From 313322eaa54877a2dd5101cc82c6b8fdd99709c3 Mon Sep 17 00:00:00 2001 From: gowerc Date: Tue, 24 Sep 2024 15:05:45 +0000 Subject: [PATCH] [actions skip] Built site for rbmi@c2e2bef798787ec7f38a0e1e974a0eb3977fd913 --- 404.html | 4 +- CONTRIBUTING.html | 4 +- LICENSE.html | 4 +- articles/advanced.html | 4 +- articles/index.html | 4 +- articles/quickstart.html | 4 +- articles/stat_specs.html | 4 +- authors.html | 4 +- latest-tag/404.html | 91 + latest-tag/CONTRIBUTING.html | 155 + latest-tag/LICENSE.html | 148 + latest-tag/articles/advanced.html | 988 ++ .../figure-html/unnamed-chunk-18-1.png | Bin 0 -> 10981 bytes latest-tag/articles/index.html | 77 + latest-tag/articles/quickstart.html | 636 + latest-tag/articles/stat_specs.html | 705 + latest-tag/authors.html | 105 + .../bootstrap-5.3.1/bootstrap.bundle.min.js | 7 + .../bootstrap.bundle.min.js.map | 1 + .../deps/bootstrap-5.3.1/bootstrap.min.css | 5 + .../bootstrap-toc-1.0.1/bootstrap-toc.min.js | 5 + .../deps/clipboard.js-2.0.11/clipboard.min.js | 7 + latest-tag/deps/data-deps.txt | 13 + .../deps/font-awesome-6.4.2/css/all.css | 7968 +++++++++++ .../deps/font-awesome-6.4.2/css/all.min.css | 9 + .../deps/font-awesome-6.4.2/css/v4-shims.css | 2194 ++++ .../font-awesome-6.4.2/css/v4-shims.min.css | 6 + .../webfonts/fa-brands-400.ttf | Bin 0 -> 189684 bytes .../webfonts/fa-brands-400.woff2 | Bin 0 -> 109808 bytes .../webfonts/fa-regular-400.ttf | Bin 0 -> 63348 bytes .../webfonts/fa-regular-400.woff2 | Bin 0 -> 24488 bytes .../webfonts/fa-solid-900.ttf | Bin 0 -> 394668 bytes .../webfonts/fa-solid-900.woff2 | Bin 0 -> 150020 bytes .../webfonts/fa-v4compatibility.ttf | Bin 0 -> 10172 bytes .../webfonts/fa-v4compatibility.woff2 | Bin 0 -> 4568 bytes .../deps/headroom-0.11.0/headroom.min.js | 7 + .../headroom-0.11.0/jQuery.headroom.min.js | 7 + latest-tag/deps/jquery-3.6.0/jquery-3.6.0.js | 10881 ++++++++++++++++ .../deps/jquery-3.6.0/jquery-3.6.0.min.js | 2 + .../deps/jquery-3.6.0/jquery-3.6.0.min.map | 1 + .../search-1.0.0/autocomplete.jquery.min.js | 7 + latest-tag/deps/search-1.0.0/fuse.min.js | 9 + latest-tag/deps/search-1.0.0/mark.min.js | 7 + latest-tag/index.html | 173 + latest-tag/katex-auto.js | 14 + latest-tag/lightswitch.js | 85 + latest-tag/link.svg | 12 + latest-tag/news/index.html | 106 + latest-tag/pkgdown.js | 154 + latest-tag/pkgdown.yml | 8 + latest-tag/reference/QR_decomp.html | 90 + latest-tag/reference/Rplot001.png | Bin 0 -> 1011 bytes latest-tag/reference/Stack.html | 144 + latest-tag/reference/add_class.html | 94 + latest-tag/reference/adjust_trajectories.html | 132 + .../reference/adjust_trajectories_single.html | 123 + latest-tag/reference/analyse.html | 204 + latest-tag/reference/ancova.html | 161 + latest-tag/reference/ancova_single.html | 130 + latest-tag/reference/antidepressant_data.html | 136 + latest-tag/reference/apply_delta.html | 103 + latest-tag/reference/as_analysis.html | 109 + latest-tag/reference/as_ascii_table.html | 104 + latest-tag/reference/as_class.html | 91 + latest-tag/reference/as_cropped_char.html | 98 + latest-tag/reference/as_dataframe.html | 89 + latest-tag/reference/as_draws.html | 126 + latest-tag/reference/as_imputation.html | 107 + latest-tag/reference/as_indices.html | 96 + latest-tag/reference/as_mmrm_df.html | 127 + latest-tag/reference/as_mmrm_formula.html | 99 + latest-tag/reference/as_model_df.html | 102 + latest-tag/reference/as_simple_formula.html | 95 + latest-tag/reference/as_stan_array.html | 93 + latest-tag/reference/as_strata.html | 110 + .../reference/assert_variables_exist.html | 91 + latest-tag/reference/char2fct.html | 97 + latest-tag/reference/check_ESS.html | 111 + latest-tag/reference/check_hmc_diagn.html | 106 + latest-tag/reference/check_mcmc.html | 105 + latest-tag/reference/compute_sigma.html | 115 + .../convert_to_imputation_list_df.html | 148 + latest-tag/reference/d_lagscale.html | 106 + latest-tag/reference/delta_template.html | 203 + latest-tag/reference/do_not_run.html | 89 + latest-tag/reference/draws.html | 306 + .../reference/encap_get_mmrm_sample.html | 111 + latest-tag/reference/eval_mmrm.html | 122 + latest-tag/reference/expand.html | 185 + latest-tag/reference/extract_covariates.html | 94 + .../reference/extract_data_nmar_as_na.html | 95 + latest-tag/reference/extract_draws.html | 105 + latest-tag/reference/extract_imputed_df.html | 123 + latest-tag/reference/extract_imputed_dfs.html | 130 + latest-tag/reference/extract_params.html | 90 + latest-tag/reference/fit_mcmc.html | 151 + latest-tag/reference/fit_mmrm.html | 138 + .../reference/generate_data_single.html | 125 + latest-tag/reference/getStrategies.html | 122 + latest-tag/reference/get_ESS.html | 91 + latest-tag/reference/get_bootstrap_stack.html | 98 + latest-tag/reference/get_cluster.html | 91 + .../reference/get_conditional_parameters.html | 101 + latest-tag/reference/get_delta_template.html | 93 + latest-tag/reference/get_draws_mle.html | 168 + latest-tag/reference/get_ests_bmlmi.html | 112 + latest-tag/reference/get_example_data.html | 114 + latest-tag/reference/get_jackknife_stack.html | 98 + latest-tag/reference/get_mmrm_sample.html | 108 + latest-tag/reference/get_pattern_groups.html | 98 + .../reference/get_pattern_groups_unique.html | 99 + latest-tag/reference/get_pool_components.html | 91 + .../get_visit_distribution_parameters.html | 111 + latest-tag/reference/has_class.html | 102 + latest-tag/reference/ife.html | 104 + latest-tag/reference/imputation_df.html | 87 + latest-tag/reference/imputation_list_df.html | 87 + .../reference/imputation_list_single.html | 98 + latest-tag/reference/imputation_single.html | 91 + latest-tag/reference/impute.html | 208 + .../reference/impute_data_individual.html | 149 + latest-tag/reference/impute_internal.html | 121 + latest-tag/reference/impute_outcome.html | 98 + latest-tag/reference/index.html | 882 ++ latest-tag/reference/invert.html | 90 + latest-tag/reference/invert_indexes.html | 101 + latest-tag/reference/is_absent.html | 101 + latest-tag/reference/is_char_fact.html | 87 + latest-tag/reference/is_char_one.html | 87 + .../reference/is_in_rbmi_development.html | 91 + latest-tag/reference/is_num_char_fact.html | 87 + latest-tag/reference/locf.html | 94 + latest-tag/reference/longDataConstructor.html | 459 + latest-tag/reference/ls_design.html | 117 + latest-tag/reference/lsmeans.html | 138 + latest-tag/reference/method.html | 214 + latest-tag/reference/parametric_ci.html | 116 + latest-tag/reference/pool.html | 148 + .../reference/pool_bootstrap_normal.html | 105 + .../reference/pool_bootstrap_percentile.html | 108 + latest-tag/reference/pool_internal.html | 131 + latest-tag/reference/prepare_stan_data.html | 127 + latest-tag/reference/print.analysis.html | 92 + latest-tag/reference/print.draws.html | 92 + latest-tag/reference/print.imputation.html | 92 + latest-tag/reference/progressLogger.html | 194 + latest-tag/reference/pval_percentile.html | 102 + latest-tag/reference/random_effects_expr.html | 107 + latest-tag/reference/rbmi-package.html | 119 + latest-tag/reference/record.html | 113 + latest-tag/reference/recursive_reduce.html | 95 + .../reference/remove_if_all_missing.html | 93 + latest-tag/reference/rubin_df.html | 114 + latest-tag/reference/rubin_rules.html | 122 + latest-tag/reference/sample_ids.html | 102 + latest-tag/reference/sample_list.html | 90 + latest-tag/reference/sample_mvnorm.html | 95 + latest-tag/reference/sample_single.html | 126 + latest-tag/reference/scalerConstructor.html | 224 + latest-tag/reference/set_simul_pars.html | 171 + latest-tag/reference/set_vars.html | 161 + latest-tag/reference/simulate_data.html | 187 + latest-tag/reference/simulate_dropout.html | 114 + latest-tag/reference/simulate_ice.html | 130 + latest-tag/reference/simulate_test_data.html | 139 + latest-tag/reference/sort_by.html | 104 + latest-tag/reference/split_dim.html | 124 + latest-tag/reference/split_imputations.html | 127 + latest-tag/reference/str_contains.html | 105 + latest-tag/reference/strategies.html | 131 + latest-tag/reference/string_pad.html | 94 + .../reference/transpose_imputations.html | 112 + latest-tag/reference/transpose_results.html | 132 + latest-tag/reference/transpose_samples.html | 90 + latest-tag/reference/validate.analysis.html | 92 + latest-tag/reference/validate.draws.html | 92 + latest-tag/reference/validate.html | 97 + latest-tag/reference/validate.is_mar.html | 102 + latest-tag/reference/validate.ivars.html | 95 + latest-tag/reference/validate.references.html | 103 + .../reference/validate.sample_list.html | 92 + .../reference/validate.sample_single.html | 92 + latest-tag/reference/validate.simul_pars.html | 92 + latest-tag/reference/validate.stan_data.html | 92 + .../reference/validate_analyse_pars.html | 93 + latest-tag/reference/validate_datalong.html | 127 + latest-tag/reference/validate_strategies.html | 101 + latest-tag/search.json | 1 + latest-tag/sitemap.xml | 149 + main/404.html | 4 +- main/CONTRIBUTING.html | 4 +- main/LICENSE.html | 4 +- main/articles/CondMean_Inference.html | 4 +- main/articles/FAQ.html | 4 +- main/articles/advanced.html | 4 +- main/articles/index.html | 4 +- main/articles/quickstart.html | 4 +- main/articles/retrieved_dropout.html | 4 +- main/articles/stat_specs.html | 4 +- main/authors.html | 4 +- main/index.html | 4 +- main/news/index.html | 4 +- main/reference/QR_decomp.html | 4 +- main/reference/Stack.html | 4 +- main/reference/add_class.html | 4 +- main/reference/adjust_trajectories.html | 4 +- .../reference/adjust_trajectories_single.html | 4 +- main/reference/analyse.html | 4 +- main/reference/ancova.html | 4 +- main/reference/ancova_single.html | 4 +- main/reference/antidepressant_data.html | 4 +- main/reference/apply_delta.html | 4 +- main/reference/as_analysis.html | 4 +- main/reference/as_ascii_table.html | 4 +- main/reference/as_class.html | 4 +- main/reference/as_cropped_char.html | 4 +- main/reference/as_dataframe.html | 4 +- main/reference/as_draws.html | 4 +- main/reference/as_imputation.html | 4 +- main/reference/as_indices.html | 4 +- main/reference/as_mmrm_df.html | 4 +- main/reference/as_mmrm_formula.html | 4 +- main/reference/as_model_df.html | 4 +- main/reference/as_simple_formula.html | 4 +- main/reference/as_stan_array.html | 4 +- main/reference/as_strata.html | 4 +- main/reference/assert_variables_exist.html | 4 +- main/reference/char2fct.html | 4 +- main/reference/check_ESS.html | 4 +- main/reference/check_hmc_diagn.html | 4 +- main/reference/check_mcmc.html | 4 +- main/reference/compute_sigma.html | 4 +- .../convert_to_imputation_list_df.html | 4 +- main/reference/d_lagscale.html | 4 +- main/reference/delta_template.html | 4 +- main/reference/do_not_run.html | 4 +- main/reference/draws.html | 4 +- main/reference/encap_get_mmrm_sample.html | 4 +- main/reference/eval_mmrm.html | 4 +- main/reference/expand.html | 4 +- main/reference/extract_covariates.html | 4 +- main/reference/extract_data_nmar_as_na.html | 4 +- main/reference/extract_draws.html | 4 +- main/reference/extract_imputed_df.html | 4 +- main/reference/extract_imputed_dfs.html | 4 +- main/reference/extract_params.html | 4 +- main/reference/fit_mcmc.html | 4 +- main/reference/fit_mmrm.html | 4 +- main/reference/generate_data_single.html | 4 +- main/reference/getStrategies.html | 4 +- main/reference/get_ESS.html | 4 +- main/reference/get_bootstrap_stack.html | 4 +- main/reference/get_cluster.html | 4 +- .../reference/get_conditional_parameters.html | 4 +- main/reference/get_delta_template.html | 4 +- main/reference/get_draws_mle.html | 4 +- main/reference/get_ests_bmlmi.html | 4 +- main/reference/get_example_data.html | 4 +- main/reference/get_jackknife_stack.html | 4 +- main/reference/get_mmrm_sample.html | 4 +- main/reference/get_pattern_groups.html | 4 +- main/reference/get_pattern_groups_unique.html | 4 +- main/reference/get_pool_components.html | 4 +- .../get_visit_distribution_parameters.html | 4 +- main/reference/has_class.html | 4 +- main/reference/ife.html | 4 +- main/reference/imputation_df.html | 4 +- main/reference/imputation_list_df.html | 4 +- main/reference/imputation_list_single.html | 4 +- main/reference/imputation_single.html | 4 +- main/reference/impute.html | 4 +- main/reference/impute_data_individual.html | 4 +- main/reference/impute_internal.html | 4 +- main/reference/impute_outcome.html | 4 +- main/reference/index.html | 4 +- main/reference/invert.html | 4 +- main/reference/invert_indexes.html | 4 +- main/reference/is_absent.html | 4 +- main/reference/is_char_fact.html | 4 +- main/reference/is_char_one.html | 4 +- main/reference/is_in_rbmi_development.html | 4 +- main/reference/is_num_char_fact.html | 4 +- main/reference/locf.html | 4 +- main/reference/longDataConstructor.html | 4 +- main/reference/ls_design.html | 4 +- main/reference/lsmeans.html | 4 +- main/reference/method.html | 4 +- main/reference/parametric_ci.html | 4 +- main/reference/pool.html | 4 +- main/reference/pool_bootstrap_normal.html | 4 +- main/reference/pool_bootstrap_percentile.html | 4 +- main/reference/pool_internal.html | 4 +- main/reference/prepare_stan_data.html | 4 +- main/reference/print.analysis.html | 4 +- main/reference/print.draws.html | 4 +- main/reference/print.imputation.html | 4 +- main/reference/progressLogger.html | 4 +- main/reference/pval_percentile.html | 4 +- main/reference/random_effects_expr.html | 4 +- main/reference/rbmi-package.html | 4 +- main/reference/record.html | 4 +- main/reference/recursive_reduce.html | 4 +- main/reference/remove_if_all_missing.html | 4 +- main/reference/rubin_df.html | 4 +- main/reference/rubin_rules.html | 4 +- main/reference/sample_ids.html | 4 +- main/reference/sample_list.html | 4 +- main/reference/sample_mvnorm.html | 4 +- main/reference/sample_single.html | 4 +- main/reference/scalerConstructor.html | 4 +- main/reference/set_simul_pars.html | 4 +- main/reference/set_vars.html | 4 +- main/reference/simulate_data.html | 4 +- main/reference/simulate_dropout.html | 4 +- main/reference/simulate_ice.html | 4 +- main/reference/simulate_test_data.html | 4 +- main/reference/sort_by.html | 4 +- main/reference/split_dim.html | 4 +- main/reference/split_imputations.html | 4 +- main/reference/str_contains.html | 4 +- main/reference/strategies.html | 4 +- main/reference/string_pad.html | 4 +- main/reference/transpose_imputations.html | 4 +- main/reference/transpose_results.html | 4 +- main/reference/transpose_samples.html | 4 +- main/reference/validate.analysis.html | 4 +- main/reference/validate.draws.html | 4 +- main/reference/validate.html | 4 +- main/reference/validate.is_mar.html | 4 +- main/reference/validate.ivars.html | 4 +- main/reference/validate.references.html | 4 +- main/reference/validate.sample_list.html | 4 +- main/reference/validate.sample_single.html | 4 +- main/reference/validate.simul_pars.html | 4 +- main/reference/validate.stan_data.html | 4 +- main/reference/validate_analyse_pars.html | 4 +- main/reference/validate_datalong.html | 4 +- main/reference/validate_strategies.html | 4 +- news/index.html | 4 +- pkgdown.yml | 7 +- reference/QR_decomp.html | 4 +- reference/Stack.html | 4 +- reference/add_class.html | 4 +- reference/adjust_trajectories.html | 4 +- reference/adjust_trajectories_single.html | 4 +- reference/analyse.html | 4 +- reference/ancova.html | 4 +- reference/ancova_single.html | 4 +- reference/antidepressant_data.html | 4 +- reference/apply_delta.html | 4 +- reference/as_analysis.html | 4 +- reference/as_ascii_table.html | 4 +- reference/as_class.html | 4 +- reference/as_cropped_char.html | 4 +- reference/as_dataframe.html | 4 +- reference/as_draws.html | 4 +- reference/as_imputation.html | 4 +- reference/as_indices.html | 4 +- reference/as_mmrm_df.html | 4 +- reference/as_mmrm_formula.html | 4 +- reference/as_model_df.html | 4 +- reference/as_simple_formula.html | 4 +- reference/as_stan_array.html | 4 +- reference/as_strata.html | 4 +- reference/assert_variables_exist.html | 4 +- reference/char2fct.html | 4 +- reference/check_ESS.html | 4 +- reference/check_hmc_diagn.html | 4 +- reference/check_mcmc.html | 4 +- reference/compute_sigma.html | 4 +- reference/convert_to_imputation_list_df.html | 4 +- reference/d_lagscale.html | 4 +- reference/delta_template.html | 4 +- reference/draws.html | 4 +- reference/encap_get_mmrm_sample.html | 4 +- reference/eval_glmmtmb.html | 4 +- reference/expand.html | 4 +- reference/extract_covariates.html | 4 +- reference/extract_data_nmar_as_na.html | 4 +- reference/extract_draws.html | 4 +- reference/extract_imputed_df.html | 4 +- reference/extract_imputed_dfs.html | 4 +- reference/extract_params.html | 4 +- reference/fit_mcmc.html | 4 +- reference/fit_mmrm.html | 4 +- reference/fit_mmrm_multiopt.html | 4 +- reference/generate_data_single.html | 4 +- reference/getStrategies.html | 4 +- reference/get_ESS.html | 4 +- reference/get_bootstrap_stack.html | 4 +- reference/get_cluster.html | 4 +- reference/get_conditional_parameters.html | 4 +- reference/get_delta_template.html | 4 +- reference/get_draws_mle.html | 4 +- reference/get_ests_bmlmi.html | 4 +- reference/get_example_data.html | 4 +- reference/get_jackknife_stack.html | 4 +- reference/get_mmrm_sample.html | 4 +- reference/get_pattern_groups.html | 4 +- reference/get_pattern_groups_unique.html | 4 +- reference/get_pool_components.html | 4 +- .../get_visit_distribution_parameters.html | 4 +- reference/has_class.html | 4 +- reference/ife.html | 4 +- reference/imputation_df.html | 4 +- reference/imputation_list_df.html | 4 +- reference/imputation_list_single.html | 4 +- reference/imputation_single.html | 4 +- reference/impute.html | 4 +- reference/impute_data_individual.html | 4 +- reference/impute_internal.html | 4 +- reference/impute_outcome.html | 4 +- reference/index.html | 4 +- reference/invert.html | 4 +- reference/invert_indexes.html | 4 +- reference/is_absent.html | 4 +- reference/is_char_fact.html | 4 +- reference/is_char_one.html | 4 +- reference/is_num_char_fact.html | 4 +- reference/locf.html | 4 +- reference/longDataConstructor.html | 4 +- reference/ls_design.html | 4 +- reference/lsmeans.html | 4 +- reference/method.html | 4 +- reference/parametric_ci.html | 4 +- reference/pool.html | 4 +- reference/pool_bootstrap_normal.html | 4 +- reference/pool_bootstrap_percentile.html | 4 +- reference/pool_internal.html | 4 +- reference/prepare_stan_data.html | 4 +- reference/print.analysis.html | 4 +- reference/print.draws.html | 4 +- reference/print.imputation.html | 4 +- reference/progressLogger.html | 4 +- reference/pval_percentile.html | 4 +- reference/random_effects_expr.html | 4 +- reference/rbmi-package.html | 4 +- reference/record.html | 4 +- reference/recursive_reduce.html | 4 +- reference/remove_if_all_missing.html | 4 +- reference/rubin_df.html | 4 +- reference/rubin_rules.html | 4 +- reference/sample_ids.html | 4 +- reference/sample_list.html | 4 +- reference/sample_mvnorm.html | 4 +- reference/sample_single.html | 4 +- reference/scalerConstructor.html | 4 +- reference/set_simul_pars.html | 4 +- reference/set_vars.html | 4 +- reference/simulate_data.html | 4 +- reference/simulate_dropout.html | 4 +- reference/simulate_ice.html | 4 +- reference/simulate_test_data.html | 4 +- reference/sort_by.html | 4 +- reference/split_dim.html | 4 +- reference/split_imputations.html | 4 +- reference/str_contains.html | 4 +- reference/strategies.html | 4 +- reference/string_pad.html | 4 +- reference/transpose_imputations.html | 4 +- reference/transpose_results.html | 4 +- reference/transpose_samples.html | 4 +- reference/validate.analysis.html | 4 +- reference/validate.draws.html | 4 +- reference/validate.html | 4 +- reference/validate.is_mar.html | 4 +- reference/validate.ivars.html | 4 +- reference/validate.references.html | 4 +- reference/validate.sample_list.html | 4 +- reference/validate.sample_single.html | 4 +- reference/validate.simul_pars.html | 4 +- reference/validate.stan_data.html | 4 +- reference/validate_analyse_pars.html | 4 +- reference/validate_datalong.html | 4 +- reference/validate_strategies.html | 4 +- test-pkgdown-workflow/404.html | 4 +- test-pkgdown-workflow/CONTRIBUTING.html | 4 +- test-pkgdown-workflow/LICENSE.html | 4 +- test-pkgdown-workflow/articles/advanced.html | 4 +- test-pkgdown-workflow/articles/index.html | 4 +- .../articles/quickstart.html | 4 +- .../articles/stat_specs.html | 4 +- test-pkgdown-workflow/authors.html | 4 +- test-pkgdown-workflow/index.html | 4 +- test-pkgdown-workflow/news/index.html | 4 +- .../reference/QR_decomp.html | 4 +- test-pkgdown-workflow/reference/Stack.html | 4 +- .../reference/add_class.html | 4 +- .../reference/adjust_trajectories.html | 4 +- .../reference/adjust_trajectories_single.html | 4 +- test-pkgdown-workflow/reference/analyse.html | 4 +- test-pkgdown-workflow/reference/ancova.html | 4 +- .../reference/ancova_single.html | 4 +- .../reference/antidepressant_data.html | 4 +- .../reference/apply_delta.html | 4 +- .../reference/as_analysis.html | 4 +- .../reference/as_ascii_table.html | 4 +- test-pkgdown-workflow/reference/as_class.html | 4 +- .../reference/as_cropped_char.html | 4 +- .../reference/as_dataframe.html | 4 +- test-pkgdown-workflow/reference/as_draws.html | 4 +- .../reference/as_imputation.html | 4 +- .../reference/as_indices.html | 4 +- .../reference/as_mmrm_df.html | 4 +- .../reference/as_mmrm_formula.html | 4 +- .../reference/as_model_df.html | 4 +- .../reference/as_simple_formula.html | 4 +- .../reference/as_stan_array.html | 4 +- .../reference/as_strata.html | 4 +- .../reference/assert_variables_exist.html | 4 +- test-pkgdown-workflow/reference/char2fct.html | 4 +- .../reference/check_ESS.html | 4 +- .../reference/check_hmc_diagn.html | 4 +- .../reference/check_mcmc.html | 4 +- .../reference/compute_sigma.html | 4 +- .../convert_to_imputation_list_df.html | 4 +- .../reference/d_lagscale.html | 4 +- .../reference/delta_template.html | 4 +- .../reference/do_not_run.html | 4 +- test-pkgdown-workflow/reference/draws.html | 4 +- .../reference/encap_get_mmrm_sample.html | 4 +- .../reference/eval_mmrm.html | 4 +- test-pkgdown-workflow/reference/expand.html | 4 +- .../reference/extract_covariates.html | 4 +- .../reference/extract_data_nmar_as_na.html | 4 +- .../reference/extract_draws.html | 4 +- .../reference/extract_imputed_df.html | 4 +- .../reference/extract_imputed_dfs.html | 4 +- .../reference/extract_params.html | 4 +- test-pkgdown-workflow/reference/fit_mcmc.html | 4 +- test-pkgdown-workflow/reference/fit_mmrm.html | 4 +- .../reference/generate_data_single.html | 4 +- .../reference/getStrategies.html | 4 +- test-pkgdown-workflow/reference/get_ESS.html | 4 +- .../reference/get_bootstrap_stack.html | 4 +- .../reference/get_cluster.html | 4 +- .../reference/get_conditional_parameters.html | 4 +- .../reference/get_delta_template.html | 4 +- .../reference/get_draws_mle.html | 4 +- .../reference/get_ests_bmlmi.html | 4 +- .../reference/get_example_data.html | 4 +- .../reference/get_jackknife_stack.html | 4 +- .../reference/get_mmrm_sample.html | 4 +- .../reference/get_pattern_groups.html | 4 +- .../reference/get_pattern_groups_unique.html | 4 +- .../reference/get_pool_components.html | 4 +- .../get_visit_distribution_parameters.html | 4 +- .../reference/has_class.html | 4 +- test-pkgdown-workflow/reference/ife.html | 4 +- .../reference/imputation_df.html | 4 +- .../reference/imputation_list_df.html | 4 +- .../reference/imputation_list_single.html | 4 +- .../reference/imputation_single.html | 4 +- test-pkgdown-workflow/reference/impute.html | 4 +- .../reference/impute_data_individual.html | 4 +- .../reference/impute_internal.html | 4 +- .../reference/impute_outcome.html | 4 +- test-pkgdown-workflow/reference/index.html | 4 +- test-pkgdown-workflow/reference/invert.html | 4 +- .../reference/invert_indexes.html | 4 +- .../reference/is_absent.html | 4 +- .../reference/is_char_fact.html | 4 +- .../reference/is_char_one.html | 4 +- .../reference/is_in_rbmi_development.html | 4 +- .../reference/is_num_char_fact.html | 4 +- test-pkgdown-workflow/reference/locf.html | 4 +- .../reference/longDataConstructor.html | 4 +- .../reference/ls_design.html | 4 +- test-pkgdown-workflow/reference/lsmeans.html | 4 +- test-pkgdown-workflow/reference/method.html | 4 +- .../reference/parametric_ci.html | 4 +- test-pkgdown-workflow/reference/pool.html | 4 +- .../reference/pool_bootstrap_normal.html | 4 +- .../reference/pool_bootstrap_percentile.html | 4 +- .../reference/pool_internal.html | 4 +- .../reference/prepare_stan_data.html | 4 +- .../reference/print.analysis.html | 4 +- .../reference/print.draws.html | 4 +- .../reference/print.imputation.html | 4 +- .../reference/progressLogger.html | 4 +- .../reference/pval_percentile.html | 4 +- .../reference/random_effects_expr.html | 4 +- .../reference/rbmi-package.html | 4 +- test-pkgdown-workflow/reference/record.html | 4 +- .../reference/recursive_reduce.html | 4 +- .../reference/remove_if_all_missing.html | 4 +- test-pkgdown-workflow/reference/rubin_df.html | 4 +- .../reference/rubin_rules.html | 4 +- .../reference/sample_ids.html | 4 +- .../reference/sample_list.html | 4 +- .../reference/sample_mvnorm.html | 4 +- .../reference/sample_single.html | 4 +- .../reference/scalerConstructor.html | 4 +- .../reference/set_simul_pars.html | 4 +- test-pkgdown-workflow/reference/set_vars.html | 4 +- .../reference/simulate_data.html | 4 +- .../reference/simulate_dropout.html | 4 +- .../reference/simulate_ice.html | 4 +- .../reference/simulate_test_data.html | 4 +- test-pkgdown-workflow/reference/sort_by.html | 4 +- .../reference/split_dim.html | 4 +- .../reference/split_imputations.html | 4 +- .../reference/str_contains.html | 4 +- .../reference/strategies.html | 4 +- .../reference/string_pad.html | 4 +- .../reference/transpose_imputations.html | 4 +- .../reference/transpose_results.html | 4 +- .../reference/transpose_samples.html | 4 +- .../reference/validate.analysis.html | 4 +- .../reference/validate.draws.html | 4 +- test-pkgdown-workflow/reference/validate.html | 4 +- .../reference/validate.is_mar.html | 4 +- .../reference/validate.ivars.html | 4 +- .../reference/validate.references.html | 4 +- .../reference/validate.sample_list.html | 4 +- .../reference/validate.sample_single.html | 4 +- .../reference/validate.simul_pars.html | 4 +- .../reference/validate.stan_data.html | 4 +- .../reference/validate_analyse_pars.html | 4 +- .../reference/validate_datalong.html | 4 +- .../reference/validate_strategies.html | 4 +- v1.2.6/404.html | 8 + v1.2.6/CONTRIBUTING.html | 8 + v1.2.6/LICENSE.html | 8 + v1.2.6/articles/advanced.html | 8 + v1.2.6/articles/index.html | 8 + v1.2.6/articles/quickstart.html | 8 + v1.2.6/articles/stat_specs.html | 8 + v1.2.6/authors.html | 8 + v1.2.6/index.html | 8 + v1.2.6/news/index.html | 8 + v1.2.6/reference/QR_decomp.html | 8 + v1.2.6/reference/Stack.html | 8 + v1.2.6/reference/add_class.html | 8 + v1.2.6/reference/adjust_trajectories.html | 8 + .../reference/adjust_trajectories_single.html | 8 + v1.2.6/reference/analyse.html | 8 + v1.2.6/reference/ancova.html | 8 + v1.2.6/reference/ancova_single.html | 8 + v1.2.6/reference/antidepressant_data.html | 8 + v1.2.6/reference/apply_delta.html | 8 + v1.2.6/reference/as_analysis.html | 8 + v1.2.6/reference/as_ascii_table.html | 8 + v1.2.6/reference/as_class.html | 8 + v1.2.6/reference/as_cropped_char.html | 8 + v1.2.6/reference/as_dataframe.html | 8 + v1.2.6/reference/as_draws.html | 8 + v1.2.6/reference/as_imputation.html | 8 + v1.2.6/reference/as_indices.html | 8 + v1.2.6/reference/as_mmrm_df.html | 8 + v1.2.6/reference/as_mmrm_formula.html | 8 + v1.2.6/reference/as_model_df.html | 8 + v1.2.6/reference/as_simple_formula.html | 8 + v1.2.6/reference/as_stan_array.html | 8 + v1.2.6/reference/as_strata.html | 8 + v1.2.6/reference/assert_variables_exist.html | 8 + v1.2.6/reference/char2fct.html | 8 + v1.2.6/reference/check_ESS.html | 8 + v1.2.6/reference/check_hmc_diagn.html | 8 + v1.2.6/reference/check_mcmc.html | 8 + v1.2.6/reference/compute_sigma.html | 8 + .../convert_to_imputation_list_df.html | 8 + v1.2.6/reference/d_lagscale.html | 8 + v1.2.6/reference/delta_template.html | 8 + v1.2.6/reference/do_not_run.html | 8 + v1.2.6/reference/draws.html | 8 + v1.2.6/reference/encap_get_mmrm_sample.html | 8 + v1.2.6/reference/eval_mmrm.html | 8 + v1.2.6/reference/expand.html | 8 + v1.2.6/reference/extract_covariates.html | 8 + v1.2.6/reference/extract_data_nmar_as_na.html | 8 + v1.2.6/reference/extract_draws.html | 8 + v1.2.6/reference/extract_imputed_df.html | 8 + v1.2.6/reference/extract_imputed_dfs.html | 8 + v1.2.6/reference/extract_params.html | 8 + v1.2.6/reference/fit_mcmc.html | 8 + v1.2.6/reference/fit_mmrm.html | 8 + v1.2.6/reference/generate_data_single.html | 8 + v1.2.6/reference/getStrategies.html | 8 + v1.2.6/reference/get_ESS.html | 8 + v1.2.6/reference/get_bootstrap_stack.html | 8 + v1.2.6/reference/get_cluster.html | 8 + .../reference/get_conditional_parameters.html | 8 + v1.2.6/reference/get_delta_template.html | 8 + v1.2.6/reference/get_draws_mle.html | 8 + v1.2.6/reference/get_ests_bmlmi.html | 8 + v1.2.6/reference/get_example_data.html | 8 + v1.2.6/reference/get_jackknife_stack.html | 8 + v1.2.6/reference/get_mmrm_sample.html | 8 + v1.2.6/reference/get_pattern_groups.html | 8 + .../reference/get_pattern_groups_unique.html | 8 + v1.2.6/reference/get_pool_components.html | 8 + .../get_visit_distribution_parameters.html | 8 + v1.2.6/reference/has_class.html | 8 + v1.2.6/reference/ife.html | 8 + v1.2.6/reference/imputation_df.html | 8 + v1.2.6/reference/imputation_list_df.html | 8 + v1.2.6/reference/imputation_list_single.html | 8 + v1.2.6/reference/imputation_single.html | 8 + v1.2.6/reference/impute.html | 8 + v1.2.6/reference/impute_data_individual.html | 8 + v1.2.6/reference/impute_internal.html | 8 + v1.2.6/reference/impute_outcome.html | 8 + v1.2.6/reference/index.html | 8 + v1.2.6/reference/invert.html | 8 + v1.2.6/reference/invert_indexes.html | 8 + v1.2.6/reference/is_absent.html | 8 + v1.2.6/reference/is_char_fact.html | 8 + v1.2.6/reference/is_char_one.html | 8 + v1.2.6/reference/is_in_rbmi_development.html | 8 + v1.2.6/reference/is_num_char_fact.html | 8 + v1.2.6/reference/locf.html | 8 + v1.2.6/reference/longDataConstructor.html | 8 + v1.2.6/reference/ls_design.html | 8 + v1.2.6/reference/lsmeans.html | 8 + v1.2.6/reference/method.html | 8 + v1.2.6/reference/parametric_ci.html | 8 + v1.2.6/reference/pool.html | 8 + v1.2.6/reference/pool_bootstrap_normal.html | 8 + .../reference/pool_bootstrap_percentile.html | 8 + v1.2.6/reference/pool_internal.html | 8 + v1.2.6/reference/prepare_stan_data.html | 8 + v1.2.6/reference/print.analysis.html | 8 + v1.2.6/reference/print.draws.html | 8 + v1.2.6/reference/print.imputation.html | 8 + v1.2.6/reference/progressLogger.html | 8 + v1.2.6/reference/pval_percentile.html | 8 + v1.2.6/reference/random_effects_expr.html | 8 + v1.2.6/reference/rbmi-package.html | 8 + v1.2.6/reference/record.html | 8 + v1.2.6/reference/recursive_reduce.html | 8 + v1.2.6/reference/remove_if_all_missing.html | 8 + v1.2.6/reference/rubin_df.html | 8 + v1.2.6/reference/rubin_rules.html | 8 + v1.2.6/reference/sample_ids.html | 8 + v1.2.6/reference/sample_list.html | 8 + v1.2.6/reference/sample_mvnorm.html | 8 + v1.2.6/reference/sample_single.html | 8 + v1.2.6/reference/scalerConstructor.html | 8 + v1.2.6/reference/set_simul_pars.html | 8 + v1.2.6/reference/set_vars.html | 8 + v1.2.6/reference/simulate_data.html | 8 + v1.2.6/reference/simulate_dropout.html | 8 + v1.2.6/reference/simulate_ice.html | 8 + v1.2.6/reference/simulate_test_data.html | 8 + v1.2.6/reference/sort_by.html | 8 + v1.2.6/reference/split_dim.html | 8 + v1.2.6/reference/split_imputations.html | 8 + v1.2.6/reference/str_contains.html | 8 + v1.2.6/reference/strategies.html | 8 + v1.2.6/reference/string_pad.html | 8 + v1.2.6/reference/transpose_imputations.html | 8 + v1.2.6/reference/transpose_results.html | 8 + v1.2.6/reference/transpose_samples.html | 8 + v1.2.6/reference/validate.analysis.html | 8 + v1.2.6/reference/validate.draws.html | 8 + v1.2.6/reference/validate.html | 8 + v1.2.6/reference/validate.is_mar.html | 8 + v1.2.6/reference/validate.ivars.html | 8 + v1.2.6/reference/validate.references.html | 8 + v1.2.6/reference/validate.sample_list.html | 8 + v1.2.6/reference/validate.sample_single.html | 8 + v1.2.6/reference/validate.simul_pars.html | 8 + v1.2.6/reference/validate.stan_data.html | 8 + v1.2.6/reference/validate_analyse_pars.html | 8 + v1.2.6/reference/validate_datalong.html | 8 + v1.2.6/reference/validate_strategies.html | 8 + v1.2.6/search.json | 2 +- 768 files changed, 44126 insertions(+), 445 deletions(-) create mode 100644 latest-tag/404.html create mode 100644 latest-tag/CONTRIBUTING.html create mode 100644 latest-tag/LICENSE.html create mode 100644 latest-tag/articles/advanced.html create mode 100644 latest-tag/articles/advanced_files/figure-html/unnamed-chunk-18-1.png create mode 100644 latest-tag/articles/index.html create mode 100644 latest-tag/articles/quickstart.html create mode 100644 latest-tag/articles/stat_specs.html create mode 100644 latest-tag/authors.html create mode 100644 latest-tag/deps/bootstrap-5.3.1/bootstrap.bundle.min.js create mode 100644 latest-tag/deps/bootstrap-5.3.1/bootstrap.bundle.min.js.map create mode 100644 latest-tag/deps/bootstrap-5.3.1/bootstrap.min.css create mode 100644 latest-tag/deps/bootstrap-toc-1.0.1/bootstrap-toc.min.js create mode 100644 latest-tag/deps/clipboard.js-2.0.11/clipboard.min.js create mode 100644 latest-tag/deps/data-deps.txt create mode 100644 latest-tag/deps/font-awesome-6.4.2/css/all.css create mode 100644 latest-tag/deps/font-awesome-6.4.2/css/all.min.css create mode 100644 latest-tag/deps/font-awesome-6.4.2/css/v4-shims.css create mode 100644 latest-tag/deps/font-awesome-6.4.2/css/v4-shims.min.css create mode 100644 latest-tag/deps/font-awesome-6.4.2/webfonts/fa-brands-400.ttf create mode 100644 latest-tag/deps/font-awesome-6.4.2/webfonts/fa-brands-400.woff2 create mode 100644 latest-tag/deps/font-awesome-6.4.2/webfonts/fa-regular-400.ttf create mode 100644 latest-tag/deps/font-awesome-6.4.2/webfonts/fa-regular-400.woff2 create mode 100644 latest-tag/deps/font-awesome-6.4.2/webfonts/fa-solid-900.ttf create mode 100644 latest-tag/deps/font-awesome-6.4.2/webfonts/fa-solid-900.woff2 create mode 100644 latest-tag/deps/font-awesome-6.4.2/webfonts/fa-v4compatibility.ttf create mode 100644 latest-tag/deps/font-awesome-6.4.2/webfonts/fa-v4compatibility.woff2 create mode 100644 latest-tag/deps/headroom-0.11.0/headroom.min.js create mode 100644 latest-tag/deps/headroom-0.11.0/jQuery.headroom.min.js create mode 100644 latest-tag/deps/jquery-3.6.0/jquery-3.6.0.js create mode 100644 latest-tag/deps/jquery-3.6.0/jquery-3.6.0.min.js create mode 100644 latest-tag/deps/jquery-3.6.0/jquery-3.6.0.min.map create mode 100644 latest-tag/deps/search-1.0.0/autocomplete.jquery.min.js create mode 100644 latest-tag/deps/search-1.0.0/fuse.min.js create mode 100644 latest-tag/deps/search-1.0.0/mark.min.js create mode 100644 latest-tag/index.html create mode 100644 latest-tag/katex-auto.js create mode 100644 latest-tag/lightswitch.js create mode 100644 latest-tag/link.svg create mode 100644 latest-tag/news/index.html create mode 100644 latest-tag/pkgdown.js create mode 100644 latest-tag/pkgdown.yml create mode 100644 latest-tag/reference/QR_decomp.html create mode 100644 latest-tag/reference/Rplot001.png create mode 100644 latest-tag/reference/Stack.html create mode 100644 latest-tag/reference/add_class.html create mode 100644 latest-tag/reference/adjust_trajectories.html create mode 100644 latest-tag/reference/adjust_trajectories_single.html create mode 100644 latest-tag/reference/analyse.html create mode 100644 latest-tag/reference/ancova.html create mode 100644 latest-tag/reference/ancova_single.html create mode 100644 latest-tag/reference/antidepressant_data.html create mode 100644 latest-tag/reference/apply_delta.html create mode 100644 latest-tag/reference/as_analysis.html create mode 100644 latest-tag/reference/as_ascii_table.html create mode 100644 latest-tag/reference/as_class.html create mode 100644 latest-tag/reference/as_cropped_char.html create mode 100644 latest-tag/reference/as_dataframe.html create mode 100644 latest-tag/reference/as_draws.html create mode 100644 latest-tag/reference/as_imputation.html create mode 100644 latest-tag/reference/as_indices.html create mode 100644 latest-tag/reference/as_mmrm_df.html create mode 100644 latest-tag/reference/as_mmrm_formula.html create mode 100644 latest-tag/reference/as_model_df.html create mode 100644 latest-tag/reference/as_simple_formula.html create mode 100644 latest-tag/reference/as_stan_array.html create mode 100644 latest-tag/reference/as_strata.html create mode 100644 latest-tag/reference/assert_variables_exist.html create mode 100644 latest-tag/reference/char2fct.html create mode 100644 latest-tag/reference/check_ESS.html create mode 100644 latest-tag/reference/check_hmc_diagn.html create mode 100644 latest-tag/reference/check_mcmc.html create mode 100644 latest-tag/reference/compute_sigma.html create mode 100644 latest-tag/reference/convert_to_imputation_list_df.html create mode 100644 latest-tag/reference/d_lagscale.html create mode 100644 latest-tag/reference/delta_template.html create mode 100644 latest-tag/reference/do_not_run.html create mode 100644 latest-tag/reference/draws.html create mode 100644 latest-tag/reference/encap_get_mmrm_sample.html create mode 100644 latest-tag/reference/eval_mmrm.html create mode 100644 latest-tag/reference/expand.html create mode 100644 latest-tag/reference/extract_covariates.html create mode 100644 latest-tag/reference/extract_data_nmar_as_na.html create mode 100644 latest-tag/reference/extract_draws.html create mode 100644 latest-tag/reference/extract_imputed_df.html create mode 100644 latest-tag/reference/extract_imputed_dfs.html create mode 100644 latest-tag/reference/extract_params.html create mode 100644 latest-tag/reference/fit_mcmc.html create mode 100644 latest-tag/reference/fit_mmrm.html create mode 100644 latest-tag/reference/generate_data_single.html create mode 100644 latest-tag/reference/getStrategies.html create mode 100644 latest-tag/reference/get_ESS.html create mode 100644 latest-tag/reference/get_bootstrap_stack.html create mode 100644 latest-tag/reference/get_cluster.html create mode 100644 latest-tag/reference/get_conditional_parameters.html create mode 100644 latest-tag/reference/get_delta_template.html create mode 100644 latest-tag/reference/get_draws_mle.html create mode 100644 latest-tag/reference/get_ests_bmlmi.html create mode 100644 latest-tag/reference/get_example_data.html create mode 100644 latest-tag/reference/get_jackknife_stack.html create mode 100644 latest-tag/reference/get_mmrm_sample.html create mode 100644 latest-tag/reference/get_pattern_groups.html create mode 100644 latest-tag/reference/get_pattern_groups_unique.html create mode 100644 latest-tag/reference/get_pool_components.html create mode 100644 latest-tag/reference/get_visit_distribution_parameters.html create mode 100644 latest-tag/reference/has_class.html create mode 100644 latest-tag/reference/ife.html create mode 100644 latest-tag/reference/imputation_df.html create mode 100644 latest-tag/reference/imputation_list_df.html create mode 100644 latest-tag/reference/imputation_list_single.html create mode 100644 latest-tag/reference/imputation_single.html create mode 100644 latest-tag/reference/impute.html create mode 100644 latest-tag/reference/impute_data_individual.html create mode 100644 latest-tag/reference/impute_internal.html create mode 100644 latest-tag/reference/impute_outcome.html create mode 100644 latest-tag/reference/index.html create mode 100644 latest-tag/reference/invert.html create mode 100644 latest-tag/reference/invert_indexes.html create mode 100644 latest-tag/reference/is_absent.html create mode 100644 latest-tag/reference/is_char_fact.html create mode 100644 latest-tag/reference/is_char_one.html create mode 100644 latest-tag/reference/is_in_rbmi_development.html create mode 100644 latest-tag/reference/is_num_char_fact.html create mode 100644 latest-tag/reference/locf.html create mode 100644 latest-tag/reference/longDataConstructor.html create mode 100644 latest-tag/reference/ls_design.html create mode 100644 latest-tag/reference/lsmeans.html create mode 100644 latest-tag/reference/method.html create mode 100644 latest-tag/reference/parametric_ci.html create mode 100644 latest-tag/reference/pool.html create mode 100644 latest-tag/reference/pool_bootstrap_normal.html create mode 100644 latest-tag/reference/pool_bootstrap_percentile.html create mode 100644 latest-tag/reference/pool_internal.html create mode 100644 latest-tag/reference/prepare_stan_data.html create mode 100644 latest-tag/reference/print.analysis.html create mode 100644 latest-tag/reference/print.draws.html create mode 100644 latest-tag/reference/print.imputation.html create mode 100644 latest-tag/reference/progressLogger.html create mode 100644 latest-tag/reference/pval_percentile.html create mode 100644 latest-tag/reference/random_effects_expr.html create mode 100644 latest-tag/reference/rbmi-package.html create mode 100644 latest-tag/reference/record.html create mode 100644 latest-tag/reference/recursive_reduce.html create mode 100644 latest-tag/reference/remove_if_all_missing.html create mode 100644 latest-tag/reference/rubin_df.html create mode 100644 latest-tag/reference/rubin_rules.html create mode 100644 latest-tag/reference/sample_ids.html create mode 100644 latest-tag/reference/sample_list.html create mode 100644 latest-tag/reference/sample_mvnorm.html create mode 100644 latest-tag/reference/sample_single.html create mode 100644 latest-tag/reference/scalerConstructor.html create mode 100644 latest-tag/reference/set_simul_pars.html create mode 100644 latest-tag/reference/set_vars.html create mode 100644 latest-tag/reference/simulate_data.html create mode 100644 latest-tag/reference/simulate_dropout.html create mode 100644 latest-tag/reference/simulate_ice.html create mode 100644 latest-tag/reference/simulate_test_data.html create mode 100644 latest-tag/reference/sort_by.html create mode 100644 latest-tag/reference/split_dim.html create mode 100644 latest-tag/reference/split_imputations.html create mode 100644 latest-tag/reference/str_contains.html create mode 100644 latest-tag/reference/strategies.html create mode 100644 latest-tag/reference/string_pad.html create mode 100644 latest-tag/reference/transpose_imputations.html create mode 100644 latest-tag/reference/transpose_results.html create mode 100644 latest-tag/reference/transpose_samples.html create mode 100644 latest-tag/reference/validate.analysis.html create mode 100644 latest-tag/reference/validate.draws.html create mode 100644 latest-tag/reference/validate.html create mode 100644 latest-tag/reference/validate.is_mar.html create mode 100644 latest-tag/reference/validate.ivars.html create mode 100644 latest-tag/reference/validate.references.html create mode 100644 latest-tag/reference/validate.sample_list.html create mode 100644 latest-tag/reference/validate.sample_single.html create mode 100644 latest-tag/reference/validate.simul_pars.html create mode 100644 latest-tag/reference/validate.stan_data.html create mode 100644 latest-tag/reference/validate_analyse_pars.html create mode 100644 latest-tag/reference/validate_datalong.html create mode 100644 latest-tag/reference/validate_strategies.html create mode 100644 latest-tag/search.json create mode 100644 latest-tag/sitemap.xml diff --git a/404.html b/404.html index 3432a098..f5fc5d52 100644 --- a/404.html +++ b/404.html @@ -51,7 +51,9 @@
main
+ id="dropdown-versions">Versions
main +latest-tag +v1.2.6
diff --git a/LICENSE.html b/LICENSE.html index cbdb7e5e..cc34fad0 100644 --- a/LICENSE.html +++ b/LICENSE.html @@ -36,7 +36,9 @@
main
+ id="dropdown-versions">Versions
main +latest-tag +v1.2.6
diff --git a/articles/advanced.html b/articles/advanced.html index 6e8c7e68..554fe1d7 100644 --- a/articles/advanced.html +++ b/articles/advanced.html @@ -53,7 +53,9 @@
main
+ id="dropdown-versions">Versions
main +latest-tag +v1.2.6
diff --git a/articles/quickstart.html b/articles/quickstart.html index 14cb8b3a..04e7e858 100644 --- a/articles/quickstart.html +++ b/articles/quickstart.html @@ -53,7 +53,9 @@
main
+ id="dropdown-versions">Versions
main +latest-tag +v1.2.6
diff --git a/latest-tag/404.html b/latest-tag/404.html new file mode 100644 index 00000000..d67e68de --- /dev/null +++ b/latest-tag/404.html @@ -0,0 +1,91 @@ + + + + + + + +Page not found (404) • rbmi + + + + + + + + Skip to contents + + +
+
+
+ +

Page not found (404)

+ +
+ +Content not found. Please use links in the navbar. + +
+
+ + +
+

Developed by Craig Gower-Page, Alessandro Noci, Roche.

+
+ +
+

Site built with pkgdown 2.1.0.

+
+ +
+
+ + + + + + + diff --git a/latest-tag/CONTRIBUTING.html b/latest-tag/CONTRIBUTING.html new file mode 100644 index 00000000..c23f35f0 --- /dev/null +++ b/latest-tag/CONTRIBUTING.html @@ -0,0 +1,155 @@ + +Contributing to rbmi • rbmi + Skip to contents + + +
+
+
+ +

Contributing to rbmi

+ Source: CONTRIBUTING.md +
+ +
+ +

This file outlines how to propose and make changes to rbmi as well as providing details about more obscure aspects of the package’s development process.

+
+

Setup

+

In order to develop or contribute to rbmi you will need to access to a C/C++ compiler. If you are on Windows you should install rtools or if you are on macOS you should install Xcode. Likewise, you will also need to install all of the package’s development dependencies. This can be done by launching R from within the project root and executing:

+
devtools::install_dev_deps()
+
+
+

Code changes

+

If you want to make a code contribution, it’s a good idea to first file an issue and make sure someone from the team agrees that it’s needed. If you’ve found a bug, please file an issue that illustrates the bug with a minimal reprex (this will also help you write a unit test, if needed).

+
+

Pull request process

+

  • This project uses a simple GitHub flow model for development. That is, code changes should be done in their own feature branch based off of the main branch and merged back into the main branch once complete.

    • +

  • Pull Requests will not be accepted unless all CI/CD checks have passed. (See the CI/CD section for more information).

    • +

  • Pull Requests relating to any of the package’s core R code must be accompanied by a corresponding unit test. Any pull requests containing changes to the core R code that do not contain a unit test to demonstrate that it is working as intended will not be accepted. (See the Unit Testing section for more information).

    • +

  • Pull Requests should add a few lines about what has been changed to the NEWS.md file.

    • +
+
+

Coding Considerations

+

  • We use roxygen2, with Markdown syntax, for documentation.

    • +

  • Please ensure your code conforms to lintr. You can check this by running lintr::lint("FILE NAME") on any files you have modified and ensuring that the findings are kept to as few as possible. We do not have any hard requirements on following lintr’s conventions but do encourage developers to follow its guidance as closely as possible.

    • +

  • This project uses 4 space indents, contributions not following this will not be accepted.

    • +

  • This project makes use of S3 and R6 for OOP. Usage of S4 and other OOP systems should be avoided unless absolutely necessary to ensure consistency. Having said that it is recommended to stick to S3 unless modification in place or other R6 specific features are required.

    • +

  • The current desire of this package is to keep the dependency tree as small as possible. To that end you are discouraged from adding any additional packages to the “Depends” / “Imports” section unless absolutely essential. If you are importing a package just to use a single function then consider just copying the source code of that function instead, though please check the licence and include proper attribution/notices. There are no such expectations for “Suggests” and you are free to use any package in the vignettes / unit tests, though again please be mindful to not be unnecessarily excessive with this.

    • +
+
+
+

Unit Testing & CI/CD

+

This project uses testthat to perform unit testing in combination with GitHub Actions for CI/CD.

+
+

Scheduled Testing

+

Due to the stochastic nature of this package some unit tests take a considerable amount of time to execute. To avoid issues with usability, unit tests that take more than a couple of seconds to run should be deferred to the scheduled testing. These are tests that are only run occasionally on a periodic basis (currently twice a month) and not on every pull request / push event.

+

To defer a test to the scheduled build simply include skip_if_not(is_full_test()) to the top of the test_that() block i.e.

+
+test_that("some unit test", {
+    skip_if_not(is_full_test())
+    expect_equal(1,1)
+})
+

The scheduled tests can also be manually activated by going to “https://github.com/insightsengineering/rbmi” -> “Actions” -> “Bi-Weekly” -> “Run Workflow”. It is advisable to do this before releasing to CRAN.

+
+
+

CRAN Releases

+

In order to release a package to CRAN it needs to be tested across multiple different OS’s and versions of R. This has been implemented in this project via a GitHub Action Workflow titled “Check for CRAN” which needs to be manually activated. To do this go to “https://github.com/insightsengineering/rbmi” -> “Actions” -> “Check for CRAN” -> “Run Workflow”.

+

If all these tests pass then the package can be safely released to CRAN (after updating the relevant cran-comments.md file)

+
+
+

Docker Images

+

To support CI/CD in terms of reducing installation time, several Docker images have been pre-built which contain all the packages and system dependencies that this project needs. The current relevant images can be found at:

+
  • ghcr.io/insightsengineering/rbmi:r404
    • +
  • ghcr.io/insightsengineering/rbmi:r410
    • +
  • ghcr.io/insightsengineering/rbmi:latest
    • +

The latest image is automatically re-built once a month to contain the latest version of R and its packages. The other versions are built with older versions of R (as indicated by the tag number) and contain package versions as they were when that version of R was released. This is important to ensure that the package works with older versions of R which many companies typically run due to delays in their validation processes.

+

The code to create these images can be found in misc/docker. The legacy images (i.e. everything excluding the “latest” image) are only built on manual request by running the corresponding GitHub Actions Workflow.

+
+
+

Reproducibility, Print Tests & Snaps

+

A particular issue with testing this package is reproducibility. For the most part this is handled well via set.seed() however stan/rstan does not guarantee reproducibility even with the same seed if run on different hardware.

+

This issue surfaces itself when testing the print messages of the pool object which displays treatment estimates which are thus not identical when run on different machines. To address this issue pre-made pool objects have been generated and stored in R/sysdata.rda (which itself is generated by data-raw/create_print_test_data.R). The generated print messages are compared to expected values which are stored in tests/testthat/_snaps/ (which themselves are automatically created by testthat::expect_snapshot())

+
+
+
+

Fitting MMRM’s

+

This package currently uses the mmrm package to fit MMRM models. This package is still fairly new but has so far proven to be very stable, fast and reliable. If you do spot any issues with the MMRM package please do raise them in the corresponding GitHub Repository - link

+

As the mmrm package uses TMB it is not uncommon to see warnings about either inconsistent versions between what TMB and the Matrix package were compiled as. In order to resolve this you may wish to re-compile these packages from source using:

+
install.packages(c("TMB", "mmrm"), type = "source")
+

Note that you will need to have rtools installed if you are on a Windows machine or Xcode if you are running macOS (or somehow else have access to a C/C++ compiler).

+
+
+

rstan

+

The Bayesian models fitted by this package are implemented via stan/rstan. The code for this can be found in inst/stan/MMRM.stan. Note that the package will automatically take care of compiling this code when you install it or run devtools::load_all(). Please note that the package won’t recompile the code unless you have changed the source code or you delete the src directory.

+
+
+

Vignettes

+

CRAN imposes a 10-minute run limit on building, compiling and testing your package. To keep to this limit the vignettes are pre-built; that is to say that simply changing the source code will not automatically update the vignettes, you will need to manually re-build them.

+

To do this you need to run:

+
Rscript vignettes/build.R
+

Once re-built you will then need to commit the updated *.html files to the git repository.

+

For reference this static vignette process works by using the “asis” vignette engine provided by R.rsp. This works by getting R to only recognise vignettes as files ending in *.html.asis; it then builds them by simply copying the corresponding files ending in *.html to the relevent docs/ folder in the built package.

+
+
+

Misc & Local Folders

+

The misc/ folder in this project is used to hold useful scripts, analyses, simulations & infrastructure code that we wish to keep but isn’t essential to the build or deployment of the package. Feel free to store additional stuff in here that you feel is worth keeping.

+

Likewise, local/ has been added to the .gitignore file meaning anything stored in this folder won’t be committed to the repository. For example, you may find this useful for storing personal scripts for testing or more generally exploring the package during development.

+
+
+ +
+ + +
+

Developed by Craig Gower-Page, Alessandro Noci, Roche.

+
+ +
+

Site built with pkgdown 2.1.0.

+
+ +
+ + + + + + + diff --git a/latest-tag/LICENSE.html b/latest-tag/LICENSE.html new file mode 100644 index 00000000..8bb1dd24 --- /dev/null +++ b/latest-tag/LICENSE.html @@ -0,0 +1,148 @@ + +Apache License • rbmi + Skip to contents + + +
+
+
+ +

Apache License

+ Source: LICENSE.md +
+ +
+ +

Version 2.0, January 2004 <http://www.apache.org/licenses/>

+
+

Terms and Conditions for use, reproduction, and distribution

+
+

1. Definitions

+

“License” shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.

+

“Licensor” shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.

+

“Legal Entity” shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, “control” means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.

+

“You” (or “Your”) shall mean an individual or Legal Entity exercising permissions granted by this License.

+

“Source” form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.

+

“Object” form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.

+

“Work” shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).

+

“Derivative Works” shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.

+

“Contribution” shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, “submitted” means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as “Not a Contribution.”

+

“Contributor” shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work.

+
+
+ +

Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.

+
+
+

3. Grant of Patent License

+

Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed.

+
+
+

4. Redistribution

+

You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions:

+
  • +(a) You must give any other recipients of the Work or Derivative Works a copy of this License; and
    • +
  • +(b) You must cause any modified files to carry prominent notices stating that You changed the files; and
    • +
  • +(c) You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and
    • +
  • +(d) If the Work includes a “NOTICE” text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License.
    • +

You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License.

+
+
+

5. Submission of Contributions

+

Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.

+
+
+

6. Trademarks

+

This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file.

+
+
+

7. Disclaimer of Warranty

+

Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License.

+
+
+

8. Limitation of Liability

+

In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.

+
+
+

9. Accepting Warranty or Additional Liability

+

While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.

+

END OF TERMS AND CONDITIONS

+
+
+
+

APPENDIX: How to apply the Apache License to your work

+

To apply the Apache License to your work, attach the following boilerplate notice, with the fields enclosed by brackets [] replaced with your own identifying information. (Don’t include the brackets!) The text should be enclosed in the appropriate comment syntax for the file format. We also recommend that a file or class name and description of purpose be included on the same “printed page” as the copyright notice for easier identification within third-party archives.

+
Copyright [yyyy] [name of copyright owner]
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+  http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+
+
+ +
+ + +
+

Developed by Craig Gower-Page, Alessandro Noci, Roche.

+
+ +
+

Site built with pkgdown 2.1.0.

+
+ +
+ + + + + + + diff --git a/latest-tag/articles/advanced.html b/latest-tag/articles/advanced.html new file mode 100644 index 00000000..d403241c --- /dev/null +++ b/latest-tag/articles/advanced.html @@ -0,0 +1,988 @@ + + + + + + + +rbmi: Advanced Functionality • rbmi + + + + + + + + Skip to contents + + +
+ + +
+
+ +

rbmi: Advanced Functionality

+

Alessandro Noci, Craig Gower-Page, and Marcel Wolbers

+ + + Source: vignettes/advanced.Rmd +
advanced.Rmd
+
+ + + +
+

+1 Introduction +

+

The purpose of this vignette is to provide an overview of some more advanced features of the rbmi package. +The sections of the vignette are relatively self-contained, i.e. readers should be able to jump directly to the section which covers the functionality that they are most interested in.

+
+
+

+2 Data simulation using function simulate_data() +

+

In order to demonstrate the advanced functions we will first create a simulated dataset with the rbmi function simulate_data(). +The simulate_data() function generates data from a randomized clinical trial with longitudinal continuous outcomes and up to two different types of intercurrent events (ICEs). +One intercurrent event (ICE1) may be thought of as a discontinuation from study treatment due to study drug or condition related (SDCR) reasons. +The other event (ICE2) may be thought of as discontinuation from study treatment due to not study drug or condition related (NSDCR) reasons. +For the purpose of this vignette, we simulate data similarly to the simulation study reported in Wolbers et al. (2022) (though we change some of the simulation parameters) and include only one ICE type (ICE1).

+

Specifically, we simulate a 1:1 randomized trial of an active drug (intervention) versus placebo (control) with 100 subjects per group and 6 post-baseline assessments (bi-monthly visits until 12 months) under the following assumptions:

+
    +
  • The mean outcome trajectory in the placebo group increases linearly from 50 at baseline (visit 0) to 60 at visit 6, i.e. the slope is 10 points/year.
    • +
  • The mean outcome trajectory in the intervention group is identical to the placebo group up to visit 2. From visit 2 onward, the slope decreases by 50% to 5 points/year.
    • +
  • The covariance structure of the baseline and follow-up values in both groups is implied by a random intercept and slope model with a standard deviation of 5 for both the intercept and the slope, and a correlation of 0.25. In addition, an independent residual error with standard deviation 2.5 is added to each assessment.
    +
    • +
  • The probability of study drug discontinuation after each visit is calculated according to a logistic model which depends on the observed outcome at that visit. Specifically, a visit-wise discontinuation probability of 2% and 3% in the control and intervention group, respectively, is specified in case the observed outcome is equal to 50 (the mean value at baseline). The odds of a discontinuation is simulated to increase by +10% for each +1 point increase of the observed outcome.
    • +
  • Study drug discontinuation is simulated to have no effect on the mean trajectory in the placebo group. In the intervention group, subjects who discontinue follow the slope of the mean trajectory from the placebo group from that time point onward. This is compatible with a copy increments in reference (CIR) assumption.
    • +
  • Study drop-out at the study drug discontinuation visit occurs with a probability of 50% leading to missing outcome data from that time point onward.
    • +
+

The function simulate_data() requires 3 arguments (see the function documentation help(simulate_data) for more details):

+
    +
  • +pars_c: The simulation parameters of the control group
    • +
  • +pars_t: The simulation parameters of the intervention group
    • +
  • +post_ice1_traj: Specifies how observed outcomes after ICE1 are simulated
    • +
+

Below, we report how data according to the specifications above can be simulated with function simulate_data():

+
+library(rbmi)
+library(dplyr)
+library(ggplot2)
+library(purrr)
+
+set.seed(122)
+
+n <- 100
+time <- c(0, 2, 4, 6, 8, 10, 12)
+
+# Mean trajectory control
+muC <- c(50.0, 51.66667, 53.33333, 55.0, 56.66667, 58.33333, 60.0)
+
+# Mean trajectory intervention
+muT <- c(50.0, 51.66667, 53.33333, 54.16667, 55.0, 55.83333, 56.66667)
+
+# Create Sigma
+sd_error <- 2.5
+covRE <- rbind(
+  c(25.0, 6.25),
+  c(6.25, 25.0)
+)
+
+Sigma <- cbind(1, time / 12) %*% covRE %*% rbind(1, time / 12) + diag(sd_error^2, nrow = length(time))
+
+# Set probability of discontinuation
+probDisc_C <- 0.02
+probDisc_T <- 0.03
+or_outcome <- 1.10 # +1 point increase => +10% odds of discontinuation
+
+# Set drop-out rate following discontinuation
+prob_dropout <- 0.5
+
+# Set simulation parameters of the control group
+parsC <- set_simul_pars(
+    mu = muC,
+    sigma = Sigma,
+    n = n,
+    prob_ice1 = probDisc_C,
+    or_outcome_ice1 = or_outcome,
+    prob_post_ice1_dropout = prob_dropout
+)
+
+# Set simulation parameters of the intervention group
+parsT <- parsC
+parsT$mu <- muT
+parsT$prob_ice1 <- probDisc_T
+
+# Set assumption about post-ice trajectory
+post_ice_traj <- "CIR"
+
+# Simulate data
+data <- simulate_data(
+    pars_c = parsC,
+    pars_t = parsT,
+    post_ice1_traj = post_ice_traj
+)
+
+head(data)
+#>     id visit   group outcome_bl outcome_noICE ind_ice1 ind_ice2 dropout_ice1
+#> 1 id_1     0 Control   57.32704      57.32704        0        0            0
+#> 2 id_1     1 Control   57.32704      54.69751        1        0            1
+#> 3 id_1     2 Control   57.32704      58.60702        1        0            1
+#> 4 id_1     3 Control   57.32704      61.50119        1        0            1
+#> 5 id_1     4 Control   57.32704      56.68363        1        0            1
+#> 6 id_1     5 Control   57.32704      66.14799        1        0            1
+#>    outcome
+#> 1 57.32704
+#> 2       NA
+#> 3       NA
+#> 4       NA
+#> 5       NA
+#> 6       NA
+
+# As a simple descriptive of the simulated data, summarize the number of subjects with ICEs and missing data 
+data %>%
+  group_by(id) %>%
+  summarise(
+    group = group[1],
+    any_ICE = (any(ind_ice1 == 1)),
+    any_NA = any(is.na(outcome))) %>%
+  group_by(group) %>%
+  summarise(
+      subjects_with_ICE = sum(any_ICE),
+      subjects_with_missings = sum(any_NA)
+  )
+#> # A tibble: 2 × 3
+#>   group        subjects_with_ICE subjects_with_missings
+#>   <fct>                    <int>                  <int>
+#> 1 Control                     18                      8
+#> 2 Intervention                25                     14
+
+
+

+3 Handling of observed post-ICE data in rbmi under reference-based imputation +

+

rbmi always uses all non-missing outcome data from the input data set, i.e. such data are never overwritten during the imputation step or removed from the analysis step. This implies that if there are data which are considered to be irrelevant for treatment effect estimation (e.g. data after an ICE for which the estimand specified a hypothetical strategy), then such data need to be removed from the input data set by the user prior to calling the rbmi functions.

+

For imputation under a missing at random (MAR) strategy, all observed outcome data is also included in the fitting of the base imputation model. However, for ICEs handled using reference-based imputation methods (such as CIR, CR, and JR), rbmi excludes observed post-ICE data from the base imputation model. If these data were not excluded, then the base imputation model would mistakenly estimate mean trajectories based on a mixture of observed pre- and post-ICE data which are not relevant for reference-based imputations. However, any observed post-ICE data are added back into the data set after the fitting of the base imputation model and included as is in the subsequent imputation and analysis steps.

+

Post-ICE data in the control or reference group are also excluded from the base imputation model if the user specifies a reference-based imputation strategy for such ICEs. This ensures that an ICE has the same impact on the data included in the base imputation model regardless whether the ICE occurred in the control or the intervention group. On the other hand, imputation in the reference group is based on a MAR assumption even for reference-based imputation methods and it may be preferable in some settings to include such post-ICE data from the control group in the base imputation model. This can be implemented by specifying a MAR strategy for the ICE in the control group and a reference-based strategy for the same ICE in the intervention group. We will use this latter approach in our example below.

+

The simulated trial data from section 2 assumed that outcomes in the intervention group observed after the ICE “treatment discontinuation” follow the increments observed in the control group. Thus the imputation of missing data in the intervention group after treatment discontinuation might be performed under a reference-based copy increments in reference (CIR) assumption.

+

Specifically, we implement an estimator under the following assumptions:

+
    +
  • The endpoint of interest is the change in the outcome from baseline at each visit.
    • +
  • The imputation model includes the treatment group, the (categorical) visit, treatment-by-visit interactions, the baseline outcome, and baseline outcome-by-visit interactions as covariates.
    • +
  • The imputation model assumes a common unstructured covariance matrix in both treatment groups
    • +
  • In the control group, all missing data are imputed under MAR whereas in the intervention group, missing post-ICE data are imputed under a CIR assumption
    • +
  • The analysis model of the endpoint in the imputed datasets is a separate ANCOVA model for each visit with the treatment group as the primary covariate and adjustment for the baseline outcome value.
    • +
+

For illustration purposes, we chose MI based on approximate Bayesian posterior draws with 20 random imputations which is not very demanding from a computational perspective. In practical applications, the number of random imputations may need to be increased. Moreover, other imputations are also supported in rbmi. For guidance regarding the choice of the imputation approach, we refer the user to a comparison between all implemented approaches in Section 3.9 of the “Statistical Specifications” vignette (vignette("stat_specs", package = "rbmi")).

+

We first report the code to set the variables of the imputation and analysis models. If you are not yet familiar with the syntax, we recommend that you first check the “quickstart” vignette (vignette("quickstart", package = "rbmi")).

+
+# Create data_ice including the subject's first visit affected by the ICE and the imputation strategy
+# Imputation strategy for post-ICE data is CIR in the intervention group and MAR for the control group 
+# (note that ICEs which are handled using MAR are optional and do not impact the analysis
+#  because imputation of missing data under MAR is the default)
+data_ice_CIR <- data %>%
+    group_by(id) %>%
+    filter(ind_ice1 == 1) %>% # select visits with ICEs
+    mutate(strategy = ifelse(group == "Intervention", "CIR", "MAR")) %>%
+    summarise(
+        visit = visit[1], # Select first visit affected by the ICE
+        strategy = strategy[1]
+    )
+
+# Compute endpoint of interest: change from baseline and
+# remove rows corresponding to baseline visits
+data <- data %>% 
+    filter(visit != 0) %>% 
+    mutate(
+        change = outcome - outcome_bl,
+        visit = factor(visit, levels = unique(visit))
+    )
+
+# Define key variables for the imputation and analysis models
+vars <- set_vars(
+    subjid = "id",
+    visit = "visit",
+    outcome = "change",
+    group = "group",
+    covariates = c("visit*outcome_bl", "visit*group"),
+    strategy = "strategy"
+)
+
+vars_an <- vars
+vars_an$covariates <- "outcome_bl"
+

The chosen imputation method can be set with the function method_approxbayes() as follows:

+
+method <- method_approxbayes(n_sample = 20)
+

We can now sequentially call the 4 key functions of rbmi to perform the multiple imputation. Please note that the management of observed post-ICE data is performed without additional complexity for the user. draws() automatically excludes post-ICE data handled with a reference-based method (but keeps post-ICE data handled using MAR) using information provided by the argument data_ice. impute() will impute only truly missing data in data[[vars$outcome]].

+
+draw_obj <- draws(
+    data = data,
+    data_ice = data_ice_CIR,
+    vars = vars,
+    method = method,
+    quiet = TRUE,
+    ncores = 2
+)
+
+impute_obj_CIR <- impute(
+    draw_obj,
+    references = c("Control" = "Control", "Intervention" = "Control")
+)
+
+ana_obj_CIR <- analyse(
+    impute_obj_CIR,
+    vars = vars_an
+)
+
+pool_obj_CIR <- pool(ana_obj_CIR)
+pool_obj_CIR
+#> 
+#> Pool Object
+#> -----------
+#> Number of Results Combined: 20
+#> Method: rubin
+#> Confidence Level: 0.95
+#> Alternative: two.sided
+#> 
+#> Results:
+#> 
+#>   ==================================================
+#>    parameter   est     se     lci     uci     pval  
+#>   --------------------------------------------------
+#>      trt_1    -0.486  0.512  -1.496  0.524   0.343  
+#>    lsm_ref_1   2.62   0.362  1.907   3.333   <0.001 
+#>    lsm_alt_1  2.133   0.362   1.42   2.847   <0.001 
+#>      trt_2    -0.066  0.542  -1.135  1.004   0.904  
+#>    lsm_ref_2  3.707   0.384   2.95   4.464   <0.001 
+#>    lsm_alt_2  3.641   0.383  2.885   4.397   <0.001 
+#>      trt_3    -1.782  0.607  -2.979  -0.585  0.004  
+#>    lsm_ref_3  5.841   0.428  4.997   6.685   <0.001 
+#>    lsm_alt_3  4.059   0.428  3.214   4.904   <0.001 
+#>      trt_4    -2.518  0.692  -3.884  -1.152  <0.001 
+#>    lsm_ref_4  7.656   0.492  6.685   8.627   <0.001 
+#>    lsm_alt_4  5.138   0.488  4.176    6.1    <0.001 
+#>      trt_5    -3.658  0.856  -5.346  -1.97   <0.001 
+#>    lsm_ref_5  9.558   0.598  8.379   10.737  <0.001 
+#>    lsm_alt_5   5.9    0.608  4.699   7.101   <0.001 
+#>      trt_6    -4.537  0.954  -6.42   -2.655  <0.001 
+#>    lsm_ref_6  11.048  0.666  9.735   12.362  <0.001 
+#>    lsm_alt_6  6.511   0.674  5.181   7.841   <0.001 
+#>   --------------------------------------------------
+

This last output gives an estimated difference of +-4.537 (95% CI -6.420 to -2.655) +between the two groups at the last visit with an associated p-value lower than 0.001.

+
+
+

+4 Efficiently changing reference-based imputation strategies +

+

The draws() function is by far the most computationally intensive function in rbmi. +In some settings, it may be important to explore the impact of a change in the +reference-based imputation strategy on the results. +Such a change does not affect the imputation model but it does +affect the subsequent imputation step. +In order to allow changes in the imputation strategy without having to re-run the +draws() function, the function impute() has an additional argument update_strategies.

+

However, please note that this functionality comes with some important limitations: +As described at the beginning of Section 3, post-ICE outcomes are included in the input dataset for the base imputation model if the imputation method is MAR but they are excluded for reference-based imputation methods (such as CIR, CR, and JR). +Therefore, updata_strategies cannot be applied if the imputation strategy is changed from a MAR to a non-MAR strategy in the presence of observed post-ICE outcomes. Similarly, a change from a non-MAR strategy to MAR triggers a warning in the presence of observed post-ICE outcomes because the base imputation model was not fitted to all relevant data under MAR. +Finally, update_strategies cannot be applied if the timing of any of the ICEs is changed (in argument data_ice) in addition to the imputation strategy.

+

As an example, we described an analysis under a copy increments in reference (CIR) assumption in the previous section. Let’s assume we want to change this strategy to a jump to reference imputation strategy for a sensitivity analysis. This can be efficiently implemented using update_strategies as follows:

+
+# Change ICE strategy from CIR to JR
+data_ice_JR <- data_ice_CIR %>% 
+    mutate(strategy = ifelse(strategy == "CIR", "JR", strategy))
+
+impute_obj_JR <- impute(
+    draw_obj,
+    references = c("Control" = "Control", "Intervention" = "Control"),
+    update_strategy = data_ice_JR
+)
+
+ana_obj_JR <- analyse(
+    impute_obj_JR,
+    vars = vars_an
+)
+
+pool_obj_JR <- pool(ana_obj_JR)
+pool_obj_JR
+#> 
+#> Pool Object
+#> -----------
+#> Number of Results Combined: 20
+#> Method: rubin
+#> Confidence Level: 0.95
+#> Alternative: two.sided
+#> 
+#> Results:
+#> 
+#>   ==================================================
+#>    parameter   est     se     lci     uci     pval  
+#>   --------------------------------------------------
+#>      trt_1    -0.485  0.513  -1.496  0.526   0.346  
+#>    lsm_ref_1  2.609   0.363  1.892   3.325   <0.001 
+#>    lsm_alt_1  2.124   0.361  1.412   2.836   <0.001 
+#>      trt_2    -0.06   0.535  -1.115  0.995   0.911  
+#>    lsm_ref_2  3.694   0.378  2.948   4.441   <0.001 
+#>    lsm_alt_2  3.634   0.381  2.882   4.387   <0.001 
+#>      trt_3    -1.767  0.598  -2.948  -0.587  0.004  
+#>    lsm_ref_3  5.845   0.422  5.012   6.677   <0.001 
+#>    lsm_alt_3  4.077   0.432  3.225    4.93   <0.001 
+#>      trt_4    -2.529  0.686  -3.883  -1.175  <0.001 
+#>    lsm_ref_4  7.637   0.495  6.659   8.614   <0.001 
+#>    lsm_alt_4  5.108   0.492  4.138   6.078   <0.001 
+#>      trt_5    -3.523  0.856  -5.212  -1.833  <0.001 
+#>    lsm_ref_5  9.554   0.61   8.351   10.758  <0.001 
+#>    lsm_alt_5  6.032   0.611  4.827   7.237   <0.001 
+#>      trt_6    -4.36   0.952  -6.238  -2.482  <0.001 
+#>    lsm_ref_6  11.003  0.676  9.669   12.337  <0.001 
+#>    lsm_alt_6  6.643   0.687  5.287     8     <0.001 
+#>   --------------------------------------------------
+

For imputations under a jump to reference assumption, we get an estimated difference of +-4.360 (95% CI -6.238 to -2.482) +between the two groups at the last visit with an associated p-value of +<0.001.

+
+
+

+5 Imputation under MAR with time-varying covariates +

+

Guizzaro et al. (2021) suggested to implement a treatment policy strategy via imputation under a MAR assumption after conditioning on the subject’s ICE status, +i.e. to impute missing post-ICE data based on observed post-ICE data. One possible implementation of this proposal is to add time-varying covariates to the imputation model. +A case study which implements this proposal and compares it to reference-based imputation methods for estimators in early Parkinson’s disease can be found in Noci et al. (2021).

+

In some settings, this may be carried out by including a binary time-varying indicator of the subject’s ICE status at each visit (defined as 0 for pre-ICE visits and as 1 for post-ICE visits) to the imputation model. However, for the simulated data introduced in section 2, it may be more plausible to assume that treatment discontinuation leads to a change in the “slope” of the mean outcome trajectory. This can be implemented by including a time-varying covariate which is equal to 0 for visits prior to the treatment discontinuation and equal to the time from the treatment discontinuation for subsequent visits. The regression coefficient of the corresponding change in the post-ICE “slope” should then be allowed to depend on the assigned treatment group, i.e. the imputation model should include an interaction between the time-varying covariate and the treatment group.

+

Let’s first define the time-varying covariate:

+
+data <- data %>%
+    group_by(id) %>%
+    mutate(time_from_ice1 = cumsum(ind_ice1)*2/12 ) # multiplication by 2/12 because visits are bi-monthly
+

We can then include the time-varying covariate in the imputation model, crossed with the group variable:

+
+vars_tv <- set_vars(
+    subjid = "id",
+    visit = "visit",
+    outcome = "change",
+    group = "group",
+    covariates = c("visit*outcome_bl", "visit*group", "time_from_ice1*group"),
+    strategy = "strategy"
+)
+

We now sequentially call the 4 key rbmi functions:

+
+draw_obj <- draws(
+    data = data,
+    data_ice = NULL, # if NULL, MAR is assumed for all missing data
+    vars = vars_tv,
+    method = method,
+    quiet = TRUE
+)
+
+impute_obj_tv <- impute(
+    draw_obj,
+    references = c("Control" = "Control", "Intervention" = "Intervention")
+)
+
+ana_obj_tv <- analyse(
+    impute_obj_tv,
+    vars = vars_an
+)
+
+pool(ana_obj_tv)
+#> 
+#> Pool Object
+#> -----------
+#> Number of Results Combined: 20
+#> Method: rubin
+#> Confidence Level: 0.95
+#> Alternative: two.sided
+#> 
+#> Results:
+#> 
+#>   ==================================================
+#>    parameter   est     se     lci     uci     pval  
+#>   --------------------------------------------------
+#>      trt_1    -0.492  0.515  -1.507  0.524   0.341  
+#>    lsm_ref_1  2.623   0.362  1.908   3.338   <0.001 
+#>    lsm_alt_1  2.131   0.366  1.409   2.854   <0.001 
+#>      trt_2    0.018   0.55   -1.067  1.103   0.974  
+#>    lsm_ref_2  3.697   0.382  2.943    4.45   <0.001 
+#>    lsm_alt_2  3.715   0.394  2.936   4.493   <0.001 
+#>      trt_3    -1.802  0.614  -3.015  -0.59   0.004  
+#>    lsm_ref_3  5.815   0.429   4.97   6.661   <0.001 
+#>    lsm_alt_3  4.013   0.441  3.142   4.884   <0.001 
+#>      trt_4    -2.543  0.704  -3.932  -1.154  <0.001 
+#>    lsm_ref_4  7.609   0.486   6.65   8.568   <0.001 
+#>    lsm_alt_4  5.066   0.516  4.046   6.086   <0.001 
+#>      trt_5    -3.739  0.879  -5.475  -2.004  <0.001 
+#>    lsm_ref_5  9.499   0.606  8.302   10.695  <0.001 
+#>    lsm_alt_5  5.759   0.636  4.502   7.017   <0.001 
+#>      trt_6    -4.685  0.98   -6.622  -2.748  <0.001 
+#>    lsm_ref_6  10.988  0.667   9.67   12.305  <0.001 
+#>    lsm_alt_6  6.302   0.712  4.894   7.711   <0.001 
+#>   --------------------------------------------------
+
+
+

+6 Custom imputation strategies +

+

The following imputation strategies are implemented in rbmi:

+
    +
  • Missing at Random (MAR)
    • +
  • Jump to Reference (JR)
    • +
  • Copy Reference (CR)
    • +
  • Copy Increments in Reference (CIR)
    • +
  • Last Mean Carried Forward (LMCF)
    • +
+

In addition, rbmi allows the user to implement their own imputation strategy. +To do this, the user needs to do three things:

+
    +
  1. Define a function implementing the new imputation strategy.
    2. +
  2. Specify which patients use this strategy in the data_ice dataset provided to draws().
    3. +
  3. Provide the imputation strategy function to impute().
    4. +
+

The imputation strategy function must take 3 arguments (pars_group, pars_ref, and index_mar) and calculates the mean and covariance matrix of the subject’s marginal imputation distribution which will then be applied to subjects to which the strategy applies. +Here, pars_group contains the predicted mean trajectory (pars_group$mu, a numeric vector) and covariance matrix (pars_group$sigma) for a subject conditional on their assigned treatment group and covariates. +pars_ref contains the corresponding mean trajectory and covariance matrix conditional on the reference group and the subject’s covariates. +index_mar is a logical vector which specifies for each visit whether the visit is unaffected by an ICE handled using a non-MAR method or not. +As an example, the user can check how the CIR strategy was implemented by looking at function strategy_CIR().

+
+strategy_CIR
+#> function (pars_group, pars_ref, index_mar) 
+#> {
+#>     if (all(index_mar)) {
+#>         return(pars_group)
+#>     }
+#>     else if (all(!index_mar)) {
+#>         return(pars_ref)
+#>     }
+#>     mu <- pars_group$mu
+#>     last_mar <- which(!index_mar)[1] - 1
+#>     increments_from_last_mar_ref <- pars_ref$mu[!index_mar] - 
+#>         pars_ref$mu[last_mar]
+#>     mu[!index_mar] <- mu[last_mar] + increments_from_last_mar_ref
+#>     sigma <- compute_sigma(sigma_group = pars_group$sigma, sigma_ref = pars_ref$sigma, 
+#>         index_mar = index_mar)
+#>     pars <- list(mu = mu, sigma = sigma)
+#>     return(pars)
+#> }
+#> <bytecode: 0x55cc34117fd0>
+#> <environment: namespace:rbmi>
+

To further illustrate this for a simple example, assume that a new strategy is to be implemented as follows: +- The marginal mean of the imputation distribution is equal to the marginal mean trajectory for the subject according to their assigned group and covariates up to the ICE. +- After the ICE the marginal mean of the imputation distribution is equal to the average of the visit-wise marginal means based on the subjects covariates and the assigned group or the reference group, respectively. +- For the covariance matrix of the marginal imputation distribution, the covariance matrix from the assigned group is taken.

+

To do this, we first need to define the imputation function which for this example could be coded as follows:

+
+strategy_AVG <- function(pars_group, pars_ref, index_mar) {
+    mu_mean <- (pars_group$mu + pars_ref$mu) / 2
+    x <- pars_group
+    x$mu[!index_mar] <- mu_mean[!index_mar]
+    return(x)
+}
+

And an example showing its use:

+
+pars_group <- list(
+    mu = c(1, 2, 3),
+    sigma = as_vcov(c(1, 3, 2), c(0.4, 0.5, 0.45))
+)
+
+pars_ref <- list(
+    mu = c(5, 6, 7),
+    sigma = as_vcov(c(2, 1, 1), c(0.7, 0.8, 0.5))
+)
+
+index_mar <- c(TRUE, TRUE, FALSE)
+
+strategy_AVG(pars_group, pars_ref, index_mar)
+#> $mu
+#> [1] 1 2 5
+#> 
+#> $sigma
+#>      [,1] [,2] [,3]
+#> [1,]  1.0  1.2  1.0
+#> [2,]  1.2  9.0  2.7
+#> [3,]  1.0  2.7  4.0
+

To incorporate this into rbmi, data_ice needs to be updated such that the strategy AVG is specified for visits affected by the ICE. Additionally, the function needs +to be provided to impute() via the getStrategies() function as shown below:

+
+data_ice_AVG <- data_ice_CIR %>% 
+    mutate(strategy = ifelse(strategy == "CIR", "AVG", strategy))
+
+
+draw_obj <- draws(
+    data = data,
+    data_ice = data_ice_AVG,
+    vars = vars,
+    method = method,
+    quiet = TRUE
+)
+
+impute_obj <- impute(
+    draw_obj,
+    references = c("Control" = "Control", "Intervention" = "Control"),
+    strategies = getStrategies(AVG = strategy_AVG)
+)
+

Then, the analysis could proceed by calling analyse() and pool() as before.

+
+
+

+7 Custom analysis functions +

+

By default rbmi will analyse the data by using the ancova() function. +This analysis function fits an ANCOVA model to the outcomes from each visit separately, +and returns the “treatment effect” estimate as well as the corresponding least square means +for each group. If the user wants to perform a different analysis, or return different +statistics from the analysis, then this can be done by using a custom analysis function. +Beware that the validity of the conditional mean imputation method has only been formally established for analysis functions corresponding to linear models (such as ANCOVA) and caution is +required when applying alternative analysis functions for this method.

+

The custom analysis function must take a data.frame as its +first argument and return a named list with each element itself being a list +containing at a minimum a point estimate, called est. +For method method_bayes() or method_approxbayes(), the list must additionally contain a +standard error (element se) and, if available, the degrees of freedom of the complete-data analysis model (element df).

+

As a simple example, we replicate the ANCOVA analysis at the last visit for the CIR-based imputations with a user-defined analysis function below:

+
+compare_change_lastvisit <- function(data, ...) {
+    fit <- lm(change ~ group + outcome_bl, data = data, subset = (visit == 6) )
+    res <- list(
+        trt = list(
+            est = coef(fit)["groupIntervention"],
+            se = sqrt(vcov(fit)["groupIntervention", "groupIntervention"]),
+            df = df.residual(fit)
+        )
+    )
+    return(res)
+}
+
+ana_obj_CIR6 <- analyse(
+  impute_obj_CIR,
+  fun = compare_change_lastvisit,
+  vars = vars_an
+)
+
+pool(ana_obj_CIR6)
+#> 
+#> Pool Object
+#> -----------
+#> Number of Results Combined: 20
+#> Method: rubin
+#> Confidence Level: 0.95
+#> Alternative: two.sided
+#> 
+#> Results:
+#> 
+#>   =================================================
+#>    parameter   est     se     lci    uci     pval  
+#>   -------------------------------------------------
+#>       trt     -4.537  0.954  -6.42  -2.655  <0.001 
+#>   -------------------------------------------------
+

As a second example, assume that for a supplementary analysis the user wants to compare the proportion of subjects with a change from baseline of >10 points at the last +visit between the treatment groups with the baseline outcome as an additional covariate. This could lead to the following basic analysis function:

+
+compare_prop_lastvisit <- function(data, ...) {
+    fit <- glm(
+        I(change > 10) ~ group + outcome_bl,
+        family = binomial(),
+        data = data,
+        subset = (visit == 6)
+    )
+    res <- list(
+        trt = list(
+            est = coef(fit)["groupIntervention"],
+            se = sqrt(vcov(fit)["groupIntervention", "groupIntervention"]),
+            df = NA
+        )
+    )
+    return(res)
+}
+    
+ana_obj_prop <- analyse(
+  impute_obj_CIR,
+  fun = compare_prop_lastvisit,
+  vars = vars_an
+)
+
+pool_obj_prop <- pool(ana_obj_prop)
+pool_obj_prop
+#> 
+#> Pool Object
+#> -----------
+#> Number of Results Combined: 20
+#> Method: rubin
+#> Confidence Level: 0.95
+#> Alternative: two.sided
+#> 
+#> Results:
+#> 
+#>   =================================================
+#>    parameter   est     se     lci     uci    pval  
+#>   -------------------------------------------------
+#>       trt     -1.052  0.314  -1.667  -0.438  0.001 
+#>   -------------------------------------------------
+
+tmp <- as.data.frame(pool_obj_prop) %>% 
+    mutate(
+        OR = exp(est),
+        OR.lci = exp(lci),
+        OR.uci = exp(uci)
+    ) %>% 
+    select(parameter, OR, OR.lci, OR.uci)
+tmp
+#>   parameter        OR   OR.lci    OR.uci
+#> 1       trt 0.3491078 0.188807 0.6455073
+

Note that if the user wants rbmi to use a normal approximation to the pooled test statistics, then the degrees of freedom need to be set to df = NA (as per the above example). If the degrees of freedom of the complete data test statistics are known or if the degrees of freedom are set to df = Inf, then rbmi pools the degrees of freedom across imputed datasets according to the rule by Barnard and Rubin (see the “Statistical Specifications” vignette (vignette("stat_specs", package = "rbmi") for details). According to this rule, infinite degrees of freedom for the complete data analysis do not imply that the pooled degrees of freedom are also infinite. +Rather, in this case the pooled degrees of freedom are (M-1)/lambda^2, where M is the number of imputations and lambda is the fraction of missing information (see Barnard and Rubin (1999) for details).

+
+
+

+8 Sensitivity analyses: Delta adjustments and tipping point analyses +

+

Delta-adjustments are used to impute missing data under a not missing at random (NMAR) assumption. This reflects the belief that unobserved outcomes would have been systematically “worse” (or “better”) than “comparable” observed outcomes. For an extensive discussion of delta-adjustment methods, we refer to Cro et al. (2020).

+

In rbmi, a marginal delta-adjustment approach is implemented. This means that the delta-adjustment is applied to the dataset after data imputation under MAR or reference-based missing data assumptions and prior to the analysis of the imputed data. +Sensitivity analysis using delta-adjustments can therefore be performed without having to re-fit the imputation model. In rbmi, they are implemented via the delta argument of the analyse() function.

+
+

+8.1 Simple delta adjustments and tipping point analyses +

+

The delta argument of analyse() allows users to modify the outcome variable prior to the analysis. +To do this, the user needs to provide a data.frame which contains columns for the subject and visit (to identify the observation to be adjusted) plus an additional column called delta which specifies the value which will be added to the outcomes prior to the analysis.

+

The delta_template() function supports the user in creating this data.frame: it creates a skeleton data.frame containing one row per subject and visit with the value of delta set to 0 for all observations:

+
+dat_delta <- delta_template(imputations = impute_obj_CIR)
+head(dat_delta)
+#>     id visit   group is_mar is_missing is_post_ice strategy delta
+#> 1 id_1     1 Control   TRUE       TRUE        TRUE      MAR     0
+#> 2 id_1     2 Control   TRUE       TRUE        TRUE      MAR     0
+#> 3 id_1     3 Control   TRUE       TRUE        TRUE      MAR     0
+#> 4 id_1     4 Control   TRUE       TRUE        TRUE      MAR     0
+#> 5 id_1     5 Control   TRUE       TRUE        TRUE      MAR     0
+#> 6 id_1     6 Control   TRUE       TRUE        TRUE      MAR     0
+

Note that the output of delta_template() contains additional information which can be used to properly re-set variable delta.

+

For example, assume that the user wants to implement a delta-adjustment to the imputed values under CIR described in section 3.
+Specifically, assume that a fixed “worsening adjustment” of +5 points is applied to all imputed values regardless of the treatment group. This could be programmed as follows:

+
+# Set delta-value to 5 for all imputed (previously missing) outcomes and 0 for all other outcomes
+dat_delta <- delta_template(imputations = impute_obj_CIR) %>%
+    mutate(delta = is_missing * 5)
+
+# Repeat the analyses with the delta-adjusted values and pool results
+ana_delta <- analyse(
+    impute_obj_CIR,
+    delta = dat_delta,
+    vars = vars_an
+)
+pool(ana_delta)
+#> 
+#> Pool Object
+#> -----------
+#> Number of Results Combined: 20
+#> Method: rubin
+#> Confidence Level: 0.95
+#> Alternative: two.sided
+#> 
+#> Results:
+#> 
+#>   ==================================================
+#>    parameter   est     se     lci     uci     pval  
+#>   --------------------------------------------------
+#>      trt_1    -0.482  0.524  -1.516  0.552   0.359  
+#>    lsm_ref_1  2.718   0.37   1.987   3.448   <0.001 
+#>    lsm_alt_1  2.235   0.37   1.505   2.966   <0.001 
+#>      trt_2    -0.016  0.56   -1.12   1.089   0.978  
+#>    lsm_ref_2  3.907   0.396  3.125   4.688   <0.001 
+#>    lsm_alt_2  3.891   0.395  3.111   4.671   <0.001 
+#>      trt_3    -1.684  0.641  -2.948  -0.42   0.009  
+#>    lsm_ref_3  6.092   0.452  5.201   6.983   <0.001 
+#>    lsm_alt_3  4.408   0.452  3.515    5.3    <0.001 
+#>      trt_4    -2.359  0.741  -3.821  -0.897  0.002  
+#>    lsm_ref_4  7.951   0.526  6.913    8.99   <0.001 
+#>    lsm_alt_4  5.593   0.522  4.563   6.623   <0.001 
+#>      trt_5    -3.34   0.919  -5.153  -1.526  <0.001 
+#>    lsm_ref_5  9.899   0.643  8.631   11.168  <0.001 
+#>    lsm_alt_5  6.559   0.653  5.271   7.848   <0.001 
+#>      trt_6    -4.21   1.026  -6.236  -2.184  <0.001 
+#>    lsm_ref_6  11.435  0.718  10.019  12.851  <0.001 
+#>    lsm_alt_6  7.225   0.725  5.793   8.656   <0.001 
+#>   --------------------------------------------------
+

The same approach can be used to implement a tipping point analysis. Here, we apply different delta-adjustments to imputed data from the control and the intervention group, respectively. Assume that delta-adjustments by less then -5 points or by more than +15 points are considered implausible from a clinical perspective. Therefore, we vary the delta-values in each group between -5 to +15 points to investigate which delta combinations lead to a “tipping” of the primary analysis result, defined here as an analysis p-value \(\geq 0.05\).

+
+perform_tipp_analysis <- function(delta_control, delta_intervention) {
+
+    # Derive delta offset based on control and intervention specific deltas    
+    delta_df <-  delta_df_init %>%
+        mutate(
+            delta_ctl = (group == "Control") * is_missing * delta_control,
+            delta_int = (group == "Intervention") * is_missing * delta_intervention,
+            delta = delta_ctl + delta_int
+        )
+
+    ana_delta <- analyse(
+        impute_obj_CIR,
+        fun = compare_change_lastvisit,
+        vars = vars_an,
+        delta = delta_df,
+    )
+
+    pool_delta <- as.data.frame(pool(ana_delta))
+
+    list(
+        trt_effect_6 = pool_delta[["est"]],
+        pval_6 = pool_delta[["pval"]]
+    )
+}
+
+# Get initial delta template
+delta_df_init <- delta_template(impute_obj_CIR)
+
+tipp_frame_grid <- expand.grid(
+    delta_control = seq(-5, 15, by = 2),
+    delta_intervention = seq(-5, 15, by = 2)
+) %>%
+    as_tibble()
+
+
+tipp_frame <- tipp_frame_grid %>%
+    mutate(
+        results_list = map2(delta_control, delta_intervention, perform_tipp_analysis),
+        trt_effect_6 = map_dbl(results_list, "trt_effect_6"),
+        pval_6 = map_dbl(results_list, "pval_6")
+    ) %>%
+    select(-results_list) %>%
+    mutate(
+        pval = cut(
+            pval_6,
+            c(0, 0.001, 0.01, 0.05, 0.2, 1),
+            right = FALSE,
+            labels = c("<0.001", "0.001 - <0.01", "0.01- <0.05", "0.05 - <0.20", ">= 0.20")
+        )
+    )
+
+# Show delta values which lead to non-significant analysis results
+tipp_frame %>%
+    filter(pval_6 >= 0.05)
+#> # A tibble: 3 × 5
+#>   delta_control delta_intervention trt_effect_6 pval_6 pval        
+#>           <dbl>              <dbl>        <dbl>  <dbl> <fct>       
+#> 1            -5                 15        -1.99 0.0935 0.05 - <0.20
+#> 2            -3                 15        -2.15 0.0704 0.05 - <0.20
+#> 3            -1                 15        -2.31 0.0527 0.05 - <0.20
+
+ggplot(tipp_frame, aes(delta_control, delta_intervention, fill = pval)) +
+    geom_raster() +
+    scale_fill_manual(values = c("darkgreen", "lightgreen", "lightyellow", "orange", "red"))
+

+

According to this analysis, the significant test result from the primary analysis under CIR could only be tipped to a non-significant result for rather extreme delta-adjustments. Please note that for a real analysis it is recommended to use a smaller step size in the grid than what has been used here.

+
+
+

+8.2 More flexible delta-adjustments using the dlag and delta arguments of delta_template() +

+

So far, we have only discussed simple delta arguments which add the same value to all imputed values. +However, the user may want to apply more flexible delta-adjustments to missing values after an intercurrent event (ICE) and vary the magnitude of the delta adjustment depending on the how far away the visit in question is from the ICE visit.

+

To facilitate the creation of such flexible delta-adjustments, the delta_template() function has two optional additional arguments delta +and dlag. The delta argument specifies the default amount of delta +that should be applied to each post-ICE visit, whilst +dlag specifies the scaling coefficient to be applied based upon the visits proximity +to the first visit affected by the ICE. By default, the delta will only be added to unobserved (i.e. imputed) post-ICE +outcomes but this can be changed by setting the optional argument missing_only = FALSE.

+

The usage of the delta and dlag arguments is best illustrated with a few examples:

+

Assume a setting with 4 visits and that the user specified delta = c(5,6,7,8) and dlag=c(1,2,3,4).

+

For a subject for whom the first visit affected by the ICE is visit 2, these values of delta and dlag would imply the following delta offset:

+
v1  v2  v3  v4
+--------------
+ 5   6   7   8  # delta assigned to each visit
+ 0   1   2   3  # scaling starting from the first visit after the subjects ICE
+--------------
+ 0   6  14  24  # delta * scaling
+--------------
+ 0   6  20  44  # cumulative sum (i.e. delta) to be applied to each visit
+

That is, the subject would have a delta offset of 0 applied to visit v1, 6 for visit v2, 20 for visit v3 and 44 for visit v4.

+

Assume instead, that the subject’s first visit affected by the ICE was visit 3. Then, the above values of delta and dlag would imply the following delta offset:

+
v1  v2  v3  v4
+--------------
+ 5   6   7   8  # delta assigned to each visit
+ 0   0   1   2  # scaling starting from the first visit after the subjects ICE
+--------------
+ 0   0   7  16  # delta * scaling
+--------------
+ 0   0   7  23  # cumulative sum (i.e. delta) to be applied to each visit
+

To apply a constant delta value of +5 to all visits affected by the ICE +regardless of their proximity to the first ICE visit, one could set delta = c(5,5,5,5) and dlag = c(1,0,0,0). +Alternatively, it may be more straightforward for this setting to call the delta_template() function without the delta and dlag arguments and then overwrite the delta column of the resulting data.frame as described in the previous section (and additionally relying on the is_post_ice variable).

+

Another way of using these arguments is to set delta to the difference in time +between visits and dlag to be the amount of delta per unit of time. For example, +let’s say that visits occur on weeks 1, 5, 6 and 9 and that we want a delta of 3 +to be applied for each week after an ICE. +For simplicity, we assume that the ICE occurs immediately after the subject’s last visit which +is not affected by the ICE. This this could be achieved by setting +delta = c(1,4,1,3) (the difference in weeks between each visit) and dlag = c(3, 3, 3, 3).

+

Assume a subject’s first visit affected by the ICE was visit v2, then these values of delta and dlag would imply the following delta offsets:

+
v1  v2  v3  v4
+--------------
+ 1   4   1   3  # delta assigned to each visit
+ 0   3   3   3  # scaling starting from the first visit after the subjects ICE 
+--------------
+ 0  12   3   9  # delta * scaling
+--------------
+ 0  12  15  24  # cumulative sum (i.e. delta) to be applied to each visit
+

To wrap up, we show this in action for our simulated dataset from section 2 and the imputed datasets +based on a CIR assumption from section 3. +The simulation setting specified follow-up visits at months 2, 4, 6, 8, 10, and 12.
+Assume that we want to apply a delta-adjustment of 1 for every month after an ICE to unobserved post-ICE visits from the intervention group only. (E.g. if the ICE occurred immediately after the month 4 visit, then the total delta applied to a missing value from the month 10 visit would be 6.)

+

To program this, we first use the delta and dlag arguments of delta_template() to set up a corresponding template data.frame:

+
+delta_df <- delta_template(
+    impute_obj_CIR,
+    delta = c(2, 2, 2, 2, 2, 2),
+    dlag = c(1, 1, 1, 1, 1, 1)
+)
+
+head(delta_df)
+#>     id visit   group is_mar is_missing is_post_ice strategy delta
+#> 1 id_1     1 Control   TRUE       TRUE        TRUE      MAR     2
+#> 2 id_1     2 Control   TRUE       TRUE        TRUE      MAR     4
+#> 3 id_1     3 Control   TRUE       TRUE        TRUE      MAR     6
+#> 4 id_1     4 Control   TRUE       TRUE        TRUE      MAR     8
+#> 5 id_1     5 Control   TRUE       TRUE        TRUE      MAR    10
+#> 6 id_1     6 Control   TRUE       TRUE        TRUE      MAR    12
+

Next, we can use the additional metadata variables provided by delta_template() to manually +reset the delta values for the control group back to 0:

+
+delta_df2 <- delta_df %>%
+    mutate(delta = if_else(group == "Control", 0, delta))
+
+head(delta_df2)
+#>     id visit   group is_mar is_missing is_post_ice strategy delta
+#> 1 id_1     1 Control   TRUE       TRUE        TRUE      MAR     0
+#> 2 id_1     2 Control   TRUE       TRUE        TRUE      MAR     0
+#> 3 id_1     3 Control   TRUE       TRUE        TRUE      MAR     0
+#> 4 id_1     4 Control   TRUE       TRUE        TRUE      MAR     0
+#> 5 id_1     5 Control   TRUE       TRUE        TRUE      MAR     0
+#> 6 id_1     6 Control   TRUE       TRUE        TRUE      MAR     0
+

Finally, we can use our delta data.frame to apply the desired delta offset to our analysis:

+
+ana_delta <- analyse(impute_obj_CIR, delta = delta_df2, vars = vars_an)
+pool(ana_delta)
+#> 
+#> Pool Object
+#> -----------
+#> Number of Results Combined: 20
+#> Method: rubin
+#> Confidence Level: 0.95
+#> Alternative: two.sided
+#> 
+#> Results:
+#> 
+#>   ==================================================
+#>    parameter   est     se     lci     uci     pval  
+#>   --------------------------------------------------
+#>      trt_1    -0.446  0.514  -1.459  0.567   0.386  
+#>    lsm_ref_1   2.62   0.363  1.904   3.335   <0.001 
+#>    lsm_alt_1  2.173   0.363  1.458   2.889   <0.001 
+#>      trt_2    0.072   0.546  -1.006   1.15   0.895  
+#>    lsm_ref_2  3.708   0.387  2.945   4.471   <0.001 
+#>    lsm_alt_2   3.78   0.386  3.018   4.542   <0.001 
+#>      trt_3    -1.507  0.626  -2.743  -0.272  0.017  
+#>    lsm_ref_3  5.844   0.441  4.973   6.714   <0.001 
+#>    lsm_alt_3  4.336   0.442  3.464   5.209   <0.001 
+#>      trt_4    -2.062  0.731  -3.504  -0.621  0.005  
+#>    lsm_ref_4  7.658   0.519  6.634   8.682   <0.001 
+#>    lsm_alt_4  5.596   0.515   4.58   6.612   <0.001 
+#>      trt_5    -2.938  0.916  -4.746  -1.13   0.002  
+#>    lsm_ref_5  9.558   0.641  8.293   10.823  <0.001 
+#>    lsm_alt_5   6.62   0.651  5.335   7.905   <0.001 
+#>      trt_6    -3.53   1.045  -5.591  -1.469  0.001  
+#>    lsm_ref_6  11.045  0.73   9.604   12.486  <0.001 
+#>    lsm_alt_6  7.515   0.738  6.058   8.971   <0.001 
+#>   --------------------------------------------------
+
+
+
+

References +

+
+
+Barnard, John, and Donald B Rubin. 1999. “Miscellanea. Small-Sample Degrees of Freedom with Multiple Imputation.” Biometrika 86 (4): 948–55. +
+
+Cro, Suzie, Tim P Morris, Michael G Kenward, and James R Carpenter. 2020. “Sensitivity Analysis for Clinical Trials with Missing Continuous Outcome Data Using Controlled Multiple Imputation: A Practical Guide.” Statistics in Medicine 39 (21): 2815–42. +
+
+Guizzaro, Lorenzo, Frank Pétavy, Robin Ristl, and Ciro Gallo. 2021. “The Use of a Variable Representing Compliance Improves Accuracy of Estimation of the Effect of Treatment Allocation Regardless of Discontinuation in Trials with Incomplete Follow-up.” Statistics in Biopharmaceutical Research 13 (1): 119–27. +
+
+Noci, Alessandro, Marcel Wolbers, Markus Abt, Corine Baayen, Hans Ulrich Burger, Man Jin, and Weining Zhao Robieson. 2021. “A Comparison of Estimand and Estimation Strategies for Clinical Trials in Early Parkinson’s Disease.” https://arxiv.org/abs/2112.03700. +
+
+Wolbers, Marcel, Alessandro Noci, Paul Delmar, Craig Gower-Page, Sean Yiu, and Jonathan W. Bartlett. 2022. “Standard and Reference-Based Conditional Mean Imputation.” Pharmaceutical Statistics. https://doi.org/10.1002/pst.2234. +
+
+
+
+
+ + + +
+

Developed by Craig Gower-Page, Alessandro Noci, Roche.

+
+ +
+

Site built with pkgdown 2.1.0.

+
+ +
+
+ + + + + + + diff --git a/latest-tag/articles/advanced_files/figure-html/unnamed-chunk-18-1.png b/latest-tag/articles/advanced_files/figure-html/unnamed-chunk-18-1.png new file mode 100644 index 0000000000000000000000000000000000000000..884f4653896c223617f93315595fb8a0147f65fe GIT binary patch literal 10981 zcmch72{@H&+xA1sR0ttLhKP_UlyR?+WXOzi_Rk)a?+2pyuQUqrvL^BLumO+!g;#**R`$%tedTI6-tZ{d#eU zL=rCQjKp*}-&Icau=A(IRf93e{byO<`yi; zv)t@xYpa-{{CYO7WoCWX9Y2ztIN+OY@s(TTI9QlZs)&U*oQ0C_4~~qOXM5!lb-t=$ zA^685|Lr{fpKf+-@d}dQt{jNgT!U{R@Mp+t?YXfV!32*r{_gH*RiO_mP)IwXV}%%x zU6Id!P}1vs_j8+xo-CBmIpQuwD1>+s&g06RGkDSMSe8= z^D=RXeM08jT~9owxs#SEk89ucv8j63xiWpp*>EPC*D$ooD0$#6GO0Q5btSdVQL00` zO}jc*tU&|STT3rYf{ON!rd)LY;5d)Q!mz}*esStgTlMd!_$qaoqsVbdLr*VB?6RIw zPj|POxp~ao4fFoR;rtfVG$t~5HW15RNvX$erF>_9;#h~gQ!!(6wAgNYd)tSIgWP`! zLF!{flR{~PT{phV`l6CG>7!b3>FC277Y#6wLCy{A%_pa@ap-JpY)Y=Mv$Okz_4f9L zgb)OXe>U!*AtxtK%P1|~@6AwZwYRojeJc^zszX>MUNgPs6M`~V#W1R>uX#y|S6Ak; z;kYzBv$z-+85tQL@3H#zH*F;smzz9?5s3%lPSRM2WN^*o*UU#$gE+Irc0Xut**}j7 z2?`Q&|M@AqX>9DU<$7grTR~xAY>eK{qr1? zUpIDt7E&Wsq!w8Anwlfw40kj%HE&S1`Qnp5efCV9f)vp!Gngj5BPI1*nN?O^UX?ZS z=;%n$aZ&H#eZ+gMT@fQbHdfG?@7L9jI#~n8Q65Z0j0KAqK~jl25#$w0>=bez598cl zZ@vf?#BIBt{AKup%w_e^4$V?5Z(O`P|M$=Q?Lhz2n1}3z+Fn&PiX}6k?llLN-X*0BWhSr4RqeNwayH1nn?!b|Kz+`~h96nlm}+em zx=UzBTv6g%*X)P34g0e(Tao{b3H{4zH!EUzEDhgCRm_OZ`tU*e?%ko!hAlBX#=Mll zzGqG&pRSPM&ZecM?Tow0lXsfs@6`s7+=Vv{4eczCU1MR%txZA@K0m8%ntuv(MRj!+ zvOsb+H8Grk7ccg z2r^=DIFEsN z9~&^9jHm0qCQ+;<@~BuPGhgj4!kPQN&rBrI>u_%i(o&<8tc2oboz2&(C(+T-rA{ja z#}tBf7>LXx$&jSj*jOHJ?(eNJHF7r)B+1|#jw}4<#*G_`uP~8mITx2=Kp%*Mva&KJ z`Ghiyq0jU4W~j#DeDi@E-5kxO!<`8yCnrd92L}f(E-o4xnh0ij0z$%^k00}NmZrVK z8D?8sTVcE4dqs{9HXI!tOJ*KEd`QM|7OD5A_53-xI#~nInc(l=(3NuM_U+r1`^%-l zRQ$`M#Y!AeBO@auC|g@wVq#(}%-`(oHvtGm_ZN8x{5R^UtQfCc87s1>8BX-pk;HVZ z@jK@eR^v}dsGjX+z>mmSQwD#ZI9i(UJlaSkK^@H{9yK*I{Fn|E9WQan)X1u^pHsBA zx4(Dqo~h}a;7VCapIM>R1Pv9Hph5jvRO8Ns*KzZ;9JNyS4;OJwu(%M43p6zCGEkb? zH1cNK;|1x%O}6IyPF5a9(a}i|r{A5E_majLLK}Ce>UwNV`qTFJ_G%T|`Zm-ogrDW| zq|Q^xWM^T?$jDI0Q0@@k(Na^R@UMB8%#BclItwL8r_%da41Rjpks!<(IWOSwkK9F6$H=pSf*4Wq>rQUA*fb-d%{wLN6)6INYa zE$FnImzP&mR78Y=nHi6Lo-_W&8dC1Iv+i8Bx3{-avA;ZC?zXkHB}8L&d~~=-ij7QH zS9t7K9IUr24CGi1=iN=dZ!uC3Of>cFTb@zd8;|{M32|{}7nk#>(b3T}XU?o|Y|x6h zM<*sGCM1jx4EXP}p5-L&jJmEX;<`DTXWA33Ze_JH-4N=v(Z>J!I>bgWp~%m#5F6K2 zKF-e1v$C+na2w*G-KXn=At7V=t&EJw*)=x%HLc2z_7hZXP#5OJ#3ycXS6a}TqAS&!6vsYRyl>r+$u->;t!5GSUg329kmGoHxVR)<=3esyQ3((Ab5+KM_1d?5t>hofiN*0Vl1t!B))#_c<|H4%gd|$ z+Secv#5;A4(4<(aee~(>x0F3V0qQ+3#ym6{ijq%QbTmaN?ZV26yo}7FLMg;MKPyQHMA0Nl@F=NA?NU%XIze-_bu+C$rUczF1uBhfg`f#fVll-uU40{#OA zA{-eFd4uFN3=0hZ8m_HO-tF9%t=6(p+q|AV-SS>yrKP1&uU`|RFi)L|!F7z@Tl@`+ zw`x`35&_M$DOHdbwzh%-T~tf#n>T)JUf*Mk*%X2^D(_Am3wuwR@=}siN~4lHyX&gd zC~#*XK*%ADnvMsHS_zP(un+#EC!;dz#7TQ&tC}DH8)p*KAaQ( z3USjjY{D}+X@w3&vH*UrZf+3s>U(EX^cj;NiztYRUji_MhyR8%RBV@?n5b)Dz;G=e zqe`4PQksW{2P*D;W#w<*zxQ`_(GdqkGlfg_c6D(%8We%^ zz5V^%J-`>J)^Cab<58d<(5#dEsH*Z_UO9~*_yK`|nHd>24i4J+W)Ixm-PP4E(met$ z0_6pOPBF6&D975`T5I#n%nWyojMaj5Y!E)|AwY5E(N>P2pkT}pY#BV1-*&vVkp$!B zH~P8GK?aj9on(gJ)GvjEgs5g~v0T5NlbsDy-QL=Y7k1+ljD+fIEr~hY(sGTC?qLg1 zl)}e`sWb@kQ&h73x60k`vB2V=Jb6-4QPB`eYxVfCQ92ufK*%*UHAzTF$jZt>Ijyjs zd>R_s@!>(H$l<1vBlI-UjX@LhvN$of9OeO~ICLuxXph%uy>?r4iAhMUU>HEI9T85Na<@fo^C-QRAgny;)V&?3Bk$i|9TmnwsXup2dLp1`m@MGgY zz#@dl6=c8&zI+DxLNk6Mq60~r>JOR4E2~G?uEbY0n;+jXeIt~!&Jd^CR7d2ulJd@z^O^5|`U030W-bW7sl5A|&pwGQTP2Kix<78w8QQ``q zP(EQmlOMx8;UDbh^5SApU?5c&g{7tCosdhWmX>`cyyP5OddqTH2&dlPpb=DjTbS7i zUQ4oaaAc>Y)%W!|^nG}60#z%IV80eOX@7$#|5uj`Z*L8>t_erxKVXEwn=Si~vUA(^Z zUJ(&fP&CSb=G78(Af@&%)m?=cL5TEu|L8^Pn5`7^o=iLkY3zhY0=25GtsO0QD?Dr% z1hq+zp2rqzY;-g(Jp7AR^;rb501hT>_r3Ma8%jrM;4@!>DkC_rT$x!OEA0?HKKR;@ zL7!<>~c(8faPcb7;Spq@CaJVQtar62 zSw}|){%}=Y-Pk}sdH@J2BeuW4pPrgZE$TTsF`>JHS42Pwlw%V+Pm9A?N5=4{u*Q$} z_*eAR#Dgnp^=v{qUF4TEY@>ap)wlWyE5Fa;lnPG`mz z)IL!tQSU_-K7kM-meLY+T|SnNou0nBsfp#rjjhEYbXs1vZl%cO%SoA;f$`_vTwMh& zg~Y~E*)2!i=T%rhj zIG|`#>b)p?{LIWu5)zVuZ0&`~Nd*N3oB3WAC#M~SAc{+u;t~^AW@fGs;V3F9RzMZZ z^<1RA4PsERQERkm%e@D zoj-3iT4Xa_f6i#~-Q5UDd$T%H27+18p@)7b-SSxexugv77LQs+C;T{0i^XJb^ zd(txc=AkKywIL)z3HT*`0AQ}4v@WokNO<#RZ@F}3efI!XI*|N2bZrA=4xBRXF|YUO zobCk?=ux4JeZO0F@EFSGstJAXqHGvFMfa%W=B?b{B(sP27j%#Jeuy656cRcBKqL*U z1L~VwSlH3n__HVRs8-Gcg8y~6(j@`{0?J@NB5pu?=n;ufPoF-`)+(-+G=SRMExR(3HSE=5$~j-p#kk*N=gd$nKME{La=kKo?Ltg zBGlA)a~7z_Sh3xm%syLzIH23r)hOV?5`kp7C)8RT@?6(>(l@8_=uOQe5egPeSy|cG z*jP(T>+$34D<8VX?&2aT_0wys4?drRjw(H!Sx_*RNb&xC>#5p+FS5|G0$(EgAnC+> z1qphik;S)``Oa;(e%522ryo!b*V#A06Fyg2c?_ksHSh7$FEh3vVVNbHt$w(TpsQ4y z_+|-BAU?ah6;V=pdY5@H5QGhSCJ2<)+6<+1`<`d)T1A~tB`|;-V1QI*;mmV@aT?Jx z-8-d`s+g;V4PlUcEO}x4GIz`(Ajsa5l~>F3+J_GxqE#FNv5@HJL8Et}>8uG03xmxJ z3Ig!~w)61dfRd_h{eTrgZcE?02mRT~+*~ox^n49V>4j=v{F}#NFJI~>CwC-@R_c8L z39RtKHI-Q%Z4>8+E-vCuD1ihxI3{Lh`r|6GQBllffjcV`R_yItN<=x}vU|09i3dqp}&wZ(?GK z^}alP`<5P*#+uuLH*a#~Y#tqKbb!v&$3NlTW0Ic&p&CjpD2d}TpOLQ28VT*DU579~ zlm{a^#Cry`mDz-=U~I;GC!G)O7X@j^cX&;_Lv zNUo6!sE9I5JtM<|gB0ZC1D%~oDw*=}w=R&77}&ppg@LlKsygr>Q>CtGaY4pvq?TwIsW4xogdJ9iG4EuQaG;_+^xY+0+^RZ=FHATu*L z8Cimmi(*QbkDnhU#ol$V-Fr4RTpO?4QP0)b9-Es}We;;rQFwl+KT-9d&aJ~XE6)uop|jvRo|fc5yB#0yfO-o0S378qQ2DU2{XvC-rXGpz z48q#{sE&qW2c2TihYzeYp@mRoe0;F5v9bSX97SbnySi)|La5n5_(h%tubavvh|gEN zn=Rby40H&hhW{`A=0BNcY}!Vu4<2lN!KSr$a=Nbm`CDp~4&}imAY7e+!AZ)Qeav!k zQqt1R?d=s#D>`@Y;^IHUM1q{aK{7N<1^V$x?@4*N2OztG!uda5ikmXAwA|X>HcBfm zFCQ5mwwDF%1jzWqG*~NodV00@*i{A4%ZyQ_J=8K_NHXM(S>Ba~dIW-62~)6SO6-HH zqyqfsI2|t$VC!BPsrrjGkeRu#;<+O@_Awx*3==t}%=+NO$1#618L6sw*W!Q&*hyD! zZ>cobh?&p7zf{jcKojb;`t=K_g}$is zq@;NuFq46ILb0M&qA)XLoY>aftZxi`{D^m=>%Vi2|7lhK+e-f5xVe_4T^0IVoZdVN z;arA=b&s?-rH#QJ(ezOpv#KavB~$i*6;MUJgn|M;4rH~z_}%{-5BVpz{o#v87ZC(6 z@Z>DOpX}T^W8-L$v!P!!a&3EaqttOp-&hO0$G}wY*V3uL@6m;Y%s$q|0v6Z^XGpO3 z>y{ZGqHw9&YBlh%o&|e9HZ`U5d6$+3Cg__h=SafK&D`nFbkQBEkW&^P~3Z z=qIU|d%yz$XRpvPvaqmRzWlW_`F86%2=w4!EU3hC8#aTr8f{8TOB*9&lkmCY2b|jhxqd4%kQ>St_{I0Mn-YgNSGrWM)2}F2BqYWnD2Jz z%>8vO1_7<1vhKk7&pz$%_G$>&QI6-16N?k-=t%K)-93$s)74$6^g)D!G<`jL5>F{z zYO?_fudj{t^*ut{s50Ll&U}Za030A)-NYfc?`zJW zLV|Du4Ko|{i#mPu<|(qL4FBik#s6m{N(@X(ww>X$I&$5?p`k3D^5J3M zpw>II9=yiwallg5OT*50n((ev9v^k5-h*a6OS=Wf-q8`557_bfJ!!INNA{{SBSKHX zm6%UFJ}@>{<5CSk_p272g5mmO?eNSuo=TCn-_oYYmAk=~Se;N~mM^I<`AnbF)bb^0Z z=kR;ogDG;@-&4OX*2w1J8H%@x83mRjDxgM8OdMzG9)9@rsdM;3jM*g^2AsA^Xw~3& zi;-j5;x3(`dBY?Ol-tGtCWjmjxG4P`wT#fm1Lgbj^t4ad`}gmyDi8UDghq!YF7j76 za?^bS8y0d<`6_A2m1+GBQT6Sjkqu6>f{UGhccQ|a{Km)reibV#E2H6&k8qIw2(D4l-|8lJ0 zPsc0@WEhFHE8Ja*RPr_2Cas%W;2aD{Y{J6AY_IuxrSK`VguJuh><67sZGFAQPK=<+ zb;KKmW0Ir_X`g=&`NV{kC!3L(shMxq_liXc6BDyyZ~ofYI>cs_uBXf1=3IMwJ17bh zUdM-Dvmi8Enwz`3yLFr=Pp3tIFGffh3S`ya-(OWlg_0&OE6Wn{6Fv-XdMnt1X)2kx zbkGD`ulV^kJv}`tYT&x(THyA!qmkhgG3%oP7vPq6?leHd3ca7OI%2o158DF@-dR#rLix%HWrz`#Jzg38Lvi?x;uCp51)4O}lU)slv2}$<1va98$W|L@3W~bmHM8 zp_el>oYvasIB2R==#nfOK)2A=(gNNdJp^hx39169H?(B*d0HN@MuI&LrvNt3 z@O99coY*Uy8DeJSIt7N^Z20S*xuH54=nHKA?R>tf&a0xv3*-}4h{}(t@iUf zLldg1rS-ATY}fs$@@S!wyJcm({2mVMWsk`D&JqW|1qc)p3^|*@5WpLl4eS#S8dkuV z2}GVq=~QqV#V$hy00jrszT#qaE9OfCCtfUAR3HwxYH5vvsdbKUudwp4ZD2rkr#Y2f zv%vl5q<>#u9}lQUe3q1GYlx7Zba}91jEoY!j$DkY+uAIP9((QeB+kF!@4Vn`iEe8M zmXiUKd-Y>{JT;PwP~crT?Gp_M5~qz5N(u(NY@)UG3V`F2d^ zHE$bi3`@bO>1i+vr>CZXkZkK7Z|MU002j(93~0hf%|Z?)CdmD-04o6j0g!pOxkFlJ z#w)!l(dZsn4G=g)MUN+I{C9VEL6L@nc7`MFc}U3i=BA8{3>-51PJ8^b0XkkDh)V!q z2uV100U^nL?b=9@O-f~@mzrAilPQPOXVpbsz~ue&<)1#gIacaqRrlfoq$h+ITsvD^ zhS^iIOG{&5)X96UN3bZRfkX^rns~X>)k35JPtrFqfUE$6|Axc7GBk+4cZvbx`ae-2 zP;fm#tk2HQhAr9OUNXpTHZeE9CndGIx*C1U!AxIYUsIC`&7IhBot0HTEoRi%lkt)k zLig_DN11`j@$6LwIvFhI6gYXB`ukzPq8F!vuCcQpwpn?m6q+vdXTkEY4V>cq?JJyh h_{W?610&z6By2GHy5;UY4JtWuS3*ua8>RR3zW|)-xMBbR literal 0 HcmV?d00001 diff --git a/latest-tag/articles/index.html b/latest-tag/articles/index.html new file mode 100644 index 00000000..897ceae3 --- /dev/null +++ b/latest-tag/articles/index.html @@ -0,0 +1,77 @@ + +Articles • rbmi + Skip to contents + + +
+
+
+ +

Articles

+
+ +
+

All vignettes

+
+ +
rbmi: Advanced Functionality
+
+
rbmi: Quickstart
+
+
rbmi: Statistical Specifications
+
+
+
+ + +
+

Developed by Craig Gower-Page, Alessandro Noci, Roche.

+
+ +
+

Site built with pkgdown 2.1.0.

+
+ +
+ + + + + + + diff --git a/latest-tag/articles/quickstart.html b/latest-tag/articles/quickstart.html new file mode 100644 index 00000000..a65178c5 --- /dev/null +++ b/latest-tag/articles/quickstart.html @@ -0,0 +1,636 @@ + + + + + + + +rbmi: Quickstart • rbmi + + + + + + + + Skip to contents + + +
+ + +
+
+ +

rbmi: Quickstart

+

Craig Gower-Page, Alessandro Noci, and Marcel Wolbers

+ + + Source: vignettes/quickstart.Rmd +
quickstart.Rmd
+
+ + + +
+

+1 Introduction +

+

The purpose of this vignette is to provide a 15 minute quickstart guide to the core functions of the rbmi package.

+

The rbmi package consists of 4 core functions (plus several helper functions) which are typically called in sequence:

+
    +
  • +draws() - fits the imputation models and stores their parameters
    • +
  • +impute() - creates multiple imputed datasets
    • +
  • +analyse() - analyses each of the multiple imputed datasets
    • +
  • +pool() - combines the analysis results across imputed datasets into a single statistic
    • +
+
+
+

+2 The Data +

+

We use a publicly available example dataset from an antidepressant clinical trial of an active drug versus placebo. The relevant endpoint is the Hamilton 17-item depression rating scale (HAMD17) which was assessed at baseline and at weeks 1, 2, 4, and 6. Study drug discontinuation occurred in 24% of subjects from the active drug and 26% of subjects from placebo. All data after study drug discontinuation are missing and there is a single additional intermittent missing observation.

+
+library(rbmi)
+library(dplyr)
+#> 
+#> Attaching package: 'dplyr'
+#> The following objects are masked from 'package:stats':
+#> 
+#>     filter, lag
+#> The following objects are masked from 'package:base':
+#> 
+#>     intersect, setdiff, setequal, union
+
+data("antidepressant_data")
+dat <- antidepressant_data
+

We consider an imputation model with the mean change from baseline in the HAMD17 score as the outcome (variable CHANGE in the dataset). The following covariates are included in the imputation model: the treatment group (THERAPY), the (categorical) visit (VISIT), treatment-by-visit interactions, the baseline HAMD17 score (BASVAL), and baseline HAMD17 score-by-visit interactions. A common unstructured covariance matrix structure is assumed for both groups. The analysis model is an ANCOVA model with the treatment group as the primary factor and adjustment for the baseline HAMD17 score.

+

rbmi expects its input dataset to be complete; that is, there must be one row per subject for each visit. Missing outcome values should be coded as NA, while missing covariate values are not allowed. If the dataset is incomplete, then the expand_locf() helper function can be used to add any missing rows, using LOCF imputation to carry forward the observed baseline covariate values to visits with missing outcomes. Rows corresponding to missing outcomes are not present in the antidepressant trial dataset. To address this we will therefore use the expand_locf() function as follows:

+
+
+# Use expand_locf to add rows corresponding to visits with missing outcomes to the dataset
+dat <- expand_locf(
+    dat,
+    PATIENT = levels(dat$PATIENT), # expand by PATIENT and VISIT 
+    VISIT = levels(dat$VISIT),
+    vars = c("BASVAL", "THERAPY"), # fill with LOCF BASVAL and THERAPY
+    group = c("PATIENT"),
+    order = c("PATIENT", "VISIT")
+)
+
+
+

+3 Draws +

+

The draws() function fits the imputation models and stores the corresponding parameter estimates or Bayesian posterior parameter draws. +The three main inputs to the draws() function are:

+
    +
  • +data - The primary longitudinal data.frame containing the outcome variable and all covariates.
    • +
  • +data_ice - A data.frame which specifies the first visit affected by an intercurrent event (ICE) and the imputation strategy for handling missing outcome data after the ICE. At most one ICE which is to be imputed by a non-MAR strategy is allowed per subject.
    • +
  • +method - The statistical method used to fit the imputation models and to create imputed datasets.
    • +
+

For the antidepressant trial data, the dataset data_ice is not provided. However, it can be derived because, in this dataset, +the subject’s first visit affected by the ICE “study drug discontinuation” corresponds to the first terminal missing observation. +We first derive the dateset data_ice and then create 150 Bayesian posterior draws of the imputation model parameters.

+

For this example, we assume that the imputation strategy after the ICE is Jump To Reference (JR) for all subjects +and that 150 multiple imputed datasets using Bayesian posterior draws from the imputation model are to be created.

+
+# create data_ice and set the imputation strategy to JR for
+# each patient with at least one missing observation
+dat_ice <- dat %>% 
+    arrange(PATIENT, VISIT) %>% 
+    filter(is.na(CHANGE)) %>% 
+    group_by(PATIENT) %>% 
+    slice(1) %>%
+    ungroup() %>% 
+    select(PATIENT, VISIT) %>% 
+    mutate(strategy = "JR")
+
+# In this dataset, subject 3618 has an intermittent missing values which does not correspond
+# to a study drug discontinuation. We therefore remove this subject from `dat_ice`. 
+# (In the later imputation step, it will automatically be imputed under the default MAR assumption.)
+dat_ice <- dat_ice[-which(dat_ice$PATIENT == 3618),]
+
+dat_ice
+#> # A tibble: 43 × 3
+#>    PATIENT VISIT strategy
+#>    <fct>   <fct> <chr>   
+#>  1 1513    5     JR      
+#>  2 1514    5     JR      
+#>  3 1517    5     JR      
+#>  4 1804    7     JR      
+#>  5 2104    7     JR      
+#>  6 2118    5     JR      
+#>  7 2218    6     JR      
+#>  8 2230    6     JR      
+#>  9 2721    5     JR      
+#> 10 2729    5     JR      
+#> # ℹ 33 more rows
+
+# Define the names of key variables in our dataset and
+# the covariates included in the imputation model using `set_vars()`
+# Note that the covariates argument can also include interaction terms
+vars <- set_vars(
+    outcome = "CHANGE",
+    visit = "VISIT",
+    subjid = "PATIENT",
+    group = "THERAPY",
+    covariates = c("BASVAL*VISIT", "THERAPY*VISIT")
+)
+
+# Define which imputation method to use (here: Bayesian multiple imputation with 150 imputed datsets) 
+method <- method_bayes(
+    burn_in = 200,
+    burn_between = 5,
+    n_samples = 150,
+    seed = 675442751
+)
+
+# Create samples for the imputation parameters by running the draws() function
+set.seed(987)
+drawObj <- draws(
+    data = dat,
+    data_ice = dat_ice,
+    vars = vars,
+    method = method,
+    quiet = TRUE
+)
+drawObj
+#> 
+#> Draws Object
+#> ------------
+#> Number of Samples: 150
+#> Number of Failed Samples: 0
+#> Model Formula: CHANGE ~ 1 + THERAPY + VISIT + BASVAL * VISIT + THERAPY * VISIT
+#> Imputation Type: random
+#> Method:
+#>     name: Bayes
+#>     burn_in: 200
+#>     burn_between: 5
+#>     same_cov: TRUE
+#>     n_samples: 150
+#>     seed: 675442751
+

Note the use of set_vars() which specifies the names of the key variables +within the dataset and the imputation model. Additionally, note that whilst vars$group and vars$visit +are added as terms to the imputation model by default, their interaction is not, +thus the inclusion of group * visit in the list of covariates.

+

Available imputation methods include:

+
    +
  • Bayesian multiple imputation - method_bayes() +
    • +
  • Approximate Bayesian multiple imputation - method_approxbayes() +
    • +
  • Conditional mean imputation (bootstrap) - method_condmean(type = "bootstrap") +
    • +
  • Conditional mean imputation (jackknife) - method_condmean(type = "jackknife") +
    • +
  • Bootstrapped multiple imputation - method = method_bmlmi() +
    • +
+

For a comparison of these methods, we refer to the stat_specs vignette (Section 3.10).

+

“statistical specifications” vignette (Section 3.10): vignette("stat_specs",package="rbmi").

+

Available imputation strategies include:

+
    +
  • Missing At Random - "MAR" +
    • +
  • Jump to Reference - "JR" +
    • +
  • Copy Reference - "CR" +
    • +
  • Copy Increments from Reference - "CIR" +
    • +
  • Last Mean Carried Forward - "LMCF" +
    • +
+
+
+

+4 Impute +

+

The next step is to use the parameters from the imputation model to generate the imputed datasets. This is +done via the impute() function. The function only has two key inputs: the imputation +model output from draws() and the reference groups relevant to reference-based imputation methods. It’s usage is thus:

+
+imputeObj <- impute(
+    drawObj,
+    references = c("DRUG" = "PLACEBO", "PLACEBO" = "PLACEBO")
+)
+imputeObj
+#> 
+#> Imputation Object
+#> -----------------
+#> Number of Imputed Datasets: 150
+#> Fraction of Missing Data (Original Dataset):
+#>     4:   0%
+#>     5:   8%
+#>     6:  13%
+#>     7:  25%
+#> References:
+#>     DRUG    -> PLACEBO
+#>     PLACEBO -> PLACEBO
+

In this instance, we are specifying that the PLACEBO group should be the reference group for itself as well as for the DRUG group (as is standard for imputation using reference-based methods).

+

Generally speaking, there is no need to see or directly interact with the imputed +datasets. However, if you do wish to inspect them, they can be extracted from the imputation +object using the extract_imputed_dfs() helper function, i.e.:

+
+imputed_dfs <- extract_imputed_dfs(imputeObj)
+head(imputed_dfs[[10]], 12) # first 12 rows of 10th imputed dataset
+#>     PATIENT HAMATOTL PGIIMP RELDAYS VISIT THERAPY GENDER POOLINV BASVAL
+#> 1  new_pt_1       21      2       7     4    DRUG      F     006     32
+#> 2  new_pt_1       19      2      14     5    DRUG      F     006     32
+#> 3  new_pt_1       21      3      28     6    DRUG      F     006     32
+#> 4  new_pt_1       17      4      42     7    DRUG      F     006     32
+#> 5  new_pt_2       18      3       7     4 PLACEBO      F     006     14
+#> 6  new_pt_2       18      2      15     5 PLACEBO      F     006     14
+#> 7  new_pt_2       14      3      29     6 PLACEBO      F     006     14
+#> 8  new_pt_2        8      2      42     7 PLACEBO      F     006     14
+#> 9  new_pt_3       18      3       7     4    DRUG      F     006     21
+#> 10 new_pt_3       17      3      14     5    DRUG      F     006     21
+#> 11 new_pt_3       12      3      28     6    DRUG      F     006     21
+#> 12 new_pt_3        9      3      44     7    DRUG      F     006     21
+#>    HAMDTL17 CHANGE
+#> 1        21    -11
+#> 2        20    -12
+#> 3        19    -13
+#> 4        17    -15
+#> 5        11     -3
+#> 6        14      0
+#> 7         9     -5
+#> 8         5     -9
+#> 9        20     -1
+#> 10       18     -3
+#> 11       16     -5
+#> 12       13     -8
+

Note that in the case of method_bayes() or method_approxbayes(), all imputed datasets correspond to random imputations on the original dataset. +For method_condmean(), the first imputed dataset will always correspond to the completed original dataset containing all subjects. +For method_condmean(type="jackknife"), the remaining datasets correspond to conditional mean imputations on leave-one-subject-out datasets, +whereas for method_condmean(type="bootstrap"), each subsequent dataset corresponds to a conditional mean imputation on a bootstrapped datasets. +For method_bmlmi(), all the imputed datasets correspond to sets of random imputations on bootstrapped datasets.

+
+
+

+5 Analyse +

+

The next step is to run the analysis model on each imputed dataset. This is done by defining +an analysis function and then calling analyse() to apply this function to each +imputed dataset. For this vignette we use the ancova() function provided by the rbmi +package which fits a separate ANCOVA model for the outcomes from each visit and returns a treatment +effect estimate and corresponding least square means for each group per visit.

+
+anaObj <- analyse(
+    imputeObj,
+    ancova,
+    vars = set_vars(
+        subjid = "PATIENT",
+        outcome = "CHANGE",
+        visit = "VISIT",
+        group = "THERAPY",
+        covariates = c("BASVAL")
+    )
+)
+anaObj
+#> 
+#> Analysis Object
+#> ---------------
+#> Number of Results: 150
+#> Analysis Function: ancova
+#> Delta Applied: FALSE
+#> Analysis Estimates:
+#>     trt_4
+#>     lsm_ref_4
+#>     lsm_alt_4
+#>     trt_5
+#>     lsm_ref_5
+#>     lsm_alt_5
+#>     trt_6
+#>     lsm_ref_6
+#>     lsm_alt_6
+#>     trt_7
+#>     lsm_ref_7
+#>     lsm_alt_7
+

Note that, similar to draws(), the ancova() function uses the set_vars() +function which determines the names of the key variables within the data and the covariates +(in addition to the treatment group) for which the analysis model will be adjusted.

+

Please also note that the names of the analysis estimates contain “ref” and “alt” to refer to the two treatment arms. In particular “ref” refers to the first factor level of vars$group which does not necessarily +coincide with the control arm. In this example, since levels(dat[[vars$group]]) = c("DRUG", PLACEBO), the results associated with “ref” correspond to the intervention arm, while those associated with “alt” correspond to the control arm.

+

Additionally, we can use the delta argument of analyse() to perform a delta adjustments of the imputed datasets prior to the analysis. +In brief, this is implemented by specifying a data.frame that contains the amount +of adjustment to be added to each longitudinal outcome for each subject and visit, i.e.  +the data.frame must contain the columns subjid, visit, and delta.

+

It is appreciated that carrying out this procedure is potentially tedious, therefore the +delta_template() helper function has been provided to simplify it. In particular, +delta_template() returns a shell data.frame where the delta-adjustment is set to 0 for all +patients. Additionally delta_template() adds several meta-variables onto the shell +data.frame which can be used for manual derivation or manipulation of the delta-adjustment.

+

For example lets say we want to add a delta-value of 5 to all imputed values (i.e. those values +which were missing in the original dataset) in the drug arm. That could then be implemented as follows:

+
+# For reference show the additional meta variables provided
+delta_template(imputeObj) %>% as_tibble()
+#> # A tibble: 688 × 8
+#>    PATIENT VISIT THERAPY is_mar is_missing is_post_ice strategy delta
+#>    <fct>   <fct> <fct>   <lgl>  <lgl>      <lgl>       <chr>    <dbl>
+#>  1 1503    4     DRUG    TRUE   FALSE      FALSE       NA           0
+#>  2 1503    5     DRUG    TRUE   FALSE      FALSE       NA           0
+#>  3 1503    6     DRUG    TRUE   FALSE      FALSE       NA           0
+#>  4 1503    7     DRUG    TRUE   FALSE      FALSE       NA           0
+#>  5 1507    4     PLACEBO TRUE   FALSE      FALSE       NA           0
+#>  6 1507    5     PLACEBO TRUE   FALSE      FALSE       NA           0
+#>  7 1507    6     PLACEBO TRUE   FALSE      FALSE       NA           0
+#>  8 1507    7     PLACEBO TRUE   FALSE      FALSE       NA           0
+#>  9 1509    4     DRUG    TRUE   FALSE      FALSE       NA           0
+#> 10 1509    5     DRUG    TRUE   FALSE      FALSE       NA           0
+#> # ℹ 678 more rows
+
+delta_df <- delta_template(imputeObj) %>%
+    as_tibble() %>% 
+    mutate(delta = if_else(THERAPY == "DRUG" & is_missing , 5, 0)) %>% 
+    select(PATIENT, VISIT, delta)
+    
+delta_df
+#> # A tibble: 688 × 3
+#>    PATIENT VISIT delta
+#>    <fct>   <fct> <dbl>
+#>  1 1503    4         0
+#>  2 1503    5         0
+#>  3 1503    6         0
+#>  4 1503    7         0
+#>  5 1507    4         0
+#>  6 1507    5         0
+#>  7 1507    6         0
+#>  8 1507    7         0
+#>  9 1509    4         0
+#> 10 1509    5         0
+#> # ℹ 678 more rows
+
+anaObj_delta <- analyse(
+    imputeObj,
+    ancova,
+    delta = delta_df,
+    vars = set_vars(
+        subjid = "PATIENT",
+        outcome = "CHANGE",
+        visit = "VISIT",
+        group = "THERAPY",
+        covariates = c("BASVAL")
+    )
+)
+
+
+

+6 Pool +

+

Finally, the pool() function can be used to summarise the analysis results across multiple +imputed datasets to provide an overall statistic with a standard error, confidence intervals and a p-value for +the hypothesis test of the null hypothesis that the effect is equal to 0.

+

Note that the pooling method is automatically derived based on the method that was specified +in the original call to draws():

+
    +
  • For method_bayes() or method_approxbayes() pooling and inference are based on Rubin’s rules.
    • +
  • For method_condmean(type = "bootstrap") inference is either based on a normal approximation using the bootstrap standard error (pool(..., type = "normal")) or on the bootstrap percentiles (pool(..., type = "percentile")).
    • +
  • For method_condmean(type = "jackknife") inference is based on a normal approximation using the jackknife estimate of the standard error.
    • +
  • For method = method_bmlmi() inference is according to the methods described by von Hippel and Bartlett (see the stat_specs vignette for details)
    • +
+

Since we have used Bayesian multiple imputation in this vignette, the pool() function will automatically use Rubin’s rules.

+
+poolObj <- pool(
+    anaObj, 
+    conf.level = 0.95, 
+    alternative = "two.sided"
+)
+poolObj
+#> 
+#> Pool Object
+#> -----------
+#> Number of Results Combined: 150
+#> Method: rubin
+#> Confidence Level: 0.95
+#> Alternative: two.sided
+#> 
+#> Results:
+#> 
+#>   ==================================================
+#>    parameter   est     se     lci     uci     pval  
+#>   --------------------------------------------------
+#>      trt_4    -0.092  0.683  -1.439  1.256   0.893  
+#>    lsm_ref_4  -1.616  0.486  -2.576  -0.656  0.001  
+#>    lsm_alt_4  -1.708  0.475  -2.645  -0.77   <0.001 
+#>      trt_5    1.281   0.927  -0.55   3.112   0.169  
+#>    lsm_ref_5  -4.112  0.661  -5.418  -2.807  <0.001 
+#>    lsm_alt_5  -2.831  0.646  -4.107  -1.556  <0.001 
+#>      trt_6    1.912   1.001  -0.066   3.89   0.058  
+#>    lsm_ref_6  -6.097  0.714  -7.508  -4.686  <0.001 
+#>    lsm_alt_6  -4.186  0.696  -5.561  -2.81   <0.001 
+#>      trt_7    2.079   1.122  -0.138  4.296   0.066  
+#>    lsm_ref_7  -6.946  0.815  -8.558  -5.335  <0.001 
+#>    lsm_alt_7  -4.867  0.788  -6.426  -3.308  <0.001 
+#>   --------------------------------------------------
+

The table of values shown in the print message for poolObj can also be extracted using the as.data.frame() function:

+
+as.data.frame(poolObj)
+#>    parameter         est        se         lci        uci         pval
+#> 1      trt_4 -0.09180645 0.6826279 -1.43949684  1.2558839 8.931772e-01
+#> 2  lsm_ref_4 -1.61581996 0.4862316 -2.57577141 -0.6558685 1.093708e-03
+#> 3  lsm_alt_4 -1.70762640 0.4749573 -2.64531931 -0.7699335 4.262148e-04
+#> 4      trt_5  1.28107134 0.9269270 -0.54967136  3.1118141 1.689000e-01
+#> 5  lsm_ref_5 -4.11245871 0.6608409 -5.41768364 -2.8072338 4.201381e-09
+#> 6  lsm_alt_5 -2.83138737 0.6457744 -4.10686302 -1.5559117 2.114628e-05
+#> 7      trt_6  1.91163968 1.0011368 -0.06637259  3.8896520 5.809419e-02
+#> 8  lsm_ref_6 -6.09716631 0.7142461 -7.50839192 -4.6859407 1.384720e-14
+#> 9  lsm_alt_6 -4.18552662 0.6963163 -5.56127560 -2.8097776 1.321956e-08
+#> 10     trt_7  2.07945506 1.1216355 -0.13755657  4.2964667 6.579390e-02
+#> 11 lsm_ref_7 -6.94648032 0.8150602 -8.55819661 -5.3347640 2.515736e-14
+#> 12 lsm_alt_7 -4.86702525 0.7884953 -6.42588823 -3.3081623 6.801566e-09
+

These outputs gives an estimated difference of +2.079 (95% CI -0.138 to 4.296) +between the two groups at the last visit with an associated p-value of 0.066.

+
+
+

+7 Code +

+

We report below all the code presented in this vignette.

+
+library(rbmi)
+library(dplyr)
+
+data("antidepressant_data")
+dat <- antidepressant_data
+
+# Use expand_locf to add rows corresponding to visits with missing outcomes to the dataset
+dat <- expand_locf(
+    dat,
+    PATIENT = levels(dat$PATIENT), # expand by PATIENT and VISIT 
+    VISIT = levels(dat$VISIT),
+    vars = c("BASVAL", "THERAPY"), # fill with LOCF BASVAL and THERAPY
+    group = c("PATIENT"),
+    order = c("PATIENT", "VISIT")
+)
+
+# Create data_ice and set the imputation strategy to JR for
+# each patient with at least one missing observation
+dat_ice <- dat %>% 
+    arrange(PATIENT, VISIT) %>% 
+    filter(is.na(CHANGE)) %>% 
+    group_by(PATIENT) %>% 
+    slice(1) %>%
+    ungroup() %>% 
+    select(PATIENT, VISIT) %>% 
+    mutate(strategy = "JR")
+
+# In this dataset, subject 3618 has an intermittent missing values which does not correspond
+# to a study drug discontinuation. We therefore remove this subject from `dat_ice`. 
+# (In the later imputation step, it will automatically be imputed under the default MAR assumption.)
+dat_ice <- dat_ice[-which(dat_ice$PATIENT == 3618),]
+
+# Define the names of key variables in our dataset using `set_vars()`
+# and the covariates included in the imputation model
+# Note that the covariates argument can also include interaction terms
+vars <- set_vars(
+    outcome = "CHANGE",
+    visit = "VISIT",
+    subjid = "PATIENT",
+    group = "THERAPY",
+    covariates = c("BASVAL*VISIT", "THERAPY*VISIT")
+)
+
+# Define which imputation method to use (here: Bayesian multiple imputation with 150 imputed datsets) 
+method <- method_bayes(
+    burn_in = 200,
+    burn_between = 5,
+    n_samples = 150,
+    seed = 675442751
+)
+
+
+# Create samples for the imputation parameters by running the draws() function
+set.seed(987)
+drawObj <- draws(
+    data = dat,
+    data_ice = dat_ice,
+    vars = vars,
+    method = method,
+    quiet = TRUE
+)
+
+# Impute the data
+imputeObj <- impute(
+    drawObj,
+    references = c("DRUG" = "PLACEBO", "PLACEBO" = "PLACEBO")
+)
+
+# Fit the analysis model on each imputed dataset
+anaObj <- analyse(
+    imputeObj,
+    ancova,
+    vars = set_vars(
+        subjid = "PATIENT",
+        outcome = "CHANGE",
+        visit = "VISIT",
+        group = "THERAPY",
+        covariates = c("BASVAL")
+    )
+)
+
+# Apply a delta adjustment
+
+# Add a delta-value of 5 to all imputed values (i.e. those values
+# which were missing in the original dataset) in the drug arm.
+delta_df <- delta_template(imputeObj) %>%
+    as_tibble() %>% 
+    mutate(delta = if_else(THERAPY == "DRUG" & is_missing , 5, 0)) %>% 
+    select(PATIENT, VISIT, delta)
+
+# Repeat the analyses with the adjusted values
+anaObj_delta <- analyse(
+    imputeObj,
+    ancova,
+    delta = delta_df,
+    vars = set_vars(
+        subjid = "PATIENT",
+        outcome = "CHANGE",
+        visit = "VISIT",
+        group = "THERAPY",
+        covariates = c("BASVAL")
+    )
+)
+
+# Pool the results
+poolObj <- pool(
+    anaObj, 
+    conf.level = 0.95, 
+    alternative = "two.sided"
+)
+
+
+
+ + + +
+

Developed by Craig Gower-Page, Alessandro Noci, Roche.

+
+ +
+

Site built with pkgdown 2.1.0.

+
+ +
+
+ + + + + + + diff --git a/latest-tag/articles/stat_specs.html b/latest-tag/articles/stat_specs.html new file mode 100644 index 00000000..2f0dd3c6 --- /dev/null +++ b/latest-tag/articles/stat_specs.html @@ -0,0 +1,705 @@ + + + + + + + +rbmi: Statistical Specifications • rbmi + + + + + + + + Skip to contents + + +
+ + +
+
+ +

rbmi: Statistical Specifications

+

Alessandro Noci, Craig Gower-Page, and Marcel Wolbers

+ + + Source: vignettes/stat_specs.Rmd +
stat_specs.Rmd
+
+ + + +
+

+1 Scope of this document +

+

This document describes the statistical methods implemented in the rbmi R package for standard and reference-based multiple imputation of continuous longitudinal outcomes. +The package implements three classes of multiple imputation (MI) approaches:

+
    +

  1. Conventional MI methods based on Bayesian (or approximate Bayesian) posterior draws of model parameters combined with Rubin’s rules to make inferences as described in Carpenter, Roger, and Kenward (2013) and Cro et al. (2020).

    2. +

  2. Conditional mean imputation methods combined with re-sampling techniques as described in Wolbers et al. (2022).

    3. +

  3. Bootstrapped MI methods as described in von Hippel and Bartlett (2021).

    4. +
+

The document is structured as follows: we first provide an informal introduction to estimands and corresponding treatment effect estimation based on MI (section 2). The core of this document consists of section 3 which describes the statistical methodology in detail and also contains a comparison of the implemented approaches (section 3.10). The link between theory and the functions included in package rbmi is described in section 4. We conclude with a comparison of our package to some alternative software implementations of reference-based imputation methods (section 5).

+
+
+

+2 Introduction to estimands and estimation methods +

+
+

+2.1 Estimands +

+

The ICH E9(R1) addendum on estimands and sensitivity analyses describes a systematic approach to ensure alignment among clinical trial objectives, trial execution/conduct, statistical analyses, and interpretation of results (ICH E9 working group (2019)). +As per the addendum, an estimand is a precise description of the treatment effect reflecting the clinical question posed by the trial objective which summarizes at a population-level what the outcomes would be in the same patients under different +treatment conditions being compared. +One important attribute of an estimand is a list of possible intercurrent events (ICEs), i.e. of events occurring after treatment initiation that affect either the interpretation or the existence of the measurements associated with the clinical question of interest, and the definition of appropriate strategies to deal with ICEs. The three most relevant strategies for the purpose of this document are the hypothetical strategy, the treatment policy strategy, and the composite strategy. For the hypothetical strategy, a scenario is envisaged in which the ICE would not occur. Under this scenario, endpoint values after the ICE are not directly observable and treated using models for missing data. +For the treatment policy strategy, the treatment effect in the presence of the ICEs is targeted and analyses are based on the observed outcomes regardless whether the subject had an ICE or not. +For the composite strategy, the ICE itself is included as a component of the endpoint.

+
+
+

+2.2 Alignment between the estimand and the estimation method +

+

The ICH E9(R1) addendum distinguishes between ICEs and missing data (ICH E9 working group (2019)). Whereas ICEs such as treatment discontinuations reflect clinical practice, the amount of missing data can be minimized in the conduct of a clinical trial. However, there are many connections between missing data and ICEs. For example, it is often difficult to retain subjects in a clinical trial after treatment discontinuation and a subject’s dropout from the trial leads to missing data. As another example, outcome values after ICEs addressed using a hypothetical strateg are not directly observable under the hypothetical scenario. Consequently, any observed outcome values after such ICEs are typically discarded and treated as missing data.

+

The addendum proposes that estimation methods to address the problem presented by missing data should be selected to align with the estimand. A recent overview of methods to align the estimator with the estimand is Mallinckrodt et al. (2020). A short introduction on estimation methods for studies with longitudinal endpoints can also be found in Wolbers et al. (2022). One prominent statistical method for this purpose is multiple imputation (MI), which is the target of the rbmi package.

+
+

+2.2.1 Missing data prior to ICEs +

+

Missing data may occur in subjects without an ICE or prior to the occurrence of an ICE. As such missing outcomes are not associated with an ICE, it is often plausible to impute them under a missing-at-random (MAR) assumption using a standard MMRM imputation model of the longitudinal outcomes. Informally, MAR occurs if the missing data can be fully accounted for by the baseline variables included in the model and the observed longitudinal outcomes, and if the model is correctly specified.

+
+
+

+2.2.2 Implementation of the hypothetical strategy +

+

The MAR imputation model described above is often also a good starting point for imputing data after an ICE handled using a hypothetical strategy (Mallinckrodt et al. (2020)). +Informally, this assumes that unobserved values after the ICE would have been similar to the observed data from subjects who did not have the ICE and remained under follow-up. +However, in some situations, it may be more reasonable to assume that missingness is “informative” and indicates a systematically better or worse outcome than in observed subjects. In such situations, MNAR imputation with a \(\delta\)-adjustment could be explored as a sensitivity analysis. \(\delta\)-adjustments add a fixed or random quantity to the imputations in order to make the imputed outcomes systematically worse or better than those observed as described in Cro et al. (2020). In rbmi only fixed \(\delta\)-adjustments are implemented.

+
+
+

+2.2.3 Implementation of the treatment policy strategy +

+

Ideally, data collection continues after an ICE handled with a treatment policy strategy and no missing data arises. +Indeed, such post-ICE data are increasingly systematically collected in RCTs. +However, despite best efforts, missing data after an ICE such as study treatment discontinuation may still occur because the subject drops out from the study after discontinuation. It is difficult to give definite recommendations regarding the implementation of the treatment policy strategy in the presence of missing data at this stage because the optimal method is highly context dependent and a topic of ongoing statistical research.

+

For ICEs which are thought to have a negligible effect on efficacy outcomes, standard MAR-based imputation may be appropriate. In contrast, an ICE such as treatment discontinuation may be expected to have a more substantial impact on efficacy outcomes. In such settings, the MAR assumption may still be plausible after conditioning on the subject’s time-varying treatment status (Guizzaro et al. (2021)). In this case, one option is to impute missing post-discontinuation data based on subjects who also discontinued treatment but continued to be followed up (Polverejan and Dragalin (2020)). Another option which may require somewhat less post-discontinuation data is to include all subjects in the imputation procedure but to model post-discontinuation data by using a time-varying treatment status indicators (e.g. time-varying indicators of treatment compliance, discontinuation, or initiation of rescue +treatment) (Guizzaro et al. (2021)). In this approach, post-ICE outcomes are included +in every step of the analysis, including in the fitting of the imputation model. +It assumes that ICEs may impact post-ICE outcomes but that otherwise missingness is non-informative. The approach also assumes that the time-varying covariates do not contain missing values, deviations in outcomes after the ICE are correctly modeled by these time-varying covariates, and that sufficient post-ICE data are available to inform the regression coefficients of the time-varying covariates. These proposals are relatively recent and there remain open questions regarding the appropriate trade-off between model complexity (e.g. should the model account for a potentially differential effect on post-ICE outcomes depending on the timing of the ICE?) and the variance in the resulting treatment effect estimate. More generally, it is not yet established how much post-discontinuation data is required to implement such methods robustly and without the risk of substantial inflation of variance.

+

In some trial settings, only few subjects discontinue the randomized treatment. In other settings, treatment discontinuation rates are higher but it is difficult to retain subjects in the trial after treatment discontinuation leading to sparse data collection after treatment discontinuation. In both settings, the amount of available data after treatment discontinuation may be insufficient to inform an imputation model which explicitly models post-discontinuation data. Depending on the disease area and the anticipated mechanism of action of the intervention, it may be plausible to assume that subjects in the intervention group behave similarly to subjects in the control group after the ICE treatment discontinuation. In this case, reference-based imputation methods are an option (Mallinckrodt et al. (2020)). Reference-based imputation methods formalize the idea to impute missing data in the intervention group based on data from a control or reference group. For a general description and review of reference-based imputation methods, we refer to Carpenter, Roger, and Kenward (2013), Cro et al. (2020), I. White, Royes, and Best (2020) and Wolbers et al. (2022). For a technical description of the implemented statistical methodology for reference-based imputation, we refer to section 3 (in particular section 3.4).

+
+
+

+2.2.4 Implementation of the composite strategy +

+

The composite strategy is typically applied to binary or time-to-event outcomes but it can also be used for continuous outcomes by ascribing a suitably unfavorable value to patients who experience ICEs for which a composite strategy has been defined. One possibility to implement this is to use MI with a \(\delta\)-adjustment for post-ICE data as described in Darken et al. (2020).

+
+
+
+
+

+3 Statistical methodology +

+
+

+3.1 Overview of the imputation procedure +

+

Analyses of datasets with missing data always rely on missing data assumptions. The methods described here can be used to produce valid imputations under a MAR assumption or under reference-based imputation assumptions. MNAR imputation based on fixed \(\delta\)-adjustments as typically used in sensitivity analyses such as tipping-point analyses are also supported.

+

Three general imputation approaches are implemented in rbmi:

+
    +

  1. Conventional MI based on Bayesian (or approximate Bayesian) posterior draws from the imputation model combined with Rubin’s rules for inference as described in Carpenter, Roger, and Kenward (2013) and Cro et al. (2020).

    2. +

  2. Conditional mean imputation based on the REML estimate of the imputation model combined with resampling techniques (the jackknife or the bootstrap) for inference as described in Wolbers et al. (2022).

    3. +

  3. Bootstrapped MI methods based on REML estimates of the imputation model as described in von Hippel and Bartlett (2021).

    4. +
+
+

+3.1.1 Conventional MI +

+

Conventional MI approaches include the following steps:

+
    +
  1. +Base imputation model fitting step (Section 3.3)
    2. +
+
    +

  • Fit a Bayesian multivariate normal mixed model for repeated measures (MMRM) to the observed longitudinal outcomes after exclusion of data after ICEs for which reference-based missing data imputation is desired (Section 3.3.3). Draw \(M\) posterior samples of the estimated parameters (regression coefficients and covariance matrices) from this model.

    • +

  • Alternatively, \(M\) approximate posterior draws from the posterior distribution can be sampled by repeatedly applying conventional restricted maximum-likelihood (REML) parameter estimation of the MMRM model to nonparametric bootstrap samples from the original dataset (Section 3.3.4).

    • +
+
    +
  1. +Imputation step (Section 3.4)
    2. +
+
    +

  • Take a single sample \(m\) (\(m\in 1,\ldots, M)\) from the posterior distribution of the imputation model parameters.

    • +

  • For each subject, use the sampled parameters and the defined imputation strategy to determine the mean and covariance matrix describing the subject’s marginal outcome distribution for all longitudinal outcome assessments (i.e. observed and missing outcomes).

    • +

  • For each subjects, construct the conditional multivariate normal distribution of their missing outcomes given their observed outcomes (including observed outcomes after ICEs for which a reference-based assumption is desired).

    • +

  • For each subject, draw a single sample from this conditional distribution to impute their missing outcomes leading to a complete imputed dataset.

    • +

  • For sensitivity analyses, a pre-defined \(\delta\)-adjustment may be applied to the imputed data prior to the analysis step. (Section 3.5).

    • +
+
    +
  1. +Analysis step (Section 3.6)
    2. +
+
    +
  • Analyze the imputed dataset using an analysis model (e.g. ANCOVA) resulting in a point estimate and a standard error (with corresponding degrees of freedom) of the treatment effect.
    • +
+
    +
  1. +Pooling step for inference (Section 3.7)
    2. +
+
    +
  • Repeat steps 2. and 3. for each posterior sample \(m\), resulting in \(M\) complete datasets, \(M\) point estimates of the treatment effect, and \(M\) standard errors (with corresponding degrees of freedom). Pool the \(M\) treatment effect estimates, standard errors, and degrees of freedom using the rules by Barnard and Rubin to obtain the final pooled treatment effect estimator, standard error, and degrees of freedom.
    • +
+
+
+

+3.1.2 Conditional mean imputation +

+

The conditional mean imputation approach includes the following steps:

+
    +
  1. +Base imputation model fitting step (Section 3.3)
    2. +
+
    +
  • Fit a conventional multivariate normal/MMRM model using restricted maximum likelihood (REML) to the observed longitudinal outcomes after exclusion of data after ICEs for which reference-based missing data imputation is desired (Section 3.3.2).
    • +
+
    +
  1. +Imputation step (Section 3.4)
    2. +
+
    +

  • For each subject, use the fitted parameters from step 1. to construct the conditional distribution of missing outcomes given observed outcomes (including observed outcomes after ICEs for which reference-based missing data imputation is desired) as described above.

    • +

  • For each subject, impute their missing data deterministically by the mean of this conditional distribution leading to a complete imputed dataset.

    • +

  • For sensitivity analyses, a pre-defined \(\delta\)-adjustment may be applied to the imputed data prior to the analysis step. (Section 3.5).

    • +
+
    +
  1. +Analysis step (Section 3.6)
    2. +
+
    +
  • Apply an analysis model (e.g. ANCOVA) to the completed dataset resulting in a point estimate of the treatment effect.
    • +
+
    +
  1. +Jackknife or bootstrap inference step (Section 3.8)
    2. +
+
    +
  • Inference for the treatment effect estimate from 3. is based on re-sampling techniques. Both the jackknife and the bootstrap are supported. Importantly, these methods require repeating all steps of the imputation procedure (i.e. imputation, conditional mean imputation, and analysis steps) on each of the resampled datasets.
    • +
+
+
+

+3.1.3 Bootstrapped MI +

+

The bootstrapped MI approach includes the following steps:

+
    +
  1. +Base imputation model fitting step (Section 3.3)
    2. +
+
    +
  • Apply conventional restricted maximum-likelihood (REML) parameter estimation of the MMRM model to \(B\) nonparametric bootstrap samples from the original dataset using the observed longitudinal outcomes after exclusion of data after ICEs for which reference-based missing data imputation is desired.
    • +
+
    +
  1. +Imputation step (Section 3.4)
    2. +
+
    +

  • Take a bootstrapped dataset \(b\) (\(b\in 1,\ldots, B)\) and its corresponding imputation model parameter estimates.

    • +

  • For each subject (from the bootstrapped dataset), use the parameter estimates and the defined strategy for dealing with their ICEs to determine the mean and covariance matrix describing the subject’s marginal outcome distribution for all longitudinal outcome assessments (i.e. observed and missing outcomes).

    • +

  • For each subjects (from the bootstrapped dataset), construct the conditional multivariate normal distribution of their missing outcomes given their observed outcomes (including observed outcomes after ICEs for which reference-based missing data imputation is desired).

    • +

  • For each subject (from the bootstrapped dataset), draw \(D\) samples from this conditional distributions to impute their missing outcomes leading to \(D\) complete imputed dataset for bootstrap sample \(b\).

    • +

  • For sensitivity analyses, a pre-defined \(\delta\)-adjustment may be applied to the imputed data prior to the analysis step. (Section 3.5).

    • +
+
    +
  1. +Analysis step (Section 3.6)
    2. +
+
    +
  • Analyze each of the \(B\times D\) imputed datasets using an analysis model (e.g. ANCOVA) resulting in \(B\times D\) point estimates of the treatment effect.
    • +
+
    +
  1. +Pooling step for inference (Section 3.9)
    2. +
+
    +
  • Pool the \(B\times D\) treatment effect estimates as described in von Hippel and Bartlett (2021) to obtain the final pooled treatment effect estimate, standard error, and degrees of freedom.
    • +
+
+
+
+

+3.2 Setting, notation, and missing data assumptions +

+

Assume that the data are from a study with \(n\) subjects in total and that each subject \(i\) (\(i=1,\ldots,n\)) has \(J\) scheduled follow-up visits at which the outcome of interest is assessed. +In most applications, the data will be from a randomized trial of an intervention vs a control group and the treatment effect of interest is a comparison in outcomes at a specific visit between these randomized groups. However, single-arm trials or multi-arm trials are in principle also supported by the rbmi implementation.

+

Denote the observed outcome vector of length \(J\) for subject \(i\) by \(Y_i\) (with missing assessments coded as NA (not available)) and its non-missing and missing components by \(Y_{i!}\) and \(Y_{i?}\), respectively. +By default, imputation of missing outcomes in \(Y_{i}\) is performed under a MAR assumption in rbmi. Therefore, if missing data following an ICE are to be handled using MAR imputation, this is compatible with the default assumption. As discussed in Section 2, the MAR assumption is often a good starting point for implementing a hypothetical strategy. But also note that observed outcome data after an ICE handled using a hypothetical strategy is not compatible with this strategy. Therefore, we assume that all post-ICE data after ICEs handled using a hypothetical strategy are already set to NA in \(Y_i\) prior calling any rbmi functions. However, any observed outcomes after ICEs handled using a treatment policy strategy should be included in \(Y_i\) as they are compatible with this strategy.

+

Subjects may also experience up to one ICE after which missing data imputation according to a reference-based imputation method is foreseen. For a subject \(i\) with such an ICE, denote their first visit which is affected by the ICE by \(\tilde{t}_i \in \{1,\ldots,J\}\). For all other subjects, set \(\tilde{t}_i=\infty\). A subject’s outcome vector after setting observed outcomes from visit \(\tilde{t}_i\) onwards to missing (i.e. NA) is denoted as \(Y'_i\) and the corresponding data vector after removal of NA elements as \(Y'_{i!}\).

+

MNAR \(\delta\)-adjustments are added to the imputed datasets after the formal imputation steps. This is covered in a separate section (Section 3.5).

+
+
+

+3.3 The base imputation model +

+
+

+3.3.1 Included data and model specification +

+

The purpose of the imputation model is to estimate (covariate-dependent) mean trajectories and covariance matrices for each group in the absence of ICEs handled using reference-based imputation methods. Conventionally, +publications on reference-based imputation methods have implicitly assumed that the corresponding post-ICE +data is missing for all subjects (Carpenter, Roger, and Kenward (2013)). We also allow the situation where post-ICE data +is available for some subjects but needs to be imputed using reference-based methods for others. However, +any observed data after ICEs for which reference-based imputation methods are specified is not compatible +with the imputation model described below and they are therefore removed and considered as missing for +the purpose of estimating the imputation model, and for this purpose only. For example, if a patient has an ICE addressed with a reference-based method but outcomes after the ICE are collected, these post-ICE outcomes will be excluded when fitting the base imputation model (but they will be included again in the following steps). +That is, the base imputation model is fitted to \(Y'_{i!}\) and not to \(Y_{i!}\). +If we did not exclude these data, then the imputation model would mistakenly estimate mean trajectories based on a mixture of observed pre- and post-ICE data which are not relevant for reference-based imputations.

+

Observed post-ICE outcomes in the control or reference group are also excluded from the base imputation model if the user specifies a reference-based imputation strategy for such ICEs. This ensures that an ICE has the same impact on the data included in the imputation model regardless whether the ICE occurred in the control or the intervention group. On the other hand, imputation in the reference group is based on a MAR assumption even for reference-based imputation methods and it may be preferable in some settings to include post-ICE data from the control group in the base imputation model. This can be implemented by specifying a MAR strategy for the ICE in the control group and a reference-based strategy for the same ICE in the intervention group.

+

The base imputation model of the longitudinal outcomes \(Y'_i\) assumes that the mean structure is a linear function of covariates. Full flexibility for the specification of the linear predictor of the model is supported. At a minimum the covariates should include the treatment group, the (categorical) visit, and treatment-by-visit interactions. Typically, other covariates including the baseline outcome are also included. +External time-varying covariates (e.g. calendar time of the visit) as well as internal time-varying (e.g. time-varying indicators of treatment discontinuation or initiation of rescue treatment) may in principle also be included if indicated (Guizzaro et al. (2021)). Missing covariate values are not allowed. This means that the values of time-varying covariates must be non-missing at every visit regardless of whether the outcome is measured or missing.

+

Denote the \(J\times p\) design matrix for subject \(i\) corresponding to the mean structure model by \(X_i\) and the same matrix after removal of rows corresponding to missing outcomes in \(Y'_{i!}\) by \(X'_{i!}\). +Here \(p\) is the number of parameters in the mean structure of the model for the elements of \(Y'_{i!}\). +The base imputation model for the observed outcomes is defined as: +\[ Y'_{i!} = X'_{i!}\beta + \epsilon_{i!} \mbox{ with } \epsilon_{i!}\sim N(0,\Sigma_{i!!})\] +where \(\beta\) is the vector of regression coefficients and \(\Sigma_{i!!}\) is a covariance matrix which is obtained from the complete-data \(J\times J\)-covariance matrix \(\Sigma\) by omitting rows and columns corresponding to missing outcome assessments for subject \(i\).

+

Typically, a common unstructured covariance matrix for all subjects is assumed for \(\Sigma\) but separate covariate matrices per treatment group are also supported. Indeed, the implementation also supports the specification of separate covariate matrices according to an arbitrarily defined categorical variable which groups the subjects into disjoint subset. For example, this could be useful if different covariance matrices are suspected in different subject strata. Finally, for all imputation methods described below that do not rely on Bayesian model fitting through MCMC, there is further flexibility in the choice of the covariance structure, i.e. unstructured (default), heterogeneous Toeplitz, heterogeneous compound symmetry, and AR(1) covariance structures are supported.

+
+
+

+3.3.2 Restricted maximum likelihood estimation (REML) +

+

Frequentist parameter estimation for the base imputation is based on REML. The use of REML as an improved alternative to maximum likelihood (ML) for covariance parameter estimation was originally proposed by Patterson and Thompson (1971). Since then, it has become the default method for parameter estimation in linear mixed effects models. rbmi allows to choose between ML and REML methods to estimate the model parameters, with REML being the default option.

+
+
+

+3.3.3 Bayesian model fitting +

+

The Bayesian imputation model is fitted with the R package rstan (Stan Development Team (2020)). rstan is the R interface of Stan. Stan is a powerful and flexible statistical software developed by a dedicated team and implements Bayesian inference with state-of-the-art MCMC sampling procedures. The multivariate normal model with missing data specified in section 3.3.1 can be considered a generalization of the models described in the Stan user’s guide (see Stan Development Team (2020, sec. 3.5)).

+

The same prior distributions as in the SAS implementation of the “five macros” are used (Roger (2021)), i.e. an improper flat priors for the regression coefficients and a weakly informative inverse Wishart prior for the covariance matrix (or matrices). Specifically, let \(S \in \mathbb{R}^{J \times J}\) be a symmetric positive definite matrix and \(\nu \in (J-1, \infty)\). Then the symmetric positive definite matrix \(x \in \mathbb{R}^{J \times J}\) has density: +\[ +\text{InvWish}(x \vert \nu, S) = \frac{1}{2^{\nu J/2}} \frac{1}{\Gamma_J(\frac{\nu}{2})} \vert S \vert^{\nu/2} \vert x \vert ^{-(\nu + J + 1)/2} \text{exp}(-\frac{1}{2} \text{tr}(Sx^{-1})). +\] +For \(\nu > J+1\) the mean is given by: +\[ +E[x] = \frac{S}{\nu - J - 1}. +\] +We choose \(S\) equal to the estimated covariance matrix from the frequentist REML fit and \(\nu = J+2\) as these are the lowest degrees of freedom that guarantee a finite mean. Setting the degrees of freedom with such a low \(\nu\) ensures that the prior has little impact on the posterior. Moreover, this choice allows to interpret the parameter \(S\) as the mean of the prior distribution.

+

As in the “five macros”, the MCMC algorithm is initialized at the parameters from a frequentist REML fit (see section 3.3.2). As described above, we are using only weakly informative priors for the parameters. Therefore, the Markov chain is essentially starting from the targeted stationary posterior distribution and only a minimal amount of burn-in of the chain is required.

+
+
+

+3.3.4 Approximate Bayesian posterior draws via the bootstrap +

+

Several authors have suggested that a stabler way to get Bayesian posterior draws from the imputation model is to bootstrap the incomplete data and to calculate REML estimates for each bootstrap sample (Little and Rubin (2002), Efron (1994), Honaker and King (2010), von Hippel and Bartlett (2021)). This method is proper in that the REML estimates from the bootstrap samples are asymptotically equivalent to a sample from the posterior distribution and may provide additional robustness to model misspecification (Little and Rubin (2002, sec. 10.2.3, part 6), Honaker and King (2010)). In order to retain balance between treatment groups and stratification factors across bootstrap samples, the user is able to provide stratification variables for the bootstrap in the rbmi implementation.

+
+
+
+

+3.4 Imputation step +

+
+

+3.4.1 Marginal imputation distribution for a subject - MAR case +

+

For each subject \(i\), the marginal distribution of the complete \(J\)-dimensional outcome vector from all assessment visits according to the imputation model is a multivariate normal distribution. Its mean \(\tilde{\mu}_i\) is given by the predicted mean from the imputation model conditional on the subject’s baseline characteristics, group, and, optionally, time-varying covariates. Its covariance matrix \(\tilde{\Sigma}_i\) is given by the overall estimated covariance matrix or, if different covariance matrices are assumed for different groups, the covariance matrix corresponding to subject \(i\)’s group.

+
+
+

+3.4.2 Marginal imputation distribution for a subject - reference-based imputation methods +

+

For each subject \(i\), we calculate the mean and covariance matrix of the complete \(J\)-dimensional outcome vector from all assessment visits as for the MAR case and denote them by \(\mu_i\) and \(\Sigma_i\). +For reference-based imputation methods, a corresponding reference group is also required for each group. Typically, the reference group for the intervention group will be the control group. +The reference mean \(\mu_{ref,i}\) is defined as the predicted mean from the imputation model conditional on the reference group (rather than the actual group subject \(i\) belongs to) and the subject’s baseline characteristics. +The reference covariance matrix \(\Sigma_{ref,i}\) is the overall estimated covariance matrix or, if different covariance matrices are assumed for different groups, the estimated covariance matrix corresponding to the reference group. In principle, time-varying covariates could also be included in reference-based imputation methods. However, this is only sensible for external time-varying covariates (e.g. calendar time of the visit) and not for internal time-varying covariates (e.g. treatment discontinuation) because the latter likely depend on the actual treatment group and it is typically not sensible to assume the same trajectory of the time-varying covariate for the reference group.

+

Based on these means and covariance matrices, the subject’s marginal imputation distribution for the reference-based imputation methods is then calculated as detailed in Carpenter, Roger, and Kenward (2013, sec. 4.3). +Denote the mean and covariance matrix of this marginal imputation distribution by \(\tilde{\mu}_i\) and \(\tilde{\Sigma}_i\). Recall that the subject’s first visit which is affected by the ICE is denoted by \(\tilde{t}_i \in \{1,\ldots,J\}\) (and visit \(\tilde{t}_i-1\) is the last visit unaffected by the ICE). The marginal distribution for the patient \(i\) is then built according to the specific assumption for the data up to and post the ICE as follows:

+
    +

  1. Jump to reference (JR): the patient’s outcome distribution is normally distributed with the following mean: +\[\tilde{\mu}_i = (\mu_i[1], \dots, \mu_i[\tilde{t}_i-1], \mu_{ref,i}[\tilde{t}_i], \dots, \mu_{ref,i}[J])^T.\] +The covariance matrix is constructed as follows. First, we partition the covariance matrices \(\Sigma_i\) and \(\Sigma_{ref,i}\) in blocks according to the time of the ICE \(\tilde{t}_i\): +\[ +\Sigma_{i} = \begin{bmatrix} \Sigma_{i, 11} & \Sigma_{i, 12} \\ +\Sigma_{i, 21} & \Sigma_{i,22} \\ +\end{bmatrix} +\] +\[ +\Sigma_{ref,i} = \begin{bmatrix} \Sigma_{ref, i, 11} & \Sigma_{ref, i, 12} \\ +\Sigma_{ref, i, 21} & \Sigma_{ref, i,22} \\ +\end{bmatrix}. +\] +We want the covariance matrix \(\tilde{\Sigma}_i\) to match \(\Sigma_i\) for the pre-deviation measurements, and \(\Sigma_{ref,i}\) for the conditional components for the post-deviation given the pre-deviation measurements. The solution is derived in Carpenter, Roger, and Kenward (2013, sec. 4.3) and is given by: +\[ +\begin{matrix} +\tilde{\Sigma}_{i,11} = \Sigma_{i, 11} \\ +\tilde{\Sigma}_{i, 21} = \Sigma_{ref,i, 21} \Sigma^{-1}_{ref,i, 11} \Sigma_{i, 11} \\ +\tilde{\Sigma}_{i, 22} = \Sigma_{ref, i, 22} - \Sigma_{ref,i, 21} \Sigma^{-1}_{ref,i, 11} (\Sigma_{ref,i, 11} - \Sigma_{i,11}) \Sigma^{-1}_{ref,i, 11} \Sigma_{ref,i, 12}. +\end{matrix} +\]

    2. +

  2. Copy increments in reference (CIR): the patient’s outcome distribution is normally distributed with the following mean: +\[ +\begin{split} +\tilde{\mu}_i =& (\mu_i[1], \dots, \mu_i[\tilde{t}_i-1], \mu_i[\tilde{t}_i-1] + (\mu_{ref,i}[\tilde{t}_i] - \mu_{ref,i}[\tilde{t}_i-1]), \dots,\\ & +\mu_i[\tilde{t}_i-1]+(\mu_{ref,i}[J] - \mu_{ref,i}[\tilde{t}_i-1]))^T. +\end{split} +\] +The covariance matrix is derived as for the JR method.

    3. +

  3. Copy reference (CR): the patient’s outcome distribution is normally distributed with mean and covariance matrix taken from the reference group: +\[ +\tilde{\mu}_i = \mu_{ref,i} +\] +\[ +\tilde{\Sigma}_i = \Sigma_{ref,i}. +\]

    4. +

  4. Last mean carried forward (LMCF): the patient’s outcome distribution is normally distributed with the following mean: +\[ \tilde{\mu}_i = (\mu_i[1], \dots, \mu_i[\tilde{t}_i-1], \mu_i[\tilde{t}_i-1], \dots, \mu_i[\tilde{t}_i-1])'\] +and covariance matrix: \[ \tilde{\Sigma}_i = \Sigma_i.\]

    5. +
+
+
+

+3.4.3 Imputation of missing outcome data +

+

The joint marginal multivariate normal imputation distribution of subject \(i\)’s observed and missing outcome data has mean \(\tilde{\mu}_i\) and covariance matrix \(\tilde{\Sigma}_i\) as defined above. The actual imputation of the missing outcome data is obtained by conditioning this marginal distribution on the subject’s observed outcome data. Of note, this approach is valid regardless whether the subject has intermittent or terminal missing data.

+

The conditional distribution used for the imputation is again a multivariate normal distribution and explicit formulas for the conditional mean and covariance are readily available. For completeness, we report them here with the notation and terminology of our setting. The marginal distribution for the outcome of patient \(i\) is \(Y_i \sim N(\tilde{\mu}_i, \tilde{\Sigma}_i)\) and the outcome \(Y_i\) can be decomposed in the observed (\(Y_{i,!}\)) and the unobserved (\(Y_{i,?}\)) components. Analogously the mean \(\tilde{\mu}_i\) can be decomposed as \((\tilde{\mu}_{i,!},\tilde{\mu}_{i,?})\) and the covariance \(\tilde{\Sigma}_i\) as: +\[ +\tilde{\Sigma}_i = +\begin{bmatrix} +\tilde{\Sigma}_{i, !!} & \tilde{\Sigma}_{i,!?} \\ +\tilde{\Sigma}_{i, ?!} & \tilde{\Sigma}_{i, ??} +\end{bmatrix}. +\] +The conditional distribution of \(Y_{i,?}\) conditional on \(Y_{i,!}\) is then a multivariate normal distribution with expectation +\[ +E(Y_{i,?} \vert Y_{i,!})= \tilde{\mu}_{i,?} + \tilde{\Sigma}_{i, ?!} \tilde{\Sigma}_{i,!!}^{-1} (Y_{i,!} - \tilde{\mu}_{i,!}) +\] +and covariance matrix +\[ +Cov(Y_{i,?} \vert Y_{i,!}) = \tilde{\Sigma}_{i,??} - \tilde{\Sigma}_{i,?!} \tilde{\Sigma}_{i,!!}^{-1} \tilde{\Sigma}_{i,!?}. +\]

+

Conventional random imputation consists in sampling from this conditional multivariate normal distribution. Conditional mean imputation imputes missing values with the deterministic conditional expectation \(E(Y_{i,?} \vert Y_{i,!})\).

+
+
+
+

+3.5 \(\delta\)-adjustment +

+

A marginal \(\delta\)-adjustment approach similar to the “five macros” in SAS is implemented (Roger (2021)), i.e. fixed non-stochastic values are added after the multivariate normal imputation step and prior to the analysis. +This is relevant for sensitivity analyses in order to make imputed data systematically worse or better, respectively, than observed data. In addition, some authors have suggested \(\delta\)-type adjustments to implement a composite strategy for continuous outcomes (Darken et al. (2020)).

+

The implementation provides full flexibility regarding the specific implementation of the \(\delta\)-adjustment, i.e. the value that is added may depend on the randomized treatment group, the timing of the subject’s ICE, and other factors. For suggestions and case studies regarding this topic, we refer to Cro et al. (2020).

+
+
+

+3.6 Analysis step +

+

After data imputation, a standard analysis model can be applied to the completed data resulting in a treatment effect estimate. As the imputed data no longer contains missing values, the analysis model is often simple. For example, it can be an analysis of covariance (ANCOVA) model with the outcome (or the change in the outcome from baseline) at a specific visit j as the dependent variable, the randomized treatment group as the primary covariate and, typically, adjustment for the same baseline covariates as for the imputation model.

+
+
+

+3.7 Pooling step for inference of (approximate) Bayesian MI and Rubin’s rules +

+

Assume that the analysis model has been applied to \(M\) multiple imputed random datasets which resulted in \(m\) treatment effect estimates \(\hat{\theta}_m\) (\(m=1,\ldots,M\)) with corresponding standard error \(SE_m\) and (if available) degrees of freedom \(\nu_{com}\). If degrees of freedom are not available for an analysis model, set \(\nu_{com}=\infty\) for inference based on the normal distribution.

+

Rubin’s rules are used for pooling the treatment effect estimates and corresponding variances estimates from the analysis steps across the \(M\) multiple imputed datasets. According to Rubin’s rules, the final estimate of the treatment effect is calculated as the sample mean over the \(M\) treatment effect estimates: +\[ +\hat{\theta} = \frac{1}{M} \sum_{m = 1}^M \hat{\theta}_m. +\] +The pooled variance is based on two components that reflect the within and the between variance of the treatment effects across the multiple imputed datasets: +\[ +V(\hat{\theta}) = V_W(\hat{\theta}) + (1 + \frac{1}{M}) V_B(\hat{\theta}) +\] +where \(V_W(\hat{\theta}) = \frac{1}{M}\sum_{m = 1}^M SE^2_m\) is the within-variance and \(V_B(\hat{\theta}) = \frac{1}{M-1} \sum_{m = 1}^M (\hat{\theta}_m - \hat{\theta})^2\) is the between-variance.

+

Confidence intervals and tests of the null hypothesis \(H_0: \theta=\theta_0\) are based on the \(t\)-statistics \(T\):

+

\[ T= (\hat{\theta}-\theta_0)/\sqrt{V(\hat{\theta})}. \] +Under the null hypothesis, \(T\) has an approximate \(t\)-distribution with \(\nu\) degrees of freedom. \(\nu\) is calculated according to the Barnard and Rubin approximation, see Barnard and Rubin (1999) (formula 3) or Little and Rubin (2002) (formula (5.24), page 87):

+

\[ +\nu = \frac{\nu_{old}* \nu_{obs}}{\nu_{old} + \nu_{obs}} +\] +with +\[ +\nu_{old} = \frac{M-1}{\lambda^2} \quad\mbox{and}\quad \nu_{obs} = \frac{\nu_{com} + 1}{\nu_{com} + 3} \nu_{com} (1 - \lambda) +\] +where \(\lambda = \frac{(1 + \frac{1}{M})V_B(\hat{\theta})}{V(\hat{\theta})}\) is the fraction of missing information.

+
+
+

+3.8 Bootstrap and jackknife inference for conditional mean imputation +

+
+

+3.8.1 Point estimate of the treatment effect +

+

The point estimator is obtained by applying the analysis model (Section 3.6) to a single conditional mean imputation of the missing data (see Section 3.4.3) based on the REML estimator of the parameters of the imputation model (see Section 3.3.2). We denote this treatment effect estimator by \(\hat{\theta}\).

+

As demonstrated in Wolbers et al. (2022) (Section 2.4), this treatment effect estimator is valid if the analysis model is an ANCOVA model or, more generally, if the treatment effect estimator is a linear function of the imputed outcome vector. Indeed, if this is the case, then the estimator is identical to the pooled treatment effect across multiple random REML imputation with an infinite number of imputations and corresponds to a computationally efficient implementation of a proposal by von Hippel and Bartlett (2021). We expect that the conditional mean imputation method is also applicable to some other analysis models (e.g. for general MMRM analysis models) but this has not been formally justified.

+
+
+

+3.8.2 Jackknife standard errors, confidence intervals (CI) and tests for the treatment effect +

+

For a dataset containing \(n\) subjects, the jackknife standard error depends on treatment effect estimates \(\hat{\theta}_{(-b)}\) (\(b=1,\ldots,n\)) from samples of the original dataset which leave out the observation from subject \(b\). As described previously, to obtain treatment effect estimates for leave-one-subject-out datasets, all +steps of the imputation procedure (i.e. imputation, conditional mean imputation, and analysis steps) need to be repeated on this new dataset.

+

Then, the jackknife standard error is defined as +\[\hat{se}_{jack}=[\frac{(n-1)}{n}\cdot\sum_{b=1}^{n} (\hat{\theta}_{(-b)}-\bar{\theta}_{(.)})^2]^{1/2}\] +where \(\bar{\theta}_{(.)}\) denotes the mean of all jackknife estimates (Efron and Tibshirani (1994), chapter 10). The corresponding two-sided normal approximation \(1-\alpha\) CI is defined as \(\hat{\theta}\pm z^{1-\alpha/2}\cdot \hat{se}_{jack}\) where \(\hat{\theta}\) is the treatment effect estimate from the original dataset. Tests of the null hypothesis \(H_0: \theta=\theta_0\) are then based on the \(Z\)-score \(Z=(\hat{\theta}-\theta_0)/\hat{se}_{jack}\) using a standard normal approximation.

+

A simulation study reported in Wolbers et al. (2022) demonstrated exact protection of the type I error for jackknife-based inference with a relatively low sample size (n = 100 per group) and a substantial amount of missing data (>25% of subjects with an ICE).

+
+
+

+3.8.3 Bootstrap standard errors, confidence intervals (CI) and tests for the treatment effect +

+

As an alternative to the jackknife, the bootstrap has also been implemented in rbmi (Efron and Tibshirani (1994), Davison and Hinkley (1997)).

+

Two different bootstrap methods are implemented in rbmi: Methods based on the bootstrap standard error and the normal approximation and percentile bootstrap methods. Denote the treatment effect estimates from \(B\) bootstrap samples by \(\hat{\theta}^*_b\) (\(b=1,\ldots,B\)). The bootstrap standard error \(\hat{se}_{boot}\) is defined as the empirical standard deviation of the bootstrapped treatment effect estimates. Confidence intervals and tests based on the bootstrap standard error can then be constructed in the same way as for the jackknife. Confidence intervals using the percentile bootstrap are based on empirical quantiles of the bootstrap distribution and corresponding statistical tests are implemented in rbmi via inversion of the confidence interval. Explicit formulas for bootstrap inference as implemented in the rbmi package and some considerations regarding the required number of bootstrap samples are included in the Appendix of Wolbers et al. (2022).

+

A simulation study reported in Wolbers et al. (2022) demonstrated a small inflation of the type I error rate for inference based on the bootstrap standard error (up to \(5.3\%\) for a nominal type I error rate of \(5\%\)) for a sample size of n = 100 per group and a substantial amount of missing data (>25% of subjects with an ICE). Based on this simulations, we recommend the jackknife over the bootstrap for inference because it performed better in our simulation study and is typically much faster to +compute than the bootstrap.

+
+
+
+

+3.9 Pooling step for inference of the bootstrapped MI methods +

+

Assume that the analysis model has been applied to \(B\times D\) multiple imputed random datasets which resulted in \(B\times D\) treatment effect estimates \(\hat{\theta}_{bd}\) (\(b=1,\ldots,B\); \(d=1,\ldots,D\)).

+

The final estimate of the treatment effect is calculated as the sample mean over the \(B*D\) treatment effect estimates: +\[ +\hat{\theta} = \frac{1}{BD} \sum_{b = 1}^B \sum_{d = 1}^D \hat{\theta}_{bd}. +\] +The pooled variance is based on two components that reflect the variability within and between imputed bootstrap samples (von Hippel and Bartlett (2021), formula 8.4): +\[ +V(\hat{\theta}) = (1 + \frac{1}{B})\frac{MSB - MSW}{D} + \frac{MSW}{BD} +\]

+

where \(MSB\) is the mean square between the bootstrapped datasets, and \(MSW\) is the mean square within the bootstrapped datasets and between the imputed datasets:

+

\[ +\begin{align*} +MSB &= \frac{D}{B-1} \sum_{b = 1}^B (\bar{\theta_{b}} - \hat{\theta})^2 \\ +MSW &= \frac{1}{B(D-1)} \sum_{b = 1}^B \sum_{d = 1}^D (\theta_{bd} - \bar{\theta_b})^2 +\end{align*} +\] +where \(\bar{\theta_{b}}\) is the mean across the \(D\) estimates obtained from random imputation of the \(b\)-th bootstrap sample.

+

The degrees of freedom are estimated with the following formula (von Hippel and Bartlett (2021), formula 8.6):

+

\[ +\nu = \frac{(MSB\cdot (B+1) - MSW\cdot B)^2}{\frac{MSB^2\cdot (B+1)^2}{B-1} + \frac{MSW^2\cdot B}{D-1}} +\]

+

Confidence intervals and tests of the null hypothesis \(H_0: \theta=\theta_0\) are based on the \(t\)-statistics \(T\):

+

\[ T= (\hat{\theta}-\theta_0)/\sqrt{V(\hat{\theta})}. \] +Under the null hypothesis, \(T\) has an approximate \(t\)-distribution with \(\nu\) degrees of freedom.

+
+
+

+3.10 Comparison between the implemented approaches +

+
+

+3.10.1 Treatment effect estimation +

+

All approaches provide consistent treatment effect estimates for standard and reference-based imputation methods in case the analysis model of the completed datasets is a general linear model such as ANCOVA. Methods other than conditional mean imputation should also be valid for other analysis models. The validity of conditional mean imputation has only been formally demonstrated for analyses using the general linear model (Wolbers et al. (2022, sec. 2.4)) though it may also be applicable more widely (e.g. for general MMRM analysis models).

+

Treatment effects based on conditional mean imputation are deterministic. All other methods are affected by Monte Carlo sampling error and the precision of estimates depends on the number of imputations or bootstrap samples, respectively.

+
+
+

+3.10.2 Standard errors of the treatment effect +

+

All approaches provide frequentist consistent estimates of the standard error for imputation under a MAR assumption. For reference-based imputation methods, methods based on conditional mean imputation or bootstrapped MI provide frequentist consistent estimates of the standard error whereas Rubin’s rules applied to conventional MI methods provides so-called information anchored inference (Bartlett (2021), Cro, Carpenter, and Kenward (2019), von Hippel and Bartlett (2021), Wolbers et al. (2022)). Frequentist consistent estimates of the standard error lead to confidence intervals and tests which have (asymptotically) correct coverage and type I error control under the assumption that the reference-based assumption reflects the true data-generating mechanism. For finite samples, simulations for a sample size of \(n=100\) per group reported in Wolbers et al. (2022) demonstrated that conditional mean imputation combined with the jackknife provided exact protection of the type one error rate whereas the bootstrap was associated with a small type I error inflation (between 5.1% to 5.3% for a nominal level of 5%).

+

It is well known that Rubin’s rules do not provide frequentist consistent estimates of the standard error for reference-based imputation methods (Seaman, White, and Leacy (2014), Liu and Pang (2016), Tang (2017), Cro, Carpenter, and Kenward (2019), Bartlett (2021)). Standard errors from Rubin’s rule are typically larger than frequentist standard error estimates leading to conservative inference and a corresponding loss of statistical power, see e.g. the simulations reported in Wolbers et al. (2022). +Intuitively, this occurs because reference-based imputation methods borrow information from the reference group for imputations in the intervention group leading to a reduction in the frequentist variance of the resulting treatment effect contrast which is not captured by Rubin’s variance estimator. Formally, this occurs because the imputation and analysis models are uncongenial for reference-based imputation methods (Meng (1994), Bartlett (2021)). +Cro, Carpenter, and Kenward (2019) argued that Rubin’s rule is nevertheless valid for reference-based imputation methods because it is approximately information-anchored, i.e. that the proportion of information lost due to missing data under MAR is approximately preserved in reference-based analyses. In contrast, frequentist standard errors for reference based imputation are not information anchored for reference-based imputation and standard errors under reference-based assumptions are typically smaller than those for MAR imputation.

+

Information anchoring is a sensible concept for sensitivity analyses, whereas for a primary analyses, it may be more important to adhere to the principles of frequentist inference. Analyses of data with missing observations generally rely on unverifiable missing data assumptions and the assumptions for reference-based imputation methods are relatively strong. Therefore, these assumptions need to be clinically justified as appropriate or at least conservative for the considered disease area and the anticipated mechanism of action of the intervention.

+

Conditional mean imputation combined with the jackknife is the only method which leads to deterministic standard error estimates and, consequently, confidence intervals and \(p\)-values are also deterministic. This is particularly important in a regulatory setting where it is important to ascertain whether a calculated \(p\)-value which is close to the critical boundary of 5% is truly below or above that threshold rather than being uncertain about this because of Monte Carlo error.

+
+
+

+3.10.3 Computational complexity +

+

Bayesian MI methods rely on the specification of prior distributions and the usage of Markov chain Monte Carlo (MCMC) methods. +All other methods based on multiple imputation or bootstrapping require no other tuning parameters than the specification of the number of imputations \(M\) or bootstrap samples \(B\) and rely on numerical optimization for fitting the MMRM imputation models via REML. Conditional mean imputation combined with the jackknife has no tuning parameters.

+

In our rbmi implementation, the fitting of the MMRM imputation model via REML is computationally most expensive. MCMC sampling using rstan (Stan Development Team (2020)) is typically relatively fast in our setting and requires only a small burn-in and burn-between of the chains. In addition, the number of random imputations for reliable inference using Rubin’s rules is often smaller than the number of resamples required for the jackknife or the bootstrap (see e.g. the discussions in I. R. White, Royston, and Wood (2011, sec. 7) for Bayesian MI and the Appendix of Wolbers et al. (2022) for the bootstrap). Thus, for many applications, we expect that conventional MI based on Bayesian posterior draws will be fastest, followed by conventional MI using approximate Bayesian posterior draws and conditional mean imputation combined with the jackknife. Conditional mean imputation combined with the bootstrap and bootstrapped MI methods will typically be most computationally demanding. Of note, all implemented methods are conceptually straightforward to parallelise and some parallelisation support is provided by rbmi.

+
+
+
+
+

+4 Mapping of statistical methods to rbmi functions +

+

For a full documentation of the rbmi package functionality we refer to the help pages of all functions and to the other package vignettes. Here we only give a brief overview of how the different steps of the imputation procedure are mapped to rbmi functions:

+
    +
  • The base imputation model fitting step is implemented in the function draws(). The chosen MI approach can be set using the argument method and should be one of the following: +
      +
    • Bayesian posterior parameter draws from the imputation model are obtained via the argument method = method_bayes().
      • +
    • Approximate Bayesian posterior parameter draws from the imputation model are obtained via argument method = method_approxbayes().
      • +
    • ML or REML parameter estimates of the imputation model parameters for the original dataset and all leave-one-subject-out datasets (as required for the jackknife) are obtained via argument method = method_condmean(type = "jackknife").
      • +
    • ML or REML parameter estimates of the imputation model parameters for the original dataset and bootstrapped datasets are obtained via argument method = method_condmean(type = "bootstrap").
      • +
    • Bootstrapped MI methods are obtained via argument method = method_bmlmi(B=B, D=D) where \(B\) refers to the number of bootstrap samples and \(D\) to the number of random imputations for each bootstrap sample.
      • +
    +
    • +
  • The imputation step using random imputation or deterministic conditional mean imputation, respectively, is implemented in function impute(). Imputation can be performed assuming the already implemented imputation strategies as presented in section 3.4. Additionally, user-defined imputation strategies are also supported.
    • +
  • The analysis step is implemented in function analyse() and applies the analysis model to all imputed datasets. By default, the analysis model (argument fun) is the ancova() function but alternative analysis functions can also be provided by the user. The analyse() function also allows for \(\delta\)-adjustments to the imputed datasets prior to the analysis via argument delta.
    • +
  • The inference step is implemented in function pool() which pools the results across imputed datasets. The Rubin and Bernard rule is applied in case of (approximate) Bayesian MI. For conditional mean imputation, jackknife and bootstrap (normal approximation or percentile) inference is supported. For BMLMI, the pooling and inference steps are performed via pool() which in this case implements the method described in Section 3.9.
    • +
+
+
+

+5 Comparison to other software implementations +

+

An established software implementation of reference-based imputation in SAS are the so-called “five macros” by James Roger (Roger (2021)). An alternative R implementation which is also currently under development is the R package RefBasedMI (McGrath and White (2021)).

+

rbmi has several features which are not supported by the other implementations:

+
    +

  1. In addition to the Bayesian MI approach implemented also in the other packages, our implementation provides three alternative MI approaches: approximate Bayesian MI, conditional mean imputation combined with resampling, and bootstrapped MI.

    2. +

  2. rbmi allows for the usage of data collected after an ICE. For example, suppose that we want to adopt a treatment policy strategy for the ICE “treatment discontinuation”. A possible implementation of this strategy is to use the observed outcome data for subjects who remain in the study after the ICE and to use reference-based imputation in case the subject drops out. In our implementation, this is implemented by excluding observed post ICE data from the imputation model which assumes MAR missingness but including them in the analysis model. To our knowledge, this is not directly supported by the other implementations.

    3. +

  3. RefBasedMI fits the imputation model to data from each treatment group separately which implies covariate-treatment group interactions for all covariates for the pooled data from both treatment groups. In contrast, Roger’s five macros assume a joint model including data from all the randomized groups and covariate-treatment interactions covariates are not allowed. We also chose to implement a joint model but use a flexible model for the linear predictor which may or may not include an interaction term between any covariate and the treatment group. In addition, our imputation model also allows for the inclusion of time-varying covariates.

    4. +

  4. In our implementation, the grouping of the subjects for the purpose of the imputation model (and the definition of the reference group) does not need to correspond to the assigned treatment groups. This provides additional flexibility for the imputation procedure. It is not clear to us whether this feature is supported by Roger’s five macros or RefBasedMI.

    5. +

  5. We believe that our R-based implementation is more modular than RefBasedMI which should facilitate further package enhancements.

    6. +
+

In contrast, the more general causal model introduced by I. White, Royes, and Best (2020) is available in the other implementations but is currently not supported by ours.

+
+
+

References +

+
+
+Barnard, John, and Donald B Rubin. 1999. “Miscellanea. Small-Sample Degrees of Freedom with Multiple Imputation.” Biometrika 86 (4): 948–55. +
+
+Bartlett, Jonathan W. 2021. “Reference-Based Multiple Imputation - What Is the Right Variance and How to Estimate It.” Statistics in Biopharmaceutical Research. https://doi.org/10.1080/19466315.2021.1983455. +
+
+Carpenter, James R, James H Roger, and Michael G Kenward. 2013. “Analysis of Longitudinal Trials with Protocol Deviation: A Framework for Relevant, Accessible Assumptions, and Inference via Multiple Imputation.” Journal of Biopharmaceutical Statistics 23 (6): 1352–71. +
+
+Cro, Suzie, James R Carpenter, and Michael G Kenward. 2019. “Information-Anchored Sensitivity Analysis: Theory and Application.” Journal of the Royal Statistical Society: Series A (Statistics in Society) 182 (2): 623–45. +
+
+Cro, Suzie, Tim P Morris, Michael G Kenward, and James R Carpenter. 2020. “Sensitivity Analysis for Clinical Trials with Missing Continuous Outcome Data Using Controlled Multiple Imputation: A Practical Guide.” Statistics in Medicine 39 (21): 2815–42. +
+
+Darken, Patrick, Jack Nyberg, Shaila Ballal, and David Wright. 2020. “The Attributable Estimand: A New Approach to Account for Intercurrent Events.” Pharmaceutical Statistics 19 (5): 626–35. +
+
+Davison, Anthony C, and David V Hinkley. 1997. Bootstrap Methods and Their Application. Cambridge University Press. +
+
+Efron, Bradley. 1994. “Missing Data, Imputation, and the Bootstrap.” Journal of the American Statistical Association 89 (426): 463–75. +
+
+Efron, Bradley, and Robert J Tibshirani. 1994. An Introduction to the Bootstrap. CRC press. +
+
+Guizzaro, Lorenzo, Frank Pétavy, Robin Ristl, and Ciro Gallo. 2021. “The Use of a Variable Representing Compliance Improves Accuracy of Estimation of the Effect of Treatment Allocation Regardless of Discontinuation in Trials with Incomplete Follow-up.” Statistics in Biopharmaceutical Research 13 (1): 119–27. +
+
+Honaker, James, and Gary King. 2010. “What to Do about Missing Values in Time-Series Cross-Section Data.” American Journal of Political Science 54 (2): 561–81. +
+
+ICH E9 working group. 2019. “ICH E9 (R1): Addendum on estimands and sensitivity analysis in clinical trials to the guideline on statistical principles for clinical trials.” International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use. 2019. https://database.ich.org/sites/default/files/E9-R1_Step4_Guideline_2019_1203.pdf. +
+
+Little, Roderick JA, and Donald B Rubin. 2002. Statistical Analysis with Missing Data, Second Edition. John Wiley & Sons. +
+
+Liu, G Frank, and Lei Pang. 2016. “On Analysis of Longitudinal Clinical Trials with Missing Data Using Reference-Based Imputation.” Journal of Biopharmaceutical Statistics 26 (5): 924–36. +
+
+Mallinckrodt, CH, J Bell, G Liu, B Ratitch, M O’Kelly, I Lipkovich, P Singh, L Xu, and G Molenberghs. 2020. “Aligning Estimators with Estimands in Clinical Trials: Putting the ICH E9 (R1) Guidelines into Practice.” Therapeutic Innovation & Regulatory Science 54 (2): 353–64. +
+
+McGrath, Kevin, and Ian White. 2021. “RefBasedMI: Reference-Based Imputation for Longitudinal Clinical Trials with Protocol Deviation.” https://github.com/UCL/RefbasedMI. +
+
+Meng, Xiao-Li. 1994. “Multiple-Imputation Inferences with Uncongenial Sources of Input.” Statistical Science 9 (4): 538–58. +
+
+Patterson, H Desmond, and Robin Thompson. 1971. “Recovery of Inter-Block Information When Block Sizes Are Unequal.” Biometrika 58 (3): 545–54. +
+
+Polverejan, Elena, and Vladimir Dragalin. 2020. “Aligning Treatment Policy Estimands and Estimators—a Simulation Study in Alzheimer’s Disease.” Statistics in Biopharmaceutical Research 12 (2): 142–54. +
+
+Roger, James. 2021. “Reference-Based MI via Multivariate Normal RM (the ‘Five Macros’ and MIWithD).” https://www.lshtm.ac.uk/research/centres-projects-groups/missing-data#dia-missing-data. +
+
+Seaman, Shaun R, Ian R White, and Finbarr P Leacy. 2014. “Comment on ‘Analysis of Longitudinal Trials with Protocol Deviations: A Framework for Relevant, Accessible Assumptions, and Inference via Multiple Imputation,’ by Carpenter, Roger, and Kenward.” Journal of Biopharmaceutical Statistics 24 (6): 1358–62. +
+
+Stan Development Team. 2020. “RStan: The R Interface to Stan.” https://mc-stan.org/. +
+
+Tang, Yongqiang. 2017. “On the Multiple Imputation Variance Estimator for Control-Based and Delta-Adjusted Pattern Mixture Models.” Biometrics 73 (4): 1379–87. +
+
+von Hippel, Paul T, and Jonathan W Bartlett. 2021. “Maximum Likelihood Multiple Imputation: Faster Imputations and Consistent Standard Errors Without Posterior Draws.” Statistical Science 36 (3): 400–420. +
+
+White, Ian R, Patrick Royston, and Angela M Wood. 2011. “Multiple Imputation Using Chained Equations: Issues and Guidance for Practice.” Statistics in Medicine 30 (4): 377–99. +
+
+White, Ian, Joseph Royes, and Nicky Best. 2020. “A Causal Modelling Framework for Reference-Based Imputation and Tipping Point Analysis in Clinical Trials with Quantitative Outcome.” Journal of Biopharmaceutical Statistics 30 (2): 334–50. +
+
+Wolbers, Marcel, Alessandro Noci, Paul Delmar, Craig Gower-Page, Sean Yiu, and Jonathan W. Bartlett. 2022. “Standard and Reference-Based Conditional Mean Imputation.” Pharmaceutical Statistics. https://doi.org/10.1002/pst.2234. +
+
+
+
+
+ + + +
+

Developed by Craig Gower-Page, Alessandro Noci, Roche.

+
+ +
+

Site built with pkgdown 2.1.0.

+
+ +
+
+ + + + + + + diff --git a/latest-tag/authors.html b/latest-tag/authors.html new file mode 100644 index 00000000..f3e59cdf --- /dev/null +++ b/latest-tag/authors.html @@ -0,0 +1,105 @@ + +Authors and Citation • rbmi + Skip to contents + + +
+
+
+ +

Authors and Citation

+
+ +
+

Authors

+ +
  • +

    Craig Gower-Page. Author, maintainer. +

    +
    • +
  • +

    Alessandro Noci. Author. +

    +
    • +
  • +

    Marcel Wolbers. Contributor. +

    +
    • +
  • +

    Roche. Copyright holder, funder. +

    +
    • +
+ +
+

Citation

+

Source: DESCRIPTION

+ +

Gower-Page C, Noci A (2024). +rbmi: Reference Based Multiple Imputation. +R package version 1.2.6, https://github.com/insightsengineering/rbmi, https://insightsengineering.github.io/rbmi/. +

+ 
@Manual{,
+  title = {rbmi: Reference Based Multiple Imputation},
+  author = {Craig Gower-Page and Alessandro Noci},
+  year = {2024},
+  note = {R package version 1.2.6, https://github.com/insightsengineering/rbmi},
+  url = {https://insightsengineering.github.io/rbmi/},
+}
+
+ +
+ + +
+

Developed by Craig Gower-Page, Alessandro Noci, Roche.

+
+ +
+

Site built with pkgdown 2.1.0.

+
+ +
+ + + + + + + diff --git a/latest-tag/deps/bootstrap-5.3.1/bootstrap.bundle.min.js b/latest-tag/deps/bootstrap-5.3.1/bootstrap.bundle.min.js new file mode 100644 index 00000000..e8f21f70 --- /dev/null +++ b/latest-tag/deps/bootstrap-5.3.1/bootstrap.bundle.min.js @@ -0,0 +1,7 @@ +/*! + * Bootstrap v5.3.1 (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors) + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE) + */ +!function(t,e){"object"==typeof exports&&"undefined"!=typeof module?module.exports=e():"function"==typeof define&&define.amd?define(e):(t="undefined"!=typeof globalThis?globalThis:t||self).bootstrap=e()}(this,(function(){"use strict";const t=new Map,e={set(e,i,n){t.has(e)||t.set(e,new Map);const s=t.get(e);s.has(i)||0===s.size?s.set(i,n):console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(s.keys())[0]}.`)},get:(e,i)=>t.has(e)&&t.get(e).get(i)||null,remove(e,i){if(!t.has(e))return;const n=t.get(e);n.delete(i),0===n.size&&t.delete(e)}},i="transitionend",n=t=>(t&&window.CSS&&window.CSS.escape&&(t=t.replace(/#([^\s"#']+)/g,((t,e)=>`#${CSS.escape(e)}`))),t),s=t=>{t.dispatchEvent(new Event(i))},o=t=>!(!t||"object"!=typeof t)&&(void 0!==t.jquery&&(t=t[0]),void 0!==t.nodeType),r=t=>o(t)?t.jquery?t[0]:t:"string"==typeof t&&t.length>0?document.querySelector(n(t)):null,a=t=>{if(!o(t)||0===t.getClientRects().length)return!1;const e="visible"===getComputedStyle(t).getPropertyValue("visibility"),i=t.closest("details:not([open])");if(!i)return e;if(i!==t){const e=t.closest("summary");if(e&&e.parentNode!==i)return!1;if(null===e)return!1}return e},l=t=>!t||t.nodeType!==Node.ELEMENT_NODE||!!t.classList.contains("disabled")||(void 0!==t.disabled?t.disabled:t.hasAttribute("disabled")&&"false"!==t.getAttribute("disabled")),c=t=>{if(!document.documentElement.attachShadow)return null;if("function"==typeof t.getRootNode){const e=t.getRootNode();return e instanceof ShadowRoot?e:null}return t instanceof ShadowRoot?t:t.parentNode?c(t.parentNode):null},h=()=>{},d=t=>{t.offsetHeight},u=()=>window.jQuery&&!document.body.hasAttribute("data-bs-no-jquery")?window.jQuery:null,f=[],p=()=>"rtl"===document.documentElement.dir,m=t=>{var e;e=()=>{const e=u();if(e){const i=t.NAME,n=e.fn[i];e.fn[i]=t.jQueryInterface,e.fn[i].Constructor=t,e.fn[i].noConflict=()=>(e.fn[i]=n,t.jQueryInterface)}},"loading"===document.readyState?(f.length||document.addEventListener("DOMContentLoaded",(()=>{for(const t of f)t()})),f.push(e)):e()},g=(t,e=[],i=t)=>"function"==typeof t?t(...e):i,_=(t,e,n=!0)=>{if(!n)return void g(t);const o=(t=>{if(!t)return 0;let{transitionDuration:e,transitionDelay:i}=window.getComputedStyle(t);const n=Number.parseFloat(e),s=Number.parseFloat(i);return n||s?(e=e.split(",")[0],i=i.split(",")[0],1e3*(Number.parseFloat(e)+Number.parseFloat(i))):0})(e)+5;let r=!1;const a=({target:n})=>{n===e&&(r=!0,e.removeEventListener(i,a),g(t))};e.addEventListener(i,a),setTimeout((()=>{r||s(e)}),o)},b=(t,e,i,n)=>{const s=t.length;let o=t.indexOf(e);return-1===o?!i&&n?t[s-1]:t[0]:(o+=i?1:-1,n&&(o=(o+s)%s),t[Math.max(0,Math.min(o,s-1))])},v=/[^.]*(?=\..*)\.|.*/,y=/\..*/,w=/::\d+$/,A={};let E=1;const T={mouseenter:"mouseover",mouseleave:"mouseout"},C=new Set(["click","dblclick","mouseup","mousedown","contextmenu","mousewheel","DOMMouseScroll","mouseover","mouseout","mousemove","selectstart","selectend","keydown","keypress","keyup","orientationchange","touchstart","touchmove","touchend","touchcancel","pointerdown","pointermove","pointerup","pointerleave","pointercancel","gesturestart","gesturechange","gestureend","focus","blur","change","reset","select","submit","focusin","focusout","load","unload","beforeunload","resize","move","DOMContentLoaded","readystatechange","error","abort","scroll"]);function O(t,e){return e&&`${e}::${E++}`||t.uidEvent||E++}function x(t){const e=O(t);return t.uidEvent=e,A[e]=A[e]||{},A[e]}function k(t,e,i=null){return Object.values(t).find((t=>t.callable===e&&t.delegationSelector===i))}function L(t,e,i){const n="string"==typeof e,s=n?i:e||i;let o=I(t);return C.has(o)||(o=t),[n,s,o]}function S(t,e,i,n,s){if("string"!=typeof e||!t)return;let[o,r,a]=L(e,i,n);if(e in T){const t=t=>function(e){if(!e.relatedTarget||e.relatedTarget!==e.delegateTarget&&!e.delegateTarget.contains(e.relatedTarget))return t.call(this,e)};r=t(r)}const l=x(t),c=l[a]||(l[a]={}),h=k(c,r,o?i:null);if(h)return void(h.oneOff=h.oneOff&&s);const d=O(r,e.replace(v,"")),u=o?function(t,e,i){return function n(s){const o=t.querySelectorAll(e);for(let{target:r}=s;r&&r!==this;r=r.parentNode)for(const a of o)if(a===r)return P(s,{delegateTarget:r}),n.oneOff&&N.off(t,s.type,e,i),i.apply(r,[s])}}(t,i,r):function(t,e){return function i(n){return P(n,{delegateTarget:t}),i.oneOff&&N.off(t,n.type,e),e.apply(t,[n])}}(t,r);u.delegationSelector=o?i:null,u.callable=r,u.oneOff=s,u.uidEvent=d,c[d]=u,t.addEventListener(a,u,o)}function D(t,e,i,n,s){const o=k(e[i],n,s);o&&(t.removeEventListener(i,o,Boolean(s)),delete e[i][o.uidEvent])}function $(t,e,i,n){const s=e[i]||{};for(const[o,r]of Object.entries(s))o.includes(n)&&D(t,e,i,r.callable,r.delegationSelector)}function I(t){return t=t.replace(y,""),T[t]||t}const N={on(t,e,i,n){S(t,e,i,n,!1)},one(t,e,i,n){S(t,e,i,n,!0)},off(t,e,i,n){if("string"!=typeof e||!t)return;const[s,o,r]=L(e,i,n),a=r!==e,l=x(t),c=l[r]||{},h=e.startsWith(".");if(void 0===o){if(h)for(const i of Object.keys(l))$(t,l,i,e.slice(1));for(const[i,n]of Object.entries(c)){const s=i.replace(w,"");a&&!e.includes(s)||D(t,l,r,n.callable,n.delegationSelector)}}else{if(!Object.keys(c).length)return;D(t,l,r,o,s?i:null)}},trigger(t,e,i){if("string"!=typeof e||!t)return null;const n=u();let s=null,o=!0,r=!0,a=!1;e!==I(e)&&n&&(s=n.Event(e,i),n(t).trigger(s),o=!s.isPropagationStopped(),r=!s.isImmediatePropagationStopped(),a=s.isDefaultPrevented());const l=P(new Event(e,{bubbles:o,cancelable:!0}),i);return a&&l.preventDefault(),r&&t.dispatchEvent(l),l.defaultPrevented&&s&&s.preventDefault(),l}};function P(t,e={}){for(const[i,n]of Object.entries(e))try{t[i]=n}catch(e){Object.defineProperty(t,i,{configurable:!0,get:()=>n})}return t}function M(t){if("true"===t)return!0;if("false"===t)return!1;if(t===Number(t).toString())return Number(t);if(""===t||"null"===t)return null;if("string"!=typeof t)return t;try{return JSON.parse(decodeURIComponent(t))}catch(e){return t}}function j(t){return t.replace(/[A-Z]/g,(t=>`-${t.toLowerCase()}`))}const F={setDataAttribute(t,e,i){t.setAttribute(`data-bs-${j(e)}`,i)},removeDataAttribute(t,e){t.removeAttribute(`data-bs-${j(e)}`)},getDataAttributes(t){if(!t)return{};const e={},i=Object.keys(t.dataset).filter((t=>t.startsWith("bs")&&!t.startsWith("bsConfig")));for(const n of i){let i=n.replace(/^bs/,"");i=i.charAt(0).toLowerCase()+i.slice(1,i.length),e[i]=M(t.dataset[n])}return e},getDataAttribute:(t,e)=>M(t.getAttribute(`data-bs-${j(e)}`))};class H{static get Default(){return{}}static get DefaultType(){return{}}static get NAME(){throw new Error('You have to implement the static method "NAME", for each component!')}_getConfig(t){return t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t}_mergeConfigObj(t,e){const i=o(e)?F.getDataAttribute(e,"config"):{};return{...this.constructor.Default,..."object"==typeof i?i:{},...o(e)?F.getDataAttributes(e):{},..."object"==typeof t?t:{}}}_typeCheckConfig(t,e=this.constructor.DefaultType){for(const[n,s]of Object.entries(e)){const e=t[n],r=o(e)?"element":null==(i=e)?`${i}`:Object.prototype.toString.call(i).match(/\s([a-z]+)/i)[1].toLowerCase();if(!new RegExp(s).test(r))throw new TypeError(`${this.constructor.NAME.toUpperCase()}: Option "${n}" provided type "${r}" but expected type "${s}".`)}var i}}class W extends H{constructor(t,i){super(),(t=r(t))&&(this._element=t,this._config=this._getConfig(i),e.set(this._element,this.constructor.DATA_KEY,this))}dispose(){e.remove(this._element,this.constructor.DATA_KEY),N.off(this._element,this.constructor.EVENT_KEY);for(const t of Object.getOwnPropertyNames(this))this[t]=null}_queueCallback(t,e,i=!0){_(t,e,i)}_getConfig(t){return t=this._mergeConfigObj(t,this._element),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}static getInstance(t){return e.get(r(t),this.DATA_KEY)}static getOrCreateInstance(t,e={}){return this.getInstance(t)||new this(t,"object"==typeof e?e:null)}static get VERSION(){return"5.3.1"}static get DATA_KEY(){return`bs.${this.NAME}`}static get EVENT_KEY(){return`.${this.DATA_KEY}`}static eventName(t){return`${t}${this.EVENT_KEY}`}}const B=t=>{let e=t.getAttribute("data-bs-target");if(!e||"#"===e){let i=t.getAttribute("href");if(!i||!i.includes("#")&&!i.startsWith("."))return null;i.includes("#")&&!i.startsWith("#")&&(i=`#${i.split("#")[1]}`),e=i&&"#"!==i?i.trim():null}return n(e)},z={find:(t,e=document.documentElement)=>[].concat(...Element.prototype.querySelectorAll.call(e,t)),findOne:(t,e=document.documentElement)=>Element.prototype.querySelector.call(e,t),children:(t,e)=>[].concat(...t.children).filter((t=>t.matches(e))),parents(t,e){const i=[];let n=t.parentNode.closest(e);for(;n;)i.push(n),n=n.parentNode.closest(e);return i},prev(t,e){let i=t.previousElementSibling;for(;i;){if(i.matches(e))return[i];i=i.previousElementSibling}return[]},next(t,e){let i=t.nextElementSibling;for(;i;){if(i.matches(e))return[i];i=i.nextElementSibling}return[]},focusableChildren(t){const e=["a","button","input","textarea","select","details","[tabindex]",'[contenteditable="true"]'].map((t=>`${t}:not([tabindex^="-"])`)).join(",");return this.find(e,t).filter((t=>!l(t)&&a(t)))},getSelectorFromElement(t){const e=B(t);return e&&z.findOne(e)?e:null},getElementFromSelector(t){const e=B(t);return e?z.findOne(e):null},getMultipleElementsFromSelector(t){const e=B(t);return e?z.find(e):[]}},R=(t,e="hide")=>{const i=`click.dismiss${t.EVENT_KEY}`,n=t.NAME;N.on(document,i,`[data-bs-dismiss="${n}"]`,(function(i){if(["A","AREA"].includes(this.tagName)&&i.preventDefault(),l(this))return;const s=z.getElementFromSelector(this)||this.closest(`.${n}`);t.getOrCreateInstance(s)[e]()}))},q=".bs.alert",V=`close${q}`,K=`closed${q}`;class Q extends W{static get NAME(){return"alert"}close(){if(N.trigger(this._element,V).defaultPrevented)return;this._element.classList.remove("show");const t=this._element.classList.contains("fade");this._queueCallback((()=>this._destroyElement()),this._element,t)}_destroyElement(){this._element.remove(),N.trigger(this._element,K),this.dispose()}static jQueryInterface(t){return this.each((function(){const e=Q.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}R(Q,"close"),m(Q);const X='[data-bs-toggle="button"]';class Y extends W{static get NAME(){return"button"}toggle(){this._element.setAttribute("aria-pressed",this._element.classList.toggle("active"))}static jQueryInterface(t){return this.each((function(){const e=Y.getOrCreateInstance(this);"toggle"===t&&e[t]()}))}}N.on(document,"click.bs.button.data-api",X,(t=>{t.preventDefault();const e=t.target.closest(X);Y.getOrCreateInstance(e).toggle()})),m(Y);const U=".bs.swipe",G=`touchstart${U}`,J=`touchmove${U}`,Z=`touchend${U}`,tt=`pointerdown${U}`,et=`pointerup${U}`,it={endCallback:null,leftCallback:null,rightCallback:null},nt={endCallback:"(function|null)",leftCallback:"(function|null)",rightCallback:"(function|null)"};class st extends H{constructor(t,e){super(),this._element=t,t&&st.isSupported()&&(this._config=this._getConfig(e),this._deltaX=0,this._supportPointerEvents=Boolean(window.PointerEvent),this._initEvents())}static get Default(){return it}static get DefaultType(){return nt}static get NAME(){return"swipe"}dispose(){N.off(this._element,U)}_start(t){this._supportPointerEvents?this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX):this._deltaX=t.touches[0].clientX}_end(t){this._eventIsPointerPenTouch(t)&&(this._deltaX=t.clientX-this._deltaX),this._handleSwipe(),g(this._config.endCallback)}_move(t){this._deltaX=t.touches&&t.touches.length>1?0:t.touches[0].clientX-this._deltaX}_handleSwipe(){const t=Math.abs(this._deltaX);if(t<=40)return;const e=t/this._deltaX;this._deltaX=0,e&&g(e>0?this._config.rightCallback:this._config.leftCallback)}_initEvents(){this._supportPointerEvents?(N.on(this._element,tt,(t=>this._start(t))),N.on(this._element,et,(t=>this._end(t))),this._element.classList.add("pointer-event")):(N.on(this._element,G,(t=>this._start(t))),N.on(this._element,J,(t=>this._move(t))),N.on(this._element,Z,(t=>this._end(t))))}_eventIsPointerPenTouch(t){return this._supportPointerEvents&&("pen"===t.pointerType||"touch"===t.pointerType)}static isSupported(){return"ontouchstart"in document.documentElement||navigator.maxTouchPoints>0}}const ot=".bs.carousel",rt=".data-api",at="next",lt="prev",ct="left",ht="right",dt=`slide${ot}`,ut=`slid${ot}`,ft=`keydown${ot}`,pt=`mouseenter${ot}`,mt=`mouseleave${ot}`,gt=`dragstart${ot}`,_t=`load${ot}${rt}`,bt=`click${ot}${rt}`,vt="carousel",yt="active",wt=".active",At=".carousel-item",Et=wt+At,Tt={ArrowLeft:ht,ArrowRight:ct},Ct={interval:5e3,keyboard:!0,pause:"hover",ride:!1,touch:!0,wrap:!0},Ot={interval:"(number|boolean)",keyboard:"boolean",pause:"(string|boolean)",ride:"(boolean|string)",touch:"boolean",wrap:"boolean"};class xt extends W{constructor(t,e){super(t,e),this._interval=null,this._activeElement=null,this._isSliding=!1,this.touchTimeout=null,this._swipeHelper=null,this._indicatorsElement=z.findOne(".carousel-indicators",this._element),this._addEventListeners(),this._config.ride===vt&&this.cycle()}static get Default(){return Ct}static get DefaultType(){return Ot}static get NAME(){return"carousel"}next(){this._slide(at)}nextWhenVisible(){!document.hidden&&a(this._element)&&this.next()}prev(){this._slide(lt)}pause(){this._isSliding&&s(this._element),this._clearInterval()}cycle(){this._clearInterval(),this._updateInterval(),this._interval=setInterval((()=>this.nextWhenVisible()),this._config.interval)}_maybeEnableCycle(){this._config.ride&&(this._isSliding?N.one(this._element,ut,(()=>this.cycle())):this.cycle())}to(t){const e=this._getItems();if(t>e.length-1||t<0)return;if(this._isSliding)return void N.one(this._element,ut,(()=>this.to(t)));const i=this._getItemIndex(this._getActive());if(i===t)return;const n=t>i?at:lt;this._slide(n,e[t])}dispose(){this._swipeHelper&&this._swipeHelper.dispose(),super.dispose()}_configAfterMerge(t){return t.defaultInterval=t.interval,t}_addEventListeners(){this._config.keyboard&&N.on(this._element,ft,(t=>this._keydown(t))),"hover"===this._config.pause&&(N.on(this._element,pt,(()=>this.pause())),N.on(this._element,mt,(()=>this._maybeEnableCycle()))),this._config.touch&&st.isSupported()&&this._addTouchEventListeners()}_addTouchEventListeners(){for(const t of z.find(".carousel-item img",this._element))N.on(t,gt,(t=>t.preventDefault()));const t={leftCallback:()=>this._slide(this._directionToOrder(ct)),rightCallback:()=>this._slide(this._directionToOrder(ht)),endCallback:()=>{"hover"===this._config.pause&&(this.pause(),this.touchTimeout&&clearTimeout(this.touchTimeout),this.touchTimeout=setTimeout((()=>this._maybeEnableCycle()),500+this._config.interval))}};this._swipeHelper=new st(this._element,t)}_keydown(t){if(/input|textarea/i.test(t.target.tagName))return;const e=Tt[t.key];e&&(t.preventDefault(),this._slide(this._directionToOrder(e)))}_getItemIndex(t){return this._getItems().indexOf(t)}_setActiveIndicatorElement(t){if(!this._indicatorsElement)return;const e=z.findOne(wt,this._indicatorsElement);e.classList.remove(yt),e.removeAttribute("aria-current");const i=z.findOne(`[data-bs-slide-to="${t}"]`,this._indicatorsElement);i&&(i.classList.add(yt),i.setAttribute("aria-current","true"))}_updateInterval(){const t=this._activeElement||this._getActive();if(!t)return;const e=Number.parseInt(t.getAttribute("data-bs-interval"),10);this._config.interval=e||this._config.defaultInterval}_slide(t,e=null){if(this._isSliding)return;const i=this._getActive(),n=t===at,s=e||b(this._getItems(),i,n,this._config.wrap);if(s===i)return;const o=this._getItemIndex(s),r=e=>N.trigger(this._element,e,{relatedTarget:s,direction:this._orderToDirection(t),from:this._getItemIndex(i),to:o});if(r(dt).defaultPrevented)return;if(!i||!s)return;const a=Boolean(this._interval);this.pause(),this._isSliding=!0,this._setActiveIndicatorElement(o),this._activeElement=s;const l=n?"carousel-item-start":"carousel-item-end",c=n?"carousel-item-next":"carousel-item-prev";s.classList.add(c),d(s),i.classList.add(l),s.classList.add(l),this._queueCallback((()=>{s.classList.remove(l,c),s.classList.add(yt),i.classList.remove(yt,c,l),this._isSliding=!1,r(ut)}),i,this._isAnimated()),a&&this.cycle()}_isAnimated(){return this._element.classList.contains("slide")}_getActive(){return z.findOne(Et,this._element)}_getItems(){return z.find(At,this._element)}_clearInterval(){this._interval&&(clearInterval(this._interval),this._interval=null)}_directionToOrder(t){return p()?t===ct?lt:at:t===ct?at:lt}_orderToDirection(t){return p()?t===lt?ct:ht:t===lt?ht:ct}static jQueryInterface(t){return this.each((function(){const e=xt.getOrCreateInstance(this,t);if("number"!=typeof t){if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}else e.to(t)}))}}N.on(document,bt,"[data-bs-slide], [data-bs-slide-to]",(function(t){const e=z.getElementFromSelector(this);if(!e||!e.classList.contains(vt))return;t.preventDefault();const i=xt.getOrCreateInstance(e),n=this.getAttribute("data-bs-slide-to");return n?(i.to(n),void i._maybeEnableCycle()):"next"===F.getDataAttribute(this,"slide")?(i.next(),void i._maybeEnableCycle()):(i.prev(),void i._maybeEnableCycle())})),N.on(window,_t,(()=>{const t=z.find('[data-bs-ride="carousel"]');for(const e of t)xt.getOrCreateInstance(e)})),m(xt);const kt=".bs.collapse",Lt=`show${kt}`,St=`shown${kt}`,Dt=`hide${kt}`,$t=`hidden${kt}`,It=`click${kt}.data-api`,Nt="show",Pt="collapse",Mt="collapsing",jt=`:scope .${Pt} .${Pt}`,Ft='[data-bs-toggle="collapse"]',Ht={parent:null,toggle:!0},Wt={parent:"(null|element)",toggle:"boolean"};class Bt extends W{constructor(t,e){super(t,e),this._isTransitioning=!1,this._triggerArray=[];const i=z.find(Ft);for(const t of i){const e=z.getSelectorFromElement(t),i=z.find(e).filter((t=>t===this._element));null!==e&&i.length&&this._triggerArray.push(t)}this._initializeChildren(),this._config.parent||this._addAriaAndCollapsedClass(this._triggerArray,this._isShown()),this._config.toggle&&this.toggle()}static get Default(){return Ht}static get DefaultType(){return Wt}static get NAME(){return"collapse"}toggle(){this._isShown()?this.hide():this.show()}show(){if(this._isTransitioning||this._isShown())return;let t=[];if(this._config.parent&&(t=this._getFirstLevelChildren(".collapse.show, .collapse.collapsing").filter((t=>t!==this._element)).map((t=>Bt.getOrCreateInstance(t,{toggle:!1})))),t.length&&t[0]._isTransitioning)return;if(N.trigger(this._element,Lt).defaultPrevented)return;for(const e of t)e.hide();const e=this._getDimension();this._element.classList.remove(Pt),this._element.classList.add(Mt),this._element.style[e]=0,this._addAriaAndCollapsedClass(this._triggerArray,!0),this._isTransitioning=!0;const i=`scroll${e[0].toUpperCase()+e.slice(1)}`;this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt,Nt),this._element.style[e]="",N.trigger(this._element,St)}),this._element,!0),this._element.style[e]=`${this._element[i]}px`}hide(){if(this._isTransitioning||!this._isShown())return;if(N.trigger(this._element,Dt).defaultPrevented)return;const t=this._getDimension();this._element.style[t]=`${this._element.getBoundingClientRect()[t]}px`,d(this._element),this._element.classList.add(Mt),this._element.classList.remove(Pt,Nt);for(const t of this._triggerArray){const e=z.getElementFromSelector(t);e&&!this._isShown(e)&&this._addAriaAndCollapsedClass([t],!1)}this._isTransitioning=!0,this._element.style[t]="",this._queueCallback((()=>{this._isTransitioning=!1,this._element.classList.remove(Mt),this._element.classList.add(Pt),N.trigger(this._element,$t)}),this._element,!0)}_isShown(t=this._element){return t.classList.contains(Nt)}_configAfterMerge(t){return t.toggle=Boolean(t.toggle),t.parent=r(t.parent),t}_getDimension(){return this._element.classList.contains("collapse-horizontal")?"width":"height"}_initializeChildren(){if(!this._config.parent)return;const t=this._getFirstLevelChildren(Ft);for(const e of t){const t=z.getElementFromSelector(e);t&&this._addAriaAndCollapsedClass([e],this._isShown(t))}}_getFirstLevelChildren(t){const e=z.find(jt,this._config.parent);return z.find(t,this._config.parent).filter((t=>!e.includes(t)))}_addAriaAndCollapsedClass(t,e){if(t.length)for(const i of t)i.classList.toggle("collapsed",!e),i.setAttribute("aria-expanded",e)}static jQueryInterface(t){const e={};return"string"==typeof t&&/show|hide/.test(t)&&(e.toggle=!1),this.each((function(){const i=Bt.getOrCreateInstance(this,e);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t]()}}))}}N.on(document,It,Ft,(function(t){("A"===t.target.tagName||t.delegateTarget&&"A"===t.delegateTarget.tagName)&&t.preventDefault();for(const t of z.getMultipleElementsFromSelector(this))Bt.getOrCreateInstance(t,{toggle:!1}).toggle()})),m(Bt);var zt="top",Rt="bottom",qt="right",Vt="left",Kt="auto",Qt=[zt,Rt,qt,Vt],Xt="start",Yt="end",Ut="clippingParents",Gt="viewport",Jt="popper",Zt="reference",te=Qt.reduce((function(t,e){return t.concat([e+"-"+Xt,e+"-"+Yt])}),[]),ee=[].concat(Qt,[Kt]).reduce((function(t,e){return t.concat([e,e+"-"+Xt,e+"-"+Yt])}),[]),ie="beforeRead",ne="read",se="afterRead",oe="beforeMain",re="main",ae="afterMain",le="beforeWrite",ce="write",he="afterWrite",de=[ie,ne,se,oe,re,ae,le,ce,he];function ue(t){return t?(t.nodeName||"").toLowerCase():null}function fe(t){if(null==t)return window;if("[object Window]"!==t.toString()){var e=t.ownerDocument;return e&&e.defaultView||window}return t}function pe(t){return t instanceof fe(t).Element||t instanceof Element}function me(t){return t instanceof fe(t).HTMLElement||t instanceof HTMLElement}function ge(t){return"undefined"!=typeof ShadowRoot&&(t instanceof fe(t).ShadowRoot||t instanceof ShadowRoot)}const _e={name:"applyStyles",enabled:!0,phase:"write",fn:function(t){var e=t.state;Object.keys(e.elements).forEach((function(t){var i=e.styles[t]||{},n=e.attributes[t]||{},s=e.elements[t];me(s)&&ue(s)&&(Object.assign(s.style,i),Object.keys(n).forEach((function(t){var e=n[t];!1===e?s.removeAttribute(t):s.setAttribute(t,!0===e?"":e)})))}))},effect:function(t){var e=t.state,i={popper:{position:e.options.strategy,left:"0",top:"0",margin:"0"},arrow:{position:"absolute"},reference:{}};return Object.assign(e.elements.popper.style,i.popper),e.styles=i,e.elements.arrow&&Object.assign(e.elements.arrow.style,i.arrow),function(){Object.keys(e.elements).forEach((function(t){var n=e.elements[t],s=e.attributes[t]||{},o=Object.keys(e.styles.hasOwnProperty(t)?e.styles[t]:i[t]).reduce((function(t,e){return t[e]="",t}),{});me(n)&&ue(n)&&(Object.assign(n.style,o),Object.keys(s).forEach((function(t){n.removeAttribute(t)})))}))}},requires:["computeStyles"]};function be(t){return t.split("-")[0]}var ve=Math.max,ye=Math.min,we=Math.round;function Ae(){var t=navigator.userAgentData;return null!=t&&t.brands&&Array.isArray(t.brands)?t.brands.map((function(t){return t.brand+"/"+t.version})).join(" "):navigator.userAgent}function Ee(){return!/^((?!chrome|android).)*safari/i.test(Ae())}function Te(t,e,i){void 0===e&&(e=!1),void 0===i&&(i=!1);var n=t.getBoundingClientRect(),s=1,o=1;e&&me(t)&&(s=t.offsetWidth>0&&we(n.width)/t.offsetWidth||1,o=t.offsetHeight>0&&we(n.height)/t.offsetHeight||1);var r=(pe(t)?fe(t):window).visualViewport,a=!Ee()&&i,l=(n.left+(a&&r?r.offsetLeft:0))/s,c=(n.top+(a&&r?r.offsetTop:0))/o,h=n.width/s,d=n.height/o;return{width:h,height:d,top:c,right:l+h,bottom:c+d,left:l,x:l,y:c}}function Ce(t){var e=Te(t),i=t.offsetWidth,n=t.offsetHeight;return Math.abs(e.width-i)<=1&&(i=e.width),Math.abs(e.height-n)<=1&&(n=e.height),{x:t.offsetLeft,y:t.offsetTop,width:i,height:n}}function Oe(t,e){var i=e.getRootNode&&e.getRootNode();if(t.contains(e))return!0;if(i&&ge(i)){var n=e;do{if(n&&t.isSameNode(n))return!0;n=n.parentNode||n.host}while(n)}return!1}function xe(t){return fe(t).getComputedStyle(t)}function ke(t){return["table","td","th"].indexOf(ue(t))>=0}function Le(t){return((pe(t)?t.ownerDocument:t.document)||window.document).documentElement}function Se(t){return"html"===ue(t)?t:t.assignedSlot||t.parentNode||(ge(t)?t.host:null)||Le(t)}function De(t){return me(t)&&"fixed"!==xe(t).position?t.offsetParent:null}function $e(t){for(var e=fe(t),i=De(t);i&&ke(i)&&"static"===xe(i).position;)i=De(i);return i&&("html"===ue(i)||"body"===ue(i)&&"static"===xe(i).position)?e:i||function(t){var e=/firefox/i.test(Ae());if(/Trident/i.test(Ae())&&me(t)&&"fixed"===xe(t).position)return null;var i=Se(t);for(ge(i)&&(i=i.host);me(i)&&["html","body"].indexOf(ue(i))<0;){var n=xe(i);if("none"!==n.transform||"none"!==n.perspective||"paint"===n.contain||-1!==["transform","perspective"].indexOf(n.willChange)||e&&"filter"===n.willChange||e&&n.filter&&"none"!==n.filter)return i;i=i.parentNode}return null}(t)||e}function Ie(t){return["top","bottom"].indexOf(t)>=0?"x":"y"}function Ne(t,e,i){return ve(t,ye(e,i))}function Pe(t){return Object.assign({},{top:0,right:0,bottom:0,left:0},t)}function Me(t,e){return e.reduce((function(e,i){return e[i]=t,e}),{})}const je={name:"arrow",enabled:!0,phase:"main",fn:function(t){var e,i=t.state,n=t.name,s=t.options,o=i.elements.arrow,r=i.modifiersData.popperOffsets,a=be(i.placement),l=Ie(a),c=[Vt,qt].indexOf(a)>=0?"height":"width";if(o&&r){var h=function(t,e){return Pe("number"!=typeof(t="function"==typeof t?t(Object.assign({},e.rects,{placement:e.placement})):t)?t:Me(t,Qt))}(s.padding,i),d=Ce(o),u="y"===l?zt:Vt,f="y"===l?Rt:qt,p=i.rects.reference[c]+i.rects.reference[l]-r[l]-i.rects.popper[c],m=r[l]-i.rects.reference[l],g=$e(o),_=g?"y"===l?g.clientHeight||0:g.clientWidth||0:0,b=p/2-m/2,v=h[u],y=_-d[c]-h[f],w=_/2-d[c]/2+b,A=Ne(v,w,y),E=l;i.modifiersData[n]=((e={})[E]=A,e.centerOffset=A-w,e)}},effect:function(t){var e=t.state,i=t.options.element,n=void 0===i?"[data-popper-arrow]":i;null!=n&&("string"!=typeof n||(n=e.elements.popper.querySelector(n)))&&Oe(e.elements.popper,n)&&(e.elements.arrow=n)},requires:["popperOffsets"],requiresIfExists:["preventOverflow"]};function Fe(t){return t.split("-")[1]}var He={top:"auto",right:"auto",bottom:"auto",left:"auto"};function We(t){var e,i=t.popper,n=t.popperRect,s=t.placement,o=t.variation,r=t.offsets,a=t.position,l=t.gpuAcceleration,c=t.adaptive,h=t.roundOffsets,d=t.isFixed,u=r.x,f=void 0===u?0:u,p=r.y,m=void 0===p?0:p,g="function"==typeof h?h({x:f,y:m}):{x:f,y:m};f=g.x,m=g.y;var _=r.hasOwnProperty("x"),b=r.hasOwnProperty("y"),v=Vt,y=zt,w=window;if(c){var A=$e(i),E="clientHeight",T="clientWidth";A===fe(i)&&"static"!==xe(A=Le(i)).position&&"absolute"===a&&(E="scrollHeight",T="scrollWidth"),(s===zt||(s===Vt||s===qt)&&o===Yt)&&(y=Rt,m-=(d&&A===w&&w.visualViewport?w.visualViewport.height:A[E])-n.height,m*=l?1:-1),s!==Vt&&(s!==zt&&s!==Rt||o!==Yt)||(v=qt,f-=(d&&A===w&&w.visualViewport?w.visualViewport.width:A[T])-n.width,f*=l?1:-1)}var C,O=Object.assign({position:a},c&&He),x=!0===h?function(t,e){var i=t.x,n=t.y,s=e.devicePixelRatio||1;return{x:we(i*s)/s||0,y:we(n*s)/s||0}}({x:f,y:m},fe(i)):{x:f,y:m};return f=x.x,m=x.y,l?Object.assign({},O,((C={})[y]=b?"0":"",C[v]=_?"0":"",C.transform=(w.devicePixelRatio||1)<=1?"translate("+f+"px, "+m+"px)":"translate3d("+f+"px, "+m+"px, 0)",C)):Object.assign({},O,((e={})[y]=b?m+"px":"",e[v]=_?f+"px":"",e.transform="",e))}const Be={name:"computeStyles",enabled:!0,phase:"beforeWrite",fn:function(t){var e=t.state,i=t.options,n=i.gpuAcceleration,s=void 0===n||n,o=i.adaptive,r=void 0===o||o,a=i.roundOffsets,l=void 0===a||a,c={placement:be(e.placement),variation:Fe(e.placement),popper:e.elements.popper,popperRect:e.rects.popper,gpuAcceleration:s,isFixed:"fixed"===e.options.strategy};null!=e.modifiersData.popperOffsets&&(e.styles.popper=Object.assign({},e.styles.popper,We(Object.assign({},c,{offsets:e.modifiersData.popperOffsets,position:e.options.strategy,adaptive:r,roundOffsets:l})))),null!=e.modifiersData.arrow&&(e.styles.arrow=Object.assign({},e.styles.arrow,We(Object.assign({},c,{offsets:e.modifiersData.arrow,position:"absolute",adaptive:!1,roundOffsets:l})))),e.attributes.popper=Object.assign({},e.attributes.popper,{"data-popper-placement":e.placement})},data:{}};var ze={passive:!0};const Re={name:"eventListeners",enabled:!0,phase:"write",fn:function(){},effect:function(t){var e=t.state,i=t.instance,n=t.options,s=n.scroll,o=void 0===s||s,r=n.resize,a=void 0===r||r,l=fe(e.elements.popper),c=[].concat(e.scrollParents.reference,e.scrollParents.popper);return o&&c.forEach((function(t){t.addEventListener("scroll",i.update,ze)})),a&&l.addEventListener("resize",i.update,ze),function(){o&&c.forEach((function(t){t.removeEventListener("scroll",i.update,ze)})),a&&l.removeEventListener("resize",i.update,ze)}},data:{}};var qe={left:"right",right:"left",bottom:"top",top:"bottom"};function Ve(t){return t.replace(/left|right|bottom|top/g,(function(t){return qe[t]}))}var Ke={start:"end",end:"start"};function Qe(t){return t.replace(/start|end/g,(function(t){return Ke[t]}))}function Xe(t){var e=fe(t);return{scrollLeft:e.pageXOffset,scrollTop:e.pageYOffset}}function Ye(t){return Te(Le(t)).left+Xe(t).scrollLeft}function Ue(t){var e=xe(t),i=e.overflow,n=e.overflowX,s=e.overflowY;return/auto|scroll|overlay|hidden/.test(i+s+n)}function Ge(t){return["html","body","#document"].indexOf(ue(t))>=0?t.ownerDocument.body:me(t)&&Ue(t)?t:Ge(Se(t))}function Je(t,e){var i;void 0===e&&(e=[]);var n=Ge(t),s=n===(null==(i=t.ownerDocument)?void 0:i.body),o=fe(n),r=s?[o].concat(o.visualViewport||[],Ue(n)?n:[]):n,a=e.concat(r);return s?a:a.concat(Je(Se(r)))}function Ze(t){return Object.assign({},t,{left:t.x,top:t.y,right:t.x+t.width,bottom:t.y+t.height})}function ti(t,e,i){return e===Gt?Ze(function(t,e){var i=fe(t),n=Le(t),s=i.visualViewport,o=n.clientWidth,r=n.clientHeight,a=0,l=0;if(s){o=s.width,r=s.height;var c=Ee();(c||!c&&"fixed"===e)&&(a=s.offsetLeft,l=s.offsetTop)}return{width:o,height:r,x:a+Ye(t),y:l}}(t,i)):pe(e)?function(t,e){var i=Te(t,!1,"fixed"===e);return i.top=i.top+t.clientTop,i.left=i.left+t.clientLeft,i.bottom=i.top+t.clientHeight,i.right=i.left+t.clientWidth,i.width=t.clientWidth,i.height=t.clientHeight,i.x=i.left,i.y=i.top,i}(e,i):Ze(function(t){var e,i=Le(t),n=Xe(t),s=null==(e=t.ownerDocument)?void 0:e.body,o=ve(i.scrollWidth,i.clientWidth,s?s.scrollWidth:0,s?s.clientWidth:0),r=ve(i.scrollHeight,i.clientHeight,s?s.scrollHeight:0,s?s.clientHeight:0),a=-n.scrollLeft+Ye(t),l=-n.scrollTop;return"rtl"===xe(s||i).direction&&(a+=ve(i.clientWidth,s?s.clientWidth:0)-o),{width:o,height:r,x:a,y:l}}(Le(t)))}function ei(t){var e,i=t.reference,n=t.element,s=t.placement,o=s?be(s):null,r=s?Fe(s):null,a=i.x+i.width/2-n.width/2,l=i.y+i.height/2-n.height/2;switch(o){case zt:e={x:a,y:i.y-n.height};break;case Rt:e={x:a,y:i.y+i.height};break;case qt:e={x:i.x+i.width,y:l};break;case Vt:e={x:i.x-n.width,y:l};break;default:e={x:i.x,y:i.y}}var c=o?Ie(o):null;if(null!=c){var h="y"===c?"height":"width";switch(r){case Xt:e[c]=e[c]-(i[h]/2-n[h]/2);break;case Yt:e[c]=e[c]+(i[h]/2-n[h]/2)}}return e}function ii(t,e){void 0===e&&(e={});var i=e,n=i.placement,s=void 0===n?t.placement:n,o=i.strategy,r=void 0===o?t.strategy:o,a=i.boundary,l=void 0===a?Ut:a,c=i.rootBoundary,h=void 0===c?Gt:c,d=i.elementContext,u=void 0===d?Jt:d,f=i.altBoundary,p=void 0!==f&&f,m=i.padding,g=void 0===m?0:m,_=Pe("number"!=typeof g?g:Me(g,Qt)),b=u===Jt?Zt:Jt,v=t.rects.popper,y=t.elements[p?b:u],w=function(t,e,i,n){var s="clippingParents"===e?function(t){var e=Je(Se(t)),i=["absolute","fixed"].indexOf(xe(t).position)>=0&&me(t)?$e(t):t;return pe(i)?e.filter((function(t){return pe(t)&&Oe(t,i)&&"body"!==ue(t)})):[]}(t):[].concat(e),o=[].concat(s,[i]),r=o[0],a=o.reduce((function(e,i){var s=ti(t,i,n);return e.top=ve(s.top,e.top),e.right=ye(s.right,e.right),e.bottom=ye(s.bottom,e.bottom),e.left=ve(s.left,e.left),e}),ti(t,r,n));return a.width=a.right-a.left,a.height=a.bottom-a.top,a.x=a.left,a.y=a.top,a}(pe(y)?y:y.contextElement||Le(t.elements.popper),l,h,r),A=Te(t.elements.reference),E=ei({reference:A,element:v,strategy:"absolute",placement:s}),T=Ze(Object.assign({},v,E)),C=u===Jt?T:A,O={top:w.top-C.top+_.top,bottom:C.bottom-w.bottom+_.bottom,left:w.left-C.left+_.left,right:C.right-w.right+_.right},x=t.modifiersData.offset;if(u===Jt&&x){var k=x[s];Object.keys(O).forEach((function(t){var e=[qt,Rt].indexOf(t)>=0?1:-1,i=[zt,Rt].indexOf(t)>=0?"y":"x";O[t]+=k[i]*e}))}return O}function ni(t,e){void 0===e&&(e={});var i=e,n=i.placement,s=i.boundary,o=i.rootBoundary,r=i.padding,a=i.flipVariations,l=i.allowedAutoPlacements,c=void 0===l?ee:l,h=Fe(n),d=h?a?te:te.filter((function(t){return Fe(t)===h})):Qt,u=d.filter((function(t){return c.indexOf(t)>=0}));0===u.length&&(u=d);var f=u.reduce((function(e,i){return e[i]=ii(t,{placement:i,boundary:s,rootBoundary:o,padding:r})[be(i)],e}),{});return Object.keys(f).sort((function(t,e){return f[t]-f[e]}))}const si={name:"flip",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name;if(!e.modifiersData[n]._skip){for(var s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0===r||r,l=i.fallbackPlacements,c=i.padding,h=i.boundary,d=i.rootBoundary,u=i.altBoundary,f=i.flipVariations,p=void 0===f||f,m=i.allowedAutoPlacements,g=e.options.placement,_=be(g),b=l||(_!==g&&p?function(t){if(be(t)===Kt)return[];var e=Ve(t);return[Qe(t),e,Qe(e)]}(g):[Ve(g)]),v=[g].concat(b).reduce((function(t,i){return t.concat(be(i)===Kt?ni(e,{placement:i,boundary:h,rootBoundary:d,padding:c,flipVariations:p,allowedAutoPlacements:m}):i)}),[]),y=e.rects.reference,w=e.rects.popper,A=new Map,E=!0,T=v[0],C=0;C=0,S=L?"width":"height",D=ii(e,{placement:O,boundary:h,rootBoundary:d,altBoundary:u,padding:c}),$=L?k?qt:Vt:k?Rt:zt;y[S]>w[S]&&($=Ve($));var I=Ve($),N=[];if(o&&N.push(D[x]<=0),a&&N.push(D[$]<=0,D[I]<=0),N.every((function(t){return t}))){T=O,E=!1;break}A.set(O,N)}if(E)for(var P=function(t){var e=v.find((function(e){var i=A.get(e);if(i)return i.slice(0,t).every((function(t){return t}))}));if(e)return T=e,"break"},M=p?3:1;M>0&&"break"!==P(M);M--);e.placement!==T&&(e.modifiersData[n]._skip=!0,e.placement=T,e.reset=!0)}},requiresIfExists:["offset"],data:{_skip:!1}};function oi(t,e,i){return void 0===i&&(i={x:0,y:0}),{top:t.top-e.height-i.y,right:t.right-e.width+i.x,bottom:t.bottom-e.height+i.y,left:t.left-e.width-i.x}}function ri(t){return[zt,qt,Rt,Vt].some((function(e){return t[e]>=0}))}const ai={name:"hide",enabled:!0,phase:"main",requiresIfExists:["preventOverflow"],fn:function(t){var e=t.state,i=t.name,n=e.rects.reference,s=e.rects.popper,o=e.modifiersData.preventOverflow,r=ii(e,{elementContext:"reference"}),a=ii(e,{altBoundary:!0}),l=oi(r,n),c=oi(a,s,o),h=ri(l),d=ri(c);e.modifiersData[i]={referenceClippingOffsets:l,popperEscapeOffsets:c,isReferenceHidden:h,hasPopperEscaped:d},e.attributes.popper=Object.assign({},e.attributes.popper,{"data-popper-reference-hidden":h,"data-popper-escaped":d})}},li={name:"offset",enabled:!0,phase:"main",requires:["popperOffsets"],fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.offset,o=void 0===s?[0,0]:s,r=ee.reduce((function(t,i){return t[i]=function(t,e,i){var n=be(t),s=[Vt,zt].indexOf(n)>=0?-1:1,o="function"==typeof i?i(Object.assign({},e,{placement:t})):i,r=o[0],a=o[1];return r=r||0,a=(a||0)*s,[Vt,qt].indexOf(n)>=0?{x:a,y:r}:{x:r,y:a}}(i,e.rects,o),t}),{}),a=r[e.placement],l=a.x,c=a.y;null!=e.modifiersData.popperOffsets&&(e.modifiersData.popperOffsets.x+=l,e.modifiersData.popperOffsets.y+=c),e.modifiersData[n]=r}},ci={name:"popperOffsets",enabled:!0,phase:"read",fn:function(t){var e=t.state,i=t.name;e.modifiersData[i]=ei({reference:e.rects.reference,element:e.rects.popper,strategy:"absolute",placement:e.placement})},data:{}},hi={name:"preventOverflow",enabled:!0,phase:"main",fn:function(t){var e=t.state,i=t.options,n=t.name,s=i.mainAxis,o=void 0===s||s,r=i.altAxis,a=void 0!==r&&r,l=i.boundary,c=i.rootBoundary,h=i.altBoundary,d=i.padding,u=i.tether,f=void 0===u||u,p=i.tetherOffset,m=void 0===p?0:p,g=ii(e,{boundary:l,rootBoundary:c,padding:d,altBoundary:h}),_=be(e.placement),b=Fe(e.placement),v=!b,y=Ie(_),w="x"===y?"y":"x",A=e.modifiersData.popperOffsets,E=e.rects.reference,T=e.rects.popper,C="function"==typeof m?m(Object.assign({},e.rects,{placement:e.placement})):m,O="number"==typeof C?{mainAxis:C,altAxis:C}:Object.assign({mainAxis:0,altAxis:0},C),x=e.modifiersData.offset?e.modifiersData.offset[e.placement]:null,k={x:0,y:0};if(A){if(o){var L,S="y"===y?zt:Vt,D="y"===y?Rt:qt,$="y"===y?"height":"width",I=A[y],N=I+g[S],P=I-g[D],M=f?-T[$]/2:0,j=b===Xt?E[$]:T[$],F=b===Xt?-T[$]:-E[$],H=e.elements.arrow,W=f&&H?Ce(H):{width:0,height:0},B=e.modifiersData["arrow#persistent"]?e.modifiersData["arrow#persistent"].padding:{top:0,right:0,bottom:0,left:0},z=B[S],R=B[D],q=Ne(0,E[$],W[$]),V=v?E[$]/2-M-q-z-O.mainAxis:j-q-z-O.mainAxis,K=v?-E[$]/2+M+q+R+O.mainAxis:F+q+R+O.mainAxis,Q=e.elements.arrow&&$e(e.elements.arrow),X=Q?"y"===y?Q.clientTop||0:Q.clientLeft||0:0,Y=null!=(L=null==x?void 0:x[y])?L:0,U=I+K-Y,G=Ne(f?ye(N,I+V-Y-X):N,I,f?ve(P,U):P);A[y]=G,k[y]=G-I}if(a){var J,Z="x"===y?zt:Vt,tt="x"===y?Rt:qt,et=A[w],it="y"===w?"height":"width",nt=et+g[Z],st=et-g[tt],ot=-1!==[zt,Vt].indexOf(_),rt=null!=(J=null==x?void 0:x[w])?J:0,at=ot?nt:et-E[it]-T[it]-rt+O.altAxis,lt=ot?et+E[it]+T[it]-rt-O.altAxis:st,ct=f&&ot?function(t,e,i){var n=Ne(t,e,i);return n>i?i:n}(at,et,lt):Ne(f?at:nt,et,f?lt:st);A[w]=ct,k[w]=ct-et}e.modifiersData[n]=k}},requiresIfExists:["offset"]};function di(t,e,i){void 0===i&&(i=!1);var n,s,o=me(e),r=me(e)&&function(t){var e=t.getBoundingClientRect(),i=we(e.width)/t.offsetWidth||1,n=we(e.height)/t.offsetHeight||1;return 1!==i||1!==n}(e),a=Le(e),l=Te(t,r,i),c={scrollLeft:0,scrollTop:0},h={x:0,y:0};return(o||!o&&!i)&&(("body"!==ue(e)||Ue(a))&&(c=(n=e)!==fe(n)&&me(n)?{scrollLeft:(s=n).scrollLeft,scrollTop:s.scrollTop}:Xe(n)),me(e)?((h=Te(e,!0)).x+=e.clientLeft,h.y+=e.clientTop):a&&(h.x=Ye(a))),{x:l.left+c.scrollLeft-h.x,y:l.top+c.scrollTop-h.y,width:l.width,height:l.height}}function ui(t){var e=new Map,i=new Set,n=[];function s(t){i.add(t.name),[].concat(t.requires||[],t.requiresIfExists||[]).forEach((function(t){if(!i.has(t)){var n=e.get(t);n&&s(n)}})),n.push(t)}return t.forEach((function(t){e.set(t.name,t)})),t.forEach((function(t){i.has(t.name)||s(t)})),n}var fi={placement:"bottom",modifiers:[],strategy:"absolute"};function pi(){for(var t=arguments.length,e=new Array(t),i=0;iNumber.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_getPopperConfig(){const t={placement:this._getPlacement(),modifiers:[{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"offset",options:{offset:this._getOffset()}}]};return(this._inNavbar||"static"===this._config.display)&&(F.setDataAttribute(this._menu,"popper","static"),t.modifiers=[{name:"applyStyles",enabled:!1}]),{...t,...g(this._config.popperConfig,[t])}}_selectMenuItem({key:t,target:e}){const i=z.find(".dropdown-menu .dropdown-item:not(.disabled):not(:disabled)",this._menu).filter((t=>a(t)));i.length&&b(i,e,t===Ti,!i.includes(e)).focus()}static jQueryInterface(t){return this.each((function(){const e=qi.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}static clearMenus(t){if(2===t.button||"keyup"===t.type&&"Tab"!==t.key)return;const e=z.find(Ni);for(const i of e){const e=qi.getInstance(i);if(!e||!1===e._config.autoClose)continue;const n=t.composedPath(),s=n.includes(e._menu);if(n.includes(e._element)||"inside"===e._config.autoClose&&!s||"outside"===e._config.autoClose&&s)continue;if(e._menu.contains(t.target)&&("keyup"===t.type&&"Tab"===t.key||/input|select|option|textarea|form/i.test(t.target.tagName)))continue;const o={relatedTarget:e._element};"click"===t.type&&(o.clickEvent=t),e._completeHide(o)}}static dataApiKeydownHandler(t){const e=/input|textarea/i.test(t.target.tagName),i="Escape"===t.key,n=[Ei,Ti].includes(t.key);if(!n&&!i)return;if(e&&!i)return;t.preventDefault();const s=this.matches(Ii)?this:z.prev(this,Ii)[0]||z.next(this,Ii)[0]||z.findOne(Ii,t.delegateTarget.parentNode),o=qi.getOrCreateInstance(s);if(n)return t.stopPropagation(),o.show(),void o._selectMenuItem(t);o._isShown()&&(t.stopPropagation(),o.hide(),s.focus())}}N.on(document,Si,Ii,qi.dataApiKeydownHandler),N.on(document,Si,Pi,qi.dataApiKeydownHandler),N.on(document,Li,qi.clearMenus),N.on(document,Di,qi.clearMenus),N.on(document,Li,Ii,(function(t){t.preventDefault(),qi.getOrCreateInstance(this).toggle()})),m(qi);const Vi="backdrop",Ki="show",Qi=`mousedown.bs.${Vi}`,Xi={className:"modal-backdrop",clickCallback:null,isAnimated:!1,isVisible:!0,rootElement:"body"},Yi={className:"string",clickCallback:"(function|null)",isAnimated:"boolean",isVisible:"boolean",rootElement:"(element|string)"};class Ui extends H{constructor(t){super(),this._config=this._getConfig(t),this._isAppended=!1,this._element=null}static get Default(){return Xi}static get DefaultType(){return Yi}static get NAME(){return Vi}show(t){if(!this._config.isVisible)return void g(t);this._append();const e=this._getElement();this._config.isAnimated&&d(e),e.classList.add(Ki),this._emulateAnimation((()=>{g(t)}))}hide(t){this._config.isVisible?(this._getElement().classList.remove(Ki),this._emulateAnimation((()=>{this.dispose(),g(t)}))):g(t)}dispose(){this._isAppended&&(N.off(this._element,Qi),this._element.remove(),this._isAppended=!1)}_getElement(){if(!this._element){const t=document.createElement("div");t.className=this._config.className,this._config.isAnimated&&t.classList.add("fade"),this._element=t}return this._element}_configAfterMerge(t){return t.rootElement=r(t.rootElement),t}_append(){if(this._isAppended)return;const t=this._getElement();this._config.rootElement.append(t),N.on(t,Qi,(()=>{g(this._config.clickCallback)})),this._isAppended=!0}_emulateAnimation(t){_(t,this._getElement(),this._config.isAnimated)}}const Gi=".bs.focustrap",Ji=`focusin${Gi}`,Zi=`keydown.tab${Gi}`,tn="backward",en={autofocus:!0,trapElement:null},nn={autofocus:"boolean",trapElement:"element"};class sn extends H{constructor(t){super(),this._config=this._getConfig(t),this._isActive=!1,this._lastTabNavDirection=null}static get Default(){return en}static get DefaultType(){return nn}static get NAME(){return"focustrap"}activate(){this._isActive||(this._config.autofocus&&this._config.trapElement.focus(),N.off(document,Gi),N.on(document,Ji,(t=>this._handleFocusin(t))),N.on(document,Zi,(t=>this._handleKeydown(t))),this._isActive=!0)}deactivate(){this._isActive&&(this._isActive=!1,N.off(document,Gi))}_handleFocusin(t){const{trapElement:e}=this._config;if(t.target===document||t.target===e||e.contains(t.target))return;const i=z.focusableChildren(e);0===i.length?e.focus():this._lastTabNavDirection===tn?i[i.length-1].focus():i[0].focus()}_handleKeydown(t){"Tab"===t.key&&(this._lastTabNavDirection=t.shiftKey?tn:"forward")}}const on=".fixed-top, .fixed-bottom, .is-fixed, .sticky-top",rn=".sticky-top",an="padding-right",ln="margin-right";class cn{constructor(){this._element=document.body}getWidth(){const t=document.documentElement.clientWidth;return Math.abs(window.innerWidth-t)}hide(){const t=this.getWidth();this._disableOverFlow(),this._setElementAttributes(this._element,an,(e=>e+t)),this._setElementAttributes(on,an,(e=>e+t)),this._setElementAttributes(rn,ln,(e=>e-t))}reset(){this._resetElementAttributes(this._element,"overflow"),this._resetElementAttributes(this._element,an),this._resetElementAttributes(on,an),this._resetElementAttributes(rn,ln)}isOverflowing(){return this.getWidth()>0}_disableOverFlow(){this._saveInitialAttribute(this._element,"overflow"),this._element.style.overflow="hidden"}_setElementAttributes(t,e,i){const n=this.getWidth();this._applyManipulationCallback(t,(t=>{if(t!==this._element&&window.innerWidth>t.clientWidth+n)return;this._saveInitialAttribute(t,e);const s=window.getComputedStyle(t).getPropertyValue(e);t.style.setProperty(e,`${i(Number.parseFloat(s))}px`)}))}_saveInitialAttribute(t,e){const i=t.style.getPropertyValue(e);i&&F.setDataAttribute(t,e,i)}_resetElementAttributes(t,e){this._applyManipulationCallback(t,(t=>{const i=F.getDataAttribute(t,e);null!==i?(F.removeDataAttribute(t,e),t.style.setProperty(e,i)):t.style.removeProperty(e)}))}_applyManipulationCallback(t,e){if(o(t))e(t);else for(const i of z.find(t,this._element))e(i)}}const hn=".bs.modal",dn=`hide${hn}`,un=`hidePrevented${hn}`,fn=`hidden${hn}`,pn=`show${hn}`,mn=`shown${hn}`,gn=`resize${hn}`,_n=`click.dismiss${hn}`,bn=`mousedown.dismiss${hn}`,vn=`keydown.dismiss${hn}`,yn=`click${hn}.data-api`,wn="modal-open",An="show",En="modal-static",Tn={backdrop:!0,focus:!0,keyboard:!0},Cn={backdrop:"(boolean|string)",focus:"boolean",keyboard:"boolean"};class On extends W{constructor(t,e){super(t,e),this._dialog=z.findOne(".modal-dialog",this._element),this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._isShown=!1,this._isTransitioning=!1,this._scrollBar=new cn,this._addEventListeners()}static get Default(){return Tn}static get DefaultType(){return Cn}static get NAME(){return"modal"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||this._isTransitioning||N.trigger(this._element,pn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._isTransitioning=!0,this._scrollBar.hide(),document.body.classList.add(wn),this._adjustDialog(),this._backdrop.show((()=>this._showElement(t))))}hide(){this._isShown&&!this._isTransitioning&&(N.trigger(this._element,dn).defaultPrevented||(this._isShown=!1,this._isTransitioning=!0,this._focustrap.deactivate(),this._element.classList.remove(An),this._queueCallback((()=>this._hideModal()),this._element,this._isAnimated())))}dispose(){N.off(window,hn),N.off(this._dialog,hn),this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}handleUpdate(){this._adjustDialog()}_initializeBackDrop(){return new Ui({isVisible:Boolean(this._config.backdrop),isAnimated:this._isAnimated()})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_showElement(t){document.body.contains(this._element)||document.body.append(this._element),this._element.style.display="block",this._element.removeAttribute("aria-hidden"),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.scrollTop=0;const e=z.findOne(".modal-body",this._dialog);e&&(e.scrollTop=0),d(this._element),this._element.classList.add(An),this._queueCallback((()=>{this._config.focus&&this._focustrap.activate(),this._isTransitioning=!1,N.trigger(this._element,mn,{relatedTarget:t})}),this._dialog,this._isAnimated())}_addEventListeners(){N.on(this._element,vn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():this._triggerBackdropTransition())})),N.on(window,gn,(()=>{this._isShown&&!this._isTransitioning&&this._adjustDialog()})),N.on(this._element,bn,(t=>{N.one(this._element,_n,(e=>{this._element===t.target&&this._element===e.target&&("static"!==this._config.backdrop?this._config.backdrop&&this.hide():this._triggerBackdropTransition())}))}))}_hideModal(){this._element.style.display="none",this._element.setAttribute("aria-hidden",!0),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._isTransitioning=!1,this._backdrop.hide((()=>{document.body.classList.remove(wn),this._resetAdjustments(),this._scrollBar.reset(),N.trigger(this._element,fn)}))}_isAnimated(){return this._element.classList.contains("fade")}_triggerBackdropTransition(){if(N.trigger(this._element,un).defaultPrevented)return;const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._element.style.overflowY;"hidden"===e||this._element.classList.contains(En)||(t||(this._element.style.overflowY="hidden"),this._element.classList.add(En),this._queueCallback((()=>{this._element.classList.remove(En),this._queueCallback((()=>{this._element.style.overflowY=e}),this._dialog)}),this._dialog),this._element.focus())}_adjustDialog(){const t=this._element.scrollHeight>document.documentElement.clientHeight,e=this._scrollBar.getWidth(),i=e>0;if(i&&!t){const t=p()?"paddingLeft":"paddingRight";this._element.style[t]=`${e}px`}if(!i&&t){const t=p()?"paddingRight":"paddingLeft";this._element.style[t]=`${e}px`}}_resetAdjustments(){this._element.style.paddingLeft="",this._element.style.paddingRight=""}static jQueryInterface(t,e){return this.each((function(){const i=On.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===i[t])throw new TypeError(`No method named "${t}"`);i[t](e)}}))}}N.on(document,yn,'[data-bs-toggle="modal"]',(function(t){const e=z.getElementFromSelector(this);["A","AREA"].includes(this.tagName)&&t.preventDefault(),N.one(e,pn,(t=>{t.defaultPrevented||N.one(e,fn,(()=>{a(this)&&this.focus()}))}));const i=z.findOne(".modal.show");i&&On.getInstance(i).hide(),On.getOrCreateInstance(e).toggle(this)})),R(On),m(On);const xn=".bs.offcanvas",kn=".data-api",Ln=`load${xn}${kn}`,Sn="show",Dn="showing",$n="hiding",In=".offcanvas.show",Nn=`show${xn}`,Pn=`shown${xn}`,Mn=`hide${xn}`,jn=`hidePrevented${xn}`,Fn=`hidden${xn}`,Hn=`resize${xn}`,Wn=`click${xn}${kn}`,Bn=`keydown.dismiss${xn}`,zn={backdrop:!0,keyboard:!0,scroll:!1},Rn={backdrop:"(boolean|string)",keyboard:"boolean",scroll:"boolean"};class qn extends W{constructor(t,e){super(t,e),this._isShown=!1,this._backdrop=this._initializeBackDrop(),this._focustrap=this._initializeFocusTrap(),this._addEventListeners()}static get Default(){return zn}static get DefaultType(){return Rn}static get NAME(){return"offcanvas"}toggle(t){return this._isShown?this.hide():this.show(t)}show(t){this._isShown||N.trigger(this._element,Nn,{relatedTarget:t}).defaultPrevented||(this._isShown=!0,this._backdrop.show(),this._config.scroll||(new cn).hide(),this._element.setAttribute("aria-modal",!0),this._element.setAttribute("role","dialog"),this._element.classList.add(Dn),this._queueCallback((()=>{this._config.scroll&&!this._config.backdrop||this._focustrap.activate(),this._element.classList.add(Sn),this._element.classList.remove(Dn),N.trigger(this._element,Pn,{relatedTarget:t})}),this._element,!0))}hide(){this._isShown&&(N.trigger(this._element,Mn).defaultPrevented||(this._focustrap.deactivate(),this._element.blur(),this._isShown=!1,this._element.classList.add($n),this._backdrop.hide(),this._queueCallback((()=>{this._element.classList.remove(Sn,$n),this._element.removeAttribute("aria-modal"),this._element.removeAttribute("role"),this._config.scroll||(new cn).reset(),N.trigger(this._element,Fn)}),this._element,!0)))}dispose(){this._backdrop.dispose(),this._focustrap.deactivate(),super.dispose()}_initializeBackDrop(){const t=Boolean(this._config.backdrop);return new Ui({className:"offcanvas-backdrop",isVisible:t,isAnimated:!0,rootElement:this._element.parentNode,clickCallback:t?()=>{"static"!==this._config.backdrop?this.hide():N.trigger(this._element,jn)}:null})}_initializeFocusTrap(){return new sn({trapElement:this._element})}_addEventListeners(){N.on(this._element,Bn,(t=>{"Escape"===t.key&&(this._config.keyboard?this.hide():N.trigger(this._element,jn))}))}static jQueryInterface(t){return this.each((function(){const e=qn.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}N.on(document,Wn,'[data-bs-toggle="offcanvas"]',(function(t){const e=z.getElementFromSelector(this);if(["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this))return;N.one(e,Fn,(()=>{a(this)&&this.focus()}));const i=z.findOne(In);i&&i!==e&&qn.getInstance(i).hide(),qn.getOrCreateInstance(e).toggle(this)})),N.on(window,Ln,(()=>{for(const t of z.find(In))qn.getOrCreateInstance(t).show()})),N.on(window,Hn,(()=>{for(const t of z.find("[aria-modal][class*=show][class*=offcanvas-]"))"fixed"!==getComputedStyle(t).position&&qn.getOrCreateInstance(t).hide()})),R(qn),m(qn);const Vn={"*":["class","dir","id","lang","role",/^aria-[\w-]*$/i],a:["target","href","title","rel"],area:[],b:[],br:[],col:[],code:[],div:[],em:[],hr:[],h1:[],h2:[],h3:[],h4:[],h5:[],h6:[],i:[],img:["src","srcset","alt","title","width","height"],li:[],ol:[],p:[],pre:[],s:[],small:[],span:[],sub:[],sup:[],strong:[],u:[],ul:[]},Kn=new Set(["background","cite","href","itemtype","longdesc","poster","src","xlink:href"]),Qn=/^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i,Xn=(t,e)=>{const i=t.nodeName.toLowerCase();return e.includes(i)?!Kn.has(i)||Boolean(Qn.test(t.nodeValue)):e.filter((t=>t instanceof RegExp)).some((t=>t.test(i)))},Yn={allowList:Vn,content:{},extraClass:"",html:!1,sanitize:!0,sanitizeFn:null,template:"
"},Un={allowList:"object",content:"object",extraClass:"(string|function)",html:"boolean",sanitize:"boolean",sanitizeFn:"(null|function)",template:"string"},Gn={entry:"(string|element|function|null)",selector:"(string|element)"};class Jn extends H{constructor(t){super(),this._config=this._getConfig(t)}static get Default(){return Yn}static get DefaultType(){return Un}static get NAME(){return"TemplateFactory"}getContent(){return Object.values(this._config.content).map((t=>this._resolvePossibleFunction(t))).filter(Boolean)}hasContent(){return this.getContent().length>0}changeContent(t){return this._checkContent(t),this._config.content={...this._config.content,...t},this}toHtml(){const t=document.createElement("div");t.innerHTML=this._maybeSanitize(this._config.template);for(const[e,i]of Object.entries(this._config.content))this._setContent(t,i,e);const e=t.children[0],i=this._resolvePossibleFunction(this._config.extraClass);return i&&e.classList.add(...i.split(" ")),e}_typeCheckConfig(t){super._typeCheckConfig(t),this._checkContent(t.content)}_checkContent(t){for(const[e,i]of Object.entries(t))super._typeCheckConfig({selector:e,entry:i},Gn)}_setContent(t,e,i){const n=z.findOne(i,t);n&&((e=this._resolvePossibleFunction(e))?o(e)?this._putElementInTemplate(r(e),n):this._config.html?n.innerHTML=this._maybeSanitize(e):n.textContent=e:n.remove())}_maybeSanitize(t){return this._config.sanitize?function(t,e,i){if(!t.length)return t;if(i&&"function"==typeof i)return i(t);const n=(new window.DOMParser).parseFromString(t,"text/html"),s=[].concat(...n.body.querySelectorAll("*"));for(const t of s){const i=t.nodeName.toLowerCase();if(!Object.keys(e).includes(i)){t.remove();continue}const n=[].concat(...t.attributes),s=[].concat(e["*"]||[],e[i]||[]);for(const e of n)Xn(e,s)||t.removeAttribute(e.nodeName)}return n.body.innerHTML}(t,this._config.allowList,this._config.sanitizeFn):t}_resolvePossibleFunction(t){return g(t,[this])}_putElementInTemplate(t,e){if(this._config.html)return e.innerHTML="",void e.append(t);e.textContent=t.textContent}}const Zn=new Set(["sanitize","allowList","sanitizeFn"]),ts="fade",es="show",is=".modal",ns="hide.bs.modal",ss="hover",os="focus",rs={AUTO:"auto",TOP:"top",RIGHT:p()?"left":"right",BOTTOM:"bottom",LEFT:p()?"right":"left"},as={allowList:Vn,animation:!0,boundary:"clippingParents",container:!1,customClass:"",delay:0,fallbackPlacements:["top","right","bottom","left"],html:!1,offset:[0,6],placement:"top",popperConfig:null,sanitize:!0,sanitizeFn:null,selector:!1,template:'',title:"",trigger:"hover focus"},ls={allowList:"object",animation:"boolean",boundary:"(string|element)",container:"(string|element|boolean)",customClass:"(string|function)",delay:"(number|object)",fallbackPlacements:"array",html:"boolean",offset:"(array|string|function)",placement:"(string|function)",popperConfig:"(null|object|function)",sanitize:"boolean",sanitizeFn:"(null|function)",selector:"(string|boolean)",template:"string",title:"(string|element|function)",trigger:"string"};class cs extends W{constructor(t,e){if(void 0===vi)throw new TypeError("Bootstrap's tooltips require Popper (https://popper.js.org)");super(t,e),this._isEnabled=!0,this._timeout=0,this._isHovered=null,this._activeTrigger={},this._popper=null,this._templateFactory=null,this._newContent=null,this.tip=null,this._setListeners(),this._config.selector||this._fixTitle()}static get Default(){return as}static get DefaultType(){return ls}static get NAME(){return"tooltip"}enable(){this._isEnabled=!0}disable(){this._isEnabled=!1}toggleEnabled(){this._isEnabled=!this._isEnabled}toggle(){this._isEnabled&&(this._activeTrigger.click=!this._activeTrigger.click,this._isShown()?this._leave():this._enter())}dispose(){clearTimeout(this._timeout),N.off(this._element.closest(is),ns,this._hideModalHandler),this._element.getAttribute("data-bs-original-title")&&this._element.setAttribute("title",this._element.getAttribute("data-bs-original-title")),this._disposePopper(),super.dispose()}show(){if("none"===this._element.style.display)throw new Error("Please use show on visible elements");if(!this._isWithContent()||!this._isEnabled)return;const t=N.trigger(this._element,this.constructor.eventName("show")),e=(c(this._element)||this._element.ownerDocument.documentElement).contains(this._element);if(t.defaultPrevented||!e)return;this._disposePopper();const i=this._getTipElement();this._element.setAttribute("aria-describedby",i.getAttribute("id"));const{container:n}=this._config;if(this._element.ownerDocument.documentElement.contains(this.tip)||(n.append(i),N.trigger(this._element,this.constructor.eventName("inserted"))),this._popper=this._createPopper(i),i.classList.add(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.on(t,"mouseover",h);this._queueCallback((()=>{N.trigger(this._element,this.constructor.eventName("shown")),!1===this._isHovered&&this._leave(),this._isHovered=!1}),this.tip,this._isAnimated())}hide(){if(this._isShown()&&!N.trigger(this._element,this.constructor.eventName("hide")).defaultPrevented){if(this._getTipElement().classList.remove(es),"ontouchstart"in document.documentElement)for(const t of[].concat(...document.body.children))N.off(t,"mouseover",h);this._activeTrigger.click=!1,this._activeTrigger[os]=!1,this._activeTrigger[ss]=!1,this._isHovered=null,this._queueCallback((()=>{this._isWithActiveTrigger()||(this._isHovered||this._disposePopper(),this._element.removeAttribute("aria-describedby"),N.trigger(this._element,this.constructor.eventName("hidden")))}),this.tip,this._isAnimated())}}update(){this._popper&&this._popper.update()}_isWithContent(){return Boolean(this._getTitle())}_getTipElement(){return this.tip||(this.tip=this._createTipElement(this._newContent||this._getContentForTemplate())),this.tip}_createTipElement(t){const e=this._getTemplateFactory(t).toHtml();if(!e)return null;e.classList.remove(ts,es),e.classList.add(`bs-${this.constructor.NAME}-auto`);const i=(t=>{do{t+=Math.floor(1e6*Math.random())}while(document.getElementById(t));return t})(this.constructor.NAME).toString();return e.setAttribute("id",i),this._isAnimated()&&e.classList.add(ts),e}setContent(t){this._newContent=t,this._isShown()&&(this._disposePopper(),this.show())}_getTemplateFactory(t){return this._templateFactory?this._templateFactory.changeContent(t):this._templateFactory=new Jn({...this._config,content:t,extraClass:this._resolvePossibleFunction(this._config.customClass)}),this._templateFactory}_getContentForTemplate(){return{".tooltip-inner":this._getTitle()}}_getTitle(){return this._resolvePossibleFunction(this._config.title)||this._element.getAttribute("data-bs-original-title")}_initializeOnDelegatedTarget(t){return this.constructor.getOrCreateInstance(t.delegateTarget,this._getDelegateConfig())}_isAnimated(){return this._config.animation||this.tip&&this.tip.classList.contains(ts)}_isShown(){return this.tip&&this.tip.classList.contains(es)}_createPopper(t){const e=g(this._config.placement,[this,t,this._element]),i=rs[e.toUpperCase()];return bi(this._element,t,this._getPopperConfig(i))}_getOffset(){const{offset:t}=this._config;return"string"==typeof t?t.split(",").map((t=>Number.parseInt(t,10))):"function"==typeof t?e=>t(e,this._element):t}_resolvePossibleFunction(t){return g(t,[this._element])}_getPopperConfig(t){const e={placement:t,modifiers:[{name:"flip",options:{fallbackPlacements:this._config.fallbackPlacements}},{name:"offset",options:{offset:this._getOffset()}},{name:"preventOverflow",options:{boundary:this._config.boundary}},{name:"arrow",options:{element:`.${this.constructor.NAME}-arrow`}},{name:"preSetPlacement",enabled:!0,phase:"beforeMain",fn:t=>{this._getTipElement().setAttribute("data-popper-placement",t.state.placement)}}]};return{...e,...g(this._config.popperConfig,[e])}}_setListeners(){const t=this._config.trigger.split(" ");for(const e of t)if("click"===e)N.on(this._element,this.constructor.eventName("click"),this._config.selector,(t=>{this._initializeOnDelegatedTarget(t).toggle()}));else if("manual"!==e){const t=e===ss?this.constructor.eventName("mouseenter"):this.constructor.eventName("focusin"),i=e===ss?this.constructor.eventName("mouseleave"):this.constructor.eventName("focusout");N.on(this._element,t,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusin"===t.type?os:ss]=!0,e._enter()})),N.on(this._element,i,this._config.selector,(t=>{const e=this._initializeOnDelegatedTarget(t);e._activeTrigger["focusout"===t.type?os:ss]=e._element.contains(t.relatedTarget),e._leave()}))}this._hideModalHandler=()=>{this._element&&this.hide()},N.on(this._element.closest(is),ns,this._hideModalHandler)}_fixTitle(){const t=this._element.getAttribute("title");t&&(this._element.getAttribute("aria-label")||this._element.textContent.trim()||this._element.setAttribute("aria-label",t),this._element.setAttribute("data-bs-original-title",t),this._element.removeAttribute("title"))}_enter(){this._isShown()||this._isHovered?this._isHovered=!0:(this._isHovered=!0,this._setTimeout((()=>{this._isHovered&&this.show()}),this._config.delay.show))}_leave(){this._isWithActiveTrigger()||(this._isHovered=!1,this._setTimeout((()=>{this._isHovered||this.hide()}),this._config.delay.hide))}_setTimeout(t,e){clearTimeout(this._timeout),this._timeout=setTimeout(t,e)}_isWithActiveTrigger(){return Object.values(this._activeTrigger).includes(!0)}_getConfig(t){const e=F.getDataAttributes(this._element);for(const t of Object.keys(e))Zn.has(t)&&delete e[t];return t={...e,..."object"==typeof t&&t?t:{}},t=this._mergeConfigObj(t),t=this._configAfterMerge(t),this._typeCheckConfig(t),t}_configAfterMerge(t){return t.container=!1===t.container?document.body:r(t.container),"number"==typeof t.delay&&(t.delay={show:t.delay,hide:t.delay}),"number"==typeof t.title&&(t.title=t.title.toString()),"number"==typeof t.content&&(t.content=t.content.toString()),t}_getDelegateConfig(){const t={};for(const[e,i]of Object.entries(this._config))this.constructor.Default[e]!==i&&(t[e]=i);return t.selector=!1,t.trigger="manual",t}_disposePopper(){this._popper&&(this._popper.destroy(),this._popper=null),this.tip&&(this.tip.remove(),this.tip=null)}static jQueryInterface(t){return this.each((function(){const e=cs.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(cs);const hs={...cs.Default,content:"",offset:[0,8],placement:"right",template:'',trigger:"click"},ds={...cs.DefaultType,content:"(null|string|element|function)"};class us extends cs{static get Default(){return hs}static get DefaultType(){return ds}static get NAME(){return"popover"}_isWithContent(){return this._getTitle()||this._getContent()}_getContentForTemplate(){return{".popover-header":this._getTitle(),".popover-body":this._getContent()}}_getContent(){return this._resolvePossibleFunction(this._config.content)}static jQueryInterface(t){return this.each((function(){const e=us.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t]()}}))}}m(us);const fs=".bs.scrollspy",ps=`activate${fs}`,ms=`click${fs}`,gs=`load${fs}.data-api`,_s="active",bs="[href]",vs=".nav-link",ys=`${vs}, .nav-item > ${vs}, .list-group-item`,ws={offset:null,rootMargin:"0px 0px -25%",smoothScroll:!1,target:null,threshold:[.1,.5,1]},As={offset:"(number|null)",rootMargin:"string",smoothScroll:"boolean",target:"element",threshold:"array"};class Es extends W{constructor(t,e){super(t,e),this._targetLinks=new Map,this._observableSections=new Map,this._rootElement="visible"===getComputedStyle(this._element).overflowY?null:this._element,this._activeTarget=null,this._observer=null,this._previousScrollData={visibleEntryTop:0,parentScrollTop:0},this.refresh()}static get Default(){return ws}static get DefaultType(){return As}static get NAME(){return"scrollspy"}refresh(){this._initializeTargetsAndObservables(),this._maybeEnableSmoothScroll(),this._observer?this._observer.disconnect():this._observer=this._getNewObserver();for(const t of this._observableSections.values())this._observer.observe(t)}dispose(){this._observer.disconnect(),super.dispose()}_configAfterMerge(t){return t.target=r(t.target)||document.body,t.rootMargin=t.offset?`${t.offset}px 0px -30%`:t.rootMargin,"string"==typeof t.threshold&&(t.threshold=t.threshold.split(",").map((t=>Number.parseFloat(t)))),t}_maybeEnableSmoothScroll(){this._config.smoothScroll&&(N.off(this._config.target,ms),N.on(this._config.target,ms,bs,(t=>{const e=this._observableSections.get(t.target.hash);if(e){t.preventDefault();const i=this._rootElement||window,n=e.offsetTop-this._element.offsetTop;if(i.scrollTo)return void i.scrollTo({top:n,behavior:"smooth"});i.scrollTop=n}})))}_getNewObserver(){const t={root:this._rootElement,threshold:this._config.threshold,rootMargin:this._config.rootMargin};return new IntersectionObserver((t=>this._observerCallback(t)),t)}_observerCallback(t){const e=t=>this._targetLinks.get(`#${t.target.id}`),i=t=>{this._previousScrollData.visibleEntryTop=t.target.offsetTop,this._process(e(t))},n=(this._rootElement||document.documentElement).scrollTop,s=n>=this._previousScrollData.parentScrollTop;this._previousScrollData.parentScrollTop=n;for(const o of t){if(!o.isIntersecting){this._activeTarget=null,this._clearActiveClass(e(o));continue}const t=o.target.offsetTop>=this._previousScrollData.visibleEntryTop;if(s&&t){if(i(o),!n)return}else s||t||i(o)}}_initializeTargetsAndObservables(){this._targetLinks=new Map,this._observableSections=new Map;const t=z.find(bs,this._config.target);for(const e of t){if(!e.hash||l(e))continue;const t=z.findOne(decodeURI(e.hash),this._element);a(t)&&(this._targetLinks.set(decodeURI(e.hash),e),this._observableSections.set(e.hash,t))}}_process(t){this._activeTarget!==t&&(this._clearActiveClass(this._config.target),this._activeTarget=t,t.classList.add(_s),this._activateParents(t),N.trigger(this._element,ps,{relatedTarget:t}))}_activateParents(t){if(t.classList.contains("dropdown-item"))z.findOne(".dropdown-toggle",t.closest(".dropdown")).classList.add(_s);else for(const e of z.parents(t,".nav, .list-group"))for(const t of z.prev(e,ys))t.classList.add(_s)}_clearActiveClass(t){t.classList.remove(_s);const e=z.find(`${bs}.${_s}`,t);for(const t of e)t.classList.remove(_s)}static jQueryInterface(t){return this.each((function(){const e=Es.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(window,gs,(()=>{for(const t of z.find('[data-bs-spy="scroll"]'))Es.getOrCreateInstance(t)})),m(Es);const Ts=".bs.tab",Cs=`hide${Ts}`,Os=`hidden${Ts}`,xs=`show${Ts}`,ks=`shown${Ts}`,Ls=`click${Ts}`,Ss=`keydown${Ts}`,Ds=`load${Ts}`,$s="ArrowLeft",Is="ArrowRight",Ns="ArrowUp",Ps="ArrowDown",Ms="Home",js="End",Fs="active",Hs="fade",Ws="show",Bs=":not(.dropdown-toggle)",zs='[data-bs-toggle="tab"], [data-bs-toggle="pill"], [data-bs-toggle="list"]',Rs=`.nav-link${Bs}, .list-group-item${Bs}, [role="tab"]${Bs}, ${zs}`,qs=`.${Fs}[data-bs-toggle="tab"], .${Fs}[data-bs-toggle="pill"], .${Fs}[data-bs-toggle="list"]`;class Vs extends W{constructor(t){super(t),this._parent=this._element.closest('.list-group, .nav, [role="tablist"]'),this._parent&&(this._setInitialAttributes(this._parent,this._getChildren()),N.on(this._element,Ss,(t=>this._keydown(t))))}static get NAME(){return"tab"}show(){const t=this._element;if(this._elemIsActive(t))return;const e=this._getActiveElem(),i=e?N.trigger(e,Cs,{relatedTarget:t}):null;N.trigger(t,xs,{relatedTarget:e}).defaultPrevented||i&&i.defaultPrevented||(this._deactivate(e,t),this._activate(t,e))}_activate(t,e){t&&(t.classList.add(Fs),this._activate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.removeAttribute("tabindex"),t.setAttribute("aria-selected",!0),this._toggleDropDown(t,!0),N.trigger(t,ks,{relatedTarget:e})):t.classList.add(Ws)}),t,t.classList.contains(Hs)))}_deactivate(t,e){t&&(t.classList.remove(Fs),t.blur(),this._deactivate(z.getElementFromSelector(t)),this._queueCallback((()=>{"tab"===t.getAttribute("role")?(t.setAttribute("aria-selected",!1),t.setAttribute("tabindex","-1"),this._toggleDropDown(t,!1),N.trigger(t,Os,{relatedTarget:e})):t.classList.remove(Ws)}),t,t.classList.contains(Hs)))}_keydown(t){if(![$s,Is,Ns,Ps,Ms,js].includes(t.key))return;t.stopPropagation(),t.preventDefault();const e=this._getChildren().filter((t=>!l(t)));let i;if([Ms,js].includes(t.key))i=e[t.key===Ms?0:e.length-1];else{const n=[Is,Ps].includes(t.key);i=b(e,t.target,n,!0)}i&&(i.focus({preventScroll:!0}),Vs.getOrCreateInstance(i).show())}_getChildren(){return z.find(Rs,this._parent)}_getActiveElem(){return this._getChildren().find((t=>this._elemIsActive(t)))||null}_setInitialAttributes(t,e){this._setAttributeIfNotExists(t,"role","tablist");for(const t of e)this._setInitialAttributesOnChild(t)}_setInitialAttributesOnChild(t){t=this._getInnerElement(t);const e=this._elemIsActive(t),i=this._getOuterElement(t);t.setAttribute("aria-selected",e),i!==t&&this._setAttributeIfNotExists(i,"role","presentation"),e||t.setAttribute("tabindex","-1"),this._setAttributeIfNotExists(t,"role","tab"),this._setInitialAttributesOnTargetPanel(t)}_setInitialAttributesOnTargetPanel(t){const e=z.getElementFromSelector(t);e&&(this._setAttributeIfNotExists(e,"role","tabpanel"),t.id&&this._setAttributeIfNotExists(e,"aria-labelledby",`${t.id}`))}_toggleDropDown(t,e){const i=this._getOuterElement(t);if(!i.classList.contains("dropdown"))return;const n=(t,n)=>{const s=z.findOne(t,i);s&&s.classList.toggle(n,e)};n(".dropdown-toggle",Fs),n(".dropdown-menu",Ws),i.setAttribute("aria-expanded",e)}_setAttributeIfNotExists(t,e,i){t.hasAttribute(e)||t.setAttribute(e,i)}_elemIsActive(t){return t.classList.contains(Fs)}_getInnerElement(t){return t.matches(Rs)?t:z.findOne(Rs,t)}_getOuterElement(t){return t.closest(".nav-item, .list-group-item")||t}static jQueryInterface(t){return this.each((function(){const e=Vs.getOrCreateInstance(this);if("string"==typeof t){if(void 0===e[t]||t.startsWith("_")||"constructor"===t)throw new TypeError(`No method named "${t}"`);e[t]()}}))}}N.on(document,Ls,zs,(function(t){["A","AREA"].includes(this.tagName)&&t.preventDefault(),l(this)||Vs.getOrCreateInstance(this).show()})),N.on(window,Ds,(()=>{for(const t of z.find(qs))Vs.getOrCreateInstance(t)})),m(Vs);const Ks=".bs.toast",Qs=`mouseover${Ks}`,Xs=`mouseout${Ks}`,Ys=`focusin${Ks}`,Us=`focusout${Ks}`,Gs=`hide${Ks}`,Js=`hidden${Ks}`,Zs=`show${Ks}`,to=`shown${Ks}`,eo="hide",io="show",no="showing",so={animation:"boolean",autohide:"boolean",delay:"number"},oo={animation:!0,autohide:!0,delay:5e3};class ro extends W{constructor(t,e){super(t,e),this._timeout=null,this._hasMouseInteraction=!1,this._hasKeyboardInteraction=!1,this._setListeners()}static get Default(){return oo}static get DefaultType(){return so}static get NAME(){return"toast"}show(){N.trigger(this._element,Zs).defaultPrevented||(this._clearTimeout(),this._config.animation&&this._element.classList.add("fade"),this._element.classList.remove(eo),d(this._element),this._element.classList.add(io,no),this._queueCallback((()=>{this._element.classList.remove(no),N.trigger(this._element,to),this._maybeScheduleHide()}),this._element,this._config.animation))}hide(){this.isShown()&&(N.trigger(this._element,Gs).defaultPrevented||(this._element.classList.add(no),this._queueCallback((()=>{this._element.classList.add(eo),this._element.classList.remove(no,io),N.trigger(this._element,Js)}),this._element,this._config.animation)))}dispose(){this._clearTimeout(),this.isShown()&&this._element.classList.remove(io),super.dispose()}isShown(){return this._element.classList.contains(io)}_maybeScheduleHide(){this._config.autohide&&(this._hasMouseInteraction||this._hasKeyboardInteraction||(this._timeout=setTimeout((()=>{this.hide()}),this._config.delay)))}_onInteraction(t,e){switch(t.type){case"mouseover":case"mouseout":this._hasMouseInteraction=e;break;case"focusin":case"focusout":this._hasKeyboardInteraction=e}if(e)return void this._clearTimeout();const i=t.relatedTarget;this._element===i||this._element.contains(i)||this._maybeScheduleHide()}_setListeners(){N.on(this._element,Qs,(t=>this._onInteraction(t,!0))),N.on(this._element,Xs,(t=>this._onInteraction(t,!1))),N.on(this._element,Ys,(t=>this._onInteraction(t,!0))),N.on(this._element,Us,(t=>this._onInteraction(t,!1)))}_clearTimeout(){clearTimeout(this._timeout),this._timeout=null}static jQueryInterface(t){return this.each((function(){const e=ro.getOrCreateInstance(this,t);if("string"==typeof t){if(void 0===e[t])throw new TypeError(`No method named "${t}"`);e[t](this)}}))}}return R(ro),m(ro),{Alert:Q,Button:Y,Carousel:xt,Collapse:Bt,Dropdown:qi,Modal:On,Offcanvas:qn,Popover:us,ScrollSpy:Es,Tab:Vs,Toast:ro,Tooltip:cs}})); +//# sourceMappingURL=bootstrap.bundle.min.js.map \ No newline at end of file diff --git a/latest-tag/deps/bootstrap-5.3.1/bootstrap.bundle.min.js.map b/latest-tag/deps/bootstrap-5.3.1/bootstrap.bundle.min.js.map new file mode 100644 index 00000000..3863da8b --- /dev/null +++ b/latest-tag/deps/bootstrap-5.3.1/bootstrap.bundle.min.js.map @@ -0,0 +1 @@ +{"version":3,"names":["elementMap","Map","Data","set","element","key","instance","has","instanceMap","get","size","console","error","Array","from","keys","remove","delete","TRANSITION_END","parseSelector","selector","window","CSS","escape","replace","match","id","triggerTransitionEnd","dispatchEvent","Event","isElement","object","jquery","nodeType","getElement","length","document","querySelector","isVisible","getClientRects","elementIsVisible","getComputedStyle","getPropertyValue","closedDetails","closest","summary","parentNode","isDisabled","Node","ELEMENT_NODE","classList","contains","disabled","hasAttribute","getAttribute","findShadowRoot","documentElement","attachShadow","getRootNode","root","ShadowRoot","noop","reflow","offsetHeight","getjQuery","jQuery","body","DOMContentLoadedCallbacks","isRTL","dir","defineJQueryPlugin","plugin","callback","$","name","NAME","JQUERY_NO_CONFLICT","fn","jQueryInterface","Constructor","noConflict","readyState","addEventListener","push","execute","possibleCallback","args","defaultValue","executeAfterTransition","transitionElement","waitForTransition","emulatedDuration","transitionDuration","transitionDelay","floatTransitionDuration","Number","parseFloat","floatTransitionDelay","split","getTransitionDurationFromElement","called","handler","target","removeEventListener","setTimeout","getNextActiveElement","list","activeElement","shouldGetNext","isCycleAllowed","listLength","index","indexOf","Math","max","min","namespaceRegex","stripNameRegex","stripUidRegex","eventRegistry","uidEvent","customEvents","mouseenter","mouseleave","nativeEvents","Set","makeEventUid","uid","getElementEvents","findHandler","events","callable","delegationSelector","Object","values","find","event","normalizeParameters","originalTypeEvent","delegationFunction","isDelegated","typeEvent","getTypeEvent","addHandler","oneOff","wrapFunction","relatedTarget","delegateTarget","call","this","handlers","previousFunction","domElements","querySelectorAll","domElement","hydrateObj","EventHandler","off","type","apply","bootstrapDelegationHandler","bootstrapHandler","removeHandler","Boolean","removeNamespacedHandlers","namespace","storeElementEvent","handlerKey","entries","includes","on","one","inNamespace","isNamespace","startsWith","elementEvent","slice","keyHandlers","trigger","jQueryEvent","bubbles","nativeDispatch","defaultPrevented","isPropagationStopped","isImmediatePropagationStopped","isDefaultPrevented","evt","cancelable","preventDefault","obj","meta","value","_unused","defineProperty","configurable","normalizeData","toString","JSON","parse","decodeURIComponent","normalizeDataKey","chr","toLowerCase","Manipulator","setDataAttribute","setAttribute","removeDataAttribute","removeAttribute","getDataAttributes","attributes","bsKeys","dataset","filter","pureKey","charAt","getDataAttribute","Config","Default","DefaultType","Error","_getConfig","config","_mergeConfigObj","_configAfterMerge","_typeCheckConfig","jsonConfig","constructor","configTypes","property","expectedTypes","valueType","prototype","RegExp","test","TypeError","toUpperCase","BaseComponent","super","_element","_config","DATA_KEY","dispose","EVENT_KEY","propertyName","getOwnPropertyNames","_queueCallback","isAnimated","getInstance","getOrCreateInstance","VERSION","eventName","getSelector","hrefAttribute","trim","SelectorEngine","concat","Element","findOne","children","child","matches","parents","ancestor","prev","previous","previousElementSibling","next","nextElementSibling","focusableChildren","focusables","map","join","el","getSelectorFromElement","getElementFromSelector","getMultipleElementsFromSelector","enableDismissTrigger","component","method","clickEvent","tagName","EVENT_CLOSE","EVENT_CLOSED","Alert","close","_destroyElement","each","data","undefined","SELECTOR_DATA_TOGGLE","Button","toggle","button","EVENT_TOUCHSTART","EVENT_TOUCHMOVE","EVENT_TOUCHEND","EVENT_POINTERDOWN","EVENT_POINTERUP","endCallback","leftCallback","rightCallback","Swipe","isSupported","_deltaX","_supportPointerEvents","PointerEvent","_initEvents","_start","_eventIsPointerPenTouch","clientX","touches","_end","_handleSwipe","_move","absDeltaX","abs","direction","add","pointerType","navigator","maxTouchPoints","DATA_API_KEY","ORDER_NEXT","ORDER_PREV","DIRECTION_LEFT","DIRECTION_RIGHT","EVENT_SLIDE","EVENT_SLID","EVENT_KEYDOWN","EVENT_MOUSEENTER","EVENT_MOUSELEAVE","EVENT_DRAG_START","EVENT_LOAD_DATA_API","EVENT_CLICK_DATA_API","CLASS_NAME_CAROUSEL","CLASS_NAME_ACTIVE","SELECTOR_ACTIVE","SELECTOR_ITEM","SELECTOR_ACTIVE_ITEM","KEY_TO_DIRECTION","ArrowLeft","ArrowRight","interval","keyboard","pause","ride","touch","wrap","Carousel","_interval","_activeElement","_isSliding","touchTimeout","_swipeHelper","_indicatorsElement","_addEventListeners","cycle","_slide","nextWhenVisible","hidden","_clearInterval","_updateInterval","setInterval","_maybeEnableCycle","to","items","_getItems","activeIndex","_getItemIndex","_getActive","order","defaultInterval","_keydown","_addTouchEventListeners","img","swipeConfig","_directionToOrder","endCallBack","clearTimeout","_setActiveIndicatorElement","activeIndicator","newActiveIndicator","elementInterval","parseInt","isNext","nextElement","nextElementIndex","triggerEvent","_orderToDirection","isCycling","directionalClassName","orderClassName","completeCallBack","_isAnimated","clearInterval","carousel","slideIndex","carousels","EVENT_SHOW","EVENT_SHOWN","EVENT_HIDE","EVENT_HIDDEN","CLASS_NAME_SHOW","CLASS_NAME_COLLAPSE","CLASS_NAME_COLLAPSING","CLASS_NAME_DEEPER_CHILDREN","parent","Collapse","_isTransitioning","_triggerArray","toggleList","elem","filterElement","foundElement","_initializeChildren","_addAriaAndCollapsedClass","_isShown","hide","show","activeChildren","_getFirstLevelChildren","activeInstance","dimension","_getDimension","style","scrollSize","complete","getBoundingClientRect","selected","triggerArray","isOpen","top","bottom","right","left","auto","basePlacements","start","end","clippingParents","viewport","popper","reference","variationPlacements","reduce","acc","placement","placements","beforeRead","read","afterRead","beforeMain","main","afterMain","beforeWrite","write","afterWrite","modifierPhases","getNodeName","nodeName","getWindow","node","ownerDocument","defaultView","isHTMLElement","HTMLElement","isShadowRoot","applyStyles$1","enabled","phase","_ref","state","elements","forEach","styles","assign","effect","_ref2","initialStyles","position","options","strategy","margin","arrow","hasOwnProperty","attribute","requires","getBasePlacement","round","getUAString","uaData","userAgentData","brands","isArray","item","brand","version","userAgent","isLayoutViewport","includeScale","isFixedStrategy","clientRect","scaleX","scaleY","offsetWidth","width","height","visualViewport","addVisualOffsets","x","offsetLeft","y","offsetTop","getLayoutRect","rootNode","isSameNode","host","isTableElement","getDocumentElement","getParentNode","assignedSlot","getTrueOffsetParent","offsetParent","getOffsetParent","isFirefox","currentNode","css","transform","perspective","contain","willChange","getContainingBlock","getMainAxisFromPlacement","within","mathMax","mathMin","mergePaddingObject","paddingObject","expandToHashMap","hashMap","arrow$1","_state$modifiersData$","arrowElement","popperOffsets","modifiersData","basePlacement","axis","len","padding","rects","toPaddingObject","arrowRect","minProp","maxProp","endDiff","startDiff","arrowOffsetParent","clientSize","clientHeight","clientWidth","centerToReference","center","offset","axisProp","centerOffset","_options$element","requiresIfExists","getVariation","unsetSides","mapToStyles","_Object$assign2","popperRect","variation","offsets","gpuAcceleration","adaptive","roundOffsets","isFixed","_offsets$x","_offsets$y","_ref3","hasX","hasY","sideX","sideY","win","heightProp","widthProp","_Object$assign","commonStyles","_ref4","dpr","devicePixelRatio","roundOffsetsByDPR","computeStyles$1","_ref5","_options$gpuAccelerat","_options$adaptive","_options$roundOffsets","passive","eventListeners","_options$scroll","scroll","_options$resize","resize","scrollParents","scrollParent","update","hash","getOppositePlacement","matched","getOppositeVariationPlacement","getWindowScroll","scrollLeft","pageXOffset","scrollTop","pageYOffset","getWindowScrollBarX","isScrollParent","_getComputedStyle","overflow","overflowX","overflowY","getScrollParent","listScrollParents","_element$ownerDocumen","isBody","updatedList","rectToClientRect","rect","getClientRectFromMixedType","clippingParent","html","layoutViewport","getViewportRect","clientTop","clientLeft","getInnerBoundingClientRect","winScroll","scrollWidth","scrollHeight","getDocumentRect","computeOffsets","commonX","commonY","mainAxis","detectOverflow","_options","_options$placement","_options$strategy","_options$boundary","boundary","_options$rootBoundary","rootBoundary","_options$elementConte","elementContext","_options$altBoundary","altBoundary","_options$padding","altContext","clippingClientRect","mainClippingParents","clipperElement","getClippingParents","firstClippingParent","clippingRect","accRect","getClippingRect","contextElement","referenceClientRect","popperClientRect","elementClientRect","overflowOffsets","offsetData","multiply","computeAutoPlacement","flipVariations","_options$allowedAutoP","allowedAutoPlacements","allPlacements","allowedPlacements","overflows","sort","a","b","flip$1","_skip","_options$mainAxis","checkMainAxis","_options$altAxis","altAxis","checkAltAxis","specifiedFallbackPlacements","fallbackPlacements","_options$flipVariatio","preferredPlacement","oppositePlacement","getExpandedFallbackPlacements","referenceRect","checksMap","makeFallbackChecks","firstFittingPlacement","i","_basePlacement","isStartVariation","isVertical","mainVariationSide","altVariationSide","checks","every","check","_loop","_i","fittingPlacement","reset","getSideOffsets","preventedOffsets","isAnySideFullyClipped","some","side","hide$1","preventOverflow","referenceOverflow","popperAltOverflow","referenceClippingOffsets","popperEscapeOffsets","isReferenceHidden","hasPopperEscaped","offset$1","_options$offset","invertDistance","skidding","distance","distanceAndSkiddingToXY","_data$state$placement","popperOffsets$1","preventOverflow$1","_options$tether","tether","_options$tetherOffset","tetherOffset","isBasePlacement","tetherOffsetValue","normalizedTetherOffsetValue","offsetModifierState","_offsetModifierState$","mainSide","altSide","additive","minLen","maxLen","arrowPaddingObject","arrowPaddingMin","arrowPaddingMax","arrowLen","minOffset","maxOffset","clientOffset","offsetModifierValue","tetherMax","preventedOffset","_offsetModifierState$2","_mainSide","_altSide","_offset","_len","_min","_max","isOriginSide","_offsetModifierValue","_tetherMin","_tetherMax","_preventedOffset","v","withinMaxClamp","getCompositeRect","elementOrVirtualElement","isOffsetParentAnElement","offsetParentIsScaled","isElementScaled","modifiers","visited","result","modifier","dep","depModifier","DEFAULT_OPTIONS","areValidElements","arguments","_key","popperGenerator","generatorOptions","_generatorOptions","_generatorOptions$def","defaultModifiers","_generatorOptions$def2","defaultOptions","pending","orderedModifiers","effectCleanupFns","isDestroyed","setOptions","setOptionsAction","cleanupModifierEffects","merged","orderModifiers","current","existing","m","_ref$options","cleanupFn","forceUpdate","_state$elements","_state$orderedModifie","_state$orderedModifie2","Promise","resolve","then","destroy","onFirstUpdate","createPopper","computeStyles","applyStyles","flip","ARROW_UP_KEY","ARROW_DOWN_KEY","EVENT_KEYDOWN_DATA_API","EVENT_KEYUP_DATA_API","SELECTOR_DATA_TOGGLE_SHOWN","SELECTOR_MENU","PLACEMENT_TOP","PLACEMENT_TOPEND","PLACEMENT_BOTTOM","PLACEMENT_BOTTOMEND","PLACEMENT_RIGHT","PLACEMENT_LEFT","autoClose","display","popperConfig","Dropdown","_popper","_parent","_menu","_inNavbar","_detectNavbar","_createPopper","focus","_completeHide","Popper","referenceElement","_getPopperConfig","_getPlacement","parentDropdown","isEnd","_getOffset","popperData","defaultBsPopperConfig","_selectMenuItem","clearMenus","openToggles","context","composedPath","isMenuTarget","dataApiKeydownHandler","isInput","isEscapeEvent","isUpOrDownEvent","getToggleButton","stopPropagation","EVENT_MOUSEDOWN","className","clickCallback","rootElement","Backdrop","_isAppended","_append","_getElement","_emulateAnimation","backdrop","createElement","append","EVENT_FOCUSIN","EVENT_KEYDOWN_TAB","TAB_NAV_BACKWARD","autofocus","trapElement","FocusTrap","_isActive","_lastTabNavDirection","activate","_handleFocusin","_handleKeydown","deactivate","shiftKey","SELECTOR_FIXED_CONTENT","SELECTOR_STICKY_CONTENT","PROPERTY_PADDING","PROPERTY_MARGIN","ScrollBarHelper","getWidth","documentWidth","innerWidth","_disableOverFlow","_setElementAttributes","calculatedValue","_resetElementAttributes","isOverflowing","_saveInitialAttribute","styleProperty","scrollbarWidth","_applyManipulationCallback","setProperty","actualValue","removeProperty","callBack","sel","EVENT_HIDE_PREVENTED","EVENT_RESIZE","EVENT_CLICK_DISMISS","EVENT_MOUSEDOWN_DISMISS","EVENT_KEYDOWN_DISMISS","CLASS_NAME_OPEN","CLASS_NAME_STATIC","Modal","_dialog","_backdrop","_initializeBackDrop","_focustrap","_initializeFocusTrap","_scrollBar","_adjustDialog","_showElement","_hideModal","handleUpdate","modalBody","transitionComplete","_triggerBackdropTransition","event2","_resetAdjustments","isModalOverflowing","initialOverflowY","isBodyOverflowing","paddingLeft","paddingRight","showEvent","alreadyOpen","CLASS_NAME_SHOWING","CLASS_NAME_HIDING","OPEN_SELECTOR","Offcanvas","blur","completeCallback","DefaultAllowlist","area","br","col","code","div","em","hr","h1","h2","h3","h4","h5","h6","li","ol","p","pre","s","small","span","sub","sup","strong","u","ul","uriAttributes","SAFE_URL_PATTERN","allowedAttribute","allowedAttributeList","attributeName","nodeValue","attributeRegex","regex","allowList","content","extraClass","sanitize","sanitizeFn","template","DefaultContentType","entry","TemplateFactory","getContent","_resolvePossibleFunction","hasContent","changeContent","_checkContent","toHtml","templateWrapper","innerHTML","_maybeSanitize","text","_setContent","arg","templateElement","_putElementInTemplate","textContent","unsafeHtml","sanitizeFunction","createdDocument","DOMParser","parseFromString","elementName","attributeList","allowedAttributes","sanitizeHtml","DISALLOWED_ATTRIBUTES","CLASS_NAME_FADE","SELECTOR_MODAL","EVENT_MODAL_HIDE","TRIGGER_HOVER","TRIGGER_FOCUS","AttachmentMap","AUTO","TOP","RIGHT","BOTTOM","LEFT","animation","container","customClass","delay","title","Tooltip","_isEnabled","_timeout","_isHovered","_activeTrigger","_templateFactory","_newContent","tip","_setListeners","_fixTitle","enable","disable","toggleEnabled","click","_leave","_enter","_hideModalHandler","_disposePopper","_isWithContent","isInTheDom","_getTipElement","_isWithActiveTrigger","_getTitle","_createTipElement","_getContentForTemplate","_getTemplateFactory","tipId","prefix","floor","random","getElementById","getUID","setContent","_initializeOnDelegatedTarget","_getDelegateConfig","attachment","triggers","eventIn","eventOut","_setTimeout","timeout","dataAttributes","dataAttribute","Popover","_getContent","EVENT_ACTIVATE","EVENT_CLICK","SELECTOR_TARGET_LINKS","SELECTOR_NAV_LINKS","SELECTOR_LINK_ITEMS","rootMargin","smoothScroll","threshold","ScrollSpy","_targetLinks","_observableSections","_rootElement","_activeTarget","_observer","_previousScrollData","visibleEntryTop","parentScrollTop","refresh","_initializeTargetsAndObservables","_maybeEnableSmoothScroll","disconnect","_getNewObserver","section","observe","observableSection","scrollTo","behavior","IntersectionObserver","_observerCallback","targetElement","_process","userScrollsDown","isIntersecting","_clearActiveClass","entryIsLowerThanPrevious","targetLinks","anchor","decodeURI","_activateParents","listGroup","activeNodes","spy","ARROW_LEFT_KEY","ARROW_RIGHT_KEY","HOME_KEY","END_KEY","NOT_SELECTOR_DROPDOWN_TOGGLE","SELECTOR_INNER_ELEM","SELECTOR_DATA_TOGGLE_ACTIVE","Tab","_setInitialAttributes","_getChildren","innerElem","_elemIsActive","active","_getActiveElem","hideEvent","_deactivate","_activate","relatedElem","_toggleDropDown","nextActiveElement","preventScroll","_setAttributeIfNotExists","_setInitialAttributesOnChild","_getInnerElement","isActive","outerElem","_getOuterElement","_setInitialAttributesOnTargetPanel","open","EVENT_MOUSEOVER","EVENT_MOUSEOUT","EVENT_FOCUSOUT","CLASS_NAME_HIDE","autohide","Toast","_hasMouseInteraction","_hasKeyboardInteraction","_clearTimeout","_maybeScheduleHide","isShown","_onInteraction","isInteracting"],"sources":["../../js/src/dom/data.js","../../js/src/util/index.js","../../js/src/dom/event-handler.js","../../js/src/dom/manipulator.js","../../js/src/util/config.js","../../js/src/base-component.js","../../js/src/dom/selector-engine.js","../../js/src/util/component-functions.js","../../js/src/alert.js","../../js/src/button.js","../../js/src/util/swipe.js","../../js/src/carousel.js","../../js/src/collapse.js","../../node_modules/@popperjs/core/lib/enums.js","../../node_modules/@popperjs/core/lib/dom-utils/getNodeName.js","../../node_modules/@popperjs/core/lib/dom-utils/getWindow.js","../../node_modules/@popperjs/core/lib/dom-utils/instanceOf.js","../../node_modules/@popperjs/core/lib/modifiers/applyStyles.js","../../node_modules/@popperjs/core/lib/utils/getBasePlacement.js","../../node_modules/@popperjs/core/lib/utils/math.js","../../node_modules/@popperjs/core/lib/utils/userAgent.js","../../node_modules/@popperjs/core/lib/dom-utils/isLayoutViewport.js","../../node_modules/@popperjs/core/lib/dom-utils/getBoundingClientRect.js","../../node_modules/@popperjs/core/lib/dom-utils/getLayoutRect.js","../../node_modules/@popperjs/core/lib/dom-utils/contains.js","../../node_modules/@popperjs/core/lib/dom-utils/getComputedStyle.js","../../node_modules/@popperjs/core/lib/dom-utils/isTableElement.js","../../node_modules/@popperjs/core/lib/dom-utils/getDocumentElement.js","../../node_modules/@popperjs/core/lib/dom-utils/getParentNode.js","../../node_modules/@popperjs/core/lib/dom-utils/getOffsetParent.js","../../node_modules/@popperjs/core/lib/utils/getMainAxisFromPlacement.js","../../node_modules/@popperjs/core/lib/utils/within.js","../../node_modules/@popperjs/core/lib/utils/mergePaddingObject.js","../../node_modules/@popperjs/core/lib/utils/getFreshSideObject.js","../../node_modules/@popperjs/core/lib/utils/expandToHashMap.js","../../node_modules/@popperjs/core/lib/modifiers/arrow.js","../../node_modules/@popperjs/core/lib/utils/getVariation.js","../../node_modules/@popperjs/core/lib/modifiers/computeStyles.js","../../node_modules/@popperjs/core/lib/modifiers/eventListeners.js","../../node_modules/@popperjs/core/lib/utils/getOppositePlacement.js","../../node_modules/@popperjs/core/lib/utils/getOppositeVariationPlacement.js","../../node_modules/@popperjs/core/lib/dom-utils/getWindowScroll.js","../../node_modules/@popperjs/core/lib/dom-utils/getWindowScrollBarX.js","../../node_modules/@popperjs/core/lib/dom-utils/isScrollParent.js","../../node_modules/@popperjs/core/lib/dom-utils/getScrollParent.js","../../node_modules/@popperjs/core/lib/dom-utils/listScrollParents.js","../../node_modules/@popperjs/core/lib/utils/rectToClientRect.js","../../node_modules/@popperjs/core/lib/dom-utils/getClippingRect.js","../../node_modules/@popperjs/core/lib/dom-utils/getViewportRect.js","../../node_modules/@popperjs/core/lib/dom-utils/getDocumentRect.js","../../node_modules/@popperjs/core/lib/utils/computeOffsets.js","../../node_modules/@popperjs/core/lib/utils/detectOverflow.js","../../node_modules/@popperjs/core/lib/utils/computeAutoPlacement.js","../../node_modules/@popperjs/core/lib/modifiers/flip.js","../../node_modules/@popperjs/core/lib/modifiers/hide.js","../../node_modules/@popperjs/core/lib/modifiers/offset.js","../../node_modules/@popperjs/core/lib/modifiers/popperOffsets.js","../../node_modules/@popperjs/core/lib/modifiers/preventOverflow.js","../../node_modules/@popperjs/core/lib/utils/getAltAxis.js","../../node_modules/@popperjs/core/lib/dom-utils/getCompositeRect.js","../../node_modules/@popperjs/core/lib/dom-utils/getNodeScroll.js","../../node_modules/@popperjs/core/lib/dom-utils/getHTMLElementScroll.js","../../node_modules/@popperjs/core/lib/utils/orderModifiers.js","../../node_modules/@popperjs/core/lib/createPopper.js","../../node_modules/@popperjs/core/lib/utils/debounce.js","../../node_modules/@popperjs/core/lib/utils/mergeByName.js","../../node_modules/@popperjs/core/lib/popper-lite.js","../../node_modules/@popperjs/core/lib/popper.js","../../js/src/dropdown.js","../../js/src/util/backdrop.js","../../js/src/util/focustrap.js","../../js/src/util/scrollbar.js","../../js/src/modal.js","../../js/src/offcanvas.js","../../js/src/util/sanitizer.js","../../js/src/util/template-factory.js","../../js/src/tooltip.js","../../js/src/popover.js","../../js/src/scrollspy.js","../../js/src/tab.js","../../js/src/toast.js","../../js/index.umd.js"],"sourcesContent":["/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/data.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n/**\n * Constants\n */\n\nconst elementMap = new Map()\n\nexport default {\n set(element, key, instance) {\n if (!elementMap.has(element)) {\n elementMap.set(element, new Map())\n }\n\n const instanceMap = elementMap.get(element)\n\n // make it clear we only want one instance per element\n // can be removed later when multiple key/instances are fine to be used\n if (!instanceMap.has(key) && instanceMap.size !== 0) {\n // eslint-disable-next-line no-console\n console.error(`Bootstrap doesn't allow more than one instance per element. Bound instance: ${Array.from(instanceMap.keys())[0]}.`)\n return\n }\n\n instanceMap.set(key, instance)\n },\n\n get(element, key) {\n if (elementMap.has(element)) {\n return elementMap.get(element).get(key) || null\n }\n\n return null\n },\n\n remove(element, key) {\n if (!elementMap.has(element)) {\n return\n }\n\n const instanceMap = elementMap.get(element)\n\n instanceMap.delete(key)\n\n // free up element references if there are no instances left for an element\n if (instanceMap.size === 0) {\n elementMap.delete(element)\n }\n }\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/index.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nconst MAX_UID = 1_000_000\nconst MILLISECONDS_MULTIPLIER = 1000\nconst TRANSITION_END = 'transitionend'\n\n/**\n * Properly escape IDs selectors to handle weird IDs\n * @param {string} selector\n * @returns {string}\n */\nconst parseSelector = selector => {\n if (selector && window.CSS && window.CSS.escape) {\n // document.querySelector needs escaping to handle IDs (html5+) containing for instance /\n selector = selector.replace(/#([^\\s\"#']+)/g, (match, id) => `#${CSS.escape(id)}`)\n }\n\n return selector\n}\n\n// Shout-out Angus Croll (https://goo.gl/pxwQGp)\nconst toType = object => {\n if (object === null || object === undefined) {\n return `${object}`\n }\n\n return Object.prototype.toString.call(object).match(/\\s([a-z]+)/i)[1].toLowerCase()\n}\n\n/**\n * Public Util API\n */\n\nconst getUID = prefix => {\n do {\n prefix += Math.floor(Math.random() * MAX_UID)\n } while (document.getElementById(prefix))\n\n return prefix\n}\n\nconst getTransitionDurationFromElement = element => {\n if (!element) {\n return 0\n }\n\n // Get transition-duration of the element\n let { transitionDuration, transitionDelay } = window.getComputedStyle(element)\n\n const floatTransitionDuration = Number.parseFloat(transitionDuration)\n const floatTransitionDelay = Number.parseFloat(transitionDelay)\n\n // Return 0 if element or transition duration is not found\n if (!floatTransitionDuration && !floatTransitionDelay) {\n return 0\n }\n\n // If multiple durations are defined, take the first\n transitionDuration = transitionDuration.split(',')[0]\n transitionDelay = transitionDelay.split(',')[0]\n\n return (Number.parseFloat(transitionDuration) + Number.parseFloat(transitionDelay)) * MILLISECONDS_MULTIPLIER\n}\n\nconst triggerTransitionEnd = element => {\n element.dispatchEvent(new Event(TRANSITION_END))\n}\n\nconst isElement = object => {\n if (!object || typeof object !== 'object') {\n return false\n }\n\n if (typeof object.jquery !== 'undefined') {\n object = object[0]\n }\n\n return typeof object.nodeType !== 'undefined'\n}\n\nconst getElement = object => {\n // it's a jQuery object or a node element\n if (isElement(object)) {\n return object.jquery ? object[0] : object\n }\n\n if (typeof object === 'string' && object.length > 0) {\n return document.querySelector(parseSelector(object))\n }\n\n return null\n}\n\nconst isVisible = element => {\n if (!isElement(element) || element.getClientRects().length === 0) {\n return false\n }\n\n const elementIsVisible = getComputedStyle(element).getPropertyValue('visibility') === 'visible'\n // Handle `details` element as its content may falsie appear visible when it is closed\n const closedDetails = element.closest('details:not([open])')\n\n if (!closedDetails) {\n return elementIsVisible\n }\n\n if (closedDetails !== element) {\n const summary = element.closest('summary')\n if (summary && summary.parentNode !== closedDetails) {\n return false\n }\n\n if (summary === null) {\n return false\n }\n }\n\n return elementIsVisible\n}\n\nconst isDisabled = element => {\n if (!element || element.nodeType !== Node.ELEMENT_NODE) {\n return true\n }\n\n if (element.classList.contains('disabled')) {\n return true\n }\n\n if (typeof element.disabled !== 'undefined') {\n return element.disabled\n }\n\n return element.hasAttribute('disabled') && element.getAttribute('disabled') !== 'false'\n}\n\nconst findShadowRoot = element => {\n if (!document.documentElement.attachShadow) {\n return null\n }\n\n // Can find the shadow root otherwise it'll return the document\n if (typeof element.getRootNode === 'function') {\n const root = element.getRootNode()\n return root instanceof ShadowRoot ? root : null\n }\n\n if (element instanceof ShadowRoot) {\n return element\n }\n\n // when we don't find a shadow root\n if (!element.parentNode) {\n return null\n }\n\n return findShadowRoot(element.parentNode)\n}\n\nconst noop = () => {}\n\n/**\n * Trick to restart an element's animation\n *\n * @param {HTMLElement} element\n * @return void\n *\n * @see https://www.charistheo.io/blog/2021/02/restart-a-css-animation-with-javascript/#restarting-a-css-animation\n */\nconst reflow = element => {\n element.offsetHeight // eslint-disable-line no-unused-expressions\n}\n\nconst getjQuery = () => {\n if (window.jQuery && !document.body.hasAttribute('data-bs-no-jquery')) {\n return window.jQuery\n }\n\n return null\n}\n\nconst DOMContentLoadedCallbacks = []\n\nconst onDOMContentLoaded = callback => {\n if (document.readyState === 'loading') {\n // add listener on the first call when the document is in loading state\n if (!DOMContentLoadedCallbacks.length) {\n document.addEventListener('DOMContentLoaded', () => {\n for (const callback of DOMContentLoadedCallbacks) {\n callback()\n }\n })\n }\n\n DOMContentLoadedCallbacks.push(callback)\n } else {\n callback()\n }\n}\n\nconst isRTL = () => document.documentElement.dir === 'rtl'\n\nconst defineJQueryPlugin = plugin => {\n onDOMContentLoaded(() => {\n const $ = getjQuery()\n /* istanbul ignore if */\n if ($) {\n const name = plugin.NAME\n const JQUERY_NO_CONFLICT = $.fn[name]\n $.fn[name] = plugin.jQueryInterface\n $.fn[name].Constructor = plugin\n $.fn[name].noConflict = () => {\n $.fn[name] = JQUERY_NO_CONFLICT\n return plugin.jQueryInterface\n }\n }\n })\n}\n\nconst execute = (possibleCallback, args = [], defaultValue = possibleCallback) => {\n return typeof possibleCallback === 'function' ? possibleCallback(...args) : defaultValue\n}\n\nconst executeAfterTransition = (callback, transitionElement, waitForTransition = true) => {\n if (!waitForTransition) {\n execute(callback)\n return\n }\n\n const durationPadding = 5\n const emulatedDuration = getTransitionDurationFromElement(transitionElement) + durationPadding\n\n let called = false\n\n const handler = ({ target }) => {\n if (target !== transitionElement) {\n return\n }\n\n called = true\n transitionElement.removeEventListener(TRANSITION_END, handler)\n execute(callback)\n }\n\n transitionElement.addEventListener(TRANSITION_END, handler)\n setTimeout(() => {\n if (!called) {\n triggerTransitionEnd(transitionElement)\n }\n }, emulatedDuration)\n}\n\n/**\n * Return the previous/next element of a list.\n *\n * @param {array} list The list of elements\n * @param activeElement The active element\n * @param shouldGetNext Choose to get next or previous element\n * @param isCycleAllowed\n * @return {Element|elem} The proper element\n */\nconst getNextActiveElement = (list, activeElement, shouldGetNext, isCycleAllowed) => {\n const listLength = list.length\n let index = list.indexOf(activeElement)\n\n // if the element does not exist in the list return an element\n // depending on the direction and if cycle is allowed\n if (index === -1) {\n return !shouldGetNext && isCycleAllowed ? list[listLength - 1] : list[0]\n }\n\n index += shouldGetNext ? 1 : -1\n\n if (isCycleAllowed) {\n index = (index + listLength) % listLength\n }\n\n return list[Math.max(0, Math.min(index, listLength - 1))]\n}\n\nexport {\n defineJQueryPlugin,\n execute,\n executeAfterTransition,\n findShadowRoot,\n getElement,\n getjQuery,\n getNextActiveElement,\n getTransitionDurationFromElement,\n getUID,\n isDisabled,\n isElement,\n isRTL,\n isVisible,\n noop,\n onDOMContentLoaded,\n parseSelector,\n reflow,\n triggerTransitionEnd,\n toType\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/event-handler.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { getjQuery } from '../util/index.js'\n\n/**\n * Constants\n */\n\nconst namespaceRegex = /[^.]*(?=\\..*)\\.|.*/\nconst stripNameRegex = /\\..*/\nconst stripUidRegex = /::\\d+$/\nconst eventRegistry = {} // Events storage\nlet uidEvent = 1\nconst customEvents = {\n mouseenter: 'mouseover',\n mouseleave: 'mouseout'\n}\n\nconst nativeEvents = new Set([\n 'click',\n 'dblclick',\n 'mouseup',\n 'mousedown',\n 'contextmenu',\n 'mousewheel',\n 'DOMMouseScroll',\n 'mouseover',\n 'mouseout',\n 'mousemove',\n 'selectstart',\n 'selectend',\n 'keydown',\n 'keypress',\n 'keyup',\n 'orientationchange',\n 'touchstart',\n 'touchmove',\n 'touchend',\n 'touchcancel',\n 'pointerdown',\n 'pointermove',\n 'pointerup',\n 'pointerleave',\n 'pointercancel',\n 'gesturestart',\n 'gesturechange',\n 'gestureend',\n 'focus',\n 'blur',\n 'change',\n 'reset',\n 'select',\n 'submit',\n 'focusin',\n 'focusout',\n 'load',\n 'unload',\n 'beforeunload',\n 'resize',\n 'move',\n 'DOMContentLoaded',\n 'readystatechange',\n 'error',\n 'abort',\n 'scroll'\n])\n\n/**\n * Private methods\n */\n\nfunction makeEventUid(element, uid) {\n return (uid && `${uid}::${uidEvent++}`) || element.uidEvent || uidEvent++\n}\n\nfunction getElementEvents(element) {\n const uid = makeEventUid(element)\n\n element.uidEvent = uid\n eventRegistry[uid] = eventRegistry[uid] || {}\n\n return eventRegistry[uid]\n}\n\nfunction bootstrapHandler(element, fn) {\n return function handler(event) {\n hydrateObj(event, { delegateTarget: element })\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, fn)\n }\n\n return fn.apply(element, [event])\n }\n}\n\nfunction bootstrapDelegationHandler(element, selector, fn) {\n return function handler(event) {\n const domElements = element.querySelectorAll(selector)\n\n for (let { target } = event; target && target !== this; target = target.parentNode) {\n for (const domElement of domElements) {\n if (domElement !== target) {\n continue\n }\n\n hydrateObj(event, { delegateTarget: target })\n\n if (handler.oneOff) {\n EventHandler.off(element, event.type, selector, fn)\n }\n\n return fn.apply(target, [event])\n }\n }\n }\n}\n\nfunction findHandler(events, callable, delegationSelector = null) {\n return Object.values(events)\n .find(event => event.callable === callable && event.delegationSelector === delegationSelector)\n}\n\nfunction normalizeParameters(originalTypeEvent, handler, delegationFunction) {\n const isDelegated = typeof handler === 'string'\n // TODO: tooltip passes `false` instead of selector, so we need to check\n const callable = isDelegated ? delegationFunction : (handler || delegationFunction)\n let typeEvent = getTypeEvent(originalTypeEvent)\n\n if (!nativeEvents.has(typeEvent)) {\n typeEvent = originalTypeEvent\n }\n\n return [isDelegated, callable, typeEvent]\n}\n\nfunction addHandler(element, originalTypeEvent, handler, delegationFunction, oneOff) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return\n }\n\n let [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n\n // in case of mouseenter or mouseleave wrap the handler within a function that checks for its DOM position\n // this prevents the handler from being dispatched the same way as mouseover or mouseout does\n if (originalTypeEvent in customEvents) {\n const wrapFunction = fn => {\n return function (event) {\n if (!event.relatedTarget || (event.relatedTarget !== event.delegateTarget && !event.delegateTarget.contains(event.relatedTarget))) {\n return fn.call(this, event)\n }\n }\n }\n\n callable = wrapFunction(callable)\n }\n\n const events = getElementEvents(element)\n const handlers = events[typeEvent] || (events[typeEvent] = {})\n const previousFunction = findHandler(handlers, callable, isDelegated ? handler : null)\n\n if (previousFunction) {\n previousFunction.oneOff = previousFunction.oneOff && oneOff\n\n return\n }\n\n const uid = makeEventUid(callable, originalTypeEvent.replace(namespaceRegex, ''))\n const fn = isDelegated ?\n bootstrapDelegationHandler(element, handler, callable) :\n bootstrapHandler(element, callable)\n\n fn.delegationSelector = isDelegated ? handler : null\n fn.callable = callable\n fn.oneOff = oneOff\n fn.uidEvent = uid\n handlers[uid] = fn\n\n element.addEventListener(typeEvent, fn, isDelegated)\n}\n\nfunction removeHandler(element, events, typeEvent, handler, delegationSelector) {\n const fn = findHandler(events[typeEvent], handler, delegationSelector)\n\n if (!fn) {\n return\n }\n\n element.removeEventListener(typeEvent, fn, Boolean(delegationSelector))\n delete events[typeEvent][fn.uidEvent]\n}\n\nfunction removeNamespacedHandlers(element, events, typeEvent, namespace) {\n const storeElementEvent = events[typeEvent] || {}\n\n for (const [handlerKey, event] of Object.entries(storeElementEvent)) {\n if (handlerKey.includes(namespace)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n }\n }\n}\n\nfunction getTypeEvent(event) {\n // allow to get the native events from namespaced events ('click.bs.button' --> 'click')\n event = event.replace(stripNameRegex, '')\n return customEvents[event] || event\n}\n\nconst EventHandler = {\n on(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, false)\n },\n\n one(element, event, handler, delegationFunction) {\n addHandler(element, event, handler, delegationFunction, true)\n },\n\n off(element, originalTypeEvent, handler, delegationFunction) {\n if (typeof originalTypeEvent !== 'string' || !element) {\n return\n }\n\n const [isDelegated, callable, typeEvent] = normalizeParameters(originalTypeEvent, handler, delegationFunction)\n const inNamespace = typeEvent !== originalTypeEvent\n const events = getElementEvents(element)\n const storeElementEvent = events[typeEvent] || {}\n const isNamespace = originalTypeEvent.startsWith('.')\n\n if (typeof callable !== 'undefined') {\n // Simplest case: handler is passed, remove that listener ONLY.\n if (!Object.keys(storeElementEvent).length) {\n return\n }\n\n removeHandler(element, events, typeEvent, callable, isDelegated ? handler : null)\n return\n }\n\n if (isNamespace) {\n for (const elementEvent of Object.keys(events)) {\n removeNamespacedHandlers(element, events, elementEvent, originalTypeEvent.slice(1))\n }\n }\n\n for (const [keyHandlers, event] of Object.entries(storeElementEvent)) {\n const handlerKey = keyHandlers.replace(stripUidRegex, '')\n\n if (!inNamespace || originalTypeEvent.includes(handlerKey)) {\n removeHandler(element, events, typeEvent, event.callable, event.delegationSelector)\n }\n }\n },\n\n trigger(element, event, args) {\n if (typeof event !== 'string' || !element) {\n return null\n }\n\n const $ = getjQuery()\n const typeEvent = getTypeEvent(event)\n const inNamespace = event !== typeEvent\n\n let jQueryEvent = null\n let bubbles = true\n let nativeDispatch = true\n let defaultPrevented = false\n\n if (inNamespace && $) {\n jQueryEvent = $.Event(event, args)\n\n $(element).trigger(jQueryEvent)\n bubbles = !jQueryEvent.isPropagationStopped()\n nativeDispatch = !jQueryEvent.isImmediatePropagationStopped()\n defaultPrevented = jQueryEvent.isDefaultPrevented()\n }\n\n const evt = hydrateObj(new Event(event, { bubbles, cancelable: true }), args)\n\n if (defaultPrevented) {\n evt.preventDefault()\n }\n\n if (nativeDispatch) {\n element.dispatchEvent(evt)\n }\n\n if (evt.defaultPrevented && jQueryEvent) {\n jQueryEvent.preventDefault()\n }\n\n return evt\n }\n}\n\nfunction hydrateObj(obj, meta = {}) {\n for (const [key, value] of Object.entries(meta)) {\n try {\n obj[key] = value\n } catch {\n Object.defineProperty(obj, key, {\n configurable: true,\n get() {\n return value\n }\n })\n }\n }\n\n return obj\n}\n\nexport default EventHandler\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/manipulator.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nfunction normalizeData(value) {\n if (value === 'true') {\n return true\n }\n\n if (value === 'false') {\n return false\n }\n\n if (value === Number(value).toString()) {\n return Number(value)\n }\n\n if (value === '' || value === 'null') {\n return null\n }\n\n if (typeof value !== 'string') {\n return value\n }\n\n try {\n return JSON.parse(decodeURIComponent(value))\n } catch {\n return value\n }\n}\n\nfunction normalizeDataKey(key) {\n return key.replace(/[A-Z]/g, chr => `-${chr.toLowerCase()}`)\n}\n\nconst Manipulator = {\n setDataAttribute(element, key, value) {\n element.setAttribute(`data-bs-${normalizeDataKey(key)}`, value)\n },\n\n removeDataAttribute(element, key) {\n element.removeAttribute(`data-bs-${normalizeDataKey(key)}`)\n },\n\n getDataAttributes(element) {\n if (!element) {\n return {}\n }\n\n const attributes = {}\n const bsKeys = Object.keys(element.dataset).filter(key => key.startsWith('bs') && !key.startsWith('bsConfig'))\n\n for (const key of bsKeys) {\n let pureKey = key.replace(/^bs/, '')\n pureKey = pureKey.charAt(0).toLowerCase() + pureKey.slice(1, pureKey.length)\n attributes[pureKey] = normalizeData(element.dataset[key])\n }\n\n return attributes\n },\n\n getDataAttribute(element, key) {\n return normalizeData(element.getAttribute(`data-bs-${normalizeDataKey(key)}`))\n }\n}\n\nexport default Manipulator\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/config.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport { isElement, toType } from './index.js'\n\n/**\n * Class definition\n */\n\nclass Config {\n // Getters\n static get Default() {\n return {}\n }\n\n static get DefaultType() {\n return {}\n }\n\n static get NAME() {\n throw new Error('You have to implement the static method \"NAME\", for each component!')\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n _configAfterMerge(config) {\n return config\n }\n\n _mergeConfigObj(config, element) {\n const jsonConfig = isElement(element) ? Manipulator.getDataAttribute(element, 'config') : {} // try to parse\n\n return {\n ...this.constructor.Default,\n ...(typeof jsonConfig === 'object' ? jsonConfig : {}),\n ...(isElement(element) ? Manipulator.getDataAttributes(element) : {}),\n ...(typeof config === 'object' ? config : {})\n }\n }\n\n _typeCheckConfig(config, configTypes = this.constructor.DefaultType) {\n for (const [property, expectedTypes] of Object.entries(configTypes)) {\n const value = config[property]\n const valueType = isElement(value) ? 'element' : toType(value)\n\n if (!new RegExp(expectedTypes).test(valueType)) {\n throw new TypeError(\n `${this.constructor.NAME.toUpperCase()}: Option \"${property}\" provided type \"${valueType}\" but expected type \"${expectedTypes}\".`\n )\n }\n }\n }\n}\n\nexport default Config\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap base-component.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Data from './dom/data.js'\nimport EventHandler from './dom/event-handler.js'\nimport Config from './util/config.js'\nimport { executeAfterTransition, getElement } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst VERSION = '5.3.1'\n\n/**\n * Class definition\n */\n\nclass BaseComponent extends Config {\n constructor(element, config) {\n super()\n\n element = getElement(element)\n if (!element) {\n return\n }\n\n this._element = element\n this._config = this._getConfig(config)\n\n Data.set(this._element, this.constructor.DATA_KEY, this)\n }\n\n // Public\n dispose() {\n Data.remove(this._element, this.constructor.DATA_KEY)\n EventHandler.off(this._element, this.constructor.EVENT_KEY)\n\n for (const propertyName of Object.getOwnPropertyNames(this)) {\n this[propertyName] = null\n }\n }\n\n _queueCallback(callback, element, isAnimated = true) {\n executeAfterTransition(callback, element, isAnimated)\n }\n\n _getConfig(config) {\n config = this._mergeConfigObj(config, this._element)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n // Static\n static getInstance(element) {\n return Data.get(getElement(element), this.DATA_KEY)\n }\n\n static getOrCreateInstance(element, config = {}) {\n return this.getInstance(element) || new this(element, typeof config === 'object' ? config : null)\n }\n\n static get VERSION() {\n return VERSION\n }\n\n static get DATA_KEY() {\n return `bs.${this.NAME}`\n }\n\n static get EVENT_KEY() {\n return `.${this.DATA_KEY}`\n }\n\n static eventName(name) {\n return `${name}${this.EVENT_KEY}`\n }\n}\n\nexport default BaseComponent\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap dom/selector-engine.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport { isDisabled, isVisible, parseSelector } from '../util/index.js'\n\nconst getSelector = element => {\n let selector = element.getAttribute('data-bs-target')\n\n if (!selector || selector === '#') {\n let hrefAttribute = element.getAttribute('href')\n\n // The only valid content that could double as a selector are IDs or classes,\n // so everything starting with `#` or `.`. If a \"real\" URL is used as the selector,\n // `document.querySelector` will rightfully complain it is invalid.\n // See https://github.com/twbs/bootstrap/issues/32273\n if (!hrefAttribute || (!hrefAttribute.includes('#') && !hrefAttribute.startsWith('.'))) {\n return null\n }\n\n // Just in case some CMS puts out a full URL with the anchor appended\n if (hrefAttribute.includes('#') && !hrefAttribute.startsWith('#')) {\n hrefAttribute = `#${hrefAttribute.split('#')[1]}`\n }\n\n selector = hrefAttribute && hrefAttribute !== '#' ? hrefAttribute.trim() : null\n }\n\n return parseSelector(selector)\n}\n\nconst SelectorEngine = {\n find(selector, element = document.documentElement) {\n return [].concat(...Element.prototype.querySelectorAll.call(element, selector))\n },\n\n findOne(selector, element = document.documentElement) {\n return Element.prototype.querySelector.call(element, selector)\n },\n\n children(element, selector) {\n return [].concat(...element.children).filter(child => child.matches(selector))\n },\n\n parents(element, selector) {\n const parents = []\n let ancestor = element.parentNode.closest(selector)\n\n while (ancestor) {\n parents.push(ancestor)\n ancestor = ancestor.parentNode.closest(selector)\n }\n\n return parents\n },\n\n prev(element, selector) {\n let previous = element.previousElementSibling\n\n while (previous) {\n if (previous.matches(selector)) {\n return [previous]\n }\n\n previous = previous.previousElementSibling\n }\n\n return []\n },\n // TODO: this is now unused; remove later along with prev()\n next(element, selector) {\n let next = element.nextElementSibling\n\n while (next) {\n if (next.matches(selector)) {\n return [next]\n }\n\n next = next.nextElementSibling\n }\n\n return []\n },\n\n focusableChildren(element) {\n const focusables = [\n 'a',\n 'button',\n 'input',\n 'textarea',\n 'select',\n 'details',\n '[tabindex]',\n '[contenteditable=\"true\"]'\n ].map(selector => `${selector}:not([tabindex^=\"-\"])`).join(',')\n\n return this.find(focusables, element).filter(el => !isDisabled(el) && isVisible(el))\n },\n\n getSelectorFromElement(element) {\n const selector = getSelector(element)\n\n if (selector) {\n return SelectorEngine.findOne(selector) ? selector : null\n }\n\n return null\n },\n\n getElementFromSelector(element) {\n const selector = getSelector(element)\n\n return selector ? SelectorEngine.findOne(selector) : null\n },\n\n getMultipleElementsFromSelector(element) {\n const selector = getSelector(element)\n\n return selector ? SelectorEngine.find(selector) : []\n }\n}\n\nexport default SelectorEngine\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/component-functions.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isDisabled } from './index.js'\n\nconst enableDismissTrigger = (component, method = 'hide') => {\n const clickEvent = `click.dismiss${component.EVENT_KEY}`\n const name = component.NAME\n\n EventHandler.on(document, clickEvent, `[data-bs-dismiss=\"${name}\"]`, function (event) {\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n if (isDisabled(this)) {\n return\n }\n\n const target = SelectorEngine.getElementFromSelector(this) || this.closest(`.${name}`)\n const instance = component.getOrCreateInstance(target)\n\n // Method argument is left, for Alert and only, as it doesn't implement the 'hide' method\n instance[method]()\n })\n}\n\nexport {\n enableDismissTrigger\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap alert.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'alert'\nconst DATA_KEY = 'bs.alert'\nconst EVENT_KEY = `.${DATA_KEY}`\n\nconst EVENT_CLOSE = `close${EVENT_KEY}`\nconst EVENT_CLOSED = `closed${EVENT_KEY}`\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\n\n/**\n * Class definition\n */\n\nclass Alert extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME\n }\n\n // Public\n close() {\n const closeEvent = EventHandler.trigger(this._element, EVENT_CLOSE)\n\n if (closeEvent.defaultPrevented) {\n return\n }\n\n this._element.classList.remove(CLASS_NAME_SHOW)\n\n const isAnimated = this._element.classList.contains(CLASS_NAME_FADE)\n this._queueCallback(() => this._destroyElement(), this._element, isAnimated)\n }\n\n // Private\n _destroyElement() {\n this._element.remove()\n EventHandler.trigger(this._element, EVENT_CLOSED)\n this.dispose()\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Alert.getOrCreateInstance(this)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](this)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nenableDismissTrigger(Alert, 'close')\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Alert)\n\nexport default Alert\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap button.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'button'\nconst DATA_KEY = 'bs.button'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst CLASS_NAME_ACTIVE = 'active'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"button\"]'\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\n/**\n * Class definition\n */\n\nclass Button extends BaseComponent {\n // Getters\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n // Toggle class and sync the `aria-pressed` attribute with the return value of the `.toggle()` method\n this._element.setAttribute('aria-pressed', this._element.classList.toggle(CLASS_NAME_ACTIVE))\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Button.getOrCreateInstance(this)\n\n if (config === 'toggle') {\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, event => {\n event.preventDefault()\n\n const button = event.target.closest(SELECTOR_DATA_TOGGLE)\n const data = Button.getOrCreateInstance(button)\n\n data.toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Button)\n\nexport default Button\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/swipe.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'swipe'\nconst EVENT_KEY = '.bs.swipe'\nconst EVENT_TOUCHSTART = `touchstart${EVENT_KEY}`\nconst EVENT_TOUCHMOVE = `touchmove${EVENT_KEY}`\nconst EVENT_TOUCHEND = `touchend${EVENT_KEY}`\nconst EVENT_POINTERDOWN = `pointerdown${EVENT_KEY}`\nconst EVENT_POINTERUP = `pointerup${EVENT_KEY}`\nconst POINTER_TYPE_TOUCH = 'touch'\nconst POINTER_TYPE_PEN = 'pen'\nconst CLASS_NAME_POINTER_EVENT = 'pointer-event'\nconst SWIPE_THRESHOLD = 40\n\nconst Default = {\n endCallback: null,\n leftCallback: null,\n rightCallback: null\n}\n\nconst DefaultType = {\n endCallback: '(function|null)',\n leftCallback: '(function|null)',\n rightCallback: '(function|null)'\n}\n\n/**\n * Class definition\n */\n\nclass Swipe extends Config {\n constructor(element, config) {\n super()\n this._element = element\n\n if (!element || !Swipe.isSupported()) {\n return\n }\n\n this._config = this._getConfig(config)\n this._deltaX = 0\n this._supportPointerEvents = Boolean(window.PointerEvent)\n this._initEvents()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n dispose() {\n EventHandler.off(this._element, EVENT_KEY)\n }\n\n // Private\n _start(event) {\n if (!this._supportPointerEvents) {\n this._deltaX = event.touches[0].clientX\n\n return\n }\n\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX\n }\n }\n\n _end(event) {\n if (this._eventIsPointerPenTouch(event)) {\n this._deltaX = event.clientX - this._deltaX\n }\n\n this._handleSwipe()\n execute(this._config.endCallback)\n }\n\n _move(event) {\n this._deltaX = event.touches && event.touches.length > 1 ?\n 0 :\n event.touches[0].clientX - this._deltaX\n }\n\n _handleSwipe() {\n const absDeltaX = Math.abs(this._deltaX)\n\n if (absDeltaX <= SWIPE_THRESHOLD) {\n return\n }\n\n const direction = absDeltaX / this._deltaX\n\n this._deltaX = 0\n\n if (!direction) {\n return\n }\n\n execute(direction > 0 ? this._config.rightCallback : this._config.leftCallback)\n }\n\n _initEvents() {\n if (this._supportPointerEvents) {\n EventHandler.on(this._element, EVENT_POINTERDOWN, event => this._start(event))\n EventHandler.on(this._element, EVENT_POINTERUP, event => this._end(event))\n\n this._element.classList.add(CLASS_NAME_POINTER_EVENT)\n } else {\n EventHandler.on(this._element, EVENT_TOUCHSTART, event => this._start(event))\n EventHandler.on(this._element, EVENT_TOUCHMOVE, event => this._move(event))\n EventHandler.on(this._element, EVENT_TOUCHEND, event => this._end(event))\n }\n }\n\n _eventIsPointerPenTouch(event) {\n return this._supportPointerEvents && (event.pointerType === POINTER_TYPE_PEN || event.pointerType === POINTER_TYPE_TOUCH)\n }\n\n // Static\n static isSupported() {\n return 'ontouchstart' in document.documentElement || navigator.maxTouchPoints > 0\n }\n}\n\nexport default Swipe\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap carousel.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n defineJQueryPlugin,\n getNextActiveElement,\n isRTL,\n isVisible,\n reflow,\n triggerTransitionEnd\n} from './util/index.js'\nimport Swipe from './util/swipe.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'carousel'\nconst DATA_KEY = 'bs.carousel'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ARROW_LEFT_KEY = 'ArrowLeft'\nconst ARROW_RIGHT_KEY = 'ArrowRight'\nconst TOUCHEVENT_COMPAT_WAIT = 500 // Time for mouse compat events to fire after touch\n\nconst ORDER_NEXT = 'next'\nconst ORDER_PREV = 'prev'\nconst DIRECTION_LEFT = 'left'\nconst DIRECTION_RIGHT = 'right'\n\nconst EVENT_SLIDE = `slide${EVENT_KEY}`\nconst EVENT_SLID = `slid${EVENT_KEY}`\nconst EVENT_KEYDOWN = `keydown${EVENT_KEY}`\nconst EVENT_MOUSEENTER = `mouseenter${EVENT_KEY}`\nconst EVENT_MOUSELEAVE = `mouseleave${EVENT_KEY}`\nconst EVENT_DRAG_START = `dragstart${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_CAROUSEL = 'carousel'\nconst CLASS_NAME_ACTIVE = 'active'\nconst CLASS_NAME_SLIDE = 'slide'\nconst CLASS_NAME_END = 'carousel-item-end'\nconst CLASS_NAME_START = 'carousel-item-start'\nconst CLASS_NAME_NEXT = 'carousel-item-next'\nconst CLASS_NAME_PREV = 'carousel-item-prev'\n\nconst SELECTOR_ACTIVE = '.active'\nconst SELECTOR_ITEM = '.carousel-item'\nconst SELECTOR_ACTIVE_ITEM = SELECTOR_ACTIVE + SELECTOR_ITEM\nconst SELECTOR_ITEM_IMG = '.carousel-item img'\nconst SELECTOR_INDICATORS = '.carousel-indicators'\nconst SELECTOR_DATA_SLIDE = '[data-bs-slide], [data-bs-slide-to]'\nconst SELECTOR_DATA_RIDE = '[data-bs-ride=\"carousel\"]'\n\nconst KEY_TO_DIRECTION = {\n [ARROW_LEFT_KEY]: DIRECTION_RIGHT,\n [ARROW_RIGHT_KEY]: DIRECTION_LEFT\n}\n\nconst Default = {\n interval: 5000,\n keyboard: true,\n pause: 'hover',\n ride: false,\n touch: true,\n wrap: true\n}\n\nconst DefaultType = {\n interval: '(number|boolean)', // TODO:v6 remove boolean support\n keyboard: 'boolean',\n pause: '(string|boolean)',\n ride: '(boolean|string)',\n touch: 'boolean',\n wrap: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Carousel extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._interval = null\n this._activeElement = null\n this._isSliding = false\n this.touchTimeout = null\n this._swipeHelper = null\n\n this._indicatorsElement = SelectorEngine.findOne(SELECTOR_INDICATORS, this._element)\n this._addEventListeners()\n\n if (this._config.ride === CLASS_NAME_CAROUSEL) {\n this.cycle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n next() {\n this._slide(ORDER_NEXT)\n }\n\n nextWhenVisible() {\n // FIXME TODO use `document.visibilityState`\n // Don't call next when the page isn't visible\n // or the carousel or its parent isn't visible\n if (!document.hidden && isVisible(this._element)) {\n this.next()\n }\n }\n\n prev() {\n this._slide(ORDER_PREV)\n }\n\n pause() {\n if (this._isSliding) {\n triggerTransitionEnd(this._element)\n }\n\n this._clearInterval()\n }\n\n cycle() {\n this._clearInterval()\n this._updateInterval()\n\n this._interval = setInterval(() => this.nextWhenVisible(), this._config.interval)\n }\n\n _maybeEnableCycle() {\n if (!this._config.ride) {\n return\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.cycle())\n return\n }\n\n this.cycle()\n }\n\n to(index) {\n const items = this._getItems()\n if (index > items.length - 1 || index < 0) {\n return\n }\n\n if (this._isSliding) {\n EventHandler.one(this._element, EVENT_SLID, () => this.to(index))\n return\n }\n\n const activeIndex = this._getItemIndex(this._getActive())\n if (activeIndex === index) {\n return\n }\n\n const order = index > activeIndex ? ORDER_NEXT : ORDER_PREV\n\n this._slide(order, items[index])\n }\n\n dispose() {\n if (this._swipeHelper) {\n this._swipeHelper.dispose()\n }\n\n super.dispose()\n }\n\n // Private\n _configAfterMerge(config) {\n config.defaultInterval = config.interval\n return config\n }\n\n _addEventListeners() {\n if (this._config.keyboard) {\n EventHandler.on(this._element, EVENT_KEYDOWN, event => this._keydown(event))\n }\n\n if (this._config.pause === 'hover') {\n EventHandler.on(this._element, EVENT_MOUSEENTER, () => this.pause())\n EventHandler.on(this._element, EVENT_MOUSELEAVE, () => this._maybeEnableCycle())\n }\n\n if (this._config.touch && Swipe.isSupported()) {\n this._addTouchEventListeners()\n }\n }\n\n _addTouchEventListeners() {\n for (const img of SelectorEngine.find(SELECTOR_ITEM_IMG, this._element)) {\n EventHandler.on(img, EVENT_DRAG_START, event => event.preventDefault())\n }\n\n const endCallBack = () => {\n if (this._config.pause !== 'hover') {\n return\n }\n\n // If it's a touch-enabled device, mouseenter/leave are fired as\n // part of the mouse compatibility events on first tap - the carousel\n // would stop cycling until user tapped out of it;\n // here, we listen for touchend, explicitly pause the carousel\n // (as if it's the second time we tap on it, mouseenter compat event\n // is NOT fired) and after a timeout (to allow for mouse compatibility\n // events to fire) we explicitly restart cycling\n\n this.pause()\n if (this.touchTimeout) {\n clearTimeout(this.touchTimeout)\n }\n\n this.touchTimeout = setTimeout(() => this._maybeEnableCycle(), TOUCHEVENT_COMPAT_WAIT + this._config.interval)\n }\n\n const swipeConfig = {\n leftCallback: () => this._slide(this._directionToOrder(DIRECTION_LEFT)),\n rightCallback: () => this._slide(this._directionToOrder(DIRECTION_RIGHT)),\n endCallback: endCallBack\n }\n\n this._swipeHelper = new Swipe(this._element, swipeConfig)\n }\n\n _keydown(event) {\n if (/input|textarea/i.test(event.target.tagName)) {\n return\n }\n\n const direction = KEY_TO_DIRECTION[event.key]\n if (direction) {\n event.preventDefault()\n this._slide(this._directionToOrder(direction))\n }\n }\n\n _getItemIndex(element) {\n return this._getItems().indexOf(element)\n }\n\n _setActiveIndicatorElement(index) {\n if (!this._indicatorsElement) {\n return\n }\n\n const activeIndicator = SelectorEngine.findOne(SELECTOR_ACTIVE, this._indicatorsElement)\n\n activeIndicator.classList.remove(CLASS_NAME_ACTIVE)\n activeIndicator.removeAttribute('aria-current')\n\n const newActiveIndicator = SelectorEngine.findOne(`[data-bs-slide-to=\"${index}\"]`, this._indicatorsElement)\n\n if (newActiveIndicator) {\n newActiveIndicator.classList.add(CLASS_NAME_ACTIVE)\n newActiveIndicator.setAttribute('aria-current', 'true')\n }\n }\n\n _updateInterval() {\n const element = this._activeElement || this._getActive()\n\n if (!element) {\n return\n }\n\n const elementInterval = Number.parseInt(element.getAttribute('data-bs-interval'), 10)\n\n this._config.interval = elementInterval || this._config.defaultInterval\n }\n\n _slide(order, element = null) {\n if (this._isSliding) {\n return\n }\n\n const activeElement = this._getActive()\n const isNext = order === ORDER_NEXT\n const nextElement = element || getNextActiveElement(this._getItems(), activeElement, isNext, this._config.wrap)\n\n if (nextElement === activeElement) {\n return\n }\n\n const nextElementIndex = this._getItemIndex(nextElement)\n\n const triggerEvent = eventName => {\n return EventHandler.trigger(this._element, eventName, {\n relatedTarget: nextElement,\n direction: this._orderToDirection(order),\n from: this._getItemIndex(activeElement),\n to: nextElementIndex\n })\n }\n\n const slideEvent = triggerEvent(EVENT_SLIDE)\n\n if (slideEvent.defaultPrevented) {\n return\n }\n\n if (!activeElement || !nextElement) {\n // Some weirdness is happening, so we bail\n // TODO: change tests that use empty divs to avoid this check\n return\n }\n\n const isCycling = Boolean(this._interval)\n this.pause()\n\n this._isSliding = true\n\n this._setActiveIndicatorElement(nextElementIndex)\n this._activeElement = nextElement\n\n const directionalClassName = isNext ? CLASS_NAME_START : CLASS_NAME_END\n const orderClassName = isNext ? CLASS_NAME_NEXT : CLASS_NAME_PREV\n\n nextElement.classList.add(orderClassName)\n\n reflow(nextElement)\n\n activeElement.classList.add(directionalClassName)\n nextElement.classList.add(directionalClassName)\n\n const completeCallBack = () => {\n nextElement.classList.remove(directionalClassName, orderClassName)\n nextElement.classList.add(CLASS_NAME_ACTIVE)\n\n activeElement.classList.remove(CLASS_NAME_ACTIVE, orderClassName, directionalClassName)\n\n this._isSliding = false\n\n triggerEvent(EVENT_SLID)\n }\n\n this._queueCallback(completeCallBack, activeElement, this._isAnimated())\n\n if (isCycling) {\n this.cycle()\n }\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_SLIDE)\n }\n\n _getActive() {\n return SelectorEngine.findOne(SELECTOR_ACTIVE_ITEM, this._element)\n }\n\n _getItems() {\n return SelectorEngine.find(SELECTOR_ITEM, this._element)\n }\n\n _clearInterval() {\n if (this._interval) {\n clearInterval(this._interval)\n this._interval = null\n }\n }\n\n _directionToOrder(direction) {\n if (isRTL()) {\n return direction === DIRECTION_LEFT ? ORDER_PREV : ORDER_NEXT\n }\n\n return direction === DIRECTION_LEFT ? ORDER_NEXT : ORDER_PREV\n }\n\n _orderToDirection(order) {\n if (isRTL()) {\n return order === ORDER_PREV ? DIRECTION_LEFT : DIRECTION_RIGHT\n }\n\n return order === ORDER_PREV ? DIRECTION_RIGHT : DIRECTION_LEFT\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Carousel.getOrCreateInstance(this, config)\n\n if (typeof config === 'number') {\n data.to(config)\n return\n }\n\n if (typeof config === 'string') {\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_SLIDE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this)\n\n if (!target || !target.classList.contains(CLASS_NAME_CAROUSEL)) {\n return\n }\n\n event.preventDefault()\n\n const carousel = Carousel.getOrCreateInstance(target)\n const slideIndex = this.getAttribute('data-bs-slide-to')\n\n if (slideIndex) {\n carousel.to(slideIndex)\n carousel._maybeEnableCycle()\n return\n }\n\n if (Manipulator.getDataAttribute(this, 'slide') === 'next') {\n carousel.next()\n carousel._maybeEnableCycle()\n return\n }\n\n carousel.prev()\n carousel._maybeEnableCycle()\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n const carousels = SelectorEngine.find(SELECTOR_DATA_RIDE)\n\n for (const carousel of carousels) {\n Carousel.getOrCreateInstance(carousel)\n }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Carousel)\n\nexport default Carousel\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap collapse.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n defineJQueryPlugin,\n getElement,\n reflow\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'collapse'\nconst DATA_KEY = 'bs.collapse'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_COLLAPSE = 'collapse'\nconst CLASS_NAME_COLLAPSING = 'collapsing'\nconst CLASS_NAME_COLLAPSED = 'collapsed'\nconst CLASS_NAME_DEEPER_CHILDREN = `:scope .${CLASS_NAME_COLLAPSE} .${CLASS_NAME_COLLAPSE}`\nconst CLASS_NAME_HORIZONTAL = 'collapse-horizontal'\n\nconst WIDTH = 'width'\nconst HEIGHT = 'height'\n\nconst SELECTOR_ACTIVES = '.collapse.show, .collapse.collapsing'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"collapse\"]'\n\nconst Default = {\n parent: null,\n toggle: true\n}\n\nconst DefaultType = {\n parent: '(null|element)',\n toggle: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Collapse extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._isTransitioning = false\n this._triggerArray = []\n\n const toggleList = SelectorEngine.find(SELECTOR_DATA_TOGGLE)\n\n for (const elem of toggleList) {\n const selector = SelectorEngine.getSelectorFromElement(elem)\n const filterElement = SelectorEngine.find(selector)\n .filter(foundElement => foundElement === this._element)\n\n if (selector !== null && filterElement.length) {\n this._triggerArray.push(elem)\n }\n }\n\n this._initializeChildren()\n\n if (!this._config.parent) {\n this._addAriaAndCollapsedClass(this._triggerArray, this._isShown())\n }\n\n if (this._config.toggle) {\n this.toggle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n if (this._isShown()) {\n this.hide()\n } else {\n this.show()\n }\n }\n\n show() {\n if (this._isTransitioning || this._isShown()) {\n return\n }\n\n let activeChildren = []\n\n // find active children\n if (this._config.parent) {\n activeChildren = this._getFirstLevelChildren(SELECTOR_ACTIVES)\n .filter(element => element !== this._element)\n .map(element => Collapse.getOrCreateInstance(element, { toggle: false }))\n }\n\n if (activeChildren.length && activeChildren[0]._isTransitioning) {\n return\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_SHOW)\n if (startEvent.defaultPrevented) {\n return\n }\n\n for (const activeInstance of activeChildren) {\n activeInstance.hide()\n }\n\n const dimension = this._getDimension()\n\n this._element.classList.remove(CLASS_NAME_COLLAPSE)\n this._element.classList.add(CLASS_NAME_COLLAPSING)\n\n this._element.style[dimension] = 0\n\n this._addAriaAndCollapsedClass(this._triggerArray, true)\n this._isTransitioning = true\n\n const complete = () => {\n this._isTransitioning = false\n\n this._element.classList.remove(CLASS_NAME_COLLAPSING)\n this._element.classList.add(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n this._element.style[dimension] = ''\n\n EventHandler.trigger(this._element, EVENT_SHOWN)\n }\n\n const capitalizedDimension = dimension[0].toUpperCase() + dimension.slice(1)\n const scrollSize = `scroll${capitalizedDimension}`\n\n this._queueCallback(complete, this._element, true)\n this._element.style[dimension] = `${this._element[scrollSize]}px`\n }\n\n hide() {\n if (this._isTransitioning || !this._isShown()) {\n return\n }\n\n const startEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n if (startEvent.defaultPrevented) {\n return\n }\n\n const dimension = this._getDimension()\n\n this._element.style[dimension] = `${this._element.getBoundingClientRect()[dimension]}px`\n\n reflow(this._element)\n\n this._element.classList.add(CLASS_NAME_COLLAPSING)\n this._element.classList.remove(CLASS_NAME_COLLAPSE, CLASS_NAME_SHOW)\n\n for (const trigger of this._triggerArray) {\n const element = SelectorEngine.getElementFromSelector(trigger)\n\n if (element && !this._isShown(element)) {\n this._addAriaAndCollapsedClass([trigger], false)\n }\n }\n\n this._isTransitioning = true\n\n const complete = () => {\n this._isTransitioning = false\n this._element.classList.remove(CLASS_NAME_COLLAPSING)\n this._element.classList.add(CLASS_NAME_COLLAPSE)\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n }\n\n this._element.style[dimension] = ''\n\n this._queueCallback(complete, this._element, true)\n }\n\n _isShown(element = this._element) {\n return element.classList.contains(CLASS_NAME_SHOW)\n }\n\n // Private\n _configAfterMerge(config) {\n config.toggle = Boolean(config.toggle) // Coerce string values\n config.parent = getElement(config.parent)\n return config\n }\n\n _getDimension() {\n return this._element.classList.contains(CLASS_NAME_HORIZONTAL) ? WIDTH : HEIGHT\n }\n\n _initializeChildren() {\n if (!this._config.parent) {\n return\n }\n\n const children = this._getFirstLevelChildren(SELECTOR_DATA_TOGGLE)\n\n for (const element of children) {\n const selected = SelectorEngine.getElementFromSelector(element)\n\n if (selected) {\n this._addAriaAndCollapsedClass([element], this._isShown(selected))\n }\n }\n }\n\n _getFirstLevelChildren(selector) {\n const children = SelectorEngine.find(CLASS_NAME_DEEPER_CHILDREN, this._config.parent)\n // remove children if greater depth\n return SelectorEngine.find(selector, this._config.parent).filter(element => !children.includes(element))\n }\n\n _addAriaAndCollapsedClass(triggerArray, isOpen) {\n if (!triggerArray.length) {\n return\n }\n\n for (const element of triggerArray) {\n element.classList.toggle(CLASS_NAME_COLLAPSED, !isOpen)\n element.setAttribute('aria-expanded', isOpen)\n }\n }\n\n // Static\n static jQueryInterface(config) {\n const _config = {}\n if (typeof config === 'string' && /show|hide/.test(config)) {\n _config.toggle = false\n }\n\n return this.each(function () {\n const data = Collapse.getOrCreateInstance(this, _config)\n\n if (typeof config === 'string') {\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n }\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n // preventDefault only for elements (which change the URL) not inside the collapsible element\n if (event.target.tagName === 'A' || (event.delegateTarget && event.delegateTarget.tagName === 'A')) {\n event.preventDefault()\n }\n\n for (const element of SelectorEngine.getMultipleElementsFromSelector(this)) {\n Collapse.getOrCreateInstance(element, { toggle: false }).toggle()\n }\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Collapse)\n\nexport default Collapse\n","export var top = 'top';\nexport var bottom = 'bottom';\nexport var right = 'right';\nexport var left = 'left';\nexport var auto = 'auto';\nexport var basePlacements = [top, bottom, right, left];\nexport var start = 'start';\nexport var end = 'end';\nexport var clippingParents = 'clippingParents';\nexport var viewport = 'viewport';\nexport var popper = 'popper';\nexport var reference = 'reference';\nexport var variationPlacements = /*#__PURE__*/basePlacements.reduce(function (acc, placement) {\n return acc.concat([placement + \"-\" + start, placement + \"-\" + end]);\n}, []);\nexport var placements = /*#__PURE__*/[].concat(basePlacements, [auto]).reduce(function (acc, placement) {\n return acc.concat([placement, placement + \"-\" + start, placement + \"-\" + end]);\n}, []); // modifiers that need to read the DOM\n\nexport var beforeRead = 'beforeRead';\nexport var read = 'read';\nexport var afterRead = 'afterRead'; // pure-logic modifiers\n\nexport var beforeMain = 'beforeMain';\nexport var main = 'main';\nexport var afterMain = 'afterMain'; // modifier with the purpose to write to the DOM (or write into a framework state)\n\nexport var beforeWrite = 'beforeWrite';\nexport var write = 'write';\nexport var afterWrite = 'afterWrite';\nexport var modifierPhases = [beforeRead, read, afterRead, beforeMain, main, afterMain, beforeWrite, write, afterWrite];","export default function getNodeName(element) {\n return element ? (element.nodeName || '').toLowerCase() : null;\n}","export default function getWindow(node) {\n if (node == null) {\n return window;\n }\n\n if (node.toString() !== '[object Window]') {\n var ownerDocument = node.ownerDocument;\n return ownerDocument ? ownerDocument.defaultView || window : window;\n }\n\n return node;\n}","import getWindow from \"./getWindow.js\";\n\nfunction isElement(node) {\n var OwnElement = getWindow(node).Element;\n return node instanceof OwnElement || node instanceof Element;\n}\n\nfunction isHTMLElement(node) {\n var OwnElement = getWindow(node).HTMLElement;\n return node instanceof OwnElement || node instanceof HTMLElement;\n}\n\nfunction isShadowRoot(node) {\n // IE 11 has no ShadowRoot\n if (typeof ShadowRoot === 'undefined') {\n return false;\n }\n\n var OwnElement = getWindow(node).ShadowRoot;\n return node instanceof OwnElement || node instanceof ShadowRoot;\n}\n\nexport { isElement, isHTMLElement, isShadowRoot };","import getNodeName from \"../dom-utils/getNodeName.js\";\nimport { isHTMLElement } from \"../dom-utils/instanceOf.js\"; // This modifier takes the styles prepared by the `computeStyles` modifier\n// and applies them to the HTMLElements such as popper and arrow\n\nfunction applyStyles(_ref) {\n var state = _ref.state;\n Object.keys(state.elements).forEach(function (name) {\n var style = state.styles[name] || {};\n var attributes = state.attributes[name] || {};\n var element = state.elements[name]; // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n } // Flow doesn't support to extend this property, but it's the most\n // effective way to apply styles to an HTMLElement\n // $FlowFixMe[cannot-write]\n\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (name) {\n var value = attributes[name];\n\n if (value === false) {\n element.removeAttribute(name);\n } else {\n element.setAttribute(name, value === true ? '' : value);\n }\n });\n });\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state;\n var initialStyles = {\n popper: {\n position: state.options.strategy,\n left: '0',\n top: '0',\n margin: '0'\n },\n arrow: {\n position: 'absolute'\n },\n reference: {}\n };\n Object.assign(state.elements.popper.style, initialStyles.popper);\n state.styles = initialStyles;\n\n if (state.elements.arrow) {\n Object.assign(state.elements.arrow.style, initialStyles.arrow);\n }\n\n return function () {\n Object.keys(state.elements).forEach(function (name) {\n var element = state.elements[name];\n var attributes = state.attributes[name] || {};\n var styleProperties = Object.keys(state.styles.hasOwnProperty(name) ? state.styles[name] : initialStyles[name]); // Set all values to an empty string to unset them\n\n var style = styleProperties.reduce(function (style, property) {\n style[property] = '';\n return style;\n }, {}); // arrow is optional + virtual elements\n\n if (!isHTMLElement(element) || !getNodeName(element)) {\n return;\n }\n\n Object.assign(element.style, style);\n Object.keys(attributes).forEach(function (attribute) {\n element.removeAttribute(attribute);\n });\n });\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'applyStyles',\n enabled: true,\n phase: 'write',\n fn: applyStyles,\n effect: effect,\n requires: ['computeStyles']\n};","import { auto } from \"../enums.js\";\nexport default function getBasePlacement(placement) {\n return placement.split('-')[0];\n}","export var max = Math.max;\nexport var min = Math.min;\nexport var round = Math.round;","export default function getUAString() {\n var uaData = navigator.userAgentData;\n\n if (uaData != null && uaData.brands && Array.isArray(uaData.brands)) {\n return uaData.brands.map(function (item) {\n return item.brand + \"/\" + item.version;\n }).join(' ');\n }\n\n return navigator.userAgent;\n}","import getUAString from \"../utils/userAgent.js\";\nexport default function isLayoutViewport() {\n return !/^((?!chrome|android).)*safari/i.test(getUAString());\n}","import { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport { round } from \"../utils/math.js\";\nimport getWindow from \"./getWindow.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getBoundingClientRect(element, includeScale, isFixedStrategy) {\n if (includeScale === void 0) {\n includeScale = false;\n }\n\n if (isFixedStrategy === void 0) {\n isFixedStrategy = false;\n }\n\n var clientRect = element.getBoundingClientRect();\n var scaleX = 1;\n var scaleY = 1;\n\n if (includeScale && isHTMLElement(element)) {\n scaleX = element.offsetWidth > 0 ? round(clientRect.width) / element.offsetWidth || 1 : 1;\n scaleY = element.offsetHeight > 0 ? round(clientRect.height) / element.offsetHeight || 1 : 1;\n }\n\n var _ref = isElement(element) ? getWindow(element) : window,\n visualViewport = _ref.visualViewport;\n\n var addVisualOffsets = !isLayoutViewport() && isFixedStrategy;\n var x = (clientRect.left + (addVisualOffsets && visualViewport ? visualViewport.offsetLeft : 0)) / scaleX;\n var y = (clientRect.top + (addVisualOffsets && visualViewport ? visualViewport.offsetTop : 0)) / scaleY;\n var width = clientRect.width / scaleX;\n var height = clientRect.height / scaleY;\n return {\n width: width,\n height: height,\n top: y,\n right: x + width,\n bottom: y + height,\n left: x,\n x: x,\n y: y\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\"; // Returns the layout rect of an element relative to its offsetParent. Layout\n// means it doesn't take into account transforms.\n\nexport default function getLayoutRect(element) {\n var clientRect = getBoundingClientRect(element); // Use the clientRect sizes if it's not been transformed.\n // Fixes https://github.com/popperjs/popper-core/issues/1223\n\n var width = element.offsetWidth;\n var height = element.offsetHeight;\n\n if (Math.abs(clientRect.width - width) <= 1) {\n width = clientRect.width;\n }\n\n if (Math.abs(clientRect.height - height) <= 1) {\n height = clientRect.height;\n }\n\n return {\n x: element.offsetLeft,\n y: element.offsetTop,\n width: width,\n height: height\n };\n}","import { isShadowRoot } from \"./instanceOf.js\";\nexport default function contains(parent, child) {\n var rootNode = child.getRootNode && child.getRootNode(); // First, attempt with faster native method\n\n if (parent.contains(child)) {\n return true;\n } // then fallback to custom implementation with Shadow DOM support\n else if (rootNode && isShadowRoot(rootNode)) {\n var next = child;\n\n do {\n if (next && parent.isSameNode(next)) {\n return true;\n } // $FlowFixMe[prop-missing]: need a better way to handle this...\n\n\n next = next.parentNode || next.host;\n } while (next);\n } // Give up, the result is false\n\n\n return false;\n}","import getWindow from \"./getWindow.js\";\nexport default function getComputedStyle(element) {\n return getWindow(element).getComputedStyle(element);\n}","import getNodeName from \"./getNodeName.js\";\nexport default function isTableElement(element) {\n return ['table', 'td', 'th'].indexOf(getNodeName(element)) >= 0;\n}","import { isElement } from \"./instanceOf.js\";\nexport default function getDocumentElement(element) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return ((isElement(element) ? element.ownerDocument : // $FlowFixMe[prop-missing]\n element.document) || window.document).documentElement;\n}","import getNodeName from \"./getNodeName.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport { isShadowRoot } from \"./instanceOf.js\";\nexport default function getParentNode(element) {\n if (getNodeName(element) === 'html') {\n return element;\n }\n\n return (// this is a quicker (but less type safe) way to save quite some bytes from the bundle\n // $FlowFixMe[incompatible-return]\n // $FlowFixMe[prop-missing]\n element.assignedSlot || // step into the shadow DOM of the parent of a slotted node\n element.parentNode || ( // DOM Element detected\n isShadowRoot(element) ? element.host : null) || // ShadowRoot detected\n // $FlowFixMe[incompatible-call]: HTMLElement is a Node\n getDocumentElement(element) // fallback\n\n );\n}","import getWindow from \"./getWindow.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isHTMLElement, isShadowRoot } from \"./instanceOf.js\";\nimport isTableElement from \"./isTableElement.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getUAString from \"../utils/userAgent.js\";\n\nfunction getTrueOffsetParent(element) {\n if (!isHTMLElement(element) || // https://github.com/popperjs/popper-core/issues/837\n getComputedStyle(element).position === 'fixed') {\n return null;\n }\n\n return element.offsetParent;\n} // `.offsetParent` reports `null` for fixed elements, while absolute elements\n// return the containing block\n\n\nfunction getContainingBlock(element) {\n var isFirefox = /firefox/i.test(getUAString());\n var isIE = /Trident/i.test(getUAString());\n\n if (isIE && isHTMLElement(element)) {\n // In IE 9, 10 and 11 fixed elements containing block is always established by the viewport\n var elementCss = getComputedStyle(element);\n\n if (elementCss.position === 'fixed') {\n return null;\n }\n }\n\n var currentNode = getParentNode(element);\n\n if (isShadowRoot(currentNode)) {\n currentNode = currentNode.host;\n }\n\n while (isHTMLElement(currentNode) && ['html', 'body'].indexOf(getNodeName(currentNode)) < 0) {\n var css = getComputedStyle(currentNode); // This is non-exhaustive but covers the most common CSS properties that\n // create a containing block.\n // https://developer.mozilla.org/en-US/docs/Web/CSS/Containing_block#identifying_the_containing_block\n\n if (css.transform !== 'none' || css.perspective !== 'none' || css.contain === 'paint' || ['transform', 'perspective'].indexOf(css.willChange) !== -1 || isFirefox && css.willChange === 'filter' || isFirefox && css.filter && css.filter !== 'none') {\n return currentNode;\n } else {\n currentNode = currentNode.parentNode;\n }\n }\n\n return null;\n} // Gets the closest ancestor positioned element. Handles some edge cases,\n// such as table ancestors and cross browser bugs.\n\n\nexport default function getOffsetParent(element) {\n var window = getWindow(element);\n var offsetParent = getTrueOffsetParent(element);\n\n while (offsetParent && isTableElement(offsetParent) && getComputedStyle(offsetParent).position === 'static') {\n offsetParent = getTrueOffsetParent(offsetParent);\n }\n\n if (offsetParent && (getNodeName(offsetParent) === 'html' || getNodeName(offsetParent) === 'body' && getComputedStyle(offsetParent).position === 'static')) {\n return window;\n }\n\n return offsetParent || getContainingBlock(element) || window;\n}","export default function getMainAxisFromPlacement(placement) {\n return ['top', 'bottom'].indexOf(placement) >= 0 ? 'x' : 'y';\n}","import { max as mathMax, min as mathMin } from \"./math.js\";\nexport function within(min, value, max) {\n return mathMax(min, mathMin(value, max));\n}\nexport function withinMaxClamp(min, value, max) {\n var v = within(min, value, max);\n return v > max ? max : v;\n}","import getFreshSideObject from \"./getFreshSideObject.js\";\nexport default function mergePaddingObject(paddingObject) {\n return Object.assign({}, getFreshSideObject(), paddingObject);\n}","export default function getFreshSideObject() {\n return {\n top: 0,\n right: 0,\n bottom: 0,\n left: 0\n };\n}","export default function expandToHashMap(value, keys) {\n return keys.reduce(function (hashMap, key) {\n hashMap[key] = value;\n return hashMap;\n }, {});\n}","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport contains from \"../dom-utils/contains.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport { within } from \"../utils/within.js\";\nimport mergePaddingObject from \"../utils/mergePaddingObject.js\";\nimport expandToHashMap from \"../utils/expandToHashMap.js\";\nimport { left, right, basePlacements, top, bottom } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar toPaddingObject = function toPaddingObject(padding, state) {\n padding = typeof padding === 'function' ? padding(Object.assign({}, state.rects, {\n placement: state.placement\n })) : padding;\n return mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n};\n\nfunction arrow(_ref) {\n var _state$modifiersData$;\n\n var state = _ref.state,\n name = _ref.name,\n options = _ref.options;\n var arrowElement = state.elements.arrow;\n var popperOffsets = state.modifiersData.popperOffsets;\n var basePlacement = getBasePlacement(state.placement);\n var axis = getMainAxisFromPlacement(basePlacement);\n var isVertical = [left, right].indexOf(basePlacement) >= 0;\n var len = isVertical ? 'height' : 'width';\n\n if (!arrowElement || !popperOffsets) {\n return;\n }\n\n var paddingObject = toPaddingObject(options.padding, state);\n var arrowRect = getLayoutRect(arrowElement);\n var minProp = axis === 'y' ? top : left;\n var maxProp = axis === 'y' ? bottom : right;\n var endDiff = state.rects.reference[len] + state.rects.reference[axis] - popperOffsets[axis] - state.rects.popper[len];\n var startDiff = popperOffsets[axis] - state.rects.reference[axis];\n var arrowOffsetParent = getOffsetParent(arrowElement);\n var clientSize = arrowOffsetParent ? axis === 'y' ? arrowOffsetParent.clientHeight || 0 : arrowOffsetParent.clientWidth || 0 : 0;\n var centerToReference = endDiff / 2 - startDiff / 2; // Make sure the arrow doesn't overflow the popper if the center point is\n // outside of the popper bounds\n\n var min = paddingObject[minProp];\n var max = clientSize - arrowRect[len] - paddingObject[maxProp];\n var center = clientSize / 2 - arrowRect[len] / 2 + centerToReference;\n var offset = within(min, center, max); // Prevents breaking syntax highlighting...\n\n var axisProp = axis;\n state.modifiersData[name] = (_state$modifiersData$ = {}, _state$modifiersData$[axisProp] = offset, _state$modifiersData$.centerOffset = offset - center, _state$modifiersData$);\n}\n\nfunction effect(_ref2) {\n var state = _ref2.state,\n options = _ref2.options;\n var _options$element = options.element,\n arrowElement = _options$element === void 0 ? '[data-popper-arrow]' : _options$element;\n\n if (arrowElement == null) {\n return;\n } // CSS selector\n\n\n if (typeof arrowElement === 'string') {\n arrowElement = state.elements.popper.querySelector(arrowElement);\n\n if (!arrowElement) {\n return;\n }\n }\n\n if (!contains(state.elements.popper, arrowElement)) {\n return;\n }\n\n state.elements.arrow = arrowElement;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'arrow',\n enabled: true,\n phase: 'main',\n fn: arrow,\n effect: effect,\n requires: ['popperOffsets'],\n requiresIfExists: ['preventOverflow']\n};","export default function getVariation(placement) {\n return placement.split('-')[1];\n}","import { top, left, right, bottom, end } from \"../enums.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport getWindow from \"../dom-utils/getWindow.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getComputedStyle from \"../dom-utils/getComputedStyle.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport { round } from \"../utils/math.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar unsetSides = {\n top: 'auto',\n right: 'auto',\n bottom: 'auto',\n left: 'auto'\n}; // Round the offsets to the nearest suitable subpixel based on the DPR.\n// Zooming can change the DPR, but it seems to report a value that will\n// cleanly divide the values into the appropriate subpixels.\n\nfunction roundOffsetsByDPR(_ref, win) {\n var x = _ref.x,\n y = _ref.y;\n var dpr = win.devicePixelRatio || 1;\n return {\n x: round(x * dpr) / dpr || 0,\n y: round(y * dpr) / dpr || 0\n };\n}\n\nexport function mapToStyles(_ref2) {\n var _Object$assign2;\n\n var popper = _ref2.popper,\n popperRect = _ref2.popperRect,\n placement = _ref2.placement,\n variation = _ref2.variation,\n offsets = _ref2.offsets,\n position = _ref2.position,\n gpuAcceleration = _ref2.gpuAcceleration,\n adaptive = _ref2.adaptive,\n roundOffsets = _ref2.roundOffsets,\n isFixed = _ref2.isFixed;\n var _offsets$x = offsets.x,\n x = _offsets$x === void 0 ? 0 : _offsets$x,\n _offsets$y = offsets.y,\n y = _offsets$y === void 0 ? 0 : _offsets$y;\n\n var _ref3 = typeof roundOffsets === 'function' ? roundOffsets({\n x: x,\n y: y\n }) : {\n x: x,\n y: y\n };\n\n x = _ref3.x;\n y = _ref3.y;\n var hasX = offsets.hasOwnProperty('x');\n var hasY = offsets.hasOwnProperty('y');\n var sideX = left;\n var sideY = top;\n var win = window;\n\n if (adaptive) {\n var offsetParent = getOffsetParent(popper);\n var heightProp = 'clientHeight';\n var widthProp = 'clientWidth';\n\n if (offsetParent === getWindow(popper)) {\n offsetParent = getDocumentElement(popper);\n\n if (getComputedStyle(offsetParent).position !== 'static' && position === 'absolute') {\n heightProp = 'scrollHeight';\n widthProp = 'scrollWidth';\n }\n } // $FlowFixMe[incompatible-cast]: force type refinement, we compare offsetParent with window above, but Flow doesn't detect it\n\n\n offsetParent = offsetParent;\n\n if (placement === top || (placement === left || placement === right) && variation === end) {\n sideY = bottom;\n var offsetY = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.height : // $FlowFixMe[prop-missing]\n offsetParent[heightProp];\n y -= offsetY - popperRect.height;\n y *= gpuAcceleration ? 1 : -1;\n }\n\n if (placement === left || (placement === top || placement === bottom) && variation === end) {\n sideX = right;\n var offsetX = isFixed && offsetParent === win && win.visualViewport ? win.visualViewport.width : // $FlowFixMe[prop-missing]\n offsetParent[widthProp];\n x -= offsetX - popperRect.width;\n x *= gpuAcceleration ? 1 : -1;\n }\n }\n\n var commonStyles = Object.assign({\n position: position\n }, adaptive && unsetSides);\n\n var _ref4 = roundOffsets === true ? roundOffsetsByDPR({\n x: x,\n y: y\n }, getWindow(popper)) : {\n x: x,\n y: y\n };\n\n x = _ref4.x;\n y = _ref4.y;\n\n if (gpuAcceleration) {\n var _Object$assign;\n\n return Object.assign({}, commonStyles, (_Object$assign = {}, _Object$assign[sideY] = hasY ? '0' : '', _Object$assign[sideX] = hasX ? '0' : '', _Object$assign.transform = (win.devicePixelRatio || 1) <= 1 ? \"translate(\" + x + \"px, \" + y + \"px)\" : \"translate3d(\" + x + \"px, \" + y + \"px, 0)\", _Object$assign));\n }\n\n return Object.assign({}, commonStyles, (_Object$assign2 = {}, _Object$assign2[sideY] = hasY ? y + \"px\" : '', _Object$assign2[sideX] = hasX ? x + \"px\" : '', _Object$assign2.transform = '', _Object$assign2));\n}\n\nfunction computeStyles(_ref5) {\n var state = _ref5.state,\n options = _ref5.options;\n var _options$gpuAccelerat = options.gpuAcceleration,\n gpuAcceleration = _options$gpuAccelerat === void 0 ? true : _options$gpuAccelerat,\n _options$adaptive = options.adaptive,\n adaptive = _options$adaptive === void 0 ? true : _options$adaptive,\n _options$roundOffsets = options.roundOffsets,\n roundOffsets = _options$roundOffsets === void 0 ? true : _options$roundOffsets;\n var commonStyles = {\n placement: getBasePlacement(state.placement),\n variation: getVariation(state.placement),\n popper: state.elements.popper,\n popperRect: state.rects.popper,\n gpuAcceleration: gpuAcceleration,\n isFixed: state.options.strategy === 'fixed'\n };\n\n if (state.modifiersData.popperOffsets != null) {\n state.styles.popper = Object.assign({}, state.styles.popper, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.popperOffsets,\n position: state.options.strategy,\n adaptive: adaptive,\n roundOffsets: roundOffsets\n })));\n }\n\n if (state.modifiersData.arrow != null) {\n state.styles.arrow = Object.assign({}, state.styles.arrow, mapToStyles(Object.assign({}, commonStyles, {\n offsets: state.modifiersData.arrow,\n position: 'absolute',\n adaptive: false,\n roundOffsets: roundOffsets\n })));\n }\n\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-placement': state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'computeStyles',\n enabled: true,\n phase: 'beforeWrite',\n fn: computeStyles,\n data: {}\n};","import getWindow from \"../dom-utils/getWindow.js\"; // eslint-disable-next-line import/no-unused-modules\n\nvar passive = {\n passive: true\n};\n\nfunction effect(_ref) {\n var state = _ref.state,\n instance = _ref.instance,\n options = _ref.options;\n var _options$scroll = options.scroll,\n scroll = _options$scroll === void 0 ? true : _options$scroll,\n _options$resize = options.resize,\n resize = _options$resize === void 0 ? true : _options$resize;\n var window = getWindow(state.elements.popper);\n var scrollParents = [].concat(state.scrollParents.reference, state.scrollParents.popper);\n\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.addEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.addEventListener('resize', instance.update, passive);\n }\n\n return function () {\n if (scroll) {\n scrollParents.forEach(function (scrollParent) {\n scrollParent.removeEventListener('scroll', instance.update, passive);\n });\n }\n\n if (resize) {\n window.removeEventListener('resize', instance.update, passive);\n }\n };\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'eventListeners',\n enabled: true,\n phase: 'write',\n fn: function fn() {},\n effect: effect,\n data: {}\n};","var hash = {\n left: 'right',\n right: 'left',\n bottom: 'top',\n top: 'bottom'\n};\nexport default function getOppositePlacement(placement) {\n return placement.replace(/left|right|bottom|top/g, function (matched) {\n return hash[matched];\n });\n}","var hash = {\n start: 'end',\n end: 'start'\n};\nexport default function getOppositeVariationPlacement(placement) {\n return placement.replace(/start|end/g, function (matched) {\n return hash[matched];\n });\n}","import getWindow from \"./getWindow.js\";\nexport default function getWindowScroll(node) {\n var win = getWindow(node);\n var scrollLeft = win.pageXOffset;\n var scrollTop = win.pageYOffset;\n return {\n scrollLeft: scrollLeft,\n scrollTop: scrollTop\n };\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nexport default function getWindowScrollBarX(element) {\n // If has a CSS width greater than the viewport, then this will be\n // incorrect for RTL.\n // Popper 1 is broken in this case and never had a bug report so let's assume\n // it's not an issue. I don't think anyone ever specifies width on \n // anyway.\n // Browsers where the left scrollbar doesn't cause an issue report `0` for\n // this (e.g. Edge 2019, IE11, Safari)\n return getBoundingClientRect(getDocumentElement(element)).left + getWindowScroll(element).scrollLeft;\n}","import getComputedStyle from \"./getComputedStyle.js\";\nexport default function isScrollParent(element) {\n // Firefox wants us to check `-x` and `-y` variations as well\n var _getComputedStyle = getComputedStyle(element),\n overflow = _getComputedStyle.overflow,\n overflowX = _getComputedStyle.overflowX,\n overflowY = _getComputedStyle.overflowY;\n\n return /auto|scroll|overlay|hidden/.test(overflow + overflowY + overflowX);\n}","import getParentNode from \"./getParentNode.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nexport default function getScrollParent(node) {\n if (['html', 'body', '#document'].indexOf(getNodeName(node)) >= 0) {\n // $FlowFixMe[incompatible-return]: assume body is always available\n return node.ownerDocument.body;\n }\n\n if (isHTMLElement(node) && isScrollParent(node)) {\n return node;\n }\n\n return getScrollParent(getParentNode(node));\n}","import getScrollParent from \"./getScrollParent.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport getWindow from \"./getWindow.js\";\nimport isScrollParent from \"./isScrollParent.js\";\n/*\ngiven a DOM element, return the list of all scroll parents, up the list of ancesors\nuntil we get to the top window object. This list is what we attach scroll listeners\nto, because if any of these parent elements scroll, we'll need to re-calculate the\nreference element's position.\n*/\n\nexport default function listScrollParents(element, list) {\n var _element$ownerDocumen;\n\n if (list === void 0) {\n list = [];\n }\n\n var scrollParent = getScrollParent(element);\n var isBody = scrollParent === ((_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body);\n var win = getWindow(scrollParent);\n var target = isBody ? [win].concat(win.visualViewport || [], isScrollParent(scrollParent) ? scrollParent : []) : scrollParent;\n var updatedList = list.concat(target);\n return isBody ? updatedList : // $FlowFixMe[incompatible-call]: isBody tells us target will be an HTMLElement here\n updatedList.concat(listScrollParents(getParentNode(target)));\n}","export default function rectToClientRect(rect) {\n return Object.assign({}, rect, {\n left: rect.x,\n top: rect.y,\n right: rect.x + rect.width,\n bottom: rect.y + rect.height\n });\n}","import { viewport } from \"../enums.js\";\nimport getViewportRect from \"./getViewportRect.js\";\nimport getDocumentRect from \"./getDocumentRect.js\";\nimport listScrollParents from \"./listScrollParents.js\";\nimport getOffsetParent from \"./getOffsetParent.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport { isElement, isHTMLElement } from \"./instanceOf.js\";\nimport getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getParentNode from \"./getParentNode.js\";\nimport contains from \"./contains.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport rectToClientRect from \"../utils/rectToClientRect.js\";\nimport { max, min } from \"../utils/math.js\";\n\nfunction getInnerBoundingClientRect(element, strategy) {\n var rect = getBoundingClientRect(element, false, strategy === 'fixed');\n rect.top = rect.top + element.clientTop;\n rect.left = rect.left + element.clientLeft;\n rect.bottom = rect.top + element.clientHeight;\n rect.right = rect.left + element.clientWidth;\n rect.width = element.clientWidth;\n rect.height = element.clientHeight;\n rect.x = rect.left;\n rect.y = rect.top;\n return rect;\n}\n\nfunction getClientRectFromMixedType(element, clippingParent, strategy) {\n return clippingParent === viewport ? rectToClientRect(getViewportRect(element, strategy)) : isElement(clippingParent) ? getInnerBoundingClientRect(clippingParent, strategy) : rectToClientRect(getDocumentRect(getDocumentElement(element)));\n} // A \"clipping parent\" is an overflowable container with the characteristic of\n// clipping (or hiding) overflowing elements with a position different from\n// `initial`\n\n\nfunction getClippingParents(element) {\n var clippingParents = listScrollParents(getParentNode(element));\n var canEscapeClipping = ['absolute', 'fixed'].indexOf(getComputedStyle(element).position) >= 0;\n var clipperElement = canEscapeClipping && isHTMLElement(element) ? getOffsetParent(element) : element;\n\n if (!isElement(clipperElement)) {\n return [];\n } // $FlowFixMe[incompatible-return]: https://github.com/facebook/flow/issues/1414\n\n\n return clippingParents.filter(function (clippingParent) {\n return isElement(clippingParent) && contains(clippingParent, clipperElement) && getNodeName(clippingParent) !== 'body';\n });\n} // Gets the maximum area that the element is visible in due to any number of\n// clipping parents\n\n\nexport default function getClippingRect(element, boundary, rootBoundary, strategy) {\n var mainClippingParents = boundary === 'clippingParents' ? getClippingParents(element) : [].concat(boundary);\n var clippingParents = [].concat(mainClippingParents, [rootBoundary]);\n var firstClippingParent = clippingParents[0];\n var clippingRect = clippingParents.reduce(function (accRect, clippingParent) {\n var rect = getClientRectFromMixedType(element, clippingParent, strategy);\n accRect.top = max(rect.top, accRect.top);\n accRect.right = min(rect.right, accRect.right);\n accRect.bottom = min(rect.bottom, accRect.bottom);\n accRect.left = max(rect.left, accRect.left);\n return accRect;\n }, getClientRectFromMixedType(element, firstClippingParent, strategy));\n clippingRect.width = clippingRect.right - clippingRect.left;\n clippingRect.height = clippingRect.bottom - clippingRect.top;\n clippingRect.x = clippingRect.left;\n clippingRect.y = clippingRect.top;\n return clippingRect;\n}","import getWindow from \"./getWindow.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport isLayoutViewport from \"./isLayoutViewport.js\";\nexport default function getViewportRect(element, strategy) {\n var win = getWindow(element);\n var html = getDocumentElement(element);\n var visualViewport = win.visualViewport;\n var width = html.clientWidth;\n var height = html.clientHeight;\n var x = 0;\n var y = 0;\n\n if (visualViewport) {\n width = visualViewport.width;\n height = visualViewport.height;\n var layoutViewport = isLayoutViewport();\n\n if (layoutViewport || !layoutViewport && strategy === 'fixed') {\n x = visualViewport.offsetLeft;\n y = visualViewport.offsetTop;\n }\n }\n\n return {\n width: width,\n height: height,\n x: x + getWindowScrollBarX(element),\n y: y\n };\n}","import getDocumentElement from \"./getDocumentElement.js\";\nimport getComputedStyle from \"./getComputedStyle.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getWindowScroll from \"./getWindowScroll.js\";\nimport { max } from \"../utils/math.js\"; // Gets the entire size of the scrollable document area, even extending outside\n// of the `` and `` rect bounds if horizontally scrollable\n\nexport default function getDocumentRect(element) {\n var _element$ownerDocumen;\n\n var html = getDocumentElement(element);\n var winScroll = getWindowScroll(element);\n var body = (_element$ownerDocumen = element.ownerDocument) == null ? void 0 : _element$ownerDocumen.body;\n var width = max(html.scrollWidth, html.clientWidth, body ? body.scrollWidth : 0, body ? body.clientWidth : 0);\n var height = max(html.scrollHeight, html.clientHeight, body ? body.scrollHeight : 0, body ? body.clientHeight : 0);\n var x = -winScroll.scrollLeft + getWindowScrollBarX(element);\n var y = -winScroll.scrollTop;\n\n if (getComputedStyle(body || html).direction === 'rtl') {\n x += max(html.clientWidth, body ? body.clientWidth : 0) - width;\n }\n\n return {\n width: width,\n height: height,\n x: x,\n y: y\n };\n}","import getBasePlacement from \"./getBasePlacement.js\";\nimport getVariation from \"./getVariation.js\";\nimport getMainAxisFromPlacement from \"./getMainAxisFromPlacement.js\";\nimport { top, right, bottom, left, start, end } from \"../enums.js\";\nexport default function computeOffsets(_ref) {\n var reference = _ref.reference,\n element = _ref.element,\n placement = _ref.placement;\n var basePlacement = placement ? getBasePlacement(placement) : null;\n var variation = placement ? getVariation(placement) : null;\n var commonX = reference.x + reference.width / 2 - element.width / 2;\n var commonY = reference.y + reference.height / 2 - element.height / 2;\n var offsets;\n\n switch (basePlacement) {\n case top:\n offsets = {\n x: commonX,\n y: reference.y - element.height\n };\n break;\n\n case bottom:\n offsets = {\n x: commonX,\n y: reference.y + reference.height\n };\n break;\n\n case right:\n offsets = {\n x: reference.x + reference.width,\n y: commonY\n };\n break;\n\n case left:\n offsets = {\n x: reference.x - element.width,\n y: commonY\n };\n break;\n\n default:\n offsets = {\n x: reference.x,\n y: reference.y\n };\n }\n\n var mainAxis = basePlacement ? getMainAxisFromPlacement(basePlacement) : null;\n\n if (mainAxis != null) {\n var len = mainAxis === 'y' ? 'height' : 'width';\n\n switch (variation) {\n case start:\n offsets[mainAxis] = offsets[mainAxis] - (reference[len] / 2 - element[len] / 2);\n break;\n\n case end:\n offsets[mainAxis] = offsets[mainAxis] + (reference[len] / 2 - element[len] / 2);\n break;\n\n default:\n }\n }\n\n return offsets;\n}","import getClippingRect from \"../dom-utils/getClippingRect.js\";\nimport getDocumentElement from \"../dom-utils/getDocumentElement.js\";\nimport getBoundingClientRect from \"../dom-utils/getBoundingClientRect.js\";\nimport computeOffsets from \"./computeOffsets.js\";\nimport rectToClientRect from \"./rectToClientRect.js\";\nimport { clippingParents, reference, popper, bottom, top, right, basePlacements, viewport } from \"../enums.js\";\nimport { isElement } from \"../dom-utils/instanceOf.js\";\nimport mergePaddingObject from \"./mergePaddingObject.js\";\nimport expandToHashMap from \"./expandToHashMap.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport default function detectOverflow(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n _options$placement = _options.placement,\n placement = _options$placement === void 0 ? state.placement : _options$placement,\n _options$strategy = _options.strategy,\n strategy = _options$strategy === void 0 ? state.strategy : _options$strategy,\n _options$boundary = _options.boundary,\n boundary = _options$boundary === void 0 ? clippingParents : _options$boundary,\n _options$rootBoundary = _options.rootBoundary,\n rootBoundary = _options$rootBoundary === void 0 ? viewport : _options$rootBoundary,\n _options$elementConte = _options.elementContext,\n elementContext = _options$elementConte === void 0 ? popper : _options$elementConte,\n _options$altBoundary = _options.altBoundary,\n altBoundary = _options$altBoundary === void 0 ? false : _options$altBoundary,\n _options$padding = _options.padding,\n padding = _options$padding === void 0 ? 0 : _options$padding;\n var paddingObject = mergePaddingObject(typeof padding !== 'number' ? padding : expandToHashMap(padding, basePlacements));\n var altContext = elementContext === popper ? reference : popper;\n var popperRect = state.rects.popper;\n var element = state.elements[altBoundary ? altContext : elementContext];\n var clippingClientRect = getClippingRect(isElement(element) ? element : element.contextElement || getDocumentElement(state.elements.popper), boundary, rootBoundary, strategy);\n var referenceClientRect = getBoundingClientRect(state.elements.reference);\n var popperOffsets = computeOffsets({\n reference: referenceClientRect,\n element: popperRect,\n strategy: 'absolute',\n placement: placement\n });\n var popperClientRect = rectToClientRect(Object.assign({}, popperRect, popperOffsets));\n var elementClientRect = elementContext === popper ? popperClientRect : referenceClientRect; // positive = overflowing the clipping rect\n // 0 or negative = within the clipping rect\n\n var overflowOffsets = {\n top: clippingClientRect.top - elementClientRect.top + paddingObject.top,\n bottom: elementClientRect.bottom - clippingClientRect.bottom + paddingObject.bottom,\n left: clippingClientRect.left - elementClientRect.left + paddingObject.left,\n right: elementClientRect.right - clippingClientRect.right + paddingObject.right\n };\n var offsetData = state.modifiersData.offset; // Offsets can be applied only to the popper element\n\n if (elementContext === popper && offsetData) {\n var offset = offsetData[placement];\n Object.keys(overflowOffsets).forEach(function (key) {\n var multiply = [right, bottom].indexOf(key) >= 0 ? 1 : -1;\n var axis = [top, bottom].indexOf(key) >= 0 ? 'y' : 'x';\n overflowOffsets[key] += offset[axis] * multiply;\n });\n }\n\n return overflowOffsets;\n}","import getVariation from \"./getVariation.js\";\nimport { variationPlacements, basePlacements, placements as allPlacements } from \"../enums.js\";\nimport detectOverflow from \"./detectOverflow.js\";\nimport getBasePlacement from \"./getBasePlacement.js\";\nexport default function computeAutoPlacement(state, options) {\n if (options === void 0) {\n options = {};\n }\n\n var _options = options,\n placement = _options.placement,\n boundary = _options.boundary,\n rootBoundary = _options.rootBoundary,\n padding = _options.padding,\n flipVariations = _options.flipVariations,\n _options$allowedAutoP = _options.allowedAutoPlacements,\n allowedAutoPlacements = _options$allowedAutoP === void 0 ? allPlacements : _options$allowedAutoP;\n var variation = getVariation(placement);\n var placements = variation ? flipVariations ? variationPlacements : variationPlacements.filter(function (placement) {\n return getVariation(placement) === variation;\n }) : basePlacements;\n var allowedPlacements = placements.filter(function (placement) {\n return allowedAutoPlacements.indexOf(placement) >= 0;\n });\n\n if (allowedPlacements.length === 0) {\n allowedPlacements = placements;\n } // $FlowFixMe[incompatible-type]: Flow seems to have problems with two array unions...\n\n\n var overflows = allowedPlacements.reduce(function (acc, placement) {\n acc[placement] = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding\n })[getBasePlacement(placement)];\n return acc;\n }, {});\n return Object.keys(overflows).sort(function (a, b) {\n return overflows[a] - overflows[b];\n });\n}","import getOppositePlacement from \"../utils/getOppositePlacement.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getOppositeVariationPlacement from \"../utils/getOppositeVariationPlacement.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport computeAutoPlacement from \"../utils/computeAutoPlacement.js\";\nimport { bottom, top, start, right, left, auto } from \"../enums.js\";\nimport getVariation from \"../utils/getVariation.js\"; // eslint-disable-next-line import/no-unused-modules\n\nfunction getExpandedFallbackPlacements(placement) {\n if (getBasePlacement(placement) === auto) {\n return [];\n }\n\n var oppositePlacement = getOppositePlacement(placement);\n return [getOppositeVariationPlacement(placement), oppositePlacement, getOppositeVariationPlacement(oppositePlacement)];\n}\n\nfunction flip(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n\n if (state.modifiersData[name]._skip) {\n return;\n }\n\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? true : _options$altAxis,\n specifiedFallbackPlacements = options.fallbackPlacements,\n padding = options.padding,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n _options$flipVariatio = options.flipVariations,\n flipVariations = _options$flipVariatio === void 0 ? true : _options$flipVariatio,\n allowedAutoPlacements = options.allowedAutoPlacements;\n var preferredPlacement = state.options.placement;\n var basePlacement = getBasePlacement(preferredPlacement);\n var isBasePlacement = basePlacement === preferredPlacement;\n var fallbackPlacements = specifiedFallbackPlacements || (isBasePlacement || !flipVariations ? [getOppositePlacement(preferredPlacement)] : getExpandedFallbackPlacements(preferredPlacement));\n var placements = [preferredPlacement].concat(fallbackPlacements).reduce(function (acc, placement) {\n return acc.concat(getBasePlacement(placement) === auto ? computeAutoPlacement(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n flipVariations: flipVariations,\n allowedAutoPlacements: allowedAutoPlacements\n }) : placement);\n }, []);\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var checksMap = new Map();\n var makeFallbackChecks = true;\n var firstFittingPlacement = placements[0];\n\n for (var i = 0; i < placements.length; i++) {\n var placement = placements[i];\n\n var _basePlacement = getBasePlacement(placement);\n\n var isStartVariation = getVariation(placement) === start;\n var isVertical = [top, bottom].indexOf(_basePlacement) >= 0;\n var len = isVertical ? 'width' : 'height';\n var overflow = detectOverflow(state, {\n placement: placement,\n boundary: boundary,\n rootBoundary: rootBoundary,\n altBoundary: altBoundary,\n padding: padding\n });\n var mainVariationSide = isVertical ? isStartVariation ? right : left : isStartVariation ? bottom : top;\n\n if (referenceRect[len] > popperRect[len]) {\n mainVariationSide = getOppositePlacement(mainVariationSide);\n }\n\n var altVariationSide = getOppositePlacement(mainVariationSide);\n var checks = [];\n\n if (checkMainAxis) {\n checks.push(overflow[_basePlacement] <= 0);\n }\n\n if (checkAltAxis) {\n checks.push(overflow[mainVariationSide] <= 0, overflow[altVariationSide] <= 0);\n }\n\n if (checks.every(function (check) {\n return check;\n })) {\n firstFittingPlacement = placement;\n makeFallbackChecks = false;\n break;\n }\n\n checksMap.set(placement, checks);\n }\n\n if (makeFallbackChecks) {\n // `2` may be desired in some cases – research later\n var numberOfChecks = flipVariations ? 3 : 1;\n\n var _loop = function _loop(_i) {\n var fittingPlacement = placements.find(function (placement) {\n var checks = checksMap.get(placement);\n\n if (checks) {\n return checks.slice(0, _i).every(function (check) {\n return check;\n });\n }\n });\n\n if (fittingPlacement) {\n firstFittingPlacement = fittingPlacement;\n return \"break\";\n }\n };\n\n for (var _i = numberOfChecks; _i > 0; _i--) {\n var _ret = _loop(_i);\n\n if (_ret === \"break\") break;\n }\n }\n\n if (state.placement !== firstFittingPlacement) {\n state.modifiersData[name]._skip = true;\n state.placement = firstFittingPlacement;\n state.reset = true;\n }\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'flip',\n enabled: true,\n phase: 'main',\n fn: flip,\n requiresIfExists: ['offset'],\n data: {\n _skip: false\n }\n};","import { top, bottom, left, right } from \"../enums.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\n\nfunction getSideOffsets(overflow, rect, preventedOffsets) {\n if (preventedOffsets === void 0) {\n preventedOffsets = {\n x: 0,\n y: 0\n };\n }\n\n return {\n top: overflow.top - rect.height - preventedOffsets.y,\n right: overflow.right - rect.width + preventedOffsets.x,\n bottom: overflow.bottom - rect.height + preventedOffsets.y,\n left: overflow.left - rect.width - preventedOffsets.x\n };\n}\n\nfunction isAnySideFullyClipped(overflow) {\n return [top, right, bottom, left].some(function (side) {\n return overflow[side] >= 0;\n });\n}\n\nfunction hide(_ref) {\n var state = _ref.state,\n name = _ref.name;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var preventedOffsets = state.modifiersData.preventOverflow;\n var referenceOverflow = detectOverflow(state, {\n elementContext: 'reference'\n });\n var popperAltOverflow = detectOverflow(state, {\n altBoundary: true\n });\n var referenceClippingOffsets = getSideOffsets(referenceOverflow, referenceRect);\n var popperEscapeOffsets = getSideOffsets(popperAltOverflow, popperRect, preventedOffsets);\n var isReferenceHidden = isAnySideFullyClipped(referenceClippingOffsets);\n var hasPopperEscaped = isAnySideFullyClipped(popperEscapeOffsets);\n state.modifiersData[name] = {\n referenceClippingOffsets: referenceClippingOffsets,\n popperEscapeOffsets: popperEscapeOffsets,\n isReferenceHidden: isReferenceHidden,\n hasPopperEscaped: hasPopperEscaped\n };\n state.attributes.popper = Object.assign({}, state.attributes.popper, {\n 'data-popper-reference-hidden': isReferenceHidden,\n 'data-popper-escaped': hasPopperEscaped\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'hide',\n enabled: true,\n phase: 'main',\n requiresIfExists: ['preventOverflow'],\n fn: hide\n};","import getBasePlacement from \"../utils/getBasePlacement.js\";\nimport { top, left, right, placements } from \"../enums.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport function distanceAndSkiddingToXY(placement, rects, offset) {\n var basePlacement = getBasePlacement(placement);\n var invertDistance = [left, top].indexOf(basePlacement) >= 0 ? -1 : 1;\n\n var _ref = typeof offset === 'function' ? offset(Object.assign({}, rects, {\n placement: placement\n })) : offset,\n skidding = _ref[0],\n distance = _ref[1];\n\n skidding = skidding || 0;\n distance = (distance || 0) * invertDistance;\n return [left, right].indexOf(basePlacement) >= 0 ? {\n x: distance,\n y: skidding\n } : {\n x: skidding,\n y: distance\n };\n}\n\nfunction offset(_ref2) {\n var state = _ref2.state,\n options = _ref2.options,\n name = _ref2.name;\n var _options$offset = options.offset,\n offset = _options$offset === void 0 ? [0, 0] : _options$offset;\n var data = placements.reduce(function (acc, placement) {\n acc[placement] = distanceAndSkiddingToXY(placement, state.rects, offset);\n return acc;\n }, {});\n var _data$state$placement = data[state.placement],\n x = _data$state$placement.x,\n y = _data$state$placement.y;\n\n if (state.modifiersData.popperOffsets != null) {\n state.modifiersData.popperOffsets.x += x;\n state.modifiersData.popperOffsets.y += y;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'offset',\n enabled: true,\n phase: 'main',\n requires: ['popperOffsets'],\n fn: offset\n};","import computeOffsets from \"../utils/computeOffsets.js\";\n\nfunction popperOffsets(_ref) {\n var state = _ref.state,\n name = _ref.name;\n // Offsets are the actual position the popper needs to have to be\n // properly positioned near its reference element\n // This is the most basic placement, and will be adjusted by\n // the modifiers in the next step\n state.modifiersData[name] = computeOffsets({\n reference: state.rects.reference,\n element: state.rects.popper,\n strategy: 'absolute',\n placement: state.placement\n });\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'popperOffsets',\n enabled: true,\n phase: 'read',\n fn: popperOffsets,\n data: {}\n};","import { top, left, right, bottom, start } from \"../enums.js\";\nimport getBasePlacement from \"../utils/getBasePlacement.js\";\nimport getMainAxisFromPlacement from \"../utils/getMainAxisFromPlacement.js\";\nimport getAltAxis from \"../utils/getAltAxis.js\";\nimport { within, withinMaxClamp } from \"../utils/within.js\";\nimport getLayoutRect from \"../dom-utils/getLayoutRect.js\";\nimport getOffsetParent from \"../dom-utils/getOffsetParent.js\";\nimport detectOverflow from \"../utils/detectOverflow.js\";\nimport getVariation from \"../utils/getVariation.js\";\nimport getFreshSideObject from \"../utils/getFreshSideObject.js\";\nimport { min as mathMin, max as mathMax } from \"../utils/math.js\";\n\nfunction preventOverflow(_ref) {\n var state = _ref.state,\n options = _ref.options,\n name = _ref.name;\n var _options$mainAxis = options.mainAxis,\n checkMainAxis = _options$mainAxis === void 0 ? true : _options$mainAxis,\n _options$altAxis = options.altAxis,\n checkAltAxis = _options$altAxis === void 0 ? false : _options$altAxis,\n boundary = options.boundary,\n rootBoundary = options.rootBoundary,\n altBoundary = options.altBoundary,\n padding = options.padding,\n _options$tether = options.tether,\n tether = _options$tether === void 0 ? true : _options$tether,\n _options$tetherOffset = options.tetherOffset,\n tetherOffset = _options$tetherOffset === void 0 ? 0 : _options$tetherOffset;\n var overflow = detectOverflow(state, {\n boundary: boundary,\n rootBoundary: rootBoundary,\n padding: padding,\n altBoundary: altBoundary\n });\n var basePlacement = getBasePlacement(state.placement);\n var variation = getVariation(state.placement);\n var isBasePlacement = !variation;\n var mainAxis = getMainAxisFromPlacement(basePlacement);\n var altAxis = getAltAxis(mainAxis);\n var popperOffsets = state.modifiersData.popperOffsets;\n var referenceRect = state.rects.reference;\n var popperRect = state.rects.popper;\n var tetherOffsetValue = typeof tetherOffset === 'function' ? tetherOffset(Object.assign({}, state.rects, {\n placement: state.placement\n })) : tetherOffset;\n var normalizedTetherOffsetValue = typeof tetherOffsetValue === 'number' ? {\n mainAxis: tetherOffsetValue,\n altAxis: tetherOffsetValue\n } : Object.assign({\n mainAxis: 0,\n altAxis: 0\n }, tetherOffsetValue);\n var offsetModifierState = state.modifiersData.offset ? state.modifiersData.offset[state.placement] : null;\n var data = {\n x: 0,\n y: 0\n };\n\n if (!popperOffsets) {\n return;\n }\n\n if (checkMainAxis) {\n var _offsetModifierState$;\n\n var mainSide = mainAxis === 'y' ? top : left;\n var altSide = mainAxis === 'y' ? bottom : right;\n var len = mainAxis === 'y' ? 'height' : 'width';\n var offset = popperOffsets[mainAxis];\n var min = offset + overflow[mainSide];\n var max = offset - overflow[altSide];\n var additive = tether ? -popperRect[len] / 2 : 0;\n var minLen = variation === start ? referenceRect[len] : popperRect[len];\n var maxLen = variation === start ? -popperRect[len] : -referenceRect[len]; // We need to include the arrow in the calculation so the arrow doesn't go\n // outside the reference bounds\n\n var arrowElement = state.elements.arrow;\n var arrowRect = tether && arrowElement ? getLayoutRect(arrowElement) : {\n width: 0,\n height: 0\n };\n var arrowPaddingObject = state.modifiersData['arrow#persistent'] ? state.modifiersData['arrow#persistent'].padding : getFreshSideObject();\n var arrowPaddingMin = arrowPaddingObject[mainSide];\n var arrowPaddingMax = arrowPaddingObject[altSide]; // If the reference length is smaller than the arrow length, we don't want\n // to include its full size in the calculation. If the reference is small\n // and near the edge of a boundary, the popper can overflow even if the\n // reference is not overflowing as well (e.g. virtual elements with no\n // width or height)\n\n var arrowLen = within(0, referenceRect[len], arrowRect[len]);\n var minOffset = isBasePlacement ? referenceRect[len] / 2 - additive - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis : minLen - arrowLen - arrowPaddingMin - normalizedTetherOffsetValue.mainAxis;\n var maxOffset = isBasePlacement ? -referenceRect[len] / 2 + additive + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis : maxLen + arrowLen + arrowPaddingMax + normalizedTetherOffsetValue.mainAxis;\n var arrowOffsetParent = state.elements.arrow && getOffsetParent(state.elements.arrow);\n var clientOffset = arrowOffsetParent ? mainAxis === 'y' ? arrowOffsetParent.clientTop || 0 : arrowOffsetParent.clientLeft || 0 : 0;\n var offsetModifierValue = (_offsetModifierState$ = offsetModifierState == null ? void 0 : offsetModifierState[mainAxis]) != null ? _offsetModifierState$ : 0;\n var tetherMin = offset + minOffset - offsetModifierValue - clientOffset;\n var tetherMax = offset + maxOffset - offsetModifierValue;\n var preventedOffset = within(tether ? mathMin(min, tetherMin) : min, offset, tether ? mathMax(max, tetherMax) : max);\n popperOffsets[mainAxis] = preventedOffset;\n data[mainAxis] = preventedOffset - offset;\n }\n\n if (checkAltAxis) {\n var _offsetModifierState$2;\n\n var _mainSide = mainAxis === 'x' ? top : left;\n\n var _altSide = mainAxis === 'x' ? bottom : right;\n\n var _offset = popperOffsets[altAxis];\n\n var _len = altAxis === 'y' ? 'height' : 'width';\n\n var _min = _offset + overflow[_mainSide];\n\n var _max = _offset - overflow[_altSide];\n\n var isOriginSide = [top, left].indexOf(basePlacement) !== -1;\n\n var _offsetModifierValue = (_offsetModifierState$2 = offsetModifierState == null ? void 0 : offsetModifierState[altAxis]) != null ? _offsetModifierState$2 : 0;\n\n var _tetherMin = isOriginSide ? _min : _offset - referenceRect[_len] - popperRect[_len] - _offsetModifierValue + normalizedTetherOffsetValue.altAxis;\n\n var _tetherMax = isOriginSide ? _offset + referenceRect[_len] + popperRect[_len] - _offsetModifierValue - normalizedTetherOffsetValue.altAxis : _max;\n\n var _preventedOffset = tether && isOriginSide ? withinMaxClamp(_tetherMin, _offset, _tetherMax) : within(tether ? _tetherMin : _min, _offset, tether ? _tetherMax : _max);\n\n popperOffsets[altAxis] = _preventedOffset;\n data[altAxis] = _preventedOffset - _offset;\n }\n\n state.modifiersData[name] = data;\n} // eslint-disable-next-line import/no-unused-modules\n\n\nexport default {\n name: 'preventOverflow',\n enabled: true,\n phase: 'main',\n fn: preventOverflow,\n requiresIfExists: ['offset']\n};","export default function getAltAxis(axis) {\n return axis === 'x' ? 'y' : 'x';\n}","import getBoundingClientRect from \"./getBoundingClientRect.js\";\nimport getNodeScroll from \"./getNodeScroll.js\";\nimport getNodeName from \"./getNodeName.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getWindowScrollBarX from \"./getWindowScrollBarX.js\";\nimport getDocumentElement from \"./getDocumentElement.js\";\nimport isScrollParent from \"./isScrollParent.js\";\nimport { round } from \"../utils/math.js\";\n\nfunction isElementScaled(element) {\n var rect = element.getBoundingClientRect();\n var scaleX = round(rect.width) / element.offsetWidth || 1;\n var scaleY = round(rect.height) / element.offsetHeight || 1;\n return scaleX !== 1 || scaleY !== 1;\n} // Returns the composite rect of an element relative to its offsetParent.\n// Composite means it takes into account transforms as well as layout.\n\n\nexport default function getCompositeRect(elementOrVirtualElement, offsetParent, isFixed) {\n if (isFixed === void 0) {\n isFixed = false;\n }\n\n var isOffsetParentAnElement = isHTMLElement(offsetParent);\n var offsetParentIsScaled = isHTMLElement(offsetParent) && isElementScaled(offsetParent);\n var documentElement = getDocumentElement(offsetParent);\n var rect = getBoundingClientRect(elementOrVirtualElement, offsetParentIsScaled, isFixed);\n var scroll = {\n scrollLeft: 0,\n scrollTop: 0\n };\n var offsets = {\n x: 0,\n y: 0\n };\n\n if (isOffsetParentAnElement || !isOffsetParentAnElement && !isFixed) {\n if (getNodeName(offsetParent) !== 'body' || // https://github.com/popperjs/popper-core/issues/1078\n isScrollParent(documentElement)) {\n scroll = getNodeScroll(offsetParent);\n }\n\n if (isHTMLElement(offsetParent)) {\n offsets = getBoundingClientRect(offsetParent, true);\n offsets.x += offsetParent.clientLeft;\n offsets.y += offsetParent.clientTop;\n } else if (documentElement) {\n offsets.x = getWindowScrollBarX(documentElement);\n }\n }\n\n return {\n x: rect.left + scroll.scrollLeft - offsets.x,\n y: rect.top + scroll.scrollTop - offsets.y,\n width: rect.width,\n height: rect.height\n };\n}","import getWindowScroll from \"./getWindowScroll.js\";\nimport getWindow from \"./getWindow.js\";\nimport { isHTMLElement } from \"./instanceOf.js\";\nimport getHTMLElementScroll from \"./getHTMLElementScroll.js\";\nexport default function getNodeScroll(node) {\n if (node === getWindow(node) || !isHTMLElement(node)) {\n return getWindowScroll(node);\n } else {\n return getHTMLElementScroll(node);\n }\n}","export default function getHTMLElementScroll(element) {\n return {\n scrollLeft: element.scrollLeft,\n scrollTop: element.scrollTop\n };\n}","import { modifierPhases } from \"../enums.js\"; // source: https://stackoverflow.com/questions/49875255\n\nfunction order(modifiers) {\n var map = new Map();\n var visited = new Set();\n var result = [];\n modifiers.forEach(function (modifier) {\n map.set(modifier.name, modifier);\n }); // On visiting object, check for its dependencies and visit them recursively\n\n function sort(modifier) {\n visited.add(modifier.name);\n var requires = [].concat(modifier.requires || [], modifier.requiresIfExists || []);\n requires.forEach(function (dep) {\n if (!visited.has(dep)) {\n var depModifier = map.get(dep);\n\n if (depModifier) {\n sort(depModifier);\n }\n }\n });\n result.push(modifier);\n }\n\n modifiers.forEach(function (modifier) {\n if (!visited.has(modifier.name)) {\n // check for visited object\n sort(modifier);\n }\n });\n return result;\n}\n\nexport default function orderModifiers(modifiers) {\n // order based on dependencies\n var orderedModifiers = order(modifiers); // order based on phase\n\n return modifierPhases.reduce(function (acc, phase) {\n return acc.concat(orderedModifiers.filter(function (modifier) {\n return modifier.phase === phase;\n }));\n }, []);\n}","import getCompositeRect from \"./dom-utils/getCompositeRect.js\";\nimport getLayoutRect from \"./dom-utils/getLayoutRect.js\";\nimport listScrollParents from \"./dom-utils/listScrollParents.js\";\nimport getOffsetParent from \"./dom-utils/getOffsetParent.js\";\nimport orderModifiers from \"./utils/orderModifiers.js\";\nimport debounce from \"./utils/debounce.js\";\nimport mergeByName from \"./utils/mergeByName.js\";\nimport detectOverflow from \"./utils/detectOverflow.js\";\nimport { isElement } from \"./dom-utils/instanceOf.js\";\nvar DEFAULT_OPTIONS = {\n placement: 'bottom',\n modifiers: [],\n strategy: 'absolute'\n};\n\nfunction areValidElements() {\n for (var _len = arguments.length, args = new Array(_len), _key = 0; _key < _len; _key++) {\n args[_key] = arguments[_key];\n }\n\n return !args.some(function (element) {\n return !(element && typeof element.getBoundingClientRect === 'function');\n });\n}\n\nexport function popperGenerator(generatorOptions) {\n if (generatorOptions === void 0) {\n generatorOptions = {};\n }\n\n var _generatorOptions = generatorOptions,\n _generatorOptions$def = _generatorOptions.defaultModifiers,\n defaultModifiers = _generatorOptions$def === void 0 ? [] : _generatorOptions$def,\n _generatorOptions$def2 = _generatorOptions.defaultOptions,\n defaultOptions = _generatorOptions$def2 === void 0 ? DEFAULT_OPTIONS : _generatorOptions$def2;\n return function createPopper(reference, popper, options) {\n if (options === void 0) {\n options = defaultOptions;\n }\n\n var state = {\n placement: 'bottom',\n orderedModifiers: [],\n options: Object.assign({}, DEFAULT_OPTIONS, defaultOptions),\n modifiersData: {},\n elements: {\n reference: reference,\n popper: popper\n },\n attributes: {},\n styles: {}\n };\n var effectCleanupFns = [];\n var isDestroyed = false;\n var instance = {\n state: state,\n setOptions: function setOptions(setOptionsAction) {\n var options = typeof setOptionsAction === 'function' ? setOptionsAction(state.options) : setOptionsAction;\n cleanupModifierEffects();\n state.options = Object.assign({}, defaultOptions, state.options, options);\n state.scrollParents = {\n reference: isElement(reference) ? listScrollParents(reference) : reference.contextElement ? listScrollParents(reference.contextElement) : [],\n popper: listScrollParents(popper)\n }; // Orders the modifiers based on their dependencies and `phase`\n // properties\n\n var orderedModifiers = orderModifiers(mergeByName([].concat(defaultModifiers, state.options.modifiers))); // Strip out disabled modifiers\n\n state.orderedModifiers = orderedModifiers.filter(function (m) {\n return m.enabled;\n });\n runModifierEffects();\n return instance.update();\n },\n // Sync update – it will always be executed, even if not necessary. This\n // is useful for low frequency updates where sync behavior simplifies the\n // logic.\n // For high frequency updates (e.g. `resize` and `scroll` events), always\n // prefer the async Popper#update method\n forceUpdate: function forceUpdate() {\n if (isDestroyed) {\n return;\n }\n\n var _state$elements = state.elements,\n reference = _state$elements.reference,\n popper = _state$elements.popper; // Don't proceed if `reference` or `popper` are not valid elements\n // anymore\n\n if (!areValidElements(reference, popper)) {\n return;\n } // Store the reference and popper rects to be read by modifiers\n\n\n state.rects = {\n reference: getCompositeRect(reference, getOffsetParent(popper), state.options.strategy === 'fixed'),\n popper: getLayoutRect(popper)\n }; // Modifiers have the ability to reset the current update cycle. The\n // most common use case for this is the `flip` modifier changing the\n // placement, which then needs to re-run all the modifiers, because the\n // logic was previously ran for the previous placement and is therefore\n // stale/incorrect\n\n state.reset = false;\n state.placement = state.options.placement; // On each update cycle, the `modifiersData` property for each modifier\n // is filled with the initial data specified by the modifier. This means\n // it doesn't persist and is fresh on each update.\n // To ensure persistent data, use `${name}#persistent`\n\n state.orderedModifiers.forEach(function (modifier) {\n return state.modifiersData[modifier.name] = Object.assign({}, modifier.data);\n });\n\n for (var index = 0; index < state.orderedModifiers.length; index++) {\n if (state.reset === true) {\n state.reset = false;\n index = -1;\n continue;\n }\n\n var _state$orderedModifie = state.orderedModifiers[index],\n fn = _state$orderedModifie.fn,\n _state$orderedModifie2 = _state$orderedModifie.options,\n _options = _state$orderedModifie2 === void 0 ? {} : _state$orderedModifie2,\n name = _state$orderedModifie.name;\n\n if (typeof fn === 'function') {\n state = fn({\n state: state,\n options: _options,\n name: name,\n instance: instance\n }) || state;\n }\n }\n },\n // Async and optimistically optimized update – it will not be executed if\n // not necessary (debounced to run at most once-per-tick)\n update: debounce(function () {\n return new Promise(function (resolve) {\n instance.forceUpdate();\n resolve(state);\n });\n }),\n destroy: function destroy() {\n cleanupModifierEffects();\n isDestroyed = true;\n }\n };\n\n if (!areValidElements(reference, popper)) {\n return instance;\n }\n\n instance.setOptions(options).then(function (state) {\n if (!isDestroyed && options.onFirstUpdate) {\n options.onFirstUpdate(state);\n }\n }); // Modifiers have the ability to execute arbitrary code before the first\n // update cycle runs. They will be executed in the same order as the update\n // cycle. This is useful when a modifier adds some persistent data that\n // other modifiers need to use, but the modifier is run after the dependent\n // one.\n\n function runModifierEffects() {\n state.orderedModifiers.forEach(function (_ref) {\n var name = _ref.name,\n _ref$options = _ref.options,\n options = _ref$options === void 0 ? {} : _ref$options,\n effect = _ref.effect;\n\n if (typeof effect === 'function') {\n var cleanupFn = effect({\n state: state,\n name: name,\n instance: instance,\n options: options\n });\n\n var noopFn = function noopFn() {};\n\n effectCleanupFns.push(cleanupFn || noopFn);\n }\n });\n }\n\n function cleanupModifierEffects() {\n effectCleanupFns.forEach(function (fn) {\n return fn();\n });\n effectCleanupFns = [];\n }\n\n return instance;\n };\n}\nexport var createPopper = /*#__PURE__*/popperGenerator(); // eslint-disable-next-line import/no-unused-modules\n\nexport { detectOverflow };","export default function debounce(fn) {\n var pending;\n return function () {\n if (!pending) {\n pending = new Promise(function (resolve) {\n Promise.resolve().then(function () {\n pending = undefined;\n resolve(fn());\n });\n });\n }\n\n return pending;\n };\n}","export default function mergeByName(modifiers) {\n var merged = modifiers.reduce(function (merged, current) {\n var existing = merged[current.name];\n merged[current.name] = existing ? Object.assign({}, existing, current, {\n options: Object.assign({}, existing.options, current.options),\n data: Object.assign({}, existing.data, current.data)\n }) : current;\n return merged;\n }, {}); // IE11 does not support Object.values\n\n return Object.keys(merged).map(function (key) {\n return merged[key];\n });\n}","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow };","import { popperGenerator, detectOverflow } from \"./createPopper.js\";\nimport eventListeners from \"./modifiers/eventListeners.js\";\nimport popperOffsets from \"./modifiers/popperOffsets.js\";\nimport computeStyles from \"./modifiers/computeStyles.js\";\nimport applyStyles from \"./modifiers/applyStyles.js\";\nimport offset from \"./modifiers/offset.js\";\nimport flip from \"./modifiers/flip.js\";\nimport preventOverflow from \"./modifiers/preventOverflow.js\";\nimport arrow from \"./modifiers/arrow.js\";\nimport hide from \"./modifiers/hide.js\";\nvar defaultModifiers = [eventListeners, popperOffsets, computeStyles, applyStyles, offset, flip, preventOverflow, arrow, hide];\nvar createPopper = /*#__PURE__*/popperGenerator({\n defaultModifiers: defaultModifiers\n}); // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper, popperGenerator, defaultModifiers, detectOverflow }; // eslint-disable-next-line import/no-unused-modules\n\nexport { createPopper as createPopperLite } from \"./popper-lite.js\"; // eslint-disable-next-line import/no-unused-modules\n\nexport * from \"./modifiers/index.js\";","/**\n * --------------------------------------------------------------------------\n * Bootstrap dropdown.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport {\n defineJQueryPlugin,\n execute,\n getElement,\n getNextActiveElement,\n isDisabled,\n isElement,\n isRTL,\n isVisible,\n noop\n} from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'dropdown'\nconst DATA_KEY = 'bs.dropdown'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst ESCAPE_KEY = 'Escape'\nconst TAB_KEY = 'Tab'\nconst ARROW_UP_KEY = 'ArrowUp'\nconst ARROW_DOWN_KEY = 'ArrowDown'\nconst RIGHT_MOUSE_BUTTON = 2 // MouseEvent.button value for the secondary button, usually the right button\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DATA_API = `keydown${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYUP_DATA_API = `keyup${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_DROPUP = 'dropup'\nconst CLASS_NAME_DROPEND = 'dropend'\nconst CLASS_NAME_DROPSTART = 'dropstart'\nconst CLASS_NAME_DROPUP_CENTER = 'dropup-center'\nconst CLASS_NAME_DROPDOWN_CENTER = 'dropdown-center'\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"dropdown\"]:not(.disabled):not(:disabled)'\nconst SELECTOR_DATA_TOGGLE_SHOWN = `${SELECTOR_DATA_TOGGLE}.${CLASS_NAME_SHOW}`\nconst SELECTOR_MENU = '.dropdown-menu'\nconst SELECTOR_NAVBAR = '.navbar'\nconst SELECTOR_NAVBAR_NAV = '.navbar-nav'\nconst SELECTOR_VISIBLE_ITEMS = '.dropdown-menu .dropdown-item:not(.disabled):not(:disabled)'\n\nconst PLACEMENT_TOP = isRTL() ? 'top-end' : 'top-start'\nconst PLACEMENT_TOPEND = isRTL() ? 'top-start' : 'top-end'\nconst PLACEMENT_BOTTOM = isRTL() ? 'bottom-end' : 'bottom-start'\nconst PLACEMENT_BOTTOMEND = isRTL() ? 'bottom-start' : 'bottom-end'\nconst PLACEMENT_RIGHT = isRTL() ? 'left-start' : 'right-start'\nconst PLACEMENT_LEFT = isRTL() ? 'right-start' : 'left-start'\nconst PLACEMENT_TOPCENTER = 'top'\nconst PLACEMENT_BOTTOMCENTER = 'bottom'\n\nconst Default = {\n autoClose: true,\n boundary: 'clippingParents',\n display: 'dynamic',\n offset: [0, 2],\n popperConfig: null,\n reference: 'toggle'\n}\n\nconst DefaultType = {\n autoClose: '(boolean|string)',\n boundary: '(string|element)',\n display: 'string',\n offset: '(array|string|function)',\n popperConfig: '(null|object|function)',\n reference: '(string|element|object)'\n}\n\n/**\n * Class definition\n */\n\nclass Dropdown extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._popper = null\n this._parent = this._element.parentNode // dropdown wrapper\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n this._menu = SelectorEngine.next(this._element, SELECTOR_MENU)[0] ||\n SelectorEngine.prev(this._element, SELECTOR_MENU)[0] ||\n SelectorEngine.findOne(SELECTOR_MENU, this._parent)\n this._inNavbar = this._detectNavbar()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle() {\n return this._isShown() ? this.hide() : this.show()\n }\n\n show() {\n if (isDisabled(this._element) || this._isShown()) {\n return\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, relatedTarget)\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._createPopper()\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement && !this._parent.closest(SELECTOR_NAVBAR_NAV)) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop)\n }\n }\n\n this._element.focus()\n this._element.setAttribute('aria-expanded', true)\n\n this._menu.classList.add(CLASS_NAME_SHOW)\n this._element.classList.add(CLASS_NAME_SHOW)\n EventHandler.trigger(this._element, EVENT_SHOWN, relatedTarget)\n }\n\n hide() {\n if (isDisabled(this._element) || !this._isShown()) {\n return\n }\n\n const relatedTarget = {\n relatedTarget: this._element\n }\n\n this._completeHide(relatedTarget)\n }\n\n dispose() {\n if (this._popper) {\n this._popper.destroy()\n }\n\n super.dispose()\n }\n\n update() {\n this._inNavbar = this._detectNavbar()\n if (this._popper) {\n this._popper.update()\n }\n }\n\n // Private\n _completeHide(relatedTarget) {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE, relatedTarget)\n if (hideEvent.defaultPrevented) {\n return\n }\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop)\n }\n }\n\n if (this._popper) {\n this._popper.destroy()\n }\n\n this._menu.classList.remove(CLASS_NAME_SHOW)\n this._element.classList.remove(CLASS_NAME_SHOW)\n this._element.setAttribute('aria-expanded', 'false')\n Manipulator.removeDataAttribute(this._menu, 'popper')\n EventHandler.trigger(this._element, EVENT_HIDDEN, relatedTarget)\n }\n\n _getConfig(config) {\n config = super._getConfig(config)\n\n if (typeof config.reference === 'object' && !isElement(config.reference) &&\n typeof config.reference.getBoundingClientRect !== 'function'\n ) {\n // Popper virtual elements require a getBoundingClientRect method\n throw new TypeError(`${NAME.toUpperCase()}: Option \"reference\" provided type \"object\" without a required \"getBoundingClientRect\" method.`)\n }\n\n return config\n }\n\n _createPopper() {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s dropdowns require Popper (https://popper.js.org)')\n }\n\n let referenceElement = this._element\n\n if (this._config.reference === 'parent') {\n referenceElement = this._parent\n } else if (isElement(this._config.reference)) {\n referenceElement = getElement(this._config.reference)\n } else if (typeof this._config.reference === 'object') {\n referenceElement = this._config.reference\n }\n\n const popperConfig = this._getPopperConfig()\n this._popper = Popper.createPopper(referenceElement, this._menu, popperConfig)\n }\n\n _isShown() {\n return this._menu.classList.contains(CLASS_NAME_SHOW)\n }\n\n _getPlacement() {\n const parentDropdown = this._parent\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPEND)) {\n return PLACEMENT_RIGHT\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPSTART)) {\n return PLACEMENT_LEFT\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP_CENTER)) {\n return PLACEMENT_TOPCENTER\n }\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPDOWN_CENTER)) {\n return PLACEMENT_BOTTOMCENTER\n }\n\n // We need to trim the value because custom properties can also include spaces\n const isEnd = getComputedStyle(this._menu).getPropertyValue('--bs-position').trim() === 'end'\n\n if (parentDropdown.classList.contains(CLASS_NAME_DROPUP)) {\n return isEnd ? PLACEMENT_TOPEND : PLACEMENT_TOP\n }\n\n return isEnd ? PLACEMENT_BOTTOMEND : PLACEMENT_BOTTOM\n }\n\n _detectNavbar() {\n return this._element.closest(SELECTOR_NAVBAR) !== null\n }\n\n _getOffset() {\n const { offset } = this._config\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10))\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element)\n }\n\n return offset\n }\n\n _getPopperConfig() {\n const defaultBsPopperConfig = {\n placement: this._getPlacement(),\n modifiers: [{\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n },\n {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n }]\n }\n\n // Disable Popper if we have a static display or Dropdown is in Navbar\n if (this._inNavbar || this._config.display === 'static') {\n Manipulator.setDataAttribute(this._menu, 'popper', 'static') // TODO: v6 remove\n defaultBsPopperConfig.modifiers = [{\n name: 'applyStyles',\n enabled: false\n }]\n }\n\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n }\n }\n\n _selectMenuItem({ key, target }) {\n const items = SelectorEngine.find(SELECTOR_VISIBLE_ITEMS, this._menu).filter(element => isVisible(element))\n\n if (!items.length) {\n return\n }\n\n // if target isn't included in items (e.g. when expanding the dropdown)\n // allow cycling to get the last item in case key equals ARROW_UP_KEY\n getNextActiveElement(items, target, key === ARROW_DOWN_KEY, !items.includes(target)).focus()\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Dropdown.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n\n static clearMenus(event) {\n if (event.button === RIGHT_MOUSE_BUTTON || (event.type === 'keyup' && event.key !== TAB_KEY)) {\n return\n }\n\n const openToggles = SelectorEngine.find(SELECTOR_DATA_TOGGLE_SHOWN)\n\n for (const toggle of openToggles) {\n const context = Dropdown.getInstance(toggle)\n if (!context || context._config.autoClose === false) {\n continue\n }\n\n const composedPath = event.composedPath()\n const isMenuTarget = composedPath.includes(context._menu)\n if (\n composedPath.includes(context._element) ||\n (context._config.autoClose === 'inside' && !isMenuTarget) ||\n (context._config.autoClose === 'outside' && isMenuTarget)\n ) {\n continue\n }\n\n // Tab navigation through the dropdown menu or events from contained inputs shouldn't close the menu\n if (context._menu.contains(event.target) && ((event.type === 'keyup' && event.key === TAB_KEY) || /input|select|option|textarea|form/i.test(event.target.tagName))) {\n continue\n }\n\n const relatedTarget = { relatedTarget: context._element }\n\n if (event.type === 'click') {\n relatedTarget.clickEvent = event\n }\n\n context._completeHide(relatedTarget)\n }\n }\n\n static dataApiKeydownHandler(event) {\n // If not an UP | DOWN | ESCAPE key => not a dropdown command\n // If input/textarea && if key is other than ESCAPE => not a dropdown command\n\n const isInput = /input|textarea/i.test(event.target.tagName)\n const isEscapeEvent = event.key === ESCAPE_KEY\n const isUpOrDownEvent = [ARROW_UP_KEY, ARROW_DOWN_KEY].includes(event.key)\n\n if (!isUpOrDownEvent && !isEscapeEvent) {\n return\n }\n\n if (isInput && !isEscapeEvent) {\n return\n }\n\n event.preventDefault()\n\n // TODO: v6 revert #37011 & change markup https://getbootstrap.com/docs/5.3/forms/input-group/\n const getToggleButton = this.matches(SELECTOR_DATA_TOGGLE) ?\n this :\n (SelectorEngine.prev(this, SELECTOR_DATA_TOGGLE)[0] ||\n SelectorEngine.next(this, SELECTOR_DATA_TOGGLE)[0] ||\n SelectorEngine.findOne(SELECTOR_DATA_TOGGLE, event.delegateTarget.parentNode))\n\n const instance = Dropdown.getOrCreateInstance(getToggleButton)\n\n if (isUpOrDownEvent) {\n event.stopPropagation()\n instance.show()\n instance._selectMenuItem(event)\n return\n }\n\n if (instance._isShown()) { // else is escape and we check if it is shown\n event.stopPropagation()\n instance.hide()\n getToggleButton.focus()\n }\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_DATA_TOGGLE, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_KEYDOWN_DATA_API, SELECTOR_MENU, Dropdown.dataApiKeydownHandler)\nEventHandler.on(document, EVENT_CLICK_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_KEYUP_DATA_API, Dropdown.clearMenus)\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n event.preventDefault()\n Dropdown.getOrCreateInstance(this).toggle()\n})\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Dropdown)\n\nexport default Dropdown\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/backdrop.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport Config from './config.js'\nimport { execute, executeAfterTransition, getElement, reflow } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'backdrop'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst EVENT_MOUSEDOWN = `mousedown.bs.${NAME}`\n\nconst Default = {\n className: 'modal-backdrop',\n clickCallback: null,\n isAnimated: false,\n isVisible: true, // if false, we use the backdrop helper without adding any element to the dom\n rootElement: 'body' // give the choice to place backdrop under different elements\n}\n\nconst DefaultType = {\n className: 'string',\n clickCallback: '(function|null)',\n isAnimated: 'boolean',\n isVisible: 'boolean',\n rootElement: '(element|string)'\n}\n\n/**\n * Class definition\n */\n\nclass Backdrop extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n this._isAppended = false\n this._element = null\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n show(callback) {\n if (!this._config.isVisible) {\n execute(callback)\n return\n }\n\n this._append()\n\n const element = this._getElement()\n if (this._config.isAnimated) {\n reflow(element)\n }\n\n element.classList.add(CLASS_NAME_SHOW)\n\n this._emulateAnimation(() => {\n execute(callback)\n })\n }\n\n hide(callback) {\n if (!this._config.isVisible) {\n execute(callback)\n return\n }\n\n this._getElement().classList.remove(CLASS_NAME_SHOW)\n\n this._emulateAnimation(() => {\n this.dispose()\n execute(callback)\n })\n }\n\n dispose() {\n if (!this._isAppended) {\n return\n }\n\n EventHandler.off(this._element, EVENT_MOUSEDOWN)\n\n this._element.remove()\n this._isAppended = false\n }\n\n // Private\n _getElement() {\n if (!this._element) {\n const backdrop = document.createElement('div')\n backdrop.className = this._config.className\n if (this._config.isAnimated) {\n backdrop.classList.add(CLASS_NAME_FADE)\n }\n\n this._element = backdrop\n }\n\n return this._element\n }\n\n _configAfterMerge(config) {\n // use getElement() with the default \"body\" to get a fresh Element on each instantiation\n config.rootElement = getElement(config.rootElement)\n return config\n }\n\n _append() {\n if (this._isAppended) {\n return\n }\n\n const element = this._getElement()\n this._config.rootElement.append(element)\n\n EventHandler.on(element, EVENT_MOUSEDOWN, () => {\n execute(this._config.clickCallback)\n })\n\n this._isAppended = true\n }\n\n _emulateAnimation(callback) {\n executeAfterTransition(callback, this._getElement(), this._config.isAnimated)\n }\n}\n\nexport default Backdrop\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/focustrap.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport EventHandler from '../dom/event-handler.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'focustrap'\nconst DATA_KEY = 'bs.focustrap'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst EVENT_FOCUSIN = `focusin${EVENT_KEY}`\nconst EVENT_KEYDOWN_TAB = `keydown.tab${EVENT_KEY}`\n\nconst TAB_KEY = 'Tab'\nconst TAB_NAV_FORWARD = 'forward'\nconst TAB_NAV_BACKWARD = 'backward'\n\nconst Default = {\n autofocus: true,\n trapElement: null // The element to trap focus inside of\n}\n\nconst DefaultType = {\n autofocus: 'boolean',\n trapElement: 'element'\n}\n\n/**\n * Class definition\n */\n\nclass FocusTrap extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n this._isActive = false\n this._lastTabNavDirection = null\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n activate() {\n if (this._isActive) {\n return\n }\n\n if (this._config.autofocus) {\n this._config.trapElement.focus()\n }\n\n EventHandler.off(document, EVENT_KEY) // guard against infinite focus loop\n EventHandler.on(document, EVENT_FOCUSIN, event => this._handleFocusin(event))\n EventHandler.on(document, EVENT_KEYDOWN_TAB, event => this._handleKeydown(event))\n\n this._isActive = true\n }\n\n deactivate() {\n if (!this._isActive) {\n return\n }\n\n this._isActive = false\n EventHandler.off(document, EVENT_KEY)\n }\n\n // Private\n _handleFocusin(event) {\n const { trapElement } = this._config\n\n if (event.target === document || event.target === trapElement || trapElement.contains(event.target)) {\n return\n }\n\n const elements = SelectorEngine.focusableChildren(trapElement)\n\n if (elements.length === 0) {\n trapElement.focus()\n } else if (this._lastTabNavDirection === TAB_NAV_BACKWARD) {\n elements[elements.length - 1].focus()\n } else {\n elements[0].focus()\n }\n }\n\n _handleKeydown(event) {\n if (event.key !== TAB_KEY) {\n return\n }\n\n this._lastTabNavDirection = event.shiftKey ? TAB_NAV_BACKWARD : TAB_NAV_FORWARD\n }\n}\n\nexport default FocusTrap\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/scrollBar.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Manipulator from '../dom/manipulator.js'\nimport SelectorEngine from '../dom/selector-engine.js'\nimport { isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst SELECTOR_FIXED_CONTENT = '.fixed-top, .fixed-bottom, .is-fixed, .sticky-top'\nconst SELECTOR_STICKY_CONTENT = '.sticky-top'\nconst PROPERTY_PADDING = 'padding-right'\nconst PROPERTY_MARGIN = 'margin-right'\n\n/**\n * Class definition\n */\n\nclass ScrollBarHelper {\n constructor() {\n this._element = document.body\n }\n\n // Public\n getWidth() {\n // https://developer.mozilla.org/en-US/docs/Web/API/Window/innerWidth#usage_notes\n const documentWidth = document.documentElement.clientWidth\n return Math.abs(window.innerWidth - documentWidth)\n }\n\n hide() {\n const width = this.getWidth()\n this._disableOverFlow()\n // give padding to element to balance the hidden scrollbar width\n this._setElementAttributes(this._element, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n // trick: We adjust positive paddingRight and negative marginRight to sticky-top elements to keep showing fullwidth\n this._setElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING, calculatedValue => calculatedValue + width)\n this._setElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN, calculatedValue => calculatedValue - width)\n }\n\n reset() {\n this._resetElementAttributes(this._element, 'overflow')\n this._resetElementAttributes(this._element, PROPERTY_PADDING)\n this._resetElementAttributes(SELECTOR_FIXED_CONTENT, PROPERTY_PADDING)\n this._resetElementAttributes(SELECTOR_STICKY_CONTENT, PROPERTY_MARGIN)\n }\n\n isOverflowing() {\n return this.getWidth() > 0\n }\n\n // Private\n _disableOverFlow() {\n this._saveInitialAttribute(this._element, 'overflow')\n this._element.style.overflow = 'hidden'\n }\n\n _setElementAttributes(selector, styleProperty, callback) {\n const scrollbarWidth = this.getWidth()\n const manipulationCallBack = element => {\n if (element !== this._element && window.innerWidth > element.clientWidth + scrollbarWidth) {\n return\n }\n\n this._saveInitialAttribute(element, styleProperty)\n const calculatedValue = window.getComputedStyle(element).getPropertyValue(styleProperty)\n element.style.setProperty(styleProperty, `${callback(Number.parseFloat(calculatedValue))}px`)\n }\n\n this._applyManipulationCallback(selector, manipulationCallBack)\n }\n\n _saveInitialAttribute(element, styleProperty) {\n const actualValue = element.style.getPropertyValue(styleProperty)\n if (actualValue) {\n Manipulator.setDataAttribute(element, styleProperty, actualValue)\n }\n }\n\n _resetElementAttributes(selector, styleProperty) {\n const manipulationCallBack = element => {\n const value = Manipulator.getDataAttribute(element, styleProperty)\n // We only want to remove the property if the value is `null`; the value can also be zero\n if (value === null) {\n element.style.removeProperty(styleProperty)\n return\n }\n\n Manipulator.removeDataAttribute(element, styleProperty)\n element.style.setProperty(styleProperty, value)\n }\n\n this._applyManipulationCallback(selector, manipulationCallBack)\n }\n\n _applyManipulationCallback(selector, callBack) {\n if (isElement(selector)) {\n callBack(selector)\n return\n }\n\n for (const sel of SelectorEngine.find(selector, this._element)) {\n callBack(sel)\n }\n }\n}\n\nexport default ScrollBarHelper\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap modal.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport { defineJQueryPlugin, isRTL, isVisible, reflow } from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'modal'\nconst DATA_KEY = 'bs.modal'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst ESCAPE_KEY = 'Escape'\n\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DISMISS = `click.dismiss${EVENT_KEY}`\nconst EVENT_MOUSEDOWN_DISMISS = `mousedown.dismiss${EVENT_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_OPEN = 'modal-open'\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_STATIC = 'modal-static'\n\nconst OPEN_SELECTOR = '.modal.show'\nconst SELECTOR_DIALOG = '.modal-dialog'\nconst SELECTOR_MODAL_BODY = '.modal-body'\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"modal\"]'\n\nconst Default = {\n backdrop: true,\n focus: true,\n keyboard: true\n}\n\nconst DefaultType = {\n backdrop: '(boolean|string)',\n focus: 'boolean',\n keyboard: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Modal extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._dialog = SelectorEngine.findOne(SELECTOR_DIALOG, this._element)\n this._backdrop = this._initializeBackDrop()\n this._focustrap = this._initializeFocusTrap()\n this._isShown = false\n this._isTransitioning = false\n this._scrollBar = new ScrollBarHelper()\n\n this._addEventListeners()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget)\n }\n\n show(relatedTarget) {\n if (this._isShown || this._isTransitioning) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, {\n relatedTarget\n })\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._isShown = true\n this._isTransitioning = true\n\n this._scrollBar.hide()\n\n document.body.classList.add(CLASS_NAME_OPEN)\n\n this._adjustDialog()\n\n this._backdrop.show(() => this._showElement(relatedTarget))\n }\n\n hide() {\n if (!this._isShown || this._isTransitioning) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n if (hideEvent.defaultPrevented) {\n return\n }\n\n this._isShown = false\n this._isTransitioning = true\n this._focustrap.deactivate()\n\n this._element.classList.remove(CLASS_NAME_SHOW)\n\n this._queueCallback(() => this._hideModal(), this._element, this._isAnimated())\n }\n\n dispose() {\n EventHandler.off(window, EVENT_KEY)\n EventHandler.off(this._dialog, EVENT_KEY)\n\n this._backdrop.dispose()\n this._focustrap.deactivate()\n\n super.dispose()\n }\n\n handleUpdate() {\n this._adjustDialog()\n }\n\n // Private\n _initializeBackDrop() {\n return new Backdrop({\n isVisible: Boolean(this._config.backdrop), // 'static' option will be translated to true, and booleans will keep their value,\n isAnimated: this._isAnimated()\n })\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n })\n }\n\n _showElement(relatedTarget) {\n // try to append dynamic modal\n if (!document.body.contains(this._element)) {\n document.body.append(this._element)\n }\n\n this._element.style.display = 'block'\n this._element.removeAttribute('aria-hidden')\n this._element.setAttribute('aria-modal', true)\n this._element.setAttribute('role', 'dialog')\n this._element.scrollTop = 0\n\n const modalBody = SelectorEngine.findOne(SELECTOR_MODAL_BODY, this._dialog)\n if (modalBody) {\n modalBody.scrollTop = 0\n }\n\n reflow(this._element)\n\n this._element.classList.add(CLASS_NAME_SHOW)\n\n const transitionComplete = () => {\n if (this._config.focus) {\n this._focustrap.activate()\n }\n\n this._isTransitioning = false\n EventHandler.trigger(this._element, EVENT_SHOWN, {\n relatedTarget\n })\n }\n\n this._queueCallback(transitionComplete, this._dialog, this._isAnimated())\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return\n }\n\n if (this._config.keyboard) {\n this.hide()\n return\n }\n\n this._triggerBackdropTransition()\n })\n\n EventHandler.on(window, EVENT_RESIZE, () => {\n if (this._isShown && !this._isTransitioning) {\n this._adjustDialog()\n }\n })\n\n EventHandler.on(this._element, EVENT_MOUSEDOWN_DISMISS, event => {\n // a bad trick to segregate clicks that may start inside dialog but end outside, and avoid listen to scrollbar clicks\n EventHandler.one(this._element, EVENT_CLICK_DISMISS, event2 => {\n if (this._element !== event.target || this._element !== event2.target) {\n return\n }\n\n if (this._config.backdrop === 'static') {\n this._triggerBackdropTransition()\n return\n }\n\n if (this._config.backdrop) {\n this.hide()\n }\n })\n })\n }\n\n _hideModal() {\n this._element.style.display = 'none'\n this._element.setAttribute('aria-hidden', true)\n this._element.removeAttribute('aria-modal')\n this._element.removeAttribute('role')\n this._isTransitioning = false\n\n this._backdrop.hide(() => {\n document.body.classList.remove(CLASS_NAME_OPEN)\n this._resetAdjustments()\n this._scrollBar.reset()\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n })\n }\n\n _isAnimated() {\n return this._element.classList.contains(CLASS_NAME_FADE)\n }\n\n _triggerBackdropTransition() {\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n if (hideEvent.defaultPrevented) {\n return\n }\n\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n const initialOverflowY = this._element.style.overflowY\n // return if the following background transition hasn't yet completed\n if (initialOverflowY === 'hidden' || this._element.classList.contains(CLASS_NAME_STATIC)) {\n return\n }\n\n if (!isModalOverflowing) {\n this._element.style.overflowY = 'hidden'\n }\n\n this._element.classList.add(CLASS_NAME_STATIC)\n this._queueCallback(() => {\n this._element.classList.remove(CLASS_NAME_STATIC)\n this._queueCallback(() => {\n this._element.style.overflowY = initialOverflowY\n }, this._dialog)\n }, this._dialog)\n\n this._element.focus()\n }\n\n /**\n * The following methods are used to handle overflowing modals\n */\n\n _adjustDialog() {\n const isModalOverflowing = this._element.scrollHeight > document.documentElement.clientHeight\n const scrollbarWidth = this._scrollBar.getWidth()\n const isBodyOverflowing = scrollbarWidth > 0\n\n if (isBodyOverflowing && !isModalOverflowing) {\n const property = isRTL() ? 'paddingLeft' : 'paddingRight'\n this._element.style[property] = `${scrollbarWidth}px`\n }\n\n if (!isBodyOverflowing && isModalOverflowing) {\n const property = isRTL() ? 'paddingRight' : 'paddingLeft'\n this._element.style[property] = `${scrollbarWidth}px`\n }\n }\n\n _resetAdjustments() {\n this._element.style.paddingLeft = ''\n this._element.style.paddingRight = ''\n }\n\n // Static\n static jQueryInterface(config, relatedTarget) {\n return this.each(function () {\n const data = Modal.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](relatedTarget)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this)\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n EventHandler.one(target, EVENT_SHOW, showEvent => {\n if (showEvent.defaultPrevented) {\n // only register focus restorer if modal will actually get shown\n return\n }\n\n EventHandler.one(target, EVENT_HIDDEN, () => {\n if (isVisible(this)) {\n this.focus()\n }\n })\n })\n\n // avoid conflict when clicking modal toggler while another one is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n if (alreadyOpen) {\n Modal.getInstance(alreadyOpen).hide()\n }\n\n const data = Modal.getOrCreateInstance(target)\n\n data.toggle(this)\n})\n\nenableDismissTrigger(Modal)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Modal)\n\nexport default Modal\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap offcanvas.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport Backdrop from './util/backdrop.js'\nimport { enableDismissTrigger } from './util/component-functions.js'\nimport FocusTrap from './util/focustrap.js'\nimport {\n defineJQueryPlugin,\n isDisabled,\n isVisible\n} from './util/index.js'\nimport ScrollBarHelper from './util/scrollbar.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'offcanvas'\nconst DATA_KEY = 'bs.offcanvas'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\nconst ESCAPE_KEY = 'Escape'\n\nconst CLASS_NAME_SHOW = 'show'\nconst CLASS_NAME_SHOWING = 'showing'\nconst CLASS_NAME_HIDING = 'hiding'\nconst CLASS_NAME_BACKDROP = 'offcanvas-backdrop'\nconst OPEN_SELECTOR = '.offcanvas.show'\n\nconst EVENT_SHOW = `show${EVENT_KEY}`\nconst EVENT_SHOWN = `shown${EVENT_KEY}`\nconst EVENT_HIDE = `hide${EVENT_KEY}`\nconst EVENT_HIDE_PREVENTED = `hidePrevented${EVENT_KEY}`\nconst EVENT_HIDDEN = `hidden${EVENT_KEY}`\nconst EVENT_RESIZE = `resize${EVENT_KEY}`\nconst EVENT_CLICK_DATA_API = `click${EVENT_KEY}${DATA_API_KEY}`\nconst EVENT_KEYDOWN_DISMISS = `keydown.dismiss${EVENT_KEY}`\n\nconst SELECTOR_DATA_TOGGLE = '[data-bs-toggle=\"offcanvas\"]'\n\nconst Default = {\n backdrop: true,\n keyboard: true,\n scroll: false\n}\n\nconst DefaultType = {\n backdrop: '(boolean|string)',\n keyboard: 'boolean',\n scroll: 'boolean'\n}\n\n/**\n * Class definition\n */\n\nclass Offcanvas extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n this._isShown = false\n this._backdrop = this._initializeBackDrop()\n this._focustrap = this._initializeFocusTrap()\n this._addEventListeners()\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n toggle(relatedTarget) {\n return this._isShown ? this.hide() : this.show(relatedTarget)\n }\n\n show(relatedTarget) {\n if (this._isShown) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, EVENT_SHOW, { relatedTarget })\n\n if (showEvent.defaultPrevented) {\n return\n }\n\n this._isShown = true\n this._backdrop.show()\n\n if (!this._config.scroll) {\n new ScrollBarHelper().hide()\n }\n\n this._element.setAttribute('aria-modal', true)\n this._element.setAttribute('role', 'dialog')\n this._element.classList.add(CLASS_NAME_SHOWING)\n\n const completeCallBack = () => {\n if (!this._config.scroll || this._config.backdrop) {\n this._focustrap.activate()\n }\n\n this._element.classList.add(CLASS_NAME_SHOW)\n this._element.classList.remove(CLASS_NAME_SHOWING)\n EventHandler.trigger(this._element, EVENT_SHOWN, { relatedTarget })\n }\n\n this._queueCallback(completeCallBack, this._element, true)\n }\n\n hide() {\n if (!this._isShown) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, EVENT_HIDE)\n\n if (hideEvent.defaultPrevented) {\n return\n }\n\n this._focustrap.deactivate()\n this._element.blur()\n this._isShown = false\n this._element.classList.add(CLASS_NAME_HIDING)\n this._backdrop.hide()\n\n const completeCallback = () => {\n this._element.classList.remove(CLASS_NAME_SHOW, CLASS_NAME_HIDING)\n this._element.removeAttribute('aria-modal')\n this._element.removeAttribute('role')\n\n if (!this._config.scroll) {\n new ScrollBarHelper().reset()\n }\n\n EventHandler.trigger(this._element, EVENT_HIDDEN)\n }\n\n this._queueCallback(completeCallback, this._element, true)\n }\n\n dispose() {\n this._backdrop.dispose()\n this._focustrap.deactivate()\n super.dispose()\n }\n\n // Private\n _initializeBackDrop() {\n const clickCallback = () => {\n if (this._config.backdrop === 'static') {\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n return\n }\n\n this.hide()\n }\n\n // 'static' option will be translated to true, and booleans will keep their value\n const isVisible = Boolean(this._config.backdrop)\n\n return new Backdrop({\n className: CLASS_NAME_BACKDROP,\n isVisible,\n isAnimated: true,\n rootElement: this._element.parentNode,\n clickCallback: isVisible ? clickCallback : null\n })\n }\n\n _initializeFocusTrap() {\n return new FocusTrap({\n trapElement: this._element\n })\n }\n\n _addEventListeners() {\n EventHandler.on(this._element, EVENT_KEYDOWN_DISMISS, event => {\n if (event.key !== ESCAPE_KEY) {\n return\n }\n\n if (this._config.keyboard) {\n this.hide()\n return\n }\n\n EventHandler.trigger(this._element, EVENT_HIDE_PREVENTED)\n })\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Offcanvas.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (data[config] === undefined || config.startsWith('_') || config === 'constructor') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config](this)\n })\n }\n}\n\n/**\n * Data API implementation\n */\n\nEventHandler.on(document, EVENT_CLICK_DATA_API, SELECTOR_DATA_TOGGLE, function (event) {\n const target = SelectorEngine.getElementFromSelector(this)\n\n if (['A', 'AREA'].includes(this.tagName)) {\n event.preventDefault()\n }\n\n if (isDisabled(this)) {\n return\n }\n\n EventHandler.one(target, EVENT_HIDDEN, () => {\n // focus on trigger when it is closed\n if (isVisible(this)) {\n this.focus()\n }\n })\n\n // avoid conflict when clicking a toggler of an offcanvas, while another is open\n const alreadyOpen = SelectorEngine.findOne(OPEN_SELECTOR)\n if (alreadyOpen && alreadyOpen !== target) {\n Offcanvas.getInstance(alreadyOpen).hide()\n }\n\n const data = Offcanvas.getOrCreateInstance(target)\n data.toggle(this)\n})\n\nEventHandler.on(window, EVENT_LOAD_DATA_API, () => {\n for (const selector of SelectorEngine.find(OPEN_SELECTOR)) {\n Offcanvas.getOrCreateInstance(selector).show()\n }\n})\n\nEventHandler.on(window, EVENT_RESIZE, () => {\n for (const element of SelectorEngine.find('[aria-modal][class*=show][class*=offcanvas-]')) {\n if (getComputedStyle(element).position !== 'fixed') {\n Offcanvas.getOrCreateInstance(element).hide()\n }\n }\n})\n\nenableDismissTrigger(Offcanvas)\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Offcanvas)\n\nexport default Offcanvas\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/sanitizer.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\n// js-docs-start allow-list\nconst ARIA_ATTRIBUTE_PATTERN = /^aria-[\\w-]*$/i\n\nexport const DefaultAllowlist = {\n // Global attributes allowed on any supplied element below.\n '*': ['class', 'dir', 'id', 'lang', 'role', ARIA_ATTRIBUTE_PATTERN],\n a: ['target', 'href', 'title', 'rel'],\n area: [],\n b: [],\n br: [],\n col: [],\n code: [],\n div: [],\n em: [],\n hr: [],\n h1: [],\n h2: [],\n h3: [],\n h4: [],\n h5: [],\n h6: [],\n i: [],\n img: ['src', 'srcset', 'alt', 'title', 'width', 'height'],\n li: [],\n ol: [],\n p: [],\n pre: [],\n s: [],\n small: [],\n span: [],\n sub: [],\n sup: [],\n strong: [],\n u: [],\n ul: []\n}\n// js-docs-end allow-list\n\nconst uriAttributes = new Set([\n 'background',\n 'cite',\n 'href',\n 'itemtype',\n 'longdesc',\n 'poster',\n 'src',\n 'xlink:href'\n])\n\n/**\n * A pattern that recognizes URLs that are safe wrt. XSS in URL navigation\n * contexts.\n *\n * Shout-out to Angular https://github.com/angular/angular/blob/15.2.8/packages/core/src/sanitization/url_sanitizer.ts#L38\n */\n// eslint-disable-next-line unicorn/better-regex\nconst SAFE_URL_PATTERN = /^(?!javascript:)(?:[a-z0-9+.-]+:|[^&:/?#]*(?:[/?#]|$))/i\n\nconst allowedAttribute = (attribute, allowedAttributeList) => {\n const attributeName = attribute.nodeName.toLowerCase()\n\n if (allowedAttributeList.includes(attributeName)) {\n if (uriAttributes.has(attributeName)) {\n return Boolean(SAFE_URL_PATTERN.test(attribute.nodeValue))\n }\n\n return true\n }\n\n // Check if a regular expression validates the attribute.\n return allowedAttributeList.filter(attributeRegex => attributeRegex instanceof RegExp)\n .some(regex => regex.test(attributeName))\n}\n\nexport function sanitizeHtml(unsafeHtml, allowList, sanitizeFunction) {\n if (!unsafeHtml.length) {\n return unsafeHtml\n }\n\n if (sanitizeFunction && typeof sanitizeFunction === 'function') {\n return sanitizeFunction(unsafeHtml)\n }\n\n const domParser = new window.DOMParser()\n const createdDocument = domParser.parseFromString(unsafeHtml, 'text/html')\n const elements = [].concat(...createdDocument.body.querySelectorAll('*'))\n\n for (const element of elements) {\n const elementName = element.nodeName.toLowerCase()\n\n if (!Object.keys(allowList).includes(elementName)) {\n element.remove()\n continue\n }\n\n const attributeList = [].concat(...element.attributes)\n const allowedAttributes = [].concat(allowList['*'] || [], allowList[elementName] || [])\n\n for (const attribute of attributeList) {\n if (!allowedAttribute(attribute, allowedAttributes)) {\n element.removeAttribute(attribute.nodeName)\n }\n }\n }\n\n return createdDocument.body.innerHTML\n}\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap util/template-factory.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport SelectorEngine from '../dom/selector-engine.js'\nimport Config from './config.js'\nimport { DefaultAllowlist, sanitizeHtml } from './sanitizer.js'\nimport { execute, getElement, isElement } from './index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'TemplateFactory'\n\nconst Default = {\n allowList: DefaultAllowlist,\n content: {}, // { selector : text , selector2 : text2 , }\n extraClass: '',\n html: false,\n sanitize: true,\n sanitizeFn: null,\n template: '
'\n}\n\nconst DefaultType = {\n allowList: 'object',\n content: 'object',\n extraClass: '(string|function)',\n html: 'boolean',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n template: 'string'\n}\n\nconst DefaultContentType = {\n entry: '(string|element|function|null)',\n selector: '(string|element)'\n}\n\n/**\n * Class definition\n */\n\nclass TemplateFactory extends Config {\n constructor(config) {\n super()\n this._config = this._getConfig(config)\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n getContent() {\n return Object.values(this._config.content)\n .map(config => this._resolvePossibleFunction(config))\n .filter(Boolean)\n }\n\n hasContent() {\n return this.getContent().length > 0\n }\n\n changeContent(content) {\n this._checkContent(content)\n this._config.content = { ...this._config.content, ...content }\n return this\n }\n\n toHtml() {\n const templateWrapper = document.createElement('div')\n templateWrapper.innerHTML = this._maybeSanitize(this._config.template)\n\n for (const [selector, text] of Object.entries(this._config.content)) {\n this._setContent(templateWrapper, text, selector)\n }\n\n const template = templateWrapper.children[0]\n const extraClass = this._resolvePossibleFunction(this._config.extraClass)\n\n if (extraClass) {\n template.classList.add(...extraClass.split(' '))\n }\n\n return template\n }\n\n // Private\n _typeCheckConfig(config) {\n super._typeCheckConfig(config)\n this._checkContent(config.content)\n }\n\n _checkContent(arg) {\n for (const [selector, content] of Object.entries(arg)) {\n super._typeCheckConfig({ selector, entry: content }, DefaultContentType)\n }\n }\n\n _setContent(template, content, selector) {\n const templateElement = SelectorEngine.findOne(selector, template)\n\n if (!templateElement) {\n return\n }\n\n content = this._resolvePossibleFunction(content)\n\n if (!content) {\n templateElement.remove()\n return\n }\n\n if (isElement(content)) {\n this._putElementInTemplate(getElement(content), templateElement)\n return\n }\n\n if (this._config.html) {\n templateElement.innerHTML = this._maybeSanitize(content)\n return\n }\n\n templateElement.textContent = content\n }\n\n _maybeSanitize(arg) {\n return this._config.sanitize ? sanitizeHtml(arg, this._config.allowList, this._config.sanitizeFn) : arg\n }\n\n _resolvePossibleFunction(arg) {\n return execute(arg, [this])\n }\n\n _putElementInTemplate(element, templateElement) {\n if (this._config.html) {\n templateElement.innerHTML = ''\n templateElement.append(element)\n return\n }\n\n templateElement.textContent = element.textContent\n }\n}\n\nexport default TemplateFactory\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap tooltip.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport * as Popper from '@popperjs/core'\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport Manipulator from './dom/manipulator.js'\nimport { defineJQueryPlugin, execute, findShadowRoot, getElement, getUID, isRTL, noop } from './util/index.js'\nimport { DefaultAllowlist } from './util/sanitizer.js'\nimport TemplateFactory from './util/template-factory.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'tooltip'\nconst DISALLOWED_ATTRIBUTES = new Set(['sanitize', 'allowList', 'sanitizeFn'])\n\nconst CLASS_NAME_FADE = 'fade'\nconst CLASS_NAME_MODAL = 'modal'\nconst CLASS_NAME_SHOW = 'show'\n\nconst SELECTOR_TOOLTIP_INNER = '.tooltip-inner'\nconst SELECTOR_MODAL = `.${CLASS_NAME_MODAL}`\n\nconst EVENT_MODAL_HIDE = 'hide.bs.modal'\n\nconst TRIGGER_HOVER = 'hover'\nconst TRIGGER_FOCUS = 'focus'\nconst TRIGGER_CLICK = 'click'\nconst TRIGGER_MANUAL = 'manual'\n\nconst EVENT_HIDE = 'hide'\nconst EVENT_HIDDEN = 'hidden'\nconst EVENT_SHOW = 'show'\nconst EVENT_SHOWN = 'shown'\nconst EVENT_INSERTED = 'inserted'\nconst EVENT_CLICK = 'click'\nconst EVENT_FOCUSIN = 'focusin'\nconst EVENT_FOCUSOUT = 'focusout'\nconst EVENT_MOUSEENTER = 'mouseenter'\nconst EVENT_MOUSELEAVE = 'mouseleave'\n\nconst AttachmentMap = {\n AUTO: 'auto',\n TOP: 'top',\n RIGHT: isRTL() ? 'left' : 'right',\n BOTTOM: 'bottom',\n LEFT: isRTL() ? 'right' : 'left'\n}\n\nconst Default = {\n allowList: DefaultAllowlist,\n animation: true,\n boundary: 'clippingParents',\n container: false,\n customClass: '',\n delay: 0,\n fallbackPlacements: ['top', 'right', 'bottom', 'left'],\n html: false,\n offset: [0, 6],\n placement: 'top',\n popperConfig: null,\n sanitize: true,\n sanitizeFn: null,\n selector: false,\n template: '
' +\n '
' +\n '
' +\n '
',\n title: '',\n trigger: 'hover focus'\n}\n\nconst DefaultType = {\n allowList: 'object',\n animation: 'boolean',\n boundary: '(string|element)',\n container: '(string|element|boolean)',\n customClass: '(string|function)',\n delay: '(number|object)',\n fallbackPlacements: 'array',\n html: 'boolean',\n offset: '(array|string|function)',\n placement: '(string|function)',\n popperConfig: '(null|object|function)',\n sanitize: 'boolean',\n sanitizeFn: '(null|function)',\n selector: '(string|boolean)',\n template: 'string',\n title: '(string|element|function)',\n trigger: 'string'\n}\n\n/**\n * Class definition\n */\n\nclass Tooltip extends BaseComponent {\n constructor(element, config) {\n if (typeof Popper === 'undefined') {\n throw new TypeError('Bootstrap\\'s tooltips require Popper (https://popper.js.org)')\n }\n\n super(element, config)\n\n // Private\n this._isEnabled = true\n this._timeout = 0\n this._isHovered = null\n this._activeTrigger = {}\n this._popper = null\n this._templateFactory = null\n this._newContent = null\n\n // Protected\n this.tip = null\n\n this._setListeners()\n\n if (!this._config.selector) {\n this._fixTitle()\n }\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n enable() {\n this._isEnabled = true\n }\n\n disable() {\n this._isEnabled = false\n }\n\n toggleEnabled() {\n this._isEnabled = !this._isEnabled\n }\n\n toggle() {\n if (!this._isEnabled) {\n return\n }\n\n this._activeTrigger.click = !this._activeTrigger.click\n if (this._isShown()) {\n this._leave()\n return\n }\n\n this._enter()\n }\n\n dispose() {\n clearTimeout(this._timeout)\n\n EventHandler.off(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n\n if (this._element.getAttribute('data-bs-original-title')) {\n this._element.setAttribute('title', this._element.getAttribute('data-bs-original-title'))\n }\n\n this._disposePopper()\n super.dispose()\n }\n\n show() {\n if (this._element.style.display === 'none') {\n throw new Error('Please use show on visible elements')\n }\n\n if (!(this._isWithContent() && this._isEnabled)) {\n return\n }\n\n const showEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOW))\n const shadowRoot = findShadowRoot(this._element)\n const isInTheDom = (shadowRoot || this._element.ownerDocument.documentElement).contains(this._element)\n\n if (showEvent.defaultPrevented || !isInTheDom) {\n return\n }\n\n // TODO: v6 remove this or make it optional\n this._disposePopper()\n\n const tip = this._getTipElement()\n\n this._element.setAttribute('aria-describedby', tip.getAttribute('id'))\n\n const { container } = this._config\n\n if (!this._element.ownerDocument.documentElement.contains(this.tip)) {\n container.append(tip)\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_INSERTED))\n }\n\n this._popper = this._createPopper(tip)\n\n tip.classList.add(CLASS_NAME_SHOW)\n\n // If this is a touch-enabled device we add extra\n // empty mouseover listeners to the body's immediate children;\n // only needed because of broken event delegation on iOS\n // https://www.quirksmode.org/blog/archives/2014/02/mouse_event_bub.html\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.on(element, 'mouseover', noop)\n }\n }\n\n const complete = () => {\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_SHOWN))\n\n if (this._isHovered === false) {\n this._leave()\n }\n\n this._isHovered = false\n }\n\n this._queueCallback(complete, this.tip, this._isAnimated())\n }\n\n hide() {\n if (!this._isShown()) {\n return\n }\n\n const hideEvent = EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDE))\n if (hideEvent.defaultPrevented) {\n return\n }\n\n const tip = this._getTipElement()\n tip.classList.remove(CLASS_NAME_SHOW)\n\n // If this is a touch-enabled device we remove the extra\n // empty mouseover listeners we added for iOS support\n if ('ontouchstart' in document.documentElement) {\n for (const element of [].concat(...document.body.children)) {\n EventHandler.off(element, 'mouseover', noop)\n }\n }\n\n this._activeTrigger[TRIGGER_CLICK] = false\n this._activeTrigger[TRIGGER_FOCUS] = false\n this._activeTrigger[TRIGGER_HOVER] = false\n this._isHovered = null // it is a trick to support manual triggering\n\n const complete = () => {\n if (this._isWithActiveTrigger()) {\n return\n }\n\n if (!this._isHovered) {\n this._disposePopper()\n }\n\n this._element.removeAttribute('aria-describedby')\n EventHandler.trigger(this._element, this.constructor.eventName(EVENT_HIDDEN))\n }\n\n this._queueCallback(complete, this.tip, this._isAnimated())\n }\n\n update() {\n if (this._popper) {\n this._popper.update()\n }\n }\n\n // Protected\n _isWithContent() {\n return Boolean(this._getTitle())\n }\n\n _getTipElement() {\n if (!this.tip) {\n this.tip = this._createTipElement(this._newContent || this._getContentForTemplate())\n }\n\n return this.tip\n }\n\n _createTipElement(content) {\n const tip = this._getTemplateFactory(content).toHtml()\n\n // TODO: remove this check in v6\n if (!tip) {\n return null\n }\n\n tip.classList.remove(CLASS_NAME_FADE, CLASS_NAME_SHOW)\n // TODO: v6 the following can be achieved with CSS only\n tip.classList.add(`bs-${this.constructor.NAME}-auto`)\n\n const tipId = getUID(this.constructor.NAME).toString()\n\n tip.setAttribute('id', tipId)\n\n if (this._isAnimated()) {\n tip.classList.add(CLASS_NAME_FADE)\n }\n\n return tip\n }\n\n setContent(content) {\n this._newContent = content\n if (this._isShown()) {\n this._disposePopper()\n this.show()\n }\n }\n\n _getTemplateFactory(content) {\n if (this._templateFactory) {\n this._templateFactory.changeContent(content)\n } else {\n this._templateFactory = new TemplateFactory({\n ...this._config,\n // the `content` var has to be after `this._config`\n // to override config.content in case of popover\n content,\n extraClass: this._resolvePossibleFunction(this._config.customClass)\n })\n }\n\n return this._templateFactory\n }\n\n _getContentForTemplate() {\n return {\n [SELECTOR_TOOLTIP_INNER]: this._getTitle()\n }\n }\n\n _getTitle() {\n return this._resolvePossibleFunction(this._config.title) || this._element.getAttribute('data-bs-original-title')\n }\n\n // Private\n _initializeOnDelegatedTarget(event) {\n return this.constructor.getOrCreateInstance(event.delegateTarget, this._getDelegateConfig())\n }\n\n _isAnimated() {\n return this._config.animation || (this.tip && this.tip.classList.contains(CLASS_NAME_FADE))\n }\n\n _isShown() {\n return this.tip && this.tip.classList.contains(CLASS_NAME_SHOW)\n }\n\n _createPopper(tip) {\n const placement = execute(this._config.placement, [this, tip, this._element])\n const attachment = AttachmentMap[placement.toUpperCase()]\n return Popper.createPopper(this._element, tip, this._getPopperConfig(attachment))\n }\n\n _getOffset() {\n const { offset } = this._config\n\n if (typeof offset === 'string') {\n return offset.split(',').map(value => Number.parseInt(value, 10))\n }\n\n if (typeof offset === 'function') {\n return popperData => offset(popperData, this._element)\n }\n\n return offset\n }\n\n _resolvePossibleFunction(arg) {\n return execute(arg, [this._element])\n }\n\n _getPopperConfig(attachment) {\n const defaultBsPopperConfig = {\n placement: attachment,\n modifiers: [\n {\n name: 'flip',\n options: {\n fallbackPlacements: this._config.fallbackPlacements\n }\n },\n {\n name: 'offset',\n options: {\n offset: this._getOffset()\n }\n },\n {\n name: 'preventOverflow',\n options: {\n boundary: this._config.boundary\n }\n },\n {\n name: 'arrow',\n options: {\n element: `.${this.constructor.NAME}-arrow`\n }\n },\n {\n name: 'preSetPlacement',\n enabled: true,\n phase: 'beforeMain',\n fn: data => {\n // Pre-set Popper's placement attribute in order to read the arrow sizes properly.\n // Otherwise, Popper mixes up the width and height dimensions since the initial arrow style is for top placement\n this._getTipElement().setAttribute('data-popper-placement', data.state.placement)\n }\n }\n ]\n }\n\n return {\n ...defaultBsPopperConfig,\n ...execute(this._config.popperConfig, [defaultBsPopperConfig])\n }\n }\n\n _setListeners() {\n const triggers = this._config.trigger.split(' ')\n\n for (const trigger of triggers) {\n if (trigger === 'click') {\n EventHandler.on(this._element, this.constructor.eventName(EVENT_CLICK), this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context.toggle()\n })\n } else if (trigger !== TRIGGER_MANUAL) {\n const eventIn = trigger === TRIGGER_HOVER ?\n this.constructor.eventName(EVENT_MOUSEENTER) :\n this.constructor.eventName(EVENT_FOCUSIN)\n const eventOut = trigger === TRIGGER_HOVER ?\n this.constructor.eventName(EVENT_MOUSELEAVE) :\n this.constructor.eventName(EVENT_FOCUSOUT)\n\n EventHandler.on(this._element, eventIn, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context._activeTrigger[event.type === 'focusin' ? TRIGGER_FOCUS : TRIGGER_HOVER] = true\n context._enter()\n })\n EventHandler.on(this._element, eventOut, this._config.selector, event => {\n const context = this._initializeOnDelegatedTarget(event)\n context._activeTrigger[event.type === 'focusout' ? TRIGGER_FOCUS : TRIGGER_HOVER] =\n context._element.contains(event.relatedTarget)\n\n context._leave()\n })\n }\n }\n\n this._hideModalHandler = () => {\n if (this._element) {\n this.hide()\n }\n }\n\n EventHandler.on(this._element.closest(SELECTOR_MODAL), EVENT_MODAL_HIDE, this._hideModalHandler)\n }\n\n _fixTitle() {\n const title = this._element.getAttribute('title')\n\n if (!title) {\n return\n }\n\n if (!this._element.getAttribute('aria-label') && !this._element.textContent.trim()) {\n this._element.setAttribute('aria-label', title)\n }\n\n this._element.setAttribute('data-bs-original-title', title) // DO NOT USE IT. Is only for backwards compatibility\n this._element.removeAttribute('title')\n }\n\n _enter() {\n if (this._isShown() || this._isHovered) {\n this._isHovered = true\n return\n }\n\n this._isHovered = true\n\n this._setTimeout(() => {\n if (this._isHovered) {\n this.show()\n }\n }, this._config.delay.show)\n }\n\n _leave() {\n if (this._isWithActiveTrigger()) {\n return\n }\n\n this._isHovered = false\n\n this._setTimeout(() => {\n if (!this._isHovered) {\n this.hide()\n }\n }, this._config.delay.hide)\n }\n\n _setTimeout(handler, timeout) {\n clearTimeout(this._timeout)\n this._timeout = setTimeout(handler, timeout)\n }\n\n _isWithActiveTrigger() {\n return Object.values(this._activeTrigger).includes(true)\n }\n\n _getConfig(config) {\n const dataAttributes = Manipulator.getDataAttributes(this._element)\n\n for (const dataAttribute of Object.keys(dataAttributes)) {\n if (DISALLOWED_ATTRIBUTES.has(dataAttribute)) {\n delete dataAttributes[dataAttribute]\n }\n }\n\n config = {\n ...dataAttributes,\n ...(typeof config === 'object' && config ? config : {})\n }\n config = this._mergeConfigObj(config)\n config = this._configAfterMerge(config)\n this._typeCheckConfig(config)\n return config\n }\n\n _configAfterMerge(config) {\n config.container = config.container === false ? document.body : getElement(config.container)\n\n if (typeof config.delay === 'number') {\n config.delay = {\n show: config.delay,\n hide: config.delay\n }\n }\n\n if (typeof config.title === 'number') {\n config.title = config.title.toString()\n }\n\n if (typeof config.content === 'number') {\n config.content = config.content.toString()\n }\n\n return config\n }\n\n _getDelegateConfig() {\n const config = {}\n\n for (const [key, value] of Object.entries(this._config)) {\n if (this.constructor.Default[key] !== value) {\n config[key] = value\n }\n }\n\n config.selector = false\n config.trigger = 'manual'\n\n // In the future can be replaced with:\n // const keysWithDifferentValues = Object.entries(this._config).filter(entry => this.constructor.Default[entry[0]] !== this._config[entry[0]])\n // `Object.fromEntries(keysWithDifferentValues)`\n return config\n }\n\n _disposePopper() {\n if (this._popper) {\n this._popper.destroy()\n this._popper = null\n }\n\n if (this.tip) {\n this.tip.remove()\n this.tip = null\n }\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Tooltip.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Tooltip)\n\nexport default Tooltip\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap popover.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport Tooltip from './tooltip.js'\nimport { defineJQueryPlugin } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'popover'\n\nconst SELECTOR_TITLE = '.popover-header'\nconst SELECTOR_CONTENT = '.popover-body'\n\nconst Default = {\n ...Tooltip.Default,\n content: '',\n offset: [0, 8],\n placement: 'right',\n template: '
' +\n '
' +\n '

' +\n '
' +\n '
',\n trigger: 'click'\n}\n\nconst DefaultType = {\n ...Tooltip.DefaultType,\n content: '(null|string|element|function)'\n}\n\n/**\n * Class definition\n */\n\nclass Popover extends Tooltip {\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Overrides\n _isWithContent() {\n return this._getTitle() || this._getContent()\n }\n\n // Private\n _getContentForTemplate() {\n return {\n [SELECTOR_TITLE]: this._getTitle(),\n [SELECTOR_CONTENT]: this._getContent()\n }\n }\n\n _getContent() {\n return this._resolvePossibleFunction(this._config.content)\n }\n\n // Static\n static jQueryInterface(config) {\n return this.each(function () {\n const data = Popover.getOrCreateInstance(this, config)\n\n if (typeof config !== 'string') {\n return\n }\n\n if (typeof data[config] === 'undefined') {\n throw new TypeError(`No method named \"${config}\"`)\n }\n\n data[config]()\n })\n }\n}\n\n/**\n * jQuery\n */\n\ndefineJQueryPlugin(Popover)\n\nexport default Popover\n","/**\n * --------------------------------------------------------------------------\n * Bootstrap scrollspy.js\n * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)\n * --------------------------------------------------------------------------\n */\n\nimport BaseComponent from './base-component.js'\nimport EventHandler from './dom/event-handler.js'\nimport SelectorEngine from './dom/selector-engine.js'\nimport { defineJQueryPlugin, getElement, isDisabled, isVisible } from './util/index.js'\n\n/**\n * Constants\n */\n\nconst NAME = 'scrollspy'\nconst DATA_KEY = 'bs.scrollspy'\nconst EVENT_KEY = `.${DATA_KEY}`\nconst DATA_API_KEY = '.data-api'\n\nconst EVENT_ACTIVATE = `activate${EVENT_KEY}`\nconst EVENT_CLICK = `click${EVENT_KEY}`\nconst EVENT_LOAD_DATA_API = `load${EVENT_KEY}${DATA_API_KEY}`\n\nconst CLASS_NAME_DROPDOWN_ITEM = 'dropdown-item'\nconst CLASS_NAME_ACTIVE = 'active'\n\nconst SELECTOR_DATA_SPY = '[data-bs-spy=\"scroll\"]'\nconst SELECTOR_TARGET_LINKS = '[href]'\nconst SELECTOR_NAV_LIST_GROUP = '.nav, .list-group'\nconst SELECTOR_NAV_LINKS = '.nav-link'\nconst SELECTOR_NAV_ITEMS = '.nav-item'\nconst SELECTOR_LIST_ITEMS = '.list-group-item'\nconst SELECTOR_LINK_ITEMS = `${SELECTOR_NAV_LINKS}, ${SELECTOR_NAV_ITEMS} > ${SELECTOR_NAV_LINKS}, ${SELECTOR_LIST_ITEMS}`\nconst SELECTOR_DROPDOWN = '.dropdown'\nconst SELECTOR_DROPDOWN_TOGGLE = '.dropdown-toggle'\n\nconst Default = {\n offset: null, // TODO: v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: '0px 0px -25%',\n smoothScroll: false,\n target: null,\n threshold: [0.1, 0.5, 1]\n}\n\nconst DefaultType = {\n offset: '(number|null)', // TODO v6 @deprecated, keep it for backwards compatibility reasons\n rootMargin: 'string',\n smoothScroll: 'boolean',\n target: 'element',\n threshold: 'array'\n}\n\n/**\n * Class definition\n */\n\nclass ScrollSpy extends BaseComponent {\n constructor(element, config) {\n super(element, config)\n\n // this._element is the observablesContainer and config.target the menu links wrapper\n this._targetLinks = new Map()\n this._observableSections = new Map()\n this._rootElement = getComputedStyle(this._element).overflowY === 'visible' ? null : this._element\n this._activeTarget = null\n this._observer = null\n this._previousScrollData = {\n visibleEntryTop: 0,\n parentScrollTop: 0\n }\n this.refresh() // initialize\n }\n\n // Getters\n static get Default() {\n return Default\n }\n\n static get DefaultType() {\n return DefaultType\n }\n\n static get NAME() {\n return NAME\n }\n\n // Public\n refresh() {\n this._initializeTargetsAndObservables()\n this._maybeEnableSmoothScroll()\n\n if (this._observer) {\n this._observer.disconnect()\n } else {\n this._observer = this._getNewObserver()\n }\n\n for (const section of this._observableSections.values()) {\n this._observer.observe(section)\n }\n }\n\n dispose() {\n this._observer.disconnect()\n super.dispose()\n }\n\n // Private\n _configAfterMerge(config) {\n // TODO: on v6 target should be given explicitly & remove the {target: 'ss-target'} case\n config.target = getElement(config.target) || document.body\n\n // TODO: v6 Only for backwards compatibility reasons. Use rootMargin only\n config.rootMargin = config.offset ? `${config.offset}px 0px -30%` : config.rootMargin\n\n if (typeof config.threshold === 'string') {\n config.threshold = config.threshold.split(',').map(value => Number.parseFloat(value))\n }\n\n return config\n }\n\n _maybeEnableSmoothScroll() {\n if (!this._config.smoothScroll) {\n return\n }\n\n // unregister any previous listeners\n EventHandler.off(this._config.target, EVENT_CLICK)\n\n EventHandler.on(this._config.target, EVENT_CLICK, SELECTOR_TARGET_LINKS, event => {\n const observableSection = this._observableSections.get(event.target.hash)\n if (observableSection) {\n event.preventDefault()\n const root = this._rootElement || window\n const height = observableSection.offsetTop - this._element.offsetTop\n if (root.scrollTo) {\n root.scrollTo({ top: height, behavior: 'smooth' })\n return\n }\n\n // Chrome 60 doesn't support `scrollTo`\n root.scrollTop = height\n }\n })\n }\n\n _getNewObserver() {\n const options = {\n root: this._rootElement,\n threshold: this._config.threshold,\n rootMargin: this._config.rootMargin\n }\n\n return new IntersectionObserver(entries => this._observerCallback(entries), options)\n }\n\n // The logic of selection\n _observerCallback(entries) {\n const targetElement = entry => this._targetLinks.get(`#${entry.target.id}`)\n const activate = entry => {\n this._previousScrollData.visibleEntryTop = entry.target.offsetTop\n this._process(targetElement(entry))\n }\n\n const parentScrollTop = (this._rootElement || document.documentElement).scrollTop\n const userScrollsDown = parentScrollTop >= this._previousScrollData.parentScrollTop\n this._previousScrollData.parentScrollTop = parentScrollTop\n\n for (const entry of entries) {\n if (!entry.isIntersecting) {\n this._activeTarget = null\n this._clearActiveClass(targetElement(entry))\n\n continue\n }\n\n const entryIsLowerThanPrevious = entry.target.offsetTop >= this._previousScrollData.visibleEntryTop\n // if we are scrolling down, pick the bigger offsetTop\n if (userScrollsDown && entryIsLowerThanPrevious) {\n activate(entry)\n // if parent isn't scrolled, let's keep the first visible item, breaking the iteration\n if (!parentScrollTop) {\n return\n }\n\n continue\n }\n\n // if we are scrolling up, pick the smallest offsetTop\n if (!userScrollsDown && !entryIsLowerThanPrevious) {\n activate(entry)\n }\n }\n }\n\n _initializeTargetsAndObservables() {\n this._targetLinks = new Map()\n this._observableSections = new Map()\n\n const targetLinks = SelectorEngine.find(SELECTOR_TARGET_LINKS, this._config.target)\n\n for (const anchor of targetLinks) {\n // ensure that the anchor has an id and is not disabled\n if (!anchor.hash || isDisabled(anchor)) {\n continue\n }\n\n const observableSection = SelectorEngine.findOne(decodeURI(anchor.hash), this._element)\n\n // ensure that the observableSection exists & is visible\n if (isVisible(observableSection)) {\n this._targetLinks.set(decodeURI(anchor.hash), anchor)\n this._observableSections.set(anchor.hash, observableSection)\n }\n }\n }\n\n _process(target) {\n if (this._activeTarget === target) {\n return\n }\n\n this._clearActiveClass(this._config.target)\n this._activeTarget = target\n target.classList.add(CLASS_NAME_ACTIVE)\n this._activateParents(target)\n\n EventHandler.trigger(this._element, EVENT_ACTIVATE, { relatedTarget: target })\n }\n\n _activateParents(target) {\n // Activate dropdown parents\n if (target.classList.contains(CLASS_NAME_DROPDOWN_ITEM)) {\n SelectorEngine.findOne(SELECTOR_DROPDOWN_TOGGLE, target.closest(SELECTOR_DROPDOWN))\n .classList.add(CLASS_NAME_ACTIVE)\n return\n }\n\n for (const listGroup of SelectorEngine.parents(target, SELECTOR_NAV_LIST_GROUP)) {\n // Set triggered links parents as active\n // With both
    and
')},createChildNavList:function(e){var t=this.createNavList();return e.append(t),t},generateNavEl:function(e,t){var n=a('');n.attr("href","#"+e),n.text(t);var r=a("
  • ");return r.append(n),r},generateNavItem:function(e){var t=this.generateAnchor(e),n=a(e),r=n.data("toc-text")||n.text();return this.generateNavEl(t,r)},getTopLevel:function(e){for(var t=1;t<=6;t++){if(1 + + + + + + + + + + + + diff --git a/latest-tag/deps/font-awesome-6.4.2/css/all.css b/latest-tag/deps/font-awesome-6.4.2/css/all.css new file mode 100644 index 00000000..bdb6e3ae --- /dev/null +++ b/latest-tag/deps/font-awesome-6.4.2/css/all.css @@ -0,0 +1,7968 @@ +/*! + * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2023 Fonticons, Inc. + */ +.fa { + font-family: var(--fa-style-family, "Font Awesome 6 Free"); + font-weight: var(--fa-style, 900); } + +.fa, +.fa-classic, +.fa-sharp, +.fas, +.fa-solid, +.far, +.fa-regular, +.fab, +.fa-brands { + -moz-osx-font-smoothing: grayscale; + -webkit-font-smoothing: antialiased; + display: var(--fa-display, inline-block); + font-style: normal; + font-variant: normal; + line-height: 1; + text-rendering: auto; } + +.fas, +.fa-classic, +.fa-solid, +.far, +.fa-regular { + font-family: 'Font Awesome 6 Free'; } + +.fab, +.fa-brands { + font-family: 'Font Awesome 6 Brands'; } + +.fa-1x { + font-size: 1em; } + +.fa-2x { + font-size: 2em; } + +.fa-3x { + font-size: 3em; } + +.fa-4x { + font-size: 4em; } + +.fa-5x { + font-size: 5em; } + +.fa-6x { + font-size: 6em; } + +.fa-7x { + font-size: 7em; } + +.fa-8x { + font-size: 8em; } + +.fa-9x { + font-size: 9em; } + +.fa-10x { + font-size: 10em; } + +.fa-2xs { + font-size: 0.625em; + line-height: 0.1em; + vertical-align: 0.225em; } + +.fa-xs { + font-size: 0.75em; + line-height: 0.08333em; + vertical-align: 0.125em; } + +.fa-sm { + font-size: 0.875em; + line-height: 0.07143em; + vertical-align: 0.05357em; } + +.fa-lg { + font-size: 1.25em; + line-height: 0.05em; + vertical-align: -0.075em; } + +.fa-xl { + font-size: 1.5em; + line-height: 0.04167em; + vertical-align: -0.125em; } + +.fa-2xl { + font-size: 2em; + line-height: 0.03125em; + vertical-align: -0.1875em; } + +.fa-fw { + text-align: center; + width: 1.25em; } + +.fa-ul { + list-style-type: none; + margin-left: var(--fa-li-margin, 2.5em); + padding-left: 0; } + .fa-ul > li { + position: relative; } + +.fa-li { + left: calc(var(--fa-li-width, 2em) * -1); + position: absolute; + text-align: center; + width: var(--fa-li-width, 2em); + line-height: inherit; } + +.fa-border { + border-color: var(--fa-border-color, #eee); + border-radius: var(--fa-border-radius, 0.1em); + border-style: var(--fa-border-style, solid); + border-width: var(--fa-border-width, 0.08em); + padding: var(--fa-border-padding, 0.2em 0.25em 0.15em); } + +.fa-pull-left { + float: left; + margin-right: var(--fa-pull-margin, 0.3em); } + +.fa-pull-right { + float: right; + margin-left: var(--fa-pull-margin, 0.3em); } + +.fa-beat { + -webkit-animation-name: fa-beat; + animation-name: fa-beat; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-bounce { + -webkit-animation-name: fa-bounce; + animation-name: fa-bounce; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.28, 0.84, 0.42, 1)); } + +.fa-fade { + -webkit-animation-name: fa-fade; + animation-name: fa-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-beat-fade { + -webkit-animation-name: fa-beat-fade; + animation-name: fa-beat-fade; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); + animation-timing-function: var(--fa-animation-timing, cubic-bezier(0.4, 0, 0.6, 1)); } + +.fa-flip { + -webkit-animation-name: fa-flip; + animation-name: fa-flip; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, ease-in-out); + animation-timing-function: var(--fa-animation-timing, ease-in-out); } + +.fa-shake { + -webkit-animation-name: fa-shake; + animation-name: fa-shake; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-delay: var(--fa-animation-delay, 0s); + animation-delay: var(--fa-animation-delay, 0s); + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 2s); + animation-duration: var(--fa-animation-duration, 2s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, linear); + animation-timing-function: var(--fa-animation-timing, linear); } + +.fa-spin-reverse { + --fa-animation-direction: reverse; } + +.fa-pulse, +.fa-spin-pulse { + -webkit-animation-name: fa-spin; + animation-name: fa-spin; + -webkit-animation-direction: var(--fa-animation-direction, normal); + animation-direction: var(--fa-animation-direction, normal); + -webkit-animation-duration: var(--fa-animation-duration, 1s); + animation-duration: var(--fa-animation-duration, 1s); + -webkit-animation-iteration-count: var(--fa-animation-iteration-count, infinite); + animation-iteration-count: var(--fa-animation-iteration-count, infinite); + -webkit-animation-timing-function: var(--fa-animation-timing, steps(8)); + animation-timing-function: var(--fa-animation-timing, steps(8)); } + +@media (prefers-reduced-motion: reduce) { + .fa-beat, + .fa-bounce, + .fa-fade, + .fa-beat-fade, + .fa-flip, + .fa-pulse, + .fa-shake, + .fa-spin, + .fa-spin-pulse { + -webkit-animation-delay: -1ms; + animation-delay: -1ms; + -webkit-animation-duration: 1ms; + animation-duration: 1ms; + -webkit-animation-iteration-count: 1; + animation-iteration-count: 1; + -webkit-transition-delay: 0s; + transition-delay: 0s; + -webkit-transition-duration: 0s; + transition-duration: 0s; } } + +@-webkit-keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@keyframes fa-beat { + 0%, 90% { + -webkit-transform: scale(1); + transform: scale(1); } + 45% { + -webkit-transform: scale(var(--fa-beat-scale, 1.25)); + transform: scale(var(--fa-beat-scale, 1.25)); } } + +@-webkit-keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@keyframes fa-bounce { + 0% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 10% { + -webkit-transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); + transform: scale(var(--fa-bounce-start-scale-x, 1.1), var(--fa-bounce-start-scale-y, 0.9)) translateY(0); } + 30% { + -webkit-transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); + transform: scale(var(--fa-bounce-jump-scale-x, 0.9), var(--fa-bounce-jump-scale-y, 1.1)) translateY(var(--fa-bounce-height, -0.5em)); } + 50% { + -webkit-transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); + transform: scale(var(--fa-bounce-land-scale-x, 1.05), var(--fa-bounce-land-scale-y, 0.95)) translateY(0); } + 57% { + -webkit-transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); + transform: scale(1, 1) translateY(var(--fa-bounce-rebound, -0.125em)); } + 64% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } + 100% { + -webkit-transform: scale(1, 1) translateY(0); + transform: scale(1, 1) translateY(0); } } + +@-webkit-keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@keyframes fa-fade { + 50% { + opacity: var(--fa-fade-opacity, 0.4); } } + +@-webkit-keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@keyframes fa-beat-fade { + 0%, 100% { + opacity: var(--fa-beat-fade-opacity, 0.4); + -webkit-transform: scale(1); + transform: scale(1); } + 50% { + opacity: 1; + -webkit-transform: scale(var(--fa-beat-fade-scale, 1.125)); + transform: scale(var(--fa-beat-fade-scale, 1.125)); } } + +@-webkit-keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@keyframes fa-flip { + 50% { + -webkit-transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); + transform: rotate3d(var(--fa-flip-x, 0), var(--fa-flip-y, 1), var(--fa-flip-z, 0), var(--fa-flip-angle, -180deg)); } } + +@-webkit-keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@keyframes fa-shake { + 0% { + -webkit-transform: rotate(-15deg); + transform: rotate(-15deg); } + 4% { + -webkit-transform: rotate(15deg); + transform: rotate(15deg); } + 8%, 24% { + -webkit-transform: rotate(-18deg); + transform: rotate(-18deg); } + 12%, 28% { + -webkit-transform: rotate(18deg); + transform: rotate(18deg); } + 16% { + -webkit-transform: rotate(-22deg); + transform: rotate(-22deg); } + 20% { + -webkit-transform: rotate(22deg); + transform: rotate(22deg); } + 32% { + -webkit-transform: rotate(-12deg); + transform: rotate(-12deg); } + 36% { + -webkit-transform: rotate(12deg); + transform: rotate(12deg); } + 40%, 100% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } } + +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); } + 100% { + -webkit-transform: rotate(360deg); + transform: rotate(360deg); } } + +.fa-rotate-90 { + -webkit-transform: rotate(90deg); + transform: rotate(90deg); } + +.fa-rotate-180 { + -webkit-transform: rotate(180deg); + transform: rotate(180deg); } + +.fa-rotate-270 { + -webkit-transform: rotate(270deg); + transform: rotate(270deg); } + +.fa-flip-horizontal { + -webkit-transform: scale(-1, 1); + transform: scale(-1, 1); } + +.fa-flip-vertical { + -webkit-transform: scale(1, -1); + transform: scale(1, -1); } + +.fa-flip-both, +.fa-flip-horizontal.fa-flip-vertical { + -webkit-transform: scale(-1, -1); + transform: scale(-1, -1); } + +.fa-rotate-by { + -webkit-transform: rotate(var(--fa-rotate-angle, none)); + transform: rotate(var(--fa-rotate-angle, none)); } + +.fa-stack { + display: inline-block; + height: 2em; + line-height: 2em; + position: relative; + vertical-align: middle; + width: 2.5em; } + +.fa-stack-1x, +.fa-stack-2x { + left: 0; + position: absolute; + text-align: center; + width: 100%; + z-index: var(--fa-stack-z-index, auto); } + +.fa-stack-1x { + line-height: inherit; } + +.fa-stack-2x { + font-size: 2em; } + +.fa-inverse { + color: var(--fa-inverse, #fff); } + +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen +readers do not read off random characters that represent icons */ + +.fa-0::before { + content: "\30"; } + +.fa-1::before { + content: "\31"; } + +.fa-2::before { + content: "\32"; } + +.fa-3::before { + content: "\33"; } + +.fa-4::before { + content: "\34"; } + +.fa-5::before { + content: "\35"; } + +.fa-6::before { + content: "\36"; } + +.fa-7::before { + content: "\37"; } + +.fa-8::before { + content: "\38"; } + +.fa-9::before { + content: "\39"; } + +.fa-fill-drip::before { + content: "\f576"; } + +.fa-arrows-to-circle::before { + content: "\e4bd"; } + +.fa-circle-chevron-right::before { + content: "\f138"; } + +.fa-chevron-circle-right::before { + content: "\f138"; } + +.fa-at::before { + content: "\40"; } + +.fa-trash-can::before { + content: "\f2ed"; } + +.fa-trash-alt::before { + content: "\f2ed"; } + +.fa-text-height::before { + content: "\f034"; } + +.fa-user-xmark::before { + content: "\f235"; } + +.fa-user-times::before { + content: "\f235"; } + +.fa-stethoscope::before { + content: "\f0f1"; } + +.fa-message::before { + content: "\f27a"; } + +.fa-comment-alt::before { + content: "\f27a"; } + +.fa-info::before { + content: "\f129"; } + +.fa-down-left-and-up-right-to-center::before { + content: "\f422"; } + +.fa-compress-alt::before { + content: "\f422"; } + +.fa-explosion::before { + content: "\e4e9"; } + +.fa-file-lines::before { + content: "\f15c"; } + +.fa-file-alt::before { + content: "\f15c"; } + +.fa-file-text::before { + content: "\f15c"; } + +.fa-wave-square::before { + content: "\f83e"; } + +.fa-ring::before { + content: "\f70b"; } + +.fa-building-un::before { + content: "\e4d9"; } + +.fa-dice-three::before { + content: "\f527"; } + +.fa-calendar-days::before { + content: "\f073"; } + +.fa-calendar-alt::before { + content: "\f073"; } + +.fa-anchor-circle-check::before { + content: "\e4aa"; } + +.fa-building-circle-arrow-right::before { + content: "\e4d1"; } + +.fa-volleyball::before { + content: "\f45f"; } + +.fa-volleyball-ball::before { + content: "\f45f"; } + +.fa-arrows-up-to-line::before { + content: "\e4c2"; } + +.fa-sort-down::before { + content: "\f0dd"; } + +.fa-sort-desc::before { + content: "\f0dd"; } + +.fa-circle-minus::before { + content: "\f056"; } + +.fa-minus-circle::before { + content: "\f056"; } + +.fa-door-open::before { + content: "\f52b"; } + +.fa-right-from-bracket::before { + content: "\f2f5"; } + +.fa-sign-out-alt::before { + content: "\f2f5"; } + +.fa-atom::before { + content: "\f5d2"; } + +.fa-soap::before { + content: "\e06e"; } + +.fa-icons::before { + content: "\f86d"; } + +.fa-heart-music-camera-bolt::before { + content: "\f86d"; } + +.fa-microphone-lines-slash::before { + content: "\f539"; } + +.fa-microphone-alt-slash::before { + content: "\f539"; } + +.fa-bridge-circle-check::before { + content: "\e4c9"; } + +.fa-pump-medical::before { + content: "\e06a"; } + +.fa-fingerprint::before { + content: "\f577"; } + +.fa-hand-point-right::before { + content: "\f0a4"; } + +.fa-magnifying-glass-location::before { + content: "\f689"; } + +.fa-search-location::before { + content: "\f689"; } + +.fa-forward-step::before { + content: "\f051"; } + +.fa-step-forward::before { + content: "\f051"; } + +.fa-face-smile-beam::before { + content: "\f5b8"; } + +.fa-smile-beam::before { + content: "\f5b8"; } + +.fa-flag-checkered::before { + content: "\f11e"; } + +.fa-football::before { + content: "\f44e"; } + +.fa-football-ball::before { + content: "\f44e"; } + +.fa-school-circle-exclamation::before { + content: "\e56c"; } + +.fa-crop::before { + content: "\f125"; } + +.fa-angles-down::before { + content: "\f103"; } + +.fa-angle-double-down::before { + content: "\f103"; } + +.fa-users-rectangle::before { + content: "\e594"; } + +.fa-people-roof::before { + content: "\e537"; } + +.fa-people-line::before { + content: "\e534"; } + +.fa-beer-mug-empty::before { + content: "\f0fc"; } + +.fa-beer::before { + content: "\f0fc"; } + +.fa-diagram-predecessor::before { + content: "\e477"; } + +.fa-arrow-up-long::before { + content: "\f176"; } + +.fa-long-arrow-up::before { + content: "\f176"; } + +.fa-fire-flame-simple::before { + content: "\f46a"; } + +.fa-burn::before { + content: "\f46a"; } + +.fa-person::before { + content: "\f183"; } + +.fa-male::before { + content: "\f183"; } + +.fa-laptop::before { + content: "\f109"; } + +.fa-file-csv::before { + content: "\f6dd"; } + +.fa-menorah::before { + content: "\f676"; } + +.fa-truck-plane::before { + content: "\e58f"; } + +.fa-record-vinyl::before { + content: "\f8d9"; } + +.fa-face-grin-stars::before { + content: "\f587"; } + +.fa-grin-stars::before { + content: "\f587"; } + +.fa-bong::before { + content: "\f55c"; } + +.fa-spaghetti-monster-flying::before { + content: "\f67b"; } + +.fa-pastafarianism::before { + content: "\f67b"; } + +.fa-arrow-down-up-across-line::before { + content: "\e4af"; } + +.fa-spoon::before { + content: "\f2e5"; } + +.fa-utensil-spoon::before { + content: "\f2e5"; } + +.fa-jar-wheat::before { + content: "\e517"; } + +.fa-envelopes-bulk::before { + content: "\f674"; } + +.fa-mail-bulk::before { + content: "\f674"; } + +.fa-file-circle-exclamation::before { + content: "\e4eb"; } + +.fa-circle-h::before { + content: "\f47e"; } + +.fa-hospital-symbol::before { + content: "\f47e"; } + +.fa-pager::before { + content: "\f815"; } + +.fa-address-book::before { + content: "\f2b9"; } + +.fa-contact-book::before { + content: "\f2b9"; } + +.fa-strikethrough::before { + content: "\f0cc"; } + +.fa-k::before { + content: "\4b"; } + +.fa-landmark-flag::before { + content: "\e51c"; } + +.fa-pencil::before { + content: "\f303"; } + +.fa-pencil-alt::before { + content: "\f303"; } + +.fa-backward::before { + content: "\f04a"; } + +.fa-caret-right::before { + content: "\f0da"; } + +.fa-comments::before { + content: "\f086"; } + +.fa-paste::before { + content: "\f0ea"; } + +.fa-file-clipboard::before { + content: "\f0ea"; } + +.fa-code-pull-request::before { + content: "\e13c"; } + +.fa-clipboard-list::before { + content: "\f46d"; } + +.fa-truck-ramp-box::before { + content: "\f4de"; } + +.fa-truck-loading::before { + content: "\f4de"; } + +.fa-user-check::before { + content: "\f4fc"; } + +.fa-vial-virus::before { + content: "\e597"; } + +.fa-sheet-plastic::before { + content: "\e571"; } + +.fa-blog::before { + content: "\f781"; } + +.fa-user-ninja::before { + content: "\f504"; } + +.fa-person-arrow-up-from-line::before { + content: "\e539"; } + +.fa-scroll-torah::before { + content: "\f6a0"; } + +.fa-torah::before { + content: "\f6a0"; } + +.fa-broom-ball::before { + content: "\f458"; } + +.fa-quidditch::before { + content: "\f458"; } + +.fa-quidditch-broom-ball::before { + content: "\f458"; } + +.fa-toggle-off::before { + content: "\f204"; } + +.fa-box-archive::before { + content: "\f187"; } + +.fa-archive::before { + content: "\f187"; } + +.fa-person-drowning::before { + content: "\e545"; } + +.fa-arrow-down-9-1::before { + content: "\f886"; } + +.fa-sort-numeric-desc::before { + content: "\f886"; } + +.fa-sort-numeric-down-alt::before { + content: "\f886"; } + +.fa-face-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-grin-tongue-squint::before { + content: "\f58a"; } + +.fa-spray-can::before { + content: "\f5bd"; } + +.fa-truck-monster::before { + content: "\f63b"; } + +.fa-w::before { + content: "\57"; } + +.fa-earth-africa::before { + content: "\f57c"; } + +.fa-globe-africa::before { + content: "\f57c"; } + +.fa-rainbow::before { + content: "\f75b"; } + +.fa-circle-notch::before { + content: "\f1ce"; } + +.fa-tablet-screen-button::before { + content: "\f3fa"; } + +.fa-tablet-alt::before { + content: "\f3fa"; } + +.fa-paw::before { + content: "\f1b0"; } + +.fa-cloud::before { + content: "\f0c2"; } + +.fa-trowel-bricks::before { + content: "\e58a"; } + +.fa-face-flushed::before { + content: "\f579"; } + +.fa-flushed::before { + content: "\f579"; } + +.fa-hospital-user::before { + content: "\f80d"; } + +.fa-tent-arrow-left-right::before { + content: "\e57f"; } + +.fa-gavel::before { + content: "\f0e3"; } + +.fa-legal::before { + content: "\f0e3"; } + +.fa-binoculars::before { + content: "\f1e5"; } + +.fa-microphone-slash::before { + content: "\f131"; } + +.fa-box-tissue::before { + content: "\e05b"; } + +.fa-motorcycle::before { + content: "\f21c"; } + +.fa-bell-concierge::before { + content: "\f562"; } + +.fa-concierge-bell::before { + content: "\f562"; } + +.fa-pen-ruler::before { + content: "\f5ae"; } + +.fa-pencil-ruler::before { + content: "\f5ae"; } + +.fa-people-arrows::before { + content: "\e068"; } + +.fa-people-arrows-left-right::before { + content: "\e068"; } + +.fa-mars-and-venus-burst::before { + content: "\e523"; } + +.fa-square-caret-right::before { + content: "\f152"; } + +.fa-caret-square-right::before { + content: "\f152"; } + +.fa-scissors::before { + content: "\f0c4"; } + +.fa-cut::before { + content: "\f0c4"; } + +.fa-sun-plant-wilt::before { + content: "\e57a"; } + +.fa-toilets-portable::before { + content: "\e584"; } + +.fa-hockey-puck::before { + content: "\f453"; } + +.fa-table::before { + content: "\f0ce"; } + +.fa-magnifying-glass-arrow-right::before { + content: "\e521"; } + +.fa-tachograph-digital::before { + content: "\f566"; } + +.fa-digital-tachograph::before { + content: "\f566"; } + +.fa-users-slash::before { + content: "\e073"; } + +.fa-clover::before { + content: "\e139"; } + +.fa-reply::before { + content: "\f3e5"; } + +.fa-mail-reply::before { + content: "\f3e5"; } + +.fa-star-and-crescent::before { + content: "\f699"; } + +.fa-house-fire::before { + content: "\e50c"; } + +.fa-square-minus::before { + content: "\f146"; } + +.fa-minus-square::before { + content: "\f146"; } + +.fa-helicopter::before { + content: "\f533"; } + +.fa-compass::before { + content: "\f14e"; } + +.fa-square-caret-down::before { + content: "\f150"; } + +.fa-caret-square-down::before { + content: "\f150"; } + +.fa-file-circle-question::before { + content: "\e4ef"; } + +.fa-laptop-code::before { + content: "\f5fc"; } + +.fa-swatchbook::before { + content: "\f5c3"; } + +.fa-prescription-bottle::before { + content: "\f485"; } + +.fa-bars::before { + content: "\f0c9"; } + +.fa-navicon::before { + content: "\f0c9"; } + +.fa-people-group::before { + content: "\e533"; } + +.fa-hourglass-end::before { + content: "\f253"; } + +.fa-hourglass-3::before { + content: "\f253"; } + +.fa-heart-crack::before { + content: "\f7a9"; } + +.fa-heart-broken::before { + content: "\f7a9"; } + +.fa-square-up-right::before { + content: "\f360"; } + +.fa-external-link-square-alt::before { + content: "\f360"; } + +.fa-face-kiss-beam::before { + content: "\f597"; } + +.fa-kiss-beam::before { + content: "\f597"; } + +.fa-film::before { + content: "\f008"; } + +.fa-ruler-horizontal::before { + content: "\f547"; } + +.fa-people-robbery::before { + content: "\e536"; } + +.fa-lightbulb::before { + content: "\f0eb"; } + +.fa-caret-left::before { + content: "\f0d9"; } + +.fa-circle-exclamation::before { + content: "\f06a"; } + +.fa-exclamation-circle::before { + content: "\f06a"; } + +.fa-school-circle-xmark::before { + content: "\e56d"; } + +.fa-arrow-right-from-bracket::before { + content: "\f08b"; } + +.fa-sign-out::before { + content: "\f08b"; } + +.fa-circle-chevron-down::before { + content: "\f13a"; } + +.fa-chevron-circle-down::before { + content: "\f13a"; } + +.fa-unlock-keyhole::before { + content: "\f13e"; } + +.fa-unlock-alt::before { + content: "\f13e"; } + +.fa-cloud-showers-heavy::before { + content: "\f740"; } + +.fa-headphones-simple::before { + content: "\f58f"; } + +.fa-headphones-alt::before { + content: "\f58f"; } + +.fa-sitemap::before { + content: "\f0e8"; } + +.fa-circle-dollar-to-slot::before { + content: "\f4b9"; } + +.fa-donate::before { + content: "\f4b9"; } + +.fa-memory::before { + content: "\f538"; } + +.fa-road-spikes::before { + content: "\e568"; } + +.fa-fire-burner::before { + content: "\e4f1"; } + +.fa-flag::before { + content: "\f024"; } + +.fa-hanukiah::before { + content: "\f6e6"; } + +.fa-feather::before { + content: "\f52d"; } + +.fa-volume-low::before { + content: "\f027"; } + +.fa-volume-down::before { + content: "\f027"; } + +.fa-comment-slash::before { + content: "\f4b3"; } + +.fa-cloud-sun-rain::before { + content: "\f743"; } + +.fa-compress::before { + content: "\f066"; } + +.fa-wheat-awn::before { + content: "\e2cd"; } + +.fa-wheat-alt::before { + content: "\e2cd"; } + +.fa-ankh::before { + content: "\f644"; } + +.fa-hands-holding-child::before { + content: "\e4fa"; } + +.fa-asterisk::before { + content: "\2a"; } + +.fa-square-check::before { + content: "\f14a"; } + +.fa-check-square::before { + content: "\f14a"; } + +.fa-peseta-sign::before { + content: "\e221"; } + +.fa-heading::before { + content: "\f1dc"; } + +.fa-header::before { + content: "\f1dc"; } + +.fa-ghost::before { + content: "\f6e2"; } + +.fa-list::before { + content: "\f03a"; } + +.fa-list-squares::before { + content: "\f03a"; } + +.fa-square-phone-flip::before { + content: "\f87b"; } + +.fa-phone-square-alt::before { + content: "\f87b"; } + +.fa-cart-plus::before { + content: "\f217"; } + +.fa-gamepad::before { + content: "\f11b"; } + +.fa-circle-dot::before { + content: "\f192"; } + +.fa-dot-circle::before { + content: "\f192"; } + +.fa-face-dizzy::before { + content: "\f567"; } + +.fa-dizzy::before { + content: "\f567"; } + +.fa-egg::before { + content: "\f7fb"; } + +.fa-house-medical-circle-xmark::before { + content: "\e513"; } + +.fa-campground::before { + content: "\f6bb"; } + +.fa-folder-plus::before { + content: "\f65e"; } + +.fa-futbol::before { + content: "\f1e3"; } + +.fa-futbol-ball::before { + content: "\f1e3"; } + +.fa-soccer-ball::before { + content: "\f1e3"; } + +.fa-paintbrush::before { + content: "\f1fc"; } + +.fa-paint-brush::before { + content: "\f1fc"; } + +.fa-lock::before { + content: "\f023"; } + +.fa-gas-pump::before { + content: "\f52f"; } + +.fa-hot-tub-person::before { + content: "\f593"; } + +.fa-hot-tub::before { + content: "\f593"; } + +.fa-map-location::before { + content: "\f59f"; } + +.fa-map-marked::before { + content: "\f59f"; } + +.fa-house-flood-water::before { + content: "\e50e"; } + +.fa-tree::before { + content: "\f1bb"; } + +.fa-bridge-lock::before { + content: "\e4cc"; } + +.fa-sack-dollar::before { + content: "\f81d"; } + +.fa-pen-to-square::before { + content: "\f044"; } + +.fa-edit::before { + content: "\f044"; } + +.fa-car-side::before { + content: "\f5e4"; } + +.fa-share-nodes::before { + content: "\f1e0"; } + +.fa-share-alt::before { + content: "\f1e0"; } + +.fa-heart-circle-minus::before { + content: "\e4ff"; } + +.fa-hourglass-half::before { + content: "\f252"; } + +.fa-hourglass-2::before { + content: "\f252"; } + +.fa-microscope::before { + content: "\f610"; } + +.fa-sink::before { + content: "\e06d"; } + +.fa-bag-shopping::before { + content: "\f290"; } + +.fa-shopping-bag::before { + content: "\f290"; } + +.fa-arrow-down-z-a::before { + content: "\f881"; } + +.fa-sort-alpha-desc::before { + content: "\f881"; } + +.fa-sort-alpha-down-alt::before { + content: "\f881"; } + +.fa-mitten::before { + content: "\f7b5"; } + +.fa-person-rays::before { + content: "\e54d"; } + +.fa-users::before { + content: "\f0c0"; } + +.fa-eye-slash::before { + content: "\f070"; } + +.fa-flask-vial::before { + content: "\e4f3"; } + +.fa-hand::before { + content: "\f256"; } + +.fa-hand-paper::before { + content: "\f256"; } + +.fa-om::before { + content: "\f679"; } + +.fa-worm::before { + content: "\e599"; } + +.fa-house-circle-xmark::before { + content: "\e50b"; } + +.fa-plug::before { + content: "\f1e6"; } + +.fa-chevron-up::before { + content: "\f077"; } + +.fa-hand-spock::before { + content: "\f259"; } + +.fa-stopwatch::before { + content: "\f2f2"; } + +.fa-face-kiss::before { + content: "\f596"; } + +.fa-kiss::before { + content: "\f596"; } + +.fa-bridge-circle-xmark::before { + content: "\e4cb"; } + +.fa-face-grin-tongue::before { + content: "\f589"; } + +.fa-grin-tongue::before { + content: "\f589"; } + +.fa-chess-bishop::before { + content: "\f43a"; } + +.fa-face-grin-wink::before { + content: "\f58c"; } + +.fa-grin-wink::before { + content: "\f58c"; } + +.fa-ear-deaf::before { + content: "\f2a4"; } + +.fa-deaf::before { + content: "\f2a4"; } + +.fa-deafness::before { + content: "\f2a4"; } + +.fa-hard-of-hearing::before { + content: "\f2a4"; } + +.fa-road-circle-check::before { + content: "\e564"; } + +.fa-dice-five::before { + content: "\f523"; } + +.fa-square-rss::before { + content: "\f143"; } + +.fa-rss-square::before { + content: "\f143"; } + +.fa-land-mine-on::before { + content: "\e51b"; } + +.fa-i-cursor::before { + content: "\f246"; } + +.fa-stamp::before { + content: "\f5bf"; } + +.fa-stairs::before { + content: "\e289"; } + +.fa-i::before { + content: "\49"; } + +.fa-hryvnia-sign::before { + content: "\f6f2"; } + +.fa-hryvnia::before { + content: "\f6f2"; } + +.fa-pills::before { + content: "\f484"; } + +.fa-face-grin-wide::before { + content: "\f581"; } + +.fa-grin-alt::before { + content: "\f581"; } + +.fa-tooth::before { + content: "\f5c9"; } + +.fa-v::before { + content: "\56"; } + +.fa-bangladeshi-taka-sign::before { + content: "\e2e6"; } + +.fa-bicycle::before { + content: "\f206"; } + +.fa-staff-snake::before { + content: "\e579"; } + +.fa-rod-asclepius::before { + content: "\e579"; } + +.fa-rod-snake::before { + content: "\e579"; } + +.fa-staff-aesculapius::before { + content: "\e579"; } + +.fa-head-side-cough-slash::before { + content: "\e062"; } + +.fa-truck-medical::before { + content: "\f0f9"; } + +.fa-ambulance::before { + content: "\f0f9"; } + +.fa-wheat-awn-circle-exclamation::before { + content: "\e598"; } + +.fa-snowman::before { + content: "\f7d0"; } + +.fa-mortar-pestle::before { + content: "\f5a7"; } + +.fa-road-barrier::before { + content: "\e562"; } + +.fa-school::before { + content: "\f549"; } + +.fa-igloo::before { + content: "\f7ae"; } + +.fa-joint::before { + content: "\f595"; } + +.fa-angle-right::before { + content: "\f105"; } + +.fa-horse::before { + content: "\f6f0"; } + +.fa-q::before { + content: "\51"; } + +.fa-g::before { + content: "\47"; } + +.fa-notes-medical::before { + content: "\f481"; } + +.fa-temperature-half::before { + content: "\f2c9"; } + +.fa-temperature-2::before { + content: "\f2c9"; } + +.fa-thermometer-2::before { + content: "\f2c9"; } + +.fa-thermometer-half::before { + content: "\f2c9"; } + +.fa-dong-sign::before { + content: "\e169"; } + +.fa-capsules::before { + content: "\f46b"; } + +.fa-poo-storm::before { + content: "\f75a"; } + +.fa-poo-bolt::before { + content: "\f75a"; } + +.fa-face-frown-open::before { + content: "\f57a"; } + +.fa-frown-open::before { + content: "\f57a"; } + +.fa-hand-point-up::before { + content: "\f0a6"; } + +.fa-money-bill::before { + content: "\f0d6"; } + +.fa-bookmark::before { + content: "\f02e"; } + +.fa-align-justify::before { + content: "\f039"; } + +.fa-umbrella-beach::before { + content: "\f5ca"; } + +.fa-helmet-un::before { + content: "\e503"; } + +.fa-bullseye::before { + content: "\f140"; } + +.fa-bacon::before { + content: "\f7e5"; } + +.fa-hand-point-down::before { + content: "\f0a7"; } + +.fa-arrow-up-from-bracket::before { + content: "\e09a"; } + +.fa-folder::before { + content: "\f07b"; } + +.fa-folder-blank::before { + content: "\f07b"; } + +.fa-file-waveform::before { + content: "\f478"; } + +.fa-file-medical-alt::before { + content: "\f478"; } + +.fa-radiation::before { + content: "\f7b9"; } + +.fa-chart-simple::before { + content: "\e473"; } + +.fa-mars-stroke::before { + content: "\f229"; } + +.fa-vial::before { + content: "\f492"; } + +.fa-gauge::before { + content: "\f624"; } + +.fa-dashboard::before { + content: "\f624"; } + +.fa-gauge-med::before { + content: "\f624"; } + +.fa-tachometer-alt-average::before { + content: "\f624"; } + +.fa-wand-magic-sparkles::before { + content: "\e2ca"; } + +.fa-magic-wand-sparkles::before { + content: "\e2ca"; } + +.fa-e::before { + content: "\45"; } + +.fa-pen-clip::before { + content: "\f305"; } + +.fa-pen-alt::before { + content: "\f305"; } + +.fa-bridge-circle-exclamation::before { + content: "\e4ca"; } + +.fa-user::before { + content: "\f007"; } + +.fa-school-circle-check::before { + content: "\e56b"; } + +.fa-dumpster::before { + content: "\f793"; } + +.fa-van-shuttle::before { + content: "\f5b6"; } + +.fa-shuttle-van::before { + content: "\f5b6"; } + +.fa-building-user::before { + content: "\e4da"; } + +.fa-square-caret-left::before { + content: "\f191"; } + +.fa-caret-square-left::before { + content: "\f191"; } + +.fa-highlighter::before { + content: "\f591"; } + +.fa-key::before { + content: "\f084"; } + +.fa-bullhorn::before { + content: "\f0a1"; } + +.fa-globe::before { + content: "\f0ac"; } + +.fa-synagogue::before { + content: "\f69b"; } + +.fa-person-half-dress::before { + content: "\e548"; } + +.fa-road-bridge::before { + content: "\e563"; } + +.fa-location-arrow::before { + content: "\f124"; } + +.fa-c::before { + content: "\43"; } + +.fa-tablet-button::before { + content: "\f10a"; } + +.fa-building-lock::before { + content: "\e4d6"; } + +.fa-pizza-slice::before { + content: "\f818"; } + +.fa-money-bill-wave::before { + content: "\f53a"; } + +.fa-chart-area::before { + content: "\f1fe"; } + +.fa-area-chart::before { + content: "\f1fe"; } + +.fa-house-flag::before { + content: "\e50d"; } + +.fa-person-circle-minus::before { + content: "\e540"; } + +.fa-ban::before { + content: "\f05e"; } + +.fa-cancel::before { + content: "\f05e"; } + +.fa-camera-rotate::before { + content: "\e0d8"; } + +.fa-spray-can-sparkles::before { + content: "\f5d0"; } + +.fa-air-freshener::before { + content: "\f5d0"; } + +.fa-star::before { + content: "\f005"; } + +.fa-repeat::before { + content: "\f363"; } + +.fa-cross::before { + content: "\f654"; } + +.fa-box::before { + content: "\f466"; } + +.fa-venus-mars::before { + content: "\f228"; } + +.fa-arrow-pointer::before { + content: "\f245"; } + +.fa-mouse-pointer::before { + content: "\f245"; } + +.fa-maximize::before { + content: "\f31e"; } + +.fa-expand-arrows-alt::before { + content: "\f31e"; } + +.fa-charging-station::before { + content: "\f5e7"; } + +.fa-shapes::before { + content: "\f61f"; } + +.fa-triangle-circle-square::before { + content: "\f61f"; } + +.fa-shuffle::before { + content: "\f074"; } + +.fa-random::before { + content: "\f074"; } + +.fa-person-running::before { + content: "\f70c"; } + +.fa-running::before { + content: "\f70c"; } + +.fa-mobile-retro::before { + content: "\e527"; } + +.fa-grip-lines-vertical::before { + content: "\f7a5"; } + +.fa-spider::before { + content: "\f717"; } + +.fa-hands-bound::before { + content: "\e4f9"; } + +.fa-file-invoice-dollar::before { + content: "\f571"; } + +.fa-plane-circle-exclamation::before { + content: "\e556"; } + +.fa-x-ray::before { + content: "\f497"; } + +.fa-spell-check::before { + content: "\f891"; } + +.fa-slash::before { + content: "\f715"; } + +.fa-computer-mouse::before { + content: "\f8cc"; } + +.fa-mouse::before { + content: "\f8cc"; } + +.fa-arrow-right-to-bracket::before { + content: "\f090"; } + +.fa-sign-in::before { + content: "\f090"; } + +.fa-shop-slash::before { + content: "\e070"; } + +.fa-store-alt-slash::before { + content: "\e070"; } + +.fa-server::before { + content: "\f233"; } + +.fa-virus-covid-slash::before { + content: "\e4a9"; } + +.fa-shop-lock::before { + content: "\e4a5"; } + +.fa-hourglass-start::before { + content: "\f251"; } + +.fa-hourglass-1::before { + content: "\f251"; } + +.fa-blender-phone::before { + content: "\f6b6"; } + +.fa-building-wheat::before { + content: "\e4db"; } + +.fa-person-breastfeeding::before { + content: "\e53a"; } + +.fa-right-to-bracket::before { + content: "\f2f6"; } + +.fa-sign-in-alt::before { + content: "\f2f6"; } + +.fa-venus::before { + content: "\f221"; } + +.fa-passport::before { + content: "\f5ab"; } + +.fa-heart-pulse::before { + content: "\f21e"; } + +.fa-heartbeat::before { + content: "\f21e"; } + +.fa-people-carry-box::before { + content: "\f4ce"; } + +.fa-people-carry::before { + content: "\f4ce"; } + +.fa-temperature-high::before { + content: "\f769"; } + +.fa-microchip::before { + content: "\f2db"; } + +.fa-crown::before { + content: "\f521"; } + +.fa-weight-hanging::before { + content: "\f5cd"; } + +.fa-xmarks-lines::before { + content: "\e59a"; } + +.fa-file-prescription::before { + content: "\f572"; } + +.fa-weight-scale::before { + content: "\f496"; } + +.fa-weight::before { + content: "\f496"; } + +.fa-user-group::before { + content: "\f500"; } + +.fa-user-friends::before { + content: "\f500"; } + +.fa-arrow-up-a-z::before { + content: "\f15e"; } + +.fa-sort-alpha-up::before { + content: "\f15e"; } + +.fa-chess-knight::before { + content: "\f441"; } + +.fa-face-laugh-squint::before { + content: "\f59b"; } + +.fa-laugh-squint::before { + content: "\f59b"; } + +.fa-wheelchair::before { + content: "\f193"; } + +.fa-circle-arrow-up::before { + content: "\f0aa"; } + +.fa-arrow-circle-up::before { + content: "\f0aa"; } + +.fa-toggle-on::before { + content: "\f205"; } + +.fa-person-walking::before { + content: "\f554"; } + +.fa-walking::before { + content: "\f554"; } + +.fa-l::before { + content: "\4c"; } + +.fa-fire::before { + content: "\f06d"; } + +.fa-bed-pulse::before { + content: "\f487"; } + +.fa-procedures::before { + content: "\f487"; } + +.fa-shuttle-space::before { + content: "\f197"; } + +.fa-space-shuttle::before { + content: "\f197"; } + +.fa-face-laugh::before { + content: "\f599"; } + +.fa-laugh::before { + content: "\f599"; } + +.fa-folder-open::before { + content: "\f07c"; } + +.fa-heart-circle-plus::before { + content: "\e500"; } + +.fa-code-fork::before { + content: "\e13b"; } + +.fa-city::before { + content: "\f64f"; } + +.fa-microphone-lines::before { + content: "\f3c9"; } + +.fa-microphone-alt::before { + content: "\f3c9"; } + +.fa-pepper-hot::before { + content: "\f816"; } + +.fa-unlock::before { + content: "\f09c"; } + +.fa-colon-sign::before { + content: "\e140"; } + +.fa-headset::before { + content: "\f590"; } + +.fa-store-slash::before { + content: "\e071"; } + +.fa-road-circle-xmark::before { + content: "\e566"; } + +.fa-user-minus::before { + content: "\f503"; } + +.fa-mars-stroke-up::before { + content: "\f22a"; } + +.fa-mars-stroke-v::before { + content: "\f22a"; } + +.fa-champagne-glasses::before { + content: "\f79f"; } + +.fa-glass-cheers::before { + content: "\f79f"; } + +.fa-clipboard::before { + content: "\f328"; } + +.fa-house-circle-exclamation::before { + content: "\e50a"; } + +.fa-file-arrow-up::before { + content: "\f574"; } + +.fa-file-upload::before { + content: "\f574"; } + +.fa-wifi::before { + content: "\f1eb"; } + +.fa-wifi-3::before { + content: "\f1eb"; } + +.fa-wifi-strong::before { + content: "\f1eb"; } + +.fa-bath::before { + content: "\f2cd"; } + +.fa-bathtub::before { + content: "\f2cd"; } + +.fa-underline::before { + content: "\f0cd"; } + +.fa-user-pen::before { + content: "\f4ff"; } + +.fa-user-edit::before { + content: "\f4ff"; } + +.fa-signature::before { + content: "\f5b7"; } + +.fa-stroopwafel::before { + content: "\f551"; } + +.fa-bold::before { + content: "\f032"; } + +.fa-anchor-lock::before { + content: "\e4ad"; } + +.fa-building-ngo::before { + content: "\e4d7"; } + +.fa-manat-sign::before { + content: "\e1d5"; } + +.fa-not-equal::before { + content: "\f53e"; } + +.fa-border-top-left::before { + content: "\f853"; } + +.fa-border-style::before { + content: "\f853"; } + +.fa-map-location-dot::before { + content: "\f5a0"; } + +.fa-map-marked-alt::before { + content: "\f5a0"; } + +.fa-jedi::before { + content: "\f669"; } + +.fa-square-poll-vertical::before { + content: "\f681"; } + +.fa-poll::before { + content: "\f681"; } + +.fa-mug-hot::before { + content: "\f7b6"; } + +.fa-car-battery::before { + content: "\f5df"; } + +.fa-battery-car::before { + content: "\f5df"; } + +.fa-gift::before { + content: "\f06b"; } + +.fa-dice-two::before { + content: "\f528"; } + +.fa-chess-queen::before { + content: "\f445"; } + +.fa-glasses::before { + content: "\f530"; } + +.fa-chess-board::before { + content: "\f43c"; } + +.fa-building-circle-check::before { + content: "\e4d2"; } + +.fa-person-chalkboard::before { + content: "\e53d"; } + +.fa-mars-stroke-right::before { + content: "\f22b"; } + +.fa-mars-stroke-h::before { + content: "\f22b"; } + +.fa-hand-back-fist::before { + content: "\f255"; } + +.fa-hand-rock::before { + content: "\f255"; } + +.fa-square-caret-up::before { + content: "\f151"; } + +.fa-caret-square-up::before { + content: "\f151"; } + +.fa-cloud-showers-water::before { + content: "\e4e4"; } + +.fa-chart-bar::before { + content: "\f080"; } + +.fa-bar-chart::before { + content: "\f080"; } + +.fa-hands-bubbles::before { + content: "\e05e"; } + +.fa-hands-wash::before { + content: "\e05e"; } + +.fa-less-than-equal::before { + content: "\f537"; } + +.fa-train::before { + content: "\f238"; } + +.fa-eye-low-vision::before { + content: "\f2a8"; } + +.fa-low-vision::before { + content: "\f2a8"; } + +.fa-crow::before { + content: "\f520"; } + +.fa-sailboat::before { + content: "\e445"; } + +.fa-window-restore::before { + content: "\f2d2"; } + +.fa-square-plus::before { + content: "\f0fe"; } + +.fa-plus-square::before { + content: "\f0fe"; } + +.fa-torii-gate::before { + content: "\f6a1"; } + +.fa-frog::before { + content: "\f52e"; } + +.fa-bucket::before { + content: "\e4cf"; } + +.fa-image::before { + content: "\f03e"; } + +.fa-microphone::before { + content: "\f130"; } + +.fa-cow::before { + content: "\f6c8"; } + +.fa-caret-up::before { + content: "\f0d8"; } + +.fa-screwdriver::before { + content: "\f54a"; } + +.fa-folder-closed::before { + content: "\e185"; } + +.fa-house-tsunami::before { + content: "\e515"; } + +.fa-square-nfi::before { + content: "\e576"; } + +.fa-arrow-up-from-ground-water::before { + content: "\e4b5"; } + +.fa-martini-glass::before { + content: "\f57b"; } + +.fa-glass-martini-alt::before { + content: "\f57b"; } + +.fa-rotate-left::before { + content: "\f2ea"; } + +.fa-rotate-back::before { + content: "\f2ea"; } + +.fa-rotate-backward::before { + content: "\f2ea"; } + +.fa-undo-alt::before { + content: "\f2ea"; } + +.fa-table-columns::before { + content: "\f0db"; } + +.fa-columns::before { + content: "\f0db"; } + +.fa-lemon::before { + content: "\f094"; } + +.fa-head-side-mask::before { + content: "\e063"; } + +.fa-handshake::before { + content: "\f2b5"; } + +.fa-gem::before { + content: "\f3a5"; } + +.fa-dolly::before { + content: "\f472"; } + +.fa-dolly-box::before { + content: "\f472"; } + +.fa-smoking::before { + content: "\f48d"; } + +.fa-minimize::before { + content: "\f78c"; } + +.fa-compress-arrows-alt::before { + content: "\f78c"; } + +.fa-monument::before { + content: "\f5a6"; } + +.fa-snowplow::before { + content: "\f7d2"; } + +.fa-angles-right::before { + content: "\f101"; } + +.fa-angle-double-right::before { + content: "\f101"; } + +.fa-cannabis::before { + content: "\f55f"; } + +.fa-circle-play::before { + content: "\f144"; } + +.fa-play-circle::before { + content: "\f144"; } + +.fa-tablets::before { + content: "\f490"; } + +.fa-ethernet::before { + content: "\f796"; } + +.fa-euro-sign::before { + content: "\f153"; } + +.fa-eur::before { + content: "\f153"; } + +.fa-euro::before { + content: "\f153"; } + +.fa-chair::before { + content: "\f6c0"; } + +.fa-circle-check::before { + content: "\f058"; } + +.fa-check-circle::before { + content: "\f058"; } + +.fa-circle-stop::before { + content: "\f28d"; } + +.fa-stop-circle::before { + content: "\f28d"; } + +.fa-compass-drafting::before { + content: "\f568"; } + +.fa-drafting-compass::before { + content: "\f568"; } + +.fa-plate-wheat::before { + content: "\e55a"; } + +.fa-icicles::before { + content: "\f7ad"; } + +.fa-person-shelter::before { + content: "\e54f"; } + +.fa-neuter::before { + content: "\f22c"; } + +.fa-id-badge::before { + content: "\f2c1"; } + +.fa-marker::before { + content: "\f5a1"; } + +.fa-face-laugh-beam::before { + content: "\f59a"; } + +.fa-laugh-beam::before { + content: "\f59a"; } + +.fa-helicopter-symbol::before { + content: "\e502"; } + +.fa-universal-access::before { + content: "\f29a"; } + +.fa-circle-chevron-up::before { + content: "\f139"; } + +.fa-chevron-circle-up::before { + content: "\f139"; } + +.fa-lari-sign::before { + content: "\e1c8"; } + +.fa-volcano::before { + content: "\f770"; } + +.fa-person-walking-dashed-line-arrow-right::before { + content: "\e553"; } + +.fa-sterling-sign::before { + content: "\f154"; } + +.fa-gbp::before { + content: "\f154"; } + +.fa-pound-sign::before { + content: "\f154"; } + +.fa-viruses::before { + content: "\e076"; } + +.fa-square-person-confined::before { + content: "\e577"; } + +.fa-user-tie::before { + content: "\f508"; } + +.fa-arrow-down-long::before { + content: "\f175"; } + +.fa-long-arrow-down::before { + content: "\f175"; } + +.fa-tent-arrow-down-to-line::before { + content: "\e57e"; } + +.fa-certificate::before { + content: "\f0a3"; } + +.fa-reply-all::before { + content: "\f122"; } + +.fa-mail-reply-all::before { + content: "\f122"; } + +.fa-suitcase::before { + content: "\f0f2"; } + +.fa-person-skating::before { + content: "\f7c5"; } + +.fa-skating::before { + content: "\f7c5"; } + +.fa-filter-circle-dollar::before { + content: "\f662"; } + +.fa-funnel-dollar::before { + content: "\f662"; } + +.fa-camera-retro::before { + content: "\f083"; } + +.fa-circle-arrow-down::before { + content: "\f0ab"; } + +.fa-arrow-circle-down::before { + content: "\f0ab"; } + +.fa-file-import::before { + content: "\f56f"; } + +.fa-arrow-right-to-file::before { + content: "\f56f"; } + +.fa-square-arrow-up-right::before { + content: "\f14c"; } + +.fa-external-link-square::before { + content: "\f14c"; } + +.fa-box-open::before { + content: "\f49e"; } + +.fa-scroll::before { + content: "\f70e"; } + +.fa-spa::before { + content: "\f5bb"; } + +.fa-location-pin-lock::before { + content: "\e51f"; } + +.fa-pause::before { + content: "\f04c"; } + +.fa-hill-avalanche::before { + content: "\e507"; } + +.fa-temperature-empty::before { + content: "\f2cb"; } + +.fa-temperature-0::before { + content: "\f2cb"; } + +.fa-thermometer-0::before { + content: "\f2cb"; } + +.fa-thermometer-empty::before { + content: "\f2cb"; } + +.fa-bomb::before { + content: "\f1e2"; } + +.fa-registered::before { + content: "\f25d"; } + +.fa-address-card::before { + content: "\f2bb"; } + +.fa-contact-card::before { + content: "\f2bb"; } + +.fa-vcard::before { + content: "\f2bb"; } + +.fa-scale-unbalanced-flip::before { + content: "\f516"; } + +.fa-balance-scale-right::before { + content: "\f516"; } + +.fa-subscript::before { + content: "\f12c"; } + +.fa-diamond-turn-right::before { + content: "\f5eb"; } + +.fa-directions::before { + content: "\f5eb"; } + +.fa-burst::before { + content: "\e4dc"; } + +.fa-house-laptop::before { + content: "\e066"; } + +.fa-laptop-house::before { + content: "\e066"; } + +.fa-face-tired::before { + content: "\f5c8"; } + +.fa-tired::before { + content: "\f5c8"; } + +.fa-money-bills::before { + content: "\e1f3"; } + +.fa-smog::before { + content: "\f75f"; } + +.fa-crutch::before { + content: "\f7f7"; } + +.fa-cloud-arrow-up::before { + content: "\f0ee"; } + +.fa-cloud-upload::before { + content: "\f0ee"; } + +.fa-cloud-upload-alt::before { + content: "\f0ee"; } + +.fa-palette::before { + content: "\f53f"; } + +.fa-arrows-turn-right::before { + content: "\e4c0"; } + +.fa-vest::before { + content: "\e085"; } + +.fa-ferry::before { + content: "\e4ea"; } + +.fa-arrows-down-to-people::before { + content: "\e4b9"; } + +.fa-seedling::before { + content: "\f4d8"; } + +.fa-sprout::before { + content: "\f4d8"; } + +.fa-left-right::before { + content: "\f337"; } + +.fa-arrows-alt-h::before { + content: "\f337"; } + +.fa-boxes-packing::before { + content: "\e4c7"; } + +.fa-circle-arrow-left::before { + content: "\f0a8"; } + +.fa-arrow-circle-left::before { + content: "\f0a8"; } + +.fa-group-arrows-rotate::before { + content: "\e4f6"; } + +.fa-bowl-food::before { + content: "\e4c6"; } + +.fa-candy-cane::before { + content: "\f786"; } + +.fa-arrow-down-wide-short::before { + content: "\f160"; } + +.fa-sort-amount-asc::before { + content: "\f160"; } + +.fa-sort-amount-down::before { + content: "\f160"; } + +.fa-cloud-bolt::before { + content: "\f76c"; } + +.fa-thunderstorm::before { + content: "\f76c"; } + +.fa-text-slash::before { + content: "\f87d"; } + +.fa-remove-format::before { + content: "\f87d"; } + +.fa-face-smile-wink::before { + content: "\f4da"; } + +.fa-smile-wink::before { + content: "\f4da"; } + +.fa-file-word::before { + content: "\f1c2"; } + +.fa-file-powerpoint::before { + content: "\f1c4"; } + +.fa-arrows-left-right::before { + content: "\f07e"; } + +.fa-arrows-h::before { + content: "\f07e"; } + +.fa-house-lock::before { + content: "\e510"; } + +.fa-cloud-arrow-down::before { + content: "\f0ed"; } + +.fa-cloud-download::before { + content: "\f0ed"; } + +.fa-cloud-download-alt::before { + content: "\f0ed"; } + +.fa-children::before { + content: "\e4e1"; } + +.fa-chalkboard::before { + content: "\f51b"; } + +.fa-blackboard::before { + content: "\f51b"; } + +.fa-user-large-slash::before { + content: "\f4fa"; } + +.fa-user-alt-slash::before { + content: "\f4fa"; } + +.fa-envelope-open::before { + content: "\f2b6"; } + +.fa-handshake-simple-slash::before { + content: "\e05f"; } + +.fa-handshake-alt-slash::before { + content: "\e05f"; } + +.fa-mattress-pillow::before { + content: "\e525"; } + +.fa-guarani-sign::before { + content: "\e19a"; } + +.fa-arrows-rotate::before { + content: "\f021"; } + +.fa-refresh::before { + content: "\f021"; } + +.fa-sync::before { + content: "\f021"; } + +.fa-fire-extinguisher::before { + content: "\f134"; } + +.fa-cruzeiro-sign::before { + content: "\e152"; } + +.fa-greater-than-equal::before { + content: "\f532"; } + +.fa-shield-halved::before { + content: "\f3ed"; } + +.fa-shield-alt::before { + content: "\f3ed"; } + +.fa-book-atlas::before { + content: "\f558"; } + +.fa-atlas::before { + content: "\f558"; } + +.fa-virus::before { + content: "\e074"; } + +.fa-envelope-circle-check::before { + content: "\e4e8"; } + +.fa-layer-group::before { + content: "\f5fd"; } + +.fa-arrows-to-dot::before { + content: "\e4be"; } + +.fa-archway::before { + content: "\f557"; } + +.fa-heart-circle-check::before { + content: "\e4fd"; } + +.fa-house-chimney-crack::before { + content: "\f6f1"; } + +.fa-house-damage::before { + content: "\f6f1"; } + +.fa-file-zipper::before { + content: "\f1c6"; } + +.fa-file-archive::before { + content: "\f1c6"; } + +.fa-square::before { + content: "\f0c8"; } + +.fa-martini-glass-empty::before { + content: "\f000"; } + +.fa-glass-martini::before { + content: "\f000"; } + +.fa-couch::before { + content: "\f4b8"; } + +.fa-cedi-sign::before { + content: "\e0df"; } + +.fa-italic::before { + content: "\f033"; } + +.fa-church::before { + content: "\f51d"; } + +.fa-comments-dollar::before { + content: "\f653"; } + +.fa-democrat::before { + content: "\f747"; } + +.fa-z::before { + content: "\5a"; } + +.fa-person-skiing::before { + content: "\f7c9"; } + +.fa-skiing::before { + content: "\f7c9"; } + +.fa-road-lock::before { + content: "\e567"; } + +.fa-a::before { + content: "\41"; } + +.fa-temperature-arrow-down::before { + content: "\e03f"; } + +.fa-temperature-down::before { + content: "\e03f"; } + +.fa-feather-pointed::before { + content: "\f56b"; } + +.fa-feather-alt::before { + content: "\f56b"; } + +.fa-p::before { + content: "\50"; } + +.fa-snowflake::before { + content: "\f2dc"; } + +.fa-newspaper::before { + content: "\f1ea"; } + +.fa-rectangle-ad::before { + content: "\f641"; } + +.fa-ad::before { + content: "\f641"; } + +.fa-circle-arrow-right::before { + content: "\f0a9"; } + +.fa-arrow-circle-right::before { + content: "\f0a9"; } + +.fa-filter-circle-xmark::before { + content: "\e17b"; } + +.fa-locust::before { + content: "\e520"; } + +.fa-sort::before { + content: "\f0dc"; } + +.fa-unsorted::before { + content: "\f0dc"; } + +.fa-list-ol::before { + content: "\f0cb"; } + +.fa-list-1-2::before { + content: "\f0cb"; } + +.fa-list-numeric::before { + content: "\f0cb"; } + +.fa-person-dress-burst::before { + content: "\e544"; } + +.fa-money-check-dollar::before { + content: "\f53d"; } + +.fa-money-check-alt::before { + content: "\f53d"; } + +.fa-vector-square::before { + content: "\f5cb"; } + +.fa-bread-slice::before { + content: "\f7ec"; } + +.fa-language::before { + content: "\f1ab"; } + +.fa-face-kiss-wink-heart::before { + content: "\f598"; } + +.fa-kiss-wink-heart::before { + content: "\f598"; } + +.fa-filter::before { + content: "\f0b0"; } + +.fa-question::before { + content: "\3f"; } + +.fa-file-signature::before { + content: "\f573"; } + +.fa-up-down-left-right::before { + content: "\f0b2"; } + +.fa-arrows-alt::before { + content: "\f0b2"; } + +.fa-house-chimney-user::before { + content: "\e065"; } + +.fa-hand-holding-heart::before { + content: "\f4be"; } + +.fa-puzzle-piece::before { + content: "\f12e"; } + +.fa-money-check::before { + content: "\f53c"; } + +.fa-star-half-stroke::before { + content: "\f5c0"; } + +.fa-star-half-alt::before { + content: "\f5c0"; } + +.fa-code::before { + content: "\f121"; } + +.fa-whiskey-glass::before { + content: "\f7a0"; } + +.fa-glass-whiskey::before { + content: "\f7a0"; } + +.fa-building-circle-exclamation::before { + content: "\e4d3"; } + +.fa-magnifying-glass-chart::before { + content: "\e522"; } + +.fa-arrow-up-right-from-square::before { + content: "\f08e"; } + +.fa-external-link::before { + content: "\f08e"; } + +.fa-cubes-stacked::before { + content: "\e4e6"; } + +.fa-won-sign::before { + content: "\f159"; } + +.fa-krw::before { + content: "\f159"; } + +.fa-won::before { + content: "\f159"; } + +.fa-virus-covid::before { + content: "\e4a8"; } + +.fa-austral-sign::before { + content: "\e0a9"; } + +.fa-f::before { + content: "\46"; } + +.fa-leaf::before { + content: "\f06c"; } + +.fa-road::before { + content: "\f018"; } + +.fa-taxi::before { + content: "\f1ba"; } + +.fa-cab::before { + content: "\f1ba"; } + +.fa-person-circle-plus::before { + content: "\e541"; } + +.fa-chart-pie::before { + content: "\f200"; } + +.fa-pie-chart::before { + content: "\f200"; } + +.fa-bolt-lightning::before { + content: "\e0b7"; } + +.fa-sack-xmark::before { + content: "\e56a"; } + +.fa-file-excel::before { + content: "\f1c3"; } + +.fa-file-contract::before { + content: "\f56c"; } + +.fa-fish-fins::before { + content: "\e4f2"; } + +.fa-building-flag::before { + content: "\e4d5"; } + +.fa-face-grin-beam::before { + content: "\f582"; } + +.fa-grin-beam::before { + content: "\f582"; } + +.fa-object-ungroup::before { + content: "\f248"; } + +.fa-poop::before { + content: "\f619"; } + +.fa-location-pin::before { + content: "\f041"; } + +.fa-map-marker::before { + content: "\f041"; } + +.fa-kaaba::before { + content: "\f66b"; } + +.fa-toilet-paper::before { + content: "\f71e"; } + +.fa-helmet-safety::before { + content: "\f807"; } + +.fa-hard-hat::before { + content: "\f807"; } + +.fa-hat-hard::before { + content: "\f807"; } + +.fa-eject::before { + content: "\f052"; } + +.fa-circle-right::before { + content: "\f35a"; } + +.fa-arrow-alt-circle-right::before { + content: "\f35a"; } + +.fa-plane-circle-check::before { + content: "\e555"; } + +.fa-face-rolling-eyes::before { + content: "\f5a5"; } + +.fa-meh-rolling-eyes::before { + content: "\f5a5"; } + +.fa-object-group::before { + content: "\f247"; } + +.fa-chart-line::before { + content: "\f201"; } + +.fa-line-chart::before { + content: "\f201"; } + +.fa-mask-ventilator::before { + content: "\e524"; } + +.fa-arrow-right::before { + content: "\f061"; } + +.fa-signs-post::before { + content: "\f277"; } + +.fa-map-signs::before { + content: "\f277"; } + +.fa-cash-register::before { + content: "\f788"; } + +.fa-person-circle-question::before { + content: "\e542"; } + +.fa-h::before { + content: "\48"; } + +.fa-tarp::before { + content: "\e57b"; } + +.fa-screwdriver-wrench::before { + content: "\f7d9"; } + +.fa-tools::before { + content: "\f7d9"; } + +.fa-arrows-to-eye::before { + content: "\e4bf"; } + +.fa-plug-circle-bolt::before { + content: "\e55b"; } + +.fa-heart::before { + content: "\f004"; } + +.fa-mars-and-venus::before { + content: "\f224"; } + +.fa-house-user::before { + content: "\e1b0"; } + +.fa-home-user::before { + content: "\e1b0"; } + +.fa-dumpster-fire::before { + content: "\f794"; } + +.fa-house-crack::before { + content: "\e3b1"; } + +.fa-martini-glass-citrus::before { + content: "\f561"; } + +.fa-cocktail::before { + content: "\f561"; } + +.fa-face-surprise::before { + content: "\f5c2"; } + +.fa-surprise::before { + content: "\f5c2"; } + +.fa-bottle-water::before { + content: "\e4c5"; } + +.fa-circle-pause::before { + content: "\f28b"; } + +.fa-pause-circle::before { + content: "\f28b"; } + +.fa-toilet-paper-slash::before { + content: "\e072"; } + +.fa-apple-whole::before { + content: "\f5d1"; } + +.fa-apple-alt::before { + content: "\f5d1"; } + +.fa-kitchen-set::before { + content: "\e51a"; } + +.fa-r::before { + content: "\52"; } + +.fa-temperature-quarter::before { + content: "\f2ca"; } + +.fa-temperature-1::before { + content: "\f2ca"; } + +.fa-thermometer-1::before { + content: "\f2ca"; } + +.fa-thermometer-quarter::before { + content: "\f2ca"; } + +.fa-cube::before { + content: "\f1b2"; } + +.fa-bitcoin-sign::before { + content: "\e0b4"; } + +.fa-shield-dog::before { + content: "\e573"; } + +.fa-solar-panel::before { + content: "\f5ba"; } + +.fa-lock-open::before { + content: "\f3c1"; } + +.fa-elevator::before { + content: "\e16d"; } + +.fa-money-bill-transfer::before { + content: "\e528"; } + +.fa-money-bill-trend-up::before { + content: "\e529"; } + +.fa-house-flood-water-circle-arrow-right::before { + content: "\e50f"; } + +.fa-square-poll-horizontal::before { + content: "\f682"; } + +.fa-poll-h::before { + content: "\f682"; } + +.fa-circle::before { + content: "\f111"; } + +.fa-backward-fast::before { + content: "\f049"; } + +.fa-fast-backward::before { + content: "\f049"; } + +.fa-recycle::before { + content: "\f1b8"; } + +.fa-user-astronaut::before { + content: "\f4fb"; } + +.fa-plane-slash::before { + content: "\e069"; } + +.fa-trademark::before { + content: "\f25c"; } + +.fa-basketball::before { + content: "\f434"; } + +.fa-basketball-ball::before { + content: "\f434"; } + +.fa-satellite-dish::before { + content: "\f7c0"; } + +.fa-circle-up::before { + content: "\f35b"; } + +.fa-arrow-alt-circle-up::before { + content: "\f35b"; } + +.fa-mobile-screen-button::before { + content: "\f3cd"; } + +.fa-mobile-alt::before { + content: "\f3cd"; } + +.fa-volume-high::before { + content: "\f028"; } + +.fa-volume-up::before { + content: "\f028"; } + +.fa-users-rays::before { + content: "\e593"; } + +.fa-wallet::before { + content: "\f555"; } + +.fa-clipboard-check::before { + content: "\f46c"; } + +.fa-file-audio::before { + content: "\f1c7"; } + +.fa-burger::before { + content: "\f805"; } + +.fa-hamburger::before { + content: "\f805"; } + +.fa-wrench::before { + content: "\f0ad"; } + +.fa-bugs::before { + content: "\e4d0"; } + +.fa-rupee-sign::before { + content: "\f156"; } + +.fa-rupee::before { + content: "\f156"; } + +.fa-file-image::before { + content: "\f1c5"; } + +.fa-circle-question::before { + content: "\f059"; } + +.fa-question-circle::before { + content: "\f059"; } + +.fa-plane-departure::before { + content: "\f5b0"; } + +.fa-handshake-slash::before { + content: "\e060"; } + +.fa-book-bookmark::before { + content: "\e0bb"; } + +.fa-code-branch::before { + content: "\f126"; } + +.fa-hat-cowboy::before { + content: "\f8c0"; } + +.fa-bridge::before { + content: "\e4c8"; } + +.fa-phone-flip::before { + content: "\f879"; } + +.fa-phone-alt::before { + content: "\f879"; } + +.fa-truck-front::before { + content: "\e2b7"; } + +.fa-cat::before { + content: "\f6be"; } + +.fa-anchor-circle-exclamation::before { + content: "\e4ab"; } + +.fa-truck-field::before { + content: "\e58d"; } + +.fa-route::before { + content: "\f4d7"; } + +.fa-clipboard-question::before { + content: "\e4e3"; } + +.fa-panorama::before { + content: "\e209"; } + +.fa-comment-medical::before { + content: "\f7f5"; } + +.fa-teeth-open::before { + content: "\f62f"; } + +.fa-file-circle-minus::before { + content: "\e4ed"; } + +.fa-tags::before { + content: "\f02c"; } + +.fa-wine-glass::before { + content: "\f4e3"; } + +.fa-forward-fast::before { + content: "\f050"; } + +.fa-fast-forward::before { + content: "\f050"; } + +.fa-face-meh-blank::before { + content: "\f5a4"; } + +.fa-meh-blank::before { + content: "\f5a4"; } + +.fa-square-parking::before { + content: "\f540"; } + +.fa-parking::before { + content: "\f540"; } + +.fa-house-signal::before { + content: "\e012"; } + +.fa-bars-progress::before { + content: "\f828"; } + +.fa-tasks-alt::before { + content: "\f828"; } + +.fa-faucet-drip::before { + content: "\e006"; } + +.fa-cart-flatbed::before { + content: "\f474"; } + +.fa-dolly-flatbed::before { + content: "\f474"; } + +.fa-ban-smoking::before { + content: "\f54d"; } + +.fa-smoking-ban::before { + content: "\f54d"; } + +.fa-terminal::before { + content: "\f120"; } + +.fa-mobile-button::before { + content: "\f10b"; } + +.fa-house-medical-flag::before { + content: "\e514"; } + +.fa-basket-shopping::before { + content: "\f291"; } + +.fa-shopping-basket::before { + content: "\f291"; } + +.fa-tape::before { + content: "\f4db"; } + +.fa-bus-simple::before { + content: "\f55e"; } + +.fa-bus-alt::before { + content: "\f55e"; } + +.fa-eye::before { + content: "\f06e"; } + +.fa-face-sad-cry::before { + content: "\f5b3"; } + +.fa-sad-cry::before { + content: "\f5b3"; } + +.fa-audio-description::before { + content: "\f29e"; } + +.fa-person-military-to-person::before { + content: "\e54c"; } + +.fa-file-shield::before { + content: "\e4f0"; } + +.fa-user-slash::before { + content: "\f506"; } + +.fa-pen::before { + content: "\f304"; } + +.fa-tower-observation::before { + content: "\e586"; } + +.fa-file-code::before { + content: "\f1c9"; } + +.fa-signal::before { + content: "\f012"; } + +.fa-signal-5::before { + content: "\f012"; } + +.fa-signal-perfect::before { + content: "\f012"; } + +.fa-bus::before { + content: "\f207"; } + +.fa-heart-circle-xmark::before { + content: "\e501"; } + +.fa-house-chimney::before { + content: "\e3af"; } + +.fa-home-lg::before { + content: "\e3af"; } + +.fa-window-maximize::before { + content: "\f2d0"; } + +.fa-face-frown::before { + content: "\f119"; } + +.fa-frown::before { + content: "\f119"; } + +.fa-prescription::before { + content: "\f5b1"; } + +.fa-shop::before { + content: "\f54f"; } + +.fa-store-alt::before { + content: "\f54f"; } + +.fa-floppy-disk::before { + content: "\f0c7"; } + +.fa-save::before { + content: "\f0c7"; } + +.fa-vihara::before { + content: "\f6a7"; } + +.fa-scale-unbalanced::before { + content: "\f515"; } + +.fa-balance-scale-left::before { + content: "\f515"; } + +.fa-sort-up::before { + content: "\f0de"; } + +.fa-sort-asc::before { + content: "\f0de"; } + +.fa-comment-dots::before { + content: "\f4ad"; } + +.fa-commenting::before { + content: "\f4ad"; } + +.fa-plant-wilt::before { + content: "\e5aa"; } + +.fa-diamond::before { + content: "\f219"; } + +.fa-face-grin-squint::before { + content: "\f585"; } + +.fa-grin-squint::before { + content: "\f585"; } + +.fa-hand-holding-dollar::before { + content: "\f4c0"; } + +.fa-hand-holding-usd::before { + content: "\f4c0"; } + +.fa-bacterium::before { + content: "\e05a"; } + +.fa-hand-pointer::before { + content: "\f25a"; } + +.fa-drum-steelpan::before { + content: "\f56a"; } + +.fa-hand-scissors::before { + content: "\f257"; } + +.fa-hands-praying::before { + content: "\f684"; } + +.fa-praying-hands::before { + content: "\f684"; } + +.fa-arrow-rotate-right::before { + content: "\f01e"; } + +.fa-arrow-right-rotate::before { + content: "\f01e"; } + +.fa-arrow-rotate-forward::before { + content: "\f01e"; } + +.fa-redo::before { + content: "\f01e"; } + +.fa-biohazard::before { + content: "\f780"; } + +.fa-location-crosshairs::before { + content: "\f601"; } + +.fa-location::before { + content: "\f601"; } + +.fa-mars-double::before { + content: "\f227"; } + +.fa-child-dress::before { + content: "\e59c"; } + +.fa-users-between-lines::before { + content: "\e591"; } + +.fa-lungs-virus::before { + content: "\e067"; } + +.fa-face-grin-tears::before { + content: "\f588"; } + +.fa-grin-tears::before { + content: "\f588"; } + +.fa-phone::before { + content: "\f095"; } + +.fa-calendar-xmark::before { + content: "\f273"; } + +.fa-calendar-times::before { + content: "\f273"; } + +.fa-child-reaching::before { + content: "\e59d"; } + +.fa-head-side-virus::before { + content: "\e064"; } + +.fa-user-gear::before { + content: "\f4fe"; } + +.fa-user-cog::before { + content: "\f4fe"; } + +.fa-arrow-up-1-9::before { + content: "\f163"; } + +.fa-sort-numeric-up::before { + content: "\f163"; } + +.fa-door-closed::before { + content: "\f52a"; } + +.fa-shield-virus::before { + content: "\e06c"; } + +.fa-dice-six::before { + content: "\f526"; } + +.fa-mosquito-net::before { + content: "\e52c"; } + +.fa-bridge-water::before { + content: "\e4ce"; } + +.fa-person-booth::before { + content: "\f756"; } + +.fa-text-width::before { + content: "\f035"; } + +.fa-hat-wizard::before { + content: "\f6e8"; } + +.fa-pen-fancy::before { + content: "\f5ac"; } + +.fa-person-digging::before { + content: "\f85e"; } + +.fa-digging::before { + content: "\f85e"; } + +.fa-trash::before { + content: "\f1f8"; } + +.fa-gauge-simple::before { + content: "\f629"; } + +.fa-gauge-simple-med::before { + content: "\f629"; } + +.fa-tachometer-average::before { + content: "\f629"; } + +.fa-book-medical::before { + content: "\f7e6"; } + +.fa-poo::before { + content: "\f2fe"; } + +.fa-quote-right::before { + content: "\f10e"; } + +.fa-quote-right-alt::before { + content: "\f10e"; } + +.fa-shirt::before { + content: "\f553"; } + +.fa-t-shirt::before { + content: "\f553"; } + +.fa-tshirt::before { + content: "\f553"; } + +.fa-cubes::before { + content: "\f1b3"; } + +.fa-divide::before { + content: "\f529"; } + +.fa-tenge-sign::before { + content: "\f7d7"; } + +.fa-tenge::before { + content: "\f7d7"; } + +.fa-headphones::before { + content: "\f025"; } + +.fa-hands-holding::before { + content: "\f4c2"; } + +.fa-hands-clapping::before { + content: "\e1a8"; } + +.fa-republican::before { + content: "\f75e"; } + +.fa-arrow-left::before { + content: "\f060"; } + +.fa-person-circle-xmark::before { + content: "\e543"; } + +.fa-ruler::before { + content: "\f545"; } + +.fa-align-left::before { + content: "\f036"; } + +.fa-dice-d6::before { + content: "\f6d1"; } + +.fa-restroom::before { + content: "\f7bd"; } + +.fa-j::before { + content: "\4a"; } + +.fa-users-viewfinder::before { + content: "\e595"; } + +.fa-file-video::before { + content: "\f1c8"; } + +.fa-up-right-from-square::before { + content: "\f35d"; } + +.fa-external-link-alt::before { + content: "\f35d"; } + +.fa-table-cells::before { + content: "\f00a"; } + +.fa-th::before { + content: "\f00a"; } + +.fa-file-pdf::before { + content: "\f1c1"; } + +.fa-book-bible::before { + content: "\f647"; } + +.fa-bible::before { + content: "\f647"; } + +.fa-o::before { + content: "\4f"; } + +.fa-suitcase-medical::before { + content: "\f0fa"; } + +.fa-medkit::before { + content: "\f0fa"; } + +.fa-user-secret::before { + content: "\f21b"; } + +.fa-otter::before { + content: "\f700"; } + +.fa-person-dress::before { + content: "\f182"; } + +.fa-female::before { + content: "\f182"; } + +.fa-comment-dollar::before { + content: "\f651"; } + +.fa-business-time::before { + content: "\f64a"; } + +.fa-briefcase-clock::before { + content: "\f64a"; } + +.fa-table-cells-large::before { + content: "\f009"; } + +.fa-th-large::before { + content: "\f009"; } + +.fa-book-tanakh::before { + content: "\f827"; } + +.fa-tanakh::before { + content: "\f827"; } + +.fa-phone-volume::before { + content: "\f2a0"; } + +.fa-volume-control-phone::before { + content: "\f2a0"; } + +.fa-hat-cowboy-side::before { + content: "\f8c1"; } + +.fa-clipboard-user::before { + content: "\f7f3"; } + +.fa-child::before { + content: "\f1ae"; } + +.fa-lira-sign::before { + content: "\f195"; } + +.fa-satellite::before { + content: "\f7bf"; } + +.fa-plane-lock::before { + content: "\e558"; } + +.fa-tag::before { + content: "\f02b"; } + +.fa-comment::before { + content: "\f075"; } + +.fa-cake-candles::before { + content: "\f1fd"; } + +.fa-birthday-cake::before { + content: "\f1fd"; } + +.fa-cake::before { + content: "\f1fd"; } + +.fa-envelope::before { + content: "\f0e0"; } + +.fa-angles-up::before { + content: "\f102"; } + +.fa-angle-double-up::before { + content: "\f102"; } + +.fa-paperclip::before { + content: "\f0c6"; } + +.fa-arrow-right-to-city::before { + content: "\e4b3"; } + +.fa-ribbon::before { + content: "\f4d6"; } + +.fa-lungs::before { + content: "\f604"; } + +.fa-arrow-up-9-1::before { + content: "\f887"; } + +.fa-sort-numeric-up-alt::before { + content: "\f887"; } + +.fa-litecoin-sign::before { + content: "\e1d3"; } + +.fa-border-none::before { + content: "\f850"; } + +.fa-circle-nodes::before { + content: "\e4e2"; } + +.fa-parachute-box::before { + content: "\f4cd"; } + +.fa-indent::before { + content: "\f03c"; } + +.fa-truck-field-un::before { + content: "\e58e"; } + +.fa-hourglass::before { + content: "\f254"; } + +.fa-hourglass-empty::before { + content: "\f254"; } + +.fa-mountain::before { + content: "\f6fc"; } + +.fa-user-doctor::before { + content: "\f0f0"; } + +.fa-user-md::before { + content: "\f0f0"; } + +.fa-circle-info::before { + content: "\f05a"; } + +.fa-info-circle::before { + content: "\f05a"; } + +.fa-cloud-meatball::before { + content: "\f73b"; } + +.fa-camera::before { + content: "\f030"; } + +.fa-camera-alt::before { + content: "\f030"; } + +.fa-square-virus::before { + content: "\e578"; } + +.fa-meteor::before { + content: "\f753"; } + +.fa-car-on::before { + content: "\e4dd"; } + +.fa-sleigh::before { + content: "\f7cc"; } + +.fa-arrow-down-1-9::before { + content: "\f162"; } + +.fa-sort-numeric-asc::before { + content: "\f162"; } + +.fa-sort-numeric-down::before { + content: "\f162"; } + +.fa-hand-holding-droplet::before { + content: "\f4c1"; } + +.fa-hand-holding-water::before { + content: "\f4c1"; } + +.fa-water::before { + content: "\f773"; } + +.fa-calendar-check::before { + content: "\f274"; } + +.fa-braille::before { + content: "\f2a1"; } + +.fa-prescription-bottle-medical::before { + content: "\f486"; } + +.fa-prescription-bottle-alt::before { + content: "\f486"; } + +.fa-landmark::before { + content: "\f66f"; } + +.fa-truck::before { + content: "\f0d1"; } + +.fa-crosshairs::before { + content: "\f05b"; } + +.fa-person-cane::before { + content: "\e53c"; } + +.fa-tent::before { + content: "\e57d"; } + +.fa-vest-patches::before { + content: "\e086"; } + +.fa-check-double::before { + content: "\f560"; } + +.fa-arrow-down-a-z::before { + content: "\f15d"; } + +.fa-sort-alpha-asc::before { + content: "\f15d"; } + +.fa-sort-alpha-down::before { + content: "\f15d"; } + +.fa-money-bill-wheat::before { + content: "\e52a"; } + +.fa-cookie::before { + content: "\f563"; } + +.fa-arrow-rotate-left::before { + content: "\f0e2"; } + +.fa-arrow-left-rotate::before { + content: "\f0e2"; } + +.fa-arrow-rotate-back::before { + content: "\f0e2"; } + +.fa-arrow-rotate-backward::before { + content: "\f0e2"; } + +.fa-undo::before { + content: "\f0e2"; } + +.fa-hard-drive::before { + content: "\f0a0"; } + +.fa-hdd::before { + content: "\f0a0"; } + +.fa-face-grin-squint-tears::before { + content: "\f586"; } + +.fa-grin-squint-tears::before { + content: "\f586"; } + +.fa-dumbbell::before { + content: "\f44b"; } + +.fa-rectangle-list::before { + content: "\f022"; } + +.fa-list-alt::before { + content: "\f022"; } + +.fa-tarp-droplet::before { + content: "\e57c"; } + +.fa-house-medical-circle-check::before { + content: "\e511"; } + +.fa-person-skiing-nordic::before { + content: "\f7ca"; } + +.fa-skiing-nordic::before { + content: "\f7ca"; } + +.fa-calendar-plus::before { + content: "\f271"; } + +.fa-plane-arrival::before { + content: "\f5af"; } + +.fa-circle-left::before { + content: "\f359"; } + +.fa-arrow-alt-circle-left::before { + content: "\f359"; } + +.fa-train-subway::before { + content: "\f239"; } + +.fa-subway::before { + content: "\f239"; } + +.fa-chart-gantt::before { + content: "\e0e4"; } + +.fa-indian-rupee-sign::before { + content: "\e1bc"; } + +.fa-indian-rupee::before { + content: "\e1bc"; } + +.fa-inr::before { + content: "\e1bc"; } + +.fa-crop-simple::before { + content: "\f565"; } + +.fa-crop-alt::before { + content: "\f565"; } + +.fa-money-bill-1::before { + content: "\f3d1"; } + +.fa-money-bill-alt::before { + content: "\f3d1"; } + +.fa-left-long::before { + content: "\f30a"; } + +.fa-long-arrow-alt-left::before { + content: "\f30a"; } + +.fa-dna::before { + content: "\f471"; } + +.fa-virus-slash::before { + content: "\e075"; } + +.fa-minus::before { + content: "\f068"; } + +.fa-subtract::before { + content: "\f068"; } + +.fa-chess::before { + content: "\f439"; } + +.fa-arrow-left-long::before { + content: "\f177"; } + +.fa-long-arrow-left::before { + content: "\f177"; } + +.fa-plug-circle-check::before { + content: "\e55c"; } + +.fa-street-view::before { + content: "\f21d"; } + +.fa-franc-sign::before { + content: "\e18f"; } + +.fa-volume-off::before { + content: "\f026"; } + +.fa-hands-asl-interpreting::before { + content: "\f2a3"; } + +.fa-american-sign-language-interpreting::before { + content: "\f2a3"; } + +.fa-asl-interpreting::before { + content: "\f2a3"; } + +.fa-hands-american-sign-language-interpreting::before { + content: "\f2a3"; } + +.fa-gear::before { + content: "\f013"; } + +.fa-cog::before { + content: "\f013"; } + +.fa-droplet-slash::before { + content: "\f5c7"; } + +.fa-tint-slash::before { + content: "\f5c7"; } + +.fa-mosque::before { + content: "\f678"; } + +.fa-mosquito::before { + content: "\e52b"; } + +.fa-star-of-david::before { + content: "\f69a"; } + +.fa-person-military-rifle::before { + content: "\e54b"; } + +.fa-cart-shopping::before { + content: "\f07a"; } + +.fa-shopping-cart::before { + content: "\f07a"; } + +.fa-vials::before { + content: "\f493"; } + +.fa-plug-circle-plus::before { + content: "\e55f"; } + +.fa-place-of-worship::before { + content: "\f67f"; } + +.fa-grip-vertical::before { + content: "\f58e"; } + +.fa-arrow-turn-up::before { + content: "\f148"; } + +.fa-level-up::before { + content: "\f148"; } + +.fa-u::before { + content: "\55"; } + +.fa-square-root-variable::before { + content: "\f698"; } + +.fa-square-root-alt::before { + content: "\f698"; } + +.fa-clock::before { + content: "\f017"; } + +.fa-clock-four::before { + content: "\f017"; } + +.fa-backward-step::before { + content: "\f048"; } + +.fa-step-backward::before { + content: "\f048"; } + +.fa-pallet::before { + content: "\f482"; } + +.fa-faucet::before { + content: "\e005"; } + +.fa-baseball-bat-ball::before { + content: "\f432"; } + +.fa-s::before { + content: "\53"; } + +.fa-timeline::before { + content: "\e29c"; } + +.fa-keyboard::before { + content: "\f11c"; } + +.fa-caret-down::before { + content: "\f0d7"; } + +.fa-house-chimney-medical::before { + content: "\f7f2"; } + +.fa-clinic-medical::before { + content: "\f7f2"; } + +.fa-temperature-three-quarters::before { + content: "\f2c8"; } + +.fa-temperature-3::before { + content: "\f2c8"; } + +.fa-thermometer-3::before { + content: "\f2c8"; } + +.fa-thermometer-three-quarters::before { + content: "\f2c8"; } + +.fa-mobile-screen::before { + content: "\f3cf"; } + +.fa-mobile-android-alt::before { + content: "\f3cf"; } + +.fa-plane-up::before { + content: "\e22d"; } + +.fa-piggy-bank::before { + content: "\f4d3"; } + +.fa-battery-half::before { + content: "\f242"; } + +.fa-battery-3::before { + content: "\f242"; } + +.fa-mountain-city::before { + content: "\e52e"; } + +.fa-coins::before { + content: "\f51e"; } + +.fa-khanda::before { + content: "\f66d"; } + +.fa-sliders::before { + content: "\f1de"; } + +.fa-sliders-h::before { + content: "\f1de"; } + +.fa-folder-tree::before { + content: "\f802"; } + +.fa-network-wired::before { + content: "\f6ff"; } + +.fa-map-pin::before { + content: "\f276"; } + +.fa-hamsa::before { + content: "\f665"; } + +.fa-cent-sign::before { + content: "\e3f5"; } + +.fa-flask::before { + content: "\f0c3"; } + +.fa-person-pregnant::before { + content: "\e31e"; } + +.fa-wand-sparkles::before { + content: "\f72b"; } + +.fa-ellipsis-vertical::before { + content: "\f142"; } + +.fa-ellipsis-v::before { + content: "\f142"; } + +.fa-ticket::before { + content: "\f145"; } + +.fa-power-off::before { + content: "\f011"; } + +.fa-right-long::before { + content: "\f30b"; } + +.fa-long-arrow-alt-right::before { + content: "\f30b"; } + +.fa-flag-usa::before { + content: "\f74d"; } + +.fa-laptop-file::before { + content: "\e51d"; } + +.fa-tty::before { + content: "\f1e4"; } + +.fa-teletype::before { + content: "\f1e4"; } + +.fa-diagram-next::before { + content: "\e476"; } + +.fa-person-rifle::before { + content: "\e54e"; } + +.fa-house-medical-circle-exclamation::before { + content: "\e512"; } + +.fa-closed-captioning::before { + content: "\f20a"; } + +.fa-person-hiking::before { + content: "\f6ec"; } + +.fa-hiking::before { + content: "\f6ec"; } + +.fa-venus-double::before { + content: "\f226"; } + +.fa-images::before { + content: "\f302"; } + +.fa-calculator::before { + content: "\f1ec"; } + +.fa-people-pulling::before { + content: "\e535"; } + +.fa-n::before { + content: "\4e"; } + +.fa-cable-car::before { + content: "\f7da"; } + +.fa-tram::before { + content: "\f7da"; } + +.fa-cloud-rain::before { + content: "\f73d"; } + +.fa-building-circle-xmark::before { + content: "\e4d4"; } + +.fa-ship::before { + content: "\f21a"; } + +.fa-arrows-down-to-line::before { + content: "\e4b8"; } + +.fa-download::before { + content: "\f019"; } + +.fa-face-grin::before { + content: "\f580"; } + +.fa-grin::before { + content: "\f580"; } + +.fa-delete-left::before { + content: "\f55a"; } + +.fa-backspace::before { + content: "\f55a"; } + +.fa-eye-dropper::before { + content: "\f1fb"; } + +.fa-eye-dropper-empty::before { + content: "\f1fb"; } + +.fa-eyedropper::before { + content: "\f1fb"; } + +.fa-file-circle-check::before { + content: "\e5a0"; } + +.fa-forward::before { + content: "\f04e"; } + +.fa-mobile::before { + content: "\f3ce"; } + +.fa-mobile-android::before { + content: "\f3ce"; } + +.fa-mobile-phone::before { + content: "\f3ce"; } + +.fa-face-meh::before { + content: "\f11a"; } + +.fa-meh::before { + content: "\f11a"; } + +.fa-align-center::before { + content: "\f037"; } + +.fa-book-skull::before { + content: "\f6b7"; } + +.fa-book-dead::before { + content: "\f6b7"; } + +.fa-id-card::before { + content: "\f2c2"; } + +.fa-drivers-license::before { + content: "\f2c2"; } + +.fa-outdent::before { + content: "\f03b"; } + +.fa-dedent::before { + content: "\f03b"; } + +.fa-heart-circle-exclamation::before { + content: "\e4fe"; } + +.fa-house::before { + content: "\f015"; } + +.fa-home::before { + content: "\f015"; } + +.fa-home-alt::before { + content: "\f015"; } + +.fa-home-lg-alt::before { + content: "\f015"; } + +.fa-calendar-week::before { + content: "\f784"; } + +.fa-laptop-medical::before { + content: "\f812"; } + +.fa-b::before { + content: "\42"; } + +.fa-file-medical::before { + content: "\f477"; } + +.fa-dice-one::before { + content: "\f525"; } + +.fa-kiwi-bird::before { + content: "\f535"; } + +.fa-arrow-right-arrow-left::before { + content: "\f0ec"; } + +.fa-exchange::before { + content: "\f0ec"; } + +.fa-rotate-right::before { + content: "\f2f9"; } + +.fa-redo-alt::before { + content: "\f2f9"; } + +.fa-rotate-forward::before { + content: "\f2f9"; } + +.fa-utensils::before { + content: "\f2e7"; } + +.fa-cutlery::before { + content: "\f2e7"; } + +.fa-arrow-up-wide-short::before { + content: "\f161"; } + +.fa-sort-amount-up::before { + content: "\f161"; } + +.fa-mill-sign::before { + content: "\e1ed"; } + +.fa-bowl-rice::before { + content: "\e2eb"; } + +.fa-skull::before { + content: "\f54c"; } + +.fa-tower-broadcast::before { + content: "\f519"; } + +.fa-broadcast-tower::before { + content: "\f519"; } + +.fa-truck-pickup::before { + content: "\f63c"; } + +.fa-up-long::before { + content: "\f30c"; } + +.fa-long-arrow-alt-up::before { + content: "\f30c"; } + +.fa-stop::before { + content: "\f04d"; } + +.fa-code-merge::before { + content: "\f387"; } + +.fa-upload::before { + content: "\f093"; } + +.fa-hurricane::before { + content: "\f751"; } + +.fa-mound::before { + content: "\e52d"; } + +.fa-toilet-portable::before { + content: "\e583"; } + +.fa-compact-disc::before { + content: "\f51f"; } + +.fa-file-arrow-down::before { + content: "\f56d"; } + +.fa-file-download::before { + content: "\f56d"; } + +.fa-caravan::before { + content: "\f8ff"; } + +.fa-shield-cat::before { + content: "\e572"; } + +.fa-bolt::before { + content: "\f0e7"; } + +.fa-zap::before { + content: "\f0e7"; } + +.fa-glass-water::before { + content: "\e4f4"; } + +.fa-oil-well::before { + content: "\e532"; } + +.fa-vault::before { + content: "\e2c5"; } + +.fa-mars::before { + content: "\f222"; } + +.fa-toilet::before { + content: "\f7d8"; } + +.fa-plane-circle-xmark::before { + content: "\e557"; } + +.fa-yen-sign::before { + content: "\f157"; } + +.fa-cny::before { + content: "\f157"; } + +.fa-jpy::before { + content: "\f157"; } + +.fa-rmb::before { + content: "\f157"; } + +.fa-yen::before { + content: "\f157"; } + +.fa-ruble-sign::before { + content: "\f158"; } + +.fa-rouble::before { + content: "\f158"; } + +.fa-rub::before { + content: "\f158"; } + +.fa-ruble::before { + content: "\f158"; } + +.fa-sun::before { + content: "\f185"; } + +.fa-guitar::before { + content: "\f7a6"; } + +.fa-face-laugh-wink::before { + content: "\f59c"; } + +.fa-laugh-wink::before { + content: "\f59c"; } + +.fa-horse-head::before { + content: "\f7ab"; } + +.fa-bore-hole::before { + content: "\e4c3"; } + +.fa-industry::before { + content: "\f275"; } + +.fa-circle-down::before { + content: "\f358"; } + +.fa-arrow-alt-circle-down::before { + content: "\f358"; } + +.fa-arrows-turn-to-dots::before { + content: "\e4c1"; } + +.fa-florin-sign::before { + content: "\e184"; } + +.fa-arrow-down-short-wide::before { + content: "\f884"; } + +.fa-sort-amount-desc::before { + content: "\f884"; } + +.fa-sort-amount-down-alt::before { + content: "\f884"; } + +.fa-less-than::before { + content: "\3c"; } + +.fa-angle-down::before { + content: "\f107"; } + +.fa-car-tunnel::before { + content: "\e4de"; } + +.fa-head-side-cough::before { + content: "\e061"; } + +.fa-grip-lines::before { + content: "\f7a4"; } + +.fa-thumbs-down::before { + content: "\f165"; } + +.fa-user-lock::before { + content: "\f502"; } + +.fa-arrow-right-long::before { + content: "\f178"; } + +.fa-long-arrow-right::before { + content: "\f178"; } + +.fa-anchor-circle-xmark::before { + content: "\e4ac"; } + +.fa-ellipsis::before { + content: "\f141"; } + +.fa-ellipsis-h::before { + content: "\f141"; } + +.fa-chess-pawn::before { + content: "\f443"; } + +.fa-kit-medical::before { + content: "\f479"; } + +.fa-first-aid::before { + content: "\f479"; } + +.fa-person-through-window::before { + content: "\e5a9"; } + +.fa-toolbox::before { + content: "\f552"; } + +.fa-hands-holding-circle::before { + content: "\e4fb"; } + +.fa-bug::before { + content: "\f188"; } + +.fa-credit-card::before { + content: "\f09d"; } + +.fa-credit-card-alt::before { + content: "\f09d"; } + +.fa-car::before { + content: "\f1b9"; } + +.fa-automobile::before { + content: "\f1b9"; } + +.fa-hand-holding-hand::before { + content: "\e4f7"; } + +.fa-book-open-reader::before { + content: "\f5da"; } + +.fa-book-reader::before { + content: "\f5da"; } + +.fa-mountain-sun::before { + content: "\e52f"; } + +.fa-arrows-left-right-to-line::before { + content: "\e4ba"; } + +.fa-dice-d20::before { + content: "\f6cf"; } + +.fa-truck-droplet::before { + content: "\e58c"; } + +.fa-file-circle-xmark::before { + content: "\e5a1"; } + +.fa-temperature-arrow-up::before { + content: "\e040"; } + +.fa-temperature-up::before { + content: "\e040"; } + +.fa-medal::before { + content: "\f5a2"; } + +.fa-bed::before { + content: "\f236"; } + +.fa-square-h::before { + content: "\f0fd"; } + +.fa-h-square::before { + content: "\f0fd"; } + +.fa-podcast::before { + content: "\f2ce"; } + +.fa-temperature-full::before { + content: "\f2c7"; } + +.fa-temperature-4::before { + content: "\f2c7"; } + +.fa-thermometer-4::before { + content: "\f2c7"; } + +.fa-thermometer-full::before { + content: "\f2c7"; } + +.fa-bell::before { + content: "\f0f3"; } + +.fa-superscript::before { + content: "\f12b"; } + +.fa-plug-circle-xmark::before { + content: "\e560"; } + +.fa-star-of-life::before { + content: "\f621"; } + +.fa-phone-slash::before { + content: "\f3dd"; } + +.fa-paint-roller::before { + content: "\f5aa"; } + +.fa-handshake-angle::before { + content: "\f4c4"; } + +.fa-hands-helping::before { + content: "\f4c4"; } + +.fa-location-dot::before { + content: "\f3c5"; } + +.fa-map-marker-alt::before { + content: "\f3c5"; } + +.fa-file::before { + content: "\f15b"; } + +.fa-greater-than::before { + content: "\3e"; } + +.fa-person-swimming::before { + content: "\f5c4"; } + +.fa-swimmer::before { + content: "\f5c4"; } + +.fa-arrow-down::before { + content: "\f063"; } + +.fa-droplet::before { + content: "\f043"; } + +.fa-tint::before { + content: "\f043"; } + +.fa-eraser::before { + content: "\f12d"; } + +.fa-earth-americas::before { + content: "\f57d"; } + +.fa-earth::before { + content: "\f57d"; } + +.fa-earth-america::before { + content: "\f57d"; } + +.fa-globe-americas::before { + content: "\f57d"; } + +.fa-person-burst::before { + content: "\e53b"; } + +.fa-dove::before { + content: "\f4ba"; } + +.fa-battery-empty::before { + content: "\f244"; } + +.fa-battery-0::before { + content: "\f244"; } + +.fa-socks::before { + content: "\f696"; } + +.fa-inbox::before { + content: "\f01c"; } + +.fa-section::before { + content: "\e447"; } + +.fa-gauge-high::before { + content: "\f625"; } + +.fa-tachometer-alt::before { + content: "\f625"; } + +.fa-tachometer-alt-fast::before { + content: "\f625"; } + +.fa-envelope-open-text::before { + content: "\f658"; } + +.fa-hospital::before { + content: "\f0f8"; } + +.fa-hospital-alt::before { + content: "\f0f8"; } + +.fa-hospital-wide::before { + content: "\f0f8"; } + +.fa-wine-bottle::before { + content: "\f72f"; } + +.fa-chess-rook::before { + content: "\f447"; } + +.fa-bars-staggered::before { + content: "\f550"; } + +.fa-reorder::before { + content: "\f550"; } + +.fa-stream::before { + content: "\f550"; } + +.fa-dharmachakra::before { + content: "\f655"; } + +.fa-hotdog::before { + content: "\f80f"; } + +.fa-person-walking-with-cane::before { + content: "\f29d"; } + +.fa-blind::before { + content: "\f29d"; } + +.fa-drum::before { + content: "\f569"; } + +.fa-ice-cream::before { + content: "\f810"; } + +.fa-heart-circle-bolt::before { + content: "\e4fc"; } + +.fa-fax::before { + content: "\f1ac"; } + +.fa-paragraph::before { + content: "\f1dd"; } + +.fa-check-to-slot::before { + content: "\f772"; } + +.fa-vote-yea::before { + content: "\f772"; } + +.fa-star-half::before { + content: "\f089"; } + +.fa-boxes-stacked::before { + content: "\f468"; } + +.fa-boxes::before { + content: "\f468"; } + +.fa-boxes-alt::before { + content: "\f468"; } + +.fa-link::before { + content: "\f0c1"; } + +.fa-chain::before { + content: "\f0c1"; } + +.fa-ear-listen::before { + content: "\f2a2"; } + +.fa-assistive-listening-systems::before { + content: "\f2a2"; } + +.fa-tree-city::before { + content: "\e587"; } + +.fa-play::before { + content: "\f04b"; } + +.fa-font::before { + content: "\f031"; } + +.fa-rupiah-sign::before { + content: "\e23d"; } + +.fa-magnifying-glass::before { + content: "\f002"; } + +.fa-search::before { + content: "\f002"; } + +.fa-table-tennis-paddle-ball::before { + content: "\f45d"; } + +.fa-ping-pong-paddle-ball::before { + content: "\f45d"; } + +.fa-table-tennis::before { + content: "\f45d"; } + +.fa-person-dots-from-line::before { + content: "\f470"; } + +.fa-diagnoses::before { + content: "\f470"; } + +.fa-trash-can-arrow-up::before { + content: "\f82a"; } + +.fa-trash-restore-alt::before { + content: "\f82a"; } + +.fa-naira-sign::before { + content: "\e1f6"; } + +.fa-cart-arrow-down::before { + content: "\f218"; } + +.fa-walkie-talkie::before { + content: "\f8ef"; } + +.fa-file-pen::before { + content: "\f31c"; } + +.fa-file-edit::before { + content: "\f31c"; } + +.fa-receipt::before { + content: "\f543"; } + +.fa-square-pen::before { + content: "\f14b"; } + +.fa-pen-square::before { + content: "\f14b"; } + +.fa-pencil-square::before { + content: "\f14b"; } + +.fa-suitcase-rolling::before { + content: "\f5c1"; } + +.fa-person-circle-exclamation::before { + content: "\e53f"; } + +.fa-chevron-down::before { + content: "\f078"; } + +.fa-battery-full::before { + content: "\f240"; } + +.fa-battery::before { + content: "\f240"; } + +.fa-battery-5::before { + content: "\f240"; } + +.fa-skull-crossbones::before { + content: "\f714"; } + +.fa-code-compare::before { + content: "\e13a"; } + +.fa-list-ul::before { + content: "\f0ca"; } + +.fa-list-dots::before { + content: "\f0ca"; } + +.fa-school-lock::before { + content: "\e56f"; } + +.fa-tower-cell::before { + content: "\e585"; } + +.fa-down-long::before { + content: "\f309"; } + +.fa-long-arrow-alt-down::before { + content: "\f309"; } + +.fa-ranking-star::before { + content: "\e561"; } + +.fa-chess-king::before { + content: "\f43f"; } + +.fa-person-harassing::before { + content: "\e549"; } + +.fa-brazilian-real-sign::before { + content: "\e46c"; } + +.fa-landmark-dome::before { + content: "\f752"; } + +.fa-landmark-alt::before { + content: "\f752"; } + +.fa-arrow-up::before { + content: "\f062"; } + +.fa-tv::before { + content: "\f26c"; } + +.fa-television::before { + content: "\f26c"; } + +.fa-tv-alt::before { + content: "\f26c"; } + +.fa-shrimp::before { + content: "\e448"; } + +.fa-list-check::before { + content: "\f0ae"; } + +.fa-tasks::before { + content: "\f0ae"; } + +.fa-jug-detergent::before { + content: "\e519"; } + +.fa-circle-user::before { + content: "\f2bd"; } + +.fa-user-circle::before { + content: "\f2bd"; } + +.fa-user-shield::before { + content: "\f505"; } + +.fa-wind::before { + content: "\f72e"; } + +.fa-car-burst::before { + content: "\f5e1"; } + +.fa-car-crash::before { + content: "\f5e1"; } + +.fa-y::before { + content: "\59"; } + +.fa-person-snowboarding::before { + content: "\f7ce"; } + +.fa-snowboarding::before { + content: "\f7ce"; } + +.fa-truck-fast::before { + content: "\f48b"; } + +.fa-shipping-fast::before { + content: "\f48b"; } + +.fa-fish::before { + content: "\f578"; } + +.fa-user-graduate::before { + content: "\f501"; } + +.fa-circle-half-stroke::before { + content: "\f042"; } + +.fa-adjust::before { + content: "\f042"; } + +.fa-clapperboard::before { + content: "\e131"; } + +.fa-circle-radiation::before { + content: "\f7ba"; } + +.fa-radiation-alt::before { + content: "\f7ba"; } + +.fa-baseball::before { + content: "\f433"; } + +.fa-baseball-ball::before { + content: "\f433"; } + +.fa-jet-fighter-up::before { + content: "\e518"; } + +.fa-diagram-project::before { + content: "\f542"; } + +.fa-project-diagram::before { + content: "\f542"; } + +.fa-copy::before { + content: "\f0c5"; } + +.fa-volume-xmark::before { + content: "\f6a9"; } + +.fa-volume-mute::before { + content: "\f6a9"; } + +.fa-volume-times::before { + content: "\f6a9"; } + +.fa-hand-sparkles::before { + content: "\e05d"; } + +.fa-grip::before { + content: "\f58d"; } + +.fa-grip-horizontal::before { + content: "\f58d"; } + +.fa-share-from-square::before { + content: "\f14d"; } + +.fa-share-square::before { + content: "\f14d"; } + +.fa-child-combatant::before { + content: "\e4e0"; } + +.fa-child-rifle::before { + content: "\e4e0"; } + +.fa-gun::before { + content: "\e19b"; } + +.fa-square-phone::before { + content: "\f098"; } + +.fa-phone-square::before { + content: "\f098"; } + +.fa-plus::before { + content: "\2b"; } + +.fa-add::before { + content: "\2b"; } + +.fa-expand::before { + content: "\f065"; } + +.fa-computer::before { + content: "\e4e5"; } + +.fa-xmark::before { + content: "\f00d"; } + +.fa-close::before { + content: "\f00d"; } + +.fa-multiply::before { + content: "\f00d"; } + +.fa-remove::before { + content: "\f00d"; } + +.fa-times::before { + content: "\f00d"; } + +.fa-arrows-up-down-left-right::before { + content: "\f047"; } + +.fa-arrows::before { + content: "\f047"; } + +.fa-chalkboard-user::before { + content: "\f51c"; } + +.fa-chalkboard-teacher::before { + content: "\f51c"; } + +.fa-peso-sign::before { + content: "\e222"; } + +.fa-building-shield::before { + content: "\e4d8"; } + +.fa-baby::before { + content: "\f77c"; } + +.fa-users-line::before { + content: "\e592"; } + +.fa-quote-left::before { + content: "\f10d"; } + +.fa-quote-left-alt::before { + content: "\f10d"; } + +.fa-tractor::before { + content: "\f722"; } + +.fa-trash-arrow-up::before { + content: "\f829"; } + +.fa-trash-restore::before { + content: "\f829"; } + +.fa-arrow-down-up-lock::before { + content: "\e4b0"; } + +.fa-lines-leaning::before { + content: "\e51e"; } + +.fa-ruler-combined::before { + content: "\f546"; } + +.fa-copyright::before { + content: "\f1f9"; } + +.fa-equals::before { + content: "\3d"; } + +.fa-blender::before { + content: "\f517"; } + +.fa-teeth::before { + content: "\f62e"; } + +.fa-shekel-sign::before { + content: "\f20b"; } + +.fa-ils::before { + content: "\f20b"; } + +.fa-shekel::before { + content: "\f20b"; } + +.fa-sheqel::before { + content: "\f20b"; } + +.fa-sheqel-sign::before { + content: "\f20b"; } + +.fa-map::before { + content: "\f279"; } + +.fa-rocket::before { + content: "\f135"; } + +.fa-photo-film::before { + content: "\f87c"; } + +.fa-photo-video::before { + content: "\f87c"; } + +.fa-folder-minus::before { + content: "\f65d"; } + +.fa-store::before { + content: "\f54e"; } + +.fa-arrow-trend-up::before { + content: "\e098"; } + +.fa-plug-circle-minus::before { + content: "\e55e"; } + +.fa-sign-hanging::before { + content: "\f4d9"; } + +.fa-sign::before { + content: "\f4d9"; } + +.fa-bezier-curve::before { + content: "\f55b"; } + +.fa-bell-slash::before { + content: "\f1f6"; } + +.fa-tablet::before { + content: "\f3fb"; } + +.fa-tablet-android::before { + content: "\f3fb"; } + +.fa-school-flag::before { + content: "\e56e"; } + +.fa-fill::before { + content: "\f575"; } + +.fa-angle-up::before { + content: "\f106"; } + +.fa-drumstick-bite::before { + content: "\f6d7"; } + +.fa-holly-berry::before { + content: "\f7aa"; } + +.fa-chevron-left::before { + content: "\f053"; } + +.fa-bacteria::before { + content: "\e059"; } + +.fa-hand-lizard::before { + content: "\f258"; } + +.fa-notdef::before { + content: "\e1fe"; } + +.fa-disease::before { + content: "\f7fa"; } + +.fa-briefcase-medical::before { + content: "\f469"; } + +.fa-genderless::before { + content: "\f22d"; } + +.fa-chevron-right::before { + content: "\f054"; } + +.fa-retweet::before { + content: "\f079"; } + +.fa-car-rear::before { + content: "\f5de"; } + +.fa-car-alt::before { + content: "\f5de"; } + +.fa-pump-soap::before { + content: "\e06b"; } + +.fa-video-slash::before { + content: "\f4e2"; } + +.fa-battery-quarter::before { + content: "\f243"; } + +.fa-battery-2::before { + content: "\f243"; } + +.fa-radio::before { + content: "\f8d7"; } + +.fa-baby-carriage::before { + content: "\f77d"; } + +.fa-carriage-baby::before { + content: "\f77d"; } + +.fa-traffic-light::before { + content: "\f637"; } + +.fa-thermometer::before { + content: "\f491"; } + +.fa-vr-cardboard::before { + content: "\f729"; } + +.fa-hand-middle-finger::before { + content: "\f806"; } + +.fa-percent::before { + content: "\25"; } + +.fa-percentage::before { + content: "\25"; } + +.fa-truck-moving::before { + content: "\f4df"; } + +.fa-glass-water-droplet::before { + content: "\e4f5"; } + +.fa-display::before { + content: "\e163"; } + +.fa-face-smile::before { + content: "\f118"; } + +.fa-smile::before { + content: "\f118"; } + +.fa-thumbtack::before { + content: "\f08d"; } + +.fa-thumb-tack::before { + content: "\f08d"; } + +.fa-trophy::before { + content: "\f091"; } + +.fa-person-praying::before { + content: "\f683"; } + +.fa-pray::before { + content: "\f683"; } + +.fa-hammer::before { + content: "\f6e3"; } + +.fa-hand-peace::before { + content: "\f25b"; } + +.fa-rotate::before { + content: "\f2f1"; } + +.fa-sync-alt::before { + content: "\f2f1"; } + +.fa-spinner::before { + content: "\f110"; } + +.fa-robot::before { + content: "\f544"; } + +.fa-peace::before { + content: "\f67c"; } + +.fa-gears::before { + content: "\f085"; } + +.fa-cogs::before { + content: "\f085"; } + +.fa-warehouse::before { + content: "\f494"; } + +.fa-arrow-up-right-dots::before { + content: "\e4b7"; } + +.fa-splotch::before { + content: "\f5bc"; } + +.fa-face-grin-hearts::before { + content: "\f584"; } + +.fa-grin-hearts::before { + content: "\f584"; } + +.fa-dice-four::before { + content: "\f524"; } + +.fa-sim-card::before { + content: "\f7c4"; } + +.fa-transgender::before { + content: "\f225"; } + +.fa-transgender-alt::before { + content: "\f225"; } + +.fa-mercury::before { + content: "\f223"; } + +.fa-arrow-turn-down::before { + content: "\f149"; } + +.fa-level-down::before { + content: "\f149"; } + +.fa-person-falling-burst::before { + content: "\e547"; } + +.fa-award::before { + content: "\f559"; } + +.fa-ticket-simple::before { + content: "\f3ff"; } + +.fa-ticket-alt::before { + content: "\f3ff"; } + +.fa-building::before { + content: "\f1ad"; } + +.fa-angles-left::before { + content: "\f100"; } + +.fa-angle-double-left::before { + content: "\f100"; } + +.fa-qrcode::before { + content: "\f029"; } + +.fa-clock-rotate-left::before { + content: "\f1da"; } + +.fa-history::before { + content: "\f1da"; } + +.fa-face-grin-beam-sweat::before { + content: "\f583"; } + +.fa-grin-beam-sweat::before { + content: "\f583"; } + +.fa-file-export::before { + content: "\f56e"; } + +.fa-arrow-right-from-file::before { + content: "\f56e"; } + +.fa-shield::before { + content: "\f132"; } + +.fa-shield-blank::before { + content: "\f132"; } + +.fa-arrow-up-short-wide::before { + content: "\f885"; } + +.fa-sort-amount-up-alt::before { + content: "\f885"; } + +.fa-house-medical::before { + content: "\e3b2"; } + +.fa-golf-ball-tee::before { + content: "\f450"; } + +.fa-golf-ball::before { + content: "\f450"; } + +.fa-circle-chevron-left::before { + content: "\f137"; } + +.fa-chevron-circle-left::before { + content: "\f137"; } + +.fa-house-chimney-window::before { + content: "\e00d"; } + +.fa-pen-nib::before { + content: "\f5ad"; } + +.fa-tent-arrow-turn-left::before { + content: "\e580"; } + +.fa-tents::before { + content: "\e582"; } + +.fa-wand-magic::before { + content: "\f0d0"; } + +.fa-magic::before { + content: "\f0d0"; } + +.fa-dog::before { + content: "\f6d3"; } + +.fa-carrot::before { + content: "\f787"; } + +.fa-moon::before { + content: "\f186"; } + +.fa-wine-glass-empty::before { + content: "\f5ce"; } + +.fa-wine-glass-alt::before { + content: "\f5ce"; } + +.fa-cheese::before { + content: "\f7ef"; } + +.fa-yin-yang::before { + content: "\f6ad"; } + +.fa-music::before { + content: "\f001"; } + +.fa-code-commit::before { + content: "\f386"; } + +.fa-temperature-low::before { + content: "\f76b"; } + +.fa-person-biking::before { + content: "\f84a"; } + +.fa-biking::before { + content: "\f84a"; } + +.fa-broom::before { + content: "\f51a"; } + +.fa-shield-heart::before { + content: "\e574"; } + +.fa-gopuram::before { + content: "\f664"; } + +.fa-earth-oceania::before { + content: "\e47b"; } + +.fa-globe-oceania::before { + content: "\e47b"; } + +.fa-square-xmark::before { + content: "\f2d3"; } + +.fa-times-square::before { + content: "\f2d3"; } + +.fa-xmark-square::before { + content: "\f2d3"; } + +.fa-hashtag::before { + content: "\23"; } + +.fa-up-right-and-down-left-from-center::before { + content: "\f424"; } + +.fa-expand-alt::before { + content: "\f424"; } + +.fa-oil-can::before { + content: "\f613"; } + +.fa-t::before { + content: "\54"; } + +.fa-hippo::before { + content: "\f6ed"; } + +.fa-chart-column::before { + content: "\e0e3"; } + +.fa-infinity::before { + content: "\f534"; } + +.fa-vial-circle-check::before { + content: "\e596"; } + +.fa-person-arrow-down-to-line::before { + content: "\e538"; } + +.fa-voicemail::before { + content: "\f897"; } + +.fa-fan::before { + content: "\f863"; } + +.fa-person-walking-luggage::before { + content: "\e554"; } + +.fa-up-down::before { + content: "\f338"; } + +.fa-arrows-alt-v::before { + content: "\f338"; } + +.fa-cloud-moon-rain::before { + content: "\f73c"; } + +.fa-calendar::before { + content: "\f133"; } + +.fa-trailer::before { + content: "\e041"; } + +.fa-bahai::before { + content: "\f666"; } + +.fa-haykal::before { + content: "\f666"; } + +.fa-sd-card::before { + content: "\f7c2"; } + +.fa-dragon::before { + content: "\f6d5"; } + +.fa-shoe-prints::before { + content: "\f54b"; } + +.fa-circle-plus::before { + content: "\f055"; } + +.fa-plus-circle::before { + content: "\f055"; } + +.fa-face-grin-tongue-wink::before { + content: "\f58b"; } + +.fa-grin-tongue-wink::before { + content: "\f58b"; } + +.fa-hand-holding::before { + content: "\f4bd"; } + +.fa-plug-circle-exclamation::before { + content: "\e55d"; } + +.fa-link-slash::before { + content: "\f127"; } + +.fa-chain-broken::before { + content: "\f127"; } + +.fa-chain-slash::before { + content: "\f127"; } + +.fa-unlink::before { + content: "\f127"; } + +.fa-clone::before { + content: "\f24d"; } + +.fa-person-walking-arrow-loop-left::before { + content: "\e551"; } + +.fa-arrow-up-z-a::before { + content: "\f882"; } + +.fa-sort-alpha-up-alt::before { + content: "\f882"; } + +.fa-fire-flame-curved::before { + content: "\f7e4"; } + +.fa-fire-alt::before { + content: "\f7e4"; } + +.fa-tornado::before { + content: "\f76f"; } + +.fa-file-circle-plus::before { + content: "\e494"; } + +.fa-book-quran::before { + content: "\f687"; } + +.fa-quran::before { + content: "\f687"; } + +.fa-anchor::before { + content: "\f13d"; } + +.fa-border-all::before { + content: "\f84c"; } + +.fa-face-angry::before { + content: "\f556"; } + +.fa-angry::before { + content: "\f556"; } + +.fa-cookie-bite::before { + content: "\f564"; } + +.fa-arrow-trend-down::before { + content: "\e097"; } + +.fa-rss::before { + content: "\f09e"; } + +.fa-feed::before { + content: "\f09e"; } + +.fa-draw-polygon::before { + content: "\f5ee"; } + +.fa-scale-balanced::before { + content: "\f24e"; } + +.fa-balance-scale::before { + content: "\f24e"; } + +.fa-gauge-simple-high::before { + content: "\f62a"; } + +.fa-tachometer::before { + content: "\f62a"; } + +.fa-tachometer-fast::before { + content: "\f62a"; } + +.fa-shower::before { + content: "\f2cc"; } + +.fa-desktop::before { + content: "\f390"; } + +.fa-desktop-alt::before { + content: "\f390"; } + +.fa-m::before { + content: "\4d"; } + +.fa-table-list::before { + content: "\f00b"; } + +.fa-th-list::before { + content: "\f00b"; } + +.fa-comment-sms::before { + content: "\f7cd"; } + +.fa-sms::before { + content: "\f7cd"; } + +.fa-book::before { + content: "\f02d"; } + +.fa-user-plus::before { + content: "\f234"; } + +.fa-check::before { + content: "\f00c"; } + +.fa-battery-three-quarters::before { + content: "\f241"; } + +.fa-battery-4::before { + content: "\f241"; } + +.fa-house-circle-check::before { + content: "\e509"; } + +.fa-angle-left::before { + content: "\f104"; } + +.fa-diagram-successor::before { + content: "\e47a"; } + +.fa-truck-arrow-right::before { + content: "\e58b"; } + +.fa-arrows-split-up-and-left::before { + content: "\e4bc"; } + +.fa-hand-fist::before { + content: "\f6de"; } + +.fa-fist-raised::before { + content: "\f6de"; } + +.fa-cloud-moon::before { + content: "\f6c3"; } + +.fa-briefcase::before { + content: "\f0b1"; } + +.fa-person-falling::before { + content: "\e546"; } + +.fa-image-portrait::before { + content: "\f3e0"; } + +.fa-portrait::before { + content: "\f3e0"; } + +.fa-user-tag::before { + content: "\f507"; } + +.fa-rug::before { + content: "\e569"; } + +.fa-earth-europe::before { + content: "\f7a2"; } + +.fa-globe-europe::before { + content: "\f7a2"; } + +.fa-cart-flatbed-suitcase::before { + content: "\f59d"; } + +.fa-luggage-cart::before { + content: "\f59d"; } + +.fa-rectangle-xmark::before { + content: "\f410"; } + +.fa-rectangle-times::before { + content: "\f410"; } + +.fa-times-rectangle::before { + content: "\f410"; } + +.fa-window-close::before { + content: "\f410"; } + +.fa-baht-sign::before { + content: "\e0ac"; } + +.fa-book-open::before { + content: "\f518"; } + +.fa-book-journal-whills::before { + content: "\f66a"; } + +.fa-journal-whills::before { + content: "\f66a"; } + +.fa-handcuffs::before { + content: "\e4f8"; } + +.fa-triangle-exclamation::before { + content: "\f071"; } + +.fa-exclamation-triangle::before { + content: "\f071"; } + +.fa-warning::before { + content: "\f071"; } + +.fa-database::before { + content: "\f1c0"; } + +.fa-share::before { + content: "\f064"; } + +.fa-arrow-turn-right::before { + content: "\f064"; } + +.fa-mail-forward::before { + content: "\f064"; } + +.fa-bottle-droplet::before { + content: "\e4c4"; } + +.fa-mask-face::before { + content: "\e1d7"; } + +.fa-hill-rockslide::before { + content: "\e508"; } + +.fa-right-left::before { + content: "\f362"; } + +.fa-exchange-alt::before { + content: "\f362"; } + +.fa-paper-plane::before { + content: "\f1d8"; } + +.fa-road-circle-exclamation::before { + content: "\e565"; } + +.fa-dungeon::before { + content: "\f6d9"; } + +.fa-align-right::before { + content: "\f038"; } + +.fa-money-bill-1-wave::before { + content: "\f53b"; } + +.fa-money-bill-wave-alt::before { + content: "\f53b"; } + +.fa-life-ring::before { + content: "\f1cd"; } + +.fa-hands::before { + content: "\f2a7"; } + +.fa-sign-language::before { + content: "\f2a7"; } + +.fa-signing::before { + content: "\f2a7"; } + +.fa-calendar-day::before { + content: "\f783"; } + +.fa-water-ladder::before { + content: "\f5c5"; } + +.fa-ladder-water::before { + content: "\f5c5"; } + +.fa-swimming-pool::before { + content: "\f5c5"; } + +.fa-arrows-up-down::before { + content: "\f07d"; } + +.fa-arrows-v::before { + content: "\f07d"; } + +.fa-face-grimace::before { + content: "\f57f"; } + +.fa-grimace::before { + content: "\f57f"; } + +.fa-wheelchair-move::before { + content: "\e2ce"; } + +.fa-wheelchair-alt::before { + content: "\e2ce"; } + +.fa-turn-down::before { + content: "\f3be"; } + +.fa-level-down-alt::before { + content: "\f3be"; } + +.fa-person-walking-arrow-right::before { + content: "\e552"; } + +.fa-square-envelope::before { + content: "\f199"; } + +.fa-envelope-square::before { + content: "\f199"; } + +.fa-dice::before { + content: "\f522"; } + +.fa-bowling-ball::before { + content: "\f436"; } + +.fa-brain::before { + content: "\f5dc"; } + +.fa-bandage::before { + content: "\f462"; } + +.fa-band-aid::before { + content: "\f462"; } + +.fa-calendar-minus::before { + content: "\f272"; } + +.fa-circle-xmark::before { + content: "\f057"; } + +.fa-times-circle::before { + content: "\f057"; } + +.fa-xmark-circle::before { + content: "\f057"; } + +.fa-gifts::before { + content: "\f79c"; } + +.fa-hotel::before { + content: "\f594"; } + +.fa-earth-asia::before { + content: "\f57e"; } + +.fa-globe-asia::before { + content: "\f57e"; } + +.fa-id-card-clip::before { + content: "\f47f"; } + +.fa-id-card-alt::before { + content: "\f47f"; } + +.fa-magnifying-glass-plus::before { + content: "\f00e"; } + +.fa-search-plus::before { + content: "\f00e"; } + +.fa-thumbs-up::before { + content: "\f164"; } + +.fa-user-clock::before { + content: "\f4fd"; } + +.fa-hand-dots::before { + content: "\f461"; } + +.fa-allergies::before { + content: "\f461"; } + +.fa-file-invoice::before { + content: "\f570"; } + +.fa-window-minimize::before { + content: "\f2d1"; } + +.fa-mug-saucer::before { + content: "\f0f4"; } + +.fa-coffee::before { + content: "\f0f4"; } + +.fa-brush::before { + content: "\f55d"; } + +.fa-mask::before { + content: "\f6fa"; } + +.fa-magnifying-glass-minus::before { + content: "\f010"; } + +.fa-search-minus::before { + content: "\f010"; } + +.fa-ruler-vertical::before { + content: "\f548"; } + +.fa-user-large::before { + content: "\f406"; } + +.fa-user-alt::before { + content: "\f406"; } + +.fa-train-tram::before { + content: "\e5b4"; } + +.fa-user-nurse::before { + content: "\f82f"; } + +.fa-syringe::before { + content: "\f48e"; } + +.fa-cloud-sun::before { + content: "\f6c4"; } + +.fa-stopwatch-20::before { + content: "\e06f"; } + +.fa-square-full::before { + content: "\f45c"; } + +.fa-magnet::before { + content: "\f076"; } + +.fa-jar::before { + content: "\e516"; } + +.fa-note-sticky::before { + content: "\f249"; } + +.fa-sticky-note::before { + content: "\f249"; } + +.fa-bug-slash::before { + content: "\e490"; } + +.fa-arrow-up-from-water-pump::before { + content: "\e4b6"; } + +.fa-bone::before { + content: "\f5d7"; } + +.fa-user-injured::before { + content: "\f728"; } + +.fa-face-sad-tear::before { + content: "\f5b4"; } + +.fa-sad-tear::before { + content: "\f5b4"; } + +.fa-plane::before { + content: "\f072"; } + +.fa-tent-arrows-down::before { + content: "\e581"; } + +.fa-exclamation::before { + content: "\21"; } + +.fa-arrows-spin::before { + content: "\e4bb"; } + +.fa-print::before { + content: "\f02f"; } + +.fa-turkish-lira-sign::before { + content: "\e2bb"; } + +.fa-try::before { + content: "\e2bb"; } + +.fa-turkish-lira::before { + content: "\e2bb"; } + +.fa-dollar-sign::before { + content: "\24"; } + +.fa-dollar::before { + content: "\24"; } + +.fa-usd::before { + content: "\24"; } + +.fa-x::before { + content: "\58"; } + +.fa-magnifying-glass-dollar::before { + content: "\f688"; } + +.fa-search-dollar::before { + content: "\f688"; } + +.fa-users-gear::before { + content: "\f509"; } + +.fa-users-cog::before { + content: "\f509"; } + +.fa-person-military-pointing::before { + content: "\e54a"; } + +.fa-building-columns::before { + content: "\f19c"; } + +.fa-bank::before { + content: "\f19c"; } + +.fa-institution::before { + content: "\f19c"; } + +.fa-museum::before { + content: "\f19c"; } + +.fa-university::before { + content: "\f19c"; } + +.fa-umbrella::before { + content: "\f0e9"; } + +.fa-trowel::before { + content: "\e589"; } + +.fa-d::before { + content: "\44"; } + +.fa-stapler::before { + content: "\e5af"; } + +.fa-masks-theater::before { + content: "\f630"; } + +.fa-theater-masks::before { + content: "\f630"; } + +.fa-kip-sign::before { + content: "\e1c4"; } + +.fa-hand-point-left::before { + content: "\f0a5"; } + +.fa-handshake-simple::before { + content: "\f4c6"; } + +.fa-handshake-alt::before { + content: "\f4c6"; } + +.fa-jet-fighter::before { + content: "\f0fb"; } + +.fa-fighter-jet::before { + content: "\f0fb"; } + +.fa-square-share-nodes::before { + content: "\f1e1"; } + +.fa-share-alt-square::before { + content: "\f1e1"; } + +.fa-barcode::before { + content: "\f02a"; } + +.fa-plus-minus::before { + content: "\e43c"; } + +.fa-video::before { + content: "\f03d"; } + +.fa-video-camera::before { + content: "\f03d"; } + +.fa-graduation-cap::before { + content: "\f19d"; } + +.fa-mortar-board::before { + content: "\f19d"; } + +.fa-hand-holding-medical::before { + content: "\e05c"; } + +.fa-person-circle-check::before { + content: "\e53e"; } + +.fa-turn-up::before { + content: "\f3bf"; } + +.fa-level-up-alt::before { + content: "\f3bf"; } + +.sr-only, +.fa-sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } + +.sr-only-focusable:not(:focus), +.fa-sr-only-focusable:not(:focus) { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + white-space: nowrap; + border-width: 0; } +:root, :host { + --fa-style-family-brands: 'Font Awesome 6 Brands'; + --fa-font-brands: normal 400 1em/1 'Font Awesome 6 Brands'; } + +@font-face { + font-family: 'Font Awesome 6 Brands'; + font-style: normal; + font-weight: 400; + font-display: block; + src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } + +.fab, +.fa-brands { + font-weight: 400; } + +.fa-monero:before { + content: "\f3d0"; } + +.fa-hooli:before { + content: "\f427"; } + +.fa-yelp:before { + content: "\f1e9"; } + +.fa-cc-visa:before { + content: "\f1f0"; } + +.fa-lastfm:before { + content: "\f202"; } + +.fa-shopware:before { + content: "\f5b5"; } + +.fa-creative-commons-nc:before { + content: "\f4e8"; } + +.fa-aws:before { + content: "\f375"; } + +.fa-redhat:before { + content: "\f7bc"; } + +.fa-yoast:before { + content: "\f2b1"; } + +.fa-cloudflare:before { + content: "\e07d"; } + +.fa-ups:before { + content: "\f7e0"; } + +.fa-wpexplorer:before { + content: "\f2de"; } + +.fa-dyalog:before { + content: "\f399"; } + +.fa-bity:before { + content: "\f37a"; } + +.fa-stackpath:before { + content: "\f842"; } + +.fa-buysellads:before { + content: "\f20d"; } + +.fa-first-order:before { + content: "\f2b0"; } + +.fa-modx:before { + content: "\f285"; } + +.fa-guilded:before { + content: "\e07e"; } + +.fa-vnv:before { + content: "\f40b"; } + +.fa-square-js:before { + content: "\f3b9"; } + +.fa-js-square:before { + content: "\f3b9"; } + +.fa-microsoft:before { + content: "\f3ca"; } + +.fa-qq:before { + content: "\f1d6"; } + +.fa-orcid:before { + content: "\f8d2"; } + +.fa-java:before { + content: "\f4e4"; } + +.fa-invision:before { + content: "\f7b0"; } + +.fa-creative-commons-pd-alt:before { + content: "\f4ed"; } + +.fa-centercode:before { + content: "\f380"; } + +.fa-glide-g:before { + content: "\f2a6"; } + +.fa-drupal:before { + content: "\f1a9"; } + +.fa-hire-a-helper:before { + content: "\f3b0"; } + +.fa-creative-commons-by:before { + content: "\f4e7"; } + +.fa-unity:before { + content: "\e049"; } + +.fa-whmcs:before { + content: "\f40d"; } + +.fa-rocketchat:before { + content: "\f3e8"; } + +.fa-vk:before { + content: "\f189"; } + +.fa-untappd:before { + content: "\f405"; } + +.fa-mailchimp:before { + content: "\f59e"; } + +.fa-css3-alt:before { + content: "\f38b"; } + +.fa-square-reddit:before { + content: "\f1a2"; } + +.fa-reddit-square:before { + content: "\f1a2"; } + +.fa-vimeo-v:before { + content: "\f27d"; } + +.fa-contao:before { + content: "\f26d"; } + +.fa-square-font-awesome:before { + content: "\e5ad"; } + +.fa-deskpro:before { + content: "\f38f"; } + +.fa-sistrix:before { + content: "\f3ee"; } + +.fa-square-instagram:before { + content: "\e055"; } + +.fa-instagram-square:before { + content: "\e055"; } + +.fa-battle-net:before { + content: "\f835"; } + +.fa-the-red-yeti:before { + content: "\f69d"; } + +.fa-square-hacker-news:before { + content: "\f3af"; } + +.fa-hacker-news-square:before { + content: "\f3af"; } + +.fa-edge:before { + content: "\f282"; } + +.fa-threads:before { + content: "\e618"; } + +.fa-napster:before { + content: "\f3d2"; } + +.fa-square-snapchat:before { + content: "\f2ad"; } + +.fa-snapchat-square:before { + content: "\f2ad"; } + +.fa-google-plus-g:before { + content: "\f0d5"; } + +.fa-artstation:before { + content: "\f77a"; } + +.fa-markdown:before { + content: "\f60f"; } + +.fa-sourcetree:before { + content: "\f7d3"; } + +.fa-google-plus:before { + content: "\f2b3"; } + +.fa-diaspora:before { + content: "\f791"; } + +.fa-foursquare:before { + content: "\f180"; } + +.fa-stack-overflow:before { + content: "\f16c"; } + +.fa-github-alt:before { + content: "\f113"; } + +.fa-phoenix-squadron:before { + content: "\f511"; } + +.fa-pagelines:before { + content: "\f18c"; } + +.fa-algolia:before { + content: "\f36c"; } + +.fa-red-river:before { + content: "\f3e3"; } + +.fa-creative-commons-sa:before { + content: "\f4ef"; } + +.fa-safari:before { + content: "\f267"; } + +.fa-google:before { + content: "\f1a0"; } + +.fa-square-font-awesome-stroke:before { + content: "\f35c"; } + +.fa-font-awesome-alt:before { + content: "\f35c"; } + +.fa-atlassian:before { + content: "\f77b"; } + +.fa-linkedin-in:before { + content: "\f0e1"; } + +.fa-digital-ocean:before { + content: "\f391"; } + +.fa-nimblr:before { + content: "\f5a8"; } + +.fa-chromecast:before { + content: "\f838"; } + +.fa-evernote:before { + content: "\f839"; } + +.fa-hacker-news:before { + content: "\f1d4"; } + +.fa-creative-commons-sampling:before { + content: "\f4f0"; } + +.fa-adversal:before { + content: "\f36a"; } + +.fa-creative-commons:before { + content: "\f25e"; } + +.fa-watchman-monitoring:before { + content: "\e087"; } + +.fa-fonticons:before { + content: "\f280"; } + +.fa-weixin:before { + content: "\f1d7"; } + +.fa-shirtsinbulk:before { + content: "\f214"; } + +.fa-codepen:before { + content: "\f1cb"; } + +.fa-git-alt:before { + content: "\f841"; } + +.fa-lyft:before { + content: "\f3c3"; } + +.fa-rev:before { + content: "\f5b2"; } + +.fa-windows:before { + content: "\f17a"; } + +.fa-wizards-of-the-coast:before { + content: "\f730"; } + +.fa-square-viadeo:before { + content: "\f2aa"; } + +.fa-viadeo-square:before { + content: "\f2aa"; } + +.fa-meetup:before { + content: "\f2e0"; } + +.fa-centos:before { + content: "\f789"; } + +.fa-adn:before { + content: "\f170"; } + +.fa-cloudsmith:before { + content: "\f384"; } + +.fa-pied-piper-alt:before { + content: "\f1a8"; } + +.fa-square-dribbble:before { + content: "\f397"; } + +.fa-dribbble-square:before { + content: "\f397"; } + +.fa-codiepie:before { + content: "\f284"; } + +.fa-node:before { + content: "\f419"; } + +.fa-mix:before { + content: "\f3cb"; } + +.fa-steam:before { + content: "\f1b6"; } + +.fa-cc-apple-pay:before { + content: "\f416"; } + +.fa-scribd:before { + content: "\f28a"; } + +.fa-debian:before { + content: "\e60b"; } + +.fa-openid:before { + content: "\f19b"; } + +.fa-instalod:before { + content: "\e081"; } + +.fa-expeditedssl:before { + content: "\f23e"; } + +.fa-sellcast:before { + content: "\f2da"; } + +.fa-square-twitter:before { + content: "\f081"; } + +.fa-twitter-square:before { + content: "\f081"; } + +.fa-r-project:before { + content: "\f4f7"; } + +.fa-delicious:before { + content: "\f1a5"; } + +.fa-freebsd:before { + content: "\f3a4"; } + +.fa-vuejs:before { + content: "\f41f"; } + +.fa-accusoft:before { + content: "\f369"; } + +.fa-ioxhost:before { + content: "\f208"; } + +.fa-fonticons-fi:before { + content: "\f3a2"; } + +.fa-app-store:before { + content: "\f36f"; } + +.fa-cc-mastercard:before { + content: "\f1f1"; } + +.fa-itunes-note:before { + content: "\f3b5"; } + +.fa-golang:before { + content: "\e40f"; } + +.fa-kickstarter:before { + content: "\f3bb"; } + +.fa-grav:before { + content: "\f2d6"; } + +.fa-weibo:before { + content: "\f18a"; } + +.fa-uncharted:before { + content: "\e084"; } + +.fa-firstdraft:before { + content: "\f3a1"; } + +.fa-square-youtube:before { + content: "\f431"; } + +.fa-youtube-square:before { + content: "\f431"; } + +.fa-wikipedia-w:before { + content: "\f266"; } + +.fa-wpressr:before { + content: "\f3e4"; } + +.fa-rendact:before { + content: "\f3e4"; } + +.fa-angellist:before { + content: "\f209"; } + +.fa-galactic-republic:before { + content: "\f50c"; } + +.fa-nfc-directional:before { + content: "\e530"; } + +.fa-skype:before { + content: "\f17e"; } + +.fa-joget:before { + content: "\f3b7"; } + +.fa-fedora:before { + content: "\f798"; } + +.fa-stripe-s:before { + content: "\f42a"; } + +.fa-meta:before { + content: "\e49b"; } + +.fa-laravel:before { + content: "\f3bd"; } + +.fa-hotjar:before { + content: "\f3b1"; } + +.fa-bluetooth-b:before { + content: "\f294"; } + +.fa-sticker-mule:before { + content: "\f3f7"; } + +.fa-creative-commons-zero:before { + content: "\f4f3"; } + +.fa-hips:before { + content: "\f452"; } + +.fa-behance:before { + content: "\f1b4"; } + +.fa-reddit:before { + content: "\f1a1"; } + +.fa-discord:before { + content: "\f392"; } + +.fa-chrome:before { + content: "\f268"; } + +.fa-app-store-ios:before { + content: "\f370"; } + +.fa-cc-discover:before { + content: "\f1f2"; } + +.fa-wpbeginner:before { + content: "\f297"; } + +.fa-confluence:before { + content: "\f78d"; } + +.fa-mdb:before { + content: "\f8ca"; } + +.fa-dochub:before { + content: "\f394"; } + +.fa-accessible-icon:before { + content: "\f368"; } + +.fa-ebay:before { + content: "\f4f4"; } + +.fa-amazon:before { + content: "\f270"; } + +.fa-unsplash:before { + content: "\e07c"; } + +.fa-yarn:before { + content: "\f7e3"; } + +.fa-square-steam:before { + content: "\f1b7"; } + +.fa-steam-square:before { + content: "\f1b7"; } + +.fa-500px:before { + content: "\f26e"; } + +.fa-square-vimeo:before { + content: "\f194"; } + +.fa-vimeo-square:before { + content: "\f194"; } + +.fa-asymmetrik:before { + content: "\f372"; } + +.fa-font-awesome:before { + content: "\f2b4"; } + +.fa-font-awesome-flag:before { + content: "\f2b4"; } + +.fa-font-awesome-logo-full:before { + content: "\f2b4"; } + +.fa-gratipay:before { + content: "\f184"; } + +.fa-apple:before { + content: "\f179"; } + +.fa-hive:before { + content: "\e07f"; } + +.fa-gitkraken:before { + content: "\f3a6"; } + +.fa-keybase:before { + content: "\f4f5"; } + +.fa-apple-pay:before { + content: "\f415"; } + +.fa-padlet:before { + content: "\e4a0"; } + +.fa-amazon-pay:before { + content: "\f42c"; } + +.fa-square-github:before { + content: "\f092"; } + +.fa-github-square:before { + content: "\f092"; } + +.fa-stumbleupon:before { + content: "\f1a4"; } + +.fa-fedex:before { + content: "\f797"; } + +.fa-phoenix-framework:before { + content: "\f3dc"; } + +.fa-shopify:before { + content: "\e057"; } + +.fa-neos:before { + content: "\f612"; } + +.fa-square-threads:before { + content: "\e619"; } + +.fa-hackerrank:before { + content: "\f5f7"; } + +.fa-researchgate:before { + content: "\f4f8"; } + +.fa-swift:before { + content: "\f8e1"; } + +.fa-angular:before { + content: "\f420"; } + +.fa-speakap:before { + content: "\f3f3"; } + +.fa-angrycreative:before { + content: "\f36e"; } + +.fa-y-combinator:before { + content: "\f23b"; } + +.fa-empire:before { + content: "\f1d1"; } + +.fa-envira:before { + content: "\f299"; } + +.fa-square-gitlab:before { + content: "\e5ae"; } + +.fa-gitlab-square:before { + content: "\e5ae"; } + +.fa-studiovinari:before { + content: "\f3f8"; } + +.fa-pied-piper:before { + content: "\f2ae"; } + +.fa-wordpress:before { + content: "\f19a"; } + +.fa-product-hunt:before { + content: "\f288"; } + +.fa-firefox:before { + content: "\f269"; } + +.fa-linode:before { + content: "\f2b8"; } + +.fa-goodreads:before { + content: "\f3a8"; } + +.fa-square-odnoklassniki:before { + content: "\f264"; } + +.fa-odnoklassniki-square:before { + content: "\f264"; } + +.fa-jsfiddle:before { + content: "\f1cc"; } + +.fa-sith:before { + content: "\f512"; } + +.fa-themeisle:before { + content: "\f2b2"; } + +.fa-page4:before { + content: "\f3d7"; } + +.fa-hashnode:before { + content: "\e499"; } + +.fa-react:before { + content: "\f41b"; } + +.fa-cc-paypal:before { + content: "\f1f4"; } + +.fa-squarespace:before { + content: "\f5be"; } + +.fa-cc-stripe:before { + content: "\f1f5"; } + +.fa-creative-commons-share:before { + content: "\f4f2"; } + +.fa-bitcoin:before { + content: "\f379"; } + +.fa-keycdn:before { + content: "\f3ba"; } + +.fa-opera:before { + content: "\f26a"; } + +.fa-itch-io:before { + content: "\f83a"; } + +.fa-umbraco:before { + content: "\f8e8"; } + +.fa-galactic-senate:before { + content: "\f50d"; } + +.fa-ubuntu:before { + content: "\f7df"; } + +.fa-draft2digital:before { + content: "\f396"; } + +.fa-stripe:before { + content: "\f429"; } + +.fa-houzz:before { + content: "\f27c"; } + +.fa-gg:before { + content: "\f260"; } + +.fa-dhl:before { + content: "\f790"; } + +.fa-square-pinterest:before { + content: "\f0d3"; } + +.fa-pinterest-square:before { + content: "\f0d3"; } + +.fa-xing:before { + content: "\f168"; } + +.fa-blackberry:before { + content: "\f37b"; } + +.fa-creative-commons-pd:before { + content: "\f4ec"; } + +.fa-playstation:before { + content: "\f3df"; } + +.fa-quinscape:before { + content: "\f459"; } + +.fa-less:before { + content: "\f41d"; } + +.fa-blogger-b:before { + content: "\f37d"; } + +.fa-opencart:before { + content: "\f23d"; } + +.fa-vine:before { + content: "\f1ca"; } + +.fa-paypal:before { + content: "\f1ed"; } + +.fa-gitlab:before { + content: "\f296"; } + +.fa-typo3:before { + content: "\f42b"; } + +.fa-reddit-alien:before { + content: "\f281"; } + +.fa-yahoo:before { + content: "\f19e"; } + +.fa-dailymotion:before { + content: "\e052"; } + +.fa-affiliatetheme:before { + content: "\f36b"; } + +.fa-pied-piper-pp:before { + content: "\f1a7"; } + +.fa-bootstrap:before { + content: "\f836"; } + +.fa-odnoklassniki:before { + content: "\f263"; } + +.fa-nfc-symbol:before { + content: "\e531"; } + +.fa-ethereum:before { + content: "\f42e"; } + +.fa-speaker-deck:before { + content: "\f83c"; } + +.fa-creative-commons-nc-eu:before { + content: "\f4e9"; } + +.fa-patreon:before { + content: "\f3d9"; } + +.fa-avianex:before { + content: "\f374"; } + +.fa-ello:before { + content: "\f5f1"; } + +.fa-gofore:before { + content: "\f3a7"; } + +.fa-bimobject:before { + content: "\f378"; } + +.fa-facebook-f:before { + content: "\f39e"; } + +.fa-square-google-plus:before { + content: "\f0d4"; } + +.fa-google-plus-square:before { + content: "\f0d4"; } + +.fa-mandalorian:before { + content: "\f50f"; } + +.fa-first-order-alt:before { + content: "\f50a"; } + +.fa-osi:before { + content: "\f41a"; } + +.fa-google-wallet:before { + content: "\f1ee"; } + +.fa-d-and-d-beyond:before { + content: "\f6ca"; } + +.fa-periscope:before { + content: "\f3da"; } + +.fa-fulcrum:before { + content: "\f50b"; } + +.fa-cloudscale:before { + content: "\f383"; } + +.fa-forumbee:before { + content: "\f211"; } + +.fa-mizuni:before { + content: "\f3cc"; } + +.fa-schlix:before { + content: "\f3ea"; } + +.fa-square-xing:before { + content: "\f169"; } + +.fa-xing-square:before { + content: "\f169"; } + +.fa-bandcamp:before { + content: "\f2d5"; } + +.fa-wpforms:before { + content: "\f298"; } + +.fa-cloudversify:before { + content: "\f385"; } + +.fa-usps:before { + content: "\f7e1"; } + +.fa-megaport:before { + content: "\f5a3"; } + +.fa-magento:before { + content: "\f3c4"; } + +.fa-spotify:before { + content: "\f1bc"; } + +.fa-optin-monster:before { + content: "\f23c"; } + +.fa-fly:before { + content: "\f417"; } + +.fa-aviato:before { + content: "\f421"; } + +.fa-itunes:before { + content: "\f3b4"; } + +.fa-cuttlefish:before { + content: "\f38c"; } + +.fa-blogger:before { + content: "\f37c"; } + +.fa-flickr:before { + content: "\f16e"; } + +.fa-viber:before { + content: "\f409"; } + +.fa-soundcloud:before { + content: "\f1be"; } + +.fa-digg:before { + content: "\f1a6"; } + +.fa-tencent-weibo:before { + content: "\f1d5"; } + +.fa-symfony:before { + content: "\f83d"; } + +.fa-maxcdn:before { + content: "\f136"; } + +.fa-etsy:before { + content: "\f2d7"; } + +.fa-facebook-messenger:before { + content: "\f39f"; } + +.fa-audible:before { + content: "\f373"; } + +.fa-think-peaks:before { + content: "\f731"; } + +.fa-bilibili:before { + content: "\e3d9"; } + +.fa-erlang:before { + content: "\f39d"; } + +.fa-x-twitter:before { + content: "\e61b"; } + +.fa-cotton-bureau:before { + content: "\f89e"; } + +.fa-dashcube:before { + content: "\f210"; } + +.fa-42-group:before { + content: "\e080"; } + +.fa-innosoft:before { + content: "\e080"; } + +.fa-stack-exchange:before { + content: "\f18d"; } + +.fa-elementor:before { + content: "\f430"; } + +.fa-square-pied-piper:before { + content: "\e01e"; } + +.fa-pied-piper-square:before { + content: "\e01e"; } + +.fa-creative-commons-nd:before { + content: "\f4eb"; } + +.fa-palfed:before { + content: "\f3d8"; } + +.fa-superpowers:before { + content: "\f2dd"; } + +.fa-resolving:before { + content: "\f3e7"; } + +.fa-xbox:before { + content: "\f412"; } + +.fa-searchengin:before { + content: "\f3eb"; } + +.fa-tiktok:before { + content: "\e07b"; } + +.fa-square-facebook:before { + content: "\f082"; } + +.fa-facebook-square:before { + content: "\f082"; } + +.fa-renren:before { + content: "\f18b"; } + +.fa-linux:before { + content: "\f17c"; } + +.fa-glide:before { + content: "\f2a5"; } + +.fa-linkedin:before { + content: "\f08c"; } + +.fa-hubspot:before { + content: "\f3b2"; } + +.fa-deploydog:before { + content: "\f38e"; } + +.fa-twitch:before { + content: "\f1e8"; } + +.fa-ravelry:before { + content: "\f2d9"; } + +.fa-mixer:before { + content: "\e056"; } + +.fa-square-lastfm:before { + content: "\f203"; } + +.fa-lastfm-square:before { + content: "\f203"; } + +.fa-vimeo:before { + content: "\f40a"; } + +.fa-mendeley:before { + content: "\f7b3"; } + +.fa-uniregistry:before { + content: "\f404"; } + +.fa-figma:before { + content: "\f799"; } + +.fa-creative-commons-remix:before { + content: "\f4ee"; } + +.fa-cc-amazon-pay:before { + content: "\f42d"; } + +.fa-dropbox:before { + content: "\f16b"; } + +.fa-instagram:before { + content: "\f16d"; } + +.fa-cmplid:before { + content: "\e360"; } + +.fa-facebook:before { + content: "\f09a"; } + +.fa-gripfire:before { + content: "\f3ac"; } + +.fa-jedi-order:before { + content: "\f50e"; } + +.fa-uikit:before { + content: "\f403"; } + +.fa-fort-awesome-alt:before { + content: "\f3a3"; } + +.fa-phabricator:before { + content: "\f3db"; } + +.fa-ussunnah:before { + content: "\f407"; } + +.fa-earlybirds:before { + content: "\f39a"; } + +.fa-trade-federation:before { + content: "\f513"; } + +.fa-autoprefixer:before { + content: "\f41c"; } + +.fa-whatsapp:before { + content: "\f232"; } + +.fa-slideshare:before { + content: "\f1e7"; } + +.fa-google-play:before { + content: "\f3ab"; } + +.fa-viadeo:before { + content: "\f2a9"; } + +.fa-line:before { + content: "\f3c0"; } + +.fa-google-drive:before { + content: "\f3aa"; } + +.fa-servicestack:before { + content: "\f3ec"; } + +.fa-simplybuilt:before { + content: "\f215"; } + +.fa-bitbucket:before { + content: "\f171"; } + +.fa-imdb:before { + content: "\f2d8"; } + +.fa-deezer:before { + content: "\e077"; } + +.fa-raspberry-pi:before { + content: "\f7bb"; } + +.fa-jira:before { + content: "\f7b1"; } + +.fa-docker:before { + content: "\f395"; } + +.fa-screenpal:before { + content: "\e570"; } + +.fa-bluetooth:before { + content: "\f293"; } + +.fa-gitter:before { + content: "\f426"; } + +.fa-d-and-d:before { + content: "\f38d"; } + +.fa-microblog:before { + content: "\e01a"; } + +.fa-cc-diners-club:before { + content: "\f24c"; } + +.fa-gg-circle:before { + content: "\f261"; } + +.fa-pied-piper-hat:before { + content: "\f4e5"; } + +.fa-kickstarter-k:before { + content: "\f3bc"; } + +.fa-yandex:before { + content: "\f413"; } + +.fa-readme:before { + content: "\f4d5"; } + +.fa-html5:before { + content: "\f13b"; } + +.fa-sellsy:before { + content: "\f213"; } + +.fa-sass:before { + content: "\f41e"; } + +.fa-wirsindhandwerk:before { + content: "\e2d0"; } + +.fa-wsh:before { + content: "\e2d0"; } + +.fa-buromobelexperte:before { + content: "\f37f"; } + +.fa-salesforce:before { + content: "\f83b"; } + +.fa-octopus-deploy:before { + content: "\e082"; } + +.fa-medapps:before { + content: "\f3c6"; } + +.fa-ns8:before { + content: "\f3d5"; } + +.fa-pinterest-p:before { + content: "\f231"; } + +.fa-apper:before { + content: "\f371"; } + +.fa-fort-awesome:before { + content: "\f286"; } + +.fa-waze:before { + content: "\f83f"; } + +.fa-cc-jcb:before { + content: "\f24b"; } + +.fa-snapchat:before { + content: "\f2ab"; } + +.fa-snapchat-ghost:before { + content: "\f2ab"; } + +.fa-fantasy-flight-games:before { + content: "\f6dc"; } + +.fa-rust:before { + content: "\e07a"; } + +.fa-wix:before { + content: "\f5cf"; } + +.fa-square-behance:before { + content: "\f1b5"; } + +.fa-behance-square:before { + content: "\f1b5"; } + +.fa-supple:before { + content: "\f3f9"; } + +.fa-rebel:before { + content: "\f1d0"; } + +.fa-css3:before { + content: "\f13c"; } + +.fa-staylinked:before { + content: "\f3f5"; } + +.fa-kaggle:before { + content: "\f5fa"; } + +.fa-space-awesome:before { + content: "\e5ac"; } + +.fa-deviantart:before { + content: "\f1bd"; } + +.fa-cpanel:before { + content: "\f388"; } + +.fa-goodreads-g:before { + content: "\f3a9"; } + +.fa-square-git:before { + content: "\f1d2"; } + +.fa-git-square:before { + content: "\f1d2"; } + +.fa-square-tumblr:before { + content: "\f174"; } + +.fa-tumblr-square:before { + content: "\f174"; } + +.fa-trello:before { + content: "\f181"; } + +.fa-creative-commons-nc-jp:before { + content: "\f4ea"; } + +.fa-get-pocket:before { + content: "\f265"; } + +.fa-perbyte:before { + content: "\e083"; } + +.fa-grunt:before { + content: "\f3ad"; } + +.fa-weebly:before { + content: "\f5cc"; } + +.fa-connectdevelop:before { + content: "\f20e"; } + +.fa-leanpub:before { + content: "\f212"; } + +.fa-black-tie:before { + content: "\f27e"; } + +.fa-themeco:before { + content: "\f5c6"; } + +.fa-python:before { + content: "\f3e2"; } + +.fa-android:before { + content: "\f17b"; } + +.fa-bots:before { + content: "\e340"; } + +.fa-free-code-camp:before { + content: "\f2c5"; } + +.fa-hornbill:before { + content: "\f592"; } + +.fa-js:before { + content: "\f3b8"; } + +.fa-ideal:before { + content: "\e013"; } + +.fa-git:before { + content: "\f1d3"; } + +.fa-dev:before { + content: "\f6cc"; } + +.fa-sketch:before { + content: "\f7c6"; } + +.fa-yandex-international:before { + content: "\f414"; } + +.fa-cc-amex:before { + content: "\f1f3"; } + +.fa-uber:before { + content: "\f402"; } + +.fa-github:before { + content: "\f09b"; } + +.fa-php:before { + content: "\f457"; } + +.fa-alipay:before { + content: "\f642"; } + +.fa-youtube:before { + content: "\f167"; } + +.fa-skyatlas:before { + content: "\f216"; } + +.fa-firefox-browser:before { + content: "\e007"; } + +.fa-replyd:before { + content: "\f3e6"; } + +.fa-suse:before { + content: "\f7d6"; } + +.fa-jenkins:before { + content: "\f3b6"; } + +.fa-twitter:before { + content: "\f099"; } + +.fa-rockrms:before { + content: "\f3e9"; } + +.fa-pinterest:before { + content: "\f0d2"; } + +.fa-buffer:before { + content: "\f837"; } + +.fa-npm:before { + content: "\f3d4"; } + +.fa-yammer:before { + content: "\f840"; } + +.fa-btc:before { + content: "\f15a"; } + +.fa-dribbble:before { + content: "\f17d"; } + +.fa-stumbleupon-circle:before { + content: "\f1a3"; } + +.fa-internet-explorer:before { + content: "\f26b"; } + +.fa-stubber:before { + content: "\e5c7"; } + +.fa-telegram:before { + content: "\f2c6"; } + +.fa-telegram-plane:before { + content: "\f2c6"; } + +.fa-old-republic:before { + content: "\f510"; } + +.fa-odysee:before { + content: "\e5c6"; } + +.fa-square-whatsapp:before { + content: "\f40c"; } + +.fa-whatsapp-square:before { + content: "\f40c"; } + +.fa-node-js:before { + content: "\f3d3"; } + +.fa-edge-legacy:before { + content: "\e078"; } + +.fa-slack:before { + content: "\f198"; } + +.fa-slack-hash:before { + content: "\f198"; } + +.fa-medrt:before { + content: "\f3c8"; } + +.fa-usb:before { + content: "\f287"; } + +.fa-tumblr:before { + content: "\f173"; } + +.fa-vaadin:before { + content: "\f408"; } + +.fa-quora:before { + content: "\f2c4"; } + +.fa-square-x-twitter:before { + content: "\e61a"; } + +.fa-reacteurope:before { + content: "\f75d"; } + +.fa-medium:before { + content: "\f23a"; } + +.fa-medium-m:before { + content: "\f23a"; } + +.fa-amilia:before { + content: "\f36d"; } + +.fa-mixcloud:before { + content: "\f289"; } + +.fa-flipboard:before { + content: "\f44d"; } + +.fa-viacoin:before { + content: "\f237"; } + +.fa-critical-role:before { + content: "\f6c9"; } + +.fa-sitrox:before { + content: "\e44a"; } + +.fa-discourse:before { + content: "\f393"; } + +.fa-joomla:before { + content: "\f1aa"; } + +.fa-mastodon:before { + content: "\f4f6"; } + +.fa-airbnb:before { + content: "\f834"; } + +.fa-wolf-pack-battalion:before { + content: "\f514"; } + +.fa-buy-n-large:before { + content: "\f8a6"; } + +.fa-gulp:before { + content: "\f3ae"; } + +.fa-creative-commons-sampling-plus:before { + content: "\f4f1"; } + +.fa-strava:before { + content: "\f428"; } + +.fa-ember:before { + content: "\f423"; } + +.fa-canadian-maple-leaf:before { + content: "\f785"; } + +.fa-teamspeak:before { + content: "\f4f9"; } + +.fa-pushed:before { + content: "\f3e1"; } + +.fa-wordpress-simple:before { + content: "\f411"; } + +.fa-nutritionix:before { + content: "\f3d6"; } + +.fa-wodu:before { + content: "\e088"; } + +.fa-google-pay:before { + content: "\e079"; } + +.fa-intercom:before { + content: "\f7af"; } + +.fa-zhihu:before { + content: "\f63f"; } + +.fa-korvue:before { + content: "\f42f"; } + +.fa-pix:before { + content: "\e43a"; } + +.fa-steam-symbol:before { + content: "\f3f6"; } +:root, :host { + --fa-style-family-classic: 'Font Awesome 6 Free'; + --fa-font-regular: normal 400 1em/1 'Font Awesome 6 Free'; } + +@font-face { + font-family: 'Font Awesome 6 Free'; + font-style: normal; + font-weight: 400; + font-display: block; + src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); } + +.far, +.fa-regular { + font-weight: 400; } +:root, :host { + --fa-style-family-classic: 'Font Awesome 6 Free'; + --fa-font-solid: normal 900 1em/1 'Font Awesome 6 Free'; } + +@font-face { + font-family: 'Font Awesome 6 Free'; + font-style: normal; + font-weight: 900; + font-display: block; + src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); } + +.fas, +.fa-solid { + font-weight: 900; } +@font-face { + font-family: 'Font Awesome 5 Brands'; + font-display: block; + font-weight: 400; + src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } + +@font-face { + font-family: 'Font Awesome 5 Free'; + font-display: block; + font-weight: 900; + src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); } + +@font-face { + font-family: 'Font Awesome 5 Free'; + font-display: block; + font-weight: 400; + src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); } +@font-face { + font-family: 'FontAwesome'; + font-display: block; + src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); } + +@font-face { + font-family: 'FontAwesome'; + font-display: block; + src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); } + +@font-face { + font-family: 'FontAwesome'; + font-display: block; + src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); } + +@font-face { + font-family: 'FontAwesome'; + font-display: block; + src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); } diff --git a/latest-tag/deps/font-awesome-6.4.2/css/all.min.css b/latest-tag/deps/font-awesome-6.4.2/css/all.min.css new file mode 100644 index 00000000..6604a067 --- /dev/null +++ b/latest-tag/deps/font-awesome-6.4.2/css/all.min.css @@ -0,0 +1,9 @@ +/*! + * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2023 Fonticons, Inc. + */ +.fa{font-family:var(--fa-style-family,"Font Awesome 6 Free");font-weight:var(--fa-style,900)}.fa,.fa-brands,.fa-classic,.fa-regular,.fa-sharp,.fa-solid,.fab,.far,.fas{-moz-osx-font-smoothing:grayscale;-webkit-font-smoothing:antialiased;display:var(--fa-display,inline-block);font-style:normal;font-variant:normal;line-height:1;text-rendering:auto}.fa-classic,.fa-regular,.fa-solid,.far,.fas{font-family:"Font Awesome 6 Free"}.fa-brands,.fab{font-family:"Font Awesome 6 Brands"}.fa-1x{font-size:1em}.fa-2x{font-size:2em}.fa-3x{font-size:3em}.fa-4x{font-size:4em}.fa-5x{font-size:5em}.fa-6x{font-size:6em}.fa-7x{font-size:7em}.fa-8x{font-size:8em}.fa-9x{font-size:9em}.fa-10x{font-size:10em}.fa-2xs{font-size:.625em;line-height:.1em;vertical-align:.225em}.fa-xs{font-size:.75em;line-height:.08333em;vertical-align:.125em}.fa-sm{font-size:.875em;line-height:.07143em;vertical-align:.05357em}.fa-lg{font-size:1.25em;line-height:.05em;vertical-align:-.075em}.fa-xl{font-size:1.5em;line-height:.04167em;vertical-align:-.125em}.fa-2xl{font-size:2em;line-height:.03125em;vertical-align:-.1875em}.fa-fw{text-align:center;width:1.25em}.fa-ul{list-style-type:none;margin-left:var(--fa-li-margin,2.5em);padding-left:0}.fa-ul>li{position:relative}.fa-li{left:calc(var(--fa-li-width, 2em)*-1);position:absolute;text-align:center;width:var(--fa-li-width,2em);line-height:inherit}.fa-border{border-radius:var(--fa-border-radius,.1em);border:var(--fa-border-width,.08em) var(--fa-border-style,solid) var(--fa-border-color,#eee);padding:var(--fa-border-padding,.2em .25em .15em)}.fa-pull-left{float:left;margin-right:var(--fa-pull-margin,.3em)}.fa-pull-right{float:right;margin-left:var(--fa-pull-margin,.3em)}.fa-beat{-webkit-animation-name:fa-beat;animation-name:fa-beat;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-bounce{-webkit-animation-name:fa-bounce;animation-name:fa-bounce;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.28,.84,.42,1))}.fa-fade{-webkit-animation-name:fa-fade;animation-name:fa-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-beat-fade,.fa-fade{-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s)}.fa-beat-fade{-webkit-animation-name:fa-beat-fade;animation-name:fa-beat-fade;-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1));animation-timing-function:var(--fa-animation-timing,cubic-bezier(.4,0,.6,1))}.fa-flip{-webkit-animation-name:fa-flip;animation-name:fa-flip;-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,ease-in-out);animation-timing-function:var(--fa-animation-timing,ease-in-out)}.fa-shake{-webkit-animation-name:fa-shake;animation-name:fa-shake;-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-shake,.fa-spin{-webkit-animation-delay:var(--fa-animation-delay,0s);animation-delay:var(--fa-animation-delay,0s);-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal)}.fa-spin{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-duration:var(--fa-animation-duration,2s);animation-duration:var(--fa-animation-duration,2s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,linear);animation-timing-function:var(--fa-animation-timing,linear)}.fa-spin-reverse{--fa-animation-direction:reverse}.fa-pulse,.fa-spin-pulse{-webkit-animation-name:fa-spin;animation-name:fa-spin;-webkit-animation-direction:var(--fa-animation-direction,normal);animation-direction:var(--fa-animation-direction,normal);-webkit-animation-duration:var(--fa-animation-duration,1s);animation-duration:var(--fa-animation-duration,1s);-webkit-animation-iteration-count:var(--fa-animation-iteration-count,infinite);animation-iteration-count:var(--fa-animation-iteration-count,infinite);-webkit-animation-timing-function:var(--fa-animation-timing,steps(8));animation-timing-function:var(--fa-animation-timing,steps(8))}@media (prefers-reduced-motion:reduce){.fa-beat,.fa-beat-fade,.fa-bounce,.fa-fade,.fa-flip,.fa-pulse,.fa-shake,.fa-spin,.fa-spin-pulse{-webkit-animation-delay:-1ms;animation-delay:-1ms;-webkit-animation-duration:1ms;animation-duration:1ms;-webkit-animation-iteration-count:1;animation-iteration-count:1;-webkit-transition-delay:0s;transition-delay:0s;-webkit-transition-duration:0s;transition-duration:0s}}@-webkit-keyframes fa-beat{0%,90%{-webkit-transform:scale(1);transform:scale(1)}45%{-webkit-transform:scale(var(--fa-beat-scale,1.25));transform:scale(var(--fa-beat-scale,1.25))}}@keyframes fa-beat{0%,90%{-webkit-transform:scale(1);transform:scale(1)}45%{-webkit-transform:scale(var(--fa-beat-scale,1.25));transform:scale(var(--fa-beat-scale,1.25))}}@-webkit-keyframes fa-bounce{0%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}10%{-webkit-transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0);transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0)}30%{-webkit-transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em));transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em))}50%{-webkit-transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0);transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0)}57%{-webkit-transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em));transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em))}64%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}to{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}}@keyframes fa-bounce{0%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}10%{-webkit-transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0);transform:scale(var(--fa-bounce-start-scale-x,1.1),var(--fa-bounce-start-scale-y,.9)) translateY(0)}30%{-webkit-transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em));transform:scale(var(--fa-bounce-jump-scale-x,.9),var(--fa-bounce-jump-scale-y,1.1)) translateY(var(--fa-bounce-height,-.5em))}50%{-webkit-transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0);transform:scale(var(--fa-bounce-land-scale-x,1.05),var(--fa-bounce-land-scale-y,.95)) translateY(0)}57%{-webkit-transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em));transform:scale(1) translateY(var(--fa-bounce-rebound,-.125em))}64%{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}to{-webkit-transform:scale(1) translateY(0);transform:scale(1) translateY(0)}}@-webkit-keyframes fa-fade{50%{opacity:var(--fa-fade-opacity,.4)}}@keyframes fa-fade{50%{opacity:var(--fa-fade-opacity,.4)}}@-webkit-keyframes fa-beat-fade{0%,to{opacity:var(--fa-beat-fade-opacity,.4);-webkit-transform:scale(1);transform:scale(1)}50%{opacity:1;-webkit-transform:scale(var(--fa-beat-fade-scale,1.125));transform:scale(var(--fa-beat-fade-scale,1.125))}}@keyframes fa-beat-fade{0%,to{opacity:var(--fa-beat-fade-opacity,.4);-webkit-transform:scale(1);transform:scale(1)}50%{opacity:1;-webkit-transform:scale(var(--fa-beat-fade-scale,1.125));transform:scale(var(--fa-beat-fade-scale,1.125))}}@-webkit-keyframes fa-flip{50%{-webkit-transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg));transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg))}}@keyframes fa-flip{50%{-webkit-transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg));transform:rotate3d(var(--fa-flip-x,0),var(--fa-flip-y,1),var(--fa-flip-z,0),var(--fa-flip-angle,-180deg))}}@-webkit-keyframes fa-shake{0%{-webkit-transform:rotate(-15deg);transform:rotate(-15deg)}4%{-webkit-transform:rotate(15deg);transform:rotate(15deg)}8%,24%{-webkit-transform:rotate(-18deg);transform:rotate(-18deg)}12%,28%{-webkit-transform:rotate(18deg);transform:rotate(18deg)}16%{-webkit-transform:rotate(-22deg);transform:rotate(-22deg)}20%{-webkit-transform:rotate(22deg);transform:rotate(22deg)}32%{-webkit-transform:rotate(-12deg);transform:rotate(-12deg)}36%{-webkit-transform:rotate(12deg);transform:rotate(12deg)}40%,to{-webkit-transform:rotate(0deg);transform:rotate(0deg)}}@keyframes fa-shake{0%{-webkit-transform:rotate(-15deg);transform:rotate(-15deg)}4%{-webkit-transform:rotate(15deg);transform:rotate(15deg)}8%,24%{-webkit-transform:rotate(-18deg);transform:rotate(-18deg)}12%,28%{-webkit-transform:rotate(18deg);transform:rotate(18deg)}16%{-webkit-transform:rotate(-22deg);transform:rotate(-22deg)}20%{-webkit-transform:rotate(22deg);transform:rotate(22deg)}32%{-webkit-transform:rotate(-12deg);transform:rotate(-12deg)}36%{-webkit-transform:rotate(12deg);transform:rotate(12deg)}40%,to{-webkit-transform:rotate(0deg);transform:rotate(0deg)}}@-webkit-keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(1turn);transform:rotate(1turn)}}@keyframes fa-spin{0%{-webkit-transform:rotate(0deg);transform:rotate(0deg)}to{-webkit-transform:rotate(1turn);transform:rotate(1turn)}}.fa-rotate-90{-webkit-transform:rotate(90deg);transform:rotate(90deg)}.fa-rotate-180{-webkit-transform:rotate(180deg);transform:rotate(180deg)}.fa-rotate-270{-webkit-transform:rotate(270deg);transform:rotate(270deg)}.fa-flip-horizontal{-webkit-transform:scaleX(-1);transform:scaleX(-1)}.fa-flip-vertical{-webkit-transform:scaleY(-1);transform:scaleY(-1)}.fa-flip-both,.fa-flip-horizontal.fa-flip-vertical{-webkit-transform:scale(-1);transform:scale(-1)}.fa-rotate-by{-webkit-transform:rotate(var(--fa-rotate-angle,none));transform:rotate(var(--fa-rotate-angle,none))}.fa-stack{display:inline-block;height:2em;line-height:2em;position:relative;vertical-align:middle;width:2.5em}.fa-stack-1x,.fa-stack-2x{left:0;position:absolute;text-align:center;width:100%;z-index:var(--fa-stack-z-index,auto)}.fa-stack-1x{line-height:inherit}.fa-stack-2x{font-size:2em}.fa-inverse{color:var(--fa-inverse,#fff)} + +.fa-0:before{content:"\30"}.fa-1:before{content:"\31"}.fa-2:before{content:"\32"}.fa-3:before{content:"\33"}.fa-4:before{content:"\34"}.fa-5:before{content:"\35"}.fa-6:before{content:"\36"}.fa-7:before{content:"\37"}.fa-8:before{content:"\38"}.fa-9:before{content:"\39"}.fa-fill-drip:before{content:"\f576"}.fa-arrows-to-circle:before{content:"\e4bd"}.fa-chevron-circle-right:before,.fa-circle-chevron-right:before{content:"\f138"}.fa-at:before{content:"\40"}.fa-trash-alt:before,.fa-trash-can:before{content:"\f2ed"}.fa-text-height:before{content:"\f034"}.fa-user-times:before,.fa-user-xmark:before{content:"\f235"}.fa-stethoscope:before{content:"\f0f1"}.fa-comment-alt:before,.fa-message:before{content:"\f27a"}.fa-info:before{content:"\f129"}.fa-compress-alt:before,.fa-down-left-and-up-right-to-center:before{content:"\f422"}.fa-explosion:before{content:"\e4e9"}.fa-file-alt:before,.fa-file-lines:before,.fa-file-text:before{content:"\f15c"}.fa-wave-square:before{content:"\f83e"}.fa-ring:before{content:"\f70b"}.fa-building-un:before{content:"\e4d9"}.fa-dice-three:before{content:"\f527"}.fa-calendar-alt:before,.fa-calendar-days:before{content:"\f073"}.fa-anchor-circle-check:before{content:"\e4aa"}.fa-building-circle-arrow-right:before{content:"\e4d1"}.fa-volleyball-ball:before,.fa-volleyball:before{content:"\f45f"}.fa-arrows-up-to-line:before{content:"\e4c2"}.fa-sort-desc:before,.fa-sort-down:before{content:"\f0dd"}.fa-circle-minus:before,.fa-minus-circle:before{content:"\f056"}.fa-door-open:before{content:"\f52b"}.fa-right-from-bracket:before,.fa-sign-out-alt:before{content:"\f2f5"}.fa-atom:before{content:"\f5d2"}.fa-soap:before{content:"\e06e"}.fa-heart-music-camera-bolt:before,.fa-icons:before{content:"\f86d"}.fa-microphone-alt-slash:before,.fa-microphone-lines-slash:before{content:"\f539"}.fa-bridge-circle-check:before{content:"\e4c9"}.fa-pump-medical:before{content:"\e06a"}.fa-fingerprint:before{content:"\f577"}.fa-hand-point-right:before{content:"\f0a4"}.fa-magnifying-glass-location:before,.fa-search-location:before{content:"\f689"}.fa-forward-step:before,.fa-step-forward:before{content:"\f051"}.fa-face-smile-beam:before,.fa-smile-beam:before{content:"\f5b8"}.fa-flag-checkered:before{content:"\f11e"}.fa-football-ball:before,.fa-football:before{content:"\f44e"}.fa-school-circle-exclamation:before{content:"\e56c"}.fa-crop:before{content:"\f125"}.fa-angle-double-down:before,.fa-angles-down:before{content:"\f103"}.fa-users-rectangle:before{content:"\e594"}.fa-people-roof:before{content:"\e537"}.fa-people-line:before{content:"\e534"}.fa-beer-mug-empty:before,.fa-beer:before{content:"\f0fc"}.fa-diagram-predecessor:before{content:"\e477"}.fa-arrow-up-long:before,.fa-long-arrow-up:before{content:"\f176"}.fa-burn:before,.fa-fire-flame-simple:before{content:"\f46a"}.fa-male:before,.fa-person:before{content:"\f183"}.fa-laptop:before{content:"\f109"}.fa-file-csv:before{content:"\f6dd"}.fa-menorah:before{content:"\f676"}.fa-truck-plane:before{content:"\e58f"}.fa-record-vinyl:before{content:"\f8d9"}.fa-face-grin-stars:before,.fa-grin-stars:before{content:"\f587"}.fa-bong:before{content:"\f55c"}.fa-pastafarianism:before,.fa-spaghetti-monster-flying:before{content:"\f67b"}.fa-arrow-down-up-across-line:before{content:"\e4af"}.fa-spoon:before,.fa-utensil-spoon:before{content:"\f2e5"}.fa-jar-wheat:before{content:"\e517"}.fa-envelopes-bulk:before,.fa-mail-bulk:before{content:"\f674"}.fa-file-circle-exclamation:before{content:"\e4eb"}.fa-circle-h:before,.fa-hospital-symbol:before{content:"\f47e"}.fa-pager:before{content:"\f815"}.fa-address-book:before,.fa-contact-book:before{content:"\f2b9"}.fa-strikethrough:before{content:"\f0cc"}.fa-k:before{content:"\4b"}.fa-landmark-flag:before{content:"\e51c"}.fa-pencil-alt:before,.fa-pencil:before{content:"\f303"}.fa-backward:before{content:"\f04a"}.fa-caret-right:before{content:"\f0da"}.fa-comments:before{content:"\f086"}.fa-file-clipboard:before,.fa-paste:before{content:"\f0ea"}.fa-code-pull-request:before{content:"\e13c"}.fa-clipboard-list:before{content:"\f46d"}.fa-truck-loading:before,.fa-truck-ramp-box:before{content:"\f4de"}.fa-user-check:before{content:"\f4fc"}.fa-vial-virus:before{content:"\e597"}.fa-sheet-plastic:before{content:"\e571"}.fa-blog:before{content:"\f781"}.fa-user-ninja:before{content:"\f504"}.fa-person-arrow-up-from-line:before{content:"\e539"}.fa-scroll-torah:before,.fa-torah:before{content:"\f6a0"}.fa-broom-ball:before,.fa-quidditch-broom-ball:before,.fa-quidditch:before{content:"\f458"}.fa-toggle-off:before{content:"\f204"}.fa-archive:before,.fa-box-archive:before{content:"\f187"}.fa-person-drowning:before{content:"\e545"}.fa-arrow-down-9-1:before,.fa-sort-numeric-desc:before,.fa-sort-numeric-down-alt:before{content:"\f886"}.fa-face-grin-tongue-squint:before,.fa-grin-tongue-squint:before{content:"\f58a"}.fa-spray-can:before{content:"\f5bd"}.fa-truck-monster:before{content:"\f63b"}.fa-w:before{content:"\57"}.fa-earth-africa:before,.fa-globe-africa:before{content:"\f57c"}.fa-rainbow:before{content:"\f75b"}.fa-circle-notch:before{content:"\f1ce"}.fa-tablet-alt:before,.fa-tablet-screen-button:before{content:"\f3fa"}.fa-paw:before{content:"\f1b0"}.fa-cloud:before{content:"\f0c2"}.fa-trowel-bricks:before{content:"\e58a"}.fa-face-flushed:before,.fa-flushed:before{content:"\f579"}.fa-hospital-user:before{content:"\f80d"}.fa-tent-arrow-left-right:before{content:"\e57f"}.fa-gavel:before,.fa-legal:before{content:"\f0e3"}.fa-binoculars:before{content:"\f1e5"}.fa-microphone-slash:before{content:"\f131"}.fa-box-tissue:before{content:"\e05b"}.fa-motorcycle:before{content:"\f21c"}.fa-bell-concierge:before,.fa-concierge-bell:before{content:"\f562"}.fa-pen-ruler:before,.fa-pencil-ruler:before{content:"\f5ae"}.fa-people-arrows-left-right:before,.fa-people-arrows:before{content:"\e068"}.fa-mars-and-venus-burst:before{content:"\e523"}.fa-caret-square-right:before,.fa-square-caret-right:before{content:"\f152"}.fa-cut:before,.fa-scissors:before{content:"\f0c4"}.fa-sun-plant-wilt:before{content:"\e57a"}.fa-toilets-portable:before{content:"\e584"}.fa-hockey-puck:before{content:"\f453"}.fa-table:before{content:"\f0ce"}.fa-magnifying-glass-arrow-right:before{content:"\e521"}.fa-digital-tachograph:before,.fa-tachograph-digital:before{content:"\f566"}.fa-users-slash:before{content:"\e073"}.fa-clover:before{content:"\e139"}.fa-mail-reply:before,.fa-reply:before{content:"\f3e5"}.fa-star-and-crescent:before{content:"\f699"}.fa-house-fire:before{content:"\e50c"}.fa-minus-square:before,.fa-square-minus:before{content:"\f146"}.fa-helicopter:before{content:"\f533"}.fa-compass:before{content:"\f14e"}.fa-caret-square-down:before,.fa-square-caret-down:before{content:"\f150"}.fa-file-circle-question:before{content:"\e4ef"}.fa-laptop-code:before{content:"\f5fc"}.fa-swatchbook:before{content:"\f5c3"}.fa-prescription-bottle:before{content:"\f485"}.fa-bars:before,.fa-navicon:before{content:"\f0c9"}.fa-people-group:before{content:"\e533"}.fa-hourglass-3:before,.fa-hourglass-end:before{content:"\f253"}.fa-heart-broken:before,.fa-heart-crack:before{content:"\f7a9"}.fa-external-link-square-alt:before,.fa-square-up-right:before{content:"\f360"}.fa-face-kiss-beam:before,.fa-kiss-beam:before{content:"\f597"}.fa-film:before{content:"\f008"}.fa-ruler-horizontal:before{content:"\f547"}.fa-people-robbery:before{content:"\e536"}.fa-lightbulb:before{content:"\f0eb"}.fa-caret-left:before{content:"\f0d9"}.fa-circle-exclamation:before,.fa-exclamation-circle:before{content:"\f06a"}.fa-school-circle-xmark:before{content:"\e56d"}.fa-arrow-right-from-bracket:before,.fa-sign-out:before{content:"\f08b"}.fa-chevron-circle-down:before,.fa-circle-chevron-down:before{content:"\f13a"}.fa-unlock-alt:before,.fa-unlock-keyhole:before{content:"\f13e"}.fa-cloud-showers-heavy:before{content:"\f740"}.fa-headphones-alt:before,.fa-headphones-simple:before{content:"\f58f"}.fa-sitemap:before{content:"\f0e8"}.fa-circle-dollar-to-slot:before,.fa-donate:before{content:"\f4b9"}.fa-memory:before{content:"\f538"}.fa-road-spikes:before{content:"\e568"}.fa-fire-burner:before{content:"\e4f1"}.fa-flag:before{content:"\f024"}.fa-hanukiah:before{content:"\f6e6"}.fa-feather:before{content:"\f52d"}.fa-volume-down:before,.fa-volume-low:before{content:"\f027"}.fa-comment-slash:before{content:"\f4b3"}.fa-cloud-sun-rain:before{content:"\f743"}.fa-compress:before{content:"\f066"}.fa-wheat-alt:before,.fa-wheat-awn:before{content:"\e2cd"}.fa-ankh:before{content:"\f644"}.fa-hands-holding-child:before{content:"\e4fa"}.fa-asterisk:before{content:"\2a"}.fa-check-square:before,.fa-square-check:before{content:"\f14a"}.fa-peseta-sign:before{content:"\e221"}.fa-header:before,.fa-heading:before{content:"\f1dc"}.fa-ghost:before{content:"\f6e2"}.fa-list-squares:before,.fa-list:before{content:"\f03a"}.fa-phone-square-alt:before,.fa-square-phone-flip:before{content:"\f87b"}.fa-cart-plus:before{content:"\f217"}.fa-gamepad:before{content:"\f11b"}.fa-circle-dot:before,.fa-dot-circle:before{content:"\f192"}.fa-dizzy:before,.fa-face-dizzy:before{content:"\f567"}.fa-egg:before{content:"\f7fb"}.fa-house-medical-circle-xmark:before{content:"\e513"}.fa-campground:before{content:"\f6bb"}.fa-folder-plus:before{content:"\f65e"}.fa-futbol-ball:before,.fa-futbol:before,.fa-soccer-ball:before{content:"\f1e3"}.fa-paint-brush:before,.fa-paintbrush:before{content:"\f1fc"}.fa-lock:before{content:"\f023"}.fa-gas-pump:before{content:"\f52f"}.fa-hot-tub-person:before,.fa-hot-tub:before{content:"\f593"}.fa-map-location:before,.fa-map-marked:before{content:"\f59f"}.fa-house-flood-water:before{content:"\e50e"}.fa-tree:before{content:"\f1bb"}.fa-bridge-lock:before{content:"\e4cc"}.fa-sack-dollar:before{content:"\f81d"}.fa-edit:before,.fa-pen-to-square:before{content:"\f044"}.fa-car-side:before{content:"\f5e4"}.fa-share-alt:before,.fa-share-nodes:before{content:"\f1e0"}.fa-heart-circle-minus:before{content:"\e4ff"}.fa-hourglass-2:before,.fa-hourglass-half:before{content:"\f252"}.fa-microscope:before{content:"\f610"}.fa-sink:before{content:"\e06d"}.fa-bag-shopping:before,.fa-shopping-bag:before{content:"\f290"}.fa-arrow-down-z-a:before,.fa-sort-alpha-desc:before,.fa-sort-alpha-down-alt:before{content:"\f881"}.fa-mitten:before{content:"\f7b5"}.fa-person-rays:before{content:"\e54d"}.fa-users:before{content:"\f0c0"}.fa-eye-slash:before{content:"\f070"}.fa-flask-vial:before{content:"\e4f3"}.fa-hand-paper:before,.fa-hand:before{content:"\f256"}.fa-om:before{content:"\f679"}.fa-worm:before{content:"\e599"}.fa-house-circle-xmark:before{content:"\e50b"}.fa-plug:before{content:"\f1e6"}.fa-chevron-up:before{content:"\f077"}.fa-hand-spock:before{content:"\f259"}.fa-stopwatch:before{content:"\f2f2"}.fa-face-kiss:before,.fa-kiss:before{content:"\f596"}.fa-bridge-circle-xmark:before{content:"\e4cb"}.fa-face-grin-tongue:before,.fa-grin-tongue:before{content:"\f589"}.fa-chess-bishop:before{content:"\f43a"}.fa-face-grin-wink:before,.fa-grin-wink:before{content:"\f58c"}.fa-deaf:before,.fa-deafness:before,.fa-ear-deaf:before,.fa-hard-of-hearing:before{content:"\f2a4"}.fa-road-circle-check:before{content:"\e564"}.fa-dice-five:before{content:"\f523"}.fa-rss-square:before,.fa-square-rss:before{content:"\f143"}.fa-land-mine-on:before{content:"\e51b"}.fa-i-cursor:before{content:"\f246"}.fa-stamp:before{content:"\f5bf"}.fa-stairs:before{content:"\e289"}.fa-i:before{content:"\49"}.fa-hryvnia-sign:before,.fa-hryvnia:before{content:"\f6f2"}.fa-pills:before{content:"\f484"}.fa-face-grin-wide:before,.fa-grin-alt:before{content:"\f581"}.fa-tooth:before{content:"\f5c9"}.fa-v:before{content:"\56"}.fa-bangladeshi-taka-sign:before{content:"\e2e6"}.fa-bicycle:before{content:"\f206"}.fa-rod-asclepius:before,.fa-rod-snake:before,.fa-staff-aesculapius:before,.fa-staff-snake:before{content:"\e579"}.fa-head-side-cough-slash:before{content:"\e062"}.fa-ambulance:before,.fa-truck-medical:before{content:"\f0f9"}.fa-wheat-awn-circle-exclamation:before{content:"\e598"}.fa-snowman:before{content:"\f7d0"}.fa-mortar-pestle:before{content:"\f5a7"}.fa-road-barrier:before{content:"\e562"}.fa-school:before{content:"\f549"}.fa-igloo:before{content:"\f7ae"}.fa-joint:before{content:"\f595"}.fa-angle-right:before{content:"\f105"}.fa-horse:before{content:"\f6f0"}.fa-q:before{content:"\51"}.fa-g:before{content:"\47"}.fa-notes-medical:before{content:"\f481"}.fa-temperature-2:before,.fa-temperature-half:before,.fa-thermometer-2:before,.fa-thermometer-half:before{content:"\f2c9"}.fa-dong-sign:before{content:"\e169"}.fa-capsules:before{content:"\f46b"}.fa-poo-bolt:before,.fa-poo-storm:before{content:"\f75a"}.fa-face-frown-open:before,.fa-frown-open:before{content:"\f57a"}.fa-hand-point-up:before{content:"\f0a6"}.fa-money-bill:before{content:"\f0d6"}.fa-bookmark:before{content:"\f02e"}.fa-align-justify:before{content:"\f039"}.fa-umbrella-beach:before{content:"\f5ca"}.fa-helmet-un:before{content:"\e503"}.fa-bullseye:before{content:"\f140"}.fa-bacon:before{content:"\f7e5"}.fa-hand-point-down:before{content:"\f0a7"}.fa-arrow-up-from-bracket:before{content:"\e09a"}.fa-folder-blank:before,.fa-folder:before{content:"\f07b"}.fa-file-medical-alt:before,.fa-file-waveform:before{content:"\f478"}.fa-radiation:before{content:"\f7b9"}.fa-chart-simple:before{content:"\e473"}.fa-mars-stroke:before{content:"\f229"}.fa-vial:before{content:"\f492"}.fa-dashboard:before,.fa-gauge-med:before,.fa-gauge:before,.fa-tachometer-alt-average:before{content:"\f624"}.fa-magic-wand-sparkles:before,.fa-wand-magic-sparkles:before{content:"\e2ca"}.fa-e:before{content:"\45"}.fa-pen-alt:before,.fa-pen-clip:before{content:"\f305"}.fa-bridge-circle-exclamation:before{content:"\e4ca"}.fa-user:before{content:"\f007"}.fa-school-circle-check:before{content:"\e56b"}.fa-dumpster:before{content:"\f793"}.fa-shuttle-van:before,.fa-van-shuttle:before{content:"\f5b6"}.fa-building-user:before{content:"\e4da"}.fa-caret-square-left:before,.fa-square-caret-left:before{content:"\f191"}.fa-highlighter:before{content:"\f591"}.fa-key:before{content:"\f084"}.fa-bullhorn:before{content:"\f0a1"}.fa-globe:before{content:"\f0ac"}.fa-synagogue:before{content:"\f69b"}.fa-person-half-dress:before{content:"\e548"}.fa-road-bridge:before{content:"\e563"}.fa-location-arrow:before{content:"\f124"}.fa-c:before{content:"\43"}.fa-tablet-button:before{content:"\f10a"}.fa-building-lock:before{content:"\e4d6"}.fa-pizza-slice:before{content:"\f818"}.fa-money-bill-wave:before{content:"\f53a"}.fa-area-chart:before,.fa-chart-area:before{content:"\f1fe"}.fa-house-flag:before{content:"\e50d"}.fa-person-circle-minus:before{content:"\e540"}.fa-ban:before,.fa-cancel:before{content:"\f05e"}.fa-camera-rotate:before{content:"\e0d8"}.fa-air-freshener:before,.fa-spray-can-sparkles:before{content:"\f5d0"}.fa-star:before{content:"\f005"}.fa-repeat:before{content:"\f363"}.fa-cross:before{content:"\f654"}.fa-box:before{content:"\f466"}.fa-venus-mars:before{content:"\f228"}.fa-arrow-pointer:before,.fa-mouse-pointer:before{content:"\f245"}.fa-expand-arrows-alt:before,.fa-maximize:before{content:"\f31e"}.fa-charging-station:before{content:"\f5e7"}.fa-shapes:before,.fa-triangle-circle-square:before{content:"\f61f"}.fa-random:before,.fa-shuffle:before{content:"\f074"}.fa-person-running:before,.fa-running:before{content:"\f70c"}.fa-mobile-retro:before{content:"\e527"}.fa-grip-lines-vertical:before{content:"\f7a5"}.fa-spider:before{content:"\f717"}.fa-hands-bound:before{content:"\e4f9"}.fa-file-invoice-dollar:before{content:"\f571"}.fa-plane-circle-exclamation:before{content:"\e556"}.fa-x-ray:before{content:"\f497"}.fa-spell-check:before{content:"\f891"}.fa-slash:before{content:"\f715"}.fa-computer-mouse:before,.fa-mouse:before{content:"\f8cc"}.fa-arrow-right-to-bracket:before,.fa-sign-in:before{content:"\f090"}.fa-shop-slash:before,.fa-store-alt-slash:before{content:"\e070"}.fa-server:before{content:"\f233"}.fa-virus-covid-slash:before{content:"\e4a9"}.fa-shop-lock:before{content:"\e4a5"}.fa-hourglass-1:before,.fa-hourglass-start:before{content:"\f251"}.fa-blender-phone:before{content:"\f6b6"}.fa-building-wheat:before{content:"\e4db"}.fa-person-breastfeeding:before{content:"\e53a"}.fa-right-to-bracket:before,.fa-sign-in-alt:before{content:"\f2f6"}.fa-venus:before{content:"\f221"}.fa-passport:before{content:"\f5ab"}.fa-heart-pulse:before,.fa-heartbeat:before{content:"\f21e"}.fa-people-carry-box:before,.fa-people-carry:before{content:"\f4ce"}.fa-temperature-high:before{content:"\f769"}.fa-microchip:before{content:"\f2db"}.fa-crown:before{content:"\f521"}.fa-weight-hanging:before{content:"\f5cd"}.fa-xmarks-lines:before{content:"\e59a"}.fa-file-prescription:before{content:"\f572"}.fa-weight-scale:before,.fa-weight:before{content:"\f496"}.fa-user-friends:before,.fa-user-group:before{content:"\f500"}.fa-arrow-up-a-z:before,.fa-sort-alpha-up:before{content:"\f15e"}.fa-chess-knight:before{content:"\f441"}.fa-face-laugh-squint:before,.fa-laugh-squint:before{content:"\f59b"}.fa-wheelchair:before{content:"\f193"}.fa-arrow-circle-up:before,.fa-circle-arrow-up:before{content:"\f0aa"}.fa-toggle-on:before{content:"\f205"}.fa-person-walking:before,.fa-walking:before{content:"\f554"}.fa-l:before{content:"\4c"}.fa-fire:before{content:"\f06d"}.fa-bed-pulse:before,.fa-procedures:before{content:"\f487"}.fa-shuttle-space:before,.fa-space-shuttle:before{content:"\f197"}.fa-face-laugh:before,.fa-laugh:before{content:"\f599"}.fa-folder-open:before{content:"\f07c"}.fa-heart-circle-plus:before{content:"\e500"}.fa-code-fork:before{content:"\e13b"}.fa-city:before{content:"\f64f"}.fa-microphone-alt:before,.fa-microphone-lines:before{content:"\f3c9"}.fa-pepper-hot:before{content:"\f816"}.fa-unlock:before{content:"\f09c"}.fa-colon-sign:before{content:"\e140"}.fa-headset:before{content:"\f590"}.fa-store-slash:before{content:"\e071"}.fa-road-circle-xmark:before{content:"\e566"}.fa-user-minus:before{content:"\f503"}.fa-mars-stroke-up:before,.fa-mars-stroke-v:before{content:"\f22a"}.fa-champagne-glasses:before,.fa-glass-cheers:before{content:"\f79f"}.fa-clipboard:before{content:"\f328"}.fa-house-circle-exclamation:before{content:"\e50a"}.fa-file-arrow-up:before,.fa-file-upload:before{content:"\f574"}.fa-wifi-3:before,.fa-wifi-strong:before,.fa-wifi:before{content:"\f1eb"}.fa-bath:before,.fa-bathtub:before{content:"\f2cd"}.fa-underline:before{content:"\f0cd"}.fa-user-edit:before,.fa-user-pen:before{content:"\f4ff"}.fa-signature:before{content:"\f5b7"}.fa-stroopwafel:before{content:"\f551"}.fa-bold:before{content:"\f032"}.fa-anchor-lock:before{content:"\e4ad"}.fa-building-ngo:before{content:"\e4d7"}.fa-manat-sign:before{content:"\e1d5"}.fa-not-equal:before{content:"\f53e"}.fa-border-style:before,.fa-border-top-left:before{content:"\f853"}.fa-map-location-dot:before,.fa-map-marked-alt:before{content:"\f5a0"}.fa-jedi:before{content:"\f669"}.fa-poll:before,.fa-square-poll-vertical:before{content:"\f681"}.fa-mug-hot:before{content:"\f7b6"}.fa-battery-car:before,.fa-car-battery:before{content:"\f5df"}.fa-gift:before{content:"\f06b"}.fa-dice-two:before{content:"\f528"}.fa-chess-queen:before{content:"\f445"}.fa-glasses:before{content:"\f530"}.fa-chess-board:before{content:"\f43c"}.fa-building-circle-check:before{content:"\e4d2"}.fa-person-chalkboard:before{content:"\e53d"}.fa-mars-stroke-h:before,.fa-mars-stroke-right:before{content:"\f22b"}.fa-hand-back-fist:before,.fa-hand-rock:before{content:"\f255"}.fa-caret-square-up:before,.fa-square-caret-up:before{content:"\f151"}.fa-cloud-showers-water:before{content:"\e4e4"}.fa-bar-chart:before,.fa-chart-bar:before{content:"\f080"}.fa-hands-bubbles:before,.fa-hands-wash:before{content:"\e05e"}.fa-less-than-equal:before{content:"\f537"}.fa-train:before{content:"\f238"}.fa-eye-low-vision:before,.fa-low-vision:before{content:"\f2a8"}.fa-crow:before{content:"\f520"}.fa-sailboat:before{content:"\e445"}.fa-window-restore:before{content:"\f2d2"}.fa-plus-square:before,.fa-square-plus:before{content:"\f0fe"}.fa-torii-gate:before{content:"\f6a1"}.fa-frog:before{content:"\f52e"}.fa-bucket:before{content:"\e4cf"}.fa-image:before{content:"\f03e"}.fa-microphone:before{content:"\f130"}.fa-cow:before{content:"\f6c8"}.fa-caret-up:before{content:"\f0d8"}.fa-screwdriver:before{content:"\f54a"}.fa-folder-closed:before{content:"\e185"}.fa-house-tsunami:before{content:"\e515"}.fa-square-nfi:before{content:"\e576"}.fa-arrow-up-from-ground-water:before{content:"\e4b5"}.fa-glass-martini-alt:before,.fa-martini-glass:before{content:"\f57b"}.fa-rotate-back:before,.fa-rotate-backward:before,.fa-rotate-left:before,.fa-undo-alt:before{content:"\f2ea"}.fa-columns:before,.fa-table-columns:before{content:"\f0db"}.fa-lemon:before{content:"\f094"}.fa-head-side-mask:before{content:"\e063"}.fa-handshake:before{content:"\f2b5"}.fa-gem:before{content:"\f3a5"}.fa-dolly-box:before,.fa-dolly:before{content:"\f472"}.fa-smoking:before{content:"\f48d"}.fa-compress-arrows-alt:before,.fa-minimize:before{content:"\f78c"}.fa-monument:before{content:"\f5a6"}.fa-snowplow:before{content:"\f7d2"}.fa-angle-double-right:before,.fa-angles-right:before{content:"\f101"}.fa-cannabis:before{content:"\f55f"}.fa-circle-play:before,.fa-play-circle:before{content:"\f144"}.fa-tablets:before{content:"\f490"}.fa-ethernet:before{content:"\f796"}.fa-eur:before,.fa-euro-sign:before,.fa-euro:before{content:"\f153"}.fa-chair:before{content:"\f6c0"}.fa-check-circle:before,.fa-circle-check:before{content:"\f058"}.fa-circle-stop:before,.fa-stop-circle:before{content:"\f28d"}.fa-compass-drafting:before,.fa-drafting-compass:before{content:"\f568"}.fa-plate-wheat:before{content:"\e55a"}.fa-icicles:before{content:"\f7ad"}.fa-person-shelter:before{content:"\e54f"}.fa-neuter:before{content:"\f22c"}.fa-id-badge:before{content:"\f2c1"}.fa-marker:before{content:"\f5a1"}.fa-face-laugh-beam:before,.fa-laugh-beam:before{content:"\f59a"}.fa-helicopter-symbol:before{content:"\e502"}.fa-universal-access:before{content:"\f29a"}.fa-chevron-circle-up:before,.fa-circle-chevron-up:before{content:"\f139"}.fa-lari-sign:before{content:"\e1c8"}.fa-volcano:before{content:"\f770"}.fa-person-walking-dashed-line-arrow-right:before{content:"\e553"}.fa-gbp:before,.fa-pound-sign:before,.fa-sterling-sign:before{content:"\f154"}.fa-viruses:before{content:"\e076"}.fa-square-person-confined:before{content:"\e577"}.fa-user-tie:before{content:"\f508"}.fa-arrow-down-long:before,.fa-long-arrow-down:before{content:"\f175"}.fa-tent-arrow-down-to-line:before{content:"\e57e"}.fa-certificate:before{content:"\f0a3"}.fa-mail-reply-all:before,.fa-reply-all:before{content:"\f122"}.fa-suitcase:before{content:"\f0f2"}.fa-person-skating:before,.fa-skating:before{content:"\f7c5"}.fa-filter-circle-dollar:before,.fa-funnel-dollar:before{content:"\f662"}.fa-camera-retro:before{content:"\f083"}.fa-arrow-circle-down:before,.fa-circle-arrow-down:before{content:"\f0ab"}.fa-arrow-right-to-file:before,.fa-file-import:before{content:"\f56f"}.fa-external-link-square:before,.fa-square-arrow-up-right:before{content:"\f14c"}.fa-box-open:before{content:"\f49e"}.fa-scroll:before{content:"\f70e"}.fa-spa:before{content:"\f5bb"}.fa-location-pin-lock:before{content:"\e51f"}.fa-pause:before{content:"\f04c"}.fa-hill-avalanche:before{content:"\e507"}.fa-temperature-0:before,.fa-temperature-empty:before,.fa-thermometer-0:before,.fa-thermometer-empty:before{content:"\f2cb"}.fa-bomb:before{content:"\f1e2"}.fa-registered:before{content:"\f25d"}.fa-address-card:before,.fa-contact-card:before,.fa-vcard:before{content:"\f2bb"}.fa-balance-scale-right:before,.fa-scale-unbalanced-flip:before{content:"\f516"}.fa-subscript:before{content:"\f12c"}.fa-diamond-turn-right:before,.fa-directions:before{content:"\f5eb"}.fa-burst:before{content:"\e4dc"}.fa-house-laptop:before,.fa-laptop-house:before{content:"\e066"}.fa-face-tired:before,.fa-tired:before{content:"\f5c8"}.fa-money-bills:before{content:"\e1f3"}.fa-smog:before{content:"\f75f"}.fa-crutch:before{content:"\f7f7"}.fa-cloud-arrow-up:before,.fa-cloud-upload-alt:before,.fa-cloud-upload:before{content:"\f0ee"}.fa-palette:before{content:"\f53f"}.fa-arrows-turn-right:before{content:"\e4c0"}.fa-vest:before{content:"\e085"}.fa-ferry:before{content:"\e4ea"}.fa-arrows-down-to-people:before{content:"\e4b9"}.fa-seedling:before,.fa-sprout:before{content:"\f4d8"}.fa-arrows-alt-h:before,.fa-left-right:before{content:"\f337"}.fa-boxes-packing:before{content:"\e4c7"}.fa-arrow-circle-left:before,.fa-circle-arrow-left:before{content:"\f0a8"}.fa-group-arrows-rotate:before{content:"\e4f6"}.fa-bowl-food:before{content:"\e4c6"}.fa-candy-cane:before{content:"\f786"}.fa-arrow-down-wide-short:before,.fa-sort-amount-asc:before,.fa-sort-amount-down:before{content:"\f160"}.fa-cloud-bolt:before,.fa-thunderstorm:before{content:"\f76c"}.fa-remove-format:before,.fa-text-slash:before{content:"\f87d"}.fa-face-smile-wink:before,.fa-smile-wink:before{content:"\f4da"}.fa-file-word:before{content:"\f1c2"}.fa-file-powerpoint:before{content:"\f1c4"}.fa-arrows-h:before,.fa-arrows-left-right:before{content:"\f07e"}.fa-house-lock:before{content:"\e510"}.fa-cloud-arrow-down:before,.fa-cloud-download-alt:before,.fa-cloud-download:before{content:"\f0ed"}.fa-children:before{content:"\e4e1"}.fa-blackboard:before,.fa-chalkboard:before{content:"\f51b"}.fa-user-alt-slash:before,.fa-user-large-slash:before{content:"\f4fa"}.fa-envelope-open:before{content:"\f2b6"}.fa-handshake-alt-slash:before,.fa-handshake-simple-slash:before{content:"\e05f"}.fa-mattress-pillow:before{content:"\e525"}.fa-guarani-sign:before{content:"\e19a"}.fa-arrows-rotate:before,.fa-refresh:before,.fa-sync:before{content:"\f021"}.fa-fire-extinguisher:before{content:"\f134"}.fa-cruzeiro-sign:before{content:"\e152"}.fa-greater-than-equal:before{content:"\f532"}.fa-shield-alt:before,.fa-shield-halved:before{content:"\f3ed"}.fa-atlas:before,.fa-book-atlas:before{content:"\f558"}.fa-virus:before{content:"\e074"}.fa-envelope-circle-check:before{content:"\e4e8"}.fa-layer-group:before{content:"\f5fd"}.fa-arrows-to-dot:before{content:"\e4be"}.fa-archway:before{content:"\f557"}.fa-heart-circle-check:before{content:"\e4fd"}.fa-house-chimney-crack:before,.fa-house-damage:before{content:"\f6f1"}.fa-file-archive:before,.fa-file-zipper:before{content:"\f1c6"}.fa-square:before{content:"\f0c8"}.fa-glass-martini:before,.fa-martini-glass-empty:before{content:"\f000"}.fa-couch:before{content:"\f4b8"}.fa-cedi-sign:before{content:"\e0df"}.fa-italic:before{content:"\f033"}.fa-church:before{content:"\f51d"}.fa-comments-dollar:before{content:"\f653"}.fa-democrat:before{content:"\f747"}.fa-z:before{content:"\5a"}.fa-person-skiing:before,.fa-skiing:before{content:"\f7c9"}.fa-road-lock:before{content:"\e567"}.fa-a:before{content:"\41"}.fa-temperature-arrow-down:before,.fa-temperature-down:before{content:"\e03f"}.fa-feather-alt:before,.fa-feather-pointed:before{content:"\f56b"}.fa-p:before{content:"\50"}.fa-snowflake:before{content:"\f2dc"}.fa-newspaper:before{content:"\f1ea"}.fa-ad:before,.fa-rectangle-ad:before{content:"\f641"}.fa-arrow-circle-right:before,.fa-circle-arrow-right:before{content:"\f0a9"}.fa-filter-circle-xmark:before{content:"\e17b"}.fa-locust:before{content:"\e520"}.fa-sort:before,.fa-unsorted:before{content:"\f0dc"}.fa-list-1-2:before,.fa-list-numeric:before,.fa-list-ol:before{content:"\f0cb"}.fa-person-dress-burst:before{content:"\e544"}.fa-money-check-alt:before,.fa-money-check-dollar:before{content:"\f53d"}.fa-vector-square:before{content:"\f5cb"}.fa-bread-slice:before{content:"\f7ec"}.fa-language:before{content:"\f1ab"}.fa-face-kiss-wink-heart:before,.fa-kiss-wink-heart:before{content:"\f598"}.fa-filter:before{content:"\f0b0"}.fa-question:before{content:"\3f"}.fa-file-signature:before{content:"\f573"}.fa-arrows-alt:before,.fa-up-down-left-right:before{content:"\f0b2"}.fa-house-chimney-user:before{content:"\e065"}.fa-hand-holding-heart:before{content:"\f4be"}.fa-puzzle-piece:before{content:"\f12e"}.fa-money-check:before{content:"\f53c"}.fa-star-half-alt:before,.fa-star-half-stroke:before{content:"\f5c0"}.fa-code:before{content:"\f121"}.fa-glass-whiskey:before,.fa-whiskey-glass:before{content:"\f7a0"}.fa-building-circle-exclamation:before{content:"\e4d3"}.fa-magnifying-glass-chart:before{content:"\e522"}.fa-arrow-up-right-from-square:before,.fa-external-link:before{content:"\f08e"}.fa-cubes-stacked:before{content:"\e4e6"}.fa-krw:before,.fa-won-sign:before,.fa-won:before{content:"\f159"}.fa-virus-covid:before{content:"\e4a8"}.fa-austral-sign:before{content:"\e0a9"}.fa-f:before{content:"\46"}.fa-leaf:before{content:"\f06c"}.fa-road:before{content:"\f018"}.fa-cab:before,.fa-taxi:before{content:"\f1ba"}.fa-person-circle-plus:before{content:"\e541"}.fa-chart-pie:before,.fa-pie-chart:before{content:"\f200"}.fa-bolt-lightning:before{content:"\e0b7"}.fa-sack-xmark:before{content:"\e56a"}.fa-file-excel:before{content:"\f1c3"}.fa-file-contract:before{content:"\f56c"}.fa-fish-fins:before{content:"\e4f2"}.fa-building-flag:before{content:"\e4d5"}.fa-face-grin-beam:before,.fa-grin-beam:before{content:"\f582"}.fa-object-ungroup:before{content:"\f248"}.fa-poop:before{content:"\f619"}.fa-location-pin:before,.fa-map-marker:before{content:"\f041"}.fa-kaaba:before{content:"\f66b"}.fa-toilet-paper:before{content:"\f71e"}.fa-hard-hat:before,.fa-hat-hard:before,.fa-helmet-safety:before{content:"\f807"}.fa-eject:before{content:"\f052"}.fa-arrow-alt-circle-right:before,.fa-circle-right:before{content:"\f35a"}.fa-plane-circle-check:before{content:"\e555"}.fa-face-rolling-eyes:before,.fa-meh-rolling-eyes:before{content:"\f5a5"}.fa-object-group:before{content:"\f247"}.fa-chart-line:before,.fa-line-chart:before{content:"\f201"}.fa-mask-ventilator:before{content:"\e524"}.fa-arrow-right:before{content:"\f061"}.fa-map-signs:before,.fa-signs-post:before{content:"\f277"}.fa-cash-register:before{content:"\f788"}.fa-person-circle-question:before{content:"\e542"}.fa-h:before{content:"\48"}.fa-tarp:before{content:"\e57b"}.fa-screwdriver-wrench:before,.fa-tools:before{content:"\f7d9"}.fa-arrows-to-eye:before{content:"\e4bf"}.fa-plug-circle-bolt:before{content:"\e55b"}.fa-heart:before{content:"\f004"}.fa-mars-and-venus:before{content:"\f224"}.fa-home-user:before,.fa-house-user:before{content:"\e1b0"}.fa-dumpster-fire:before{content:"\f794"}.fa-house-crack:before{content:"\e3b1"}.fa-cocktail:before,.fa-martini-glass-citrus:before{content:"\f561"}.fa-face-surprise:before,.fa-surprise:before{content:"\f5c2"}.fa-bottle-water:before{content:"\e4c5"}.fa-circle-pause:before,.fa-pause-circle:before{content:"\f28b"}.fa-toilet-paper-slash:before{content:"\e072"}.fa-apple-alt:before,.fa-apple-whole:before{content:"\f5d1"}.fa-kitchen-set:before{content:"\e51a"}.fa-r:before{content:"\52"}.fa-temperature-1:before,.fa-temperature-quarter:before,.fa-thermometer-1:before,.fa-thermometer-quarter:before{content:"\f2ca"}.fa-cube:before{content:"\f1b2"}.fa-bitcoin-sign:before{content:"\e0b4"}.fa-shield-dog:before{content:"\e573"}.fa-solar-panel:before{content:"\f5ba"}.fa-lock-open:before{content:"\f3c1"}.fa-elevator:before{content:"\e16d"}.fa-money-bill-transfer:before{content:"\e528"}.fa-money-bill-trend-up:before{content:"\e529"}.fa-house-flood-water-circle-arrow-right:before{content:"\e50f"}.fa-poll-h:before,.fa-square-poll-horizontal:before{content:"\f682"}.fa-circle:before{content:"\f111"}.fa-backward-fast:before,.fa-fast-backward:before{content:"\f049"}.fa-recycle:before{content:"\f1b8"}.fa-user-astronaut:before{content:"\f4fb"}.fa-plane-slash:before{content:"\e069"}.fa-trademark:before{content:"\f25c"}.fa-basketball-ball:before,.fa-basketball:before{content:"\f434"}.fa-satellite-dish:before{content:"\f7c0"}.fa-arrow-alt-circle-up:before,.fa-circle-up:before{content:"\f35b"}.fa-mobile-alt:before,.fa-mobile-screen-button:before{content:"\f3cd"}.fa-volume-high:before,.fa-volume-up:before{content:"\f028"}.fa-users-rays:before{content:"\e593"}.fa-wallet:before{content:"\f555"}.fa-clipboard-check:before{content:"\f46c"}.fa-file-audio:before{content:"\f1c7"}.fa-burger:before,.fa-hamburger:before{content:"\f805"}.fa-wrench:before{content:"\f0ad"}.fa-bugs:before{content:"\e4d0"}.fa-rupee-sign:before,.fa-rupee:before{content:"\f156"}.fa-file-image:before{content:"\f1c5"}.fa-circle-question:before,.fa-question-circle:before{content:"\f059"}.fa-plane-departure:before{content:"\f5b0"}.fa-handshake-slash:before{content:"\e060"}.fa-book-bookmark:before{content:"\e0bb"}.fa-code-branch:before{content:"\f126"}.fa-hat-cowboy:before{content:"\f8c0"}.fa-bridge:before{content:"\e4c8"}.fa-phone-alt:before,.fa-phone-flip:before{content:"\f879"}.fa-truck-front:before{content:"\e2b7"}.fa-cat:before{content:"\f6be"}.fa-anchor-circle-exclamation:before{content:"\e4ab"}.fa-truck-field:before{content:"\e58d"}.fa-route:before{content:"\f4d7"}.fa-clipboard-question:before{content:"\e4e3"}.fa-panorama:before{content:"\e209"}.fa-comment-medical:before{content:"\f7f5"}.fa-teeth-open:before{content:"\f62f"}.fa-file-circle-minus:before{content:"\e4ed"}.fa-tags:before{content:"\f02c"}.fa-wine-glass:before{content:"\f4e3"}.fa-fast-forward:before,.fa-forward-fast:before{content:"\f050"}.fa-face-meh-blank:before,.fa-meh-blank:before{content:"\f5a4"}.fa-parking:before,.fa-square-parking:before{content:"\f540"}.fa-house-signal:before{content:"\e012"}.fa-bars-progress:before,.fa-tasks-alt:before{content:"\f828"}.fa-faucet-drip:before{content:"\e006"}.fa-cart-flatbed:before,.fa-dolly-flatbed:before{content:"\f474"}.fa-ban-smoking:before,.fa-smoking-ban:before{content:"\f54d"}.fa-terminal:before{content:"\f120"}.fa-mobile-button:before{content:"\f10b"}.fa-house-medical-flag:before{content:"\e514"}.fa-basket-shopping:before,.fa-shopping-basket:before{content:"\f291"}.fa-tape:before{content:"\f4db"}.fa-bus-alt:before,.fa-bus-simple:before{content:"\f55e"}.fa-eye:before{content:"\f06e"}.fa-face-sad-cry:before,.fa-sad-cry:before{content:"\f5b3"}.fa-audio-description:before{content:"\f29e"}.fa-person-military-to-person:before{content:"\e54c"}.fa-file-shield:before{content:"\e4f0"}.fa-user-slash:before{content:"\f506"}.fa-pen:before{content:"\f304"}.fa-tower-observation:before{content:"\e586"}.fa-file-code:before{content:"\f1c9"}.fa-signal-5:before,.fa-signal-perfect:before,.fa-signal:before{content:"\f012"}.fa-bus:before{content:"\f207"}.fa-heart-circle-xmark:before{content:"\e501"}.fa-home-lg:before,.fa-house-chimney:before{content:"\e3af"}.fa-window-maximize:before{content:"\f2d0"}.fa-face-frown:before,.fa-frown:before{content:"\f119"}.fa-prescription:before{content:"\f5b1"}.fa-shop:before,.fa-store-alt:before{content:"\f54f"}.fa-floppy-disk:before,.fa-save:before{content:"\f0c7"}.fa-vihara:before{content:"\f6a7"}.fa-balance-scale-left:before,.fa-scale-unbalanced:before{content:"\f515"}.fa-sort-asc:before,.fa-sort-up:before{content:"\f0de"}.fa-comment-dots:before,.fa-commenting:before{content:"\f4ad"}.fa-plant-wilt:before{content:"\e5aa"}.fa-diamond:before{content:"\f219"}.fa-face-grin-squint:before,.fa-grin-squint:before{content:"\f585"}.fa-hand-holding-dollar:before,.fa-hand-holding-usd:before{content:"\f4c0"}.fa-bacterium:before{content:"\e05a"}.fa-hand-pointer:before{content:"\f25a"}.fa-drum-steelpan:before{content:"\f56a"}.fa-hand-scissors:before{content:"\f257"}.fa-hands-praying:before,.fa-praying-hands:before{content:"\f684"}.fa-arrow-right-rotate:before,.fa-arrow-rotate-forward:before,.fa-arrow-rotate-right:before,.fa-redo:before{content:"\f01e"}.fa-biohazard:before{content:"\f780"}.fa-location-crosshairs:before,.fa-location:before{content:"\f601"}.fa-mars-double:before{content:"\f227"}.fa-child-dress:before{content:"\e59c"}.fa-users-between-lines:before{content:"\e591"}.fa-lungs-virus:before{content:"\e067"}.fa-face-grin-tears:before,.fa-grin-tears:before{content:"\f588"}.fa-phone:before{content:"\f095"}.fa-calendar-times:before,.fa-calendar-xmark:before{content:"\f273"}.fa-child-reaching:before{content:"\e59d"}.fa-head-side-virus:before{content:"\e064"}.fa-user-cog:before,.fa-user-gear:before{content:"\f4fe"}.fa-arrow-up-1-9:before,.fa-sort-numeric-up:before{content:"\f163"}.fa-door-closed:before{content:"\f52a"}.fa-shield-virus:before{content:"\e06c"}.fa-dice-six:before{content:"\f526"}.fa-mosquito-net:before{content:"\e52c"}.fa-bridge-water:before{content:"\e4ce"}.fa-person-booth:before{content:"\f756"}.fa-text-width:before{content:"\f035"}.fa-hat-wizard:before{content:"\f6e8"}.fa-pen-fancy:before{content:"\f5ac"}.fa-digging:before,.fa-person-digging:before{content:"\f85e"}.fa-trash:before{content:"\f1f8"}.fa-gauge-simple-med:before,.fa-gauge-simple:before,.fa-tachometer-average:before{content:"\f629"}.fa-book-medical:before{content:"\f7e6"}.fa-poo:before{content:"\f2fe"}.fa-quote-right-alt:before,.fa-quote-right:before{content:"\f10e"}.fa-shirt:before,.fa-t-shirt:before,.fa-tshirt:before{content:"\f553"}.fa-cubes:before{content:"\f1b3"}.fa-divide:before{content:"\f529"}.fa-tenge-sign:before,.fa-tenge:before{content:"\f7d7"}.fa-headphones:before{content:"\f025"}.fa-hands-holding:before{content:"\f4c2"}.fa-hands-clapping:before{content:"\e1a8"}.fa-republican:before{content:"\f75e"}.fa-arrow-left:before{content:"\f060"}.fa-person-circle-xmark:before{content:"\e543"}.fa-ruler:before{content:"\f545"}.fa-align-left:before{content:"\f036"}.fa-dice-d6:before{content:"\f6d1"}.fa-restroom:before{content:"\f7bd"}.fa-j:before{content:"\4a"}.fa-users-viewfinder:before{content:"\e595"}.fa-file-video:before{content:"\f1c8"}.fa-external-link-alt:before,.fa-up-right-from-square:before{content:"\f35d"}.fa-table-cells:before,.fa-th:before{content:"\f00a"}.fa-file-pdf:before{content:"\f1c1"}.fa-bible:before,.fa-book-bible:before{content:"\f647"}.fa-o:before{content:"\4f"}.fa-medkit:before,.fa-suitcase-medical:before{content:"\f0fa"}.fa-user-secret:before{content:"\f21b"}.fa-otter:before{content:"\f700"}.fa-female:before,.fa-person-dress:before{content:"\f182"}.fa-comment-dollar:before{content:"\f651"}.fa-briefcase-clock:before,.fa-business-time:before{content:"\f64a"}.fa-table-cells-large:before,.fa-th-large:before{content:"\f009"}.fa-book-tanakh:before,.fa-tanakh:before{content:"\f827"}.fa-phone-volume:before,.fa-volume-control-phone:before{content:"\f2a0"}.fa-hat-cowboy-side:before{content:"\f8c1"}.fa-clipboard-user:before{content:"\f7f3"}.fa-child:before{content:"\f1ae"}.fa-lira-sign:before{content:"\f195"}.fa-satellite:before{content:"\f7bf"}.fa-plane-lock:before{content:"\e558"}.fa-tag:before{content:"\f02b"}.fa-comment:before{content:"\f075"}.fa-birthday-cake:before,.fa-cake-candles:before,.fa-cake:before{content:"\f1fd"}.fa-envelope:before{content:"\f0e0"}.fa-angle-double-up:before,.fa-angles-up:before{content:"\f102"}.fa-paperclip:before{content:"\f0c6"}.fa-arrow-right-to-city:before{content:"\e4b3"}.fa-ribbon:before{content:"\f4d6"}.fa-lungs:before{content:"\f604"}.fa-arrow-up-9-1:before,.fa-sort-numeric-up-alt:before{content:"\f887"}.fa-litecoin-sign:before{content:"\e1d3"}.fa-border-none:before{content:"\f850"}.fa-circle-nodes:before{content:"\e4e2"}.fa-parachute-box:before{content:"\f4cd"}.fa-indent:before{content:"\f03c"}.fa-truck-field-un:before{content:"\e58e"}.fa-hourglass-empty:before,.fa-hourglass:before{content:"\f254"}.fa-mountain:before{content:"\f6fc"}.fa-user-doctor:before,.fa-user-md:before{content:"\f0f0"}.fa-circle-info:before,.fa-info-circle:before{content:"\f05a"}.fa-cloud-meatball:before{content:"\f73b"}.fa-camera-alt:before,.fa-camera:before{content:"\f030"}.fa-square-virus:before{content:"\e578"}.fa-meteor:before{content:"\f753"}.fa-car-on:before{content:"\e4dd"}.fa-sleigh:before{content:"\f7cc"}.fa-arrow-down-1-9:before,.fa-sort-numeric-asc:before,.fa-sort-numeric-down:before{content:"\f162"}.fa-hand-holding-droplet:before,.fa-hand-holding-water:before{content:"\f4c1"}.fa-water:before{content:"\f773"}.fa-calendar-check:before{content:"\f274"}.fa-braille:before{content:"\f2a1"}.fa-prescription-bottle-alt:before,.fa-prescription-bottle-medical:before{content:"\f486"}.fa-landmark:before{content:"\f66f"}.fa-truck:before{content:"\f0d1"}.fa-crosshairs:before{content:"\f05b"}.fa-person-cane:before{content:"\e53c"}.fa-tent:before{content:"\e57d"}.fa-vest-patches:before{content:"\e086"}.fa-check-double:before{content:"\f560"}.fa-arrow-down-a-z:before,.fa-sort-alpha-asc:before,.fa-sort-alpha-down:before{content:"\f15d"}.fa-money-bill-wheat:before{content:"\e52a"}.fa-cookie:before{content:"\f563"}.fa-arrow-left-rotate:before,.fa-arrow-rotate-back:before,.fa-arrow-rotate-backward:before,.fa-arrow-rotate-left:before,.fa-undo:before{content:"\f0e2"}.fa-hard-drive:before,.fa-hdd:before{content:"\f0a0"}.fa-face-grin-squint-tears:before,.fa-grin-squint-tears:before{content:"\f586"}.fa-dumbbell:before{content:"\f44b"}.fa-list-alt:before,.fa-rectangle-list:before{content:"\f022"}.fa-tarp-droplet:before{content:"\e57c"}.fa-house-medical-circle-check:before{content:"\e511"}.fa-person-skiing-nordic:before,.fa-skiing-nordic:before{content:"\f7ca"}.fa-calendar-plus:before{content:"\f271"}.fa-plane-arrival:before{content:"\f5af"}.fa-arrow-alt-circle-left:before,.fa-circle-left:before{content:"\f359"}.fa-subway:before,.fa-train-subway:before{content:"\f239"}.fa-chart-gantt:before{content:"\e0e4"}.fa-indian-rupee-sign:before,.fa-indian-rupee:before,.fa-inr:before{content:"\e1bc"}.fa-crop-alt:before,.fa-crop-simple:before{content:"\f565"}.fa-money-bill-1:before,.fa-money-bill-alt:before{content:"\f3d1"}.fa-left-long:before,.fa-long-arrow-alt-left:before{content:"\f30a"}.fa-dna:before{content:"\f471"}.fa-virus-slash:before{content:"\e075"}.fa-minus:before,.fa-subtract:before{content:"\f068"}.fa-chess:before{content:"\f439"}.fa-arrow-left-long:before,.fa-long-arrow-left:before{content:"\f177"}.fa-plug-circle-check:before{content:"\e55c"}.fa-street-view:before{content:"\f21d"}.fa-franc-sign:before{content:"\e18f"}.fa-volume-off:before{content:"\f026"}.fa-american-sign-language-interpreting:before,.fa-asl-interpreting:before,.fa-hands-american-sign-language-interpreting:before,.fa-hands-asl-interpreting:before{content:"\f2a3"}.fa-cog:before,.fa-gear:before{content:"\f013"}.fa-droplet-slash:before,.fa-tint-slash:before{content:"\f5c7"}.fa-mosque:before{content:"\f678"}.fa-mosquito:before{content:"\e52b"}.fa-star-of-david:before{content:"\f69a"}.fa-person-military-rifle:before{content:"\e54b"}.fa-cart-shopping:before,.fa-shopping-cart:before{content:"\f07a"}.fa-vials:before{content:"\f493"}.fa-plug-circle-plus:before{content:"\e55f"}.fa-place-of-worship:before{content:"\f67f"}.fa-grip-vertical:before{content:"\f58e"}.fa-arrow-turn-up:before,.fa-level-up:before{content:"\f148"}.fa-u:before{content:"\55"}.fa-square-root-alt:before,.fa-square-root-variable:before{content:"\f698"}.fa-clock-four:before,.fa-clock:before{content:"\f017"}.fa-backward-step:before,.fa-step-backward:before{content:"\f048"}.fa-pallet:before{content:"\f482"}.fa-faucet:before{content:"\e005"}.fa-baseball-bat-ball:before{content:"\f432"}.fa-s:before{content:"\53"}.fa-timeline:before{content:"\e29c"}.fa-keyboard:before{content:"\f11c"}.fa-caret-down:before{content:"\f0d7"}.fa-clinic-medical:before,.fa-house-chimney-medical:before{content:"\f7f2"}.fa-temperature-3:before,.fa-temperature-three-quarters:before,.fa-thermometer-3:before,.fa-thermometer-three-quarters:before{content:"\f2c8"}.fa-mobile-android-alt:before,.fa-mobile-screen:before{content:"\f3cf"}.fa-plane-up:before{content:"\e22d"}.fa-piggy-bank:before{content:"\f4d3"}.fa-battery-3:before,.fa-battery-half:before{content:"\f242"}.fa-mountain-city:before{content:"\e52e"}.fa-coins:before{content:"\f51e"}.fa-khanda:before{content:"\f66d"}.fa-sliders-h:before,.fa-sliders:before{content:"\f1de"}.fa-folder-tree:before{content:"\f802"}.fa-network-wired:before{content:"\f6ff"}.fa-map-pin:before{content:"\f276"}.fa-hamsa:before{content:"\f665"}.fa-cent-sign:before{content:"\e3f5"}.fa-flask:before{content:"\f0c3"}.fa-person-pregnant:before{content:"\e31e"}.fa-wand-sparkles:before{content:"\f72b"}.fa-ellipsis-v:before,.fa-ellipsis-vertical:before{content:"\f142"}.fa-ticket:before{content:"\f145"}.fa-power-off:before{content:"\f011"}.fa-long-arrow-alt-right:before,.fa-right-long:before{content:"\f30b"}.fa-flag-usa:before{content:"\f74d"}.fa-laptop-file:before{content:"\e51d"}.fa-teletype:before,.fa-tty:before{content:"\f1e4"}.fa-diagram-next:before{content:"\e476"}.fa-person-rifle:before{content:"\e54e"}.fa-house-medical-circle-exclamation:before{content:"\e512"}.fa-closed-captioning:before{content:"\f20a"}.fa-hiking:before,.fa-person-hiking:before{content:"\f6ec"}.fa-venus-double:before{content:"\f226"}.fa-images:before{content:"\f302"}.fa-calculator:before{content:"\f1ec"}.fa-people-pulling:before{content:"\e535"}.fa-n:before{content:"\4e"}.fa-cable-car:before,.fa-tram:before{content:"\f7da"}.fa-cloud-rain:before{content:"\f73d"}.fa-building-circle-xmark:before{content:"\e4d4"}.fa-ship:before{content:"\f21a"}.fa-arrows-down-to-line:before{content:"\e4b8"}.fa-download:before{content:"\f019"}.fa-face-grin:before,.fa-grin:before{content:"\f580"}.fa-backspace:before,.fa-delete-left:before{content:"\f55a"}.fa-eye-dropper-empty:before,.fa-eye-dropper:before,.fa-eyedropper:before{content:"\f1fb"}.fa-file-circle-check:before{content:"\e5a0"}.fa-forward:before{content:"\f04e"}.fa-mobile-android:before,.fa-mobile-phone:before,.fa-mobile:before{content:"\f3ce"}.fa-face-meh:before,.fa-meh:before{content:"\f11a"}.fa-align-center:before{content:"\f037"}.fa-book-dead:before,.fa-book-skull:before{content:"\f6b7"}.fa-drivers-license:before,.fa-id-card:before{content:"\f2c2"}.fa-dedent:before,.fa-outdent:before{content:"\f03b"}.fa-heart-circle-exclamation:before{content:"\e4fe"}.fa-home-alt:before,.fa-home-lg-alt:before,.fa-home:before,.fa-house:before{content:"\f015"}.fa-calendar-week:before{content:"\f784"}.fa-laptop-medical:before{content:"\f812"}.fa-b:before{content:"\42"}.fa-file-medical:before{content:"\f477"}.fa-dice-one:before{content:"\f525"}.fa-kiwi-bird:before{content:"\f535"}.fa-arrow-right-arrow-left:before,.fa-exchange:before{content:"\f0ec"}.fa-redo-alt:before,.fa-rotate-forward:before,.fa-rotate-right:before{content:"\f2f9"}.fa-cutlery:before,.fa-utensils:before{content:"\f2e7"}.fa-arrow-up-wide-short:before,.fa-sort-amount-up:before{content:"\f161"}.fa-mill-sign:before{content:"\e1ed"}.fa-bowl-rice:before{content:"\e2eb"}.fa-skull:before{content:"\f54c"}.fa-broadcast-tower:before,.fa-tower-broadcast:before{content:"\f519"}.fa-truck-pickup:before{content:"\f63c"}.fa-long-arrow-alt-up:before,.fa-up-long:before{content:"\f30c"}.fa-stop:before{content:"\f04d"}.fa-code-merge:before{content:"\f387"}.fa-upload:before{content:"\f093"}.fa-hurricane:before{content:"\f751"}.fa-mound:before{content:"\e52d"}.fa-toilet-portable:before{content:"\e583"}.fa-compact-disc:before{content:"\f51f"}.fa-file-arrow-down:before,.fa-file-download:before{content:"\f56d"}.fa-caravan:before{content:"\f8ff"}.fa-shield-cat:before{content:"\e572"}.fa-bolt:before,.fa-zap:before{content:"\f0e7"}.fa-glass-water:before{content:"\e4f4"}.fa-oil-well:before{content:"\e532"}.fa-vault:before{content:"\e2c5"}.fa-mars:before{content:"\f222"}.fa-toilet:before{content:"\f7d8"}.fa-plane-circle-xmark:before{content:"\e557"}.fa-cny:before,.fa-jpy:before,.fa-rmb:before,.fa-yen-sign:before,.fa-yen:before{content:"\f157"}.fa-rouble:before,.fa-rub:before,.fa-ruble-sign:before,.fa-ruble:before{content:"\f158"}.fa-sun:before{content:"\f185"}.fa-guitar:before{content:"\f7a6"}.fa-face-laugh-wink:before,.fa-laugh-wink:before{content:"\f59c"}.fa-horse-head:before{content:"\f7ab"}.fa-bore-hole:before{content:"\e4c3"}.fa-industry:before{content:"\f275"}.fa-arrow-alt-circle-down:before,.fa-circle-down:before{content:"\f358"}.fa-arrows-turn-to-dots:before{content:"\e4c1"}.fa-florin-sign:before{content:"\e184"}.fa-arrow-down-short-wide:before,.fa-sort-amount-desc:before,.fa-sort-amount-down-alt:before{content:"\f884"}.fa-less-than:before{content:"\3c"}.fa-angle-down:before{content:"\f107"}.fa-car-tunnel:before{content:"\e4de"}.fa-head-side-cough:before{content:"\e061"}.fa-grip-lines:before{content:"\f7a4"}.fa-thumbs-down:before{content:"\f165"}.fa-user-lock:before{content:"\f502"}.fa-arrow-right-long:before,.fa-long-arrow-right:before{content:"\f178"}.fa-anchor-circle-xmark:before{content:"\e4ac"}.fa-ellipsis-h:before,.fa-ellipsis:before{content:"\f141"}.fa-chess-pawn:before{content:"\f443"}.fa-first-aid:before,.fa-kit-medical:before{content:"\f479"}.fa-person-through-window:before{content:"\e5a9"}.fa-toolbox:before{content:"\f552"}.fa-hands-holding-circle:before{content:"\e4fb"}.fa-bug:before{content:"\f188"}.fa-credit-card-alt:before,.fa-credit-card:before{content:"\f09d"}.fa-automobile:before,.fa-car:before{content:"\f1b9"}.fa-hand-holding-hand:before{content:"\e4f7"}.fa-book-open-reader:before,.fa-book-reader:before{content:"\f5da"}.fa-mountain-sun:before{content:"\e52f"}.fa-arrows-left-right-to-line:before{content:"\e4ba"}.fa-dice-d20:before{content:"\f6cf"}.fa-truck-droplet:before{content:"\e58c"}.fa-file-circle-xmark:before{content:"\e5a1"}.fa-temperature-arrow-up:before,.fa-temperature-up:before{content:"\e040"}.fa-medal:before{content:"\f5a2"}.fa-bed:before{content:"\f236"}.fa-h-square:before,.fa-square-h:before{content:"\f0fd"}.fa-podcast:before{content:"\f2ce"}.fa-temperature-4:before,.fa-temperature-full:before,.fa-thermometer-4:before,.fa-thermometer-full:before{content:"\f2c7"}.fa-bell:before{content:"\f0f3"}.fa-superscript:before{content:"\f12b"}.fa-plug-circle-xmark:before{content:"\e560"}.fa-star-of-life:before{content:"\f621"}.fa-phone-slash:before{content:"\f3dd"}.fa-paint-roller:before{content:"\f5aa"}.fa-hands-helping:before,.fa-handshake-angle:before{content:"\f4c4"}.fa-location-dot:before,.fa-map-marker-alt:before{content:"\f3c5"}.fa-file:before{content:"\f15b"}.fa-greater-than:before{content:"\3e"}.fa-person-swimming:before,.fa-swimmer:before{content:"\f5c4"}.fa-arrow-down:before{content:"\f063"}.fa-droplet:before,.fa-tint:before{content:"\f043"}.fa-eraser:before{content:"\f12d"}.fa-earth-america:before,.fa-earth-americas:before,.fa-earth:before,.fa-globe-americas:before{content:"\f57d"}.fa-person-burst:before{content:"\e53b"}.fa-dove:before{content:"\f4ba"}.fa-battery-0:before,.fa-battery-empty:before{content:"\f244"}.fa-socks:before{content:"\f696"}.fa-inbox:before{content:"\f01c"}.fa-section:before{content:"\e447"}.fa-gauge-high:before,.fa-tachometer-alt-fast:before,.fa-tachometer-alt:before{content:"\f625"}.fa-envelope-open-text:before{content:"\f658"}.fa-hospital-alt:before,.fa-hospital-wide:before,.fa-hospital:before{content:"\f0f8"}.fa-wine-bottle:before{content:"\f72f"}.fa-chess-rook:before{content:"\f447"}.fa-bars-staggered:before,.fa-reorder:before,.fa-stream:before{content:"\f550"}.fa-dharmachakra:before{content:"\f655"}.fa-hotdog:before{content:"\f80f"}.fa-blind:before,.fa-person-walking-with-cane:before{content:"\f29d"}.fa-drum:before{content:"\f569"}.fa-ice-cream:before{content:"\f810"}.fa-heart-circle-bolt:before{content:"\e4fc"}.fa-fax:before{content:"\f1ac"}.fa-paragraph:before{content:"\f1dd"}.fa-check-to-slot:before,.fa-vote-yea:before{content:"\f772"}.fa-star-half:before{content:"\f089"}.fa-boxes-alt:before,.fa-boxes-stacked:before,.fa-boxes:before{content:"\f468"}.fa-chain:before,.fa-link:before{content:"\f0c1"}.fa-assistive-listening-systems:before,.fa-ear-listen:before{content:"\f2a2"}.fa-tree-city:before{content:"\e587"}.fa-play:before{content:"\f04b"}.fa-font:before{content:"\f031"}.fa-rupiah-sign:before{content:"\e23d"}.fa-magnifying-glass:before,.fa-search:before{content:"\f002"}.fa-ping-pong-paddle-ball:before,.fa-table-tennis-paddle-ball:before,.fa-table-tennis:before{content:"\f45d"}.fa-diagnoses:before,.fa-person-dots-from-line:before{content:"\f470"}.fa-trash-can-arrow-up:before,.fa-trash-restore-alt:before{content:"\f82a"}.fa-naira-sign:before{content:"\e1f6"}.fa-cart-arrow-down:before{content:"\f218"}.fa-walkie-talkie:before{content:"\f8ef"}.fa-file-edit:before,.fa-file-pen:before{content:"\f31c"}.fa-receipt:before{content:"\f543"}.fa-pen-square:before,.fa-pencil-square:before,.fa-square-pen:before{content:"\f14b"}.fa-suitcase-rolling:before{content:"\f5c1"}.fa-person-circle-exclamation:before{content:"\e53f"}.fa-chevron-down:before{content:"\f078"}.fa-battery-5:before,.fa-battery-full:before,.fa-battery:before{content:"\f240"}.fa-skull-crossbones:before{content:"\f714"}.fa-code-compare:before{content:"\e13a"}.fa-list-dots:before,.fa-list-ul:before{content:"\f0ca"}.fa-school-lock:before{content:"\e56f"}.fa-tower-cell:before{content:"\e585"}.fa-down-long:before,.fa-long-arrow-alt-down:before{content:"\f309"}.fa-ranking-star:before{content:"\e561"}.fa-chess-king:before{content:"\f43f"}.fa-person-harassing:before{content:"\e549"}.fa-brazilian-real-sign:before{content:"\e46c"}.fa-landmark-alt:before,.fa-landmark-dome:before{content:"\f752"}.fa-arrow-up:before{content:"\f062"}.fa-television:before,.fa-tv-alt:before,.fa-tv:before{content:"\f26c"}.fa-shrimp:before{content:"\e448"}.fa-list-check:before,.fa-tasks:before{content:"\f0ae"}.fa-jug-detergent:before{content:"\e519"}.fa-circle-user:before,.fa-user-circle:before{content:"\f2bd"}.fa-user-shield:before{content:"\f505"}.fa-wind:before{content:"\f72e"}.fa-car-burst:before,.fa-car-crash:before{content:"\f5e1"}.fa-y:before{content:"\59"}.fa-person-snowboarding:before,.fa-snowboarding:before{content:"\f7ce"}.fa-shipping-fast:before,.fa-truck-fast:before{content:"\f48b"}.fa-fish:before{content:"\f578"}.fa-user-graduate:before{content:"\f501"}.fa-adjust:before,.fa-circle-half-stroke:before{content:"\f042"}.fa-clapperboard:before{content:"\e131"}.fa-circle-radiation:before,.fa-radiation-alt:before{content:"\f7ba"}.fa-baseball-ball:before,.fa-baseball:before{content:"\f433"}.fa-jet-fighter-up:before{content:"\e518"}.fa-diagram-project:before,.fa-project-diagram:before{content:"\f542"}.fa-copy:before{content:"\f0c5"}.fa-volume-mute:before,.fa-volume-times:before,.fa-volume-xmark:before{content:"\f6a9"}.fa-hand-sparkles:before{content:"\e05d"}.fa-grip-horizontal:before,.fa-grip:before{content:"\f58d"}.fa-share-from-square:before,.fa-share-square:before{content:"\f14d"}.fa-child-combatant:before,.fa-child-rifle:before{content:"\e4e0"}.fa-gun:before{content:"\e19b"}.fa-phone-square:before,.fa-square-phone:before{content:"\f098"}.fa-add:before,.fa-plus:before{content:"\2b"}.fa-expand:before{content:"\f065"}.fa-computer:before{content:"\e4e5"}.fa-close:before,.fa-multiply:before,.fa-remove:before,.fa-times:before,.fa-xmark:before{content:"\f00d"}.fa-arrows-up-down-left-right:before,.fa-arrows:before{content:"\f047"}.fa-chalkboard-teacher:before,.fa-chalkboard-user:before{content:"\f51c"}.fa-peso-sign:before{content:"\e222"}.fa-building-shield:before{content:"\e4d8"}.fa-baby:before{content:"\f77c"}.fa-users-line:before{content:"\e592"}.fa-quote-left-alt:before,.fa-quote-left:before{content:"\f10d"}.fa-tractor:before{content:"\f722"}.fa-trash-arrow-up:before,.fa-trash-restore:before{content:"\f829"}.fa-arrow-down-up-lock:before{content:"\e4b0"}.fa-lines-leaning:before{content:"\e51e"}.fa-ruler-combined:before{content:"\f546"}.fa-copyright:before{content:"\f1f9"}.fa-equals:before{content:"\3d"}.fa-blender:before{content:"\f517"}.fa-teeth:before{content:"\f62e"}.fa-ils:before,.fa-shekel-sign:before,.fa-shekel:before,.fa-sheqel-sign:before,.fa-sheqel:before{content:"\f20b"}.fa-map:before{content:"\f279"}.fa-rocket:before{content:"\f135"}.fa-photo-film:before,.fa-photo-video:before{content:"\f87c"}.fa-folder-minus:before{content:"\f65d"}.fa-store:before{content:"\f54e"}.fa-arrow-trend-up:before{content:"\e098"}.fa-plug-circle-minus:before{content:"\e55e"}.fa-sign-hanging:before,.fa-sign:before{content:"\f4d9"}.fa-bezier-curve:before{content:"\f55b"}.fa-bell-slash:before{content:"\f1f6"}.fa-tablet-android:before,.fa-tablet:before{content:"\f3fb"}.fa-school-flag:before{content:"\e56e"}.fa-fill:before{content:"\f575"}.fa-angle-up:before{content:"\f106"}.fa-drumstick-bite:before{content:"\f6d7"}.fa-holly-berry:before{content:"\f7aa"}.fa-chevron-left:before{content:"\f053"}.fa-bacteria:before{content:"\e059"}.fa-hand-lizard:before{content:"\f258"}.fa-notdef:before{content:"\e1fe"}.fa-disease:before{content:"\f7fa"}.fa-briefcase-medical:before{content:"\f469"}.fa-genderless:before{content:"\f22d"}.fa-chevron-right:before{content:"\f054"}.fa-retweet:before{content:"\f079"}.fa-car-alt:before,.fa-car-rear:before{content:"\f5de"}.fa-pump-soap:before{content:"\e06b"}.fa-video-slash:before{content:"\f4e2"}.fa-battery-2:before,.fa-battery-quarter:before{content:"\f243"}.fa-radio:before{content:"\f8d7"}.fa-baby-carriage:before,.fa-carriage-baby:before{content:"\f77d"}.fa-traffic-light:before{content:"\f637"}.fa-thermometer:before{content:"\f491"}.fa-vr-cardboard:before{content:"\f729"}.fa-hand-middle-finger:before{content:"\f806"}.fa-percent:before,.fa-percentage:before{content:"\25"}.fa-truck-moving:before{content:"\f4df"}.fa-glass-water-droplet:before{content:"\e4f5"}.fa-display:before{content:"\e163"}.fa-face-smile:before,.fa-smile:before{content:"\f118"}.fa-thumb-tack:before,.fa-thumbtack:before{content:"\f08d"}.fa-trophy:before{content:"\f091"}.fa-person-praying:before,.fa-pray:before{content:"\f683"}.fa-hammer:before{content:"\f6e3"}.fa-hand-peace:before{content:"\f25b"}.fa-rotate:before,.fa-sync-alt:before{content:"\f2f1"}.fa-spinner:before{content:"\f110"}.fa-robot:before{content:"\f544"}.fa-peace:before{content:"\f67c"}.fa-cogs:before,.fa-gears:before{content:"\f085"}.fa-warehouse:before{content:"\f494"}.fa-arrow-up-right-dots:before{content:"\e4b7"}.fa-splotch:before{content:"\f5bc"}.fa-face-grin-hearts:before,.fa-grin-hearts:before{content:"\f584"}.fa-dice-four:before{content:"\f524"}.fa-sim-card:before{content:"\f7c4"}.fa-transgender-alt:before,.fa-transgender:before{content:"\f225"}.fa-mercury:before{content:"\f223"}.fa-arrow-turn-down:before,.fa-level-down:before{content:"\f149"}.fa-person-falling-burst:before{content:"\e547"}.fa-award:before{content:"\f559"}.fa-ticket-alt:before,.fa-ticket-simple:before{content:"\f3ff"}.fa-building:before{content:"\f1ad"}.fa-angle-double-left:before,.fa-angles-left:before{content:"\f100"}.fa-qrcode:before{content:"\f029"}.fa-clock-rotate-left:before,.fa-history:before{content:"\f1da"}.fa-face-grin-beam-sweat:before,.fa-grin-beam-sweat:before{content:"\f583"}.fa-arrow-right-from-file:before,.fa-file-export:before{content:"\f56e"}.fa-shield-blank:before,.fa-shield:before{content:"\f132"}.fa-arrow-up-short-wide:before,.fa-sort-amount-up-alt:before{content:"\f885"}.fa-house-medical:before{content:"\e3b2"}.fa-golf-ball-tee:before,.fa-golf-ball:before{content:"\f450"}.fa-chevron-circle-left:before,.fa-circle-chevron-left:before{content:"\f137"}.fa-house-chimney-window:before{content:"\e00d"}.fa-pen-nib:before{content:"\f5ad"}.fa-tent-arrow-turn-left:before{content:"\e580"}.fa-tents:before{content:"\e582"}.fa-magic:before,.fa-wand-magic:before{content:"\f0d0"}.fa-dog:before{content:"\f6d3"}.fa-carrot:before{content:"\f787"}.fa-moon:before{content:"\f186"}.fa-wine-glass-alt:before,.fa-wine-glass-empty:before{content:"\f5ce"}.fa-cheese:before{content:"\f7ef"}.fa-yin-yang:before{content:"\f6ad"}.fa-music:before{content:"\f001"}.fa-code-commit:before{content:"\f386"}.fa-temperature-low:before{content:"\f76b"}.fa-biking:before,.fa-person-biking:before{content:"\f84a"}.fa-broom:before{content:"\f51a"}.fa-shield-heart:before{content:"\e574"}.fa-gopuram:before{content:"\f664"}.fa-earth-oceania:before,.fa-globe-oceania:before{content:"\e47b"}.fa-square-xmark:before,.fa-times-square:before,.fa-xmark-square:before{content:"\f2d3"}.fa-hashtag:before{content:"\23"}.fa-expand-alt:before,.fa-up-right-and-down-left-from-center:before{content:"\f424"}.fa-oil-can:before{content:"\f613"}.fa-t:before{content:"\54"}.fa-hippo:before{content:"\f6ed"}.fa-chart-column:before{content:"\e0e3"}.fa-infinity:before{content:"\f534"}.fa-vial-circle-check:before{content:"\e596"}.fa-person-arrow-down-to-line:before{content:"\e538"}.fa-voicemail:before{content:"\f897"}.fa-fan:before{content:"\f863"}.fa-person-walking-luggage:before{content:"\e554"}.fa-arrows-alt-v:before,.fa-up-down:before{content:"\f338"}.fa-cloud-moon-rain:before{content:"\f73c"}.fa-calendar:before{content:"\f133"}.fa-trailer:before{content:"\e041"}.fa-bahai:before,.fa-haykal:before{content:"\f666"}.fa-sd-card:before{content:"\f7c2"}.fa-dragon:before{content:"\f6d5"}.fa-shoe-prints:before{content:"\f54b"}.fa-circle-plus:before,.fa-plus-circle:before{content:"\f055"}.fa-face-grin-tongue-wink:before,.fa-grin-tongue-wink:before{content:"\f58b"}.fa-hand-holding:before{content:"\f4bd"}.fa-plug-circle-exclamation:before{content:"\e55d"}.fa-chain-broken:before,.fa-chain-slash:before,.fa-link-slash:before,.fa-unlink:before{content:"\f127"}.fa-clone:before{content:"\f24d"}.fa-person-walking-arrow-loop-left:before{content:"\e551"}.fa-arrow-up-z-a:before,.fa-sort-alpha-up-alt:before{content:"\f882"}.fa-fire-alt:before,.fa-fire-flame-curved:before{content:"\f7e4"}.fa-tornado:before{content:"\f76f"}.fa-file-circle-plus:before{content:"\e494"}.fa-book-quran:before,.fa-quran:before{content:"\f687"}.fa-anchor:before{content:"\f13d"}.fa-border-all:before{content:"\f84c"}.fa-angry:before,.fa-face-angry:before{content:"\f556"}.fa-cookie-bite:before{content:"\f564"}.fa-arrow-trend-down:before{content:"\e097"}.fa-feed:before,.fa-rss:before{content:"\f09e"}.fa-draw-polygon:before{content:"\f5ee"}.fa-balance-scale:before,.fa-scale-balanced:before{content:"\f24e"}.fa-gauge-simple-high:before,.fa-tachometer-fast:before,.fa-tachometer:before{content:"\f62a"}.fa-shower:before{content:"\f2cc"}.fa-desktop-alt:before,.fa-desktop:before{content:"\f390"}.fa-m:before{content:"\4d"}.fa-table-list:before,.fa-th-list:before{content:"\f00b"}.fa-comment-sms:before,.fa-sms:before{content:"\f7cd"}.fa-book:before{content:"\f02d"}.fa-user-plus:before{content:"\f234"}.fa-check:before{content:"\f00c"}.fa-battery-4:before,.fa-battery-three-quarters:before{content:"\f241"}.fa-house-circle-check:before{content:"\e509"}.fa-angle-left:before{content:"\f104"}.fa-diagram-successor:before{content:"\e47a"}.fa-truck-arrow-right:before{content:"\e58b"}.fa-arrows-split-up-and-left:before{content:"\e4bc"}.fa-fist-raised:before,.fa-hand-fist:before{content:"\f6de"}.fa-cloud-moon:before{content:"\f6c3"}.fa-briefcase:before{content:"\f0b1"}.fa-person-falling:before{content:"\e546"}.fa-image-portrait:before,.fa-portrait:before{content:"\f3e0"}.fa-user-tag:before{content:"\f507"}.fa-rug:before{content:"\e569"}.fa-earth-europe:before,.fa-globe-europe:before{content:"\f7a2"}.fa-cart-flatbed-suitcase:before,.fa-luggage-cart:before{content:"\f59d"}.fa-rectangle-times:before,.fa-rectangle-xmark:before,.fa-times-rectangle:before,.fa-window-close:before{content:"\f410"}.fa-baht-sign:before{content:"\e0ac"}.fa-book-open:before{content:"\f518"}.fa-book-journal-whills:before,.fa-journal-whills:before{content:"\f66a"}.fa-handcuffs:before{content:"\e4f8"}.fa-exclamation-triangle:before,.fa-triangle-exclamation:before,.fa-warning:before{content:"\f071"}.fa-database:before{content:"\f1c0"}.fa-arrow-turn-right:before,.fa-mail-forward:before,.fa-share:before{content:"\f064"}.fa-bottle-droplet:before{content:"\e4c4"}.fa-mask-face:before{content:"\e1d7"}.fa-hill-rockslide:before{content:"\e508"}.fa-exchange-alt:before,.fa-right-left:before{content:"\f362"}.fa-paper-plane:before{content:"\f1d8"}.fa-road-circle-exclamation:before{content:"\e565"}.fa-dungeon:before{content:"\f6d9"}.fa-align-right:before{content:"\f038"}.fa-money-bill-1-wave:before,.fa-money-bill-wave-alt:before{content:"\f53b"}.fa-life-ring:before{content:"\f1cd"}.fa-hands:before,.fa-sign-language:before,.fa-signing:before{content:"\f2a7"}.fa-calendar-day:before{content:"\f783"}.fa-ladder-water:before,.fa-swimming-pool:before,.fa-water-ladder:before{content:"\f5c5"}.fa-arrows-up-down:before,.fa-arrows-v:before{content:"\f07d"}.fa-face-grimace:before,.fa-grimace:before{content:"\f57f"}.fa-wheelchair-alt:before,.fa-wheelchair-move:before{content:"\e2ce"}.fa-level-down-alt:before,.fa-turn-down:before{content:"\f3be"}.fa-person-walking-arrow-right:before{content:"\e552"}.fa-envelope-square:before,.fa-square-envelope:before{content:"\f199"}.fa-dice:before{content:"\f522"}.fa-bowling-ball:before{content:"\f436"}.fa-brain:before{content:"\f5dc"}.fa-band-aid:before,.fa-bandage:before{content:"\f462"}.fa-calendar-minus:before{content:"\f272"}.fa-circle-xmark:before,.fa-times-circle:before,.fa-xmark-circle:before{content:"\f057"}.fa-gifts:before{content:"\f79c"}.fa-hotel:before{content:"\f594"}.fa-earth-asia:before,.fa-globe-asia:before{content:"\f57e"}.fa-id-card-alt:before,.fa-id-card-clip:before{content:"\f47f"}.fa-magnifying-glass-plus:before,.fa-search-plus:before{content:"\f00e"}.fa-thumbs-up:before{content:"\f164"}.fa-user-clock:before{content:"\f4fd"}.fa-allergies:before,.fa-hand-dots:before{content:"\f461"}.fa-file-invoice:before{content:"\f570"}.fa-window-minimize:before{content:"\f2d1"}.fa-coffee:before,.fa-mug-saucer:before{content:"\f0f4"}.fa-brush:before{content:"\f55d"}.fa-mask:before{content:"\f6fa"}.fa-magnifying-glass-minus:before,.fa-search-minus:before{content:"\f010"}.fa-ruler-vertical:before{content:"\f548"}.fa-user-alt:before,.fa-user-large:before{content:"\f406"}.fa-train-tram:before{content:"\e5b4"}.fa-user-nurse:before{content:"\f82f"}.fa-syringe:before{content:"\f48e"}.fa-cloud-sun:before{content:"\f6c4"}.fa-stopwatch-20:before{content:"\e06f"}.fa-square-full:before{content:"\f45c"}.fa-magnet:before{content:"\f076"}.fa-jar:before{content:"\e516"}.fa-note-sticky:before,.fa-sticky-note:before{content:"\f249"}.fa-bug-slash:before{content:"\e490"}.fa-arrow-up-from-water-pump:before{content:"\e4b6"}.fa-bone:before{content:"\f5d7"}.fa-user-injured:before{content:"\f728"}.fa-face-sad-tear:before,.fa-sad-tear:before{content:"\f5b4"}.fa-plane:before{content:"\f072"}.fa-tent-arrows-down:before{content:"\e581"}.fa-exclamation:before{content:"\21"}.fa-arrows-spin:before{content:"\e4bb"}.fa-print:before{content:"\f02f"}.fa-try:before,.fa-turkish-lira-sign:before,.fa-turkish-lira:before{content:"\e2bb"}.fa-dollar-sign:before,.fa-dollar:before,.fa-usd:before{content:"\24"}.fa-x:before{content:"\58"}.fa-magnifying-glass-dollar:before,.fa-search-dollar:before{content:"\f688"}.fa-users-cog:before,.fa-users-gear:before{content:"\f509"}.fa-person-military-pointing:before{content:"\e54a"}.fa-bank:before,.fa-building-columns:before,.fa-institution:before,.fa-museum:before,.fa-university:before{content:"\f19c"}.fa-umbrella:before{content:"\f0e9"}.fa-trowel:before{content:"\e589"}.fa-d:before{content:"\44"}.fa-stapler:before{content:"\e5af"}.fa-masks-theater:before,.fa-theater-masks:before{content:"\f630"}.fa-kip-sign:before{content:"\e1c4"}.fa-hand-point-left:before{content:"\f0a5"}.fa-handshake-alt:before,.fa-handshake-simple:before{content:"\f4c6"}.fa-fighter-jet:before,.fa-jet-fighter:before{content:"\f0fb"}.fa-share-alt-square:before,.fa-square-share-nodes:before{content:"\f1e1"}.fa-barcode:before{content:"\f02a"}.fa-plus-minus:before{content:"\e43c"}.fa-video-camera:before,.fa-video:before{content:"\f03d"}.fa-graduation-cap:before,.fa-mortar-board:before{content:"\f19d"}.fa-hand-holding-medical:before{content:"\e05c"}.fa-person-circle-check:before{content:"\e53e"}.fa-level-up-alt:before,.fa-turn-up:before{content:"\f3bf"} +.fa-sr-only,.fa-sr-only-focusable:not(:focus),.sr-only,.sr-only-focusable:not(:focus){position:absolute;width:1px;height:1px;padding:0;margin:-1px;overflow:hidden;clip:rect(0,0,0,0);white-space:nowrap;border-width:0}:host,:root{--fa-style-family-brands:"Font Awesome 6 Brands";--fa-font-brands:normal 400 1em/1 "Font Awesome 6 Brands"}@font-face{font-family:"Font Awesome 6 Brands";font-style:normal;font-weight:400;font-display:block;src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }.fa-brands,.fab{font-weight:400}.fa-monero:before{content:"\f3d0"}.fa-hooli:before{content:"\f427"}.fa-yelp:before{content:"\f1e9"}.fa-cc-visa:before{content:"\f1f0"}.fa-lastfm:before{content:"\f202"}.fa-shopware:before{content:"\f5b5"}.fa-creative-commons-nc:before{content:"\f4e8"}.fa-aws:before{content:"\f375"}.fa-redhat:before{content:"\f7bc"}.fa-yoast:before{content:"\f2b1"}.fa-cloudflare:before{content:"\e07d"}.fa-ups:before{content:"\f7e0"}.fa-wpexplorer:before{content:"\f2de"}.fa-dyalog:before{content:"\f399"}.fa-bity:before{content:"\f37a"}.fa-stackpath:before{content:"\f842"}.fa-buysellads:before{content:"\f20d"}.fa-first-order:before{content:"\f2b0"}.fa-modx:before{content:"\f285"}.fa-guilded:before{content:"\e07e"}.fa-vnv:before{content:"\f40b"}.fa-js-square:before,.fa-square-js:before{content:"\f3b9"}.fa-microsoft:before{content:"\f3ca"}.fa-qq:before{content:"\f1d6"}.fa-orcid:before{content:"\f8d2"}.fa-java:before{content:"\f4e4"}.fa-invision:before{content:"\f7b0"}.fa-creative-commons-pd-alt:before{content:"\f4ed"}.fa-centercode:before{content:"\f380"}.fa-glide-g:before{content:"\f2a6"}.fa-drupal:before{content:"\f1a9"}.fa-hire-a-helper:before{content:"\f3b0"}.fa-creative-commons-by:before{content:"\f4e7"}.fa-unity:before{content:"\e049"}.fa-whmcs:before{content:"\f40d"}.fa-rocketchat:before{content:"\f3e8"}.fa-vk:before{content:"\f189"}.fa-untappd:before{content:"\f405"}.fa-mailchimp:before{content:"\f59e"}.fa-css3-alt:before{content:"\f38b"}.fa-reddit-square:before,.fa-square-reddit:before{content:"\f1a2"}.fa-vimeo-v:before{content:"\f27d"}.fa-contao:before{content:"\f26d"}.fa-square-font-awesome:before{content:"\e5ad"}.fa-deskpro:before{content:"\f38f"}.fa-sistrix:before{content:"\f3ee"}.fa-instagram-square:before,.fa-square-instagram:before{content:"\e055"}.fa-battle-net:before{content:"\f835"}.fa-the-red-yeti:before{content:"\f69d"}.fa-hacker-news-square:before,.fa-square-hacker-news:before{content:"\f3af"}.fa-edge:before{content:"\f282"}.fa-threads:before{content:"\e618"}.fa-napster:before{content:"\f3d2"}.fa-snapchat-square:before,.fa-square-snapchat:before{content:"\f2ad"}.fa-google-plus-g:before{content:"\f0d5"}.fa-artstation:before{content:"\f77a"}.fa-markdown:before{content:"\f60f"}.fa-sourcetree:before{content:"\f7d3"}.fa-google-plus:before{content:"\f2b3"}.fa-diaspora:before{content:"\f791"}.fa-foursquare:before{content:"\f180"}.fa-stack-overflow:before{content:"\f16c"}.fa-github-alt:before{content:"\f113"}.fa-phoenix-squadron:before{content:"\f511"}.fa-pagelines:before{content:"\f18c"}.fa-algolia:before{content:"\f36c"}.fa-red-river:before{content:"\f3e3"}.fa-creative-commons-sa:before{content:"\f4ef"}.fa-safari:before{content:"\f267"}.fa-google:before{content:"\f1a0"}.fa-font-awesome-alt:before,.fa-square-font-awesome-stroke:before{content:"\f35c"}.fa-atlassian:before{content:"\f77b"}.fa-linkedin-in:before{content:"\f0e1"}.fa-digital-ocean:before{content:"\f391"}.fa-nimblr:before{content:"\f5a8"}.fa-chromecast:before{content:"\f838"}.fa-evernote:before{content:"\f839"}.fa-hacker-news:before{content:"\f1d4"}.fa-creative-commons-sampling:before{content:"\f4f0"}.fa-adversal:before{content:"\f36a"}.fa-creative-commons:before{content:"\f25e"}.fa-watchman-monitoring:before{content:"\e087"}.fa-fonticons:before{content:"\f280"}.fa-weixin:before{content:"\f1d7"}.fa-shirtsinbulk:before{content:"\f214"}.fa-codepen:before{content:"\f1cb"}.fa-git-alt:before{content:"\f841"}.fa-lyft:before{content:"\f3c3"}.fa-rev:before{content:"\f5b2"}.fa-windows:before{content:"\f17a"}.fa-wizards-of-the-coast:before{content:"\f730"}.fa-square-viadeo:before,.fa-viadeo-square:before{content:"\f2aa"}.fa-meetup:before{content:"\f2e0"}.fa-centos:before{content:"\f789"}.fa-adn:before{content:"\f170"}.fa-cloudsmith:before{content:"\f384"}.fa-pied-piper-alt:before{content:"\f1a8"}.fa-dribbble-square:before,.fa-square-dribbble:before{content:"\f397"}.fa-codiepie:before{content:"\f284"}.fa-node:before{content:"\f419"}.fa-mix:before{content:"\f3cb"}.fa-steam:before{content:"\f1b6"}.fa-cc-apple-pay:before{content:"\f416"}.fa-scribd:before{content:"\f28a"}.fa-debian:before{content:"\e60b"}.fa-openid:before{content:"\f19b"}.fa-instalod:before{content:"\e081"}.fa-expeditedssl:before{content:"\f23e"}.fa-sellcast:before{content:"\f2da"}.fa-square-twitter:before,.fa-twitter-square:before{content:"\f081"}.fa-r-project:before{content:"\f4f7"}.fa-delicious:before{content:"\f1a5"}.fa-freebsd:before{content:"\f3a4"}.fa-vuejs:before{content:"\f41f"}.fa-accusoft:before{content:"\f369"}.fa-ioxhost:before{content:"\f208"}.fa-fonticons-fi:before{content:"\f3a2"}.fa-app-store:before{content:"\f36f"}.fa-cc-mastercard:before{content:"\f1f1"}.fa-itunes-note:before{content:"\f3b5"}.fa-golang:before{content:"\e40f"}.fa-kickstarter:before{content:"\f3bb"}.fa-grav:before{content:"\f2d6"}.fa-weibo:before{content:"\f18a"}.fa-uncharted:before{content:"\e084"}.fa-firstdraft:before{content:"\f3a1"}.fa-square-youtube:before,.fa-youtube-square:before{content:"\f431"}.fa-wikipedia-w:before{content:"\f266"}.fa-rendact:before,.fa-wpressr:before{content:"\f3e4"}.fa-angellist:before{content:"\f209"}.fa-galactic-republic:before{content:"\f50c"}.fa-nfc-directional:before{content:"\e530"}.fa-skype:before{content:"\f17e"}.fa-joget:before{content:"\f3b7"}.fa-fedora:before{content:"\f798"}.fa-stripe-s:before{content:"\f42a"}.fa-meta:before{content:"\e49b"}.fa-laravel:before{content:"\f3bd"}.fa-hotjar:before{content:"\f3b1"}.fa-bluetooth-b:before{content:"\f294"}.fa-sticker-mule:before{content:"\f3f7"}.fa-creative-commons-zero:before{content:"\f4f3"}.fa-hips:before{content:"\f452"}.fa-behance:before{content:"\f1b4"}.fa-reddit:before{content:"\f1a1"}.fa-discord:before{content:"\f392"}.fa-chrome:before{content:"\f268"}.fa-app-store-ios:before{content:"\f370"}.fa-cc-discover:before{content:"\f1f2"}.fa-wpbeginner:before{content:"\f297"}.fa-confluence:before{content:"\f78d"}.fa-mdb:before{content:"\f8ca"}.fa-dochub:before{content:"\f394"}.fa-accessible-icon:before{content:"\f368"}.fa-ebay:before{content:"\f4f4"}.fa-amazon:before{content:"\f270"}.fa-unsplash:before{content:"\e07c"}.fa-yarn:before{content:"\f7e3"}.fa-square-steam:before,.fa-steam-square:before{content:"\f1b7"}.fa-500px:before{content:"\f26e"}.fa-square-vimeo:before,.fa-vimeo-square:before{content:"\f194"}.fa-asymmetrik:before{content:"\f372"}.fa-font-awesome-flag:before,.fa-font-awesome-logo-full:before,.fa-font-awesome:before{content:"\f2b4"}.fa-gratipay:before{content:"\f184"}.fa-apple:before{content:"\f179"}.fa-hive:before{content:"\e07f"}.fa-gitkraken:before{content:"\f3a6"}.fa-keybase:before{content:"\f4f5"}.fa-apple-pay:before{content:"\f415"}.fa-padlet:before{content:"\e4a0"}.fa-amazon-pay:before{content:"\f42c"}.fa-github-square:before,.fa-square-github:before{content:"\f092"}.fa-stumbleupon:before{content:"\f1a4"}.fa-fedex:before{content:"\f797"}.fa-phoenix-framework:before{content:"\f3dc"}.fa-shopify:before{content:"\e057"}.fa-neos:before{content:"\f612"}.fa-square-threads:before{content:"\e619"}.fa-hackerrank:before{content:"\f5f7"}.fa-researchgate:before{content:"\f4f8"}.fa-swift:before{content:"\f8e1"}.fa-angular:before{content:"\f420"}.fa-speakap:before{content:"\f3f3"}.fa-angrycreative:before{content:"\f36e"}.fa-y-combinator:before{content:"\f23b"}.fa-empire:before{content:"\f1d1"}.fa-envira:before{content:"\f299"}.fa-gitlab-square:before,.fa-square-gitlab:before{content:"\e5ae"}.fa-studiovinari:before{content:"\f3f8"}.fa-pied-piper:before{content:"\f2ae"}.fa-wordpress:before{content:"\f19a"}.fa-product-hunt:before{content:"\f288"}.fa-firefox:before{content:"\f269"}.fa-linode:before{content:"\f2b8"}.fa-goodreads:before{content:"\f3a8"}.fa-odnoklassniki-square:before,.fa-square-odnoklassniki:before{content:"\f264"}.fa-jsfiddle:before{content:"\f1cc"}.fa-sith:before{content:"\f512"}.fa-themeisle:before{content:"\f2b2"}.fa-page4:before{content:"\f3d7"}.fa-hashnode:before{content:"\e499"}.fa-react:before{content:"\f41b"}.fa-cc-paypal:before{content:"\f1f4"}.fa-squarespace:before{content:"\f5be"}.fa-cc-stripe:before{content:"\f1f5"}.fa-creative-commons-share:before{content:"\f4f2"}.fa-bitcoin:before{content:"\f379"}.fa-keycdn:before{content:"\f3ba"}.fa-opera:before{content:"\f26a"}.fa-itch-io:before{content:"\f83a"}.fa-umbraco:before{content:"\f8e8"}.fa-galactic-senate:before{content:"\f50d"}.fa-ubuntu:before{content:"\f7df"}.fa-draft2digital:before{content:"\f396"}.fa-stripe:before{content:"\f429"}.fa-houzz:before{content:"\f27c"}.fa-gg:before{content:"\f260"}.fa-dhl:before{content:"\f790"}.fa-pinterest-square:before,.fa-square-pinterest:before{content:"\f0d3"}.fa-xing:before{content:"\f168"}.fa-blackberry:before{content:"\f37b"}.fa-creative-commons-pd:before{content:"\f4ec"}.fa-playstation:before{content:"\f3df"}.fa-quinscape:before{content:"\f459"}.fa-less:before{content:"\f41d"}.fa-blogger-b:before{content:"\f37d"}.fa-opencart:before{content:"\f23d"}.fa-vine:before{content:"\f1ca"}.fa-paypal:before{content:"\f1ed"}.fa-gitlab:before{content:"\f296"}.fa-typo3:before{content:"\f42b"}.fa-reddit-alien:before{content:"\f281"}.fa-yahoo:before{content:"\f19e"}.fa-dailymotion:before{content:"\e052"}.fa-affiliatetheme:before{content:"\f36b"}.fa-pied-piper-pp:before{content:"\f1a7"}.fa-bootstrap:before{content:"\f836"}.fa-odnoklassniki:before{content:"\f263"}.fa-nfc-symbol:before{content:"\e531"}.fa-ethereum:before{content:"\f42e"}.fa-speaker-deck:before{content:"\f83c"}.fa-creative-commons-nc-eu:before{content:"\f4e9"}.fa-patreon:before{content:"\f3d9"}.fa-avianex:before{content:"\f374"}.fa-ello:before{content:"\f5f1"}.fa-gofore:before{content:"\f3a7"}.fa-bimobject:before{content:"\f378"}.fa-facebook-f:before{content:"\f39e"}.fa-google-plus-square:before,.fa-square-google-plus:before{content:"\f0d4"}.fa-mandalorian:before{content:"\f50f"}.fa-first-order-alt:before{content:"\f50a"}.fa-osi:before{content:"\f41a"}.fa-google-wallet:before{content:"\f1ee"}.fa-d-and-d-beyond:before{content:"\f6ca"}.fa-periscope:before{content:"\f3da"}.fa-fulcrum:before{content:"\f50b"}.fa-cloudscale:before{content:"\f383"}.fa-forumbee:before{content:"\f211"}.fa-mizuni:before{content:"\f3cc"}.fa-schlix:before{content:"\f3ea"}.fa-square-xing:before,.fa-xing-square:before{content:"\f169"}.fa-bandcamp:before{content:"\f2d5"}.fa-wpforms:before{content:"\f298"}.fa-cloudversify:before{content:"\f385"}.fa-usps:before{content:"\f7e1"}.fa-megaport:before{content:"\f5a3"}.fa-magento:before{content:"\f3c4"}.fa-spotify:before{content:"\f1bc"}.fa-optin-monster:before{content:"\f23c"}.fa-fly:before{content:"\f417"}.fa-aviato:before{content:"\f421"}.fa-itunes:before{content:"\f3b4"}.fa-cuttlefish:before{content:"\f38c"}.fa-blogger:before{content:"\f37c"}.fa-flickr:before{content:"\f16e"}.fa-viber:before{content:"\f409"}.fa-soundcloud:before{content:"\f1be"}.fa-digg:before{content:"\f1a6"}.fa-tencent-weibo:before{content:"\f1d5"}.fa-symfony:before{content:"\f83d"}.fa-maxcdn:before{content:"\f136"}.fa-etsy:before{content:"\f2d7"}.fa-facebook-messenger:before{content:"\f39f"}.fa-audible:before{content:"\f373"}.fa-think-peaks:before{content:"\f731"}.fa-bilibili:before{content:"\e3d9"}.fa-erlang:before{content:"\f39d"}.fa-x-twitter:before{content:"\e61b"}.fa-cotton-bureau:before{content:"\f89e"}.fa-dashcube:before{content:"\f210"}.fa-42-group:before,.fa-innosoft:before{content:"\e080"}.fa-stack-exchange:before{content:"\f18d"}.fa-elementor:before{content:"\f430"}.fa-pied-piper-square:before,.fa-square-pied-piper:before{content:"\e01e"}.fa-creative-commons-nd:before{content:"\f4eb"}.fa-palfed:before{content:"\f3d8"}.fa-superpowers:before{content:"\f2dd"}.fa-resolving:before{content:"\f3e7"}.fa-xbox:before{content:"\f412"}.fa-searchengin:before{content:"\f3eb"}.fa-tiktok:before{content:"\e07b"}.fa-facebook-square:before,.fa-square-facebook:before{content:"\f082"}.fa-renren:before{content:"\f18b"}.fa-linux:before{content:"\f17c"}.fa-glide:before{content:"\f2a5"}.fa-linkedin:before{content:"\f08c"}.fa-hubspot:before{content:"\f3b2"}.fa-deploydog:before{content:"\f38e"}.fa-twitch:before{content:"\f1e8"}.fa-ravelry:before{content:"\f2d9"}.fa-mixer:before{content:"\e056"}.fa-lastfm-square:before,.fa-square-lastfm:before{content:"\f203"}.fa-vimeo:before{content:"\f40a"}.fa-mendeley:before{content:"\f7b3"}.fa-uniregistry:before{content:"\f404"}.fa-figma:before{content:"\f799"}.fa-creative-commons-remix:before{content:"\f4ee"}.fa-cc-amazon-pay:before{content:"\f42d"}.fa-dropbox:before{content:"\f16b"}.fa-instagram:before{content:"\f16d"}.fa-cmplid:before{content:"\e360"}.fa-facebook:before{content:"\f09a"}.fa-gripfire:before{content:"\f3ac"}.fa-jedi-order:before{content:"\f50e"}.fa-uikit:before{content:"\f403"}.fa-fort-awesome-alt:before{content:"\f3a3"}.fa-phabricator:before{content:"\f3db"}.fa-ussunnah:before{content:"\f407"}.fa-earlybirds:before{content:"\f39a"}.fa-trade-federation:before{content:"\f513"}.fa-autoprefixer:before{content:"\f41c"}.fa-whatsapp:before{content:"\f232"}.fa-slideshare:before{content:"\f1e7"}.fa-google-play:before{content:"\f3ab"}.fa-viadeo:before{content:"\f2a9"}.fa-line:before{content:"\f3c0"}.fa-google-drive:before{content:"\f3aa"}.fa-servicestack:before{content:"\f3ec"}.fa-simplybuilt:before{content:"\f215"}.fa-bitbucket:before{content:"\f171"}.fa-imdb:before{content:"\f2d8"}.fa-deezer:before{content:"\e077"}.fa-raspberry-pi:before{content:"\f7bb"}.fa-jira:before{content:"\f7b1"}.fa-docker:before{content:"\f395"}.fa-screenpal:before{content:"\e570"}.fa-bluetooth:before{content:"\f293"}.fa-gitter:before{content:"\f426"}.fa-d-and-d:before{content:"\f38d"}.fa-microblog:before{content:"\e01a"}.fa-cc-diners-club:before{content:"\f24c"}.fa-gg-circle:before{content:"\f261"}.fa-pied-piper-hat:before{content:"\f4e5"}.fa-kickstarter-k:before{content:"\f3bc"}.fa-yandex:before{content:"\f413"}.fa-readme:before{content:"\f4d5"}.fa-html5:before{content:"\f13b"}.fa-sellsy:before{content:"\f213"}.fa-sass:before{content:"\f41e"}.fa-wirsindhandwerk:before,.fa-wsh:before{content:"\e2d0"}.fa-buromobelexperte:before{content:"\f37f"}.fa-salesforce:before{content:"\f83b"}.fa-octopus-deploy:before{content:"\e082"}.fa-medapps:before{content:"\f3c6"}.fa-ns8:before{content:"\f3d5"}.fa-pinterest-p:before{content:"\f231"}.fa-apper:before{content:"\f371"}.fa-fort-awesome:before{content:"\f286"}.fa-waze:before{content:"\f83f"}.fa-cc-jcb:before{content:"\f24b"}.fa-snapchat-ghost:before,.fa-snapchat:before{content:"\f2ab"}.fa-fantasy-flight-games:before{content:"\f6dc"}.fa-rust:before{content:"\e07a"}.fa-wix:before{content:"\f5cf"}.fa-behance-square:before,.fa-square-behance:before{content:"\f1b5"}.fa-supple:before{content:"\f3f9"}.fa-rebel:before{content:"\f1d0"}.fa-css3:before{content:"\f13c"}.fa-staylinked:before{content:"\f3f5"}.fa-kaggle:before{content:"\f5fa"}.fa-space-awesome:before{content:"\e5ac"}.fa-deviantart:before{content:"\f1bd"}.fa-cpanel:before{content:"\f388"}.fa-goodreads-g:before{content:"\f3a9"}.fa-git-square:before,.fa-square-git:before{content:"\f1d2"}.fa-square-tumblr:before,.fa-tumblr-square:before{content:"\f174"}.fa-trello:before{content:"\f181"}.fa-creative-commons-nc-jp:before{content:"\f4ea"}.fa-get-pocket:before{content:"\f265"}.fa-perbyte:before{content:"\e083"}.fa-grunt:before{content:"\f3ad"}.fa-weebly:before{content:"\f5cc"}.fa-connectdevelop:before{content:"\f20e"}.fa-leanpub:before{content:"\f212"}.fa-black-tie:before{content:"\f27e"}.fa-themeco:before{content:"\f5c6"}.fa-python:before{content:"\f3e2"}.fa-android:before{content:"\f17b"}.fa-bots:before{content:"\e340"}.fa-free-code-camp:before{content:"\f2c5"}.fa-hornbill:before{content:"\f592"}.fa-js:before{content:"\f3b8"}.fa-ideal:before{content:"\e013"}.fa-git:before{content:"\f1d3"}.fa-dev:before{content:"\f6cc"}.fa-sketch:before{content:"\f7c6"}.fa-yandex-international:before{content:"\f414"}.fa-cc-amex:before{content:"\f1f3"}.fa-uber:before{content:"\f402"}.fa-github:before{content:"\f09b"}.fa-php:before{content:"\f457"}.fa-alipay:before{content:"\f642"}.fa-youtube:before{content:"\f167"}.fa-skyatlas:before{content:"\f216"}.fa-firefox-browser:before{content:"\e007"}.fa-replyd:before{content:"\f3e6"}.fa-suse:before{content:"\f7d6"}.fa-jenkins:before{content:"\f3b6"}.fa-twitter:before{content:"\f099"}.fa-rockrms:before{content:"\f3e9"}.fa-pinterest:before{content:"\f0d2"}.fa-buffer:before{content:"\f837"}.fa-npm:before{content:"\f3d4"}.fa-yammer:before{content:"\f840"}.fa-btc:before{content:"\f15a"}.fa-dribbble:before{content:"\f17d"}.fa-stumbleupon-circle:before{content:"\f1a3"}.fa-internet-explorer:before{content:"\f26b"}.fa-stubber:before{content:"\e5c7"}.fa-telegram-plane:before,.fa-telegram:before{content:"\f2c6"}.fa-old-republic:before{content:"\f510"}.fa-odysee:before{content:"\e5c6"}.fa-square-whatsapp:before,.fa-whatsapp-square:before{content:"\f40c"}.fa-node-js:before{content:"\f3d3"}.fa-edge-legacy:before{content:"\e078"}.fa-slack-hash:before,.fa-slack:before{content:"\f198"}.fa-medrt:before{content:"\f3c8"}.fa-usb:before{content:"\f287"}.fa-tumblr:before{content:"\f173"}.fa-vaadin:before{content:"\f408"}.fa-quora:before{content:"\f2c4"}.fa-square-x-twitter:before{content:"\e61a"}.fa-reacteurope:before{content:"\f75d"}.fa-medium-m:before,.fa-medium:before{content:"\f23a"}.fa-amilia:before{content:"\f36d"}.fa-mixcloud:before{content:"\f289"}.fa-flipboard:before{content:"\f44d"}.fa-viacoin:before{content:"\f237"}.fa-critical-role:before{content:"\f6c9"}.fa-sitrox:before{content:"\e44a"}.fa-discourse:before{content:"\f393"}.fa-joomla:before{content:"\f1aa"}.fa-mastodon:before{content:"\f4f6"}.fa-airbnb:before{content:"\f834"}.fa-wolf-pack-battalion:before{content:"\f514"}.fa-buy-n-large:before{content:"\f8a6"}.fa-gulp:before{content:"\f3ae"}.fa-creative-commons-sampling-plus:before{content:"\f4f1"}.fa-strava:before{content:"\f428"}.fa-ember:before{content:"\f423"}.fa-canadian-maple-leaf:before{content:"\f785"}.fa-teamspeak:before{content:"\f4f9"}.fa-pushed:before{content:"\f3e1"}.fa-wordpress-simple:before{content:"\f411"}.fa-nutritionix:before{content:"\f3d6"}.fa-wodu:before{content:"\e088"}.fa-google-pay:before{content:"\e079"}.fa-intercom:before{content:"\f7af"}.fa-zhihu:before{content:"\f63f"}.fa-korvue:before{content:"\f42f"}.fa-pix:before{content:"\e43a"}.fa-steam-symbol:before{content:"\f3f6"}:host,:root{--fa-font-regular:normal 400 1em/1 "Font Awesome 6 Free"}@font-face{font-family:"Font Awesome 6 Free";font-style:normal;font-weight:400;font-display:block;src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }.fa-regular,.far{font-weight:400}:host,:root{--fa-style-family-classic:"Font Awesome 6 Free";--fa-font-solid:normal 900 1em/1 "Font Awesome 6 Free"}@font-face{font-family:"Font Awesome 6 Free";font-style:normal;font-weight:900;font-display:block;src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }.fa-solid,.fas{font-weight:900}@font-face{font-family:"Font Awesome 5 Brands";font-display:block;font-weight:400;src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }@font-face{font-family:"Font Awesome 5 Free";font-display:block;font-weight:900;src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }@font-face{font-family:"Font Awesome 5 Free";font-display:block;font-weight:400;src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }@font-face{font-family:"FontAwesome";font-display:block;src: url("../webfonts/fa-solid-900.woff2") format("woff2"), url("../webfonts/fa-solid-900.ttf") format("truetype"); }@font-face{font-family:"FontAwesome";font-display:block;src: url("../webfonts/fa-brands-400.woff2") format("woff2"), url("../webfonts/fa-brands-400.ttf") format("truetype"); }@font-face{font-family:"FontAwesome";font-display:block;src: url("../webfonts/fa-regular-400.woff2") format("woff2"), url("../webfonts/fa-regular-400.ttf") format("truetype"); }@font-face{font-family:"FontAwesome";font-display:block;src: url("../webfonts/fa-v4compatibility.woff2") format("woff2"), url("../webfonts/fa-v4compatibility.ttf") format("truetype"); } \ No newline at end of file diff --git a/latest-tag/deps/font-awesome-6.4.2/css/v4-shims.css b/latest-tag/deps/font-awesome-6.4.2/css/v4-shims.css new file mode 100644 index 00000000..a85953d1 --- /dev/null +++ b/latest-tag/deps/font-awesome-6.4.2/css/v4-shims.css @@ -0,0 +1,2194 @@ +/*! + * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2023 Fonticons, Inc. + */ +.fa.fa-glass:before { + content: "\f000"; } + +.fa.fa-envelope-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-envelope-o:before { + content: "\f0e0"; } + +.fa.fa-star-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-o:before { + content: "\f005"; } + +.fa.fa-remove:before { + content: "\f00d"; } + +.fa.fa-close:before { + content: "\f00d"; } + +.fa.fa-gear:before { + content: "\f013"; } + +.fa.fa-trash-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-trash-o:before { + content: "\f2ed"; } + +.fa.fa-home:before { + content: "\f015"; } + +.fa.fa-file-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-o:before { + content: "\f15b"; } + +.fa.fa-clock-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-clock-o:before { + content: "\f017"; } + +.fa.fa-arrow-circle-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-down:before { + content: "\f358"; } + +.fa.fa-arrow-circle-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-up:before { + content: "\f35b"; } + +.fa.fa-play-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-play-circle-o:before { + content: "\f144"; } + +.fa.fa-repeat:before { + content: "\f01e"; } + +.fa.fa-rotate-right:before { + content: "\f01e"; } + +.fa.fa-refresh:before { + content: "\f021"; } + +.fa.fa-list-alt { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-list-alt:before { + content: "\f022"; } + +.fa.fa-dedent:before { + content: "\f03b"; } + +.fa.fa-video-camera:before { + content: "\f03d"; } + +.fa.fa-picture-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-picture-o:before { + content: "\f03e"; } + +.fa.fa-photo { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-photo:before { + content: "\f03e"; } + +.fa.fa-image { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-image:before { + content: "\f03e"; } + +.fa.fa-map-marker:before { + content: "\f3c5"; } + +.fa.fa-pencil-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-pencil-square-o:before { + content: "\f044"; } + +.fa.fa-edit { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-edit:before { + content: "\f044"; } + +.fa.fa-share-square-o:before { + content: "\f14d"; } + +.fa.fa-check-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-check-square-o:before { + content: "\f14a"; } + +.fa.fa-arrows:before { + content: "\f0b2"; } + +.fa.fa-times-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-times-circle-o:before { + content: "\f057"; } + +.fa.fa-check-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-check-circle-o:before { + content: "\f058"; } + +.fa.fa-mail-forward:before { + content: "\f064"; } + +.fa.fa-expand:before { + content: "\f424"; } + +.fa.fa-compress:before { + content: "\f422"; } + +.fa.fa-eye { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-eye-slash { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-warning:before { + content: "\f071"; } + +.fa.fa-calendar:before { + content: "\f073"; } + +.fa.fa-arrows-v:before { + content: "\f338"; } + +.fa.fa-arrows-h:before { + content: "\f337"; } + +.fa.fa-bar-chart:before { + content: "\e0e3"; } + +.fa.fa-bar-chart-o:before { + content: "\e0e3"; } + +.fa.fa-twitter-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-twitter-square:before { + content: "\f081"; } + +.fa.fa-facebook-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook-square:before { + content: "\f082"; } + +.fa.fa-gears:before { + content: "\f085"; } + +.fa.fa-thumbs-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-thumbs-o-up:before { + content: "\f164"; } + +.fa.fa-thumbs-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-thumbs-o-down:before { + content: "\f165"; } + +.fa.fa-heart-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-heart-o:before { + content: "\f004"; } + +.fa.fa-sign-out:before { + content: "\f2f5"; } + +.fa.fa-linkedin-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linkedin-square:before { + content: "\f08c"; } + +.fa.fa-thumb-tack:before { + content: "\f08d"; } + +.fa.fa-external-link:before { + content: "\f35d"; } + +.fa.fa-sign-in:before { + content: "\f2f6"; } + +.fa.fa-github-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-github-square:before { + content: "\f092"; } + +.fa.fa-lemon-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-lemon-o:before { + content: "\f094"; } + +.fa.fa-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-square-o:before { + content: "\f0c8"; } + +.fa.fa-bookmark-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bookmark-o:before { + content: "\f02e"; } + +.fa.fa-twitter { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook:before { + content: "\f39e"; } + +.fa.fa-facebook-f { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook-f:before { + content: "\f39e"; } + +.fa.fa-github { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-credit-card { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-feed:before { + content: "\f09e"; } + +.fa.fa-hdd-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hdd-o:before { + content: "\f0a0"; } + +.fa.fa-hand-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-right:before { + content: "\f0a4"; } + +.fa.fa-hand-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-left:before { + content: "\f0a5"; } + +.fa.fa-hand-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-up:before { + content: "\f0a6"; } + +.fa.fa-hand-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-o-down:before { + content: "\f0a7"; } + +.fa.fa-globe:before { + content: "\f57d"; } + +.fa.fa-tasks:before { + content: "\f828"; } + +.fa.fa-arrows-alt:before { + content: "\f31e"; } + +.fa.fa-group:before { + content: "\f0c0"; } + +.fa.fa-chain:before { + content: "\f0c1"; } + +.fa.fa-cut:before { + content: "\f0c4"; } + +.fa.fa-files-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-files-o:before { + content: "\f0c5"; } + +.fa.fa-floppy-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-floppy-o:before { + content: "\f0c7"; } + +.fa.fa-save { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-save:before { + content: "\f0c7"; } + +.fa.fa-navicon:before { + content: "\f0c9"; } + +.fa.fa-reorder:before { + content: "\f0c9"; } + +.fa.fa-magic:before { + content: "\e2ca"; } + +.fa.fa-pinterest { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pinterest-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pinterest-square:before { + content: "\f0d3"; } + +.fa.fa-google-plus-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-square:before { + content: "\f0d4"; } + +.fa.fa-google-plus { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus:before { + content: "\f0d5"; } + +.fa.fa-money:before { + content: "\f3d1"; } + +.fa.fa-unsorted:before { + content: "\f0dc"; } + +.fa.fa-sort-desc:before { + content: "\f0dd"; } + +.fa.fa-sort-asc:before { + content: "\f0de"; } + +.fa.fa-linkedin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linkedin:before { + content: "\f0e1"; } + +.fa.fa-rotate-left:before { + content: "\f0e2"; } + +.fa.fa-legal:before { + content: "\f0e3"; } + +.fa.fa-tachometer:before { + content: "\f625"; } + +.fa.fa-dashboard:before { + content: "\f625"; } + +.fa.fa-comment-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-comment-o:before { + content: "\f075"; } + +.fa.fa-comments-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-comments-o:before { + content: "\f086"; } + +.fa.fa-flash:before { + content: "\f0e7"; } + +.fa.fa-clipboard:before { + content: "\f0ea"; } + +.fa.fa-lightbulb-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-lightbulb-o:before { + content: "\f0eb"; } + +.fa.fa-exchange:before { + content: "\f362"; } + +.fa.fa-cloud-download:before { + content: "\f0ed"; } + +.fa.fa-cloud-upload:before { + content: "\f0ee"; } + +.fa.fa-bell-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bell-o:before { + content: "\f0f3"; } + +.fa.fa-cutlery:before { + content: "\f2e7"; } + +.fa.fa-file-text-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-text-o:before { + content: "\f15c"; } + +.fa.fa-building-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-building-o:before { + content: "\f1ad"; } + +.fa.fa-hospital-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hospital-o:before { + content: "\f0f8"; } + +.fa.fa-tablet:before { + content: "\f3fa"; } + +.fa.fa-mobile:before { + content: "\f3cd"; } + +.fa.fa-mobile-phone:before { + content: "\f3cd"; } + +.fa.fa-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-o:before { + content: "\f111"; } + +.fa.fa-mail-reply:before { + content: "\f3e5"; } + +.fa.fa-github-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-folder-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-o:before { + content: "\f07b"; } + +.fa.fa-folder-open-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-folder-open-o:before { + content: "\f07c"; } + +.fa.fa-smile-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-smile-o:before { + content: "\f118"; } + +.fa.fa-frown-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-frown-o:before { + content: "\f119"; } + +.fa.fa-meh-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-meh-o:before { + content: "\f11a"; } + +.fa.fa-keyboard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-keyboard-o:before { + content: "\f11c"; } + +.fa.fa-flag-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-flag-o:before { + content: "\f024"; } + +.fa.fa-mail-reply-all:before { + content: "\f122"; } + +.fa.fa-star-half-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-o:before { + content: "\f5c0"; } + +.fa.fa-star-half-empty { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-empty:before { + content: "\f5c0"; } + +.fa.fa-star-half-full { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-star-half-full:before { + content: "\f5c0"; } + +.fa.fa-code-fork:before { + content: "\f126"; } + +.fa.fa-chain-broken:before { + content: "\f127"; } + +.fa.fa-unlink:before { + content: "\f127"; } + +.fa.fa-calendar-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-o:before { + content: "\f133"; } + +.fa.fa-maxcdn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-html5 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-css3 { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-unlock-alt:before { + content: "\f09c"; } + +.fa.fa-minus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-minus-square-o:before { + content: "\f146"; } + +.fa.fa-level-up:before { + content: "\f3bf"; } + +.fa.fa-level-down:before { + content: "\f3be"; } + +.fa.fa-pencil-square:before { + content: "\f14b"; } + +.fa.fa-external-link-square:before { + content: "\f360"; } + +.fa.fa-compass { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-down:before { + content: "\f150"; } + +.fa.fa-toggle-down { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-down:before { + content: "\f150"; } + +.fa.fa-caret-square-o-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-up:before { + content: "\f151"; } + +.fa.fa-toggle-up { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-up:before { + content: "\f151"; } + +.fa.fa-caret-square-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-right:before { + content: "\f152"; } + +.fa.fa-toggle-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-right:before { + content: "\f152"; } + +.fa.fa-eur:before { + content: "\f153"; } + +.fa.fa-euro:before { + content: "\f153"; } + +.fa.fa-gbp:before { + content: "\f154"; } + +.fa.fa-usd:before { + content: "\24"; } + +.fa.fa-dollar:before { + content: "\24"; } + +.fa.fa-inr:before { + content: "\e1bc"; } + +.fa.fa-rupee:before { + content: "\e1bc"; } + +.fa.fa-jpy:before { + content: "\f157"; } + +.fa.fa-cny:before { + content: "\f157"; } + +.fa.fa-rmb:before { + content: "\f157"; } + +.fa.fa-yen:before { + content: "\f157"; } + +.fa.fa-rub:before { + content: "\f158"; } + +.fa.fa-ruble:before { + content: "\f158"; } + +.fa.fa-rouble:before { + content: "\f158"; } + +.fa.fa-krw:before { + content: "\f159"; } + +.fa.fa-won:before { + content: "\f159"; } + +.fa.fa-btc { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitcoin:before { + content: "\f15a"; } + +.fa.fa-file-text:before { + content: "\f15c"; } + +.fa.fa-sort-alpha-asc:before { + content: "\f15d"; } + +.fa.fa-sort-alpha-desc:before { + content: "\f881"; } + +.fa.fa-sort-amount-asc:before { + content: "\f884"; } + +.fa.fa-sort-amount-desc:before { + content: "\f160"; } + +.fa.fa-sort-numeric-asc:before { + content: "\f162"; } + +.fa.fa-sort-numeric-desc:before { + content: "\f886"; } + +.fa.fa-youtube-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-square:before { + content: "\f431"; } + +.fa.fa-youtube { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-xing-square:before { + content: "\f169"; } + +.fa.fa-youtube-play { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-youtube-play:before { + content: "\f167"; } + +.fa.fa-dropbox { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-overflow { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-instagram { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-flickr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-adn { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bitbucket-square:before { + content: "\f171"; } + +.fa.fa-tumblr { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-tumblr-square:before { + content: "\f174"; } + +.fa.fa-long-arrow-down:before { + content: "\f309"; } + +.fa.fa-long-arrow-up:before { + content: "\f30c"; } + +.fa.fa-long-arrow-left:before { + content: "\f30a"; } + +.fa.fa-long-arrow-right:before { + content: "\f30b"; } + +.fa.fa-apple { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-windows { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-android { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-linux { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-dribbble { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-skype { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-foursquare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-trello { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gratipay { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gittip:before { + content: "\f184"; } + +.fa.fa-sun-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sun-o:before { + content: "\f185"; } + +.fa.fa-moon-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-moon-o:before { + content: "\f186"; } + +.fa.fa-vk { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-renren { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pagelines { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stack-exchange { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-right:before { + content: "\f35a"; } + +.fa.fa-arrow-circle-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-arrow-circle-o-left:before { + content: "\f359"; } + +.fa.fa-caret-square-o-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-caret-square-o-left:before { + content: "\f191"; } + +.fa.fa-toggle-left { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-toggle-left:before { + content: "\f191"; } + +.fa.fa-dot-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-dot-circle-o:before { + content: "\f192"; } + +.fa.fa-vimeo-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo-square:before { + content: "\f194"; } + +.fa.fa-try:before { + content: "\e2bb"; } + +.fa.fa-turkish-lira:before { + content: "\e2bb"; } + +.fa.fa-plus-square-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-plus-square-o:before { + content: "\f0fe"; } + +.fa.fa-slack { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wordpress { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-openid { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-institution:before { + content: "\f19c"; } + +.fa.fa-bank:before { + content: "\f19c"; } + +.fa.fa-mortar-board:before { + content: "\f19d"; } + +.fa.fa-yahoo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-square:before { + content: "\f1a2"; } + +.fa.fa-stumbleupon-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-stumbleupon { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-delicious { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-digg { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-pp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pied-piper-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-drupal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-joomla { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-behance-square:before { + content: "\f1b5"; } + +.fa.fa-steam { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-steam-square:before { + content: "\f1b7"; } + +.fa.fa-automobile:before { + content: "\f1b9"; } + +.fa.fa-cab:before { + content: "\f1ba"; } + +.fa.fa-spotify { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-deviantart { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-soundcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-file-pdf-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-pdf-o:before { + content: "\f1c1"; } + +.fa.fa-file-word-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-word-o:before { + content: "\f1c2"; } + +.fa.fa-file-excel-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-excel-o:before { + content: "\f1c3"; } + +.fa.fa-file-powerpoint-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-powerpoint-o:before { + content: "\f1c4"; } + +.fa.fa-file-image-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-image-o:before { + content: "\f1c5"; } + +.fa.fa-file-photo-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-photo-o:before { + content: "\f1c5"; } + +.fa.fa-file-picture-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-picture-o:before { + content: "\f1c5"; } + +.fa.fa-file-archive-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-archive-o:before { + content: "\f1c6"; } + +.fa.fa-file-zip-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-zip-o:before { + content: "\f1c6"; } + +.fa.fa-file-audio-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-audio-o:before { + content: "\f1c7"; } + +.fa.fa-file-sound-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-sound-o:before { + content: "\f1c7"; } + +.fa.fa-file-video-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-video-o:before { + content: "\f1c8"; } + +.fa.fa-file-movie-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-movie-o:before { + content: "\f1c8"; } + +.fa.fa-file-code-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-file-code-o:before { + content: "\f1c9"; } + +.fa.fa-vine { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-codepen { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-jsfiddle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-life-bouy:before { + content: "\f1cd"; } + +.fa.fa-life-buoy:before { + content: "\f1cd"; } + +.fa.fa-life-saver:before { + content: "\f1cd"; } + +.fa.fa-support:before { + content: "\f1cd"; } + +.fa.fa-circle-o-notch:before { + content: "\f1ce"; } + +.fa.fa-rebel { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ra:before { + content: "\f1d0"; } + +.fa.fa-resistance { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-resistance:before { + content: "\f1d0"; } + +.fa.fa-empire { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ge:before { + content: "\f1d1"; } + +.fa.fa-git-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-git-square:before { + content: "\f1d2"; } + +.fa.fa-git { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-hacker-news { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator-square:before { + content: "\f1d4"; } + +.fa.fa-yc-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc-square:before { + content: "\f1d4"; } + +.fa.fa-tencent-weibo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-qq { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-weixin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wechat:before { + content: "\f1d7"; } + +.fa.fa-send:before { + content: "\f1d8"; } + +.fa.fa-paper-plane-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-paper-plane-o:before { + content: "\f1d8"; } + +.fa.fa-send-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-send-o:before { + content: "\f1d8"; } + +.fa.fa-circle-thin { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-circle-thin:before { + content: "\f111"; } + +.fa.fa-header:before { + content: "\f1dc"; } + +.fa.fa-futbol-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-futbol-o:before { + content: "\f1e3"; } + +.fa.fa-soccer-ball-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-soccer-ball-o:before { + content: "\f1e3"; } + +.fa.fa-slideshare { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-twitch { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yelp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-newspaper-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-newspaper-o:before { + content: "\f1ea"; } + +.fa.fa-paypal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-wallet { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-visa { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-mastercard { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-discover { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-amex { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-paypal { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-stripe { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bell-slash-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-bell-slash-o:before { + content: "\f1f6"; } + +.fa.fa-trash:before { + content: "\f2ed"; } + +.fa.fa-copyright { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-eyedropper:before { + content: "\f1fb"; } + +.fa.fa-area-chart:before { + content: "\f1fe"; } + +.fa.fa-pie-chart:before { + content: "\f200"; } + +.fa.fa-line-chart:before { + content: "\f201"; } + +.fa.fa-lastfm { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-lastfm-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-lastfm-square:before { + content: "\f203"; } + +.fa.fa-ioxhost { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-angellist { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-cc:before { + content: "\f20a"; } + +.fa.fa-ils:before { + content: "\f20b"; } + +.fa.fa-shekel:before { + content: "\f20b"; } + +.fa.fa-sheqel:before { + content: "\f20b"; } + +.fa.fa-buysellads { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-connectdevelop { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-dashcube { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-forumbee { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-leanpub { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-sellsy { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-shirtsinbulk { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-simplybuilt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-skyatlas { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-diamond { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-diamond:before { + content: "\f3a5"; } + +.fa.fa-transgender:before { + content: "\f224"; } + +.fa.fa-intersex:before { + content: "\f224"; } + +.fa.fa-transgender-alt:before { + content: "\f225"; } + +.fa.fa-facebook-official { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-facebook-official:before { + content: "\f09a"; } + +.fa.fa-pinterest-p { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-whatsapp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-hotel:before { + content: "\f236"; } + +.fa.fa-viacoin { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-medium { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-y-combinator { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yc:before { + content: "\f23b"; } + +.fa.fa-optin-monster { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-opencart { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-expeditedssl { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-battery-4:before { + content: "\f240"; } + +.fa.fa-battery:before { + content: "\f240"; } + +.fa.fa-battery-3:before { + content: "\f241"; } + +.fa.fa-battery-2:before { + content: "\f242"; } + +.fa.fa-battery-1:before { + content: "\f243"; } + +.fa.fa-battery-0:before { + content: "\f244"; } + +.fa.fa-object-group { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-object-ungroup { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sticky-note-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-sticky-note-o:before { + content: "\f249"; } + +.fa.fa-cc-jcb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-cc-diners-club { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-clone { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hourglass-o:before { + content: "\f254"; } + +.fa.fa-hourglass-1:before { + content: "\f251"; } + +.fa.fa-hourglass-2:before { + content: "\f252"; } + +.fa.fa-hourglass-3:before { + content: "\f253"; } + +.fa.fa-hand-rock-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-rock-o:before { + content: "\f255"; } + +.fa.fa-hand-grab-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-grab-o:before { + content: "\f255"; } + +.fa.fa-hand-paper-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-paper-o:before { + content: "\f256"; } + +.fa.fa-hand-stop-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-stop-o:before { + content: "\f256"; } + +.fa.fa-hand-scissors-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-scissors-o:before { + content: "\f257"; } + +.fa.fa-hand-lizard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-lizard-o:before { + content: "\f258"; } + +.fa.fa-hand-spock-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-spock-o:before { + content: "\f259"; } + +.fa.fa-hand-pointer-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-pointer-o:before { + content: "\f25a"; } + +.fa.fa-hand-peace-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-hand-peace-o:before { + content: "\f25b"; } + +.fa.fa-registered { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-creative-commons { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gg { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gg-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-odnoklassniki-square:before { + content: "\f264"; } + +.fa.fa-get-pocket { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wikipedia-w { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-safari { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-chrome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-firefox { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-opera { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-internet-explorer { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-television:before { + content: "\f26c"; } + +.fa.fa-contao { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-500px { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-amazon { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-calendar-plus-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-plus-o:before { + content: "\f271"; } + +.fa.fa-calendar-minus-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-minus-o:before { + content: "\f272"; } + +.fa.fa-calendar-times-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-times-o:before { + content: "\f273"; } + +.fa.fa-calendar-check-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-calendar-check-o:before { + content: "\f274"; } + +.fa.fa-map-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-map-o:before { + content: "\f279"; } + +.fa.fa-commenting:before { + content: "\f4ad"; } + +.fa.fa-commenting-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-commenting-o:before { + content: "\f4ad"; } + +.fa.fa-houzz { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-vimeo:before { + content: "\f27d"; } + +.fa.fa-black-tie { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fonticons { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-reddit-alien { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-edge { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-credit-card-alt:before { + content: "\f09d"; } + +.fa.fa-codiepie { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-modx { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fort-awesome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-usb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-product-hunt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-mixcloud { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-scribd { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-pause-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-pause-circle-o:before { + content: "\f28b"; } + +.fa.fa-stop-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-stop-circle-o:before { + content: "\f28d"; } + +.fa.fa-bluetooth { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-bluetooth-b { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-gitlab { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpbeginner { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpforms { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-envira { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wheelchair-alt:before { + content: "\f368"; } + +.fa.fa-question-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-question-circle-o:before { + content: "\f059"; } + +.fa.fa-volume-control-phone:before { + content: "\f2a0"; } + +.fa.fa-asl-interpreting:before { + content: "\f2a3"; } + +.fa.fa-deafness:before { + content: "\f2a4"; } + +.fa.fa-hard-of-hearing:before { + content: "\f2a4"; } + +.fa.fa-glide { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-glide-g { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-signing:before { + content: "\f2a7"; } + +.fa.fa-viadeo { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-viadeo-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-viadeo-square:before { + content: "\f2aa"; } + +.fa.fa-snapchat { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-ghost { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-ghost:before { + content: "\f2ab"; } + +.fa.fa-snapchat-square { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-snapchat-square:before { + content: "\f2ad"; } + +.fa.fa-pied-piper { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-first-order { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-yoast { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-themeisle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-official { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-official:before { + content: "\f2b3"; } + +.fa.fa-google-plus-circle { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-google-plus-circle:before { + content: "\f2b3"; } + +.fa.fa-font-awesome { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fa { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-fa:before { + content: "\f2b4"; } + +.fa.fa-handshake-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-handshake-o:before { + content: "\f2b5"; } + +.fa.fa-envelope-open-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-envelope-open-o:before { + content: "\f2b6"; } + +.fa.fa-linode { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-address-book-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-address-book-o:before { + content: "\f2b9"; } + +.fa.fa-vcard:before { + content: "\f2bb"; } + +.fa.fa-address-card-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-address-card-o:before { + content: "\f2bb"; } + +.fa.fa-vcard-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-vcard-o:before { + content: "\f2bb"; } + +.fa.fa-user-circle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-user-circle-o:before { + content: "\f2bd"; } + +.fa.fa-user-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-user-o:before { + content: "\f007"; } + +.fa.fa-id-badge { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-drivers-license:before { + content: "\f2c2"; } + +.fa.fa-id-card-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-id-card-o:before { + content: "\f2c2"; } + +.fa.fa-drivers-license-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-drivers-license-o:before { + content: "\f2c2"; } + +.fa.fa-quora { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-free-code-camp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-telegram { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-thermometer-4:before { + content: "\f2c7"; } + +.fa.fa-thermometer:before { + content: "\f2c7"; } + +.fa.fa-thermometer-3:before { + content: "\f2c8"; } + +.fa.fa-thermometer-2:before { + content: "\f2c9"; } + +.fa.fa-thermometer-1:before { + content: "\f2ca"; } + +.fa.fa-thermometer-0:before { + content: "\f2cb"; } + +.fa.fa-bathtub:before { + content: "\f2cd"; } + +.fa.fa-s15:before { + content: "\f2cd"; } + +.fa.fa-window-maximize { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-window-restore { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-times-rectangle:before { + content: "\f410"; } + +.fa.fa-window-close-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-window-close-o:before { + content: "\f410"; } + +.fa.fa-times-rectangle-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-times-rectangle-o:before { + content: "\f410"; } + +.fa.fa-bandcamp { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-grav { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-etsy { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-imdb { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-ravelry { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-eercast { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-eercast:before { + content: "\f2da"; } + +.fa.fa-snowflake-o { + font-family: 'Font Awesome 6 Free'; + font-weight: 400; } + +.fa.fa-snowflake-o:before { + content: "\f2dc"; } + +.fa.fa-superpowers { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-wpexplorer { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } + +.fa.fa-meetup { + font-family: 'Font Awesome 6 Brands'; + font-weight: 400; } diff --git a/latest-tag/deps/font-awesome-6.4.2/css/v4-shims.min.css b/latest-tag/deps/font-awesome-6.4.2/css/v4-shims.min.css new file mode 100644 index 00000000..64e4e8d8 --- /dev/null +++ b/latest-tag/deps/font-awesome-6.4.2/css/v4-shims.min.css @@ -0,0 +1,6 @@ +/*! + * Font Awesome Free 6.4.2 by @fontawesome - https://fontawesome.com + * License - https://fontawesome.com/license/free (Icons: CC BY 4.0, Fonts: SIL OFL 1.1, Code: MIT License) + * Copyright 2023 Fonticons, Inc. + */ +.fa.fa-glass:before{content:"\f000"}.fa.fa-envelope-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-o:before{content:"\f0e0"}.fa.fa-star-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-o:before{content:"\f005"}.fa.fa-close:before,.fa.fa-remove:before{content:"\f00d"}.fa.fa-gear:before{content:"\f013"}.fa.fa-trash-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-trash-o:before{content:"\f2ed"}.fa.fa-home:before{content:"\f015"}.fa.fa-file-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-o:before{content:"\f15b"}.fa.fa-clock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-clock-o:before{content:"\f017"}.fa.fa-arrow-circle-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-down:before{content:"\f358"}.fa.fa-arrow-circle-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-up:before{content:"\f35b"}.fa.fa-play-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-play-circle-o:before{content:"\f144"}.fa.fa-repeat:before,.fa.fa-rotate-right:before{content:"\f01e"}.fa.fa-refresh:before{content:"\f021"}.fa.fa-list-alt{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-list-alt:before{content:"\f022"}.fa.fa-dedent:before{content:"\f03b"}.fa.fa-video-camera:before{content:"\f03d"}.fa.fa-picture-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-picture-o:before{content:"\f03e"}.fa.fa-photo{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-photo:before{content:"\f03e"}.fa.fa-image{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-image:before{content:"\f03e"}.fa.fa-map-marker:before{content:"\f3c5"}.fa.fa-pencil-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-pencil-square-o:before{content:"\f044"}.fa.fa-edit{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-edit:before{content:"\f044"}.fa.fa-share-square-o:before{content:"\f14d"}.fa.fa-check-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-check-square-o:before{content:"\f14a"}.fa.fa-arrows:before{content:"\f0b2"}.fa.fa-times-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-times-circle-o:before{content:"\f057"}.fa.fa-check-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-check-circle-o:before{content:"\f058"}.fa.fa-mail-forward:before{content:"\f064"}.fa.fa-expand:before{content:"\f424"}.fa.fa-compress:before{content:"\f422"}.fa.fa-eye,.fa.fa-eye-slash{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-warning:before{content:"\f071"}.fa.fa-calendar:before{content:"\f073"}.fa.fa-arrows-v:before{content:"\f338"}.fa.fa-arrows-h:before{content:"\f337"}.fa.fa-bar-chart-o:before,.fa.fa-bar-chart:before{content:"\e0e3"}.fa.fa-twitter-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-twitter-square:before{content:"\f081"}.fa.fa-facebook-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook-square:before{content:"\f082"}.fa.fa-gears:before{content:"\f085"}.fa.fa-thumbs-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-thumbs-o-up:before{content:"\f164"}.fa.fa-thumbs-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-thumbs-o-down:before{content:"\f165"}.fa.fa-heart-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-heart-o:before{content:"\f004"}.fa.fa-sign-out:before{content:"\f2f5"}.fa.fa-linkedin-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-linkedin-square:before{content:"\f08c"}.fa.fa-thumb-tack:before{content:"\f08d"}.fa.fa-external-link:before{content:"\f35d"}.fa.fa-sign-in:before{content:"\f2f6"}.fa.fa-github-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-github-square:before{content:"\f092"}.fa.fa-lemon-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-lemon-o:before{content:"\f094"}.fa.fa-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-square-o:before{content:"\f0c8"}.fa.fa-bookmark-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-bookmark-o:before{content:"\f02e"}.fa.fa-facebook,.fa.fa-twitter{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook:before{content:"\f39e"}.fa.fa-facebook-f{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook-f:before{content:"\f39e"}.fa.fa-github{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-credit-card{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-feed:before{content:"\f09e"}.fa.fa-hdd-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hdd-o:before{content:"\f0a0"}.fa.fa-hand-o-right{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-right:before{content:"\f0a4"}.fa.fa-hand-o-left{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-left:before{content:"\f0a5"}.fa.fa-hand-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-up:before{content:"\f0a6"}.fa.fa-hand-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-o-down:before{content:"\f0a7"}.fa.fa-globe:before{content:"\f57d"}.fa.fa-tasks:before{content:"\f828"}.fa.fa-arrows-alt:before{content:"\f31e"}.fa.fa-group:before{content:"\f0c0"}.fa.fa-chain:before{content:"\f0c1"}.fa.fa-cut:before{content:"\f0c4"}.fa.fa-files-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-files-o:before{content:"\f0c5"}.fa.fa-floppy-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-floppy-o:before{content:"\f0c7"}.fa.fa-save{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-save:before{content:"\f0c7"}.fa.fa-navicon:before,.fa.fa-reorder:before{content:"\f0c9"}.fa.fa-magic:before{content:"\e2ca"}.fa.fa-pinterest,.fa.fa-pinterest-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-pinterest-square:before{content:"\f0d3"}.fa.fa-google-plus-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-google-plus-square:before{content:"\f0d4"}.fa.fa-google-plus{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-google-plus:before{content:"\f0d5"}.fa.fa-money:before{content:"\f3d1"}.fa.fa-unsorted:before{content:"\f0dc"}.fa.fa-sort-desc:before{content:"\f0dd"}.fa.fa-sort-asc:before{content:"\f0de"}.fa.fa-linkedin{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-linkedin:before{content:"\f0e1"}.fa.fa-rotate-left:before{content:"\f0e2"}.fa.fa-legal:before{content:"\f0e3"}.fa.fa-dashboard:before,.fa.fa-tachometer:before{content:"\f625"}.fa.fa-comment-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-comment-o:before{content:"\f075"}.fa.fa-comments-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-comments-o:before{content:"\f086"}.fa.fa-flash:before{content:"\f0e7"}.fa.fa-clipboard:before{content:"\f0ea"}.fa.fa-lightbulb-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-lightbulb-o:before{content:"\f0eb"}.fa.fa-exchange:before{content:"\f362"}.fa.fa-cloud-download:before{content:"\f0ed"}.fa.fa-cloud-upload:before{content:"\f0ee"}.fa.fa-bell-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-bell-o:before{content:"\f0f3"}.fa.fa-cutlery:before{content:"\f2e7"}.fa.fa-file-text-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-text-o:before{content:"\f15c"}.fa.fa-building-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-building-o:before{content:"\f1ad"}.fa.fa-hospital-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hospital-o:before{content:"\f0f8"}.fa.fa-tablet:before{content:"\f3fa"}.fa.fa-mobile-phone:before,.fa.fa-mobile:before{content:"\f3cd"}.fa.fa-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-circle-o:before{content:"\f111"}.fa.fa-mail-reply:before{content:"\f3e5"}.fa.fa-github-alt{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-folder-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-folder-o:before{content:"\f07b"}.fa.fa-folder-open-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-folder-open-o:before{content:"\f07c"}.fa.fa-smile-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-smile-o:before{content:"\f118"}.fa.fa-frown-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-frown-o:before{content:"\f119"}.fa.fa-meh-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-meh-o:before{content:"\f11a"}.fa.fa-keyboard-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-keyboard-o:before{content:"\f11c"}.fa.fa-flag-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-flag-o:before{content:"\f024"}.fa.fa-mail-reply-all:before{content:"\f122"}.fa.fa-star-half-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-half-o:before{content:"\f5c0"}.fa.fa-star-half-empty{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-half-empty:before{content:"\f5c0"}.fa.fa-star-half-full{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-star-half-full:before{content:"\f5c0"}.fa.fa-code-fork:before{content:"\f126"}.fa.fa-chain-broken:before,.fa.fa-unlink:before{content:"\f127"}.fa.fa-calendar-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-calendar-o:before{content:"\f133"}.fa.fa-css3,.fa.fa-html5,.fa.fa-maxcdn{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-unlock-alt:before{content:"\f09c"}.fa.fa-minus-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-minus-square-o:before{content:"\f146"}.fa.fa-level-up:before{content:"\f3bf"}.fa.fa-level-down:before{content:"\f3be"}.fa.fa-pencil-square:before{content:"\f14b"}.fa.fa-external-link-square:before{content:"\f360"}.fa.fa-compass{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-caret-square-o-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-caret-square-o-down:before{content:"\f150"}.fa.fa-toggle-down{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-toggle-down:before{content:"\f150"}.fa.fa-caret-square-o-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-caret-square-o-up:before{content:"\f151"}.fa.fa-toggle-up{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-toggle-up:before{content:"\f151"}.fa.fa-caret-square-o-right{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-caret-square-o-right:before{content:"\f152"}.fa.fa-toggle-right{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-toggle-right:before{content:"\f152"}.fa.fa-eur:before,.fa.fa-euro:before{content:"\f153"}.fa.fa-gbp:before{content:"\f154"}.fa.fa-dollar:before,.fa.fa-usd:before{content:"\24"}.fa.fa-inr:before,.fa.fa-rupee:before{content:"\e1bc"}.fa.fa-cny:before,.fa.fa-jpy:before,.fa.fa-rmb:before,.fa.fa-yen:before{content:"\f157"}.fa.fa-rouble:before,.fa.fa-rub:before,.fa.fa-ruble:before{content:"\f158"}.fa.fa-krw:before,.fa.fa-won:before{content:"\f159"}.fa.fa-bitcoin,.fa.fa-btc{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-bitcoin:before{content:"\f15a"}.fa.fa-file-text:before{content:"\f15c"}.fa.fa-sort-alpha-asc:before{content:"\f15d"}.fa.fa-sort-alpha-desc:before{content:"\f881"}.fa.fa-sort-amount-asc:before{content:"\f884"}.fa.fa-sort-amount-desc:before{content:"\f160"}.fa.fa-sort-numeric-asc:before{content:"\f162"}.fa.fa-sort-numeric-desc:before{content:"\f886"}.fa.fa-youtube-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-youtube-square:before{content:"\f431"}.fa.fa-xing,.fa.fa-xing-square,.fa.fa-youtube{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-xing-square:before{content:"\f169"}.fa.fa-youtube-play{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-youtube-play:before{content:"\f167"}.fa.fa-adn,.fa.fa-bitbucket,.fa.fa-bitbucket-square,.fa.fa-dropbox,.fa.fa-flickr,.fa.fa-instagram,.fa.fa-stack-overflow{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-bitbucket-square:before{content:"\f171"}.fa.fa-tumblr,.fa.fa-tumblr-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-tumblr-square:before{content:"\f174"}.fa.fa-long-arrow-down:before{content:"\f309"}.fa.fa-long-arrow-up:before{content:"\f30c"}.fa.fa-long-arrow-left:before{content:"\f30a"}.fa.fa-long-arrow-right:before{content:"\f30b"}.fa.fa-android,.fa.fa-apple,.fa.fa-dribbble,.fa.fa-foursquare,.fa.fa-gittip,.fa.fa-gratipay,.fa.fa-linux,.fa.fa-skype,.fa.fa-trello,.fa.fa-windows{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-gittip:before{content:"\f184"}.fa.fa-sun-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-sun-o:before{content:"\f185"}.fa.fa-moon-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-moon-o:before{content:"\f186"}.fa.fa-pagelines,.fa.fa-renren,.fa.fa-stack-exchange,.fa.fa-vk,.fa.fa-weibo{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-arrow-circle-o-right{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-right:before{content:"\f35a"}.fa.fa-arrow-circle-o-left{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-arrow-circle-o-left:before{content:"\f359"}.fa.fa-caret-square-o-left{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-caret-square-o-left:before{content:"\f191"}.fa.fa-toggle-left{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-toggle-left:before{content:"\f191"}.fa.fa-dot-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-dot-circle-o:before{content:"\f192"}.fa.fa-vimeo-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-vimeo-square:before{content:"\f194"}.fa.fa-try:before,.fa.fa-turkish-lira:before{content:"\e2bb"}.fa.fa-plus-square-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-plus-square-o:before{content:"\f0fe"}.fa.fa-openid,.fa.fa-slack,.fa.fa-wordpress{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-bank:before,.fa.fa-institution:before{content:"\f19c"}.fa.fa-mortar-board:before{content:"\f19d"}.fa.fa-google,.fa.fa-reddit,.fa.fa-reddit-square,.fa.fa-yahoo{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-reddit-square:before{content:"\f1a2"}.fa.fa-behance,.fa.fa-behance-square,.fa.fa-delicious,.fa.fa-digg,.fa.fa-drupal,.fa.fa-joomla,.fa.fa-pied-piper-alt,.fa.fa-pied-piper-pp,.fa.fa-stumbleupon,.fa.fa-stumbleupon-circle{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-behance-square:before{content:"\f1b5"}.fa.fa-steam,.fa.fa-steam-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-steam-square:before{content:"\f1b7"}.fa.fa-automobile:before{content:"\f1b9"}.fa.fa-cab:before{content:"\f1ba"}.fa.fa-deviantart,.fa.fa-soundcloud,.fa.fa-spotify{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-file-pdf-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-pdf-o:before{content:"\f1c1"}.fa.fa-file-word-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-word-o:before{content:"\f1c2"}.fa.fa-file-excel-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-excel-o:before{content:"\f1c3"}.fa.fa-file-powerpoint-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-powerpoint-o:before{content:"\f1c4"}.fa.fa-file-image-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-image-o:before{content:"\f1c5"}.fa.fa-file-photo-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-photo-o:before{content:"\f1c5"}.fa.fa-file-picture-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-picture-o:before{content:"\f1c5"}.fa.fa-file-archive-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-archive-o:before{content:"\f1c6"}.fa.fa-file-zip-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-zip-o:before{content:"\f1c6"}.fa.fa-file-audio-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-audio-o:before{content:"\f1c7"}.fa.fa-file-sound-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-sound-o:before{content:"\f1c7"}.fa.fa-file-video-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-video-o:before{content:"\f1c8"}.fa.fa-file-movie-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-movie-o:before{content:"\f1c8"}.fa.fa-file-code-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-file-code-o:before{content:"\f1c9"}.fa.fa-codepen,.fa.fa-jsfiddle,.fa.fa-vine{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-life-bouy:before,.fa.fa-life-buoy:before,.fa.fa-life-saver:before,.fa.fa-support:before{content:"\f1cd"}.fa.fa-circle-o-notch:before{content:"\f1ce"}.fa.fa-ra,.fa.fa-rebel{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-ra:before{content:"\f1d0"}.fa.fa-resistance{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-resistance:before{content:"\f1d0"}.fa.fa-empire,.fa.fa-ge{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-ge:before{content:"\f1d1"}.fa.fa-git-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-git-square:before{content:"\f1d2"}.fa.fa-git,.fa.fa-hacker-news,.fa.fa-y-combinator-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-y-combinator-square:before{content:"\f1d4"}.fa.fa-yc-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-yc-square:before{content:"\f1d4"}.fa.fa-qq,.fa.fa-tencent-weibo,.fa.fa-wechat,.fa.fa-weixin{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-wechat:before{content:"\f1d7"}.fa.fa-send:before{content:"\f1d8"}.fa.fa-paper-plane-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-paper-plane-o:before{content:"\f1d8"}.fa.fa-send-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-send-o:before{content:"\f1d8"}.fa.fa-circle-thin{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-circle-thin:before{content:"\f111"}.fa.fa-header:before{content:"\f1dc"}.fa.fa-futbol-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-futbol-o:before{content:"\f1e3"}.fa.fa-soccer-ball-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-soccer-ball-o:before{content:"\f1e3"}.fa.fa-slideshare,.fa.fa-twitch,.fa.fa-yelp{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-newspaper-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-newspaper-o:before{content:"\f1ea"}.fa.fa-cc-amex,.fa.fa-cc-discover,.fa.fa-cc-mastercard,.fa.fa-cc-paypal,.fa.fa-cc-stripe,.fa.fa-cc-visa,.fa.fa-google-wallet,.fa.fa-paypal{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-bell-slash-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-bell-slash-o:before{content:"\f1f6"}.fa.fa-trash:before{content:"\f2ed"}.fa.fa-copyright{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-eyedropper:before{content:"\f1fb"}.fa.fa-area-chart:before{content:"\f1fe"}.fa.fa-pie-chart:before{content:"\f200"}.fa.fa-line-chart:before{content:"\f201"}.fa.fa-lastfm,.fa.fa-lastfm-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-lastfm-square:before{content:"\f203"}.fa.fa-angellist,.fa.fa-ioxhost{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-cc{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-cc:before{content:"\f20a"}.fa.fa-ils:before,.fa.fa-shekel:before,.fa.fa-sheqel:before{content:"\f20b"}.fa.fa-buysellads,.fa.fa-connectdevelop,.fa.fa-dashcube,.fa.fa-forumbee,.fa.fa-leanpub,.fa.fa-sellsy,.fa.fa-shirtsinbulk,.fa.fa-simplybuilt,.fa.fa-skyatlas{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-diamond{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-diamond:before{content:"\f3a5"}.fa.fa-intersex:before,.fa.fa-transgender:before{content:"\f224"}.fa.fa-transgender-alt:before{content:"\f225"}.fa.fa-facebook-official{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-facebook-official:before{content:"\f09a"}.fa.fa-pinterest-p,.fa.fa-whatsapp{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-hotel:before{content:"\f236"}.fa.fa-medium,.fa.fa-viacoin,.fa.fa-y-combinator,.fa.fa-yc{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-yc:before{content:"\f23b"}.fa.fa-expeditedssl,.fa.fa-opencart,.fa.fa-optin-monster{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-battery-4:before,.fa.fa-battery:before{content:"\f240"}.fa.fa-battery-3:before{content:"\f241"}.fa.fa-battery-2:before{content:"\f242"}.fa.fa-battery-1:before{content:"\f243"}.fa.fa-battery-0:before{content:"\f244"}.fa.fa-object-group,.fa.fa-object-ungroup,.fa.fa-sticky-note-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-sticky-note-o:before{content:"\f249"}.fa.fa-cc-diners-club,.fa.fa-cc-jcb{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-clone{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hourglass-o:before{content:"\f254"}.fa.fa-hourglass-1:before{content:"\f251"}.fa.fa-hourglass-2:before{content:"\f252"}.fa.fa-hourglass-3:before{content:"\f253"}.fa.fa-hand-rock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-rock-o:before{content:"\f255"}.fa.fa-hand-grab-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-grab-o:before{content:"\f255"}.fa.fa-hand-paper-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-paper-o:before{content:"\f256"}.fa.fa-hand-stop-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-stop-o:before{content:"\f256"}.fa.fa-hand-scissors-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-scissors-o:before{content:"\f257"}.fa.fa-hand-lizard-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-lizard-o:before{content:"\f258"}.fa.fa-hand-spock-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-spock-o:before{content:"\f259"}.fa.fa-hand-pointer-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-pointer-o:before{content:"\f25a"}.fa.fa-hand-peace-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-hand-peace-o:before{content:"\f25b"}.fa.fa-registered{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-creative-commons,.fa.fa-gg,.fa.fa-gg-circle,.fa.fa-odnoklassniki,.fa.fa-odnoklassniki-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-odnoklassniki-square:before{content:"\f264"}.fa.fa-chrome,.fa.fa-firefox,.fa.fa-get-pocket,.fa.fa-internet-explorer,.fa.fa-opera,.fa.fa-safari,.fa.fa-wikipedia-w{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-television:before{content:"\f26c"}.fa.fa-500px,.fa.fa-amazon,.fa.fa-contao{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-calendar-plus-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-calendar-plus-o:before{content:"\f271"}.fa.fa-calendar-minus-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-calendar-minus-o:before{content:"\f272"}.fa.fa-calendar-times-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-calendar-times-o:before{content:"\f273"}.fa.fa-calendar-check-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-calendar-check-o:before{content:"\f274"}.fa.fa-map-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-map-o:before{content:"\f279"}.fa.fa-commenting:before{content:"\f4ad"}.fa.fa-commenting-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-commenting-o:before{content:"\f4ad"}.fa.fa-houzz,.fa.fa-vimeo{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-vimeo:before{content:"\f27d"}.fa.fa-black-tie,.fa.fa-edge,.fa.fa-fonticons,.fa.fa-reddit-alien{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-credit-card-alt:before{content:"\f09d"}.fa.fa-codiepie,.fa.fa-fort-awesome,.fa.fa-mixcloud,.fa.fa-modx,.fa.fa-product-hunt,.fa.fa-scribd,.fa.fa-usb{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-pause-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-pause-circle-o:before{content:"\f28b"}.fa.fa-stop-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-stop-circle-o:before{content:"\f28d"}.fa.fa-bluetooth,.fa.fa-bluetooth-b,.fa.fa-envira,.fa.fa-gitlab,.fa.fa-wheelchair-alt,.fa.fa-wpbeginner,.fa.fa-wpforms{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-wheelchair-alt:before{content:"\f368"}.fa.fa-question-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-question-circle-o:before{content:"\f059"}.fa.fa-volume-control-phone:before{content:"\f2a0"}.fa.fa-asl-interpreting:before{content:"\f2a3"}.fa.fa-deafness:before,.fa.fa-hard-of-hearing:before{content:"\f2a4"}.fa.fa-glide,.fa.fa-glide-g{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-signing:before{content:"\f2a7"}.fa.fa-viadeo,.fa.fa-viadeo-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-viadeo-square:before{content:"\f2aa"}.fa.fa-snapchat,.fa.fa-snapchat-ghost{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-snapchat-ghost:before{content:"\f2ab"}.fa.fa-snapchat-square{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-snapchat-square:before{content:"\f2ad"}.fa.fa-first-order,.fa.fa-google-plus-official,.fa.fa-pied-piper,.fa.fa-themeisle,.fa.fa-yoast{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-google-plus-official:before{content:"\f2b3"}.fa.fa-google-plus-circle{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-google-plus-circle:before{content:"\f2b3"}.fa.fa-fa,.fa.fa-font-awesome{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-fa:before{content:"\f2b4"}.fa.fa-handshake-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-handshake-o:before{content:"\f2b5"}.fa.fa-envelope-open-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-envelope-open-o:before{content:"\f2b6"}.fa.fa-linode{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-address-book-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-address-book-o:before{content:"\f2b9"}.fa.fa-vcard:before{content:"\f2bb"}.fa.fa-address-card-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-address-card-o:before{content:"\f2bb"}.fa.fa-vcard-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-vcard-o:before{content:"\f2bb"}.fa.fa-user-circle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-user-circle-o:before{content:"\f2bd"}.fa.fa-user-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-user-o:before{content:"\f007"}.fa.fa-id-badge{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-drivers-license:before{content:"\f2c2"}.fa.fa-id-card-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-id-card-o:before{content:"\f2c2"}.fa.fa-drivers-license-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-drivers-license-o:before{content:"\f2c2"}.fa.fa-free-code-camp,.fa.fa-quora,.fa.fa-telegram{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-thermometer-4:before,.fa.fa-thermometer:before{content:"\f2c7"}.fa.fa-thermometer-3:before{content:"\f2c8"}.fa.fa-thermometer-2:before{content:"\f2c9"}.fa.fa-thermometer-1:before{content:"\f2ca"}.fa.fa-thermometer-0:before{content:"\f2cb"}.fa.fa-bathtub:before,.fa.fa-s15:before{content:"\f2cd"}.fa.fa-window-maximize,.fa.fa-window-restore{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-times-rectangle:before{content:"\f410"}.fa.fa-window-close-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-window-close-o:before{content:"\f410"}.fa.fa-times-rectangle-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-times-rectangle-o:before{content:"\f410"}.fa.fa-bandcamp,.fa.fa-eercast,.fa.fa-etsy,.fa.fa-grav,.fa.fa-imdb,.fa.fa-ravelry{font-family:"Font Awesome 6 Brands";font-weight:400}.fa.fa-eercast:before{content:"\f2da"}.fa.fa-snowflake-o{font-family:"Font Awesome 6 Free";font-weight:400}.fa.fa-snowflake-o:before{content:"\f2dc"}.fa.fa-meetup,.fa.fa-superpowers,.fa.fa-wpexplorer{font-family:"Font Awesome 6 Brands";font-weight:400} \ No newline at end of file diff --git a/latest-tag/deps/font-awesome-6.4.2/webfonts/fa-brands-400.ttf b/latest-tag/deps/font-awesome-6.4.2/webfonts/fa-brands-400.ttf new file mode 100644 index 0000000000000000000000000000000000000000..30f55b7435491ed4c2b11de8ab5e5c7e1e1ed669 GIT binary patch literal 189684 zcmc${34CQmmH1zE-*Vr+@7Z47OY%CMq_eyvFTLi`3pCI`BRe$G4T9{p*a(6^R6tZz zP((n)sK_`YGK|ZNGor?27)No2pFelei3;M3jCPW4x^ojP^SsZ+O=R!Rlblya%!DW?sNTyWMauTV;_B6asgSMAyR;QaSbP|E+PQi|1hPtx8by%s)?-YG?xjpKbs~+}W5Z!Q41ucpxHT#?Wv&-R;8EZ<<@a$8Tl;DTI zdgjQCQr6Uw!$;<nxVv}mce@Ivr-|_ zq`YOy%rwdp(vJ?(JiM29!J0CmQ@6b5*}PXyoIExVZqySZ9y58Ia#d5_iJP*#H+4yP z)VsRsA}!i@ucf|bUgBvW)@Vc0Tbl7^D0u;&-cS7kloy~4r*E^3`lgmbw^dhBgD1%c z84!0h%Sj#7=LN4mlQyN?roOtVSH|h2?`^czXmhC_v_t+xv%Y42!l?e0soN>PU-_hr zsk<50mCbXzNpsp;9+R{~+6o*itIm8er_FIU>tj55>hSSCOqv_Wnek4U_9R`sET~3* zrc5}{=tqPw*@)Y;?=^9$SMoV|O=zBH8g)y$;Ii&O1FwQxKjScUn0m-Zn!$yHEAk0$ zih$&eOWr1sF9|3O2O>?%CZugCzoJh?+9^mrAloe42&G;#=e%puiKKbteWQ#sPcte= znr+p|FXM6g>T8aBsr+)eWd>>MF2RG)fYQ^Ae55y@O`a*9>ndyNlsK?d|4!PutC4?7wMxA}o;*ds zf-Q!Y`k_?Oyqj67BPVz=ZILErO?=A4g=aL=EJM%5<-Dexly%B$t4`i2o~19O@odUd zU(!7Djx?no2WA@Jlx^nMjk=V6%BiD~&nGyOxK#&)o~BmRL7eBX{&^!$gGN^7p^VU- z4_wGNOnIpb{Ip2E#(QUOo%))6GGht{T`lRD3J>i?ptt66rrK8Ke} z8OxNFasLm&l$$a1AF;MkeulQ{@LQ>;&U_Oxj%M3!M?E|5gf@g{J3MHLJ(0O-j@{Hp z`DXt}&rxqgo{6_M+LyTSSiR!e8K=-E>yGypAo|-*3uD zIX~~xq?t8LUfBa$%s6FDD)KqJH%>gwdrfHO#JqFn+AHy98^mQV6<#Vd>F|n0Ws}7X zxPUGvZvP!mkO|K&q&UU1+rF2YzCog`zM(o*x9U@C)wybqx=>xDUZpNpm#9~(OVwrS za&?8eQeCC4RUCmhjU8k;BZ%{W>hAXF4POqF%Iji!%S$j4)TbOO1 zJ%08TvuDlTI(x^@7JvTqFY3S8{fi$y8+~@&vnM=z|Fa)>cKW&D=k`AL*mM8z%iu3t z_P=`nW&7W}|IYpI+5dt4-`)SSx#Zlcxz%&ya~tN)m^*jwlDW&~E}y%4?zMB*% z4yF$F9~?e7e(;!s?>YF;!H*vN_`y#d{OrLm9(?lPQwRU~;M~DO2Y>%!^u@%BlP_+5 z@q;gZ*I zHy?iJ@FR!6aroPZe|Gp+M~*PZovN%>t0A@F|JAuXWA?h)xBiK_9AYjXeeOx-GH2%U zO=d3d-~a6ubGg1Tm#=0nubA7*T)uAZ4Rce><=f0$KEPZ~&wXa@iMemgJw5mQ+`rH5 zXD+RI-+XvJ##|0Fm+R(FWG;8lUo?LebNR;ksrg%(%l9&u_cE6soBwm>@{{wAGnZeR z|L*)x%v@f&aQ(t93vXdA-?4D_!u<;$T=?+9BMYBlF2CBC%Wp83-)1ghAULx0I!e!el6|H5339-cgU zE|1I}`IjR@2i|xf+G2QX&j$d^Atm7*kGwmm~-Ov_neW7(<>tt)X z)!Xv9mcMEFbW2y^mxWIh?kSvFfEOb!&PKl<{dV-J=)KW*M9(!T6H#=sXch3}*k^Pb9Bfp6JAoAVFlaa4RzM@p*uOpv|{AJ`ZV)sPuio7fG z=E!R!dn2!jTphV8a$#goWH)cl2F?O@M9?K8$49nDwnf%O`Xj3&z2V2g9}RyvJRN>0 z{9yQj@crTY!uN*X7rrO_-tgVwcZc7__V~{5o#D5I?+D)>zAb!f_${Q|626(guMb}v zz9#(I@Lu9qhffQi5;0qt9)HnrUZ)YIzxRLR_a6PAen8)@Z`F4;s`(!U`b_z<<&S17cd@^Elvl-7LM2s7rBy~{RSpfmpjuR`YE$j1Llxn~C3tZcdutE& zf>o*)zTBt!)qq-~2H9V)Q8%bN)z{TQ^#`qVP=82;r0W6XMDz0;e>)q%MonFcx*UOG zHDMp%0fvCJfb9Gm09mPVU{3=wgcmk|mCOF_K#owbC6EU$Za{(Xk_NO8zPbUegvk5` z+6XUeKs({(4d@`mGGw4gcx3}*99K0!DB|h{$e3Q!0FhSqHbBPt+6MFx3T+BhfY&u( z72&=H^b%g%0Ktt=w1D8~x(4(UUf+NL!Z$QP==6pL4AR7~1A^PiX$~;Us&cvmCVmES zK-P8zo+V)7?;{RC9cq^O5RmzqO*){cdlvi(s1ZVNC!nf?$2*`#318uW8Y85gS*d@V zkg?3(N}TzgeJgMW@#B>G*`foAI)2XT5Kt#5^^3X#>O{ib4yYZ3KXgEygb^<4fI69Q zodfC=LdpxMorL#0piU+HfCGv?K0EDz0#DB|&gU4P0#DEFbwHg-_$crg>5SpICxL$; zey&o#3_7585kiZ<6k0o9sr|2ZKybJp8WWJY*#9Qr&BXT*-synQ^8WWYAauN+e(tA# zLd*NV4Sbh4G`s(24hTKYF&6?tk8{wFfXwL}bR!`1I0vo;gihzy1G285(>dlsK&0zA z#vmYcI(LZ!tYI}r|L3I7*AQOrfQhp%=BQ8TbPoK?QBG)ZPVxalgLAI~_7T64P~P24 z{3gOFfbpxh5HkM)>Ndi+0q-P!JK;SJsCN+F=K%ag%{>4JPRKX+kOS)7gww!ZkbW28 zXMn#UPQJM(fG-l4v3vt~iugwep9a29{9}aA1OH0=lZ5{c{D%1Bg!_Sm#6Ly&dk5f; zYTg3Cnfe@|5AYNJJHjv!Ax@j~F(5;nw&#Z&5I!^yP0f#x{`Z762NX0kzYf?$`qv4e z@A)0XzeV^;2h=|k?gpS2^)w;0GS3{SZxdbx$XxuGaIXXECxmYVZYKSwgj0af8?-YI zzUSXg{8_^HI3P;h{CffLu6|AUJ_khmn!gu-78SUe2d4r$NC@ucA0d5?@Xvuy5MLmC z)B*MaHUCNAG16Zod>mkI)L}xw9iaY`@N2-6#1{#_3;c-sq1$;G1AW&v;j9B1Trcc$ zKnDmf1+I1avv56e=u77bZvm!=!^0P#vjyl&cM{&=fbJrE2LSEpZo<2P_Y+@D$XFL1 zAkJJZd=P+kbd~VKz{AAH2^seSbfKBI1;(`S1>(mM&N!e?CHyMD*z_w18J~bYosjt! zkhxxHfIfpb^tk|i>N5$MV*z~*A#=R&4DoXb|HT1)F(Grg@GR+<5dIQ)f%xTw(64}g zE#Yq+khQbG+z9Av2oD0kBmO!<@Gbz#)dA?>fQLA^JOJJggo#5(2jW1MIJi6jeh+|4 zk+u$$9nkM31djsh4}?7c_|wqSfs-82(}dtcK!eKzXE~rhLdZA;^hXK5>wpH=2N>Uh z=Scq;A!8IU^*>Ks`u}H29Si^|;(tfj?*P(_I!OBh$TjL9b0#3N#KB`65ZXTY9tZT_ z6F%gCev*)W2x#W%;Kv=%%+FymQ2(E{cS?VBcT5`;iLolJA|7Z(BSMvXjMRekC6VoNWb*= z3F(7?{vjcBB%pt!)S)2&{Al>bq4OQkKOwvUxRLaq65i&3o+W&n1Nvu#?{q+HdWY@; z?k4{)2;UFfPn!15AOw}1t` zAEvJYmX9VrWx%mMK|1{00noBOOyUH<47eG+huS^7^FaCDTM=IAnLUFU>rzZ*Ibbi6 z@NT7oK7h0!GZ5N<4o7v75HN-2*#$hTRICU*2%(ZU{y9wXL%_!&V$zemPttyhI#N$# zTBF|duQ(@s98=zQ%z-@TnCW~SnC94N74R93d0GMb)k@xW;_c6HJT;|M(Ffe8REctB z;$76!-49Slg|MGC`$_93{{XONmr{fDbC7-v@;n>@_9O5;s?;cDWPy#}0T5qHed}m* zJ>@sj+!4^UA|Y?H)p?qTHSbQ51+@M4pSDK`~%XUPT)h z(}znabIIpW2KE9^Ayo11vL7J$--EE90#(%*C?Z~&o)wr?fxZNTkyfV8*1 z5z=Ce@8J1P(%;4NyLTbX(B|DUO1+oz_iP2I_uj)wy`S(t`traiLJsYGkiL9~`1Dgs zeRv1~Cj|UTsfSk~;M@bepcIT!{n>t{KK`Imf6nt`txEmnAC&qlpHiPbU8&Df=CeHi z4P`zD{N3Y9eV*_OwEx8ozyYPc^fjeso>uCs`;__`?fm_fN77dbFZ%kwsrx(h@w;~c`<43Mv{K)v&L7hL51&D3A^ZvDf6ACi^Z_O8uKcNC^SFdx5_F`}IowhWEdn03Jm;z7wH@{)qHB zPucmW*x?>#=~CxG%D?z4R=H29-|b{G8CB{Cd5%1T&{4-y?N-{oUun+|l=eP~@Iji7 zH2?KV2W|y;7rsmB$itj@pRRPAFbPXY-lufxL8Y^IDxIUQ{2!EVp?uqn((OACM7Anj z+@N%c=Poxw2T+Ltw6p3SrF*w4-Mpp zlgV=m_3Z3Y`V7*~FZvF5V1??8z^_fVWn@pUg?|MO5aTRTlNB9SNct)y_tTz z`40#d#BZe^x6UYid%x0erT#kG#r_y%=?=32Q?+-YXApd=D zRQmq=lzxEp4^Zz1Df7W+l>QKXm<}OqP~S&(0G~s^*r4>o#6Px5=|^bq&jtRV^rPf? z^l7C(nN#|)DGtFxN`LAur9TaP_64Q?`e6sh70Jqi&2_7h5fx6Wof1n~T0o`1Yw>7P)~PwCT7 zf1vcwZ&mu2yMVitem(>|$RaKDJz%)_9`np zs;uaDlocnQ-KwmTTUq5PWp(XTR?laY)%$T}^*yYtLBgTk${NlAvr5w zS~hOi%Gp}?cx`yR)|ts>vT?8NwL5z%J(YO<+;c0RKKGodbIz%L`kZqYf2qTflrE+s zVJ)%6S&6N?{r1HJ{F%93hxl`QZ0YsxW=z98m9p^qnN{0)NU9=I!)Rr=r(CkV>3Axe zsgBgf$KtN>+Bogf0s2w*`y&xQKk~Pj4Fv+Btn*h-Iq64~6Kb`%TPIVgq;~$&7Gs{( zGqiPv*g}$->~JQkt2OGUW{HkuGwGD=?bG&(C=Jtm&!jGcf@ZW-=@}cXjjz|W@^X}l zIw)Eze>P&ywaWiD^}g7gJ?TIyQAO)54q2Ln@1u$)ZH zXZ?u`(^ITF6=B|onD=YIzz7&<(<9ZP;mok0Kn6IddnAy@4m~nV(0oI(U`GbLQ4f`e zhRef35d1b#n9WE`QU>+#(8{-@3%;t;Ai{F#p|Er_7>EoGwYO@Q+Y07}T{@G^r81F_ z*BejdQ#og7%wE2b4td-jZzvd!r_-_Fmb_)TT!H+sE0d!@IBa{8$$TnH0X@T?F71=5 z!yw*gheNTb$7@GJ+T-(uB88S>TQV8&xjLO0DZ6aj?+tPK;P!ZIuh-=b1`{zU5Y%2u zOMzBXpiF^=jLo{4GW0s2*yP5BM=L_J+M9N0R@)!|WPvz_t&e)R&hHO|Inbb=66*RP z-BB!d=*D06-xW{COBTO>amERC-QSf=B$8du-y>?}6KhEu`ksQm(KZ@$>yWnXjq2ts z4(jDebjZ0j&cdgcGIt%?Ow$a5-5U&tY}@4uUGuu2%jNZj!oj`HoV*5fId85Bxjdxx?U9tJsnK`f+?ddKpNQ3$$s& z3qb37dhIZTjVx2x0yyu}V_G>qy31+cuA`sy-j_{oWNPq(y5T()FlM-&Pu${HBhx^-Z{YKGIjEiGENwDdMZt(vc=mLj_)!&Ebei3tC5=8WxM z{05R#AJvV;H>u-GgSVDO9G}*)cx-pja@oFcD72>TjBxjEC#>tKB$QQ`cao=L@fl~d z&R@NIxlI}OYT7K(qQk=`^>|H23b8cVUs+hbq|3nHUCOmPl-SX?VbiK!OZUF*(@wib zRN8MxzLeEEn=R?xxekZC+gEF=ch#m1eRnk4PbOnu%#}*nEaPsBUHY+>evGo$tcL{% zA2z*U4y0f3BOy%TMwx6?Wv}30QeJmTvCtB=JZ^W0AKMDI zozjs?X`M=STxv2tM6b<$p_A$8^-@Ag#$#<#z?O3Eyvc5gNgmo*T3;XHBnfI^r!vdj z1+V-Q$mPSHChFY5{Ao?p-4_k`txkP= z&T{&*w~4n{wBT>z&6i%nTQC%Te8n2;RO?q@O~^czPNj#~CZxa3d97trvf>OyI$JE8 z2HP?l^RN6`vj>L!cXux>J!^GeeKHpgXR`TxmYrZZrmv8!QsmLTwOVVdxe_<`4|q=e zbs^*A&J>noN8>f$pvkJpKemfLL3Ta0@R(k}K<}SW6_OcaPIAeUn*~zt`T7+wHNL@x__A)^S}I!q->J-?zL)(&hbh z0EuB*N`Qx$sz1*38nY1BiUFrr~Uz~pFGqS)6L3n26gYYTF zGUbdZ=@ra}o4bn0^gRrW@wudf+Ftx!b4a=_Lt32qFNP#zf|iG%<*KSPCbMrEuH($! z@=iS}TgUiD5rG_1ZwmF4S(fQ{3iUXo*VB$vVl4Gk>fL%y0=|hErNujb@)03T&tVvcEKzokN*RQh_5GI0S20 zQ=7FUnMe@`TChu9(6m;s7$3K+@$pwU-Ppc%U8$sXskCnG_C_4BV4$_D+U#y$(HC~| z@>dGhj}i%|BuVOYFmhB)rq-enF*-Z7;LcQ<^^tdHbE}cEGe--Kj$Smye73-yL<)g8 z91TE3!zx&jv5XIDSF&8o{?wmN`>tPwL=JZgc{`pgdI-Z%)2i!N-?e?azEf1L#o68R zmemm6u|r-WWQTU^;^Ki%eiHIr=7%k;i&0uNGtee$ZzUTPjFz+IvF4^0)ka1zoUS}p zZ!)1N8EHJ;+Gd8mo1f;J-Mg3Qgb@m*O;4M5pr_o{7B3A+(U~Qdw#3S&miGH;@o3OJ ze@=9ydjqmf9oUt)oPUu1vDQLppY z5tfcQW?(Hp{hK!jLL&Azc&N-v&Pa#L>&)Tw$VY;nc(oii8;3k78R729k!oAJ*6nT8 zkx7Gz>D{}VJWxBRG4C4rD5C3&GfjDx>1m8z_AADI)Z8qIqGK!M@a1T|iTGKEif0?F zYr6ToauOUOo;oU6*)}$bX4^|^Y`Liw9Wbpr)%=O}W}NO!zieAyVw|;RTS9@dyv;^$ zbbNS%><6|W*x8C3JV8=PnQh{BeP3VbCa0mhQf$5cO`FbLITvSdl+8z`w|`RaZsJ>a zl_#8TMIza=*RHdiiE|2CHK7$LwrW{s7Meg$>TXiwMr%pZ>=e!DC=1F)-FftGaYQ+- zIe)d9Y2cry zoL-1RylmKM_V*>Upv7L1Hl`?<2&1VoHxAQ~DT|AVK}(}e+&0G8P|kQV4IWui&>9_d zxF)$w504tJto^2rAGMaTyM%jPTV{|oU1PN~zaLn$ay-Y}CfYL!neWcG>C4UX4onaJ zt%Hok!`C~JE4Iu8T^3e~aPXV&G<_&GWJZxiov}B$OrM7FjG6J)DLv!VFzW|>Ekrm-jA7Kq0Kw>40j(I4_8 z4j)cFmC+AeKowdt~KvNDb6TuZHw*V5^zwi~sl($U4= zHRJjrZ*1{vdX-b};#9_ml(=#62hDaez6P(9vE{|D%g9*{w|2#*MyKgx*tJ~T~$JVbO8*OKASsC9=&Ysn)dz>C@==Dbo4s11KVf662wA5eT?$bIb+uCs*y4kkQk8NGa z=o-9hY^a%c&5R5~r_KygP!i8rU26{P3rIvOw<8ljbK_cRgRB8g%U0m8OAUd)hP1G} zD=e=;VFWUem12XvVuQHg)n+lW(oSEM&uME_@5a6Sl(HF(^igv5rktZ+-pAMht4tiwPw*4Ext&t*a` zAC^?DJy`Xme$2O!i{*>_QD%>)jxk<)+@@Bk-Gynlqr(?n!3RXbY-&90AB`Ct`{PYx z%At4RUBsrRm$r(=)ab^7SW&mg3yw`&a4UqcyhjL$3!Y6%kxH8aveV28x!N=|+Z4!W zo%Uy&g1kI8=Y0y_L*dQivZ`7{*O5sRV?j^FB@>+z8Tid_X$r{GB08fOdcyjH@^|qc z^jc(NkGx-;60uoN5%o0cw}h@_OiT42-3s)AmD(;jZKW}&dagdhm(BXlxY|q>W}LhH znXG;4slH6ce;4h9^+WP^@khE#sD1Gn-6O^x$-Ow0w$eJg_^%R7NnMcl3fziawrTT< z8hf#jHs>9N@)5~J z?%*dLZ7%(fepFO@so~L2tmuoQr408tJy@|5o3jV8c30Mk2{gbWf54gF2jo};HN|)P z?fSivF7Lm8uQR##eqY{5jwha2Ic8}`?2?WR=xD2G4cjzZu%dchPx*bJ#b5uyscmsa z9(7tC&DN{q$=Kp&oqiCH>K(*|r-hDuO)uyt(1!43N@CRU7;8u-JCqhu<+nCkmhH;< z&8D)cDyL%Wp)?_I-iw~;m`fbS0?mp2Xz8~Gqm*ifnsrEMY)#Jk5QL2mznQfZj)YV0 zP&}5*V=d6-blRKGvg)y%$se673>FH$KrEW*w|%WG8wUz4E**;&$n9FdU2f-8>XKYqhPrwrq~tKA!6Xsd&O(UDO?!Osb`vv2?nGQXJG4KeR+7 zla2XcH2Hyno^I`GZS8O+k`|keM|(Xs^F&jY)!v>g1k))$h5W&AKHr1I)ZSXF6+5-H zcH-=@a+zzIeuWHWRai$MEHQ1k{%poa`)vG{F9qY^!l<@_E?0G|%)S9pmD3`?*0N*O zbk!iTPp4t$vQ3MgHO9F^HBDR~TPfS+8bN1V1Yqazs}e^e6aG&9<)mG$++2(VGuiew z3nN@=)hVakr`c>H5!>t0|ClX#+uHm31}=!K(ug=0Trtr9$l6-5y}fjTr`?llEw)ZA z9?+p1>UA;Ojn}R?bm)qW8?|1)Ztv;7n5|o^w)Si$80ko(a4i1EJCBQa+oXX5em&U<#TzHaT+eWTT$Zfo%jeecNdt1qsOYJZ_% z_F9WQ?Tg^7RcyA#Vr0%=h1jx;()@+Y7;nYM2ZL|{G1y5`6`N0Wq{CwTwtn{-ZB1^v z;DSw)mV~`M9(UUa`xLA$eeAk4Cx7ns>meJRUgyH4WqXdz!^&+ZhwIF-Q_g2d-_u`wfg2;kVkp*kESet5{ zjYVW@*?o)N2-*)m-1?&{XlR(Dr_UoPnL2Scgis(3t6 z$j9{Y)y|S`+n~3PK*8Dmfwk*LSeb=R#K)I>?} zp1x;|GqN1z zw<2>YjE`UhhLRepF``fBG^#Sn7|nCOeM& zUeD^=xtY)p=amTr-z&w3@k+Mh@K4#mdBp^o>3dx`m5Q<_1$ONM6Om{t6?&7~{fk|@ zkPX5-g{R!^H-%EE7s6Puf$UzCZ3p~WUnXUz(`|z; zNFr)ki9|~>&vrq&fq-xEln&SU z`1bANHFwGH!*3z1t-*3RY&^5^{Uj3f zN24uWnT#tGvidPBuyuITK~%C#cWbLhyDYudh0wrC_(&?}4kE+3A)tWpOslTn0LOCt zm6F{56MJ=8&Q-eEyvNejG16QmWx??;r zubpEB{S<0Yb+mrj`gOr#ZQ>+XzL3wk9`_(8u9dx}vvX|o)~!8#lanLEZoTfA*ygph zwl-J2e$G%=x0_8dn;C6QBqN12gWGCDgPBYqkiD$U_T_Suqrx-QBwtoK2231_Z_}%} zt@lRynXb})!$6H67wjrq>oI-yW|v$VMmY?^T$nXhkT$H`%SLX*AIx3C`k+=}cOx9m zirWS0NJt}N)XDI~Uy5hOoU&_n8NC}9sLR*7<58S-V$7}`i>0&i_O{-RJS-;@$>%%z zTH28dOVL6po(?6kZ${Gj2=?)GE~+!pWH83bYQP^&1w*flXD4%&TnzGyY8{IOB9#i( zaqU0crMnBGZMjqawKpBYq?Sy>7eYGT(F*r%85+_pF$(0{MuI_iBp;6ZePMi9f~h2w zmN+FJIVT9^`GdIsM1${&eSU|u5X)p@lm26jYff_4uIuZc6W#^w@#QMTuGFrN)jRdM z`VxJOzDa*l|Be1CvbV~9~nItAP0S-d4uY)TTVX2pxk z#A;4B32U|4;UyeOqg4=N8#4zz5*xx9jFSW8I3Z(F*ytoSRvU#~nHUBN2?xgtFk(q^Jn!i;>E=n^;9GNU6DltTEa40n)Y4_AB zLV>)MmszJY!!BoGrTh+2z%l#q)F-_%Bar$9H!RLl8@K|GreaeGC7V_1|IFwlEY6+N zQ42v7?++b79dQ>d%d4?s;BtmeSUQgoYejX=tvOE98JFwjTlUc6Q7v0?Uh7H=$Nw+drq!1;UO-)X zna0y@_q}1aue*0uj}IQ>E&6=-yIcc$$Q=lHwdF!fwgad5{5EO}8*fDa%rX%jw$3gx zCYQG`7I5R6XGgRZ30oh~lP;Df3VYy|IH^817ZF}CiDOT2|3lxV9BzE>mfp_Jbiy4h zWADIw%=aN3VH=V9|K888;YOj>e8gZ0rxy7Bzd|Xa`)^>BIb?Q@LTO_%**E_SjJp2& z2u=U@=!j{2IYN4q6)wjUWejIxfwRV4y6kpiVvh$n#AW|&L7V|kR@lr3@lQuw9;&1Euj_?gY*vXRKyRo=88jrR5?;q<$Rj`c{qJwO>B*Z+UR7s5 z+>Q2kp73X9p-V3(wdewjfzBh|$q zPY8$OoxQ#7?baC+sYD{M_y=G2IgxZapDUo5z}-&uYCU!G#6+=PE=QuecwC{Ur+=a& zok#`)`CMgnM~BsiY_E{lu(>GaDyE8f{~T-f$a0A-E#25yDm7f@Mo_nfS(i}UJSHL; z0C^`f1=E%tEM*>LU)Am@5I6NEkb}>T#@TO+*00fevq2XrJl@g>-m}@dip-m}CeZrJ zKh=8apUjK?iu`$oG_8N~6ZuQDU4Kbp^2hP@EgCwCqZ@aD%Q*9Hh@CB5e#;(F(3=>p z#s#gqUTka6WHIB0vYGa_;^NFSKYF(DxOs4m*1N^Y#=LFhl`PYZINnri1~-F8Bwcjj z-+@O*=2h8h&C`v1h0!H>|G(`+*B*HN^H05S?Y>tRpIUqV(=Yt^pTDIMhP4Xg<2{dU zVGo$^!9TesG|jJ}ceCdJWj3U;XvYcsv+v z@95ut%rUKD%N2%7bJ>oPWo1eomW6$n?I+;(p;yErQMSflJRad#B^3(=z0eeLSPUsF zfdm%{M{Ub3ekCxwpzXe>t)rv6zrVk;6iKzUAKTT_(Gm>yw09&kutN)XCCq{Nn4}AZ zbewaGxZmYTrjp5Yp)-%<8IQpoBZ-9H7qtV)SWJ5|*@RC#mMx2$1E{l*D%T;GYz{$m zihfmBJHQ*9MOHauM5JAgJ9ebFAi!9R;(~%C+_Bs#N}W00>=cVY!;EMu(8KH2 zat}5mKU(~GXHNWObDg@q8JkQb3yVK5h?93gw-esf%>K~}!;vgY{y{=S=qsPu;>@(Yw(V3}KIrV!CI@$Z9Y@KGIxRMdv04=K$^n?_I+&uH#x^i;(032GAKGGbaY#UwkHY1>H?hPxuV zR0K)aDf_6)a)q>Y?TH>w#^ZSve_gOT*FLS^Z@Es;o@fN-cY=i+=D8S2#JU(p>hDH8 zN(98xP+hEYhX+2L9h-iab4_7{pbU3;qO5l(kxi9(@B0)m!dDBRI^Yh)KoKktT} zmkowc!6D7DhSqrRt!64?)SoVYs=KYNXP~F0z1*f-y9V3i;!la<8|SAj;k9jVBIUt4 z17~_ToNvW!8fnReOU3q>Z6h_?Q8p0XJ%Rl&TfpZBlX8nL`FnbjffFaQN(QT$JFa8ITRZ~&dy^odI_*Ga16K?J`f!RbJD$Fc3Y>zK2; z!I$v78GJ-%mh35{{{L|Q%cZLDrGIMdadGa4SI2BDIF)$1Vvm*MTt%y%cA7r^6|Z>X z8&3b$KNa^59&^l~-ucY&$3L_9{u^)9moXL}u!H%(ob#Ou7MfG}Ld{j`qw+11TFHhp z8CE4u`i7SomS)(p-BZDaAr#TzKf>%Wze4~*4rU=h6Ho;O8%v1H!-jsASC7Ndpsel^ z2mNDaP1UgOiNqq-6laZQXsw5pl^Qn7Pe_S`N@>i7m6o|tMI+Kz=@~K+<%ZLy_^`-B zY14v_il+VuVEu?mS&S?hIj;12{4Q^P15 zhxq|g%N?<}P9~Fjw@WtC6trun;LdcwiUQh>>b?Yrb-zz-l6Rk?r~LD?F?W?x$t!iJ z56)ifwARxm?b;sCxdR^YaSH@I16Dd!Ds>dG=A?@qrBW(wU6I3~KAOv-u*9-)r%X(D zSXee)-l*-4M8e4e+`{q)v=u@Ww4}}$2M#UVSkN00F(Or8*4FKGgRZpGnXIqg>v4Gv z?+8IV`}kJa1RgS{a^i4{x(~{6rop-WQblnXub3-u5Qb32e~Ap~kGb zPFWgaU#wLfUd)N2=BB3Et^~_i(q%m%bcr+~DvpuH#*px&0%7o&nM{FUGB2k&d)~1A z*Sy6dShe`MRgk~L+VZ%W;EHhk*(XZCHtx%}t;$z)z#mzhiQtfn8N5!Y7Z0=Z`Tfxd zb`6{s179+aktpOx9!|o~ZmE33skYGfWThpq3pmT5z;D~;#|jzo6?FdCsCczRzasTX zsju|;BV0+83STBL{92s~oeG}z@P(`!)q9aqjTFFeZ86abcUjWM*J1)28Y`EKU;q{c zmE*$7$n1rz1i+E-IY+HudhaPgnXpPUuQDfQ&4*Nh4kW3gKWg<>d}=)_>A^#;F~aj=G6Y)6Z=q26xWH_+bilN*+%~X}rHDA9Q&ar(q@9+N;^_Upr{x??TeM=R_{^Kv|PmVENC!`GnLM`^f7ZT~7R5O&2T^x40`+niFnM%!6zs7j-H6Eg#SYTeYaDz zUAabyk}YJ2@p5F+;wTrMF4Wk8*9hJzgowEYw+LKVZjFJ|*ntsPjQCca4+KxjbVT>$ zIPtzP(4O6q4hD8)Isz9;!k%bH=A?Vmu^pjkBy>_V)4e{ES>K(Bo)n5kgF9mB;<{{h zUGW2c|4C`RCzH7_v*QGx@1)G0Y<5rPq#e@~@Z}0ZxnpBDnB)@wsoVf5DSoE|>4<9vbSt zqZzV+l}dSjN~Hq4DSgySGToiaEoW$q?KiBWd-*Ehc6APGNbZ}-@-YT#vmRI-tO;q@ ztQy2XQAtElkl$f$1vSNJ5x<4*f#IwpVwY@8?~*)hO|`X-4U|g#MH>~BBR4%<#*W?I zp>w(3-mz{TT8hQWNN+D4U;GgF?s6T40@xHx6_4>X7$VSDycuZsZoHwA zm-`8Yi+T@TwP*E7VAINOe*0^5((m=S&e$#E;0z3!l{I)0yzvX11NZ5ZkR}bk6W+)a zjWd%Y!^|cdLaL4BXtoELT{*p%ZQa>8pakc*i_6d?IqXel-esN~9?M}@3b_r!aR^lE zGTs1ETgHZ}jOW;x9K0C?gF{_mmoP~=D&Zi7o$>zhxVl8p>k{#Sny7&$tiM3*OyFUI`q}v5jWkfk zVwx^XT++=S!nU&}*6=Zi;-<+H$U_}@i`qFXarL|0UjEPxo-OH!-&-9$Lz+4 zcrcmHSc8(ziM&+pauv?;Y}k0#S;v*K*#O(u6_(C)cJ}lXOBu_a2U!@1B zi$0n<78j+0bZ4o%r?bTUT7xISm<+-)`FkqJW#&i5Chu-YZ(Vo%@msd6!bob6YF$)d z!?Zje*Px4k9uG*e3TJsXY&!F-kSwN{wZ!tP;m3`Kv*mUCN%39gPe8Ax>{aE z=AO4h(9oWoQa0AWuzp*}KUwPREKQ07#NUYAgb%0hk<(ypBOKQ89fE%f1tvTBne>|+ z^4|Y3-WNvDgm{l{w|8J-V&mk*!~j}>@2|PHj2QOtwzJms_jPf76OM&vlH{4(_|iOQ z4E6Vy$rJRmLHbnm$WwX_e&fR`Gp082McDJ@Q~(; zX526%*+8UBeBZx zziDF>OY2i&@mww*bN=Q^-s@edWT~`z_2A&@zRprI<+|QmDz8~nUOdd_7I4Z-Tl#7X z4Gf*kdP7SYRnnWmj+w&aE1&Tee%;xL!zT{oejCpqmb~Tl`eLaTY#517eQswQn@3A3 z7DplsBolEyy3wisB4s%>{`*o#=+1c^Yu2ppEf&R6QY`kaUbCj7o$c$}X{^L_BBddF zGln&elYYR{{EUnzP|DsB4CS(jc&7bEe>{mOPWm0~p-?+oWHy=%B$9D@;>Wcr5*G*wQ5{^MC8|##(@(DPkvpJT9#*dLyFjS$95N|ide^{lds-_3t~*Zg7m#;#_9;Z3BmbhmjNf<)8?4ex?qOl=Qpq+?RZ zB6}~Vc|}n&IuhCqbYL_iW2UI3^_v!FS`+-V>ZR*=Lq2beRS^sF6O9G}(M&YTVO%8Q zq-3K0pv9pMXGsZlJ32|aTTWQNQtK`537c|^-V z&%@A2f#IsiSDYPH5k)96$W7#>j|z!b8b`_UMWU1YX;}Wj5!-KUYwi|2((wTe$!7GH znoGFu__mBsZcI#@4;U?1VXFP|K29%$*SdD?a`qj0aPQNFmO{MBM=n3F6>%V z4*&l*{r2=$pt96oX7WI*L3$ zxMl}#72N?l5kPQ*KhYj%WWho!ywewrwzl$d6jz2baDQA*`g6FQ$RIBemwG7oq;0#a z{R8Fh&R90fAj5tfAJZ+le1J~C$*~qt&7-G=%K06i{iGk14c)gZR#0L>avMsCo zdRUaW>qe3ZT>cw3(N|$ka!G5hn{YOflV#T`Y12Z5$D9n00wl>1a76&-_3+!)*?jOJ&WTwp zm1@hkb9yki`1tC^kKQ>5Q(cjxwXIx=J&~ih82)BgL!ct+|Dn zN=L zK4wGCHHSMx*BDxc$15@{v-WJSJToE|6KNUeiI~XW{9?>YdpYq&cTAyYl|hTt&4MXa zGLp&y=4wY{0kiqn1aYEiW_j;?EoBIyp;RuF*XmJcwbCC}l(}lKtI?R3>N7SB4n+xmm4-Q) z1ZphNA)|oHXSEm?431W$^P;~wmtMeQb_lGIj#QM9u}Kz~12M9J1O75AbWczKPqIgc zOT$=StCSlXW(Il~uj!9OzyggWGwJa%8e&z1CK)4@U%9Siul|{8Jxz;v(%e)re^&U^u<@a&m?X~S7erMhoJj#QypC#zRP-S*0HZ<0v zhjTIwn}Y-SU@W}BX~<{c9PCCEjv(0E;iymB?A5%=r$%~L=TJGM9y<<$BZiF@$0&bL zv&zG$Y+UD)J5sb8L{Y(9AH<(h2W*WCSIC2IAqS?o^Xfd8%}fP0YM{sC6Sg3#mDCBZ zBPYy5n&rt0-;SkxVTII))i*Ri#Jo8(K8rAb6<&_E#_0`XgL21k#l`s# zRZM)CY}?ac&@NoKdO>Z}FW)WTz6_px0d5hntqtb$QH@G2)WQ@%m&lP)So*p{@XC)r z7W8`~KHVCP`f)(?@Wl^pJ?()mJUWoUAKYaH*$~;F7=pb@MkvI^LFnRd1Kba@Wmdgh zXl06hy0?(a;8>|7tJu?&h#a$ z6P<#fxZtLfCxbf2JZw-`6>D48me&m57|xm}sXU9|xQseJ>DE9TXk$x)KY}(6$Jle= z&*ddeW?83MocU!t+PEuQ+IU_JK{A$2_2@cev^~O?2*WXtJL5wZovXG zxAzXN$rkNGqAVubO`AHqbZaaT=hHncF-}WE(R>ca2<<|x(l|PyejKayR)2uAM-GC* ztm>ojT5_!5FNar;oh?^zn8uF*qbu%T)t5zu?!EMe3r;+9@HN+7xaE|=p1){)wD9u7 zIQ_a0UwGjy_n&+2AAa^T)<6s2U3{MJE{5d)9N_qJxiMGORZi;Lj(gykLbG|-+%YrPdt0uZHsT$Cog_V^qytiE-l_uFHa$!VMkBtsbDItiMcUT zg90|9&kJ4i-KN>7$VsURoG}>@H!kYKx%cWkv z30iDRWRt#hDiKM(JSzTdV0B+>tJbZp;ZO>en26`u4mjD1C!+0%gbP`Rd-R@QHf;G) z5%CsVmTd=lSkm6tYjeMZ#bEUCA?N5mo31v!z|1`|eOL3=)Y~Q}bw1yhj&qL9cT4rQ z;JUXpE_Y2gFIU~Sy|yMVe&-uQ!RX-j`rZL!liI3j^<;T%fHtqSHfG{ z`mYt*@h|BMDf~8G-|$I$01u8wWn{uBUczf!C390#1T-;X^H=msTq}gpLP~~bIF1)G zVxcsgER5kF<2+Iu3NntehY+5$1GYx1BbXm6QVQ`ei4YRxHbfF(qRVHei zInr(KA8haH2zPe0A)v3ay~mkPxoVtp`{nzfNRX|q6}(6~*0$w_+T??v{q4*_(N1)y z-Qi*udezEzlCSfX!MZ{`1%x?le9s2U_}EQb{+ z->i@tOQMR>Rd(335Pim6jzu~Fxkle|h@7QjGw#?v-ygxTP?k2%}EKId=se@ z*8xJvdU0$*!2};JafkXlJ1ws#1TFa!xxCfhS?u6T&#@3Iz{R(Tk&6n2HtZvb9c!S_ z&eFu%WA8x>=PmBX&g!u`#oEDoVz!-EuBv0l5P_2Qg^()75Pgfab?fDrrV2Xj4rci% zg2$WTx(fm~vMcv>TfDh|l}a9quR+M0NEBR=u#0tqqGr1aiG(+VC(=+hgYFb8cejQ4 z2uGn_KjT=wRM4Rp|E$rpco>+qW0gfz7vO3UiwAaKXijklQRew-XwJxAh+x8^oL^%X z5KeBe4<#;nf{?~6nk`_7&GGD%tB71GbYRZBg@TRx?l9JdWim^%hGKilh%TsHUbucV zwYH4t)7Q6o=jz^YILB%RUmCs5)800=rj1QClj%%y_s;F%Ah(zd!vqEIUN075g#Y9E z`k0sHh0q}PU^sy@c_!lyv5)unZJy-B+v@Mz*w)e-a(UfB)EgIHrit}+6fND_GEnX4 zzyUIhb9Y<1=!(Z$;&|a`=Dsb2=_uN|Y2(h7>uAOPeTl-KUzV)7wkensJp4B?C|iob zAef{IC>%LODg*Dhc#v9&OI60cg~=O(^otNN$U{qtMw5a(YxU~Qr}VClgz-!>*c0Tn zfjp5&GU-yHiM#~<;83K`%jIy(b&$OAJmp9liK7l;fXR62-P_X09w`!x&P|5c9W2b+GQyQ5yjNtk@J*B$j|A^#6 zUeaNiJBJ!1D*A_6AdEf%g+`p=E z`b4iB)Iy}vC%saNlfx>f&q#5vI{*ANS=>lCppXkl9OUS?pN}eBDq|Tv|6I)%fl6DE zMwIUBmA4%+4uLwP#NwmxX+h%T8{X~r@G{AHkX2c=>0$bW=u4kw!Bt#s=6`y;j$WBB zHG?COE@kngyN&+GULv+hFFocL+nbX?G(p)t=%+}V$XI;z!VF4=V}vx;4Y3C2YJ9(e z6z7Y%QqVY3{GH#IN@DWyCsXKaJytSR?pod3gI4eEkuSHU(z@5?Pfc zqRT)xSLWCmu-L@92S)0{xjb?$8VBEPNZCFt&#`=Vq+aE72`p&C@5s$}9QraDtP*&5 zXEJ>?scU83!jlwVF3~s2|JvAH7Jl8QQN7AQMK}m^`K>*p9JR)VH|$w|+J-$F2K2s- z-Cdy}&q54I```GI6R=zNui5Y~OK*AyI|Iqf{agtrt zy>IVxs?MpLt4`${I;ZOD>Y47D>Fya#&;)6MMw%Io63RjdkibNF2m*tVM2_MjBQQ3P zAdmqO41VL`p=+}t|~g+uLV zbSGs-2D0-hjvM2HX?HM39caY+`lX&){oYqo$9(s^F1hhoOdSqxd^)b)#gX28`Q_J+ zO-^2atanW0hzGy>E9l$X+)Rd+o}D@jE8?6DftB_I}LTuVgvz6?6VU=2(B1T!6nM)?;5P*enoD|3~*&~5K8Z~-n?P&T)t_E<3D_Tqf{V(p3K< z#N4-i&+grOKw2$LJ6Zd=qW%Vgxg6vRhPv+GZs%h#R4CxP6pdG&QIvmkjHm5j2ZRk= zUV7ZfXwWZhqUd{(`;oin-sI->Ymms;@%GT)+o4u=etu%YG$$tJ=d-PlS7u`@+Gkt8 z@dr?E?b*7WQ?8JYWH{M!#c2hH`v+T2rJP0Qak7=N^W-B(d4{g7|LgRFlz1($(q-zy zV@9G0AzN~@p_a7CAt!%N62o~x+G{^zWa9;2U@h#yDK+VhtOf0Z+IZU7UFYDUX~- z(z~NTHgpYm4$Vv=FWvt+9Xs$Eju4&hBs0)cbJZHUWp!@&UQKKGNOk!BHWQPX$#*-9>swiunT|) z!;RQSck6lO{9ne;zj=_U3;=|*YMZuLO;zbfqo{p?=5 zYvY(yb8pkPoJQXn+B&X;luo9V4W2q3TavoEKVN(G+S80`wZ*Qu@$GLf*MFy8R@ob# zb84$8uyy(P_rABVfLyb{=19J6f;>=Rsg%x{)J|^7ebgH@J#C^nc=veXYH!%wqaudd zm{R3ii|@}Vh#lz%8#kh^hSYCGJsVHH4Uj&M0Dr-GwZxo<&k*bf(o)|H+3R&pM=AFt zwC2Jx$D9WntQg7~31_RtU@qTWu2w^#8NUinCliK>?(?hT_-Xtnz*%857#U``5c(v# zMzvOWb6`&pGNM{7ru=s+|1rP1FAo)$7+-5XYbd|ZYx%wYY&}FSQ|y>NNd#LjvPABM zFljvfBDG`lnSQTh5Wi&lg3&2I)-R8W^TLZyz$VmGtL^TLC(9-LPf$6a`(c))GlgO@S1DT`%AvuM?G&Lv zPD6C-foOSoY5{t(5m4UYyF0@2*Lh_P4!+*UWx$%mY|YtS?T&4%a)@1THE>mK{d>!Cn!$TTp>Tz~A8#yg zKX`Eaazp1JpI47D4x|>wo8Wc(^2k&8+iz13s?TuGDd=6apHDyqb;)?^N*rjGNInQ6 z!b5@-$aLWjo0BNJyj z!GDsOb?l0PD%c5Wv^KkOnm(jnb27`wI_X_0z#ifYu%$17N@HvH;DgiRDR5tX?RAqQ zaTK~8IEV`;91M5aGtK51IVGD42w||59M0k3`t|hdAAAB%uJ;o23gt`w1kS}e_fq&T zoI+N|^&iR7g)7jN-Z=J9eTKYoR_JzQUKgwa@^AAfT$3Ox0IFM#FehkL6Y;_idb0iu z=t&uWLvT&Iq(dqB|#=X3{2M@N1+~f$b%O4No1rNF;y)z#jgQLkk+vNn_>42$ zP6nw0=e3F9BrIprYS5EoF7rN9K>t^()aHQ`AP z6AJ?7>?0^0#zh}h=N)0_m{U2BP$aV zgvDIBLR`>HrxTe>CPaW6h#2jI|0)4;0XY)#%cVyt7A>n2@rWPDnlv`J2`{)UKiC8g z;87^lvQmE~i!7^+zo)Ij0yI$LEhm|bsAxQ%G0a@4BUNUgN1nF?6?YfydoBF^a z>pMehmYE5Wve3)$<|@(brzMOWkAW+7W<+C?Pqeoh*{aWMUH;ItVNXo-eSz(zqM0@F zg{6YySl#*g&fj?*r*NoG#ezy@>ScjlyN>yN+h=Fyt>Vqz`B^H|unH2ZThAk*De z%eJ%Grh1&2Z8LhYb@_!$hXDInDJp8^?5vf|W@edUfD6I`RIks1#8mSb2VY?nc)S_> zDx;%Q+fRI?0RVxr8DLrLmOnT)wwN6Di#wba5j#WH%UZoX`^bxJw5`CtB7wC;AL<6W4IAorJ%nSfBpTNd^q|UYeiXc+ z%KYJcqfSSUCPO5MWqwfe-{ z(~i*CdJvG+%50~! zipSfN>9lU6-&fC9zmE^|QhJ?e`)Tc!b=|JqHPa@Rk|ZG`-G!-65}la3Xgz5Vno?do zE9Nv=?BTfvWUO{?{7>@5ao7ud-?yt+q?hsVj^{wBsoB`}7@&ze#7}ZYUo6kph#m-V zbw)f!4U_0!;kNpHxcDL)N1{k)L-`I=Yq-G>88R9CbX8rt!iZ0XlLkt$AI1~}gy}QW zslCXo<9KLrGhMLv3ut(I26xCUEr{KyJHQ19iYd3jqZJns8LUznL^~;Uv5Y}FF&(3% z)NgD&3O)npXe_HPA1?i&kvlFXA@`LtaNs# zPSwW8wL!T7F((kzjpws5J3lbo`2qc$T-zouzGgQ7cX8=}P9^iQbYTr1VhNPw&$o{0 zt;=`6;03rAv*|>F*hwNxq6J7R-?NX?AH3&1#Fi8EFv!B7-L*nYKSHm$WxTZ{i&bAE z_J|%!8M*}hhZpTA7l=|JDv|Bz!3K;#zU)d*;<_C@!eH%Vy9)Fz$W6Sh6B7p$2PY<+ zUs*BLMd!@UIt2P=>Daj<3lJfvi`L<>aYMc8+4meC>bBK#+54(XnM+HhTs9gZl@i2` zXSbT0y9yaiw;?z11Ng2NxU;K~&#!>z)#bBywICS>w7RK9h{#pBRauEb3XoKmNT=wI@>#ZGjkFG((WMe`kHmMZ6!lBCV4FUzr#Dvbg8n}Nm0+ynWCPHG zo97qj=7?(9w`7Q6q?SO}0wq zFtkcBV3Nti_}Ic_kxHdjL2ZeYN);gL;gbQ>kx*Jtk->D$Z+U$3ry@ymqycb|_wxJ-1q@jE^D!;@-sY#plWZQD+dt@S3Vl~AZ!ne459WqoW^o!+($5^Uw1 zSX-~{K6?%E^M^J*rAo!l=&dAt*U26to3w5%lQW*r%J4|q|vv)K@I4giNz7lkF2T*OV0OeUH{SyQRybShOWn4ta1Hw5xG?X)4$g1aN= zBOe~()wN6pPKXvX`?jrZNzX_EpT#Gz)uMQL^;03$8(0PfM#Ur0-eJ^wwRBJZwPeyM zDP4K0lTZ&al_jry6uMgV(8i7W@65*E>5qJKPo|Vioj#pPmNL#2S3K{EE544Sy~Lc0 zpWtaSD5W!z<R;+H?fxu*$Va7vw7A#K8*tI@Yq@U6`!=7|B5X>Z# z;g+A_Q6L90hJ0V2e-;n#JExDG=%y13#e#|wBf<@!JpJoZ$*C%nf?*#q)h><4cuMCu zMe&h#$qKv@E~1x@JUED#>FbsYCSoUyUmL?7k+=s#{b{;yzl>~wB zdAfzL^QOgW4FgaeGf_|Cm{EXO)bbTe(=#O>;F2W3YE-1dGqn_0cfZ22VI>Q!JcMo; zR&QD@Ra$kW);`DnYqNsH$Wa3B;!1lEWpuH(zD~&jG~BJr&c;9FyYs6vqlemLY?5mf z3@+-}yM!^Zeg4Or&TwIPwdB97_~AZ}+0K?f?}vfsv6luym8mZkXJ=QIrf=S=t!!Ps z$XWFj$ebX$|Bo3*Z+00dTDldb1@%~wJ{<4I37^opGfv-8u|21j5cs{`iGH1%Z?~5B zeCjo=^61|AhCf#IkB)9z9PI=r0);}WQ!M(_iQ2}w`-|y>J>f4Wua`L}iVyCm*cvwS zj;LwU)G+09Y$XH#wb8Ca$ zw6=R@`}WBR6Q0erxpEl}y!-#r^M&WGuZzEN(<(D|tx2Z+_}<-&>5 z9Qcq;!XtMZQb{gDB!YWk7wxaw=IYbtVo_Ok2Wk$wH710FZ{%6XRWE2~9Fm!?&HcCt zGM4&sW3uC)VrT<9z@a$3^=h{+zc%V`bmL3Ze__+8y~ArHyA6LdZ+ThKYAr2MuEfZA zt^6fu8%%s2PpZ*?niQYR@Zb-SGb86Q=eM@p*5$E4);toDjR4e`67IG59+a`QN-htxqncODaYO#2aCZ#-=;>&b9t^z`y$+B9ug7Z@OAKAzFhl^vs_xT12FRl1pY zctX5h5qxKhe9L)!cCbJU3r9S0w9jwJx!r^e-5#QyxRWkX4R)v?2anX6WL*U8lfG# z(=D8yuE4^aj;WUqqK}oCU|>eRpT!uaH)&bNJ^yd80BK!s=fVvRZU6$#ZpRUJI9DHK z1LR&~bLb7>-L&feR^&R);;yJK=-zPh`;D#v`T-9HJtAkE9DXn1Puus|z1p==sm|<; z#r9=M{_+L)Z;wSGF9cUtDNjEl9qE$ItsZs_+{zYPaYn8g#uV)jmMhf0VR#A>? zZkA1Ox>)Lfz_c2q*>(kiDIO2rB-Y^eDZBxAAaP0RiYK5%qmQ1~f3J{PXNc9*t~eRV z)FiR25I9QQ$Qmrqao!$P=O5)y+F5u4tKe*|Ln8=kt+tNOZc^Lhf-ix!5y6{o0q@cu zrR;Fp9(+inu{C-f`6u7^jvc#Cu!m#3-*>E1`A2zY#mg7?QK?V<+o9K;J^Q*(7RP?Z z`xtMZy&jbFHZI0gU8)Iuos zpVH|HPQ7rZybz8`2L{LwZ^<622F7;thRGp`0Ux}W4EQ7w&05`|c<6r#biFz9gH$r= z7!UD*Z|d?qY;xtd@o;Pq5tpEweRt%qN1ht_vCK-^A5|gsmR4c@mD_B0kiUc^Bpe}F zWp}Q`O)B>*uLf$aJ3_FxfC2UGbk7uB3sObhmbN|&;x+>qnf66$=)oqtF`{TKX-p1< zhUUJrOaTE#n!ur5F(abJ71WE!8eVR=R!-nnI(G-ad`~=+?n`!c?||nr10JhW5iUwz zIvJLO{7gb+`20Q;qeh0^>*3+^`>hTxeBAPc27;L9H&1~cq7Y0ZT%Fry15Ls;mnbKE z#cIX(9krZoG?p0B2wM{D0)DJDD^At4I7|m90WlBQ>7eQnLq?Pb$w`z%I*tcQ*?JRe zt!lMaC-z`?a0no@VPg(@eP&Du6{%lDG837eRz&BrE3*}i*j}GM9*ehX5PIM!_*%$3 zlFd~25ygOVnSDro`cpw6dC!x8Iu%H9U?D^ent1k@R;g%2gg`mhB+~>8KmugzsSI8z zrl5!wrp%6$4Rd|C8<&rVofLpBS;wjGs_bayfxt)4$cWhGz2Qb z21%?~2aC;6k!1R?WW0u=0Sjx7^BA_B%lQ$`l)b?_sf<>0rd}=)z=_ao2`*^5U}oRB zBhvi2O z6vrD4W3hMq*wT{6Xf(z@LDdG4rqUo}JStx_5b}c$o4R~F8O;+_iB?jCTau~bz2sGT z$OKF*1GJ?jf{9ohKLJ$Ra1_I9Q7RVlh0(dW-N!jJ)#p9l-E(uJ-wOh*l{gs=gp^Tm zh{#QP%=xz(tx*yY!|^&XnRvR8O5TpY*OPaWJL33?!TQI8aR89YXil`IQ-r!Twsa8x zZo|L%vXPG<4Yh47(4Ixd)iecH?G9f(R&|`eR{`WD?Z->|_EWZlg2XTu@gM`Zt(#n$ zor*o8m=0cu6m1>XJEC>>r9tH=>^5V_*7_w+iuUg45VD?ZWz1&eY=alNf6j@;J@eC( zz^@7P-8k9CrAmK}rYw}caQIb|$Z;U2aIPRYBa>5cCqf*K?7*0Z%3Y~&F6-cOCdz2j z>G1U7PMErpxM4IJolF;sc`x9DJX%RK;+&Z5gr(Sq6$z3n6j0I0$%xqG@mQXUPNZbz zQUt0zBulr{jj~T9k_k^tMJdF|+2zvo-ccfdd7`kH7fo7R8rP=JHZ`~4iN&44xsVE} za5l%ay_@4E5fomS)u$$J9NtKVv6U9jfHXloX7;AIILjn%HaOhOf|n|2a=36fO=AdI z8!4u-CbRFzNYGeSFT%G}fG27kzdo@GEq&WISZ8TwNXP}qDNl*=LNXO=Y1?SYr0h;~ z@{rsiCNmzxgxnTw%rt&9tE#n`k9=&t*)rDNdms~q3^sk)@#9xu!jiD2Zh7;Ym#Wnr z%Q(#%jU87PcUrM{e5d-n`hwM4@ZA09>D~fH^~U>i*~LrJHxc5=A|{Ui!wp6+9}N7j zM&r@#qa8*_dwEB*rLK$=deK%WkGPygPFdDJG7wpEUh-lYpM$<<6zg&i99RF1WZanx(1fk~j!_q0xz~@!FEP z%PhX(V)Oi?M~+N1@<8KrXU-N3Ox+mkuC9Rqiv*Al6g8+%VKL9sbMklab6Y*6vn|f*mrvS-lzKL%AHNM;t|_=*>Kq zE7w+?YjKX|%c;}}T}(S)=t!}SolpwJq*hR~>3t==?x^3B)2A5}`+fobFVOxG4-6K*4{3HRdq`a57d+R%H z2YFEs7V;F^|rS{zHj8v*RVF@9?{Y#dcB>UYa|+PU0PS-LMZW?C zk2FwKI;8uZ{zACEH~Xoy?z0B=ZnC^Hz1|`)0}q$HbH~bJzz7$hUQn~UR;i~7e!&2a z=2UJx45FlBTiY(bYz=kG!j&KGtszdK*)~(pZnAE?%~SAtXa|~2C_yS7B7*CtD*FBC zX(|z8GBKy@a6%pORBa#G5a~4fU?^+FH*24tC#m8*cDvx2F^j}yVa+vqeGzn;d(^e53~lv7j#6 z)nR1Coz$s#2l>FCANk{vKSz>MCZo};X@_p>6(kFKC1Ib{cvYYfKeHD&e#o$BZF2OO$A3;qq1qhpZa&Dv0mNIP-W4sDT3D(W%3t5i0X8M422(e$`82 z5evn$=rD2oWt~6}A)@-mcXhB|Dh?@Yk&B{Vq3$V*4#?N|NqwqH z`LWecdky^FFnc^~;3i=!F4*4wh}UqeH_1`oWbIT|ztJR**!~5&J(pTTvacxn4Qmp) zUwea1@Y{OJZb2XIehb%W-|sHp&$LW8Wp4fTVlz}QSY>F=tKVnH-mWy9quzQfu=6-+42h!_aX+O>)AK8&hv@jxd3+S-H3qpjQq}s=jMOov z+cLb|sw@mG=*_Vtae1L6bo#*jdrqhCq$Q=mc6$6{DXsqHA3~vj2n5{!Pkrq0wUgH# z{@9oM{V%haIl@oI{j91F`Fsyqp@)LDEZD(^Le}+ncX!=#%dYO-54BpC{+)cdU%VUl z4_8Thn&Qncw&S~+h5zyq+LU1FpgG&_{DL^7hLg0Uh)Eed@{%cBPvSDch|dCTTLb;w z)vio+H`BZ6o$b!1b5B1p_mJ+P28w{JA4udadIIN=r|cfpR=;H2Sjo|`x%t^K29s$_ zcRD1l3R6L;*lN^+!Fv7bt4GTvDB5H7ba8TJ2jx0Dqn%N6cM;0=QiSxfdK;w}2W)zD zeqo{2#Q2;D1WQ+WlL-{&7EoVIP=)_?e3PDdQ|-Cj{2*r^GRC#|f^L1h}i@IWEl2Yd?xaO6196 zl@0p<5Z1oTjv#heCHgMZUD{})$|N7$yjk~A_4F6AXQQq4I9K~nYF~eeTxlx4_Z23K z*E^llwLZxX$G73fS}if3LpFp#gmel5$SI}KBup~}Jx(;*2I?7>Xe}afwAu8i_1({1 zqSEHJZF^T17Mh5yW|J=I;1)B;9L9^A3Jt{Xnb?PoM))^1v`V8{4BQU2=kL-XA<(83G!`6SE2E-91L%?RNK)i)i!TI}{{CG6V1@HIMCBP|WiVt=TfNRJ;n!}I_X^bHCezp|%m zDGc=&v3G6JM`(MF$q5(x`ZO%cC;5-4<6CydN0t)iIUXSYIje%8b$NES9PQ8iIIjZU4;>kr9WUZ`Q05+%BsW+NQvsj$P?-QF2 zXE%Ok7b}&b9ghU4%QfeZ#-AUajzQfs141%sX4SUnR5&}>B^uiJiR>gFOEVnd^;=^} zTbFR*L3c=Z>2V$5E(c1$h{j5$0Fo_fnuKH6=cXX#rG4;% zh@Nc~a|*YH+BIad88X-WEz`AV7Yd$1n%YP?Dikqy?-9JpIQ|3*xJ4mf@;O(8y$c8@ zco{;bXa7xh5#^)ZMDM9up0H_5I*AvG&@(K!$`D~b>mDPOG|gnnc%v8~QXE}}40yl9 zaWa`{%V)QXfn+FvBZh7@l@;oS2z|#N$i^wyVR&y7{-1-EN(Cq_2X=W!4lSoRO0?3m zKF)TGZTuF${0VdO1H^H+v$z_v9)y{iq*3N}yrQLyqY1>K< zRGV*+ph!bL@}-0GpC`b~?(QhHr5s5ISVRWNa3nq;WliQJ8A9xizC^a1l!PW=l@uE{ z)Bo;eu!>41XH6w=ff~sS?%V#I289E5!``V0M$uAQy9{bjlk6QxL|RNq-8^QlKM%Om z@5|q3d*GCTwhdVL3FS-eLPj>_@2OI&32xHsbqb-d*;eK@SYN{7vZ|w-CK995(ReTr zO!#qdam@W4!0|-H1Q{A@7sKYas4L|4MDbmTL)HH}L-?M>g+xBUjQ|jsP=AdB!8=#` zU7uO~nQAsr_5OPlTu$S5T(7EW#-cSRlm>u3c0IIoL9Zu(4ss!XCWOW`l8o2lH6dh_ zm!rcry1gA~G8)ZBzuH($|7GR!RvFSinF*mN{x*!?1NN@ zZq2+d`Dvfy-VUI@4G@q$nog89w`X$ca(B`#-HY#Og$@(Uy~WLTknQD#d8R&1fhGb? zp?ox+@K}{8@80YyAfv7+LdcT-u`vQd6JMEKKbUH)^0zTV<`4CPn`Vd(D z46H+%DM?Hoel38~-b}HO#W4~JJJ|)iC)q&g^p2f|v17-XP$0{Sg{)BZFlNK>(Raah zxcL0TRD`{aTb_6132;kXu&o~;(oqkojyy}T?|DpJ76$BDvewmk+@17<+I3{Be&=ah zb)WW0y^4}P{lx>v`+dVWl-%RBifEOVF+IKSR#L+p5+0B)zR(+BZG>!7{2A&IV|I4; z{^@Cht@g{aUW}GeME=T0aK!7Az3#ZM$o4Dkwvq!L?Jn05zbK38^@&N#WK-A))6mS{hZ&e#7p`iA8EUI)LBx6J&2gZ z8c55I*ajRxEHc;z?ev$1ZnwPDk;p%|`EfuK@_vhMk=%F>UODM5K_Wec%L!1`s)YhE zBJ{&Tp<090;j#k4C{zIrn)jG>s5b$U@rg(@9BPsiQxp~#7-|XtH=7cr8lTwLY=+n_Nl9=6wk=}Y6cqnz z4#m=#`HF7qEaUJ;w6)}M#{`8W^^H67XG8JP@Al(N-{3FfHTC*-`}R)fL)YJP{ozk^ z4jkx1IE#UA2kv4n2_7TU!g}5{#Qg1I2f{D2?g@sX_l=f7 zr1{TK|IkyWB6;9jUVp&%8A<+nf=COQSIF~`U;Yg>gdZVq;&SoKOP}VKGFR<89*%O;6@5tuTlY~p- z&1N?gq<*XCUobnZWSvrRB^!pMxa5rnS0yw`Q9GN|r^R*~$Ga!}?qH!<%+szRbq~rF z_myb3r)O}oiwE${CitWUY$A%!-_ocXc=IAE65d4}KJhp2fX^i+eZYf^e+pON!`iRb zB2#H3DI$$W3Zh6#Ez2V(6u}EcP{ND)LxYE|zj8-=yYv2jDU=L^!fCHDdp79ZS-KTx zYR&7L?R7f#3j^);wmbjb6FlWpDeC}91{B>>Yo`u$XGdGDMyr!Z958~%Zd;h0o3yVw zdiI(dZ#{eV$R3CV{d*6+=1tQJ3%$E;)_T@c#wynu<>}0i^wD|mVrJ4;)u7|`HJ|lG zgC^d7!+p7mQP)c6TkjW7p=tn6lCmvgk3ZT@UdQ#j{ zH9DS5I_B*e5MUt{lqZAVq(^L&}?u6)+M0@_V%B2k8uK-9hYMdPP{RAkkRc})7RiB{6rGJS2(h@^ZTLu!YYG}JqK03s) zVF}j^{wZj!Ei8De2fg)Nn-pDMAZQsK!nrM2T5SE^> zZ3OwdT$$bjx}Eg!aIBIGlGq-2o!%_4fO=0VOVVl$DoR02DXo<1;+%Hdtv9dRlCI25 zFxPXsvOn#i?R!^3xkw@Pw?uj+3qWoB2|-wLE?*2lSo!V%;=tkEfp)==A4ivMEK_|00&bZ3EF8|{rx2P6`f+7j>);3F z+QGHd86Qv}Arg&I){>`7jx2{07Up??-8I2eMMB+$C>CfTI)iK-S4)2?WNi6}#wZ4gi%pQn|k zF{wZZlEX`rdLn#_ix9r`1mEh9K5q*$!oh~oq+=d5YoWcfzskr;XDAS6tJSu&3IplE8@`I zgWmXI71_A!x>Hx}+?CiEx1$1K^85cilnZ}Ik2(All2(i~tfzS-_J5!B8%X$8+5WF^$w$0%|LK>Rw;mArp+$mSd!7N2x7x zY~x?K$9k<&30-F|TGYD&b+`F^h4)T!mX8u)1E1h%p%Rz*kf2Hi%UMKDhdC4n5aB(V z-mbMasi=9l{dA}A<*9*u>oK-QBVA?w&o}@w1)I#?Oww>}AJ~9{pj9+Gjhe)s@blu7@Uec2_4u zwNxirz0iEJgw?dV`uPt5$(Iw|GrjM?%*_7%)4Pw4k6m7Su;x@V|Kr9RZ+!67sRwV| zUwrVv;$_p*;6R6c-3KDHN^2aXr@wCGp^?W&zCQAV)(i(kz?dVp=y%LJwZXE?K^Ip; z1g0^eP_R?FOt7eiC>afLcgsv5QwtenY4_U&1_cIDN4#@%eLZbS``2}gr_eE7+{f^M zgfK6DKV37!k~G zyU*h3&uU{8vJ&`BVrEk6Jdw6LrLIVN{Sk?y8|5g0=C~Ax9$m#xYJl_hBoLe;+yIH; zqnEWG7#<=`2KeS7NLJK`h#{Cg`WQ1NNT?XcQ?b)rQjAa0FQ<$szq3Zch~cnNyr+aP z_0<;>%&(6MV(?~I?F*OzQ+~)VCCMpSmhdAgofoI^4Oa`451#{~D${@$}sZ6}kJ@*w46?UA;Fgs5EtYQc?I#hJDGaHP_u#___&i!=30_??h&{(VZ}@ zB=K77QequHGq;TgU`a{~N)J6j#NfPxBGc9o4Qw*v4ft_|CXX*glW^KLs)inMA~NS3 z#$L}R{ztC8%`lV$h@uacupz;j!LDp8;*d}BXpo+sZ(XCny>XP~ z3+g}SJkTC6{k$t495iE z2%$?@lBw z;)-q4(|Hgqe82IeHCR#D_>oSj8n-r{ctV!+KULi`HVy@zrM;bwZ~=j*N@T@Iey z8Tr!5J^fR5$mS&e(wB^P}n%nJy_-FLio9B@7 z5?zUgh;K$>Del9Ot96};dT$1?yT4pVhciixMNXs60Erc&0G=|+Rz@Ne17biy+nh*J z{u{Iue4kXb6nb2%#}qMj>8b9#*bt3|84L*1HJ;ocz{lE0;m!!g>W~8k6kI~W?Ewcf z#)dzXNJM~|TaQn!O_9NcOFIHt3P%Tf37L*B5V674rBcjE!k*ywC;nPOf=5E`pa}PpzH{!Hb*ebjStbCNlY{ zNd_w0#bZg4&C#W8)bFNaT~*8H0(YQd!a*J=mCF^t|JF4tG*d7*brTs0Dh`EmG4?gm z=JGioF`iV?ad7t7>I-v@GhP!{Y!sI%Ge3MOeyQRnN;E-u8kE@|*nTJs`XPjqA}F|L zJm4wJ8t%omg8ovu)vDeLXdMqCv;A-!x-!eeFIvTW5_c4#8hs3Z-@8Fd%K6KVrmJ4by1A%K7R`r zzP*EQbzyJS=i9)>-IJ4(YO2d@y`&aAW_Q|<6?;`<^@cA6GeLFy^5d6Vm+i4$5r2VU zY&UK={v!Viw*9u}<${zu`1bCt_h8p{7gts;8*4VlkHzn>(GH(;M!ibDzy9j>)wjI_ zo$mDA2^f1MRWz>FiNmBD)9PDSnx3$69RW3QS>Q`Ejq8Pb>uQ`Eo@=QhV4i|LjG@P< zgQ@VeRN1)J428`9^U$Fa5a4uo0nNM)WV3PFSh)IT>N~60{>!5WS32q=yKh#1fM#^^ z#8t<+PrT3<*USYg+#|lG^L<%PD#nisbGP|yrIDWR>K6KU8$;9#p(JN11 zD;r#UZ3u75)yFeCc3hHXLVxb{%zGcYjN4_rFo!3R%O6KB$C;U!m1I8BQdg$we%y8^ za2?Uo^HbVssC|FY$q7^=*ZJ?VaP!CE(-K(~xnGjPx=*RAh>di9*d|Iuq^Pa_k6Sms z&KL|oINa{#8sF|gfA|~phxdclER2kbFL@9<6J=8_q%{=$axHW?Strt39BCc-pw**3 ztzIh6cX#HpXD_*=q27=#yzuDJdNN->cI@tG)>@gJsZEm*7fF>pp<53fY81+iwbhev z%;$H`cgrPZH80tF&F>eIjble&SWMr2{CNG}+f%iyX z;mQy!U-He)(Pm@#G)5Fi8SO(PC?l7?*w?^bvT>z3Clo6e9hoJP9aE+yQEW`sp*d^q z*-@{F2A5`LmTF~$T?UrlatGo zA#4B{l)c)ON<$s}1*9ZC_&kh+niy9bpM;TUY)nC|cD63cu%YT3k4(YuLj+f)H+~fL z`%3~#3_p#q@$NuAT`4oOj=V`DyG;$bu5NJD4tW|f!C_=D!O}PA8R_o- zyAMXuW6aF%Tb`VXW-Co6tIQfH5=OnTpzd8D@IbE`-}smjt}s?2l`wTsBNfs2`FHxl z+AEE4N!FIa#-AsW`(~+X(`al%lgnls1a8XZ<^twB>lPr>3VxUfUidCK;9auxTG{ip zvh;{7Jt9lrmv6s6LLTSHFTX~g{F08H1+l-9$nF9dtOnmrfSlV zZRB0}vOX~Kp^=Y{d~)Qmk!M*kRUZ)=X z-Jx9RxM>cZS-|$O42W?;b%CItD4n=OLkUbi%&=HK?boZZ`~e8WBhiozNyuYSao2}Q62Wy!oAk0W0QVhf85{Lw* zSRN1iBeQ{Upu*=i6$sVkq-AaW+4#%)zk=`lGwMimE?jm_-IYLnAr7LbZNv{DdQrI2 z;5krS62v6Y`NZWwKKvE+kJl$^>gdMpH{77!x$(-ACvo-tL`^I&zq%HvJraC3|E~20^w3@BII^`!-yu&awIEsLtHq_&rJO zR)@02#$RJJmWo8CNdGC87}n4H%%yVu9_*l3fM%4q(7?PAX~Mj&m$JGEwWc8;zWn~vfVMGfJB8P(c6H$?#yW_K%9D3o?|+T3bF zQr~~A3-O83Zq3cMS_UTh*f^g<=%i9Ek}8;%m%71tN15PoC%n^n}dQ^5D0&kZy^A??EIr zpIz}S?{4KX-Ad9e^gOSHedC*#uRyYyrlUv#0g+^*)7i05!~93BPSulG{ldQN@CXbX zH6TrJT=CUAQJy@dnn^RqK2dhlaW)0za|a3s02MQas{y<(#YKSn5& z0@_fjk&Y9S+M4;X@rgz{Ej~VToVqgP{&M(IX=-Zb(B!1HpIyB@2O9Ps8pEBHv?koo z5GatRu0ymYswbdzw<8RkRyM6boJ$&lmu@VLR&bYCPfcGYW(`I{sUSC8+kLW>(Fd09 z)^4a*uAkXCKab8(s=Rn^o2Ugo2R>g}MnSdwFyLpBK_@Iu-ti zWGWs*EA$r%NphU=jo5*V)Mya7;`(#Wp$Eu}hg3oBZ#W|J<*$#>*MBBjygIjf^qQlq zv$L0kkKJCs23aEg~NvzWEb~6h-3f2f!SGO_@1CL)Y=*dr{Vjs4bIaB9i{L-ayq{zUKL$+Wx`;xQTvR6I@$1%JSeSa*Ld#i_jN|&nH3Bcc|irTSa@RIV42h@B+yJB+^HCP*}Lx zO)f*bb5`MxvMOrBB;o!DXoz)>qk=tKcNAP+@^O z<{VA7_FA3PT#ql@gJtmFa(+GxJX0wfJ(w-I2QE|YNDi1bMf}K0sQdJn?(Tfa6J;CM z14bLleaIuy%_%$U$BQK-&^Xq+`^i=FtWXo~ zWgVvVledB1FXNsn)c~a>%ASuF7T4F8EpOf#ZDxYPd*U(iFo&QkB5KS$WKw;WxFw-^ ziS&w9j`dpec}WKMQ|;IC6O`nXs60kCn=M8^`q3yrQew}Sg1biHL9}^sQ2|q5Pd{2jhs~N=7bU8vTPRj12(kK?1)Ty9@ojh=}dL_ehkloN+g-d(jGWYjVL16 z^hcwxev9gc)a%U8wA;0?H;u%UH2Xl-Niy3ZOgSQD%cYn%-ly@UrFl&IWAA%kZw0_3 zGv|(eptrnj1`m|aU^^sj)VQ4sqM~d_;d~1wv(6ViIk>YUdzUtzgvdgiQ(?S(OcPe{ zyBYBXI%Kb=@S0aoJohMl4gq%&iM*n~3Ts+1+G&{XBx2Q%qv zO-d(a(zR+j^9ghV+jb&Mx8&Q)bPC`0B6mi`E#*(8J$Qk_5>)rXdn_=0ikbsCNzok$ z0Sf&FEeA9>7!a6-;M1GMVl-ALVy%B1XbJ5Yg>=n?cL2t!^)!qlrVX5&-Z@o`my6j| znY3#ZvS<7RQngSyQJIoX<^h8O16Hb#vmk^dQjnn7JO$a~S@rU!cxj8x<&EEiUOYkt zJ{sSLJrj6Te2c|!*l)$45|8G|VE!plRDz96<@8&hy_>DrmK!BdiVijoO4OoDkX0qT zBw9E6OXJtoZ!=>x1XbP=8M+CGK;Timmo>QWwEZV@K|>Aj*&QuYL^8UV2pgHBrIPvx zp-w^@B#i?a*l?i??q}6}$CW1`{)W(}Qoi)S{V#2|BauccKi=9`sni?y+FLiCb`Rhl z4Cfx+rE#CXSSD#+8Ifo{UvG@HE8APmIP>iYo|Qq~Qyg1+1tj@dyrgftn9jhJx^mb- zVP)PCe+7b^=}SH!N#)J;O?1+DVMs+;z*B*rlx>8{xkrkjOd}$nwNOSryRD(yKr1HD zs|^}T#|-q=Y_Ao?#?Mc$teED?%IV?z^{P-D8^gW;VIyf~WkSWW_+umX^B5I+`S)|> zVIcWE>V6f|-^Jmhhw`JbH3ewv^6J8ZP`xfJtX^1B@7Q`)Is1KbRylhCq3$b;Nxsrx z`I~a)2Z)iz9(coj9?yN^6r4>5Dt><@0C6clQB_dSl18;rf5xfxMYuUEPod*HOrL9! z#c~uRAeinJWz*&Xp~*xBZ96ZGm}AbCT5R$XeedE>hX&nKD-beKqeJL!h$C6z((aSN zSKYJRS=HeSo-WUPQJ+RQFcy32(UQmyq?E@Kwgcl}VIsazm~suo<`D`vUlfz@;%x{L zZ?RO&B4c)h(Ul*owcFC3rDD5NZeyYu^;+k1W}|F0NY%{ecD@|S|8G`J24k8q0aVKu zWv_dFJStTLBSkDJ{91F7xYtiSqe&q=2LzHJ0>H+)kOwov26L{gY$C+>=Fkrou4<1 z`T0vns~VV;s$O1k7b-u$SP1`*&m5H$gns|%nWNj6jbd?ns;8d6^+0mqjUR=Jg(h+} z^u!aPVzJpQ6v^YcSQusFI*M6deiep{>MdN=o0x#>L2$-HIZ)*OPm&=u(zm$_05cMh zBWNM_FzgKTPWS0wx?Y0|Uw589zFilCGz#N;tB*t<^yrKQtXlDBRy zuN?jRIJHD3;idpUBe|hO^5!O}g4Nuz9=0ATObph4i~62ROS|_{=(!5FYJcg{y-O1L z-`HVp+kVBdZC#$b_ufV}bog+gmpyXuInBZ1;e)Sy<$u6G@rHZv zZDxaq59fQiBL|JaV)pRCd++6XE#e#7mDGtm$Xqh;IgxEJ=sv=xCH+8VJ)N{L{2;2f z!^(lhV~L$Hm~W)(N!MXC=}JS|hSeTdGG<8HP9Gv+3bA;)!3;1A|u z-?FT5G)y?x-A-nSG zJTAzLxG3wxcd(E`!ng??yH1uizJ=qdUUy?k`u!7xxG7gGx*LYSX<3wBp>%gRMH>N^ zkIt5oKo>(Zz(HbRp~Ina&DPU!y6c~L7CH0vTTdbTol&k|u5%J`z&`SVJn5 zLe)mK56%`-k`p;Ad~$r{(|%gDfk4QEmzN}AX;i1awF|d5{2tKzg|GFEHE>yqs=zA^xX^8rjFV&!yXVXq z=fhTDW0|GE0a&CyL?1w(u|PkB z5um~Ff~wTh48T@;nK?R2Vw&+Xo8;+p>i&?~PPJB#AK&-YMsw$iQpb)SKc1m)kuqe4 zamd9Yg+gsR?U|%KFqLDez~6-3CU>0qw;b3 zSw#EO;+s6l#z;x$@Lx(*fSX6|9(gSq?ZZU-zeuG2nS(uwxI!e= zidKY;kH@CjL;Rwu@~pe+Ok7aVL-%#kW{vK^Wn@Je_UIj>#7M z8-4nb1QKFKF{5n`d<}=iOKOlTYN&Phptjnr4{PXewjT7(NAG#Gq4ygcWAhx!{&@RN zbxb{r?)*s$JDP)Xv2xT?!_&*U`h_b6(8BHFT0t-8;>Ka5Rw`8sQFGda~Zp`VnQah8VG^_d5u+<~FV{urvf+Uz9} z501F`Y2f|DrK>%hsK&CT+$Fa!71K6<#B=ksAMshZKS8Dr6pOIOEKq|=uS+I_PG&*8 zC%GJs4p}=lGt-KOGKu2MbQ@X&6c4;Wv2c*yX4>V-Xl7!3Y$|SK;Z5+7Tp6b(iaWT! zg~O3rCE`EQ2Vz`o{SkM{v;GV=b>F03A08}*11Und!N`q`;LyP>(ugG5W22=p2nTcZ zSQHR1MjeF?NTo=ocTLYw`p+!Zx7FwEL?X3)T^ONGZ`-c5^-FA9$3C7=%+`2Ngok=y zFAO?A&k7MHFFkvS1Z_8xB8X*$_O0_S8}oS7_LJQOueV!lx2F1&d#<;v^~D|(&#stI z*YBR}-&_T}F;U=Wf~9JY4CF*leE_5W=&?(%aLPLjPrcUf?{=HdTq$qdO*AzVpPgA- zo0*Nn8)gG_hOQOk}r&nO=k1C!lldG z^Dl)!5{i9wfFz^j#x>!5zA@cw`u)v&aEgqN;THMU<1ymOR9M2tXr+p!hN>pB+4NF4 zJYjYht}K>H(StXXMn{{ov6$_1pw@*ZbMGWx>Rh+hYSq8ksZ`TBWi%QG?;Ll*0^?tT zfQbGO&BpM%k)woP?de#>kO~{0I$ce_kpO6|6+*)ZhJvYf4Q=fW=E{P* z<%1q`ZeqO7KI#>$N#^F4`YXu%cy*zap=eSd7b+GG8~_H1ZxdUge8;wcKdQHNpKzp|a(2^xCUW9|bbZ$mlCD3NMw<0~lT@yvxu%V>G zYX_K1(mvM=GmMO({bo3HTdf}0Qvaxg4^rtuAx#-bNGB#I@yK-g{kBli z;oA=xfuD#Y*YG~27T4B@=~1d3 z)g6k3u{oUh9;|rasH4nMnMf)%qzX-$te=i~5`v_G`OkTQs zbTklfoMkQ*M?+FoX7-VAiUC0A>fl|f-6xLEJs308u=nooFx^FX5K~tHxJVxmc>&lLB zd*#80jXe^~7bL?W>%`4B ze85b>Fr4ilh2Y;0(|>EE^8*J3AH$qir9`Rn3D&@X8uxA8<< zx7B&`)R|qKqHQOlk%YILjz%i^LUJLLN+jA8eq?H|oxQ}P+G{WTwF9Nx{tg&E3ZfNZ zF83aKeWAE(GFyWQ;kl+k2>>ngIRC@+1dbuq;`XNE2MlkTPzZX?d7S9bDtB>`a2t+IuVAI7 z6eMrY-OP0Cj-0)(i>&J__%{mP(!R!?;F95;e2pZfLd2pD6~GL2Rs2#=%{lvW!yC)^ z?MjuJj-Dd+V3B%S5`4cOa%jYZ(E`b9dUJWhcbC~p?JVxQG~00hB1Lp4`Vn)GK)USSVI<1R2s9vn2v(&N#D z2UDc9iGIF%plFfk%4B)EJlW2W#ih>^mm$+ok2}%+vEo&@JBaa7P(7XvUhgw2NBdEy zDAiFYWV^C_>h$soMRsvuM~WMNGH{Iy93EeO&1*>t4*47R#g zres|x76-M1YuoomZ#xF2%gK@^vo5OD2!}a~RI)6@Jj_2pm%Wq? zu&u={$7gftFJL>Uy&yJ2hiJ{!;yseCjoX6lX;&ikyOj3ROYfH!MAdDe@iMarj(4C@ zx&EO!uQn(oa4r#kj?&jDiWyK~DN&POvTOf|ZQCJ`hRmoB&WuDtV`1UQwysky;{k~o ziFEVK@^*k&oo!3go>AaO>WE5YXLc6zolM4zrl)86GZT3G0XMy`4M4O$&rhoXD@Kq? z%(&aKl&mod6GcetH@-SGK3Yod-G6peM1D=hrlxw6MZ#C1c(XNLDGK^0E%Oa!a|dnl z4fS1oqhVqq`;iec9ZKv1S<&n24kAr5ylJ^X;PrM#H0sB3Yc~Kwz{!(-*qRRbl!XX)W&zG|8?Wkldt^O|B`+3#Ct!|zV3Aoe&IW% z?S~%x#x~tvQXBA}=sV&QIVUnzXCR7sq|uCncT@&Syoj0tj6FmZiQS9fR>+RFd}%LI<{s&Ioq5qcx;G$tIQ-m9~+{ob1W4Wpim^ zUX)^V8GSgD=M*LrS*!mPf|)G{^MVg@7X7bKNhX+CgW;Pj_&+7COS)h=pAhMRTQo^_ zQJGM2%h3t&Fl&d)5daLqu<8RRIptC3y(ZBg!`n>fz{x2c!qf*ZP(`Z(`MF|OtfF#? zT8R$0hRGFJMlF$m7pH+Y$5=GH$fRMNz7SFgyqS)N%QPkKRCgFMD4#hJ z!BAuJbMJH#8iP@{m#Q>-_mXDyY4t2Fo^#NtwO%s6x0TJQAF|=4?9tjfI=3$4+sLY) zUn;wZ+#Ifp%(2Tlg`zZZV?vp2mv^NCMn-u>B+fBdt%cD5H5+B@CE&krxxRj3bnJJ!|pgXbi6;D~P{AR=W0IpWqSO54#N;jc3*D%)Y&olWEhK zZx@z!k2dP2QD~$wSQ9wxvubCqEuA8nZa^VjxaRWJT4P6)LLj|PwN{4h-}1))u+%-& zh6TeRQG0*3(@dpWxd3JmIuOZ-dBDxdWI3Otv@?p_Ej_pHqFh`a7aSzNsH z9S@D+JnHAvfAG%Mt6$x^)BT~B&1+WL`1C72_*-|jU;XO#oxkycSG;25!Hvf^HXh_X znQWf^Jag_RWq%u>XoV80ouC3e`+8Q!B4l4v>}ILu$mF7l_mrc4Jwh*ZPKd)0G)Bzo zXICLV);}!loCB7RZKkQOYN;mMI=d8V=Lh2B8H z1-`ZMTW`IQw#{!tKAX+;=Im@%$&9kIv(2?u6Oua2=k`v%=tU>@a{1xm&n_(d$CdSs zg}JS*#UK92N8aA;_lZqPr7A*@tX2E{?%A_vUWoT)iXqT;0Y>5ufy|iOKm^~GH8jLgIstcPf!avYjMYJvc0Lv z3)K1^5p&b6(Ri|DE~k?D!ynEIOQv96#S*xc*lYIo9>Hl1hUQgabNCO}@k#7Fdhn&s z@wwyR(VaUUc)+~r@P~mon4ZP*@P}3UhK-FIO!)AJb@o31uss~Ue`)Eu>B=7s21n$9 z#>@d6npJ?{I%!*kc&-tR5%e2={b31YdOFOJ@8-sp!x}v^AG~-QJecQRteuM`yPKQG z!5PXW3%6EcQ3u}vOxJ$DzuqJ2GnOdVssx<@C3?aVG@ZWvEw*>$23JeJt%HIsR{k9Mei7htC&2Z}=!W|J!rf zC=Z}V0QEF{bp9?b&q6`2HHB%+j zA0PW7W>R_TQ;CkHk(Z%G7v)ni39v)nQ6)$_L={XbVOl*C79Gw1l?w80m8shF4LAug zt}+U#Ms2IeM>jZ#P1yX17X@s)0FNE=5_pYl?~aDW!k~{PHtNJnWkrkN2NlBmFuBF&JAlJ5A z%PWg#dp#3A1!N;A!DtNB1~F%L)}Nz*yR^LBNs~$(j41#_k@w{o>E3bnEWYu+Pd<=Q zF%qQ@m*{^kH2l-ln+H8Z&M|Zth6t85E9pR1w?<`~3gs#{uSme5>Jw!bWrNxpO|)mC zhJ5L7)Y90l!^X0XlFk%omk54#I|;O&d!a{+d{|?#YHe+!)s&L#Br()?{*vShbl6_Bjf?egcGTJ8h{R(uG(zEJ1{;|PydejYxD7$ z@0xkqj!)+HDfLwy1z+~ad7l9rd?)<6`jg}$mPhHg`dRQjx>wTmWLsCY4tM0OE>G`a zmnaB1Q(e_6I);-AO>V2{+*&`_o;=vb6taIp7p#n-GFL8pWny9A6xWLwyzEPficB7I zZFBPYp82m406fqXIbGD8(#fIr4B<56CCD)=mIQGuUFa?>bPH*)55$^~Y&L-tJP;}9 z3Q_N2D30Vit^@9SChXya42gg`hco$H5d_2_#tm=kVivHph>&|Q4rMc>M~ig9Q$WKS zPUmKscu5LW#^*hmn$1aJ?8TBf@HInYIdS8io%2H3P#D+s1fx$=IZsV^O}JE^pD&lf z0ESv+B1a-ICcJo<;$BWUuW{oZGZDltrUFBl5J%8$8YNZ$1v^i43n{h$flOu-nB}sm zJR3xlnJP~1por;pxEp|aH4^TT8!a2b(V9JY2!oqvBCgUNVKU~|MJFA`wl3+DLiogF zg$9QQH#>C*FL;FoKoAv*j;6y`dsg#NAG>#nVo^wC(hkw+P1Eq|1xiPp>~R zmkux26mD5F2f-2!Jx2f60056Ro+cE4v{vBB;mVSZ6WJj)002YW$%TMnoHDs8=oi#9 z|4uAKKL8?^9pPLI5Bs{Gih)ZbR{?_Gx2YrO?EKLb&N|48XPyedKwq6O$WNe7PwT0D{LGYCE67v?`Y^D%;9aIeE|Z3d2;c>>dp&KoN$~ICtj$fIUe_x3=X?D ztj=S$D6~Q5b!xQ>+}u0-g(i-p9GV+!tgfz^kH(MBPS4Zwj=fZ7<17+`<2PZpQq9H- zs*4L73PL>mj%u$r(xLpy`SU=V%f~wm&-2UhJW;sC7ZU6K5We^`W9zTL&zSrkyvmV( zI=BMCj{IXd9r4mf{+SCjn;-e_`8ndwt(z)3#O;wyzzx8drk$&GHXNmX7i(d;RA?^e zQ=5B<$iX$nTyqdf>}{s<%gq8ARHOk9mCO0YaxS%@5@%H+oY>n)<(3=yvP%4cKDjUG z^JzW;uNMaXbomGGuxIQ~uIW#&Ik{^xth-)&dUDsvHSJxGmS{aabLo=dKmMjS{new7 z{?*GL`SK(DZ3_B8Xa&=ym23VzGUw0YBe*G#n*yLIA}OL304IvZs5=0gG9fh_(~j)` zkqCDjzu@PH3fx2%g;l!ym%N{A(o>7YrR2~W#;c=0JG)pPKsJ(@&T=0oH8-D|-`{xX z((lL7Q?7aY#=Nlr^iK1)W>IAnh@>3+7MbwOvDe=4vi0>+DcuIMrBW#xyzQ2jUhzTW zzxVL>Ga)cbGx;*Yz3}QTTN^-X=5_Eb{}cI}{ta=DpPTthMnjN;RiqXUVz?a+;`?}u zo)~9dQ2cW6FK$9>8X{%>`36D+gW2?tir&2MlINU}+S+YUb+ixS-@ovf^&v5ja|ncC+&vmwlBYmMf-Qy@l-aJY5- zXk#6ASV8{4%1l;nsw~WJUAG8`02b{YfpU7rsGc*H#fFy8yYWh8X|+vk;Ceh&D;2{f zyc{0J@xqHll{w{!#%P2qQYqFJ6bzb|=|5Pa@?b&NF^dOI1H8hbO(GszNc9$H7Ge(;*y-uky4T!51i=lIGaugWwHKjShzG$9DTYdQbxhy)CPNR{{wL1ke z=yW<5zEMSxacqZ(f>-JMUY|iL9b=$xK~o&yV`;2vm_U6rk`#+$_lPY+(wo>p1ML{2 zy@3}IUpplAy6Dp{C*vK)4tYeD8|89COA;H@we*RPFOV@O9RzRlTP|esd0UqL)B&md zvBOUg)q_c1wioXJp9fZAUyNx1rw|Pv{<}%!_oZNoQU&%wha=%sD8xG@n9qKJ`AnK& zAW}(SRUXMSZU4y&ZU1=7lAf0^rk2@O)Wj)c9ywu}BlwS(W?nsaL+Ly2`L@}GcBxs) zoq_wg88!fm+RCg`^^TkHU{I{$-(gNayk8r>Z+&U>y`9X<>#3X1?GP?BX5>~HH+A#< z-1SS;vqSFsz*nOuUSVKqzm;{RZy8PLTiI5*tx)NE(LFHl26am#phLxR!!y5#qjWUa zC{oGJiQ~82e4NM-^0(}s%#_Q6xk3@6=VPRf>#dfPL=NB~oykAqx{tip&F$gLL}v%d zWc|h4ip1G@(b@Gi(AG$fAqoMEduTR zD!Hl#`CPxPz+`j}$~98)7v_(-@SUFXvo%;|FSCEoL#I-TzV3HMqs@)suuH$0#z)nJ z)`T|n`KXXTJ%n10O0nNb?b`kX)2W)Y#{h`4`z*PF=9Zo}sq7mxs3Ctfnf63&&xxbM|^@sND`H9mA(|2@z%*xXh}W*TrT~_qm%Q|^+g!$a2}42 z2hIZhlWt(tcAa{&jGq&T!$r6$q(~SXskRs*AidXW6~>ZZanY@U$AV-Tk3~|jYlQZr z<9Gc$2n;1*%!YEznAC*FK#NDJm0rJ|&F53)BJ(d9T7r4|61KOO(I=-ve~5iB7}@*^ zAzbZ?V1fDHHnDAVGQZjRT-JZTL~pZHViUA{5@BE|wf$_g0i_TY_~a<&WMw@wcinmC zBcv!*wg@KYj&*A?lWO0;+5}9xp(3CgS6=qHOYtH5IzFP9vjOqnn!OH z4woPFKvrs`&^OSmklftxhaXfxGEmCYY(B%@i&}0$+N%-MdBO0$}$a$K} z&P6?PB2w(#qg;M59^}G(%KVD?aV(Q%ERxdSzjo$*Gf!Bn`(_E*3J%QcS-g3skd|XT z1sE(aF;@8`5Wlf}TK9~`oeY@kKKvR(BhV)?dEq#ThCt)Y(o#S#g9Ln<0y$)7ruBks zZ)mzGwuW}7gLZv#mc zeV=(p0f+h6;oIK!u6JE``d#1)u|gd#(9n30Ipkc49;H{OfsV62ad(uLn5ChhPN4*Yr&-MI#yhMPZpn>3E0 z0@2Tm)_|U;p8g{FVLt)p`CIA5Qwm6hOuxVdLIu?h5z8^D15ap|S|DIJ)PVLY^`E*d z8oz*;J@c3BDd_nK2chBCEz4pJxFxV!{#uV3iI-yWQf;=G&3QN{GajXN%4bW3Vm=XnrzyzXGP_*RVWMf?Cd}#3!f%eq4sAqUT}g-TZx_`{wiQ z8bHCBEVvvhAcz*gd%bj~SSk6+Q3kL?t(4EEaw*7))NgeAObnO^>1zw+$}HnY+N^;+ zJs!`!-2j6VNDP-T(daXc*9b)wmB_tT?h@FOydw3~Q=TF+1&O znt|yj(dT4ez{2@f2sST0Je

    2P)_s*k^L4o2|`nlt-f>02$G2A`cL9QYnoAgei%H zc;qH3LLL)?c%lSBRQ1NQ827G}f|5Ea#F;@ME*4H@Ny7PrZgcd8y7l4bxKeQz0Ib<8 zs&L2Coyuij8uw5t7wjv~jRs$gg%xao&iY6pJbu|J44eD;u|l7nk<-fb|J4E$#s{ z=a?syB_y%Fetuy%YTOlN>&nJo>jdln^fJLE zzl%%V_0g9+_#oMFn`!S&T#okj>K&X;D67~w#u5KyVbW*_zBKwo zsPSohRWI=ny`aV?2UL*R!Y059PEtSO5(*yU8*J&9n{n6am&;9NDLq8+-P{pB7kt&% z{PMOxed-4wJ+gzl?~7xJ!qp;qC#*G7x0`rtE)~z}pS()w?vF15Z3<-js4e zXnR7*5mUS&AD(k-Dw@dXKT`0#cCDFgcTmWfR;OD|1CWE`N2Qp}&6n2KmbY=MZF!N( zxp0;_LbDU;c3}kY8zg0hd?mcNa(5t_g|UZXPfcTBOQ0JfmYb|N4scZ-$}qBeO7tKO z00lOQkCI7dEmHBJ0N^NRrjvdpOz>|Z8^Xc9xFBZOQ<;R5par1LpmHFw1qJJ8u& zM;4wpPNhN=3FtQAO-Ki(5e@$b8)LhPI))fIl6k0y;W&F#U$brdk3I67`I!n9X6RWi zQMoVpX|RuQ^`x&L8o)&Rm0YTrf|{q(*iXyo@qkxK6%)wVWS0OcDW1^&Paa;LA|9B7 z=~^3=(Bb8YUgvY=D+O3J>Jai=y-?@U+8YG@?koV3mPwxhks|%0N3W@CAAS&Ba=+gl&5h5_ zlk;LUm$Q7uH}GM7jG6CVwEm#~jMh{|(=XiCL|wyDUq#*$4-j-dxP`0E2DU0yNA?R} zC(dq7ZjICWB%_07r$E~nvEk&f=U0NOVL;uPJ`3@hZ zdOZXxoVOdT)=+2Q;_ivnRU8|j9`er- ztbFWxaraOaf}$h{)BRDlrzDEOe&TvYx(U+7Ig@;tGsuQTG}o^NgW$t6za(x@mG>;` zdw3YkV>H238K)YRBq+Diptq~Bg0*Q)Q`gqOBR*P}Ww2o1hW)nCPbe7*xw6_y=N&!M zSHSe7;0`k&stC<_x~$bqP`4P@*%fphib!49Gk{L_aDP)!-i*~Dq{6CLFXu3sSdhM1tN4nueVvW$8IGO6P?PimI z#AKxBhZR-{mQi!Z;U8iQ4-vcqE^o1T&E`4~C~l~9-kSY0<}Z4tv9U>%ykBXx)4d2G zRS;#m1TcaoD<`-}lON^#V zbKmhBu0K9VVo=^ejktf~-hRJt%gss+CG&Hw%EmfL$`rmo=*lRo8-Q9Db7W8^fD-ymFU*qxIp!>HY|PK)d{kpm z@PKy)iwo@tVc>8_8S+iSs;sU&xY1r6%I-$kX`$2UnkUGm6vj8givx?TU_~a5?hvO~ zG6<%7J+ML(gkk{I2l}0Smbx)ZVK;T{ye%ZvnP>E*0RCn`E-6=Y?(CgmdJnGzfd*pkZ zw>WPB)y^~sY=9yfzW-Z}O&TP0KQ zGEUAkqq%S$*SPa13(?NWnjQlG%()&x)8f4ogXAamBf1$yr|P zWk$|m&KN7{Z!^7doF5A`Ao?9U*W|xe{ z4?%m>{=~9DQat8Ad@IK4YAre+eNMeX!Bwcnu1p6C6d4Aom_icv7CVz(5~V@3fo_~^EFH;Nv6YxvlM@f9!h9rt+w zbpC_0w|CFo`+LrGY0hUe(M0Y?uymzm_kTk_mxyMvU+!k2@m$R-?8_0LiykYp(ruQ? z#X_`w3gF+Ac^+l`-yglWZlZXPsSczFZp@Cgigz^Lrn*dktdE)XIAr_5PO45Idf=Ph4MpP0niAqgW&wN#7SQ{abHy0fv#6pJ{FW0|$ zh%_JdvgNquOSqR=e`nG zjFEq>SWY6-<9TxKqkGhpRHZq+eY=c*zB6nU^^N=|46=kah}YXPrNM=wPX7KqfdViQiG4?>=?6gDLmK?4`j*=?E31j zypT<@VR!Okl6C3uEu$SH-p(T%0)DNBmw~-M__bB?IqZy=mT|ucoxgb7?jP3L+ntpx zfY^K8Z#jF@%@;0gn)Uh9XKuOW^y!6c;qP~ze3HKl4M0Jw)dj2<@x*z8)mIumvd-#v zZh>LjzScDN2XLDG{;7P1p=jISDR^4RIYh_>+HO~ZWJirq5QpS?Je)L{V}Az*fN4%r zDUBlm7R{#l^}{Ed`10?DbJ?ajc4hT=`wf1foS)yy9#0oa*A`;02)EpH7B}ZuC3GK^ zX9~sU;lFJL2&zr9rM1JKNge&NS9^6&v7|%Jc1^^M??Ou&Xd`M!j(0&4f4N1S6(Q zU2drl@L2lFgMjNu=V1+Jk{o%!U@o+%k2(-sebimI^P%2GBY`79^`lYM1PVr$-D!&x zoTa<`JP~KYBS(uCs|+(RndMSFJ^+lq%*?_Z53LCk0@JD%oI9}uQZiFPjfg}ROaDxQ zz#fb!t2Xch(bZ~din_QSc5SSsIQV&s^K!8q69tZ;P*|7|&jzQBRA3x)CxF4k?x(Bx$SH*{QXk7F|SJq+3f zzj}Q zwLmcnxwjj#p3=4*m_BNX5b$Wc*PD&R3ACx=AbQjWwKj9DM&)y89ozsdK|RSrCICl7 zH_-Iul}3{IaZDTyh2uTh3n0i;aW`2WIECWbT$^O(=HS4Di=|5LESjwYW}oUxNpKsb zl`2|5^Mtf~M^8R{8n+xc$b9cFZtzJ9Wq;bVu z5Yg$VQ^uS_J&Y&YqscbU@jRT`d)-X7mOzP+;3pOvc+6EyVq}P;(er3c=`^VxYH2qy z3xCf~k7V$8H{|P){b;S0N4?>OioPF;dF3+xJwP$#3I+G_QD=vv>%A&`^>Sp1@flbo3t#?Xwy_?tf_V)KSNXnEtbH^R0m00EhbgyZzkx1|bdZEf?^&oZ1=i|qyL zmRH$4vJN7FCGe`-Ng3-pak8FG5&J&AkXC0vN_A^IhZsOe^yM2PP{! zSJvKjWsQB46YLnB$|OE{1RkFGAam&sS)x!x^77VC10A#NGQOR1Y(OxIN~F8M-e&Qw zP(o9Tg0`rNOM|Virc7L9qJ~@85b;zmJBIZsfs# z@=45vH#Sb6-q;AYZM3g3FCZd?s2e5~BE@`=eTywpPb|nchB+=@iYP-tv{3>C%b0&; zs$dXGk%(hKG;*IxVV}`%B5e8n&Lwzjb~0PV0bt`9F5b!<7I#MQ*B)`H81wr5+wPp7 z3;mM9nbAdqVx?J>QJq_Mld1j+=6)ZSLH2~oSwA9q1M%_b{IE51BiZg}h`oh(u?=K@ zRiQy8A{Sw&5Q*S&=2vhVD+=L=fjQS0 zgi`7DM!A$di?6z#I@xG+<7;c{>&ew#6+5Ci{A?&5>$QmnXOPFDtwq$#ZuD~IrVS<; zeAi-bHhby-10K5C#2=?ru}q-;#(dLSsT{9(fH`Xm zqaNcd^fK`$bv6|3krQwQ+U^yU3nZ{6a&lYDoeJl^<-kk2XZ+$e9xJK}6BBT)SL zd?~J3X4YO_zGk{K!)O~6zTr3?2dfmn^&%`oLjGfk)C=+Z484{$T{Q8RNBujFt{+}H zdO=;7I^ln!=a15Bkt@C4XnVZrhc~R$GLe|6m7rc=mYkVCJ@%)6y8G3qX9g{TM1TZu z4X&@NG=9zbpZ~H?N*$OUdLO|4~^wn)i z2S}Yu;|>AY#wP>n4t|$3IYHl}j6W%_Zbu7fD_EmFkiT&1wY^YW3aR6U$W0SOCJJ6z z?~SjnGR5kK)1x22#(HuN84tLtMBJ|&%XPYgL4DCjL7!59bMj*0O6SK1epZ|V+HWbFiENSBTXcS1MTB8yD`#BlX?a!k~P_erG z`H+bc{ce9=YvMJd|a|sjemP&vE zXIt%7o*bZPjd5&iR;%e`lnDbzSU-h7uTSJj7m$a1u{&&(@S;d1=N_Is2v0LNeG(?F z>64h~y;VI)6+)V@^719*Ta5CS&ymcaOZG-)GeR{Po0qIv5B-~y#;ns?n0V4$YCfrCH@W1fm`9vr(H%J5VQG*x9 z|M>52CUpHcb9lHH3YAElwpPRu#w+dS32`Un%iLH1g)EDX4M?u9uklmx++2~+1yoW| zj>UAXm*I@;L^@0(ZW$H%b`G|mlUEzSX+)#hvk0btis)+M-)Z9alZ_M!2?v*A$*(8; zd4iEmEy~R1_R>UWxhP9?p^(8Thy)iI3_Dv&c-4)5cPyTHz%#vlbUN9SK} z!cDjvA_T}(K?s+r&*|61OM$)yN8%^o`0Fz>vsi8l9gIxtn;EleHd|h@4Zqe|+Z8%sm)Dd^q#r zSU4aN{`t~9MLQ-01`Kj5*Fc)obph4%H_+sE`DY^WhvP5>?UQ$PGE6Cs6Km?eUW84{ zr=rMN8_7j>qeM_ZPz*jPaGu5W*uuuGZR>z;?gvFsF>$344`M}7_+sncA*RY+f*SG& zhvXmji_2RW6MMc=EXtyjxq&SMmA8*# zk8@ee+S?;yJWJe+SC~Km@Tm4R*b@Y7b0xi)+~x(6rl?(BH6qQZeOoF?7uWj_ISCGy z$y;p{9Hm`#LAJQgaCAF~0b*S-fAO|o*iA*!QG7h$&k_8v;-hGaV*|mtOkl|EM9M=? z{d+mT0Di1;?FnOvfeJiXJ`^VJtC(_<5Hmk!O0#V=VCdrOm8F(R%ZoVQs17_ikGr8H z5tqjNRYh)Uz~|O8grWjfkw`EtkU6$r=enUmfg6VL4A09eT)Zl_s``KgXmy+;em0j( zk>J)z3ay-nKuhjOCx__a5tt9&MiJj-G*9AoT`UEhjJcAi9$aG(4{#I*xRhfZwn#gO z6cqI<%_`;_@F=k`x2p)`fUO+9HJUCJ;<$H^w+}EC={KSolv+~kaUrsFaaH_fnA`Ah zN|OOlj5hiP0#-BFBhdMTNydkK?T~v?N;JnS?#M zAqEmHm0YockZckK0(XnNV76bY`|WSdQC>LyguL(!;T*FrhAjI)roO_B@=2ODl&&+& zK=pC_>eg+$^T-iZgZV^JYRrh+C?sZ^9po=s(JPg5*}4>&q<>tGd0E`M>J=#hp~`fN zN)p4*Y;8z{ahSBi_!`9&nuB{$8iN|eWC5jrh-|q9&X#<>Gi!>0+uDCrbc9zJR~BoSMi5 zDs(2=mrA0uWKhH42uU#l5J>w&CP>h9dUHR?L3QVAR3m?8fQWwCCV1l}NFAncjo+!WV|#DMe3NxfqE4<;^n$^uxg$}RQM znMavs6iK2XAE?52AS>GtjUY>uWo$WE!UFd{P--B{p^X(e3Ol#`oR-zMZ*AeTk()=4 zUCOd@Kx6=30>SThY;LkIyTHM6wphxaK6=nxLo-@cXs`r??i(v1-`&do}QW9M9a4PFrS{eax=T8xHkYA2K!ckNaz9a6LuPCb}+7t7Fe9%r9lK6_s!|!h?a~EH5 zHKDp(=eIyGC0{l@36JPo7-!K+br>a#?a2m!${)|adGUIXrDdd4%mpUI)ZOp}fF;nO zZZqMMg?2O9J9cL4vDkMXM3RYoE_pD-CR&CY4<#ar0raH64b4vj{nmQwqhpkWkR>s+ z$!s=-S+iPO{5=%?ju+{~w;ZU^?+YQruj>>-y{W_ss|L^9#rBy#D%?6>~q(zVI_(PbJX2jYeCy z<1jZ{{rY|Pb+6ys+xytD`GtISwsva&_N~zf^%^-rnmenjWI(^)tgKvr{heRC?>@De zJ5zo7Dd*+zaLPXcKA#!#P}107x@HzJ-S}bvN92LB)w;7*+9p=ciZ{BKZ_!2@M z2qMNr^nyEXzl}U7RYRPriAfy>Jt1(O36M)N5^BDwt{d~2A2|FQWY8)uc02<1%|Ir7 z#`c8oy-c08kYTRI@LDY*{LI?Aq!zwZ6kiux!8kDUP2c-Z_BS`dRr-^Co0C3B5brdq z7m-Q&q(d0=v5F3MFiJKW8ykCWx4X22ngRiRb8|09UVr#RvUtO6cUn}-Kt4B*G5%cQ zoZ8SQIIx)GN1@Uw%8l-PIcWtQ;OziQc5gU{kW(nU&Mawm!DT zh+`Doyz*mDHZM8D=jJnK9=E@ylq?mC;eI-f0zQ-O9o~dHK>gSwZ@%M>d6McCnATH= z#unuq{{H=m6aRK^?*~qtxNwrsgD7C#UOk>JHyV|gLkf!CneD9y(9gER4BJ@!g#A!f z{a-ZeDQMOc%mpXW^Sl%(1A=7R&$b>dsMmoo>226=X9Ihj?O?Fl<6fsU_B~hZzdE=V zm~x?g0W_Yqt_D}~%u|ouU1_%~RU#hULb2c9TB=qPdBDpW6{0v&Wi*u07Lfy?VzC+y z9a~t0+c38xz6^#d!%DDYl%>$(m|Keo2`eP1|CF%UwbYuql0Iw z#J&QAQwrFk7UbVtUyc%9%tjhD`IjoBlAMaiJ`kygRym{=duI-;(TXzpkyR)&md-thpxu$K`C(VCC*3Qhd zY1fUJ-<pseVY5{qKOMI^?tf!!1PMQ{SA zm|b{UOe%4=fUkMv^Q9aS_Bi;bVc9~zEoeQd>3&Q<+A#Z3eEQ1|W0udYt>LRjAOmbO z&X)?ir$2zDlZvITS);2YLGMf|E`VSGj}l9ma!`P0a*g|dV=aP{m}zLv#4Jos4R~^b zforqv`nJI3cp?EE!U{+Cu!WbAbFAli1v0{n8ujq6h#dY`-*0!5@>?gG2jwijark|0 zAw}Rj4+lW}x;eHjeBi_3 zS0Om?-dF!Wj06;a7$OuSR7;T%Xt*U5z`_&$Dn>tLs5xti&5?B;kx+t|wY}m8e;+`f z`COVTD+Ia#R1`NAjH-sbJ$=D_@?s>rK)!)=#un&~fWA4T8xhZ(D@33LsmzDlK+id3 zLL=&479MIPhK2liIGl$M2<5;iP$*jH4!wLN+y;JyW>GEb)ycw-D!huE^Ri53S#t+!Q#sg{Su%i)dG)pDn4Sh!@&;)l z6NfB?Z@%t=(AuuO_M1L%9hg+95qpUH z$v5T?x!-%R5be#}#OwzK9P?RZQx2Fm$Bw6a@7T%ALk3d&DC>P>6Xs0$c3_r*d^YBTk z^cDiV39V=H-UKg!F0ZbkueIFTjwnKk z{D5&J_}aOi`U$>D4Z|0`C~Mls7IFf=h!ZFQZ7~$4EXyh&A%ngAYEA!}R>vnjUulD5 zKE-s}CR>zKSuQS>D?6=TlSy)Y&1Y|L2e!(Jt>idMm=Ebb?X0mb_2dS>Bz9ztU+vM> zx-bdWg8kZK_v;u(v~JH~Vhc*Jh1Gg;CVPXUj8@KDmn(t*4D`FTzQHKoP-05}Pq#Kv z_HL^b7HE%omjVC1iuI`LDSiJp4JYUm`8=>C~f|eB`gAoT1J}KDQxF=O{1IS zYvDRf>ly(QeTXFP1~0GP8*LsQoRY419+h$}r)Ge_Gh8fVi?XkF*WC zFPc(NPnV$2&;PLk-sG^;n4d?+W9&?KOqS1BSOu0XBFb++&t7Wh2I|t_Xz*+GQ%83^ zbA36UF1F^Z@1y;hYZcRa(Z*{Qy2Au(~q6pu54;cryc!VIjFOa{skOu z@H;&L=I5Co#~IaXh}ZhWNHrO(J>&OT=U6pxa8(}EZUuLJ#z~g`akDE;WY_Fg@MD~d zrw;$oFSK&8@QqCgL5&;3v0STAIj{PvJ!XGv!Ee~SI3)O4tEh!DS%(|o-c1v(O-iWT zV}N>Dn*UWWX&y(y?#`TL#eSej97y$ zw!reNH(E{NugL@EVR$8XZY26^jTUb8Amh~6%zQWsntiz}m25GZSl+p3ZxSlPau!6% z`#lt1$waEvs>2pWFf8Zuv3iaxrrWK?dh=D_UXWy7EqdE(iBf#c*aIO?!bzjlO6n3^G~HIXL}5GI4Qh(*a~+ zmmtsBrt-9ohWh{(<-+JiQ3(4;w<0DDe8AyKdHpZ|oc=z>L7ZPV!B~#(cDu&e+`5=Z zK5+WL$)Q{h=TW@upSmfLy6Kdh)ALW_M^=R?qOY09`xR8096MDFGaPUIR zify#}VF72HACIf|#Hz*k058H&GE*83gUnFFVJSmEMLIQz7pt**s^gvW2OFCW9yioJ zcI@0;2M48xL+-a`E?juU#**%_wsZ5rsnIC(gU0+|Xf!%?aP!XEhhQ9)R?t}u9;=K~ zhWs*~Lhthg8tpYWLI7Hi7)dAnZ7TmriAOWx+E9Xqi>~)jB@efb4F|>yhR1g1_D|o9 zUP=g^XrI%d@!OGz$DyRmvW^UEmqr5K}B%7mZ zO?q-vZOe2UFWIThcB0O#@rr|fe;{$DFqJDR$-6n3QTy^%o@4^K_g-Us(Imi;v&_-DdXi z&noYEkI5c><2@$xUh%3`WTLN8Z!YnvMJpMN!is{=$hc%8i?j}=eDvSZk1^6>^dQa+ zHG;GZ607C;Nce#eL(>`MOK5=G>%6~)j>@mqTK!rT5SMnN(P;Mv%WRc;#bOejK&Xnm zTqq%+qh8AtMyLe=*8_g9(Q0QsGALwIDW0xK0s*KvLnNWhu+46FWYe!ACp)yw+i9D5 z;(G3f3cMY^@rzg@-elh4^qei{gmbg=0_P#ZQs0R@ExX&UAt42R6&mXNQ$@@ZOg59A z&D220=E!7w3{Ma14!=wdYe;BdFvH*v0g2;E!(dD>I?DbyAVD++N=Yk{afhJ#86Dh0 zeg=}M<&&XbwVQZx>)09`QA;Y|4iHycl1Nk8xFmmT&eAhbezHE<2Sd#o#5tm;K^ zf(NtrVjm6@ssTMYY7*owL_OLgC@%X^a4?v?0<}42iy-}|3QD#2NH`tRvy$&jII7Y-R%3KQ2z_zDY)jnj(c5 z=o0{62uU3M7R5p(Vno=Y3^&FH8PIqkd~;?Z0G&uiQ4D%$y>!{&2Bc!ftd26lEs!t* z*HDKon?65q7tz3y`k#M}20eH2IJnWTXuURUfREfA{q)yaM`NGmgj~9)XZX}swf;*Sc z1R{xj5Z42cKZrt)?>D7vbjYsc0bwQkFAg#^DDh|Pz%7P39V0RL*YZe6#Yq+Kkk=d# zW?lhMn}oMa2mtR8g-|Dz6gJ8?9=)Fl8}7JY__0Fs7B!wWcpSC3So zL{+PUrnm@YO$=ppoU>_8!6TA)7+5Imq-p^~(H!zA?dN%k2o0C5gM_>YlyEkb7Zetr zg~`P}amGF;6P1^?FgR!f;jU5oioFVZaiV(#3rv8&00(fyAvw*Qhd&f=!y5x#@Nr%( z!Zbq%M;|UP2rA!0txZkqsTnh#o;X6l6bM_S_z9%|8ej)-VDxGR4+;VXg;2?2WqdVl z1Vu)&HJ6UoqzHyaVE#!o$=k+FDwQTMWD1CrzPA?r0f1`5RZ?Bjfs2hrr5lcC$-?1- zu1}YY08QijPDPF_+4!}MB8(GqMZ6j=nE@0M02$I2ay$74D8DsX5G?4 zTZX9yPO!_osqfIzLzUr6NDIHB`1aTizJ%Hao|80=T?jtMq*@Y#K7sJYe=#V_FC=|( zwK7{$Vo_4WUst|*A7A&~ks3=XsGtlpk=;9);?{b?+tW)~=S5k4xb=tyr1>g%(1stV zm`lXp@q%bmZC!1x{b3^(KwmwZB!>na?n3c)wq$v90nFTl}J}COn?XQr;eAQ zfG({f3@NroNhmRc(H-@Xl$UN+H` z6EO;qY#R7B$VD{D67x^84#b9RFmdoadKzKqF&Sd| zu`eqM6iImm9L1O&M3p7*L}F>VwegM=l@$e`EocVyXW^Lu2!@;ks}*0t^NrUe^$=7( zL^Z&fGV8I2>oX6E1hD}Pu`?zf%$mdWaPPzfMG#L+u77wl%2j0j0HwEUPR>*RnnUeH zRE5YGM`Y0={>n(oL4-L1!X&~xH>Bf*>8<)ohd=Q*3VJcx{pZ26`0g*aZXGm1IM579 z=C4NY;&TI4{QAzzU-7SwVWY?B7iAh6|7#x0%I}wve=Z}peC~Ok$^}RZLnN`>UGXW_ zEXpWCHO#N&YZycj1dV-^>ijayAPYUzF`R%MmEkuTzEaoo)tjW@$x7nl$NRP3rpzR00if`0k%Jw)6z>ZFb|Z*&WVS!9OL zAVn*oeo9qBVLuegz)1W~rx8^ZIUGA$a>AceP}+3B{-{2`3?!t_s-LN2+B zIZQM**MPlwL`W853L-dO5-g-#RLHAzHHxcWB_YY2p&cwxc3y52iXk4gkjMG85Hhb1 z6DD-{O?oDX0zQ`X!uaegK{<>G+~#VtvxrZ)meRp&A*M%`Nkrnkhq4}2K;2Lu=3NG@ zAICe(HdinPK_zw2_(qPx?gNVj)<&oMd@= zrs!+np`ykgZ@Q8|x8<=Md?)TJWAiFvq1)}2nMYQ984`KB(_GN%Nq+5iyLENVTs}QN zS0R5E$-K8t&&~UlY_VG1KKz{$*;cM@2ZeaPsl0ujC=Pk%7k!W3CP8B&Ol^(EcB-=C zlhqSWHJ{87wg=L+wA5p(=j@CpSb=&g?a%>}7@69J=b_z9CbOW*lR~SrG9C=j;x&ny zAmp@2j4AHTcsP#g;CF&#l=jQk@(ylg^20A~1fUL$>GJD?-Q~^(mV2p8xo9M^Aw08V z8vudsr<%Y*TYjhsWlf|TFamkcQxXjuc9tSYLC-nck{KbsHt*)jQf-A3n%EFCM)& zbx#94{S_OFg4Zd%kvbMtq*nNfx3JP4T+*bjizHaUe29@=bvP++ORK*5A_>znle5`I z2^Z8l`C8tdTm(xY1KI@sqWId!G+n{@dP|Foiv+;vdex|{j zSI?A6xKJ}qdDK86i<2*g8jgER$lcD(xP=Qd8e#CZRcx6^7_&(zn-e7wU{Ej;ZZziS z=DL7Ykgp&e12ZxIt=F7yHkmoGeGC8eYw(sY0psAYng0xCKn-Y~ErNM2nirezF^`*{ zG{0f~gZa7>a(2+Yyx4iz`F83pke*d_bU0fL6VF}t1l2VCMc*>fREgvV-|4aG((aWH zK_{qDSXS86=~0@vK=4sIB!9Q!X8kBn+0F)vHT0ZwKm{-Y_`9_AHJ;1=YuVB8Q;x z_T8n)V!kZ{HAIWB)RnsHkbk{`dg+k53&^EzPK-5B*xXcC_U!92@%x0(Z|Drn%PPga zMm+b`Kp#CB70j_QhtbLCz4>yF$rmpMJ)O?B-+<58(ZJTFM&h`(byrFw221;Fn{DF! zCk=IkbMyeZ;qb% z6sYEboj@2xkYo51Nxqx;74GL>@c{S$%#WICb99be9lIV{{fQXHU z6KH$m7Uce?z39p@*cq5_e^&B&owMAF+B&gu#2Rd`(q8>vbW0hsWhNGKiK zPE#Y~e&gL7cAz`*V!+}M;S%oOjvE)2Y}j&GbPw^FjBYV$G!(M&H?U7>nc9K)N8#ms z3dWTMe?Wmna(U zn03R)3;AJ{F&Q;k)D_I}3?YelRJ`MGfJDMngZO3=D*)J$Hd0So3E2vvtTG2xRTZdd zYyDPd7k{RD7j@kj_CsNawcHk?Z* zNsr@1)A)RO2}$MBaREceTJ#+J2)E(2l%quVb<#j!NYH}O1B#1wXDrirfN6$>XX<)! z0$WfnMbKCg;$^C_XuTo5YBEZQk&C_-(jinEFOpxCk`g_Rw1~Vo&J`idsR1wdBp5cI z!jiN>4%N5AVp#T8ge#zcS12S-Re=sBAZNjT1f+<^4l6QQN~$yYT6(A}pdx2d;|swQ zPd^$8;37C%l-F{1?C+uYpp<#*{xzqL9qadHaXo>>>iF&;Sl?=K9#TAWKb^(lImhF< z3}g873po-7))A>3(?1xHuq>Qj*tq@nn|G_VSbBMReZBsvyzl#vOyd*_NciNafx$q# z9j)Ll{;?H$M%KK4&}{7vqR@F@slsk4CNpAocabxABwJj!FxToGUD-n(ThHpzx%)V#lggT z`t#^BK1F=YshPXUJ^bz&6dkA3B5FH}#Sn-&;?fudR?Rrjh7Rx0a2%E<)N(l*6$q0Y z5BLTKpdCdchD@NK^aqw{q1m~bM_m%O%Y}%#5gRMqE-`Ka$6*H;_pz{zs&<`qaB}71 z${a}XX8pvOEqq1Ll99xr2r5*eSTPS^w_lcLb0)#W4cW!vn=BItrc^%s^|-UbuuB1T zS;C*qU9M3`b_s;0S6ayw2{jQuwQshsW_04TYbvPQOxw5%T#<)D#>E$-1gxF+k88)S-NIW;zAfAf$dF74Ie6#X z(y;V!B7+ZkIJ0zOX;@yT+^p;{GQq@_)zlJkos*~Wp2qIwERMXW_Po-GEiLbzm^{|F z+jZX3%WZAtdU_gM8syIr_?k$@Eox+jJF3B)23|)^ms4fBqI#lU)AU(8<87&hD1`Sy zz*G{PX||uc2R|uxt0+5lF~{qjRALhXT-N8{F%l_Uz?a(ATyg(YP@jZNEc;NEWUBD5 zv5qAY{=$uH5qX+8bv%(uCm7@TgQ1lZpR#hIJh#Qv>p@F7X|Is{_NWm$Dev=)2Yr93 z)aon4`=C`Uy|&P7R;m^>%af0`$`NyN7=^Ahj7uoNs&=z6E}%Rx7;W zn~DLncIVOaV9t8TX8$jGomZnFc=OB$7#UB@d}QWhGoOGy$nCScy@s+Mb^p2$Wr*BI zK`x~a&aL*hVLWsO#P`c*lemN}N_Mw{!o}Vi24l_+Fk^YG7U7dc>zb--?riE6g9K-T zb>AapH_b3EjB@xN<8@2I7o27(i2Z%7w1Knit^sNrgl#Z4NvXQMYPLb091-ZmR%sm& zJ7ZoN3f(Wdeye`&4~1T;W$0JMKpAs6bZ^KUzU9_iFMf2jv(o)v0JG$CPxRJ1?z#2E z*-w1ea%Z)>66Nr9t6g)Wj+wFRj_ryuuUz@9$r>*3)18N(3_Gte<~2_EmD_IU5n~=$ zSzS$kp;r9-?BSbVP6XlX|1mjfE4{qFz0y7Z&^V?_63$y8!m6ad4d$w@5=6KPm zK<`=ugXuUJKd+v_m1IyVVsu``Wrqcrg}9}XPu8M?jCx)AG&|T0u-Z0IB-(+0y0J;) z#`ZXhsI1ZoBTU@y}nsAN;?=)MiJAzch#Q4H>&e=A72fIosQ}+_Jp|2Jqsw8H|y%5C8(R zv#n-n?jJ6OoQ<_mXtK~s=%PzXbrv)ucV$C+cn$@wq~Ol9@4;u^MfZEp>4indSzI{1 zJ3k-CRtFX~^E!-P&iDPLb=AG}@d@ULH_fEb#NW#!#E!o>lYbl^R(2!$ovhqy;gfk?Uif zM7LCN4!;bqVs1jmJX_CXLg3-0qQkj9*vCS zO3%9qx0#!y7Q4~)A9!6N@w#^q-UAyJH6;w2u6r)&krZ+6e?>XvSRa_rGVbGe!jYNT z%Z)t7-Pm`%5gYzEH!7LW9(?G>%)uZ1(P!K3Q}^9>${YZkWM=NU$1Vs*_=9}j%=Dk0 z8ISFPI+V`wG855d8-;h<4B1?GcP326y84r9Vxk>T#VG=Nk}OoU8_bBH&;rsXjBkGj zce{}kk^ZK4WIebf*M6g;RK{*oe(^nWm2c@JiZEGXBFW@B(>amjI(7S9Ztjb@>oB`0GB{HAb-2u#xb zRa;xVo&l6>t5vPd7V)ow!6o#_w%3zR3*M*-s@1jNTFp>Xa)PnleZs?{Pf`U3A#|!p zs@Kk&;$lXYVcgMP$VIS!=)nv#D45JpXzG`^jIzcGx=)%zzZ`wNv%Paiy?O4~|Igc- zz)5zOb-#7asdG;4`>9j=zVyCT*V5h7Jv}`$naNBNCX;oBEEBRq!j?otfe!GG8HzbN0Md9idjc7oxB0=x#@~YS6RpGtwOL4!?|5RmqLK4E~bMNiyu0D0@ ztpD@>KmTX>J-=sEuWOp{k6uzKT|MaQ+U$Y7!^?r_>gAUm*^5ROW64q~we{yBN7q(X z>g3qR=4`t$vvY9cE=*&R!g-r{=561{CvM!^gD+wqx2c`j+q?0$In%%Fqz^}?;IYJc)m3|*IgJ5msc%w<~7fA+{sx*c3kkYPkHo2dNli68gKS9OTN#4 z{_m&1+M}Q6hmW8CJcExh^A#|u=fTTjTX?Oj6iL-$($>%VRQKr)KRS#7hy@*q;sUwB znFW=)tjA6`KmL&tDoe2Z&jq1tEB|a6B{EdLzlaD5m2v_>50`%5d;ZFheH&c_YaHzK8}ZarH9H+=N#|q4hq>1D||wlFwW3`P2#}swK|x`n2PuCyTWe zZ{Y=sCO?{}VH1f5C;2pS&Chv@pWCb@p7s{U|Dtq3&qj_(OSUn1F>et8WQGr&w!HPc z2#=>^ON^+eox7(#9343_8rJW*>#p%v9{C_z)V|&lJ-Is+{AjH?Mkw>2F z%K4*#LOH6Q1iBUnkzoh*tO6OLC=&lfL1Q%x?tCZTGi%GS(8{{n zoKr44w=Gn=#>(pM?&_-Ett|9<^MBOa-CYZXFuNB+;WG;hk*%#0C$=`D3k!wZ+-Ni! z&gTlB2b=38DW`#5OdvJzcE8CdDwgVjtkW~CpRlxUn;>bnP{g+?~aL@`iwb2q*=Nr;c{!Ics10ZN|$d=&f)jD?)PBNogQ2^$#`2O9(_6Vhvx? zHAVg?>yEG30568vM8^v(}z14J{=0~ zAP2-t!IZpg7?115#|`;igCSZoDQ~a$sWqKiNv9u|Uwo>M2Se}6IPAU;@Z3@tkY5a}GAx+z?P$C9!TCl>U zr2S?dCK8Z8CT+Z{?`k!qLllok8KgWYemop)r;bQ#_Uop~S0ri5qfVIolh6DyR%%*p z{Pg)p_1*Eg^Sia$qvv;bpMHV`7I(>Ptks^Voj-T({G+w={9{2l9(+6GvF@RXWqk9# zYkUv+zRUMv-_Q6y?)xp~B($xb!U^y^Rv8RKo}8M_)il+V2!VQF5K4&L;)ypWCOazG zRC{q4- z(Sg)z zX}Ee-Cp7@y6(KUcTCZ2N0tib&;0`d$g7zT?n>z-tU%(NV4E4X$AP8M8HrrtpSZh0E zSgDVpQJy0+1&un|Y{babVJX&X$1cUo3!hTLS|%Zf(iJKD|Jo`S4(1ELz#duP^ME-4 z4-^V=Vd(5_G_gi=h0kg7vD;h~1SYIbCsUagnQx9G#p4_&S6CDxaBTNt(3C-(5cH6a z=abHyqE01V1df&z_(NR`>m*T&9B^-_Kfu-@6e4qCH8TD7cN3w3KXi4#_|apG^L|1_K4;o|!h~38Zj+zQ9w`Mq0hLu&@Ga zXRQwMFu8l~+%B{4`0oJ1fMj{$_v0`8wfF7+dmH66GqioxRolXNym!-0d*~L1kTaHB*Bhl}U0W$N>g~Js z-{;;|qy9Q^x}1hhFpDPd!z;v2yS` zfr$@v*a1V@`e;SFymDasir1{vE0y}n<6#n2s3CY~B~nRR1uJ=!x@O|Glv0;4HW_** zlVcXL;h`F#&qdMY-pKqX&4WDfs5R{sH3xBbs01>-2$)oEkv}!Ifba`SRi+!!iqYj{ zlA#%xse}O-5JFZ^v&su8;S_HkQxlXbwc>cQ6k&Jv)ZU_3D>Xw;mJ0>_la%yK8~$Pf z;aVy!pwk)P*tl6hB87B3Tib>gO34BECNBY?K-IUOxkcOIt(Cdrqbi{7MZ%d{vzbc+E%Ap5EB0p^^>o@kFk?r8 zej}G#TU%YtFj2u^b`3+!_%q@+0?4lZ8F=~*dPqTqiBKfM_I*rbiB{*md0VzhF~dk4^u5zK_tZs-KGbgU3Zm z`Ya2++oQUpV`m+#2e^slE>~7jfGpG>qJ}H00<&<`o%0n@476gLTY|L|J{k@q^_yiN zsJT|=*WqNYYXkZe!8HBbELnOkdG*Dk$kLt8RTrx&L zA~K*g2o8vc#=Ca*lC#UT$iG0eeamnJ!HXRThpYG>Q8#?Cw>+kM z`Y_F|o`QTc7&e4P$kTqLhbyyEGNptKLP!zhd8snJ9LeA@N#Kj;Nm)>tD&#d(o2Y}T z2Q58D?Z!cz=^@D(R1QFOv<4vsbtd+fY&Es{kl{ub;+I#mKY_eWt`@!vj9Gbgn%xxw z0YHwGCg$}mxeoO$h#hrT1GfzaOJu-{ zBhav{Zn5MRs@3IYt1Xr7#rC6`JMKm+IWt@NPSS7?vEkpT*Xxq)!A^R#({AE9q*lAB zLsG-miMW7Kf~5@9oIgvIsU*5Tc=S}NG_$tWmV@fW`az*S>$*yItWPOF4+%nQzVX{55%cBnGpJ12!u$-N@cTA z3e+%<*d`T2NQS_7mvbxie8Hq%52KtZBAJ}wE=vFd2ny1KaKaj)@rSfjk_xygH32^= zunb9909!>ui~%BwJp~V?Q-lN-ojVfFc%_40B<2fKH zP6>2T*YR=6A%Sp#`0^)h%Y9U?#|o0qBz{|}z^CH5e-@~SACg=cbz=b&BO3kT9c<@N zotbM8Lz{yjgeB(w*ZMAcL;-#4#AJ4pAQzLq3OYVBById>szWBbO2eYxN)JasGTNkP zBZ`L*!ppEG*kyI~EwZ?@&wPI&11u|cTVt-;XjW!Bt#*DhkG4~;ER3IQ zh~dZV{Nkqx4_CBDq@q`* zS{;ot8RxAb_>xFiS{XFGy^^c(JW~xm1F!{;X^$|2mtdk;X4dslY*+4*!DhK>Zs(xR z$7O9GRd~%#&$UI87?Y_MP_RAZmbLGHS{ISKa4Jw{+(bdEfM6+PdT>>3Uu}PoBu#I(U7Rm%*F)wuslUFHcuYF0SF@V1e8jKpn-qB|({Bc6Mbe!$sZw zC{bgwxPDN4MN(n(b$2U>H`JmVYjxDryT8{>3*8xnWNw=Slx*2b{=O{C@hhcth75oA z4ks3+=_vV5?_~?r0+Q~BdVsfxLFjVuG~4GQQke8o#}e*mhpJD07Ra860l28mY&24bM)*F z#$wKhj`f(lU06ll3R^#Wzn>U6vDgo0b#78JfsXUHZ;f2H-~v2*r{MdJ7xZaM_d_M( z<;^FuB*V~~Exj3qxkHUbHTa0@snIAi%9^*&q-V$&m9_l$j`+;p-nJJRKl@$CAI(x6 z4U9l8KA=Nx5d=;ac!T$w14@L-a0gVWDW|3RoIO3Fnv;@s9|X# z>WHOn>|{#auX3_rCe86pa3RSgeVG(4xj;pErefIB{xSob^)=L5`&Pt*(oNJLP+>qd zuqK|krnt!DE9O~I1b7;>KAw7oCYA@GIux3_8fD8kZJKIp+_$nuq~>G`=h-W?-6yDL z0+C2-@XW%SgErVaCeBhzx2=#J^xqFokD`qM?JDqv3=85W5UR%$jRbt9k*ulp=w-2p zb&6C<+Y-t$i=TbVwTn)m&h&=@+)TgotRyQp?qV(t20?M!CKNb`HS%ZMjdTV)uX`*& zp(dW7I-+2>IQngT2+|QuPU7LuyuKVos%{{iB%G=sX1E@Q1Z7a1q^k*fM)lJ;4mTUZ zK9`leJjWN6yCD^BfK3ehWAV->N}sBoJ+$ZiWdU<;9oJtAjlsF`FG4HhFS(;*UiVa; z<&SYx{v`3DST{lL?cik_S_~8H>Rp1IdT6OVTPF$DDi#~j_Nj&!#hV!9;|CtRlu}D3 z{`x2M9c-~AtbTp$k+*RV4X#?G>huvxVm)gnk6As`-u^Xm7fmk?#nYx&OoyIKld_w9 zo~Ut1S=Cp#d9g6SVDTLRWIofMq)9}a%Q~%uw-Hq(7Uu?o@mG2OSjX*tAr3_mh1Ib% z`1G&~Kgrs#)ykIO+Hx5W7Dj}>*YooaM6pdd+_h4+)q*31Y^6eB{67{K&|&hz;$MmN zbmP$8t+(zSGUWdkIiQ^C`QN#OQL9)WP1}hB?+Z#^bdr>m^}W6IN~y%agDVf9V%(uL zd!g8xU0Ry`*GMK27LHC_bqh12-MKkkpPSp=ou5;(e7{T_yr4`>HU=;0-& zagk{Jt-8-E#D4hfYpAv0p1k^+v+qSA34Z{C$2AItxc~O&7dzt{9!xUT+FnrDwPMry9M(duJB3#!`1-wpD7DTXweOmalK7nyIT(xm4~% zIbK4QYDDUbp-!;96=iPcZ+%afZDjFf@E z+eI&%?Ii#_7=Gc%U~uGWEe^9iYQNjw5?-cmXGQ`=luOhvDlm`l>>v&!u-z8!hCRi zh{77la4Zg!1;s{UG3*D!UKRG@xoX8+w-y%mdfiYa9%l%WJCRApvJ|tI@__2-YVJo* z5#L7~#ODOhfcM5r`@v>a4TK-gYnAKg4;|VVrN*y^h{SI$*H-lT)z$7y?3{ba(Nm`e ze@Au0wca7K(}o4{2S!&U8_l(~-B(_9_1ap%(=)vJPU<)K%r72jxE*u2{I1J-2&s_t45yT}XIqv&EuJpTyAw4+D7KACJeN z7^E7diyMLOOf-`eo|HI-BSyyPl54I#c))M&$x-peFf&;O!G=s`?N4THsR!XW-5@q9 zSsOMzjGPh7yX0I(o-@W-Y9<2F0?U$Kv>L6NtZC91TLx|BX zyfa3;;4oN3Y!bliqL_niw5mEER|RX?VJhO?ixJBv+MD&CwCqjGJVNz(>uSro$+myM z3M|^@A94OqF>8MoHO>A5+noQX9Uwo{+K>%zHmw^1)(2%n>;3wVoBjpMIwXtSxov*PvgUr&G`na3L)q{K%Q|jakCv5Pv3rmzMaHY zIqAERPip?>4yN!67vRIHgtd9-tOg^=15{6y=j*acoIcE<(;gys-&MN8gkM@TVcAqV zg7#fxk>m?0vR4Xu$PCtTVXnT?@8;@NqWYV+xaDJdt+ujC$oD_kvOpS$QAqPFp$*Jf z68!G4v-TvkzjfGWc&6D}9hx#NCP%w)?3G$ge@R_7NTti}-QAdR2de~kvHExs?v5~p zMybU>QV2F`4K9*o@o|o`@2KjzKcVmP+@Tc_4Wq|>d0`E?L(fm|W$)26qLI?grIFA# zc~>u`n6jkWWU>|L!W-fMas{^K^i_~jj0t_5Ggz){~$a^sQ1Yi3YzC+_ zK1mLl;1?LN67&tA!b?^55c)KL$T^*jd1|L`<`8M>PD2_JWFMOhCJY-5q`a4;6{>WFZpvDtn4K!yCPI8a< zW4R)^L0C1VE)j`gVg@v(0qj;0jh0i*_?1t5F?+(oy+PVb=iZk;t53u zo08%qmnhdxGl&JGJY|0565hZ>h}t+Qm7Rnq2up+OVB0{@0Vd0Tl6xGeKeqO027_2VjllQ z{}($QD7EEv4}k{_;|2=OLEQ!{-F3a!vc`g-sZoktED7ytv(O!#N(Mr-6g@#4?}u+3 zWo^gKoEgR66#wbdKX%_8U#-6J-Ko{3PtM;Har~Wf1n3DGG}+P%R~%{QBT_F?D*1Jb z6!$t|Mwfa=bE!e-4XJzH+&OV;Cw29=k8TMj+u^()@&f{G{D{y#if{WInqHT9q%gAG z?>i3yWd-3sR)~2euSn!nh^Q%Cjgcc!OEDa#C>}NJ ziC&;Bq0I_hSHv6y+(1`kH%w87TprPhzHS}k6k0Ncwr))>&Z9?lZEXn@##bMMm;BC1xY54z=n(@9 z#^RcGAYjMHU__?UQzIeDDh2}N3du+EDBz#}HsaY@0y(V38;N*9*9%=C!IPNg2A_d) zsL!#;h8FAu;(A&SLi}?u&<(_lv;otE5euvbwxmbm;FP||0C{}1UITB0BC=Sgs#m!w zdI@8!USGpql&CMxXd*jY^Z2wbhHv=9Fqs?u!JhHD|89UZB}d8OIC-=+!}#-b+6~q?My5o zM@8;i#O@cs!mRs7m&${W!Y8z#mk6BlsyxtC+hhPZ+EZOCW1IwRV zK5*vFJ2Uqh)_103$(QT;tMGf=dC||0y-L?#PKM@W<@gW&&s1#u=ke74E-U^`TMY%q zKaGU5f9;(jzwe8s`|?E%zFh4$Bc#|G{tdVw5>1%PB1e!0XA~dBy*#gNYQR3k6H^ig z$BNRVP^P@u7hRCw$*c=mx$t3v>G1+cY$ZJKa0+yxbZnq6c+oAkXK$e|7hF3OIZEHi zz{D;nvVE_q4x$`6-buRJd#XudmgIxt5`1sesZs+s7^f5;rB(T=Ubpe zpZ2buS{;zOW!F>jCzHAm(xj75#$opp2VtqWs!l;xEQeRHE+${nHxL;7%VlsN=L|Ws zT!>$idfChz|CcABh=V4H{U}_h>sxU|hCdP8cMh-a%O|zJ(tZ?7dXFmZzwG-R-)DVa z^!?*>9uUbbt)`yCBsxcJ3klGBZXXyE_#PEk^eWsx;5`QrEh+_$P3X33tClLJ%VzVs zav}CXgfcjX_>!vHmL>;7dN4Z(#jWVp!go*nQ@%z?+tPWQLswMH>HC(h0+0gnnw-nB z=MD5f(d*cYn5rsEz_HNrWH#aK&wOzAf8g$`H4;>JM;q|vOqMrBERTnPK6>k$o9n$2 zbHe`Rx1`CUNBgi%nw?fB{FnN3o*YqUD{v*r{Z_`N7$XW^FhJ=k(arEBBVNghMu|fi z$B&FoWa(Cd9B2%77d1!@#q>Z(AP8VDa7X%9d2l2HpoDNHfWak*3D6Dg>n8tyac@Y0 zk6L3mJalL{Ysp(vd_$+A*UFlnB?%2(? zjBKVHy3_RAEjmcnSxrvmgg5@dRwzYeVv*CevhJ6N1J`cF86bR+M{djVFbpSyFW9;L z2rG$&R&T9gLxF&Z!!r=)u7fEjc=&M8p+sna;+15aN3}po>!Eq-*$4wK(>_Si8Pry( zorQ-M-TPo$)c!HQ^X&!q6!T}8Mj~&Rqc4EZI=i=zf*K1t!8P~oRJ|y z-eGA0KfJ77R^K%nJWVocUBdQNpH);CjZkFbQ&o(W`AcovJgc{h>fA z?}TmbU17?gW6o4Yw>OrSW@b|B_^AbKKoRal5*PtPBOEDLM(4J7ivP5fqhKs{2HF~t zn{xFSxOzDcLWNjlEQrv%@=DvPquW@+y=IW{A-e#%*K56_1oiQpKJ}mjBJa2 zLlGFZRozpjiDgaLk7*QEvIW~n&*nVh1L(axPh@z61dr9jNc|R8F>W!mb$_JJTmX(S z9L_hjcRuH4S{toCthO79Z#60t>pI7!G|=L=L0x$BM+G zf{roqc-Xi^45(zxG;&PbD5@x$LfatAu=dHjfA*Qj52UlF+w+GPDPpsDo8)(M z4YHTrjcZLzA~{-jh_ljD!`KsaEVfdd>_jK7E5{sz%$3PyOXIt$c2Hl|v?oeNCR9c^ zA`-K9B%KM6|B}I|Wpzcu0}<#M!cHb&m&1-;{#H^qh0*{E*86{adFbzE2C-zf>qO_G zS&AyehMA5z7+77WACI?NZe%XvTK=$MCUch7BA*_{lf_P{6pW%c=}FU~dYi3Pogg$h za&`wn>371U+Hp|<2L>kKe#MAml*@(_DM19SGf}tMNyOLP8B5Nb zT}#HBoosA2p6Rw?iSW=CgLL@^cL4}$v%<9I8e;I%7cb4ms z@XFRsZ>@q{@@kR&GBJiDU>x3rZ6ZROyH+onl$Utq<)ttWSmR225+!E3*)+FIT+!-f zP82NfFsfl~Yw{giplGUc-kiy^bg(eh;fC71b#MfB89pb{eYXJO*luo)v<=J5I=OuO zmkbn3W@59(j5v`&;=T;RucLgabF_E1Qs@axLTYCs8_u@dOU10qvqX{k8ITU79N}ILxph=d zkoKKYxsZ(nlSzl_!8->JG#hw|tV~HaW=EsdIwJ#iY$;|POXHaOa5xe)f)3WNR`r{u z7_tccf%*-;F}i62e~6Eo2}5aXnrBN(l9G^aO8e4w_%I&6ZucC9xHdm`@bqwLsoccH zCPib)5B~ON6ZmgV&CZN{zfF%Q6b^3Z^4b?UzUN0>;`pF$-M-Po>tQ&}x4wFK`lj`7 z>+SxSHupDLcKqQFv#rhF0d#`HR4Y9IzVEc}RpcZhBhyw>9)8w#1;k);fnA=d8h;)O zDc7f5C-PK1C0KgEiUcK_%3tU`Zd_NAIn9LMcbVz&#E5%KbZPd+y~%rwY-wbX>R7P z)bw1NQp#vg%hGt40VXi=1LV+gEFtuzDgx0B{5XLO34i`@P>fjCGXDoc4?#FU{lT)7 zUO20FguW(iBr}D!rq`TM<_?DhylhMN;}Y}x%c1t)6t$sNI4B@t`~lJ5d$xWc7xg+E zp**SRNemJKxxmBFXb__k3xWvEi6NWCmt>HsGsr|D4=q#rfpjE6z~f=ib?W{y{~oDc zdF8DHDU*8RE}oa)S0rCU>I~(5hkREM1DM3^g#|_pA6nldd{}d3#-b&8$@JrmDFSnN zIM*&<#~E;j5~iMLO%9cQCZNT%aIgLhtc7DFog_?<0E414)v&}NKo5p`Vv%bEV#|F>&$G`GZ3(HIOnSOt% z)owe9<)sDM2;=E%{|j03fNzT#=hg67z1#QwzTfcusqY`ioNAJucRBW6v2#= zH`vbdR74BFJn?!pHi?RNFuDe45@ZoLQVokDSa_1xx^3~Jf%5V$MMd7Z7*4MMMr6`* z8Im9gkqVozt@o|0?aL)PHWWmSrY#INJ=uL_8>ciI?Y}(^w|`HLD%z*oV~vy*wkC%F zl~psv%2Q-BKO^i29a8u~`#E%`kX}FBL34BbpM* zB%{TUQtEBN+o(C+x+!D&jc?Vk<^@0VN7|RQ$M81aj@%KC(KJpkLPmzfL~L~Avk08& zdif6j-w-ke&s|Ix76Bvg-Z7?oOph=TT><}r17|PU+MMY|Vsmq+qh_&SM&O%HWhfKq z;DrpkF+i36@JzFj6?BWEnf2PXiEB|eK@DZyYPFIhf8U((V+3gaL8BiEG;z8fJv|y3 zAG>pJFB-_@S5^#g?h0-eg?C#9l)hf z)y>T{r1go{pO<$8d5BCQ z^72kCU&wc|itoHFxggQSFMOjm<~vF}+1@ox56L(5EIHovcC`;F2RlrnPNrO`1Ning zt5qi>Wid!Ko0Y8VLUB&fmReTEsa6?^XzVsQ+myM(-;Z6tfWZQb)ZE)kIU$}E?%R4J z<674JfdCjC@br~z4&)43GDVcSsLM~393@%K=5kqC{v|XC42D1;25iaBId<^QL=1&L zet$GysK{7xGmSb52MCLtZ8geqhL_WXeJ~ym-Jh?fl2Cp#*z3)VYnl*S%1<=$0M*T1 zx5Su9H)U@>WsO3(H3N1_FhH)``91-QF|J95^!{V;gJxf>V{3tckXUjR1Yx9FrSy|w z#4?V{Tm)TMFgRbSuuZs3v>MLaB7`=PT5W;xuE#KWsB0a+2A8K5C>Fb@L`Dft**_)O z$IQa8`E3LM_;H=1aG8j}gysa`8V+|T8k`GzlJ;|K^7<#NklF5(NjNY*4_QF)^cAMrhUY0h7@KD?N z;1&7T=dbwDnOS}IYV8%Fk7PbDUi(Psk+k;MxOU~0+SB8oeCu16tBH-!a-td>iI3ms z8*uDP(T>l;{P&x_KWDu9+EPOhVtOX+0opM=(^YSn8C?k$X9e4&=+MQ_KBSMM6pQElE zFbP?4+!n8(z?Pm$Ky%Y7CXzf8g|mP~4g4a8X}m_=!)P=ye%RJd?%$83mW;*Ji#XQP zz{Ycs-um^wgT+^ej#eEFEs)}bcQT!#IPn zuS^@oyGqUiVIC@~hs`Z5_VesW0v$V2OOzcktmF(@E*h3IX#NHWt4L1&KFOiej0PFa zh?>YZJoHiYv~JhQrYK9&qP|HTN}0W?UY5Cn{`NJmFY9QkS8I2}d-+a@=^h07FWrMD zgjglY%Cgu@6EUp%nu^gXx#_J}@4`We#bs@93zI3AkL+$snL^n`HuI{;u*K>+x+<@! zTSoeiI*BD~5mJ-TlNZ49)_LDg`kJygWQ}~5OQloE1$fiL{`CV1cd?ax;}G%Rn1BYn zJ}X{)08T5W?c>CVxro=^)R-TC9XlL2MwI&A~Ne7fYKb*Vn077H>6sAPSi< z*fRvgw_2TBDV2k<(Z(*+kg$f zp(?BdjKh|Qmu98KLIzaGY$UbVC??1?gp#$o!FppLvZHVph5c4K#p4oYLt2nLv?4SAd*ZMv zT7Zf(Y%Lu|TdLpSx8kI0rc*9^StLt2*8D>h{^S0A_YOYt5$*h$Gvo8x`P!#HJ^rFrm$*g( z9NA;U_zgULWStCqU3+M!-C^3mHDfo^zj9fp(|NM?*kiRTRtv8xuDtaZU;5I2JTd;q zm%U8OjQ@RaPiu)>!%OJ9o_TbJIXX;rS=UzscYcTOzxn=^W6FVsZnF!&Go&e8nk`)6 zxcG!fiv^FS2v>%RWIrRE7^2Fjqy&LO1r8+C#P!5L^^h!Tf>RP>X!nPb-^4qdy~;^N zpb;&B({e@;rozK3_{a>PPsX~4rjB6Jn6Q~9nRY2KCO#|va5Zw;)qS728oY}az8|V1 z2r9YHB2z5~lXMzxvw3%VJ)PXyNiL?lnb-1fF}ZVPJpM}A+ez7$Q`w+JlQaKDkK|f!QUnBfW`RXxw!`) zymfy5&Iiw(d(eOW{QdXeABprXT~`k(+K&(jC*rFP@Cc3QhbQ=B#6BW(JExP@ib6OLwj5}#C< zndycuWzjisBFj6yPfZP?*ztMA`(`pt(9vXuW}Pf~Tfi)#wh_8P>124}WeSty(n;0y zLU>TRsEQm-jwCBMG2tWCGrdF5{kc+gdUlrG2`>AlkNOms^mIwF=QN``L?m7hK&<^|6(0_NO` zfQb*Qtu=y`wn+g~Nk$DunN&pTBJ(W^MUUB5gF3;2MNXt66LNrRhnxvHDw7mFoiDMW zaI<2AAz!gfB6cL>0wa<+3ONj>LFn5tO28Q5(ZM;i~p_n8&Bts{PYlJDCyN*T6NCYkm zWK=30L-jDRS8EVel6Y%jo3`8a8kjE8w*^xr9C$Kcp@%Txpvc19f-^c62e6n*1-Vr- zkk1Lvj9gzJo}dDb-b_>TR~lIibB>ghI}7rOF&${8L>F?il%b{B^rZ0q=s7nozCLM2 z+8RF?q%b^Oj*67PN33r`ZDA{*zY#EVG!>~-G|wB^jObf5E0buvP&D3|MI?suSQ6kZ z$n(bg$Yt02G(t-VP*JpQ#W zk1x}HQU1RCWyQI8m72EQWL#wNx_PBx@%IXphME>F` zEFvRSUXsS*0l^rllW{Bp1CW;p3*-yRC}SKXraSN=p_5%1POE*j%%K>YOh5>iZXd_2 zQq;*mfHp`bK+Enk^nso2KbMji`_9yM*jt?67oYJxolI&YJ?d5$wTG!rfTtFQk3gin zxp`u9vr3U{v9}m43?ORL$4Rxg@Y#rTQ4Z-hJU8R+>9&Tkv6#g}gTH}(JGU7MW|Nr) zE?hgJ6)P~I=3})|G0GrS2fX+MSuQ*GDUk@?fzMHC7=tYp*5midrRDP|z1r(v z^{P)6SB21v;ettqZpsfX8-dse$Ob2lEXX&<3E0yAXT?EUo$jLh=P2Za-5E!D%;&l% z%3xgL)oj^deq;pF8Pc5@4x=~zxWI3zoIiq-1G=Ojhe3o zhW!@b%fT7G1Ee9@tV+Kb4Exe$`gAwf;|L(Gpcq+)wlg%>5x}ZXivFScMYDtDITU#> z9SGw{vAWVZ<#0qqIGn}m-T%0VYqjec$HBDmNFk_q7qozzC%ui(SuPX@iW?`P1~K{Og#~Rn_^yluW6g$MmM4Y#0*ZYaGm-gR@@FI z`>Q3^hl;u!UZc`lkS%=7#fp63eBLgqQfA7hi3*U+l5 zndGF6J<5eigUgEd>&x{{yK(H;_3P_o48uuRuS!~9Jf3UK&9+Zod3HEZ7gn$1!USMe zrvh2>ZMZP!<}O<au(zW_HK^1?A;pW5|R7Kt*bKC2p3E=xYE+mKsn5GI4;YI+ai8A(sRBnAhiGjX(z zOJo#5NOG41uhv&ZHmVy`%9&7PaR70P$O9A%qO^xX-HK2ilG8F#za+vZFDwpq8PfN8 z;U4g=K+Y@|%fZ))o6A^|j6_mBL@48`QRnjP7+K-n4127RiRit6wea$8LG7+U!g5d< zW^9I^QFfD8pO~IHE_-kf4J;P6o7_H~j&&V5EXR>cQhUgD%uZArl$f4?E<630L3%?t zyuDYeEkj{@^-K7~n&p1GL+X))&(O*9v)wBXF~NVp?<7UW@*^d`DAg?{cd=24h4fIQ zCJqk2e;Io9JjIe3lHn>9Ge5&7;8rX$Dq_c&t0AL?CKmGxbyJ=4SAZ(0$s}mtY z{V0=Lg=GzUHvHK90N4rU0p+)v91-nE0(Dx08OqS!fG?7hR}#v3VT9^L=HPg$CL@9d zV(VCWQ=MBT1-Ss$k=z*ktSqQs?NSD`ZI!?ihp3R0ut6J5WF-W&lv8pkh1{YIM=CRovHB_f{< zhR&R&`PoO)Zu3K~K_E~tBTwtQ_ye*;0*`~@`GS@uRQd)+>pChv{hbMD(r2ZIVkXo5 zRLNX#T+ulv-JH$ZN$H7oR_rTCGzEOUPyr)*_CXLn0+g5I@RBcFk;AeT7R+Qc z_ViFvce8`GSqPuSiYSxR$jbdg5zKu@k%+zV2Q zR!JapGNiprlXqrrvD4HGApwyfSx)3g$uPxy;$5Y{Ok{y1DwCV4B$N^aVyb3Do;|b2 zdjCy~mJDMgSWL#W_uDq_bOPwuq^6!wx6M2kR^|`Uxf2pd=f@AkNxBMNF*_SJjeJhJ z9MB|;23;%D+axy`r#F<-+MA7d99>>IhJnt`2nUixM<%ij*8pQ(e22kcJP}~nz?~Ii zL4P;AfQ(^q6#1~!-7H{;2_QgAk+CUV)spD{JN^E>V_9crrdSyDFltCo;j#utYE6hk zITOMn>(;6ZUDL3FYm~o)$1jDqPmYkLh9OQg9APrHsNTUH{3j+OC~ur^&|yU*2sIRh z7-t3_#a808lyhV4NhX8w&~w3ga=Ia*Q}jlFeu`L+CAspn9XPmy_6k;+Pgjez=tB5S zGP3^;>?ODsSdANl>pp`^PKf_xc=9g!%#zG4twyg=t{V|>7P&>Y{;WJaihU7tv9NWr znLN#@E2psxYcj$h?hz z5#QjiYaf93tb~5*)9Nysp9Ky#N$u@{CQB0->l2OB9vDAfAUH&+ih_0w$tX+t@t>UA zdG(c__|V?DC$4{;_5t~F{P^@s?U?Lv-Tn7J_~7_o?!EVc2b3>|+?%gyzYe=gm2=M$ zpS!?f;teGw3%YVi`0qLSmd+aL%&1gz zd2Dal6W2yQSFIRE6(Gd^b(Ja){6b!e)IIR3JW}X@SF-NlXr`*l9cRv-0e_+jG5&%q z!o8#7r~&=W@D+ur&3QXo-gkq4KsWWOae<@ii`=GGirs9G=%O;nVO~WdA}G;SuwT3q zT_(oIM17)Sg{V4w%Ojo;dntkK>0bavJy%(~Bk)pwSuY1s!B(Y?J6PK)p2SI%a3!Y3 zc6^du#h`BfFJso=_vU=7=;~?_^~Y*7FiEvU(I{zu8Om=)LRQYLGPH}OZU)oAFl&8C zxa)0_g5qvIlhFK$WWWrJznaYD7=FgAp06*H1Ep0f%LP0}u!6wg0b$2`#h?|_mLvt3 zaJ7bE{H|gAMk-7lFMa&?hVhW1yWY+H=R=yl}v3fv& z`x^;rA`_3#q|YOev+nH2kq&3la@wV8Mf+$foiBPEBSkpQw6AbtEPqbSWk|oa`O9I0 z-o-fU-mHKW&%JVmuZlaWX$4oEzF9RtZV z>nY+6wVx>Lq*U8Xi82J#uvH*>ag>5-BSP?v_SSZEOSZ|(kt5gbUp{mE(z$sJ zi1Ve#&+K2m0|H2wv`6q5aKV0C8;;|ADjm;|(iW>7tN%({U?301upS`bT7543`UFYDQodKI9B||POC*5qPv%K8tWI<_6m)>8%QFsiAoWEy*np0_b zWD*~FY5_NRAQA5*GTB6xiPzH4edHDUw=`njmPnllwRQdj?)qi25sx7tWgyVGak@^% z5N+xz%Z~NlLz6XFAnMs{XhZebloc~ElBD~C?VE44rD9UMHGk>iqTIhfqt)ue;gO?~ z!>Xy}qs=C&vvvh?gus+ZB&~#*nxx`=5yW-71N(LxPB5d_JB|WwEw1n1_lPWu%&_@MEuU-XPe{r*QUde`2Qg%U5DrAX+1IMEm-pG~uazt;OW{<2|E zu-e6r)-GQ7cKJe9Za-@a#%w^RvFRINIJ_#36Q z@JDuJ$f>rYhoDO<=_^49LhWiaO5Yd)^`4Gz2^a8%CuCK3<48CGIh;~n`L8x$H}442 zBjseyg;N1VRaxn)S-K(TR~vXqt0=nDXE_uSQNEtU@l_{fdo7C}2nSB3)u?AmA##Q& z^X)d=c6(>LcdZ>_7%#5DtP{{FBM$#GDVXj1{5h%#1_EUUHI8vYrjmyTVmZBz5Z#I4 z&!|aST9Q&r!6j;1=uUNOYh$xW2riV07E6(E^Ul*(cDwnfsra(8(vQb#S-0$xCGv~g z2X`0a@wt`3pxecv8SBk2RI63w3!oR0ehx;0Z&!9Kp)GVq+ZNxV5r-NQi>?-rAH*f8BQpV@WihK1D^9xIu>y zke5P%57p%OT=S#Eg(s7vGyu)h)wW8Pmw{m1ff0U3v(acakBvq(L~FA#8tok!jT&_{ zh1zIz{OpLd(~Zq*uRO42=o=f?jqiB<>t!#D_k&ILe1le}RNEydMnm{Nv^u+7a+uv4 z+M=OrvAd+n2g0-tGTVwZ+ZD zx`FT6oGnr#y3?sNvRYm|RiH*Q^Y74;$?bVJ9O@!g>iC|h{V3+A8QdrouiZQ#@uUmw zbqRS)_t0X~5#imU*F0WJBt0-5@qVW#4n9Ezq74m((sm+sfgWTgW2xKh(Ubd43Hs7f zo+Py8lZ+TTk>CWoU%7NN6s8MBIJ-9*HL*q1?OwaFsfQ2wUAGu&HW%jHkPRF!n@$H_ zdfv!nAfm*^y4-)PWSCC1lnT4u1Fe8{Wv$gLmx7w~1?wSNu0dZrJV`*TR)?b#-eVeX z8ylO~G^^zb3TU*l1|>pl39%6ll}8$;0(1z!k*-977OwQ*G6wH#DBNDkAbRPj8`qWc zwA{sQw?R#vt6y~6DaZx!E=ZxKk@R7+KXf6aB0Y%QTB-!nPA}9(jDlm*M!`3iHm%*= z?L$`zZ$ziFas8$-v^od(=G~dS(&^0n+!1MwfN-(CU~AM$qi!VC9gSKo3e&lOe!eqh z<$9_6&xNk-wVG8Nj@){!xOh>YSwJ%zyx=JNddZ;7)w1vfedWc9gqwbmAC>3%;sn8W z&(EKlo8uYnmj_3QHGSjWjfUV(B1_8DQeuJb-1 z*jkVA{}Mm7{Ys@=lFCOd+()reG8N^M;gU0keM~JTypW|Pr5Y&yCxHgH^_(ey5pbOFx3W@56=qw{j=NRQt~>lLo)^cH+k#QB$n+7O&hEfYM;bjU;?w5 zS-XzAN}7DvCelqr^F@z>4*`^dTh%a07wjf?4{ja)fz?a2*S?lgwe4=Jzqt5KjF3Mx zE};d`FHpfE2_TxgIbaIC^1>`oEpHMOj(6tM^J$TF`^S;6V0?2eVvBk%kykZ> zrkJB{PkAJiwtzJZDyV07U%X6s72zY-jjxs{w}K+ES8X&xJH~pN=SVnIs-Ub{>9y6& zH_<2`nRRfxYfS6a;@Zp%xkb*#QYy7P1L3z{TUcFvRy+K}H`xyE0HcB0lOozPtLuyq zcs!&SC5D}YBcX{ONLr`6zM5q+l}-fah}lkX6r}g@*~#?PS`b})FF7#+d1hoGkGw;p zvZA!fkq6>+;OKbnIpvG)X3DvX01*cP`Xnq8_|5~HJwT&)p#vdtj$_s9ID_ST!e+|p z8w3~2c3|+BG}jkn*aG6EhY(O0{Md&-0sl}1<3%fUebiU`o&#J6(B5cdbJsS4w?ysi z0`aNZjR1uNMi?0$N6m-WDi%F>b6AU9S4mcIUb7Qda%KYue&90v%T5U?8pyni^?;a; z-atGE&+)03iC_9zbJ}^|!{6+a1v9{8ptzoF_3kb2e;2M{#Npad97wQaJny?CISSTB z>X2a7*q3~> zPmWe)0Cglog&Y)BE?COFvea)nLz%x`bbH@UNGAIBh&I63484Q@zc#Cdhxa^o&pqEn zBR!}^KPJZ+XpvtAkRBb}cY8#idH?l4^g~~FzdPLTwcq6Za!U?Qfz94j2my>+1^( z#`OO{3&d_q$8*)Hv6N4dER9Dd`Unk zej-#r1aWsLj$IC>-vmq~oLQi%vcKW4&fq3JM39_xxOyH7v8H9)l-sef&MRG)gmH7b z)Vg(ize`Lp>ShTn#FsB;CbA!4L^B#U8*V+PW?nO5e(&lNap;e5XI@#gpb7bVxjv7zx77(rmOUx$$yB3 zct{}#GRy$GYZ<3igE|BJVG2M|xa^dvARjC-)}qA5ex7(xz;}+8{sG?yC5wm1g6Mtx zs#vx3sE#czF`2(;afRHViQG(IqTp4T#rQ<{L)w?^WDij6(s+CVtvpm|V)BV+*6!K_ zyTurnZ0!O2Apu>BiIP9osaa6I`+ssY(e zdccLQ49aLbPT01_bVsc+Cs@cVA|+ioHeon0DDt(GTRe1VyTG$yA5gR52*SSkHD8=u<;%r-w|T+?0?K8o}owA%%kX|nRxr*SeY(NDF0lg z_)P2s{OCU?u;qk?e*;&Fw~Wf*y6f9>9lE1vWwk=V(zchD@fglms}kHl2~iZ}Zsaei zotN=*#hhx5VsBI~BV7~?CAv~7M#5lY2#JIv#nMVJxH;OGn~(Fn6IyW3h$=(+ z=xn!Zyy@ON39g~?!GmQ|5CZvotGOYSW3yT@!(huo2sm_Wp8fx{ew4PVU^AUY`uN(D z_UfuWE-^QOwGO~Vijn9+&4=R6R{{ljgd18$0E0(br|HOlSHSq5$eFsG zy+c=?KD38-O+UPM*KMu(%xezMy1DV9v9oj2ja%S&jsR*QT>#D10WahrCG<~=uk1^) za5a&jW*|uHA{3hbT=5ITg?XXHDHP@xhL^7QqdVJX@Z`P4W@|P!f|#&YJ9@GPI)WaK zM=CQnpH88|J(;Z5W;z&Cqyl9nym6^*Sw@z=Bfg}k6?di|X?%z8US_6bJz2+Vsw4x1 zG}3Fq^&AH!ILE)pSGQ594dr^BD?9TW!hLxZx1IeY{XiS54+C z*xn?2&a%CLEvQG5ii+qJ(AI*uWiZEuI9S|YI7f9VB}am}L>%~aGMkCSU2=;kgPjsy z6NDbOguHOHRE6=9zdve+A}vkpcmd$?&(o{o$t7)xv=zoTEfIfZTu@7>p@3~&*bvRO z0|mj`L&0c`Q-cx|MF}YZ?7K)wi~?g663#`)-SEmNza_YCeUaEkAFuRJ(Q*Q2Q}GOL zRc_YnBmAF?7~x- z(UT`{COW*m^)AQmw6+f%J9gNw|Bx1)U0keg?G0~7!nQ~cw{*J_h?6r0K$C2_xL^N{ ze5qrT*F%jas+nO2=%FZ?XCfTs5k(>8pR+?v0fB@-_aLjum;gBwibzQqe}IB!b}+0_ z?!KYDfAi94q1aGY$5j%5vv-$`RMa_s{KlI$H$bQ9flUAVP95KCL#5Hwjq!cSnCtdl zekb&~hP84am-1VwL4qC>fP~#|!Izb=BCq4sDr`pZP--5`W~rKSrs|?)&~wle*7e zBI@v9AfEqPZ7?{ze|dXtwL)3ca%FXGd;juTA^|vn&QAaTiWNJtop6RNH(xUG14sPD zg4-Ho6FVnj*5#*xTVHWui=v;XP}D3HvaO+$*gg@pueeInPG7F{&*%1_<-Y#XCYSm9 zUw?7lg^o|{zd*UrAIZDk31ik3S4}QyC;7}pM`KWZs4b=!efn}PdMA+`G_!>w1u*2I z5u?b_JUHwwvcLdY5*IG`v<;i0jJZDlp;m-G5%l`9cCOWIUY+UnGPAR%nyuE=b8}9w z=V+ych52v$)aK@={+N2kET3Im{A2m(6P*4t_EI0_vLkS7-Vgr%tswBfTUfH(7wA-z z7^$Z+dpHzNR5YPR7H=q;u#2NdvI}jl5wl7H1hz|G@uorXn_^bcwYu~#@cqd7UZ-1E zin?$H$U*qzBHT?rqBHam_5%t_Kd)Q$(E2*+Q+0j)(1oSaQaiP)Qrd3Rp>H8Y3 zEHBS1hi%j8THOv$zj@2+m~dX;SMtK6B1kwVCjrx|MFQ* z@qdR=2-C=3g&ZdCo5n;4M@UkRMabQWx@NEtG~(e>G}O|JT|;YyqNQ-$V6Ev=CqP)c z;x+u{rE=3SX6EJ!u{71k=9+GfgijzuIk!1iDuvRq!ra^p(?q#Ma2FZ|$v#a^|AQJ0 zK?SDXp}K=-&wIZw5u?aV4aJ#jiPH?VSk#UsN=~|h3V{+)NoUI1BgN$A=IK1zh)Q>t zbhD(JKfSpLh{~`^ArL*OBwB1H3t{+dy3=TM(zC?GQDMR{D8ch)sANH&iQZLVBJxu$ zw2&nWP!dEE6h4?NWed6hPxVTr4!_B{{nsz|5qRbfxDRquruPs=jXA7`QxG;eRWv^u zy6QmXMCy`8sXQjMLQunKK*szfdf<-R?pm;UuGV*z{R`Mn@^dIR+ zhz6N^cx`J=Uzy)BHzDzcs2fCQ)puxy{HJHgL3ybj-U03-8m7f}%B*L3ia6}D5nV-l z_aX*WT+f8V>9pPfWWqW1Cg5_Q5L(5AB!)X;zUEWz;6d1iF{1=$O0k5@!GpO5)3y*k znPwH(I}FMGCaRSSn5BcsMh$m^gJX?=XOJPYxOkcW^2J3_QZ?AUPwe#jwfcOw3;W1! zZ>`x(rC^sUlaGI3e!g6S>MxaAqr9t*jWS$XK7MSpAt?f6oNXLCwz4z~23J~ZAnT&B zQh9#<0M)%KwR(x2n$5M|E`i`qcfMZh_jkx{S=36!R;yGbtW;{XAm3X|lIm{y%FHbv zmZzhATKjKMY8FK1gL3yWcqOAmlK03wQQYjFAfcTHUD3*=rM*YT_>*VDu=SpUQW^Q) zk?+NLkw_U!{1=Zl!D8iA60bCaU&i!0MN!xnJ(TmoEUc7Ib!P7wudj-A&lYkH*-Kv1 zZM^x%JCDmct`(D#lrXg|+k5hWBU5#S@5QFz%H%54mC5Xa4keo_-VCIXauX5tM>dJ? zRdK}23B21?q%}&{rN?%Zh*eGDH3-|tO>(o|rFj^7Ik9@F8wMIe(-0WSDCSP~rm*jY zJDK!ybqevtsXG)KTX6n}1v!y$^m(sIgUHQz8If`zHc)M)o*XU4^N?pJu_(UeYdh0i zUj_566nc(_-s=1V8m%I}&_Tnw!qA~kO_LU#GvXC5&8}VwN zaS+J`;IqY87Ua5IBb#yE>S`JJ0=yRyAQwhLFdDx_4v*Xb7$;6a3OAc`Dgqvp+mx;O zj)PlCGMl~oHZUqHawPde0<@EJ=*MKm^oR6dZ}B<+Tz1Ml6n2w|^=PA>`hN&}4|r+P zy3V(&s;jDV&N-a!J~{R2)8|d|hKX;OJi`q0GE9I;Fac+V0VO!73_;w>5ClP#1q>^) zaPpAQC9CYVBpOC{+{Z7-vc_{?sK}UtDbu5iT@mm@z}91 zuBOQAl+87)=1PHZ_be4+vYtSY;g^XCgFGK9(o4hq;JE;zk4J2Lnn@*)RO<=rhpL8f zZzLa64``(m!QYyG!MR{BmOvyzIBmFwW2v3D zK5GAibFK5^&ey~8l-wSZe}+_W$Xx(#Gg?oeBX|S4iV|}3*5?7>){|V@Kpxy@p|yDH z^8%Ee1ywRo2igo6KKmCu5B>vaKl=>8A)-NCf9C(?K%9uIBY4#OGXU`h!p$0L8cs)m zt`Zta!gxW-cmdY(hyWRwMgiQ^+&+?w3v_}WAaH&0t;8VmgW`>at^uezmXGDdz6#43 z3}-;_3>(RPn6W)jm+;=a3w+}}C0xoIa`!vlbM(NsFsOM80zt0N6`ZR%&fo3ZV2O zB92@!5qKh?6YLcV@Nl44oDe6I*{OgQW_NM}Qfe!aYpujFiWjgq2o^{!Jb)P_!)qp?G%ba?ZR1l&CLt_{Y>f;#p-f z^jdZYP!Du7p3dZFNZs)XP?j)$Mvxj_B#rjMwiJ925`_=6^7^8+8b_hw;3@iHVzd}c zh@Omg(=EB+X*Td*@pFdo(s)o!%p-t0*b?MtIve(xaC0q{>GYTk2*)e|^;3)#;!^^S zfSoDmA<=^A?Dt_zMtuE|d|O-q2cO6w&oO0+RGf}Ws?Md+&+ zCp3gQQmv*o71BuSq%Nu=l|{bJi^dWhM&B9@^C;709dRHY)s(hAslxo>i5bP^S*4{| zgY-jyivJ@LdnJm-H##h=Zf*)b@cVtxR2J8hy+krk1RU6N@$FPj6(Dh;_gFlw!Npc0 zj<;vfmXC9SQSlJ!7+iTOIo8+)u=lf7lDenR14}i?IxX$ z2Mn9FpGQ%<8WZ!Nqv-^zUtL4Rz>`%!W=*b}$)Q*^3kTXVZk%d)GO-7O&7cYtok?qt zp*Af?H?JP4H)1*nqo~MH1lki%LIWWWF@MpWLzQDn)JV1XJ720nd**5fkXr#KW;fVp>>&q+0nXxAF>KE{4nu7H~1j0cyr9K@hpa^Bcqg zKo5%>J_(&@_7-Zn9Lg}Y)a=28`DZO~zvD^uuP z|IYr(^4jY1!r;`p57cioTP+u#xJi#t|HSzz7qg1RZadk0yIshYh}gWmFg4`7d2NTL5QtRxxk(z@(dUi&G?|*C7QFBTv?g0HX|(p zugKxCzYaxpIA7RVJKb-D!@a>Jt6S{}2?nlb9gs)@4-naP20Ggwu@5d|^a&H_A#2mS zH01~H-I4j}LmBxx0fi#kLNtje5A?Y+$;Vu-8@l4i6|T$2D-!2xweyKr9QZ9g2WP&r zJzQ8AZU>+JdaYLP2cOpa|Am|8l{gMHz4*d?y8FV7+#Ztu?z5kP7y5+p?UYvj1!$s` z8~2@|4~9M#`dsL@=;QyNb`1JY>tQAhnXSG&mP6!Zkv%guyklHNuxaRzeY-kOP5~DyWSK(8aYoI^8l?981e(5!k9|({ z$f?PWjIG<9oyn<#$M5}>_YR)eyy>R5oxZXD>cU%p_UUUA*eKZvys~zp$q3R-%TNAj zk|_E*h(2CSAS<=v#q>Ll?~u|{xv<7N#}6LgansJr7vJ}5Z;P*wMy=2MSZTU@)%l}G zo#^UgCvLj&=}nyfkDk9gZdWSdWHFt!KJi?dl>H{HolH)inoQ*I({4{Dr%oa`<@of* z4eOu&)JA`;^65`kuB+@_UaWX-r4Y^K5Dp|sAVYSWH@FE7QVBQq{Fs80mkZJCN7dT# ziR0rj4#HNeJsuxFF;*{ra{Q@JZ{IL({|FM()(s~Ym#qDp)y__H@6(?;o*y5&jxN~0 zTC9Y@%rL%Ns4P>ZaML+kG=v_V$`j6zP{mszth5jV;wa{ON{s|##8B6bA0ZGh> ztZ}NzM)@+*o2yf?H9()Tz*8W*W-sX$!7aW=K(MquCAZoL-My~_X}$t&273B=A=Nr3 z@~0e>1p?Wedcf2?Y$%Nz>)5QfIo~_cb!4l%QbhllRgE0qZDm0RvG>>s^-bV34& zipbf$NC$tuwf+Dg2)UA|VjtM@6X{~{4&p=-Atz2UIfgW0c?E0>&lDEmJaYAQ5|v=T zVA(bM!~bfza5y_FC!MrKYzpPx8icM7JsJ9lB2x&3f}8_!2rF-H6ebNH@1g_&d86_1 z=Lnf^A0GPx56bQ0cLVK#E6mS5LsOh@B}m%6C}1&SY(o&RK}u6c#V36UH&m^88!5I~ z=hNU*dr38&bIR3xHkPa{q|e&2290Fe_>54m|^|keYiguTEmX?R-pUH9p&rYwkR}9mc$u)x zSz;JmNl<1z6D?I5Vyv{jah3(Y+V5qHWoZ7c*nmboo$fs)H#s^0Ig8ShU}i+jEt_Yj zFbin((y3alzp!G3_rFzHUAf=AL+wW#*T%n{00>>y;TP3Mh{fzZ8}UFwc$t zCa~8!D@Fl9I7*gX1TLve3LP!c#r_NGV^&$`nNmLZ9K8fCmdId5v&0ReN402au-Q;!B9fj(50>!$y5la9VUNnrLD8LA=(^!U71| zwYuOE%YJv=3{a6xqAE^UZAk7i)$cR=V46KT2u43e-<)h9epl_zzApvPl`_(Iy zE9>iP>tzfCYT4|i*ld?hO4kgs*(5@3zMUi&L%R&5dDP7z?{EqPir8s)i1ZxA;ia&1 z{XJHMjMFqFj|)3|WwVq*riDEM3`Ma-lw7fVVaB@v4MZpG6nq<W94Gp{RZ<=q``3^}aY*EUCs)iry2``p>>v2AaU&mI~KYHhzIZflzxw$7d9d;jxf_?=>LOHj<-j+v-t^Xc9IdThdg&^Of0q`IJ@n8GyA#{qJ9P6GKT^nKebzMCi6d$e}|w;*K2tB4e(+TAwAa)-Q&JMAr$q58nlCzb6l>br;^OK~-?Cn0QSVUR zdTi%zXMNM4_M+==ecSRmE=kvD@!W2&%Wfs_BH-oAmPND*<2dNF^CW=*bK1KMePGx8 z*T^T{5^BOD-x#`|HDnMNdIN#^G{Kj)r=H?l3_z=o{X zLN(nU3I$Wg)XY{=oqEJ8UV;)SJ@1_D9&hi-&XfW(fHeF}oechk6=q+x1r z2p|EeS{5V8sG&*<&4S<=kQbBe0oni`FEc1EL=ok$`H zXfH3f;rL12_OF<3|LJxRac-NyIEh7;JG|DSQtzm(6>$Im0QZlH?{>kL39vlUw0 z6z!Vk*{;*%o1N!~VZgNnO)dCo$1t1af&o=Q+Opjd7KvJ<+zncjb{BS3x7-myz^KEb8DemQ-`vZ}2gunK~u6L6xwj+^!B9%YX z`XLv=v}Du&2yA#gu%Szww2+C#Nu^9wS2L6q$>8)Seh`LpIxdoFN11Hw8@~15^75z5 z+Y1F4;$;3=(w1Ms|FJi|fDh~SqP_ROcp>-Tx{mh0_{yuVe&rXhzWR&KgAY6)Tgkbc zFG*zJrisW&47yR#?`Nmu@?<(@cBYg3c-pt}5W_`(>{#P$KkKxuEPAmYzvIE@-M#qG zt8Te#|I(|zbo0(N`=8pm?%LB=En2%bUiU|Dx$3GD?|s2>>rYxA`>x~r+t!Eo$CJO` z|NO}MmHqOOdmjD6iS_mUE38lM|EhKD7w)x6lUJE|lN*f3^&>ONXRv5SSAV}&`~6?Z<$mU8Ubxgnbq98WId;72Dzq`2 z7v7Z(f*i!Hv)d?pe6at*NtmoSGV^340q+UvY@0MgAcQEXyi!MuhEc22Ndf*NS7I3e zyM-PfvJ~6`UPw!;JNaGY!i`(#-*89!LpfUoy<%uiQ zQ6WY#?$N4s)&-!fD4={9RV46OMUu;zP^ zx_^dz3jfKz+P==d$9|dpX8XPNPuRa@|CvM1*+E&oqU&15yfqksaT-S~VRJGNyHf^o zVXAIp@`cGU;mY4d`b6{WQb3*AvU0lSn}IjBaCa! zHR?h_E~6pN?y~og6mOY2c@R`>L3y-ILw7;fW04#EAcPj2o$QVLsm#~lR)-@@jg)w3 zJQ)~Sz&7|&$deQ}*M#CUE z7!pB-!kjTzIERXYc5Y9(Dn77ZzGw`%SseK&9Q!=T$4Xsy@T?vOQyoAJ1FiJdm4N%* zrH4C+8mWzpAI7vBT_%H63L2}Tx>%-f+&nR!=f^P>2a1l$WEg=3W7=!mom#S1n?@w}3+l|YW3*X>78I#*DT_(i05leL6h!d6*U4@L?cPlvlL z=#YqOXX5!ZmJ3bdF{GRV5yxZer(W>FULdY6$Y+5F%8K4T)2>MDsfiF&dokV^HDbg6g(dRAe{AN~}bJ zO8A9jlJLpv>@jP#hD@G0AfY7;Ej`1qf}zf&8yYorl?(@rgj)h5BBHA_)mWLy*XxsP zZbP}pFujOJQeMo16kbAxkf~U8#n1Av@J^<~slNabwuNMUoOk4iiVQZ46Rn(D@RV|i zBZ}0KMq~GZB$0TU{u**dtmPEBkg;C0Qwb1o@-hs=Jul-BaTF6FGHzn7CX!awqD9a# zC=AgpvR#b*ahTS0d2mXWyl1mmUUjibb#d?6DC(SWAU`~H%WK`GcjWI2mE0So5Ko{652<10fK zdoSjY6)9mgu#|#v7Clu;6O1lXuXO5lT4*EHf`6l4Yy=R>-GpIF8s`kAE=leIkz6c< z`-p*-!4YmD>TbC3K3wLxQM6c*NR`xf&{7WkE*6n2fo)9uu_BHkg?B4`9t~vKOuXNt#{HXI`=cCZ5pib(5Au*#z05+z8-*W*wXUlz5f8$b6 z>!=42A*w|h!_9!cBkhtb?|C957UtY&4K<6#6x^E)v1zBktf1XuPOVoEpoHKerAXp%eN@=uPg!o`Gc|%}|Mje?6 zMe0gvOtsBXw0E$q?e7M&9Zs!1BM)PmNc!6!|O9?fqaYb@`BlBSD{ z6i;Cyi;f;0yy=g|f%x+*MS1}@yJg5ltrXS@&-4vFt3^4|3gBK{fu5&y5z-x308Nct zRA${`fhznjV#hMAhFY56q&Y>V;Q4<-FqGw$P6shXwAWo+Y&BeGX}m3Mj}vV*2g|*# z7rpDQcGTpW0cPjYzg-LTj9fQNmwZA(td(jL1Ab8U&h7(ELp?s6XhSM&QI0BP*?sdZi)X;5^Zy;Y1GwDvi zfVA#oH9!&5fzVmM)XMuk9K(Re6Xv9hnpppp}+Q78N&Xqo zO&GgrO`Tkb)o=X~L?q&NxIGM6K%oG=L)Zwl=m)FCt5!P0PKV&_kB&0vGdfP#!i;>S`dUUk&C$rXP8OiY!^*nyy^Sbu@%W6F!J8mv5H&95_ITU#-bFR#_HYmPqiM7~9p$)iyPQPQ*UkBml| zY!TKl42pV?VxpLgSi6wTxwkMBUWXn?`N6is44QS3)-TT^jwEex8(0Uh1T11|;{f8I z9I;qWh6G}zQcyx1t|Q2W`h)<0AhYCm7I-JyUQd6)%;3kA8)DGEV zp204{dmtEU?$K#;w==*3>0t(h!N408{|A|tYu~d9=o)1SMnJM{=n?AoEH9a00qJH+ zF~l6<{2|JbQ-{T)@#p(p#!zr65^3!J1(l#zEah@s_H#MxyTy8FJIZX_Ugu#*So7J6 z_n!!thGb5iz-}E&=u8Hd&GzyA3F8+4N$!*P?$^ZQFa*~98ckwQ(pLNrybS4CkluuD zL&iBE=f@%Jc7NE|SboN;TODf`iO)UOW7hXuZ?pcb^-=40t*=@8_E`2uhW|Ih3vk^K z>qO;F!_r8oVRL3jLyUmQ4)T_g6hsnD5Gn~d56BGaqY&VaKFCO-dwq|D9Y6p=ec%oR z22&wH54?r=0znK^0Tj$G>^$O5oVaAHSss;i22rhIZS zKsgXKpmLad-74e^X*cZeB3&&}(Jl?xrc20kg&(&(zIO?Txr_4{9W_bf4Jwml!8AMg z#wn$3_0XasyY1@{9w~=tJET{coT!1h9V0>M6$|rYS}U5+v{#0B8y3!^yLitrMCfKG zhCFOqWBr;U|3;=b+R|M}z-it=9j!0L39=6j8FN5z#Ilc2%oIi{qB?1i5297j26bvk z_Y{I;h<;e3PdD4<`I3<^usjoIb|auOwTvc)Kpjt}T#*3U+``i~89R-csE5sQ&}dVE zrd+OphS;Sgwcc5)cn zU;9=mzhOg@)<~NQ<_nNP+$vquEdcy@*8hLIq!Z2c+9Z$5G}3F@Hv50iHUnt*F#})S zEPhzr#g1l=u%!Y25DHKXNR7>=1W8gKr$~h+u>l*It;BN#z7RljLHkLiC;){J9??(s zEQ%dkqJL}uk3nCEoD=aq#r8;1_qP> zv&Rh4Bbj+|+RUK{Iv%M0F^zI3qIE!6=qsO+LM`HWxj{gcr;_MHEzemGK`Dtoj5gC9 zCMrxw1{}&=jGI-NMQBj~Ib;e#sbacFlZ)q2_*W3Ro6l=ld}NFWKBK4n;m|*YXWY+N z-bhOdJqjofDvWV)a_SMW80ZYT5*E-}bU8^vljzlv$YwN?Puv{6Q`ZUY%#G@TdO@-- z$Xq0O*bs7;^l5XwMFM@^M(BjeDF^6LOk@)UaL^S{$O1FQUBRI&;GpjqTMuB3K#2*+ zD+NXo5COM`%FhGRY*##L!*}2!` z?bc(+HI`smZrPpE&-pb~ustA=st5xkkc8|{pbSlQnuH<6$rZy>%p!lnU6^45_# z7yQOe1TZh}wcEpcm zYOq~#s?nI6%w*V8(Egxv6l4)>U!uQK$MO&>BM>_V{LEQ~_1JD=!$^bz%uS)u4c!*c zF`P1Xh*M`FcImU|fUVH8p}*A2`f)w=kI?!YCLe17y)kQmi1iLuAs9Xw-zJ`#&7sso zt`vzLts_#3rc-!8y3i&(6~p6F1x|sdvX)Td`NS28$4FDHr$*z2V!jZw?s=9+UVJd# zKIn^~8(xD4&>C^g-V}N-{yx7$iL0#FS|7K5%X$`}^0xgv`*Hhe`;*Q&=Vs@2=e5oc zIX~k3r0ltby%=YWvG2+rStA_G5B6OKZ_H-NE25`ib;3IbLq5{Y`U~dK2tR^J7OFP1 z2?7G+O96i42zdi5Sr;_71)c*LVKCcpn~iuwe61Ams$f&}eh)k_Fb2E7fL za2*%TGsy}oCwE^DP_Qk}2er~H<7tBNw9fQL!7AJumyMdih%*$5S6%kEChR1XUbK%b zs2=s91E7b@+bAQHv=H+wX+p-F6?&ZpL&qIxK#-)M8sW;0v@ZyYwzvAbK8D_Cfj7}U z<0t~MrjgK9MN=aL4xV>NQ1p;sWt&+TNnxfjr=w6HfxtsOgO-6xgU~@GG#mD*N)W9q zs36FYJ)KC10xify5{$^#U@;+PmVC9)jJqxmi54BmjYepFXx>DkisDd(3C5#@j9B+g zNwR4AD98E&Y8wJ;l&J%C9_QJ!sye8c#{iy&Q`*OxV6ycSYJ~m=ON^%#F)^1lyB<>9 zBCH?dI3!Ni)|C0%LM4fhgvdq_pb}isV1`%pD@Uqay+iK8V5^ql$V|9z)JbTnCR188 z9ho4YEHTr(5vw-1nTi6j97TdQqdQ~-5rYVvs5G@BL74)*_@@}3S_u3u2SvDyp0m7b z;xS7%CcPpzjRw%wTAL^dH9g$HnlKBM1)_UZvq->zE>@+Cq8UEKSzKqTG%6aZKC(^3 zcTC5W8vX!mgb-J-A)2(V7jnX>VPk1)YYReXYjb01wLwC8+xifK0pOjyRy}L`-ZN*H zm%;FxhYokG-9x0BkG<=(M6T(^GZSEATTUt7P{OpUc^>X9*4M8}N zOA9ygL^Y)Vo{n7L9dudd1-&S&3FE2fDvFN2!_E{04>nXb0x?;L*G2LG-(awzPSyJVo*)oKuL*-C~xQmT{V|FQ7|_J94m-HAU0M60xuB~5`4gjL(yXX5BUk@ z1Z^6NC(*B9=n@nZWed<%HQoSXOhgpTz!(kbegs?}Lkj~ihChx=45Ft=gF!q8C1apl zHZcQyeC@)iWx5FcqUWpiMk%L;qGpWaXv#wn)1XuWkA&4aC^hMQVolUostC%A;eyg( zV2B6cpfC~_%ovFwhMx1RI&ur?vLLJx#)uR|=IZDc|Tbc~TW?e`T~mNxb@xTR%xp^;cBcYVk%+DF1TLfc6TQcYy@i=+`w zTH!kqsjOm@I$8HfIg4!+WH)QdsFu8Fuh?mK2D?X&)=6Y~Kg>2``c||AUMb#O?X)9_ znB_HFYv(V!bU4H!xp(E2-}PG#$>PlvKrV*8V-o$?s7Y;K6q~5Rds()a_F>eYPWk5` zJ5#M<34f#1(Du?R*(}DRN8^suH>oMVKkju-_^XUC!({s?aSl6pC7EYA(!IrR;_ub2A7p zff2wKQ>o!2)O@%*4FVgOrcjVNo`-X%9UI2k;_hXPBx!zbO|HB!GAPRYkg3WL{v6?vi|Q8yIHsibv8Dtz#goTi7OZafjN?2ib~!rI_+^K$W>gs*vWzgS z-7a(zK*q_e3X|DC6K87>HiMn*R#)gSW(S19CWR^!%~@zTJ5+{-UV?`9<;y*o_OVAT z=biC1k8#~^-rCvw!k>3O|IW@6zux`x(GTBY-M#;SW!<^|>;Gl{gVsB_zZP=1|Csg5 zM8(R74wJpdWZEf>+|;1zQw-FeyRO#`vaj6 zv=%~&k&mbWipw08{eDkkpphpf7#fYDN6J+cDIlpK)EDSlYLPzn>L!`+s0c;}4G@HYugbxTQxfUT%1nv+JF7XkU;4LgBYh!kk!r`* zk`gjB*%@tOciHzyc~Rb`6l5hMzu6yMeDqGge4#JCxGvow6Y1johI!-EG`c`ay3M{v z0nokjdTj-*>6c69u~=qfDdjU0i<0x8 z0-#+XYs?ix&#ErSg^{sCG@41th)ijJ7A$gRlBBy!5D4a}A~1LY#1b@-JYhte={`gJ znI};PN+Q)x&p3wl3dj=4^GwOzi?xYZDeJ6yquMUgFnWx!bQ~!xq=aiM5}L+b>Sr+f z=xQ&H(bONwpIeK|O#M`0ptj(F8PxHPZtgK3m{=8w9(8a6XJb6=|05d=HqZ9GW9(jx zJ}{!W9Xt+IBya_!%Mi#!|L*lEqDRC*(C~DnaCn(rmVp-oF0o?~eZ<*^?9*!jfKrQ) zJQNU&x=D}~QiL3@*AYvnhGZGF%Qy3ik78v^ zOlEcddFCYb5-BEz9V1Gv&lwLz_2Iu|bHolIBkaN=!JBIJWYR{Yl3HGwZfthSkPu3z zv)k$7vRrL8$zjDG_59oS|1DHZwz0CZwXsnEd)CV< z%K*cewKY4xy|IxeIzXemP%ed$qlBZZLV#t|TUhX`fYMmBy0@2fRyTaD`H4`75p|Y>HoPV1NUAC5%YZrQjBT!7xW=SXvr`ySe2X55c=gP*BbsvK{{ub4ObjehvZ;v&fz(j(U$GPz*n zZtqw$e74tPdBi=&dYHyO6uZ+{+)+v*{RLYiKy752}3=M|95CCx_o%?K@-*HR%Gj^X=?$;SX4%_8(fZV-+X5$cem z5tg)rbt=PBD!P#Y-kF}AAG+ZlvJV`*hmkP3HP~;3eAqAK@vwJlnY8yPI_GH61pJ+ z*xf*^x{_{N%_INeCo?Tl2`@pbC;s%Y$FDqFx9+Q3eTcdfS4zQDT>I>24Q`#wr&37Y zLdHvtS|!T>egg2|Zvk+r`9YEwuLVe9UJ}e1aNV=^9|Zgcy(q?R za9}uAJ;I10sMNS=7~^fe_M!bH5ACQ5+-bq|A6kX_xY( z*OVg!NVYhpOp#2axFBQ0OJZG}vwH1wPh7ngTaCxp_L}i0_x7HQU;AMDF24A&|GMsN zne(0NtYg=-Z%dzV|J>D`wI6)wT-Q3*KA*m=ea(Ycw~w7$d*<9jUs{XDSErAh&)n9% z&U#_{>IeCYJsFEVx%U<7ovy}qFQWDhra)YyjU<_7+w!zk&r1 z?So(_kl>WeF4~|75OP&1!Xd`MPW& z6&Z&U_<*(^KOGcUNJdY2iJdIrEEXeCWB`w*d~ASIySV+Wjad16$>>RFb(Kgi5<)Eb zDA6&9sK1*^uC6Y6*vCQ7hu&^K8Tt$4CVstp~vVkxy4TAX-`OMqEM#kgdF9YUSK^T)ss>naeYu=JA3cfLV=51h| z5glvn%rCC$7!ujkJ{F)UI|F^2fo z0G;4h$a8L8|=6}Yk@2Z}3{S0kzhbOFQqQ#l@tNt!&e&rB{Um0q#;E;7EAQfV&+ z(61(^M<3eSl8<}4m03n{W8DmVua*+lZ`&iu$KKwd zL+g9n+k0=W4cFHfYqjCp`cmzKM=_e+Jj*yR0?jz#T1%DM(l1^ei48JYkF=$%RDz9z z6cW|y(QLuWZ_P%G#B8`!{ZzZxSZysmVR4I;WXri3g(QpQirwhaapu9-}YJ0rs7>a7C1tXNoZF^-e15MjNx zw~rs+9yb-%tBIriN64UPS+_9bEtFoY5Jl0DcV9Av=ko&kPXglfyXA7Yo6mQVbScm* zt0-(ebFiA(F}WB&a=eXAm6gq~a%HDN6z)L*w4KZslL>3UN?jmBh=|0_E)pKewm_|z z?xU;DhV2|S*`CWfo5!ITk}v%Xp3W}~y#hO6GgfB#1bnLJGg9!Z!FJOo)<5>BoMBAh z^0DuoARE{UXf*9|7)D7B(BK3ym}g^0;#ejX$iQ#GKtt%MJa{q%nZER)CDH0jOSXR4 z!y2OWrA6fDI;H;)oN5FVLftym$>-|!>}SKS=kj-3b#pOaCz<8EVB^%OyDuJFcj^JE ze5cXaF~^l#mn5K{ozm$$bNM>eJ$a%IIl`g&yc4oShXkF-HI7>+`Prb*>t07I)QHzX zOU!l21u#jFwfsDLH)MQ74|v06)nUwW(AD6dOP;tuZxb9JJb&;Q2#*35oeK0GJDatp zZ>g!!QVDdHi`ERo+te;N*E`NOKS1X-H0*bxSHnRft1J{#g--PHXsOxA<_M=LeF448Wf!dXQ}MSw;87f-F(j>7^{+96 zrD7iZPGI0j^S%xo_5c@*ZnnO#S|z<_7)V*ac42YglJjvT6Jv{Fy3-_UE#t*MJjq|e zll0|vd;w%ZXs{M$Z~BWnAN|$Z7)e3vTYok=cKP8~A3c&t42XkDn$c!s@BYyu(d6XA zxS2@&)j3{Ypwy999X&$c&Y)DIIlX^zWXS@SQmNE!DiyRNlms_Dim&fW)ehebKa*gF zIe?m)68&a*0yFqGjntgY7OiMOol`S)JX_N9a-1CqI19MK^#y#BZ+};z*&!!vrc#{} z4JApihfF#VkEL??&J-j9E6{0|5EdkotpSl`(v`|^P^*y)BEPrmheOK2<@EY3 zOg1z7zhQOQF1c*osCUTA6jZC5>q|>`9i_8S0a!NY!1vD5()wn#3h@vtBY9P1 zvxSkItFpO4KZrcpALP(^74oA@b}12WCK~`D0zDO}I+v?)AmmPN1m^Kw%;Vcb?+^VP zvZ|r=W`2Lr`UUGV*6&$=flUAbXa^&4dI&bbD2CWXU#>UDEPRFr@h9JAa5cdE7d7I# zG}6cXA0Vw#N@Q9n^o}-l^&)2)_sm-+DZpBwk-4oI?W>vt#lj3%dyqPz9$8 z0m@d!zsx2Qnor~|RGm24!NH>J1$YmZdNf)oA)PDWq+hPA8iu4_==@_ zd1x1BT!rt?q)V?t9cSY=DFZnS0jVGTH)=JM?8yc%;Q@ z#7<15OQiyqXQ>Rz+2y|B4f00x%}XE^2CVM zLUHVsN~Lnr>PiVIk&%Mfqcp=Il96Shh#6$*m7V@cq#0y7q_{Uvb!iO}DgE|X z8?0{H*v}2B$hD2?wBi&935Far=l)WL*w=PVtliysxKC~xYjL5RGJCwkWPWL9s1j*LHN39IIrU4I1Y}ZDOVu|4jIp3PL-EQ}5 zwOTGjgt{nn+TX#~hdE9lJ1c~$#2x9uCm^u!2S~x(Q9ti=H>|RghjSzb)Bw?j+ihF< zM*h5ajrFo8-{9|mHGck&zuRhDf9yw}`pRugxOdzBtK2uf&T8&|dF9@xtycB>U-EeB z?XUmY(*8eK>03{IkT36hlezD@{pPo)e)z`ES!5x#Lq%}shrykd(C7dSB)2&PjaYL{ zg%u{mVF7X)MBy<9)`xMXeI9|}>=Ej~f5Ec7HNt;zAXQn##cN4Hr1g z_#BlopJNU$Kxq0Z7Py(frDNPA?~kIx-E zw7_SHYm&a}jCuDrecd7!&QuM~F?Dbi6Dtsy!75*hjutn;O=q#jXb~e8m3*SHeK|ew zD+LTH5q+cxYXYE__>|phz$@E5MkaESd35smO7&2!)yk}Uo;T>F)9lnOQZJIQK9$|7 z6H{4&CBQPK<@ojjEc_FlHty^(qM)JAsZ<;yM-daYhE@z`25ZC#os5v23hFLlJs&s> z+$OG1G_q<%>$QzoAz#X8M{f8~p;Aec7BXEWZ6a!j)_sfCN^eb8!$Th7wCA6Ii&PPP=6Epr)(Rgtv!mM`4JQ zJ&m&~vOyyKos?U?k=OXpbo2OAt4NTqL2meX=#A_UgX`Jt!E~BAoXryRGczP)v1rz& ztO!s(G=cmq!TN+-Fv|1YDP9!?D33Y8p_QPO!ZMoDqlyugSo+gbI7o|#^;6OV~TPjnf^qjgNtROeqq*t;E25i z(&t2}I8S4cD3T9%7S(khcIH+W2wN$~x-Y`|;bv`%n=lSMa{1*aPi|WGE-l}3>+;ey zx7~K*Z+42v2jLm2m1GA)E0IYI$F;J3%A>N)W}3KHV(f?P2YpHf-}I-$<@@hjh_UGX zfAhc}o*53htZl119Gv+>5T4fW9VXGO%b@;LgXZ zHCVa9FPUD7OE;izBo7*Z9_RFix;8^ryr}0AM=*bP^Va7HWl^ZJMG0AyAjCv6rN9gj z0M5%UFA*)NyR?IF?Ni|^uYB2Xd1rHY=Xht~Tc@^Pu_ZJ5FiIYv#?sPJUW<#i^;4I9 zp@lTp!GcQAh!+kQQV2-{1ZftOjsvcNz0U2tgMkcIE~oTt4WW2OE}0@j&TD~!mxOIhy`np)nokoch+~$o zkkWzo$a-@vTUd%Et5qewhZZ!3n-F5`x`=#1AO)e-u;aq76vI&uYpjgyD||drR2=>q zKpd5)MK~r##IpO?210+_YduGv>TAOlf?HvO7*BX9tQWA}!pRug5I-AW5grdHDV0oz z1W|DeBzy}$7rz{-RZ}t7&t(gQb%_?O`-mcfc_a%3tAjZ1ds;FssW~A{1+Ve%$z}6V zh(tvFSRP_!Mo|ZY2ounc$0008FJPR)Z5<{bvsgC=8^tDk{=q=62anHUZsO7Ju-z>d z+{_?L>wpaw|17_1quJ%+gi)MKpn7g@$zF9uP4q`ryLB3W9??76_bhC6=m#Qw%D1T+> zM{ryP5f9$nX|?Lxo?9;WmZ6cDulhSbgJo8>@se z!t2yd6<4inv$36NHb$$>W^B9BBnV14e0F1Dm5c-F)rF1IjD7p$+A2Y}n$4xvy@?%e zG)AdTXE^L^!rEfCkWN={8M*Azt!lNBc;izDrjXYZ3Y`abh$S-_4zPboEUrb-ybOjD z^wgka(W-M>g6E^VeJMEjSJ|Oo4*gF-D+7*Z`$J<9Y$QzE`2hi^QPRc$na%{O5JOr5%)lHm0Dvk-h} zs%MCWBjg-11tY-)?K01$EqPg*?Ur}dBm^nAiF#^-^g8?!I`SYNtpA)RQ7G87R}(i* z9=pLIKa(v~GX=jI&t-u2M|yZ*mroK}$wKjuPg*QF+Dwr>tI}(e^e&3_rqt^_zu5!= zCs(iOhF4uDogTj@O{y>CWyvpPGVyrJ3D6JJqkg~AsWk0eQMfpr zBCu+{;QOud<(qLFdWq&kEVaEO#BQ^WEU%R9`r=xHnC;LMhR?OXpWsi#Z({E!qF$rZ z?ZdR$r7uI6g_DIsZl{|gF?wy3_0sivp<=~ayG7S7l@_t&hUN=fWI`ufDH#QlIm>Sp zbHsozZpl60Vl^*S)-Y}G)74u2C3_3C4i+=vJsgH|FhS*?g;UIvJ@4bB9%uMqt?l7D zeEV9nnar*ppIA;K2%uZJ_KX!hL=gGB?N&3{1||v&aREApw#T8D7FG{6EEMD;gnvL8 zc{fQ_gK{Qe$3Jk0R8t8sir5MG3IExz*oWBfKDGyE(DWfsMG-Pae61B})>oHBC~*_- zfK)Nu4lE8SIyL=DaSzEeCmKy5Lp_>`SXS0G*7A5|+xi3s%OJCKW&aaZCJkadIT~SN zi!W5VYF%~U-0$6eH(MDmHO`evquBNi)&Gi_Pxnf)hm7{k!_$#=d}2W+q0{>>-QAFU%nUa^;#Pdu|injw-R-LSiB zTTdnvxf3V$e`h7ZdS1>YlKan`K+|;dvXUSiUs<-^Nd}^JhdXz7Sv!oY#khVi<60$W z*_+`%M8`t#;yq+oPGZ49B}m1YKcEEG(xQFp2@*$I+cUBWo%Zz0^gy6YLR76dDeGB# zfG@xl{?G;>QG;2ex+(CR64(^r)cyWrr0;Z3f>jCZtS}@X=z6VEw$sHz{Cb!K zav8Ga31ev;h0e9?M{xRxHSufyd=s3*h7~P9;%N->5v<1Y6pY21sY*pY7wu${EG$(l z@^~<=41-JweLa*-9*h@{_m1uE_IlPH$|BsWBjx`}+6X|F?P66)L`buzt-oguj-u6i z2NvElF3>Cijh=)R^KW6`iPpw}Z*6G3nKK3yxQ=>HgUWv}(3$}=s+t|C54i&Kh<7=d z0Q_KPMwRk5GdP^+enX;Opcue)V7@6)ag?{XG@Tf9b5*Wfl5b>A3TyPtMZ9s-NMsSf zrjqFY&B+|Un2+4n^744Px0n5aT&JCnlVMQ=UpT#T=FFMX%ZlEBaRg`c&<5~8#a?a+ zelNAUN`$OLFOwzH1;5LkOqQ6+OWAC@%y~j_Wi!3ookB5!CJxH8;4~u&74kj;dnbzI zERw@$u_%4|bAIFDnW$9*BhsguO+3P#^5H{!dwCEdF;?=2_6{E^Ii8Oxk&NJ)yNC@i zwk1Iqlxlw?nOKua2dPCQQm!bYbu7M?NN)7|_NHAZBrQUBVDe#slTgbk1}M5rbYgJ?c(q7Ksm`MGY18tX$c@&p)yHQ4&CTb<`@DLbEHF1h3 zfatMZWEr>@?80xtNpc5|@G)iQ$>xob!Dz>DOaZON9lUwC@-3UeQ5)gJG-PZZ2~K+c zgw!>&KJhU6M2F{)O_D@pn-EzVcbx^r79{3Xv@YgK59X5!1vu%PVYC7h9G&^+!vOA* z?`v^9^#8tw^gM3ANvuQgFULEM|C%+3J{qfU>;JL0HczGd3mc`C)#a7^F_xdRoq+^v zZyX(VH#@ajdUJKP(X_txO)PA15tO(&IAL%>K1yq)Ehy6SUD&)N8O{La#HchaCm5$`xeaJ{5S{!N6bwK# zZFrb8@vN@CD{>8D5U|j?Y;iGBhojsilSFnaI#?KwyGx5Bq?y)c52;HHCHlg`b|*+s z!vdr?stGpPP80o+BLEle#Cy!jlGKXdbW#3g8WNBAd;vqzzo1)Tg_y8sN zX|+qYNIc0sp6^erta+ZSc{Tdxd5~b{c{(|J;9(7g%x@WOMvXX$Rw{-W7NJBK7T&s1 z$~qQU0o!)L22&9M9G)oPVMW3E-in}$ z_Oa}(*teoTP@BrSNM|J9#(;*Bzw>ClTvqIea=HF`oHGy-yrEFgNrklL*OCf4s;O>GYd=#E!N~rQW-_6(Y7mC9o_79BGO4{GI3QT-JGIbIk5|C6kyd zl}Pkg?!CW9VhMB;$}jUx^2^jh?}0`%=rw@w0mz{nf`5|08*4}o`)az8@yvKXaf;@g z&Qp}J31s*HXrp1$Xc4u}ROE{;o)u(}=H%C$;!;tbY``5y0CD}IycIeqy*POMR8Gn} zCf9C2YFQof4dZUgfaFI^D`6_iL^&8K08=CzMy$d@W9c9jB1~1pPnOEjcrsaW;I%qf z)jN?ias0K-(SwEyL|%Y6SZFqpyXQ;gY|bf&1a`ym=N1#c)ym+oC}pVA?yR5~j4Hu3 zs6Uq?_9|I!2g7bvPM}R>)0lnmP*jRYEgKtWn4xq8dtySD)+#WVaJq5qR`W1CHlaL= z8C+H?$T86P!J+Jwa4cfeMwC-a>lN!bXz6c))7QwZb30aC-v^&!OaU~ATD=fxBj(8< zI%b3cpOMELY2e}3jf6$BbzxS)#+nGyvSA3JDGE%#M8*+}0D@XazNm9ibDDNDk32FE zSy+D?Yqc+-Gj%0mj5Hc>lbK?T^m>O@NL!t$2=;`t$Bv%4_UO^{lOTyizEs-UJbH4n zE0@`Le0c;3T1iG^YVXA>mBPOxhbq2UW#{E&=)fI3RT*FH$d?L)V9C_AGA6GBa?7Ik;2yMgj|pGg1V#=9s8W2-dw5#39<$5&|E)Z{Az!v1^P@M7f8dqwh*7t?GD1M4?c91ieR^eTDJ@pf?pz8%}&DI_J1;De!6<5TWy!cNXyUuQ1TC(^5bZ6xT zyL_ruE$^gySGDb!d(%yx4)Se zef7y#`C03)!4H>&&Z0kgQRq>4^rx63gDIdkS+$~u^)|a*$WTOkkrN_D4PrE)U$B6& zDXWhoiSr2z%i-?GS1L*Y9W%rJ2x#I9i%?p22L^(Ga?Lpru2L~hRRL0>-_MPAuD4Hb zurI6YxrK%PLUz5%kBu|dJ=3Xm=d|TiM(=Kx zT(RhRe3ZO*G$Q?eq)tFMo%AuKywG@MSf`#c@ookMt z7z_j;PaMDI;PK5rc=b~o3Tz08-9;-7K&;o!zqeCbh(`vsPWru{&bXer0D)3^!Q}q^ z&s0-Y3?UVQ0v45b zMKo5@q70~>J^_EsSZTwuQnkou!ZTCCAMFU=iKD{7YHBRs_~nhVu~o0WRtv8u*Or$5 zY;%*ivWO_aRwazJ*vNzsFFN0dJ06Z_JL`e>hmBbpnI>RFATZ$Lk-%bf(0S&%>#n}~ znJ1oj1{%3g*j&eoGyL{@*}q-#epzR7MI1G)XP^#J-41KEBS=ip{fDZ=AjR5^!DP+2 z7wEh=nFN=KsdbzcLszu_=oeO!f?oe3<>vs?=y|Z^a}Kb#|pp4uGdN($k_kGdJg5<{=0soOz>X?0JgvO8+NBX zS|&XeeSyxI!QU%PE-Ks^G%^o zAve&g9lT7jWIe^{D>4`(GDA3A3Us@_IH5w;2)(KpPEGHJL z9!9K>3$bf0ptzaW!7RgOy3ml2vGWF8xO(8hWaIlQg?J_IU9Nd76~!l|I~pw{Qk_;i zzB(LQ$Gbf-^Mlc-w{YkXmPPDxG6^x{jJXdUP#($}IN)Ei5W#qa_s9$(Lk0qb1u%$U zi)FKE40eO5FpjYS_SnF2e+TD8L={*mGs>X)XAiETi^tO|o`&pdwWr}}y9Egbk!|?c za0iB#K#s9eqZz~LnxTiw3wMm)rz!D^T4~cO4ptQ$@Bi&mr6PCjICSL__H?B@^M3?i5E1?VKVkYB<=2eU>Q6;iv6#Ick?j>fBGr9{P;R}J}TaBN|7_CvE{#xV2; zvttK)_&=E)hYyr-;WLY~^B!aU*z7olU&fWQ<9MhX`qb<=W1_qU&&rzmk*0@5rR>t|D|Fay&yMX-%KoLNVcY`L@Jtm?nwk%u4eoP5PSk3I6xJ)PaosYma6_{EPs@192=d*G3WJJad*`sUW5 z2~5MJv-oXAp9qQ^?(DAbtZy&fbpJh_SKM>==&{a2k39Bx=Vgz)?Bx&M^=RjT7g5V& z_dMRA*3RRPJo4a6A9#FS^~|2Q8$56AhI{T)`EPr|yfK~G#Y_Lc&aMMmmZU7-`e&wl z!o4r-+Xdk$URlC>V!y|RM_2?|7LcSIqDZdpuI{co)m{Cns%NIJAUUWcMUW^GB#B56 z5d@T+Bp!mWAUTK#0)k}xs%Pfz-MzaUjxS7y>8bet-}n91-Br47jm!(TeLOj_z5mj} zx!PWI>Qsh=`LZe}6J7oyiAG)V@j|Y~geh)gd*fofo+QwfdZWa48!s(wN=i&G?es%okq&mA?Di?M6oWvtUC-K`8AiAQ< z+m|H9V~7^tm&ng{C9JVmZ!{YUWit3i!pVKjDph}#L+vc2(kcASx|%uE zbbqg$>}p(IA-}6i^I#udv7`}tdENT(MjxBw&(AK!J+3SH<44&+1Hdah4Y0L=WvBgf z(ijHABv_bBWD~R4dH#=in_2#CaIj$7?$5m+^4lwQvV_8&7Rr zo1M$9gOShcvFo$r>;z(f8(>}Xh@E7ou)g4V?0n4Gb0Is8k;fZj%>%a3D^XHQ^HWKUv$iaD#F zj2VZX!k&sZP@cw~&Yr=Z$)3fYja5RP1LgQ!_B{4{_5$|T?1k(_?8WRQ?4@}6_GMTB z`sKKL`wI3-_A2&j=#JN5F3Zg?5*r=?CtCw?49gg z?A`1=?7dj>{C({G>;vqB>_hCsm;>`8?4#^s?Bkd@`xESw>{9kA_Gv79{2BIH_Br-> z_67Du_9gaZ_7(P3%*gOH+==}<-jVqR`zGFn`WE{(`wn8N@3QZKQNNG7Za-u{Vn4=e zY(K?YG#frf$IvonDfTldV|aXoDWEXh6XvTrM288BR$>{OV_5m-9J(4^oqma~LDwX_ zP)NUm`3!!Qw&~aC+H@{mhptQ4qwCXgIzhiqH=y63h)&We+zL7mb9Y`q7t(3E5oT<< z3Eh+~qMOlg(#`1>^jmaGx|nW7m(Z>0HgsG1ZMq%Zp6-Bm@a}~5;eMCyj5Xr!LU*OR z(cS4Dm;v0$J6dIUX^9z~C)$IxTxkFX-$pU~sz@$>|GB0ULn zj{X@vnf{!fLQkc?z+6*Lr)OZrx@Xa|=`ZOy^jGv;dLBKWUVt_1UPv#Z7t>4VrSv!S zGWuJ3IsF~If?i3lqF2-3(`)D-=(Y5Z^g4PyR+M-Hy^-ET|4jcvZ>G10jx6^nUsPeULsxAI9ox}AJb3h zr}VSl2J}DlB5ntZMWT4g9au9gxGfydC#hE8w8+Z5x-w55DPe8o3SI@`Ov9Dk%#Gkq znC{&F$1@LS(uhniqS)xU6=on)!PTggiP5pr`B95}jEHI}Qhe07qN${7Cn=YzEwwNG zk<~S_vZ#aM<)D-cVM1HzT4rsK3b8B9M5MWhl*oC~j&rSZg;aUlH;r|HlT)Xs!=|#e zLQ{+rrJFQEQ;0Bcq)J8FFXT)NFFZfWjc)2;mzUD%A)Pn~2OFiLR=N#Qz_@i{)Kp2q zjT7myId@4>@+vAd3a*W;^8Q?>O;Bl8dw(~k>IT2@G2Shvn{@5EW_ z;;;HxHFj&=-`J1x$vUe@R^w%I5mzoK`65ZHLE%bux}R8kK`(Ym*y;u~7ySh~Zn8?+ z?}w?;b*vYMV06he(omO~((}=t6$Y7-$<*|CT8(1q;wG62SXxuY%B(B7JR0!2RwA6k z4B!MIEWv1*4p7WyF-(n&<5-D-owl_YXSy-VQUuNjrSuRM?<6dlX4Ao3$XEwPRQNZl zc`lHKKqqxYgu}ziaq0M& z&VoUpDx#@%6(!OnN=){h8l}iPk-D+{ROb2CWeyzG^`U)7zU5QXz%qj!txLtjSfH4R zSR2ghaA2L_<+|+ML1=3LK-P{^F@sGyINaFkrb;{a?axs6FoBPtM#CMO$rMg9Fd`Nz z5M>QN8Lv%6x*iN+QlKdSW|f%REhkV0I7Qc_bmmNe9rXLy!ZC$~bBn;+W>WN9p=x+w z>z!+5XmhUMk71HTGimu2wk1cAO~Oq&!BHx0;w{Es!Ar3iVY^Jh?^$PnbODgaW#zin zmq(%0ivr=w$akbt(zAKrXJM)#8avUl3QG8EQ*O2qB9~ZJ z9O$;CYbQIb^VJ=CV?v$XrdZUt71h3tA4xO$#E`RRBP{LlQ}N(8S-czSe|hsgQPHs7sD0b3{Wz| zk9HtZ;ddyX(gyH`CUjg6PT%l`^&$X0?rI={ZgzK3Yq(1^8^zvXA}7VjgQgD`?dH-Y zfFe>>CTMZi=xk8jeS~&PV97UZjql^GJ14kR2X|&;oGEfbAF~tWj>!A~1 zUC{C{2D14xeH_jD!r8Vj%QOygB$R1~-YOGhII#_Sr`F&@i~8>MO(}$H>T9k#e}Deo zW%N`W=1Bq!lYphjcgzsSjn$bO^3+HFTpi+B2112q=_(a+1!|QXJ?+nK!K+NWHpCHZ z9SOrMw)XZSsjY`=Va6QH3i35_8uBLfd4}A>kXZEi+ye4T9q+&!Lx8_`mp-=bxU(Mc zsA)s5Z*oxNY8%Bz;s!RVfluCDpdQ8^fq)ul%B3W+30Ldg5=6M!*-baF0il!B$XyLR zb$LP?CyjGTWYQL48lml@6sYWFN~%F$!cJj@TR>;&xQcWFYzRAGGPuqI6qrT?sZPMR zegkOSN)6B4`K#Zot!PbALE3WEX)L$_kVuolG>sQ7<28Fn*;#wLC3;E##mu;~xGv?+ zs5+O5GI?ab*Z2yB-mqeuj!@#M;Zsq8pm32G!7kHIk9Z4R?nU|L%34vp**??dEhtmg z9tZ}xf&cX}kW;T!!+r_?IzlK#1@IlnP-wTqO^C+uv=d0zfLbTZO$0F~!W~gfLH~mt z4Je`=J6c)ljt!=wg|?VXp$I_6=vuS2Z$Fxb(3;*0`kt`+s?A(jg1#%KL!c}S&JM6O zZmEpDN%w#nK`D13hXcT+Fgk>Dd(;iRD@HrESJh>&vNxGj4Xo5lLb(`tQE(yHN@XGq zYYq*ep%jGi{auSmUGUh*#6$Vfy3mjUiztKd3J8Sh1md_w@qJPOL~kL|pl)4(6aavc zZ)OilUPc&b2CzwrFRWHz;(Tm6X!ht6`dtrPHYt=`Oe_p0DPRk-+C&U6lW=0MSV9Ya zGjai*7ktX=G0wJVAn3%+P5`XLEe_DGAnvkJP&W?VsF9^deIN;>RUagXzyYM-CTK4M z07b%Wh|WjTro+bCrmA=m%s2-n4QA3`-vNGaR?XISz9zuIyeJcUWKT08UIKMOB0>%Z zEgVfO&R%YyFwr4k#!ioWKv_l|o8Z2^gI=cEzVEZ1wi5b;CvKBB2rCd7`9P-cqprRM z_YG!^*zp3f8+r^p+&DcDpmyWw0a<~ofM)?}$7kGcK!d~OCb9$9*7}0+lGVK4@WSM> zqoKz{BbqXpYJ;3cz@gHcpjk_?l@=aB*+Tk4%OEPMZLcos(U}I8pK!<)?}w%6@1Owt zbsqZt*cw^a6d%Ry!E;q|wFB2xNhF#pJ-t(3Nebz~b9H+41F6Rl`XTzf)++E{u_ z4jLPIRowZ952e?{y+q4KCD46Ga}LD<4~gKCO$=3jc<&4V7-@`mAeEMkKS89L0>ll7 zY*Q;Z_3AfA)@a}{4e?3UM<7}pft>+sfF)2<-pG&5waQ>xP)IR%;1!%37TQz@V-eOP zXio|_%dkWoUPBZGmGtpY&xqO3$Ln+G>Q$80#i@uhVS|Z=`WCTjdvm$yLnY|(5~nfr zKMkflN05SsGE>A)RlgG8=-omVO@RDi=9@?a6-lF3IJpIlg~kBm8Lh;j6){G!XmyqL zAf3<7d?;bt2v&}v`zQzc*|X0Y@(_)DTyJVxZ`JyDkEJb_X&qIR+l! zKkeiNMoZv|7*}wCg(OZ29~zkif<=hFZNThJ4E$?) zO>O&)#Yf9c6>w?dDjxgJLm?rJM~Kc*8^h}$Zn6w?y;Ra(s0|Tln2!2IqkR$-52+E4ee9Mq11&AOAAf>ftamWB7)(HTBtA6vKjOaS4-2LDTAR5joaKmGFCAAkMxpa1=3VC)WvWjn6t8=n9A>)(A&>#D&gsD22IDzHW#{!vzi z7{NF3i0nn2su9@P0f@UDh$tOlBsq1DC6Mp0|LS~d0IbNJAa=ijZjyls9_`?p_W_i4 z=bmhOksWlDlFgod2&aC{n;Fdr(1J0^;C#gFk4{QX0$y-a^~7r6m>a_n+sjX`TDt|Ns4%>Q_})>aO3NtGcSXx={lS zStub1NhqyC0!2nbGR|!q#bIQ$!Zu2d$dUChnYCGOJL5Ip!69QgB(04zma$jUKv-+l zO2!?7dn}KB2{SdxlmC5L+PkOw#@YV1WbE1)yLLAA|D5}B@m^*{=w)>;vZ}Gj>c%3g z8;h)NEV3HR>h9sM1~8)r$oTp=wel8XAIAcUxjOzZZK)h~gvnqkY5(U58Nd!>ELf@z5Zwx?-?O1R3uFW|L{ z;Y|iS2FWJj%=&$zoMVdP7#0MhQBA~I|BpW&wcQlLjx89CbS4NSk|{p#{Il`U{OWIn z`ab|16Mz3>{Poq=omz2#hxStcalpwCS9hSc{%8Fsy{OLl@mHwNf3Et;MFu!;gwXQj zN%>N4AO*6M90P27e`mkZ#3i7l5=>W1fq$P(`|i0jj;?xLj;1(_T$==0l1G6~5DS?4 zpW;9aIkDw!;wXsef_vB09mrf&AGYxd|64+@?e~M)13O;T&bnULR-HZex?e48LZm#j z@=2c=3LWA|smc4z+9%b9ve#3}xj-SKkmYlNNTHx(>6vzNsC)2IJXHN$L$5-9JWtw* ze7dSHT4ff6mVCNAiGn9T!d2C|Rz5f8S+)1GNj2nkXtkg(FVWY^k1|AQF?jp}OOhvT zqNDM~9LQ(4GNj-oD~SH8c;Qz-FD$u-j{hGu?V!6dJFn1qm+D_FlvQoqbTil` z-U3TL1{`njxmaY06c+wPzXvuHRulgpg# z!(cd!fRQi?M#C6zKsZFeSQrQ6VFFBqNiZ3v6e(AvLy?X}Iu+?sWW5<+W-zmxxy@!~ zd$Wr<&s^mEANeY(ZdBi>&(TJ7_2}l&o1?cz?}{lGP`J<1dS#K&7e87gx1hHT2Gs4D;=Ptbc{~WIl4p-=nMTI zlY8=bp3bv)J}=^xypgx?PTt37_&VR>d;EYO@*{r6ulO7Px~canM#ZXMc1F9TUCyp-*R$8yJM6voA^VJd-o9Z!u%Fp4ZM3!h z+hw@YyXv^=ySBTYxSqMb>2kW4o~q~T#d^73tvBn_`mDaDALzIGgLdjDcP)2)_YU^~ z_Y?OUx6>W(@c=+RC;-KwEY!5;&rK(DqPZ}uY>Xr3Sj>aikl3trVOxXZ7czWR&vQ%AWgy6b#P=`lS&JzqT^J@4U%9g??L-rRY!=FOBRDNj@$ zXP!HGZsl%~Yf7$=oXI)I<*bu4Q%?UJmvUUpaW+T6?6KL8W?z@RUG_!*0ETING~O7` zj19(8qkHMQ?NyD607e!7BLu(*Hi7_*K*QJYF}&nIsil%EQCc!iM#)d|y?h|=$~yq^ zf;=Nn$`b%`om?eX$T>1xI^-BRT8@(aWk1;$Kz0R?T>xZD*&IMNlTBqKSzVTt#bq&Z zLhKd0#V)Z!Y!h3=X0b_Z5bMP{u~w`WE5!-`v0N+@i^U?bK+G5O#9RO|Q_KJm6U2BC zA;N_NK#Ue0M4LQhcg*e(y&XLZk&B)N0ElLy!KgRtj2fb9l#SAng!S;#@PqKZ@cHoB z@Ye9=a4F1%+2H@+vA_mL1qTNE1$zg32D=1H!D28K39cgbaPiCn|O^OJpKk2Baw7VneuAXkB$Q}W!M!8tCKnxhg! zGM~O$8E^Y}*v;4ef2PKmuYMOICE?e zIBRSOID2duIA?4GICpFmIB#qWIDc#$xZqPKU|_0(6b$THK?Mf(Lf-71KIhk){DFNF zPIGIjAPsS*f=P(W6-+_gT){NN?G?;GynO|;5brS{a}e(h@xH)3#OFeMiOn~N&%@*S z;sh+um;a{?!DMz>=h zL+OsBI||!=cS@?THQiZ#Y(sY;-Nkrr>)xV!7qfo%ehtx& zMN%KT5la$FVbzayQ-#6AMiurXw(HX_C$=Yc#H=4XCFEN>6T4xxE%qSx#GxO1om=Q2 zVsBy}984TY9EL-QBZwn$G;s_K;8@~B;#3?*oI@2(CN3DjDa1v@#W(GkDlF5++EZ%jcBA_wCE zas)XN50PWZad?EBKu(Fr$oa_mupK!+xd5Ie7e$-&Jh=q93|=5tCRf3$*N;X zmaE1la%*yDyhZLx?uL)aJyIW^l82CotznzUBgmuhC3y^a5_;rWX#j)Bv&nPt19>hD zUxF4*!$y zk{{6;`6>AsZ8-S_RkV@RCVko{Y6ogxnYO47pbn)?Lmf^XL7Ry>nmU#?3w1);#x^^3 zGIcs_4(d$meA>L!h15l~`Ke2(%V`TzS5nvPHa1b$QFqanpzfg_r7cfAkp^gMQcqLQ z(AJ`!ql&f;_57F@r~%r#)JxPWwDqVrXn?jM^%nIBZ6oS)+GN|7`jYC=wxb59A89*L zKT}29llrB?DO3&6_M(2H{-W(o{ZCum@arHRNR5f5q^6|S#7a_oQdeRFsXM91j>}KI zQbimf^%=lFq`ssP#6i*&s))0snH6!4G_NAglNMCO1=3ps#6{AVq_2oeq^}8M4E_!2 zPvQ!>1-TP(KY4Jf(1ARnLPzr03hl|0Dl{VRuZX9}rz_%V@`Z|chJ3Xmo+aO`i08<6 zD&l$a!xixY`RTEEkz5fkkzc8Zm&w0X#4F@d5wDW}s)*O9+E&EtR2?ef4XOzh@g~*e zig=6a)By1|)pJzO6Yo&HNEPuO)rSMb`;_675yS_SB{V>ML|IN*L3~WvL>2KVWm`pj zM%lH6@i}ELWgqbcXgrXELR>Iu}7iT|ljQ$>S@)R!w7G@^dKqCsQo*DD${q5iC*K~w5) zKU;%lBHhFq`HfIx{(m&q#-kc)s-?F2Fd_Q2_a1^Oa#^WBR;j9nWi>9Vexec;2q|Pg z%d-ZMzTgSI< z`B9VFVU)92{MOwzoqqE4gj~adgrrAEIL*gdI*=j`B9#=As+&LDcdw{<>RQ%KP7GYfGiIYMp1|Z>%wFA^aRIPkO#?QgoKG2CmxEbtV)%r#6vMr zNgPSB0hzr(Aw{0$*#T0zz(JnPrd7F*iZ)YLWf!uj((iYrgn7earscR&Qe#i9@vHGi zs`^=L`B_DLBffz}Z2zh&C34h-IZ{cg>V>)LN}V>^!o|@M0dY@{F~Tu<4I$w~jgvMe z#b}(2RiY9pq}WC#^}SEh#blhQq_&t8GLEE32YGfLqjWS*$65ZPQe#~umBDV6ZDTxY zu^m-e&8AgZEL_8&3?oPClv$>`J?gB0acb4a42h~oiE9fH)EZIU+Lst0Y&2tAyhE{+YuU4_mp({-ezx^!&c(}eVFXu58@%~r1+ zhNjN?u3;F4n=&a4VcU+)HBAb^gk{xyO-Rpz(52%xTfLPqG#bd%Z56R+5s=>!#zFhOs>U z?8Mwy4QsV<^~paai6?)~bFI@89AQDu+n4Y@MhXu$P8pJrZdT<1?!&6=!c+qvyN2U||^UJ1$GcY|F9rD_5oM*cM~r^4l?8 zd;6u&_m)@Vmd%*F{0`lo0>nT44?Kf+l668532YnjU?x(@J`^03=Mfq@rg^r38HiNo zoldi#ca~f~o~n|D5)<@1{)9OpVZ1!wSZP@eH^4o_2_-xAzH2Vqt-}OVz;#k7G#x>Fv~L!4!G+8fdZ;@)*A;4 z_mMGz4>HG5Xc*>PcWm3DOm>%l`YjICb2x6+91C)WkZ|C^t(&vy z0aVZ@nFj6SR-HPL_te+N`(D%d06jj|vb|^g z?8rXZ|5(?9kWn1Pqw#3$VSTbZ%PSShv4~MzX765A788}IB>o2jFfGS6-Zi9d_^mm! zF+Zp~PNEtOl{ilB)OeZY*oFa^-@dU6fO=!crOhqv_J3|BJys>nV-elD4P4GG`iV*; z+X+lmA@1%Pq-N^+iEi7r&6xPvUj)|O@_c`JWHTnDW81jLFkHfT=XJ?B@(3Zw@^O}@ zVyy^-6mbR+KjAdb^0G3e&I;pYRgA`?@y&wSXcwb#I$CGZKw192!w9RxvR4jQfwbS-|9bfx2$c&IMQsZP(M+c#Egi^#x7k*hqUmL~fke${6=f>Ur$iWbKABhUTNF5{GuOc*XIT%!%ZysKt-_S3Y4Eta?BOL=Kt1Np5+Sjb&~talG3ug zPk3uy+ua)sfWcsI_gZ>>tF^whvRX8G_eM{5WP58`miNs;;({YumiOIPmUg(iG?c7+ zFvIry{l_@WQ@UZe_qdxEa9>%{WAMoN8b88%(3nEun!+so!PPEQ8`>i5UjMjK7Dw2cxcTg z4i9xDd4vUOwbo)`=^pLXVVSnu!C+JlS8;q~&ZS(u{J_ zxm%9w<8$Z6+uP%F=f+0vl=4@63Zq(e419b}=~^jmgE~p&c|B z9-NnJ=htd2%$LX5b`f8=zYY1M5YEy`M=a(* zW*U#>)af3dTDrbZ`$;TFr65T)jq}|}47|8q5PrZr@iDFajSc%|8@{M_My?Cv!iC4= zaf(OIn}A{3j=6b&!&st;)pVz6x97U*k=;EipbMdu;W(xV7;CtBpCc^DIw3=ioH$R> zrq>6a4vTX0^XP**aZdv$9yLIYk)RTnd&H)=%j1>DI?;Q^llAP!DUMUywShoPE(N(N zL^%vdUgPKpLRqBHWbsBsbp5t^CKMH-G%lvP;;)2b}ca}6U-lQJbhQ-Nyr zG)Pk@JQcn{4HuaMPMp8Wqa*B$|0+~Q@?t7fhH2W4`H{1I%DMO@y6spNW%^fqDPB6o zo91HshXB`O0?m_V6hCuWV0j1A{!9^?7>k8io`(#OUY5 za?uJPQ`Il`INtQjE1*(mmwpGq&l$A!QA zS*(rOzDJ2JJe*(tVO^^eo!@ITNTH=vPTFkRjpOqyRU*dVeCGWHsgsT2Fp1-2INV5G z{0Z8f>1=<0Htn?W+Hn{s!|cqN>}vVCkj43;=yp2YqBwsCuG7(}8#hUYn-Hxjce3j| zRp#7J<5#7w=5Tn%=ZBPoIW*^rl2G$%{Nz~W&2ioOSnmB>*}K*st-~=z4qh_|o;Ghb+f2 zbijJ>Of?S={qeyqSxWPv^SkEdhnhQM_&it1$TOtoq4SW24!zPz*c&gJkOMQSKd{!Q zgRZTgf9k2Ho*JkGNYubbdB^t9d-t?_CA=wEP0yZRU!x}rBx*2F36BvSU_qYSGSD|u zYBWxKHQj{XMZvRKo~7C7A4W|`(;y#>uK=W&P&ujUIGU2OypoxGUWYS{JMA5cMysjn zt|Q#N>S|bTL>i}X2!w~la-;!7VdU)gZu5?FujlSnSFJd)9Vr;MEXRx@Q`2^Pc-U!0 zhH2DXNW=8}c4u{7Pi&@3Nde8a97kxJG6*TC?!F~8ZLX9JjhB4EjuebBPP<**^Ig5& zNN9W?j_Lq2$DyDBb0!uGcOhQh#iU64#p;mzRE|95C%QeyOwTn8fMi;o9*V7DLcJ4E zuCRmx^Svj&DLBF>;;bvmS_^_Z1IdKUa+XzP71_2ezVM4c@>yFLajY*it~vOa#x-a8 z3)pouu4%SyFONbX#F1^=S{*fdfPiHQREQM=_0|mj4*j7)Op}YXo!j zfWguE@i9_#5iF#pVOZhKI$?7P$9VhO4aYGpK>zBmHXg`F;6DA;UyYYpg>}F7Yb`)G z9LM^$Z=e44ulqXl@utjg|292pWcj!iX>UAU=56e=r#rT7GOB;rs|@;Koifw59sSlV zyvJlz*B#sbgZHb`%kKSuVB3zaQ)d3ykCD1kJkTP{XivpJc48hGy1o48&noA&m&cKz z+lW8QG|mK}zx;mhHe?WbxWh19LMX9Me-{6ZA0v#sg^+Nx3ym3?PShwi2lZ(>#`~E< z3KglSNQ%k6Hs~bU1YfZtUu!W_Ln8V62szI3tQwW$Nxq3dw~R8+^6B^sacDTM6LCB6 z!-jHzG>%0>Q2;o2W`cTiyV=xD->Yp(eWi7NtJz}ky+WE6EX#Ebz~r3idKODia0bV6 z{J`L#==H1W$`h9_c~Unx=37xvlV^J9CP~yv6N-2+7{CIhlmf6o6Xoj~r;V+xYzVfp z(q*-f!ZI}oP3k&@$tmr0!lo5R2Bk18r_soK&y!cGs@DhI-Fak|raFj0dZ#Be$AZ|z zCv8%aOXS()5%PTUVnS@TxQvJuv$Rqyo5rXiQnMm1hO^)X;v!K(rg2eL4Me-M zv9Wc-T?5wK8!y}1{Knm?*XazNt#!2eN^j+8`9HAVGM@vrT3Nm9|NsAG=g$NCdrv)} z`w}hM?x@6ayRqwH`I)bLmaB1`<(hZ)@v9g5ecQgDZfxNjv9)#k%qj(TfBP-`}PTrc_qw9nz%WH5l*+}horO!o0baXic>&6Q586*oPp zo3&bm!C=D1ql@$Lh!H|S$mubTaU4?(s}c`l8`%M7GOlK27fBmp8&Xxd6#4SZ*H#s) zSswq-J)Gar_#x-_{OVm)yP-kcbJwr_>RtB;Xg4%^*RSIFXF1=Ybcge^PrUKp&ps*W z4%MF0uzmK8Z#=sV?J14!Q1Rs1e}Cf>gb+4f7g!?+IZMbeOQ{afwF`zpKVa!B1o`snJFXDc^7n9jeFlH6mBhAX*>TeJUB}+}e&IM#;<&CG+mVUw%maq?nvlYAo~)ocx?d6pu)`I#J~gESLkOGR-M z1F%G*l4?>+3V(z(>5S=YIxF)mmjfThGgwy$A}NZ5UhgHONUMJMjG(rRV47zsi=;?J zI!FikAkSvmY&x4()2b}WVp2?K!%~tsiX(ZFy0&Wedhq<@rjfYyMkF*%#PzzH7@94; zLD0@^<;_MMnTEz|wR&CE9k*T|JZNj0Vb~U+3H|ZqB63~Vjc|DUXavob+-)|SO*da@ z0yz)w|DxAxD%(vH-&=3MO-1!a!`{ChKOUcPow};mYc;MJW)wFD_4<2Z*@n(FM+)(A z*Nqx%`g?Gbw`*QVI>&rp(>3UE9_JX_~YrrD-TViDVqN zw^`zQ9;LNfcNj(%JTEXc&STrMD66YUWSYKZ`U%%?1G+19YPxuY0WeK{d3TqU<+W>N z#RrDo1su@rG<7It%oLj&J3D92uql3k*Er{_6G_ofbFLXHxfH!_kNufz8J^c#O%i6? zbQ6XFDMW1Pwv!B3Rx~h*UB*F8L;KpO&Mn(yoDgCXLO|zuGug8gghX-HujG{Egba&W zI*W@LD`u%y%u@9~FE3`PEN1Dfn5Focn{SEb`I|T2fAi)CzWLtw=FMNZ{(@`Qmj8PF zJFh?JmG^>#P|`g8Mf@Fph%hoCq#BRLnyz}OdvG_pDyEb0=#w8+G=KX6W{AuLtz|vrAX5WE%$ulQGBMpZb?}t9F~s=Z}t7ha4JL z$#kU_y3MoOSF6!>l9;A?Nn7gm`oWY4LdXFQZG=>DAtc$+S&hr8 zEJ{_idqSvf4R)GaeOb;}0T0lph>O`Ymm(F0zg0|BBoR^Dte8}j*?u1@1s!{tqY@R# zN~U?LWKq3zmj_-P`@R%H`hKi}PJ6xEXt+t@HX7ZHRtFRVuQ>=}J9JFbjT^4#x^dk@ z;)RxP>bhxok!62gpbqMJ-3QwFwRm& zzI-+hSzb-wcN;d%cYH*HZDi>_4B9?_kfuIcETgfIAF4+2Ocz3?FMUU;97xecBuB9| z9U$@`;;4%xmO>?6MDh1$E7WA1C{w*u93W9L3P!QI4<>4|*t5X_CTcWRNijJoyz;*GL zH1U!Hhd$C1lrqtrncUX6bU|H*egp^1GyvT&pAIxGbqk(t0fuQhmd$@*<3gQlg16TD z{kX=h6bPA=`X}JR0bCb9XK>EB;R5c@yNm^Wvy?q8Z&;uG=F@F6-T(ypTv2xa9AZz; zfmoNGC#85U?+d7t2DMZ$25FeIWJo!$X?kGlh5^%TxsJZ> zq49I&H&!{f49*`gm1*iQ9H$oAj?gLFwi}!`^tu|Z*~0gIW_Uuw|4T3qxF6P~E^o#t zSN_zswg?#x@;IU*NgwNYrzlBwy!b(}&*LGhaD1g|uZ+f{QQ8+;y|KAf)RV+>T~8%- z<+_hhYoojRZ~6C3*Vn_qw)1>4KD(t7@tD+Q6xCH6N?q5(uz@Ent7u0cgisP23@wR8 z$T05DinutBG%j$woPXsve&aX(@uxq1^XAXri~%0Ipv<$p8l=L5DUBv7iN*HcmWN118*$tTaC;iX2mhW@0Q?`5 zFx1`P+=YwtdM%E_n$)$mRucfjw{b9G8sSSBr!a(R)+Q97yokfl;GoYiXjomH9WhEN zXVi7$0R?b*&}}p?I^gWJXg78pFiI(B@Z8v-01JXLT?GNr2*Hoy zh-8GIAE`toN>sYq)9n?@Xgb59JHB7$_|fIF9Vh6ouXj51!JVj9Gnb#$hmX2(+^9Dj zal|?96~NJ5J3GC3nz|l(&uV7b=1w=Rg_hZ<=V!WIy0J+J;e?!?P=W>VNsr9PZGN&X z9mFz>bluabRCs_87xHZX{V(h@~w6e}6U$v!3rn9gK6h^0_b)^KH>rQk;ZJ^BIw zcpAuo%fPFEOGBogzIUHKygTo{L|*~WOTa4t;HUl^_*bY206z@?fBNSD@MnJpoQ!qw zlK{TCS6=sYsZT%>vPuYoN&LuNObhcY)l`+s{wV8Wb@H?^@3lLLvTfT|NvGXg9xoP) z1x~VlfJ@tFfy3poTtBl|EEbE~#xTd(?MwS4=E~C(d8*hyLkx z%iqJ+8~*-R|K;_sdr|LKcc1*tfBBQ&_%-B&fH4gz_?4bg+yy9lF=`72Io8<%9#&-+wi~S(5tWJ0H+Qo}24rLDKR$X-mlqQ2NrIzDx zN=-vIP2YDtjWR0;T#M1jw=E$6Na0JvtA%l7JD#N6v27csX-WRPcDFm++}!LB+^F5T zZ8ht*EGz4D!^AK^13;6y;5=?NF>2_Zxygm>s`5k)r1oZLakkO{<^Z*QI^mlOqwN)&4p3$i@RLt@w)tjeCS zKT%063t122y}gF(xNiNUiS_?@`B(kA0#v<^&hr@@)WYWSubN@47Bo~T;CjAF3N`RB%cRINDcW#=OuUdi06Y3jSXpJckcp~< zOjKEAxhlOXZ%y+om*0+!R8mYj5A)*3KhWRKD6>Idf4in7n)ZB6OBm&hy$<*QWzUA@ zxs1|hQ)WPW0i}$-fP)&tn9?|=G@eiVkPdcBY0N*bzA!1wA74~G7AS2vg@ zn9bN86|WW40cHHb%Xk+yZV6}{p!C)Th0)a59rVebb%QGkT_dw~CAqBqeHl$pg z^6p#KyY>@(>Z$zG-^7358PX)1iLp` z!oc;;_V&(b<4o_T2Yo|x|5MO&=O4#2>*6-ff#vf$TU)b!w`NfYDFl~y>5MVWb*W)c z*VPzfIuAD*%`jRWE`Q`Rk_#aPsMTvUo;ZJj6McPYFQ!LO#3OxDklV;3z`L=}@)?(LHcih&&>cxkLgb&3>%FJ8FJ9cnJ%4)r`kyX8 z@Yc8DC7B|JBjgTxgqM;w$;pJ=OP)twLB5Xs1|iiz&Y0GWI733>fZJ=OGRtRWl_(** zEp+nUjGgMn&4sK#8U4T^!I#s5*p(&6i5d=ekMI@3LoEywiYSQ~11FE#D4EzL9m)6}+T91RBDUK~YnuR9n- zF@0Ix^L?+beBbv~0N)3EA6+U!;_YO)WP z!iFPWSZSm2Xq*ZUg)FLcn5Cm}I*@D>M`|=K$|{_WSt>jX@HKxlUhDiUAB~ZUW!1NM zmX5|+Ro-u=!h=-8Muqb+^1q_M`%EEtOE%uesRfSr4Vo>pW%#u=#rrf^_X2cXLsGnC z+AXjNwyx2~`MQQDeso=PT|N39L$$8qiTP!NEk9`j>%E#Jts<=27%9G{2fZ$exDsn@-uBc|a0BL2R0@O4&J`%=L|e$R1hzaUauOh52e1<+0=E7 zZ}gR}8NHpe*+A2}gK{%#SWGMz6cY3)2+&t=PhXtAIRy;eGzF(ov|*Ud#@2<6o=(w<+LpPc%T{w^y0&SW zO@D<$g8`~jv|#``Z2?rLRIdS))h$aiwrYW|TTZkFfDne4WS`=g_@qy6Bcv)7SL9Uo zDJBP0Y$Mx1P0mUO9`Z>Si7HR_-YSVB4=EPx%T$l|5?%b4Y2BH0z30^JVcl-+ezhac z1e$s#?5g+Iy4~7+RyRER{=hz?Tc&>23WpnExG@Z^v$|zzXY8Q2F2s88BZ_i1fcwJm z{_xC=vV1nYPl)@%vuCWcDu8;{TCa7xwRP*Prk$~1z1Hp4))7Jk@dl$qB%^?D6J&Yd z@DSvo^w-acg^(ZQ*>C;b?0_sUSM_RniG1o#aj2G;$?V|o;P9Xv4DcZy3`#s0l(VT- z2S|gV1O4SL7Gw?uoREFu$O4JQnYBjl>%MUuzj-(w58r(M5ktKZMfG~U9z~5>4T1$u z5XWzRSFN^?XbA?cWd=px6!aHz~dkYQ^)Q6+ZZ5@iqY7#k_A5qUUwp$gKqbxLen^FHk;@0I7^ceF1)hJNEdb>;~J#HD(6*NiIB4qRFWV` zRd#{Hg3jJR7Ky6rCypDcN*2i*nEuw+bNThJSEM(9diRYV>n(nAo=B99+}V8ZV^m_w z2NvKEwAn9%`W-ss#Iguy`gf=RQw?T6p1$%GX&rWFhm8m3|MQ9a7S1wCvDka-li(># zBA4z(2qA{h(IFhcAv%j*iGB<;ScJ0(iI6kSQsD9=)kI9Pi3syh_-$Y^AQa&R;xo}r zvf4GePOu2Jj@oe8(OJeVO<`J@ZdlR0rRf}j-Po5Bt zvYOGJ=%@WN3vZY+|Azykv*s}n{xm)hL zZ*O;B>-eF@4&toTF&^z``6EgtLP+C^w7GTLZKon3RG{!Y3}L^YB)ura5}`mbsU_*+ zVw%)Gu$~QOGXQ{~9}Sa!6r7O|fWU!h{Fr)@*3O59)0j;FUJ)E$zu|`c{ke39HV+s- z(7-^kB#{vzBO*zd0&d(UZEjz_?bJ#CbjTAqs{hGi4CDl*5}|av(I2)5l_&uq@%S>1 zvvEv-oFoz@K*&y`KWGz)QmU%Xe2JNBQBbs%q-Euck`hHI7|%Fc(t&FM$)YsmjA!Iju7nV8dnj0%{S6>X9dNVl)M}1> z@9Ciyv6m6wi=u^v(P&|@A9+4`8H?hTm3a1F5cmXw0K*HgA7C8#_~p$wZZ=q0Hx1u6 zje5u$f7|a9Syog<;WCu~6U>-w`%TUqw-0yp9hdQ@Z#$Bts-Ek}q~C`h@-g-UjDx?g zIkxTW(pKErU0GR}@AVwVaeBS^g_V_V2cYvC0l|KN!G{2j!>)ux{c%--5;7$<5^vV@ znxGi=LH22Ll52@5>UMO!(~Tlwd8+Fv_2vxZg7y0dHlY-dE zgCxnFxQK(;nOii-10Dx?ac}RS*XzNxgI=%KJM5JVvD@qI?j9bYYrS6Y-fMt*y&m*t z2fZFF8SL)v@)Pd@j=lju2_Hv3x`Yr=&Qgf7jR}p&&2*^sIO4FvF=A2`<>6Q>YMAdpMiH18Lt>$c z*)@pCo?Aj5_8}N!=)AH$V!VD~j0u5dP`Dhzenligp*#`FTmiVY8ssYqe)1{W*Y3gO zuDi&JiqNN^(QG*RJO&K&xzlJiq}NrWZrzhrbPU~yox%n%Sd?)TN*2}Es!JtNrEknFheJjv8pppK{j*+02m>bHy935LJR=zf)_MHA!E>3 zkNDj7>@$Q=ObErGQ-UcWnEVwcK*%M>AwARJiXwNqwvDfVL?lclOt7R<001G(NHJgu ztTDi7~PwJgzvV3AhIIKG#P;}o9nzg!$Dfyw_xgc#ZT_cjAk@s=i399WqYBGkwbw(oq(Og(e2w{wA zrswL8txO-$-1{gdlwd+$jfsRYC1j2OA-Jb&py_%|=!T{nddHGfl>(NyEWx}z_JvVO z2qi#?E^6OHxQ3<(RicAJ?9o&Zk6)#?UO(lI8WItgKke)->)bFx$23ga_uKUj^m?P& zXBTcx2)B%SJy`1WTkSZqKP+0dBg>kpX#m#txZ8u(*9dyOQN9F}m|N-lJIF%8(II>e z4v`JN0>2Oc0{@FS?%+ARgg5X>dRG`*K)X%doQ zITK+q&ZZ(1lQrfCp-9p-NO&k%mIOW?e$UIC@r;Qi9k1bOkrYgfgOHa+!hJr@SdbPr z({UE%U7BKPHYJ=ir`b4zvfPRAC1RY3F&m@3EJ6`h;^+dOGxc`nq*#Npnq*}<&M@3T zr&Y$MS(HVApO5o#kp(PB(r};H8RxPzNxd*A<4HEj1xwSb+=pc~2{_}^c$`JWv|`*} z1IGOwC@Y?%S(1kp<0ObH^3)BRPO~K4f$|R3hB4WZ|1QT-V_|Nakxz#JS7Ct%WPK2dkiz_5^{uU}T`sE()`fgkm5eG`xp# z&d1<7ICoWA07>RKj+`?vErnqu31Cb~b@4Xd3j#@uTNOrW8b&%SiahsR0%&JvhKMss zf^nm@OOybZ2Jltmbm1gW)^!z&tE$uLcuYt)1MrefZ!1WVW~9LwG&mg?A*5@W*kSAr z!5PUjxcNLnYPy)YT7G(b~H!xXdOf?(?7C(d&2-@EH%mgv! zg1AOe1n`V!v>_nRbL$hpS;QGQkClm;CV*RG2@Kps5K=;LMp6r|l>j&({d%_9T#CX% zvd$Ecj1jDcvc^CQ?i!;Q6P)1V!Y>RM6Nx(2p!2KTmC}1Mj$Uvi49$J=yWiG)60REc9Fi4<=x=IMv2n7ja z2{S&2F|J@+t^>^eQDqY6G7!R4N#a*VDah>8J-vcP15se*AXo6N7lr{S~c z7W4o@!w9kPWAtL)8oeecM%oRJOQqRM5@}h<5Og;Q-U-iC$E<&_E5xe3q>BvB00z9| z>XgR$o$y$d0c6!<@c3%Wm{-H4%a<2ZfOPTljcJV#Sq+)j7!6uo0`HbD2Grxe(W@nF3E z=_5pp*Pk@cCIHWt_u5F~ogujc8;#Kjk7oyUPoXfMrzvyTRAtKs3 zHRu8Kh9^%AJ-Nxv_WD|@*=((4O^aa0ENVF2nipVjolf9luKD2E!cSmV!uYhQ>BO)l z8p;ZQrkMcOHnGTNA6&m>HM~Zn9>dD~>C^M07184jHwK8K?d|>mTDI#LrqZ-S47ydX z*%k(3DH>qkZUa6Ea9mM41j~wIFcv#-ub*2JA>m8Dz9vds#3_%fG#6FQ(^%xYr{gsm z<{y2zB^iS}Y@J^&7 z2O-Ee_$GtoB=+FxQy>1~+n@ZlQ{VgPcRln}eeVAkj^Lf2{ik=m>qr0W>8EFJg>$pd z>+X&&KxdA|WWGv%l1}nT6{SfUR!N#{!N~3w!d7NiiYms-%3?Z*?2w;ibc1kyVRLOs zmPb)&JIu5~%Q75OmO-w^(Y)k&y|ybnIq+T6I~|X^?_XY6Y_$MdEkpM)kzA(^GANem zxK_t?NgN6QU($quW#6RKzFjf$;M+H#h>N^HmFuAxhwmpvkTb!cB^!r#DyIf|%gzqe z>x+S7LsuvYoY2vsj8*4nbVxy4Gvup$K^Z*RpxY{b=@=d;1Y?@SwLHRRhpe2O4|8 zqup2Tpw(V_F+vC_2pwHNrp6QKCA_pR;fK+8qfa6PoI6V*wWga8|Iw>}UQtitC;@9- zghJ}A2M9LiRdXO@y;DPvr z@s~6FN$x@rb`Th*|4JP|ZHo|t4H%ei+t?36AKSLh0Am}%u+52VnL1Zw&?v>SB&qVS zCX6#LjcCWm>2hq7{Sm@2q*%$8-T*QmxecX-1$w- zOxi9LdH!)b;GP$gF@Th048@+ugZ8oKlZ<%cG6pV$F~~t-q?k_j4^HnqIs%T4?mRu% zPtM4m!HyaBL)!DHvody%2IQQZ+;gaBM0wuP8|nDzL@tFezD5u5frPX z^0ToiO2pHY5xP`wv2FPGs3y1Fc*WCqdH#K;JDrX-HuR@uiM(^j5Y(s54MKl#crw@- z`r~207-Cn+yKBIwtPG%Bfe7;Ze(j#&+0Y15Od%MyCqgnd&tX=9U|P`hKs1ST&uH*YFVmg zcYY_h1Q!@W7w5IWp@0w&H{N*r?S$jw2NF=6WSQ35BdKEezMJHQ$GyYxn56KC$Rw>c z4aTFP??sVkh?u7oqPWxPM?iA_@ewedoL;!|tB5foM$BalV2sAZI0NJ45ixdZ#T|bFqwfe7!5ByhKJ8N4$_I4>Ui0+To-2%{7+e)5)wCm z5A$pYpx%x37UdKrZov<-|E;y=ObVzlOfOUwXn+64i}v;dzf`Dkr({Zset&b^>j6bR zNlxISm83}m#f7qM&n0e=#~ZZv<4jD?$K&6^u1H}D(EN~gjH-TCgm*8qo~}3^@B@`rR>#u=A2p`OODMwvxKfjb&INM z)~n5mT^u%5UiFfB7qh?atw!^>3ClvPD0O zUhiNvZS9Uvh;uq-g`qc5=CIqrBP$r7?~vm=4!ylT1P5qUpiaA0?1Dqt$wyJn_Y^tX9?P>gpew?!|lxnT7_! zWypgr^udMQGc&$Cme8ix&r?lYnp)d-s$pH_rw=1dif^8|s< zLkmVvG9c1zPjWaQP=R8ly*bZ+Sdo2Ck!8j6Wd#!K`SD<3J|URWWN~pg^aGgZgz~6f zuP-gnr}cWh&ncDXU>96(5QdC}LWE2TMeKP(MA2Y8h~iMd1y4~F#q$(JQM{Kru8Xnj zI*#W8xSo?wmWQ?-b0~q>_HcQ!o7L*JrkS$rtBTKMQ`79anC#|Jtrnwy9S{N(pjL|( z!;s4WG7rPW3S7@wKXUdKmN0@>q5z>`T$O3E2qgAm5_>55eQN>y%6(HJ%XYST-^PW_ z`!<*1sjcBaA4~sgtIF(eAe{XTg!jVI%H}uxu?OD)TtgD7A=Cs>d3`AsxdmA~g1iem z5T`ju@y9+E&kkVvob(yvet3Y5Nm;P(*Z*09YqO`OQ+V4!_P%EGOk!Ev?;fN6v&eP0 zhHQj@@U+Ug*nxg^yY+B=_ZvUypO>F{qc?ljhO^r2^A3CfuFc+g%PsfJFD>17zBrE% zLG~l*^R%x}(hVGS#x0hC^xHFbP)GK$6s5*TI?ZW?RXS6VHo3P9?Wi zb3FjhtJNKeNGxczgAhQ3t#%;MU-7pWT$kQ_7S7!aFa<)K-_r?(_2c{d$8i{}u810c zJ^;q5R{dABqBsdu~sjxT!((dpI%U{ULI z#ZY@W18pd}otnTPYTb_b`gh-YzK-c{8xM4vpT%UTS+O0M#A#CHlP**|6CsbI5$y4U zYy(36-MHp7W%ZarPXH`Q<}r;g|KU6Dl#F9KyNaFuRj%$C^dHD#JJYfLiZePk&VJAT zf4`3PA0+LK@6oZ25F!X2UH5;FCP8as(Oig=9T;c(gWN7l?9(#FpC==;|65T=)}`u- zEcNhMlK=3LJ)(?cX^(8WLgFKZ3_bVaIw-f53Mdu1q2w4!P6?O1gQj1cOb0<7d!>|4L3#L%%zJLFI~KN@#5^SE?v5K@mnrlEYp{+ z$G3+ZU58g|1(O!?P!pkHnnLIt6FlXDa8dC%$VI=1gMJZ1{4?^;Y<#%>A?Zu@7kj;z z_U`}cSJT;F&%q;eZ<$Z$UvtnsP%aHGf?m5;`+>Q+mtDJt5Rwo&vY+;KP>jat3VJu% z)Ah4AFa$VW%~XshDG#Pt7p7?vCvg-3c}6j%BWEGKVw6wDgCA!O$z` zhJY~))5;qT21CD0hX7-v<3~|T;aq7&{h(vGuCv_e1pPVh9r-~0Z^R+r_N}sBAAbDv z^m;)6KV8STuF0IsTB}Nj06Sp^e#!6U006_Z@&|*#(EsK(4*|Y|`;Tljo%qzx(ko4h z{pY32Xd-=%T8<0gI<4mY!!|!&xSSU90>25a(_BH<3W~+T+>nty)oG6^>lsMJ27yN z(}WP-W$>s0%wusfR#H&G`N*>dXthS;?)&WRu81e&aRl>rm`4mgx8u4V1LSor9fus} zg?e&OH>`O@LOM*d+0kFTK2}8^+0ymp$;cxrii##pF%VFF*8|LHLE7-kCi1mn6jHU zAACnaROF21=l?rrEH8)(By&bgO18Lk?b@XUladjeU!B#8uBm!<^(&|-=>MW${-_W_ zytvzau@FK$T3xz$-+dP^RXXJ87Jd}BuDLYWmpI?){Jir~XuKX@h97sFG+QmOzM}KR zOil7)wOpxXvzRx_#d5Q)W^924#VVcEi}j*Q0NmuxhX?A@<@pqFia8#pq>`0;aYSCI zTyd-r!Llq8SV&Q8+iYtVzY5i?p8oZ>P z(#tWFnC5!gS%bX6)0vqyi*2o~SO0_j3I;i1BRD!;kuA7eI5M@9D~n@bB@c^oSt|6e zT&&31gBouTyWteN_0nvwb;2Z6e1%WXpCFi6hi9IbPgaBhgLX!3v3j)RzL8Wde zq)-9pi4-=LTna9HBFTB#Y8ORYamg6t!V+o4IpfmxY$jc+6iMv~=DNO*OmNP%R>Idp zgzy=+))Hf!6Uz$5BoCExtrbdfA&u)=#jUkMaNoL&0iqjOmtxKV(H;!@AeQGzQ52q# zL;_&k0*Q5933x^kREQTGFm97LFj8>>2qc9UM@BG!7{?s|2sGOT4}7Te|D-(lh5+nO z%QOuT#&JQc-S6iYYAUnSEj%?I#qCbN+Yw4IL13PgQUb2dT}uGzNbm?i{=40rwTU{B z=3IGhg0m=UMc@EgZJ?Cpa9!8ffS8hstd)TD(}d0#3BWQ~MqtACBymGbaHg~t=Re$L zup0-uPH zu&r({)^S`=uhlZjL}8jF!e~G66j7+NsPrNvoF|^Aby^bTajInKmr*DSMN%kI%J*!n z&|};HMo0ky0fK3Q;S3-^I*xMm3%`bs<3mozshy2;oAZeCtn(Jffy8OqK-qMOjth~A zP*b%waLktFMU^^?olvbeMT2L<)1~MNHX5%rV-^pD#dc7Ree*7-Z$Bn=aL{ObvEO4Y z>U2-LWhqz7W&QV}EW7tNjj_Yw(I?xhS06F@;(XrO&?E7|!S%wrw7zgU|2VA4SJSCd zt=0}cWNel8w$uAb2$>a~PFuBPt5wXaiSm4}8q3LKo^p=mtj>QSmh^Zik|f^UJ-YbV zcU9AAdFkLFETozmcW>{aQj#4`ciOg!8KDL6d8-p}kwHFx*^KKd#ivw13Bl-t zULQ1`>>iBA1f|2+YU`%Ei#on(({a= z$QTW3ds1)^=_oolOp`Ed`_{l$Qlj*Qlpq<$v%@_1#~1z_-in`dcAR^W*(-CezzgDP zzSx$_1$+=KH_f&d8sf@^-sCMKPwP6z&h69l#X5&bx(!#T^J|;!m0N5d zKqa)?m;;K*Df_?|oPV$tg+Lg!@X*wm{X1$u>S_fj?LO)${fk<84;SV>>?w`s5k@T* zMt9%!9jkcnD2&1g=Re?7p7Pw)T5I?Gt)9|adGB4LelKo9|7Nn|EI1O&=plMkAdab7 zUQA`7GWGE`{#x?wpZJLyXyr-b`bpy3G|5Zow)$%F*I)N_wkQ%$3O|7F3+W|AVR;-M zHg)c-mAS#BFxlF+HnsFD9idfagR`5J`pjD)=4fR)Z)^Uri0{3Oi}K*$zU01xgR=0A z=Fy6ZyfrVgOwVN;w|iNpH!9D2@`1g5!e>5Ot!}8-UmFa>XWq;5yx(uvhijHuuXUTQ zOZOqmZqE^NIsixTapa&WI*IN=FF>zCPv^Avcu0Q^OOuL35nZp0b4K~3nq*UA)p?Z! z)D>L)ASKLxq}|8pW;0DVvG{0a!KkunLT;5u`c*P{DNo@8lCHZBA$BTiDmP7XSY}zb z3;4!-d07YpOA>+YICiPNFG*Y5kGzwutn3||nkqCGwtyTAlHJe{rE+`#xGwxb0wO+uiQKW0IRME{d8u`-P5S zsA{9$Y8r;D)N7p%r9=&NN~jv@2IdMSvf{`21dXH7vq(qvE2PM{2tmsVkEMYpz)uTs z>74yK%+2quV!XC?$NaHkvDel0UU#Y3`-#2zIk;nO4dYc17mw}r_pjc)zi)qZ_Bm*` z(z)??E^W2pw*KC+iyyW3_wT;Cj}Rbq^hj5QtFcN5r&7bm2R?3dM0f|6>G z$QNN4!nb_#5fU+JR^ZE>Qoupb?RL9?58d%-L)Wvp`DW1Wv^!xr7=(dhaS}y?Sl)>G zrU3w0gqqgf)O1zBU|XHeiWEitKH&z`YH7OsripD+sw%KOhygSeL%W?T0KQj-O!7Q; zun6G$8-ee)n#A;63}HANu81V*bTQU78T-Czna9Eq@cfXt9_JB42vOu79m1#K5Lw7a zb(A1f^#h-?Mc_porO6iV7yY2<7yV$^XPmX6pB^XIg^zk}%e(EfE!TsO&K~?fxO4Uw zMXx8G+j=T&d;T4F_+C2{moB~F(xsmV&ufQa+w*e$9gUCHBaPjzRW0JUP_=B6wYBroTTn5!IBzlz zCJ_#GCD334#8oLI2kJDBi#Qg?q!}3GG{0h0&FAwb^CIfl)W!6^)r)YqY52Zj02qcp zsuj%j4Rhoh<|5u`x#~+MdVlNz*Wsi8a(sH-%igORVtq{RbBLoG(0Oz>dO3P8LQR@) zy{x3l1YvY7pEP?txzJz6IGYHcGcGFO5(s{a&5X*btgO63iDU#-7=%`oL6QX>n1h#x zj}zXI!lvGd-+rj<^#NQsPb>fjtEvqE8<$pBMs1n7u7_oiNpN=5_lXP&c&_WZ9w;D_ z;7kZ$z}t4)-C+c9?8^Yov2nWvP~U=n-ypbn4SLdM2L=58$Y z6CRpZKBcXHfm=SX-r%J01;he7o*vGG|u3=mh7po`&NQN87Kll1! zXYS-AR_vZKH@8;KrP`v}Y!1^_ONNX6*}*fdz~zfd$GZ<9f~=$K@UL(Uapa>KLLN_J z!E;YA&VU~7r!$k`R_}d0z55wGv5iJ6j=^zidsiR53i&(U@!xmAg*W3UZZ!-$ zsX5NSf8!f@bUyj3@K^BMfrjVB1W(Qds(9cqaXau=c=y;7!@bwVL&x?<>+4I41TQWf z%Q_v)B+vXE{o&{D?cq^gHr_e5K*{{V$^E4zY|7l*yX@8wlCFh7MlqJ$?#B?S)Po1d z&RNJ^ZY5O(jHgf~Kob`^jDH9S4i|6*a|OpULG_D%QD7{TiYr#eq+Rn5)E-)4XgJDXSNlnEp%NiCVG$A%3TzAOZ=)tk z&<1)wLc_3Q@~g6$&_WvtpLf5glHb0dvP#k_<`r?U9pf_xN#$(-mo<6kt*tHZNjNBnn$>2k zj`7^w=GNRC0I4(9ewa$GD^W^KlhU$|alM9Rt~GI8<1((*@mJhle~Uh&HtCbbbrAc^i)en42camdo^MtJ zJT2N@tmBL7`wN--fnR7z#%*k;JA-ww9e^P0cEbQ7f$bky`r;H3I5w=SK-xYJ_Vaqa zjj?@Y#RjmSNS?kh=n7R0Y4F}IctS50OuE+4SrG0B)NV=NO$m6Bv^_FfAPKWh<6v`EJEjU%`hF{Js=M-fWVl1v_pgS?2_ zUh`&{-ANZY1Zx2=*l|IX2XT?+L0klR5EtjqKb?2GFn|91(@&r8cK`1D`KO=GyWLk# zr_(9;pZVW2Z@hZ-jh|`F|Jxg{UY&2v|J!HYc=hUw^Zd+bK7%BLj;@arGfmV&8QMp; zBjkBh`B4fXxF--14}3Zg9Jd@=!eHV!xNjU(MVO0enkA_l2?`6ARayLsq8J+InxQC# z#+YU(2xixB{J5#s{P%hMgzNWQXL+jI-w^~|Nn%FWnHp9v2*9y573guQY4$!nPC-*` zN7cT|_5FVR41}T|i4c@uRdcD{7xJ(`8kY2pb|p}h6UWhxdf^sbz%55Pg>#2) zpE)0OKIS-4Q)Za2sa6&(fMzOEf4)d`#%>Csl(bDuR2x_`tukH&D&Ls9ZMCt@n5KX0 zP!m)1g$dgmGk4ZM8U?y`_Xlr?u&3yV_jghtTzyt|NZXo4zM=lZPVc?Gz z7gQ{mlp+}%QSsaHS_Sl21 zVssag2$BS7g~t62Mu4~_r9QG5@gR-?mj%I*B?_q}fbwN&&XnxMNo$f7o)l8H0~vW> zKMdgfvyZ9@PHwA06_`{>@P00Z@W8njr#E+sJ-k()$uLYNJ^5|4Gdoqn&|>FzET zw>^4#%D3A`xVUYx*!@e_CveUF&t){dm$M7U$h8eFF!0r%cb{ z6yO4}8z*TD+BujWXK8(^{$%dVdD!`6=eyW95PZ#)qs^&28)A5h#|6!-o^6JhM~71= zp3PLbtufjg6D0@N$k0Ug5%TCNU?MxIVcb+_+FIAM6KX?5&#-4RYih~S!7dvoCFk*S z0dd?eqnIy`52G-&*1r01=KF#v8D>(d%=bU@P~}BYNH+{!i{S7mE`uwffq2yPXn|5NURF`05vvD3sE<@1q(vh$FT+s(nu~rF?gn&6BkV zpMIY2=TfTFw1+TBVzxcVSu}`-cj3jzLQOP83+Ony5usdImd7c?+p6gZ@-pRSd=Ux} z7kP`@77ybh4?4TlWoF`LTj#lHo)$t8|J4|J{n7V-axxg=V;{aDv~0r&ZoY8gQi;K~ z>+r}s-?`fBZEXxV?0# z9~TWx{pUFT?D|}aAxSp221B^bkrlklc(=f~KnNj@kRGT2M=q+NWi&y1=x&56mcADF zbH^7U$S0L>zgmW55YLM^;L{WvM}W{DwB! zA+5K#WWCk=?74G`aRVH;e&^Mu@52Wix8L(T0N3kJ?1(6e62U_sY2qog&KnY476RIB z_;83@DUW&H3sPNewp8`dFxzG?3;_I~*Y|uFDO7?8FiFQMwSz<%I*sm-oO+p}MD?xX zMHPR9!Hnjy!Ac&TkWy9OWSVeai}AElaw+B5L|hD!lwY}uWy!wn*si!o^1MdJ_b>8y z*{IP>=b8=JsQFK;CyqZf!r#Q~lU>CPd{0qi#rM6Q#UzParuFG&P=ePCPH^;1Gp1o0 ze(Y90&L%gfHQNq??q;(wKi7bST{s7q!Km)v-4)u)InZ6M$a3KOz9P#8vrNlkjJ?iv znhn==-A2=Kt^KRx+=O93yXlGgY^%yNOGc<`bnwt=ZE4SHc1$2jAuhEs&SD;l8aFk9 zK*xHxB=rzPeWGDiKKcWHg)zzOxSlJ^vh2B@E4NLC@-InqvdlPOU!u4np124MX=_L)Q)WPRlTv&bi`xp2uaGJ33>M2`9iX z{qGzb_*NL!YXG(SFsav9JDvw%T4MOE!`ilQfMeI|z|p6u*KG$Z&~?w#b@;LpouBg? zC_`t_!!%mm8wOejJ1|YEzuvHwZ%3@Im;)|`n zBWLfSO^NCNU|QIuQkNPg>=9@wiURj6V}x){W|EY1`Y#w$j5`&RWlCu?@X-PeA6|e7AwZsFJ03;? z30`KKEKRDGkU{?aC=3C@Fq-|Y!M_OSlmaUmr64h&6j-yA^E>45*a=Ec93uc^S$gaQ z1)wJ$lWNlIj!V+<*Gc!1I3^^HNeFmtmEg5C3>rDUN66lBg2~C_lpa4xo`jouX{PYG zUQd>HKQCbm08989*3=Qf1QWv7zgxnBz3sRp9e*3!23ExbP%-~+8ABLG{ZPOr@V6Y} z0Q}fnILN@>wa)<9{<|1Z{>(lDVEfN-gb)IRj{Y1D;3H^)&@f;zZHpLPv*~CvXf2)Iz;6(uqbW zM+nl)EW_+Txdqb^2%Z*kz|)EgUWl|3K^07}VNnTQrBwj`@i)5uH>x@{*!54Iy=Up3 zv!DF&s`~Lyp1mhLckbM|u*~wq@(c$pM zzj#r;^T;#P>2&(cZ`yCOC4}%g9H23}ioQGNkduU5R9Qj7nZd+k;A;(J6Mz2LPdRj{@s%DM|Xt0-eQ*D4J?ainQcbk|*TowlM_b`-RhHn-|ElFp@bWWQyqO53D7iV}<^N~K_KGMx?w z1hhp}Yu_$;ZoTf=fo)rQw_THYz1Ad{QZ6yc3<41v2H+^}B{dOxo~Ij{CTap>LKQ+u zFR$qu{Y}D|5CcF>q{DvS|5ywF0GqvD&*%1>5I$fc z&xH*|Nz)xo3KtfVTWXdC;8>w!SxVRtjfSQNgit~WQG6~l?zxRP@WXDqVd;|OHNuc_ zA7e@+Ns^WwB?ub`0n(4Y3jYevp~n%Dk~B-wEUC(>tftu{V4QI#+*TUnth}uJBu;f} zgE6F+uE0qtxaqO)F=J;rtkcCpLcuwGWI@IOPF1VDF5ph50v3jnv{KLa`84GM<}xUb zi`$;At12ulnUsQN+0YJj!yv?WeBZJXDj9}l0mT^R1_KIv`_Es6C|+ATxjCH<0O(*a z$tMC71vlDlKLAhFda7xH@Lb3z!t=1JsnAnQ3ya~f6JSsk7$j*F2O)*}%JP*bj~~Yf zAqAmvZoY}SXa#MdGw2cYI`kfN$Rh^s3!0C!5*4YEbXui;fI%oKw?4}gsWej*QCbmE zR%w~*h&n}CLCUybKms}qtXY=L23i9ry0Uu=lacaJ@GxLp>=!wiaHwXLq}5YHN?6PI z5#xa`cql3ix(BGNQs_C(#(3PZ70sR7mi|^Kpb9424SFDTu)ZD|(=b#-9UM~7~&DE9V>f(w~5zfWq*OsJq7^Q?TiZs)GDO905O;^ubU zFig`h>ekd%b_v;4+^JQ5|G-;`M_R41VjA{}QJei6Z*{vZ?wD$}zMd(T^DV}TZJ5fa z-69^T!J4&f)HZ9g^x41ID@89td!(4go1QlZR!jWheDis#=^S(@+fqgw>)Gcecgb%E zP1p6X;V-$;J|X+%6``){nm8T<;C*>AZleYop;Zqi?-@29I?LGficOQW+yNG3Oq9hm z9Xct=;9eibkp(b|i29ELCRhNQhwDz%8wHcJ5^1yrF%R>Q2VorXAg_>53Qe;jFXC~= zifIuu9!&C*13#SMLz*IF8C$k(1F%*d>NNp7Xf`Tux&Nmy!q^cIW?{QUurEM2$^LNw z-&^2ZR$Rw20c3H>kfwwZ9b=u)(+}~M#2^{q8tyS_5WFPZ7y3HCIx>|KLKK-jg?*0@ z&&O{~l)ghw2oZ)WOy)^T_4FanN;ez=M=d3RfC0v~VuL41ygnl)?vu!DVT_eHYeUN3 zE*0Q6_7TTdSUS!N|AQR7*V%I}I`=p)cV6eb*KyKHjif5diHjF277BRuNU5szly8w- ztXe4p#1mvjNmXt%$?QdQnpG}WWnRzf?P^(GwoGjjEw+DwL9%L!6w1f_p$02ZA_+U$9eEJNVm> zqOf`c7_h^la9nyhXV`lMq;&AWy!Pu(%hF11wJD0`8ytt6_6xW0+FS|2 z?T*7#4C9ranN$r}B_%o17jQ)*NgHV!mkaGFVpiYn`%L$`TQ>|xWfX#X-NCZeQt!ET zaNv7g?Ruhc-D+obv7Qmyty#Sn5(`d`kGnn4$;n;w5C~rR&_hkH)gnY;YZ8RX=AOHs z!pCSjJ#89a%HZVqXfOck`r`3;5{8z1U$|Mex4XXU`GW{fE0j z;z=nKgqjy$fWOJKq)r@*=%%H^xkMBytMDVrT4Q{#6UGUb!_m%8n(pk3hLR_7*ztU0 ztW^S$RN8XE>e=Hju4^W^)k;PXJ>TjPrUnK`LA*C`zRk@s|y%kY5+q z8U*#KLeRRMqLfO^6ma3T*{;aC9NN@tu3VLrfX}ohXtwt}+B8J>r?(5)>l-8K`1r2Z zl|bofF#E@x_l2%8#<(q@g>R$z{I*3X<2_S(qzj1Jg4ayQyxvrA)$;SrD65n{a-X+?tFJIo>C1~{LWv@SONvV~T z6esi>x~4dP;THZMZpHmJo^?K*!HcQ|a3WfGL?%_5RlZ*p-c zS8n<@_|29D--3m#7n5p|t4)OP(Y$iJRr$}V-uCXqNfLLytVkB^xNnm1{q+3nGT$cx zxl+jPLs=Y;N8K(M;~d@YXdK7si@H(>Z#dtrJDg2orZdjl_3Xsx7S0YCW^vSNN0IL6 zCR$Nk5<8 zvp3ZBx4+^YuXx}G>jxgV{RwRF)LY*2mfwHo{3o7y<~!j0*L$vALz3sEaSNZGqo`x) z+~z#yI1MvI$4gl#H<+ub+K-cEQ8rRk+X-e$mf@9H0qPxFPgy*G?SNiKUOtTxG}Y+gAkh zOz3%hz^t+VQ5dU#Jm5U}h*b)r?U3WV@aOmpe4Df5Jmfe{%e|J$4lvuK+h*CsmctxG zvKgl7>jmT_p`?pzJ77}o;1&o+wQW>J{Y~~Bj^RIQgyr3*uNK^hoHA&W<_&=&$ zk-=v-xH{v|fn!&0$j5WTVLVJ-_Xez;e{4F=miXm{*L+I!_-i2$(LIX5G6}k&OiO?_s~%Gou_N~CdOK|b}8?Hi1<9KH^OLZ7JHEO|V%{XKeDUVpoN zWoI#^rey9y%^fV>U7xGZy)6ns_$rn3TF3P?T@L{`R{}?3cDv`f@Jz33vCRcaUQ72|mli=s9$UV}-1;y;w1MHwr`87rb}oF&;b zn`YB|oK3TF{+N|bf^jwu_&Cp&yaJ1H7G-ge7n#UY&o@p>=c1k@GGgDW1sgAjrU;|c zJXF9jgRj<`*=nh>d^Ow5R$Guqk#3x@WoNPy4k=|}zS^t^!#P_WdKGJ6tsyp5 zzAD$7c|BiDHuGw}m@MX1R>?(HQ>|>3t(J9XH_Q6WXew1#`C?P9_9HSr&t}4G)$upL z>CDIZue+`V?7Iw7=r`I$tKWbJ6)t+<`_Akg=wP?Lv{yCQR(qG~U3_%2*=*j6KuKn# z(3*KJX)xcCKGIyrK&-jmC0_wjN~T>4@-(27WX5%|)A?rkzSbJr@Sy8#7r35@7kxW; zNh@Q7Am&P?xOH8?a}8HY-q6Z*0oPS}LP9XD1?RM@m1~JyS85FsB=Mj*0Op_|aO~C> z_p2Sx_q=Jff3e=htdb^S-TImZv_ZjPJ9q%%_W_@}GDrI*7k6uv^V@DeDM598=}m{% zt{oj+yLR|M0B9xnRPa3KLhu{~7iGW&l7ys~c07&-x8upWTyQA~)+my(a$ObsV15fo zLe8`lfGW7=(_90j6U|5{$#@WQrleqG60SK9Le8|9Lhxy>0n%ZrIS-b!*7pekuesDq z`wjdw&IRY3i-#`NH6agScVTg1p(`MrTSrK5gY!9Rp=Gp&cF}qC5PB_orrn6XAEDvY zgni&5BYK@ra&V?~d@kZN4&pSYOtelAr=rSvJc1wvCmnMSzf24pfop*TO+gBN(ryrk zH7~8goX5OF<}#kgMGh=}Cd+DQFn<{q(-g~=Bw2FPy+vS{{t~6OX@b}ALu{`uw_C0D z@`*5X2XdTxhG7_Z1$TAoWjD6oynf|a4zOI9& ziQ~2n-wWpK$$PKdzP;nl7Pv*J#TDiM^t$mqASrkA+ksHmqjA4~-gM2-JYQ88FOp|+ z4tj>cJd<JI=YVel<6#Px&2mj2f7=fu;Nr3tb=AJnZ`U1@=D~sskQzPAsT4W`&L0( zNqG<#?{Y>~@i-0QA{RcBhBIWkBk>4$o{ry$zBf6}{jGE7_kOwC+ir>8^uV)=+5Ub$ zzmOk4o-bTY)7igWc*QF&+SeU=1|Bk-sg@5_rgAcyr%9VFKc)InDceHL^SwY(Q9>~aN ztdM;ZdI-G{eH1;1eja@d;Yz@OTX7Wd9L?12&)b(;ueg%p}ip`Ezcwt}7YhFz({8 zPsFq8r;&%CEX3;t6DH;mLZc@b*BL=b^?SX-2(DQh>OFhS~hph4(? z#hfhLy~a{T9hZ2L%wl~m30!Hgl+g!v^P}eMeRUW`F2KJ(v7WG$5V4pedO9OSDIxs% zjKYkH)a664h-ToYvcha3mw~b78g$F>^QeMFLqwzyxw`U z^BK-pJI-{bRst?O*ROw8vq^{vsbsww_=q`c&a%vB)r_{6ZK`D8CXXe{*fja7oTW)n>I(o2H!Axmuu{VsKJC*I)zf+SG|2P(`xFJ+PPcE{xxjK;vF%>V^*p!583Ke* zh7p`Y=Mbt=pEDjMfUOFEF;7qhgBA>IdU|-CETS@~N9KK}P3!6EPrSg7yPgL@*jn&h zlSq0L-&pngfV;g^Vgmwm3v@%hbD`74_@u(yjsvi{6ox7#PN#kDSUfafh`j>9m>jV=uMSpuFr-f#gkS@wGXzTelh-6VlDIr%1x4HF}T z5sFjPKA(go&qbnyrCt+bSZrBDzBx*4m~?x>y1`G)U0?{f4w^R0h{oLZyGB7E6Rc=@ z!!=A@_d?y!EvtJD!}-j0HH^7owuS(j=19^KfbTWd*bq1C03o5Z0E>f(a9|7mJh*OC zmV2%XrWrLX8&m~)!@c;WD+EMs-bM4?fp16>0jwr%U4t5$r8_JYA;w{0#=UrL_0rnPN&}*(8ndsnj}LCn{wf0&*z9Xx3o#BSW}tU`e!)z2 z2kQcdAuf9~?!C0ShN0CO;V{Ia{==)GIt`8xL(4~p@NPIn+vpbbFnT5W8u~+o*de5R z>^vT}A{8Vo>+A&C<{t!S7-m7xn)AEIL+y-{-| z*Prrsh-DViwMK)& zFtRR;mn1=`l>E@)^`pr+_KXRX=esVLF-lo&1Etd>%UqWOLKss5f&-qPcwy)vVl3%) z@*H5yi+jCJ$G41(?Y`2uT)2jD;6KH~;cyUVK>%uZ#*?%#O$h{}Gs!p@Q_?m4 z!E`$MbVe}NSjHU3ahT)0@KM~t^A2-b&d71nvbu^mpzh&yQL$B1u0DG3_`ydzlZW^E z%WuB?nYXr{!QS)F-=%WtIR6>!pZ~!#&*)FKIe6hCw8RIUJDq!-N1RtXZ*kt{yvO-e z=QEwpcfQ>DYUcyacRJtie9-v`=jWYYbN-|AyUrgw|L8c=db^pOAgUy&X4OPes;pG) zA&ifM)MBl$3oQ5u#iYt1qC0c(Fn?7oK1^rQi-5Q z%VNB|coj&3r&Yj3T#cX-TvUR`sTYzYyr(>^Ql3_M9E!5ad7AU8%mpukO7OG@<~E26 zk&K|od7LLH8@}gPobfd0JCO4#u7uAEO$FZhmMBi&*Ox^19!q}DP#1$ zyOO&edl|)W$BSIU2;$bl*U4|3Idc)iINvD&?!_46JMi?bhewoxefk#brp*+-fB#|lDP7kt zU%dT1JAbO>zy#nyp!A{JUxi=3wMhYJSuzZzf9mFN=hEVZt4VU^_T|g&#&;2bJMN(X z^qzZk4sh3naBJ&C-~xXBeE{M2+zhF!69^;k=ny_-n`sUZg19e-K(Fs`n9pjF!>T;~q8jg5cl?!w z|MAKG2oB+6$V5#vhYE!3XMS0+Uh{^HKh&$Fm=l&&DEv%POV2aLpxv3|+x6Pj6DMML zOVE7bt+z&ABRYTnMOKY%jz|5Kv?xu#L-j|`oQa#A_}K2{w>BEvlf2UgHaKze?jLJ< z@%dX{*a}{B;X?F($#Or-HhJAQSIc*Gn(?`_k7@Ms-d>ClvaAFMui=|a;$(1Ri2p8q&7ZE|^_I3e8m;y_olbXo zw6!p|@ugvZy1KczxVbv*4PjyX)LNFUo!VYljI!y*($dB>i{frKSsjg5lWsSLTmKu{ zEtwmDU07Uz*=Ot;%+Ev3hN}_aZWlu|`|f3^1(2o?%>K$!ly+NDEQFAR*wJAN1_ zD4xP=rg4O!QD2yS5*u9;W3y}ED`0j_nCQ7<$w%LYjrO_THt=t{-l_2jLmaR90JU1I z=~kyRn8qPK|h2(gPuiSMn8gn z0{t}ldGt#ydE$4_AE9p|R0Zq>Ra)UGs{lUCque7_x?|%YuRg1?tHH!t-xcM+3Gwyy zvdRV{V07%pNjcmc>wh+7X_a|H2Gca*<95%|ILM{Eh=*~I6Gv1I;i=QFL%-+;5;@6O z6$iPHCcG{)C)$fm@ zXcR@y^!xYBoWVW){@bHy_Hj)+8AT^Wr2SyqV$x8D{B#VqZHF$#GB8zBWD=m+Tz737 zutjZVzmE23wr=rC^+&X*w z{`=wmvsYif4Cd@#U~yyPb$zw}-P#BHYX3HEum9cAr!-9yk)~-Vx5G&(LhyfZfNE#~ zZ6X9b_euK&+23>@gO}li5`}?awOXDyBe+!v#}FL8`M4(YAC|fHc#YsX{8Sy2+U$!h z55B&m)$%;gYyH-wTCh1dkhwNDr*RqAW?!gbT!S+;Jo`iN+;%I~fo-+%?-waUUf(YU zqWAz_g&x7NI3!5}T@Y^ z(sn&frYZpQo6YuCngF=&sMmv+=G`9PWSEVILyWt<`Gsx|bj`y=#h^0Iq?YXf%%#Wn z=H~&=&rg?nJxrp06k(7gFcn2nC%rC!qADCflUb-~I2mrKngs362q6Rr9sN8Uz*o=( zggk!18M=xqkLCx2$LEgY*%%BmU)EHn#{40-saLn8X05>DIL0 z15jk&k37S~nhqdDD6lGVO;Jo6=Mn8`no1{l%r7j)LBM4oWTDwyHeFXypxs_x9-UcQLIk0s z>u|`;I9NeXai3Q&YKm839>S@oYKN&2@Ty7_#1TQi#Q3hHH*u~*u+3B!#R&~ZW=D2# z9UU+89g)QF>b*O+CzDPa+TE9AYul5&(*f7%eJo$(Cq3E%TujfMDJx8h;@m@$X-Zt8 z5+hWuxvoXIB4}5CExOOB<%8E4og33fm3t*#&5KCX?;W{6*d7 z?AO}mxw+o#hoDZ~<9xX|H!Ywn&zvi!_^pI9EOCi*!X(W!n~YIYl0cDFS<(q`0IF%8 z@7NSTX*NBLV2n9eLy2HOxLNuj9xS_w5X3xAV-W}W=Z~rKBHn6}<{S=oZ@v50-SPN@ zasH7jR~|WU9v_bnn`INO9UgwmxHtaV*T%i^x6Hn_yPJz9Qt<)2WSn`x67PS7l%Yje z&<`s_G2#LGeDQ3aW1Qz_&rD~XfnHi~H0o*6U?DU&`F2B56ovZwI`9&lPtTs6=J?AG ztl{zah8xCX`~$nNw0P{;;u3V>g>D%|4npAcoj$2z&iDvo5#$9N&UWGOx6bufIMbKo zvu%IBuf0d;-FKQs^UinPeHW@Bbo6U@7e0r+1N|WS3i^5Uo9Iu_KLUUSJ;-4fUIK50 zYw$VvS@<3J6ZkhQ;~HMT>j(`?o}?vD(rFy=ginjfnfk4Wc*LVLiOZ@K#pJ}~6+X`W zQ`_T>yFxGk6@CU(ILgMg{9qzN5%Gv8AUqp|BFuw?Cvg%mo`}zk5B0;Up=eG=P?8Do zxLO6r!R3ZBw8_yVA7_$BD)S6^qH)p;{Th@BbpUd`i}4DZOkytF{N4?8n%^@NX&CI8 z%9<#ft2RfcemH<+pRY4P9 zy^Z5>HK5~N96$$!D&{(PGbe`;r(__PO^qj|()4W(Zz-0~3$|xFkiV+}$Z$PZM#LCV>}o0VpjC#zGTDVXUpa ze?OF~fIN?lwFpDkBtlA~LM}yU0BW^bz3_X#H|(`qE#O`v&oBnj7NJrgW@5&e)w1Ps z#-(7Ml;=-_G3^7?7KRa&4y3k>vDnO|lAZ^pBL1OKkpp=#8WlN^7o&Gb;aNaP5e8d; z+bA*&K(Bv~jbkf7N#eQye?yWI0Md~Ve8+P^!o(4iN$LSP&r47!0cOHbOQ{r~s=8jD z%w}U>Zuk4aou}u25X3RTSl_rV7gD-^R0JWYEIJV=iaj1it-pj+ z+LgpeX={NWcBSOPb**O&D2{_d5hLL%CE;4#4Y`()65JKcSZhpTy-d2o8i0`pCb)6K zFbI5K3&u2Ko)B!Ob-;N52qO~D*db~GXb*z>#57|(8ILoQef{sddBgw~S6i zfe`TGIA$h5zXbt?!T4K?d^FArpvcFgy!gQ&Nt7ys9!N$pW1L5!+%mz_)WBppUjcp; zX(MzLg+l;V2TEoxKnMi^2Bc)<+719>jP3M#VbtwaQvHChHC!9E;+TmrN(8@=XT+Hj zM2v7WiU+;cH7S8{tRx_9HueU|@Q46#sYp)H>(53-)`8yO{>hn+aW{}haqbcIqNt<`PTwOVV&xG;eq&_0xut|ZQc z(wZFRI4}GOeFnbMiJi*1-EpRwTF7cr&o=8u$1qRW zyc2%eBm$-KVqcryV31{?AnbG|)!uA)ak>-7JlrJq>EVr?6X&vXkMmOJJuE)Os{!@5}&Lr7J$s@~L*qDz8ak3$oR*o|2oS5|csi7n#P zdb0ZX)0ZzZcKPzt(?wo^Zf|d|*M&|rXfPNI=y&X66n#9R|1v)gZa6m_Uy5k?mr`tP z7+PC}1YN+j^?EoQ4%h2zbBEylNB-oeFJES#dopx;dtD9a?w%h6{=ZKQzZnrl?~Q=y zy>lt+h5p_g{u69qxScKb}&r<6l`QcBaln=V9j!&Zj%hbhBJ7 ztNG|a&(ki!Yc8r5lPV9{Tqb~BQ&d?wh;VZ{6Rl=j&o;Jc=`|;p&Bm`MV^hxR&2}@Z zw}+y$&J!R$*cSpG;BoFNyAQ?#?fEL;3_jpL2fu`Oir;ksBt@^^Yvl#be2?Kr$Fo_l z3-o)ldN7+zh$iFuCs{S1iUjYpuY5}4dA~Vgen2@Y76c5z?!JF(98R+wK~I9vX)l6U z7=+QzEDWK0sgQLoMcRA1&mdcU!cCoAs(y738}NV+T?45EOq2O)Bheo?HN-05SGN(3 z2bD^ykKMOi5G|JX-Md^8EtmK1j_z}*K6+?4bG^floAF;ozw*q(O+(Z)4?pwp#Y@!d z9UpD*)}6fp-nU%P`9J!-?#@oH=Y8ZOUaz;a)9v}V+a~`iIe(V6+lQW9Z-7mE`1JN- zaj{+pEQMqXZJ^8OQG}4UbWDCt3(ynZ;|Wdd`^i_J<{K(VOtzojUdFx+0CLv3%p+jJ)rU}@xvPH**fn$Dl^+A9)ydZezx(U zhaYL2nI0T?_2p-u%_|xyM_+~i#<;*ecPyJlqH}mgl1nDJ+o%KwXl*!uvg|dliEH}V zv(2J*?xqI@-)`yIn_m5D42qMyl_8%6!xP52|bcJ`*%yao}(jt*fxSLEbP z=&IhS(S`6F*)`5M!n(Tyy@mqyl0@nOBZr>sL=oCW$To!230l_&9K4`j_kuG9W42{= zSI$_r#ki@cjc(xA>p@*nRmn1asMnX48g<_?nW8H7f5(GiRSgGt`fz)2cQhw^p}`mq z&RCYsnBHAE$hIk3qw8bF49hTFok|uqbc}TaTN2e>!?1q28V&)5!>Sq#0fvLB>AJ4l zB=)21FvObHPOdlaTkgfB>$M(u=$-~4P+d!iF9xc;07koolNv`DT1cc36X6c9Pia== z2y1OR%JezVG{WW1`>`KYnBx6oO?Ra|&bLO&PMPcHDN$?Wu}l$O=}}RK#$g>&}}t<1Vt_ z;Edkjy)7tLgN(j1}2XG;J8fI2RfJf)C*B==lf%q9|?&iORQn8pVyM zPSU@_vlG|NqD{fENt*Cgu(%bM#k3d{Gy-PTOArcg{mTK7;e&Zgv73WI*F5ify$)~& z@n9IMp$NXqc5I33npG1*xB!U5I~4T3{Vu?Y&QF=Wl=MQXvCqv+-^1u zol-CjV9aQXdQ0v18f`);F>vRVO1InhER#aRVGK-srK*FKyjhIeXa#Md8_^Z?FnSSs z19}g-hCYdY1br3#GWr8szsJKqmugP6Xp`J5+$xqORa%vIo`z81Fsafk>mRMPF8`Zs zxz?z7R(2ue87pX3c_<(OwP*!(cJp5GQe+{ovYY$zlWH@pgD@>CLd6Cwc{5qqg>3&^ zcXgF*IGgev5QU~%XIGr0$G#2ll2dmKHaTDas6~>1jg(lN5Pm-=oMA>d!2s(VfO7zT z6CngRU?Ne>FxzEJlO!hTl&%4{IZW(lZf~2RKnSrICfwrWZcaES3{#0<*xPZyVgIMb zOS}Gwp7`ujpN+Sb{W!;YQGkCcc>>PA6uw0J8Ah5#?b*BX6!ro_NCbP*))Uo~U)rfG zP8KNDB_>H4W8eO&_jAMEX5UOOK=qh!?iy^>CTQc?Rh5cdWWRf!cLmw6(0_r`ZVvQ>$DMeOfNje_EI2&iv zA}Pk%xJZg|F^;EcF^#81l6`K=##vHKNeK;;X480@j*D@Y6ytPUjMHg4E}~+ZCaZ@@ zHcqF`FiVQ0NT=yIoyOy0Jc6hgX9*VueCRQ#F65zFlu>+1M10KG$T+(Zt7N$Y<7}Ky zsvON?&J|&ky2)Bq1`oPE$S1Xfq5(Zt_(5nw`@+ZBw1EEtEQPh)1SarM#bxVhhO9EC z8tTN^_@;aE2gARon1&)tRACq!9H^wV089ykVj8jO zCi8Rcc}0opk!?{`1(S*CMzrIH>!FQ~!LqA$wJ;6D`2pxS5 z{s|6Ij5g57RxSWUZ+fX91QVL<*Hr<2~Sv!}{fXDoJ@O87@~#$M0iouPnOV z7H^B;zWt@@;a(JX7n{vybFml2y~1%_w}6k6&COfSpNIm#vxRBYud4mF^4^fm&g?}V zK>jcrkQgM7d`!+9SC{OzhxVi0Tk2h++wP# zs+#M8-NnQL_}m@6;H|3f^@lpQElt<_q_6An7P2{AdqnS`BwJjFRMmu6nX2m5C#%&a zrQr0Y?a(xtTGw068*XUSWta27uXmoerYhD?j@1X~9sMGF9KM7eK(9paMu!Mhoh+X0 z>Z}lAiF_rn-zI7xP{v4mfKyLdFwR2affY`=B(*rm+^Hn!9xp8wB_gpTc^(Rh3SeZO zhPBdAWMLNO(_qpszLU4$6Xa9Jk1$a0tIHAg1tp?x+ z&1PU?0+22(^^+v6ssv=20(Nz*{)>bGuyh^DW5>p049T!8CzHl5P|Az>xmHu>n95ul zm*rea{$%ac$@Mi48wOa%b}wEyMHPiV3j`#J2{9$gF#(B5lH(H2C6`f#F{X@Bmr)%* zs!9?e7|TZUYT9T5w3_o-yKPcK2QZAej_Zb|?~g=ZNUWEZWtt=)D-2{zlpj%mNt7@N zo-P3vmu}pjn^RRGHh@w^!5(9_rAYv?%$8J@%i8RVC8Y}s=M@af?UNk#xuQtVmoP{G zgqRZL7z2~I<1)^v%P0p7j7csdI!1^hc61%S0@u(YT0zF0F%$_Z8o&p_N7Wh0&I_5jC&#f3VvEoBv( zCWcylcQ1}^G(8VDB!(gIeIMgytNCWOynL=$SZsH6iSF9@Jk0ZxC+;+rIbBf+Wt=k8 z)G$yigJBrDWl9)h3EHw!uTQEVP?UjUS@J4aR`{Nv-6pu#O?fQK*tQys-InUQsy1Rx zpZ{*H(InuxHDNQ1F+eR0VV>H!#<|8lUtuVfjU|a;0FWd@H#oawI!D*xvv3V52o3rC z6$&IL2LG`SzW}n2zW9A`_}Hb{Om@=$C;RDP9*Qc<-)o`)lV})NflM^@we65xxpOCN zaqe2CD|G_P>^7R-RQFvsX<-}&VgKq03dyk-zVwE6{dAf_&@_xz5Mb%do0_eirCOg- z_4x$fju1x3j_vA5HVNQqmF8Ta)^x(w)w_0gpW8iVzy4y(e&ouP=PqAckb`q$z*(K&f+agtc(=!ukhUzvZjtA_6Q1GRr~%;N zw_;4BdIQT(l3{QfgQoUbgZxXK@aHqkIv>QCkUF5g>^O42rnXutqR2bC4u8ihE;xy< zVgjs)GN(W+U*w}V{As2{fvV|nSH+*wXMqx#^A7J4A;)!5E%f~p!*jx_&bMv{O|uy{F{}~SNo2pX}axbIs_reZGiFMXB$$Yzwg_qZDnTyY4dxZNExDGE+@=prEOYTiWOxXJtHI z1COP!6*D=8vyDuOotMD*12+7p%^fI3$j6zVjo091JEt$Mt+(3{5Aw=$K>{03r{~sk z(dj4vHg}b_56F=AZp;P09lTV>E#nYi#6Obnx0obJj@C%B-rD+E z5xTq^R;%cT50kBhTXq)~02UT@Z(Q8yuB@zXO|nkA-N`0fo8xik-vJ8gc9-sZ|B3lI z6>1I)hPzMPuXj%nys)sdqi8!CHyWGc<5mFe_7wlaqdDeBI21v;12GpB9|oebAg?$) zIDGovi(eNXzHnvsyDR@QyYlr{|Ie@1zJBq;-=Ez3#&`Vi@3hy?yyL5D=TF={I)s0K zL%m3JfY30GLJX5dh&&o)dMe>joHq@3#8IEdQS!dFIO0WFz^b+dQJnB#!>;Ko6E{a# zZV&PziHE*HaPJM6v>|5&1Bn3sx=CRGv?&c~iw?02cmtc3sipy?5|_EGD7vnIX=0E8 zWIT|X6eh|B+NAJh+-x+O_{%_N*J+E=7JU!UrX)2fT$6gwQrhcL+Uqf#5IP(L*e1mo zVh4JfvUcm%o3jd&Ms-~Gukgv!v zu~D<@xw@)q#zV|=8=YIdEYfMX9^=f{bgvNv9f=u3LNRB>vc%Bnn3|?)22@m{S124x zqyWIuAgI?|7n=M2V-@5kG#lNTZG*6^!0&YYz_J8U)TK?`AQ1-35}{?|zU$WNK`arB zODyGrn$b~;N`Ag_gCR1gSw@aBSk;;AR|n0IrCl8f@2-j_-pP#7&Hwci*zxkGHJ3EGyDpztd7x zCOJRd&d($%2;pNmT}ubPKdh@f@Ec7p#JIuQ;CY>f7jU(~*batK+B{{(o(rm4Zwv=< z6d;5s>Kz@zH8?~B@jI)H^HlIE<#{s$o-rU6(Wro6W-PA!hfRPwuT`Ma_BA!82RJ-D zJivFA#s1!f3r9ztHahLYqYIBOm-yxvecHX!@O zhhO>?j}PDUrs3o4t6sUcTrQW(pMC8&{nq27H@#`}_^-VGwXa>idijmZ<*V_hGk%>M z{DiZ1oGIsI!bdu6N5OPd!!0dZcxebn^2jL=4JfC_#5d zo;Sf8HY2H&9Btl!iR*{|lYT;QO1N9V`TO3-IRk;X_zBwG-|OH-yDbjz*6u@{z5QLg zQg->mNAZt%-kCWE&Nd&JTUZKvCJbe0LIC)oT!e8oU2UqowDZGb?RK$G)s?DMT}j3JXqs1>RSd8_S5Xb|%KO#L1CCq1mz;lf;`yGJ z;th%CPa(`FoSnVJ3P9iu0_Xqlo_p@O$KAf`+V6b$!yo?eW7pobee6}Q!aL8uI`zCc z@2%&h=Uno&OlX+Qr zWki`cQ_k{524Ya%{;aEIJsX|#O%FfP??bVNJ9ipyvRYl7=UMEf54DV@VU|Wj<<8Ec zViXv+=(H11J{(-a^G&n2fTyQBiGJiRkS2CeuU7YO?mTuGQxavrKW6M?u_!|;byCkN zqNCm2J|>l5&v{dPXJ;Tv)N>A^@T0*{6jT|n){<16LxsU|P@1_5piat49sH!IL!P&6 z{tZkUra`fi2By*V(!bMZcqjB4tmQ)VOUA&s0&d%OyX-N7AY0@N#wbD%sL1FbXir;h z!-;rz==wr4;K29y_a!I@qO#TUIO03TD2MzR8HS8N5fP%O+q+Jzt`8n1uuI0KN}l&f zN`W{I+hIt({x~y!1O_n2A!mfkd*)&1b&g|XCxU!-f7F3che+zl-p%*bC2!f9;K2XH z2|k&t8VDAiF)4hF((Noug#cGB^?I8N^(;%LJ2x08!!&>Ul^c`M+}|z;nB8LVl+HDKDe!_Ag*@1 zpsWl7!C;J`ZnxZRm0*WQn{nWAMxIszBt#elK6~u8+gwIv*`ChoOlsp6xx*ax!Y%wP zZaK=ivdhI@mkfk7+;oxdQbNGk7>5G9PDFDze_~Rz*GMk zhOB!yPGUB8F`J$7w;(0Jn09Br91cL!>E(8d*}>?M`JAzBbuvGhS9?mOY5C?LP19Cx zr0{mevhY~Zc%iw_oBO72Zy_#)azf~t%18g_d=5-Cl@Zx7rC8HHy8Kz$d)>|8Yw>c9r;Gqmf{cSTyvWzg@?^=2H69$8I zmMRLvSy>`Yhr=k85=kd>U2<^;%~~Z?=((JCdRY+1iDj@^UTAoJ=y|S*{GbgJ>>JUy zRn_e1cr+&E2NIq3bX>NiKOF4r^j&v(6n499Om-eQZnvjt%v5hO>3<~!CI})a^Dq>y zp9GdOrPB6bP?T`U@n86D{15z)^V!Y|jx(Lrvuak)>Y1Jj?_ps&F;SyT(q`XFDw$7e zrG9nG({O+4ovE^Dmd~VbezRt~S|V7PYPQWrLEb8fr&`WvX+P_kosk3i4vj)@>9$|# zOU^5ot75aBMpgGBCYZ_=Sp!H4g#|aXHec;Rr10OR3-vFeNg}zs?jeQZfi%kPO9=uq z#tYKP{{AElJY$$JG}d>OkpV)`Vv-2NWHM$HX&DL?M4X_cc8D;EGh;nFx1Hu$lSN2E z$q8V@qEN{|PGZj}#YGTzJ}37+5qBYkfdJZ zrl}kGNis-c0PQ4k?JgAnk*R|Nr45GWf4?KqBH zD1fQXqZ+uxU8R&54hP^&*5|kVRgjK@JaU{Z&RmXz5(;ncoOq`@^TXj>38`}z)AUYG z#@)-+x;fQw0%0l3vmGb^NN==8Gk}HIUGGYO;{c9KVFvp)HVQmKW7*(ZW?% z;G~)_PHV0gRjYH=nXQ94XRG_-=J~Bonx<(7*B(cl3*!R%D@@$=Y#^2Py)^ZFtz=+5 zm$B=kuWJN1T)3dZSA59i4B%wA!FYxmpU&nDG*$ zf_^{HI`Ir+TuALn0>krZ+S%pc{1=7cTP_hX&LlVqA*2)_)3t=lT!a9U#9h~AfR_l# z0I_f=BB@(j}rxm!5p`(j~%0s}4cS<$Wi6 zt)fUr?RG?+&fStRb^hanD5B-^zTN5aFyR&}f|8s%lPse^D&Ho4R+ReFZsIQ`#s zEi3YLaFqC~Ggn@_9k~6PbkA0Pax_Tug1fHk@}fxkN3p*=bLF+$fZMML_iX9MNByKI zxa7$nsBeI{N!km z=H+r_&F}4r_q*e|d@VlJy&k^c+`I|r=1u#~cmC76-t|wfdCQ-?HIM)k{fOQSL@Utm45jC9H(rT`oYku`E#qq2tm0YS z6zZ9uRzEt(Gi2GJH?yR_B(&?lyX~MiO;gVc$45J1z*!RSU0FVN^A9ZjBnR;ty?pnUJ}@U`EB^1bJO5OS3BFexrMj&+b*nll^E zV6|E85sV!Pw5Gdm#d(I8dq9{YRaOnvP;AC4muV!nCVu*O<6n#vlW5DFB8OE8WP8ggF0Mqr;GM$QGI~ z)MlyMM6LGTxXW`T=l#JRr3q1z(t?tdC{1VqL;rCspX5H!+TUB>b8rATNRrCL+xb4) zY)7oeNy2Qab$Bw0xmZ}glV>9LJiJazrKC<7OP}x8b(!V48TrNlV+HQZvDQz40>X4v zlC)Yy(dta6{XT_S5f~|9y%9>nC?b-&9@~gBhQn(beo&Oz2SUBwvl9lc>$<_~2fe-h z{eB-vUAG|D_m(fDMCmAHl#QM}PnM2e&;PcgULS0=zN_6{Uq0U7A370C-RFh9S$**8 z{oxQa+3f8>aK7DX*6R)&oB==WH=G9v1SVxXl&%U^F40V`%Bx?OhcMveca8hKIF939 ze>|_od4Fsa_j_+A%lMt|B#eg5KkQW1PNgjW82OSo>i5Uvem{yw;}k_P9v20IpnYK7 z?RbngRMk#Z<$+-K7M^$R#JEYJ9RhjMHdv74bGX2b$3ht{2)KEwTW1V3S>6KN^6WUz z%A>*Hs9zNQqru>)*X{O>27{wANy4-sZcO^dQIUp8GJRf|#K6>?hCi@Cefej;(3vDuj%;@-nsPyY7EfoKQKvl;(HGr$00|(@KJq@Smj)H zoT*kNBnXZR9yBMb%?YMYnhA|ssv<8Yt64K^4nQ-QA~H!Oh~*h$n)x?@QmAuA~#Il!q{8h%HlI?EojoI>&4TX4Y1ihb=zW|Wnm{y zfHW-%d$9-uP~PH5(o*0XzX|xJt5e5u9N{p>p)=fcdd|?PoYy#?Wr`@?>Ho6BgZ~;hW!X%C~i$GxZyE(6j zI1_$3j6(Q+w;u||{o8Q;y?7}Tf+GwM0Ngp^hBXf3`7>U;DD&4>=M~pNK2_YqyZWMAJ zdtMkavW&q-X@(>CwIzQ|=qcAVjOB>myt7PFAp`^XiDAAcn~W!uvICDX*J$G^tr;U7 zrU|3VI_|KZbNgGRPQqL&#<&o+7Afo(2S51z7N%9RDrIl!{KsGXv#87hWFdCGpa_ZT4^PC@Qyo9PsgVTPnnnMgMc%U7%e1pWTguFtt4U88x*;Q z)^Qd^hDiY2C4%c34{oQ^;?gZ!%(PmCU`%+{NIxNBBoIZcOiUQ%z7UMZ-EI$BsVL2? zY`3bNUX&z$k!4CoPTg&8a>-6bMpv=k>~;hCQ`FPzo8?Mb2?eU3&$}G$v$~p;`4%@; zqFKpYsw@u(x#p~%)ysNP=h-Dp5#T18YQ>*;{PDMF)pjjF)^wF7wtMZ`?YD3Fr4%U% z%C)U8^w|?grIcVDE$|U^2wcewS-L!WYA=c+A&OV4Znxj}8F-t(rHp5nFJC#^ON8X0 zw%&SOW&=!w<)}ef!hSo*n7+2{u;saNw5wI=dH}w-j*|oH;akr7D1HIo;&7*M4rKTn zyw&+K=Xu9zs?93D3ODoX)p@m9Wo0p>4I)hPy#&-D+vJI(Fk3NCy;_ubwK%oTl_yPR z8xG-9O;tho=%0&Rc=aM*9C>-1DXHdaz8KFJvwXbjMB{lrzW^aMA1YqgvwHmW&dv>U zG7NHmcuWAlz0=l4;QHT}3#q(=N|RoFAAWJdg;YuiBj5AJx%Ndc8l1UH~CTF%X#E6+4ON=3eG5+~Y-@yGT$~*X+ zj(}mw`KM|r!BgBcO4?4Hk{thqkKljegU;)nPj;MXDVV;slZuLgu2fG&R9Kx04W-fH z+^(}Mm#gd&maFaPUiVjWy_q-j#b&nA*W+iFs5Yy7TjGCsb|}H-#^#;wAc~b@98m-? zN}@c?604&i3w&&9Tg8QELN`py&LGRAVhke?u~ykB!$3)bl!+fI@c%=73yd+DQuo~B zuKqv>>n6#l z*Q#~oPIYiF!f`ilUAcPo%AF_2lEQHC#FeX8uDR9;y}gTPr{sl<*j9L9ZUSpUpDdC(*_H9M>IOQwewEX?^Vt~oWmX#3O6@&v!CVXGesb*FDE4iH zC^lXa88`U_Puaj#*2g=2;+_ZY)npBrHT00L-O$%=C{v|Ioq;zR_W=yYlOn)4PC%_J ziudxx{AjbgOG71FcZ$f2GL1kPEhU(>Zp^_Iplsvg28-#u!qrle!NL~^ovFoR?>o|_X9J$RA_c$-( z*q}F(%qYvV#XM?AbG=!g$!D%0t=P|emnaB|Ff2mX4O_9Fxz>hKYZ+SW7GYTY#Q80f271JWUSvDD*8*d(3&lx> zKzPe~5qAOb^@Kd~0WHCRX9bi3AMw{BlEj{Wbem8L@8RX(kQTW5T6oKGSS-!H-;G<& z#<|ORiQ`ON%`Mn8s>r3PM;hByy}gS1YKO+Uy0sx>81x;{CDEbxmg&W;sUshad&hAJ*-a7WA3Zvl6x5&~gB^`yq z4QXuf^~)_<1Od*!iCXQQok_jh+_twTRlOUxEjIob-NgHxBj=^g*E-I0Q_qVX7C!Du zvjEZjmGv$2xdG$io|VFZ)4D_1XR0qf2h1u*)C^@`qU=B-Sk6mE#bn&iQg@AuFS*&) zxUtg8it+ehIjc!<;j(VG*JV&IrFJ`y@Vr$@@oulzBcb&a&IZHX-Qj>qrDN4;cRC7z zl17XthbPk={&g&L4fT4x9(RpV7j(O$q!yE^+7Telzz>fuUphJ#jJv*X*<(p<~-=U$@xm>+nk?vM&OOW znYgQ-S5*|6EjG)&9j59wK2t}#)og2#nFspSc%-GaZZI|jicOftg?f*dlbTzX6lOTF z-qM@&s=5&)J=0=REzr3Dk7k)!UMN}Nug@>VQc4-)mlG+aOwK=2v^%bF5o(`t*ELtJ zTAIa~%Y=?1U)V92>(bpB4#jU72>^eev##yRIOLWWjy(Ei~; zXxbu)d%az7o`M()v5xwKuxtgbb`nCK{|TU7FX1$Ku4@TtGQ{&z#^;~Bt)(USoJ7i> z4V7y9f|&9H-xtG<+v)6iz84Hfk_%#rx&X

    (0wGwLXB2naKMKgASi>6$6=JG#)$u zbC-d;S^#C1-M(7*QN(zD*Uny!

    eR%{X2~@BDL9_h6`Z>+T&N~A8DG43_l*mJx3sN~p1gPw{Nkl2KJqRl z2iu)?n^CuSPxGms{~Nb3o?N(a=iL`CRuv7o%oUHp zE?;`;@rxJf{L5Y^WDVAC?;Q3AK)W?lqRVxho=&HNc6YCy?eFv67YIfQ|7_rp6Ta|K zd=Sq&H=IWuC!MH;R4Z``vf8pFHHotIh%(FVgYc9WS;lg;t>bMyt5XYyo(3Io>|-bM z#jfG4^^C%}o&CMTJ!|M_x!l{0OTnQ{x4WGl?2n`(>Fy^+}c>#D{+?EVr5_@OUDbByWAjFX+^1?v7 z#&XAX9A`l{@t~79rSqWkGUqwxjm{@H-|zf{^PA2`9A_F*6H*hR2~juYRj8{7A?gr~ zR8q-$7b%5d^&4D8INwRsRg{goijdVI)!_KhrVp>OZ79JC%0_}4Pc$b8=_<;*hFUMq z|IRX7UB$Mno8``{3stIm?VU>2A?gs*a<4uMslwT%t`g#ZEv*m!J`>$!6;-UF3A^T< zj9No~^wQt&`91&qUsK*=CJfR}6oHa>*mhk8R)?O)*mbr;J2a&!HRZ?@>^|1A7x}LK z^uw-+Lt_X^MTy&Gr_^Jv)|5+~6`!xe@_YHN9$jb+ZFeU7Tn0hE>v;rL7XcsnqnW1wTvW6a)lIo0E%*)H7|vxGjGo_if#y+M2MqL9J!Bu0$+9vvLhiw^1N zpbBf6)-)nm*JW&fm#*GmkAQKl8AHD^027wAxYrK62yMf_Xv}H{&tSaejGYV4W1Jph z&9*E;5L$tgd4JV(6l)=5B;Zeko zbbTY!VeFb0rEVPQuoZpJCM8PynF?dKlf|CLg-v&eInmkokLc{|CC^+rqsMPGa90#+ zH~t#$Kfvlg9PDK%`4%|;Z*CN-uob3X{1XXLvbuf*wLY?M zWFEf~NhM#|&r}$>MVtqo;Fj}rv`&Z;)(<23K%m^Ht<%Ytk`!;NVynY247H8l56=%K zk*k8)VA?9uL`ywxkS1h1lH)uFKoL_JpFV_nen{74AtF`6P z-Oa0}lC^ARLlpH^%}RL{E430SvriwdWnJaf2B=m|&A0^v3*xSoT1@{#&zOfg=~u29~Y&4{nneUnw9ygSt%-!$vn%e^=7`<#@~BKC9gL7kkCm$6T1KdUn=w;H2omW7nF;St3!-|8IX=|FA-z2J zIXBXEv%>R~3lw*L&spZR_sdeoxl~dnXOorBcdYAqBbQMLgcRBU@a>+<5ECMPoaHdO z!xTe7mhA2C_WKt7FS>>==5y#fQwK515er3s(^Az;W0h6_MbxS(8nvpoO}VPKjjEM| zIrG;jk{1sSA9>ZA-}KCjUUGWjbp7^T@9rr5nx9`vc`!QZk6(SZfBWP$#iS~~VBG5- zJ$Fwp|JnAqnmqTSPVbA)n&r-pAC%>4KMpz5+4UO_Md9C^|8=>ycRXpO-stRLciUf0 zz^5B+?|Ms8JiND&c>Yse?Qq9=;s4T2e4Znn(y5$N$C>70x)D>=xE3+1okE}uXj>WC z-$e*Mr_=k5blcF)^IPY)a1Gaf>7;*jv~3QL`e6L7hab7{=bh1Jd>pmg{j_k{nGd8?{o`nGVSUFO}TBT>|u`po#bjvP|-d?6fvbT;d25EjrYF;abEQkV^OpCu<;(4Um;QTiS?o_vG zfb}3tasC~_TSD)ee>RVXQ8IXSUwk4TkcktYWTM`!aiN@sRHVe)NW=Lj9|ove)6h%K6>9Q3zfS^20V1C?PrxmQ%+7W+sqnIQ_-r( z%fUO<5RkGDLR7{Znx1b{Zd_qnuGD=QFrJ7dOFOoG#R%1EcQ{|dTp2VSBX zjmN}vf>^|HqA1?PiEP*jeEvcI*lhQN8!C(oPo zqX+LZtxnBa^cU8@BnLELluMI3=M!?;x-1#q4OQi33but^ljj^KEG zon;sfFLTb>qrjXism2;$Fd3k2RduCB(rH^~9O~faY@-bGn4l+_Yf-$Sbg8PpFt77M z$pc7I#(ZZ9wo#kAMrn3iJc?q0Yu6yMycOS;rF0ESwU5(@5goUcLV`}Z7qB(wFU*Pp z)=jE^ysy*-WihRKMXU|9D$-nOXcPHOO<@gv;h%C|?|jnOimpKeHkw*J2o|-X54%r@ zVl}TO^_Vec&ZTTbTeIv1Tg5Vc7y!{#@&d4nb`g8Vn?S)+Utl||WXUssr`_mo9bZ*+Qt zG|RnnfP+tc-OQkjb7TehEw}B##o5`Fv!yZCzwy{(*L`Emt3tt}7w_$56!;>Fqr-#i zU-i=C<4goT#k;!?MS_P{vT9J+r@OfCU%ToD94rWkcB-SJYKNR4?f6eSjKU~7@vOC1 zE?R57^OutE%ig{BJRWiuUOPKGKF(T9X#;MuTip2zlgB8v9z%5NWf9zOl{be74~a0&?#& z&2qI|Q37*IBr>k55pjCaKu?LPXL6ZmC6d+gRiTow12)^uZ1b&5$|9A5v?3w0j?hHn zN^LD9X?WgnZUUE#t^ZOTk~KR!4?VE6<682pRm$0Emb)vK%j?&dOEI$eEgg61IT#EEx}Qgqfd6mQW348QSlDR!asW(f%>lRp1MpdgzEjmm zX>Gmj80z7Nc6N-V+6B}A#H`)fdF1iEUH;RW>8Uh>VJDHhX2(a&^LLKJNS9G)KlH^Z zqEZ%lmUA2L6Rn;5o!2;y^`rEmodelWnfjf=FR%Hw>pG-#>SKQYN}}^FJxg~>m2%iZ zNrhkFMr45yA|UMcr`!)l%Oa0%OR?o6_xk;bozE8w@1$y52Auyqx276t!vdd-9v*0) zF?}n1V40=lF&Gv_SFeR3Ga&b`d>>*lQsr`QFj6%vsop&z%P!s1X?wGFTLC0@+EZIi z#$$O+&d@ImGy%f*?XNk$45aU~0KGOr8rM@!e10omJiA&>ABSw#*16d4c_x$o~?S7|m{RXkKk8F2QO$rl^ z4Jli_YT44%HJC*~TZ9(tI=F{rS+^TS^!UDf{NCoY*VAS?J$>|{hc;V*s=9pn?jLgx z4lbYV9T?r&+Z|J19$w zupfbvEWwFpa2J20lBBcUnfiXx5}XqYbM?kqII4R zI{m)>_-+V@rf0B8UVCdk8v3Q}sOw}D)cuR=A99V#%4&;Yr_Gu61;q<>GHs*M{BdD(Nz zy-u2}hTwMRBep)mC=3blIGzV4E%f zPX1Q)De`~(amt5ImS55D-#ZJUGaR3=lkvqPe+@r`pIm{Vha-fEZ8LHs;(NkJjp50l z(-{oqUEH95IOueO?6%3tNz?Ut9W*B=liRZQw!#pD{^4PNfG}*osGj9TkL>Ie`K*4> zaimkcaEtcw3(jTd8=b$yODLhzanf0nmD`4#i!@N!_NmXRb>D_yL4kuIdzsG{lYG9I z)v}y{kEbF8x}I&zrkYoC)mR}GzwUA~U(9Q%=8I*HG)ga-=mKi6&F8M;Cgz(;RDqG2 z+RUptmeXi4tLF<94~4K1PRZ9;H&U&e?PgoMBS+Ir7nvwjw|lF)=}&+@EXu9j>st2qL&eVc6~>uot-bfou8XZ5z+ zo~SI!*#>7eMo7c;V%u+mi3@Hg1M-B6i1!SJ#cm9Qd3Pi@5u;w~aEQPa z3Q4~`Rll6*zp<3)4{?YvsBc=%(&Yj~Mtgb$uncmPw*epmI_y?M?Ug zdc73%{juju1M=dW6K=#5BX^T>wDMhTO_mu~a7J`?Nak@tlyVs`#+d@ZeWRn;C18xu zgJqG1E?a|kLIm^+VAKu;m;UhllXZ}%R_MsJq4iJ}hYp$G@EVZCnI8m<5quL0(howg zN2PGJP#Dct~K&AWujudv{SftuVoD2Yc7I|)hLOPOAISRNTRG2x*puL zO_UWtwA1O8qfd#A@#UYRI46RLIX9N`%`1-HF@VI2IC#)IfV4t@_9i_Jz#)hvd*4g% z2Q$WqBuU^1dl*z;xS@O8l@tmwC`Kb4FsQt2*XGLjK*OgctiwJF~D*^ zPLjU;eqTc*d2E%`#zRRKfg(dJNws4CTTpsl5crINTn|KP82Ca)@wqq4c;xvpV}24u z0CTyG;zU7e!$?a-;=9F|l7f0~Ofus#aL&ngK@d7}T_RO#6aE%pJ{V&O2yUB^5K4vw z=4+{>>9!FoM`j;)aMMYhX?&^Z70$aH$C`xkXx(+=u(45Dq$qb|S@qoK^_1X}yWbp# z}dL8C@yvJNOkMcaeW=t3w^XQfMnX6Tl=g~fM-6G2JIm*`3M|1G6k> z=B$*aR?B9M<*=TsviOdEYnUcoIJ6_O(!7Xs(yjJP2VwAQ{Wx}iD0%n6y<^$&e9l3} zKuZ6apwr27NWmY0$lbd==n*}(?e>iM+-uTvli0g9Q&CZHV%(MPG>%1Du`yMe;a%^okMxOV2`RSk)1VH9+N6Vd)%u&vfvvQog^wjg! zhCL`-0Te1pg*7XcR2R}Y03=~Mb@9Z#_g=Yz_a}S%r;p!z@9{C7MZbS?(hQ2yO~SCQ z*Nh0u<6byFrxVYSsBos>J2^@z2Q0q*dncEUX*j;Twj%~iZWYm zoAqXkV?fc_sODcQ>hACGZ2$0E9t_Pe#W@7`bi~!tLP2k3ISbuxO*~ z0LJ&WW5;8zpQvO8?v!XRfS>#F^M64cx7%?9#8JDQ#*gmTdK{i2M-H8(9WPQY0$e1N z5sDCOicM;Tkwfi!$6sGrt%d{qbzFMJi=RvV_QOSp#AIy(JJT(mN58*Zp0UZ~@Nhh4 z7z|dcvjFz<&!6|Q`?{0K>)YwhwgtEFJ`8~wh2x;wlqxTBau$@ES#HisIYWi_zW2Q^ z!`oi|@+6FtpZmGreHNoH`I6CRy&4bGW9;1drBC+_a^sKEOYa(ruH zS>-E060yyy%2yj$uz{MLq)lGvbbz>`s&+bX zefE}-V<tU-DJj>dGpY9*T1Uxi3Ir-;|4CJr!!-M^fl*acaY3f4Px&=Djvb4)b zuJ?BL_U3zgB@fE;txVH&5_GZ*h@w^=MO>W!3Eo>Qj!>8kN1;+)R| zpEE{_9UjH8N5uVpP{OA;*6ZiGp7T$J@RS&%e82C-WcXqw#We;hC#dDiBWKT9I{)7J8|Pn9 z#3n+lk?fh0^<<;A<)Ua7D^tN?h-}NNo@fz|1R1qJLZ<9;Q4aDG6QCE4FSnqcECvTd zsbuXZ9=Q4t!pUydqoz1>QhnH9Gphrca6HCc8EVN2CYO035nzfZoRb9(iyhr8PN#ah zy=9#&WtYBMk$F7v{y@0tc|BnGtRt@Y2 z!qggE5-7kGohE7VI&dD(=kURxl+@M|!6@QfSkE{9?Q;O0C*b)6@_Z<3=`di3A`0XF z%d;%aj10oQ(tr8+5pwFsaQ3xVM09v?4ia@KDAIS6nUY4VY zCx|IcFQ!PMB^OwOm=V%2Vu8q+$+o#)AUr?9uq?otQc0R5Qftka#QQ9P3n7?bu#!mz zxRyvrA>IJj@y`8l-xhOjUzY*K^*sV3ZHa+g*Ciq83Pe7;YA@54)9oONMedVW*9LkMqyDHMFfz@vZC-D$Kj6i!vCV1_##I+iIY3K zj+0jLYF2NXQmTbit64p(n+sH^EFa6VD9vV`s-#-QPdxGJ`w7?nYt?S^??qAU{KJCI zzZm23y{p-@^K57IFuv&gjSqj$JMK3q%IUN$V6MGVM=#zsM8o0!{J6sV9mjFtlrP-E z9&R};$C)>+ukuyC+Mo=c^4!5LioDm1M|k+*S3m#K%UKZOMU0I` zXLr8zqaXFdapkY;ii>DkeSdXPif81+hjS960#3T2ZYVMy!jK}CVmxYw($IK-ur5U$jUSk5!DfK!-2UV_=0?N5o<@{~+W`GQj z4Kn*s_GXNm*KXeW&YMqnZhq&jo1MuO0H1F+_`4o`s27eTL*zT>Wlqy^hEfIDK7$(@+Qy7IOb`RY}@dVEER_v~s(%T;dmVov;S zw$tNVZ=cQ1SIfnA3V`I^g5_e}tebf=U#y#XvtBGW>t?x`Z|19YvtF$i^Nj&sbGzAY zrp#Y2wu|**yIHTAbu-^AH}i(;X1?(d5^Ls*`DQ&&v|g<@^GBRUua{ug8}wMwqM2{z zPw^%Qmu@s)tT*$`e3NbFo8@NCv=qrwV%kH1=(eGx%1oQTSp!B|{PFCzT&^~dl_&Bo zWv20EKctqeCdi>N!|~0k=;LwDB`oug6r2|fVDI`pqu^kl!+4lTvPpyOuUnl?YrV%v zDK0qIn(@N{_vPHtRVrVLsK2+j5DUsQOV91nZ93xRlJgw9+}rED1ZmfDZ(>I_$cDT6 zk5GdA!ghyQzzc@{gU1j_K!IGBpsZC&N+~(xFor29m1_Y>eXxT6_{I8NurQ2YJbQ`AA z-NVgdfkb=Q@-=Z!RBK-B=J^sqZhw_Nxe^HU@YVDGU#7#@i2Db|THC*0n0T1B?qUGz zyzkLfopynzx$=EsZAf4XM;+5_05Mg90qlJp#_VU;`WyTLzR@Y1iE~@)7%-mDcvRMH z@I;Kk$RoFnusA{UxU1`O|E+7OFju|)a1dD6jbmk!BuSLiztkTNea~~_-W+>EyG33U zZXkH3-J|VG&MZ1!8qUtBD+QiaaqN1oKOFY@N=ua_Nn(1jb=_bz>@Nmyh>bNi&5E`k zFd>xW(MRoAak%#14%>`bp*eZch}xO!!}gXGr=0>tNi$&>BCy_R!(xOm;&cZZ)! z{B`-YYfs*BdOG69ivDVO^{%FAzr?gNJM{z3y)d86qC@?uW;V-1kMqEv+N^C}(r&hQ zU0bgD!Wuq0J-zdZYuEDEarPw? zrxTohlu7CLare{nD2H7GrE<=xK9C?Eh@YU!S4BBmHyCdBXY1416qwFVx4T!aKXYZd zr0MiHTrRH84-R(__`BX4-nDL_-Jag8D$HgVF3e`AuU^+lIYpN3m)#D1;#1fgF4{Sl zq25!FJNq%|5nN3LjFSkK2T}I(*UC1F^=7f&RQbf822L;OhN0GTR}Bcc{PwKXd{On& zUcWXt#A&gq*BoK#xS07MmHmKVjt3ZRn>Zk=MZ`|pQHFcqg z0_TPy(C_wQ7c%H}2S?@kVXN0Ja8wAR)?XotPv%eWA_@Z_&vuIPQoj#&0C3;uWj<0; z=?Nk72U=^}@7Kc<$=(aUh*#m3a|Nk0+Zk%77S`L%`UIJh$luu|#y$tD&8mH6Hc??& z7Mau{+$)0zbNDm!fYOZMt@d_Tv48E+13PyB=X7}V)XpT&OJ}br+I04Xci-LV;PSy? z=zHFA*2Y^f8sBlex0hQao&Ej8?Pz?S!1jZ>aiejjY?W3a#?9D8nbvsp+rRbOzU2!a z#`fX!-!{7Mm=c0T5cdak|Qbgw;`%;AObsUb-T>pdo)3_|oqG>JE)B#4!K-8)f z-;Em6TH%gAL+kSSf6jp%IDY}(bLsl^%RzYl~u$&*Gi<9I9wdm*_!yF+E4`qA$o7Y3l-(i>?1>(kj%s!h+kJ-CDsC zV6IzG=jzgZw71|)%OW5s%DFtuU4(RjaZJ4^n_>u}T&;4o;Cuw23Zj+5Fe)Ovfr<~h z5vjmMRPvbI%&U+&<4w&w2udJm=ex7#@_MjYZ}f?4mE_39E|O95;h@YEEPqwX!A8)Z z1fQ zf?BVx^9WLt>Ja&L)(>#p1Wy;`e329oW#7nixT+k9q~zZ?3J5VBo7KglSmZQ>v%nt+ zCQw=>$n?RH2bjb{cPA`FJ(DX>X8fddtgTSF`GmtX;X<o)IGAuckR&pJ!LAZ}w8Jz=dG?2hAeFKq1K=^&Ell7mr2wY2lEfgC=SwbG82Q`{ zLznwe$RwA(rvw;Dr8NUQlf_Koy37Mu;af#X_Fs8AUQuL9D&A_jgI3D}yjE-Al`XEM z%8J74=r51cM6|7y9A4HxAb75gmQC7p zEBbrJnFs_p3k4%Mj>8@AgxFT|E$ zbWbar(oPA&*L9@-Z4?F7hVzQC-q^|XwIS^ha zB95qCD<|Jrnl40Is2Dk>WAgVn7v=~T^Tk%=1q1j-o~fNfHP@V1S%qR83XJZinnxyB z(hh|DWhrJhIr0==t;dS$>%|IiZ}>ZbK(-IIIED--%fWpXJw10!pX#a?en%C$mrr5Ptk#e_Mb z5a8pdAsM1tGX@RLaqszA9s50lnQTW11wU}KJXx_P1Q`Fzg+bc|Tn8{j( zNZd?NKA&ikEKrG`Vgzrnq znUp>!$P7u80Uvexf( zyEcO^O6ZHMxUA{ve*f&bNJ&OQ>YT+nLFv+}(sh4{qA01YTPj;>#hIDYW(s0bMX$Ni z?`sv;D&PWr&D+0)|Abpk53!Rg3CmMUg{P)wz(G>6hTMFD;qV5O#b`9dUaN&yq9{h= z!G5cSS6<$4wO(5_8b13@#jWdmyRBA;yxm$~-`y))QPys6&VM^^x4$^yghPF9eOD%} zfa!7dtjdyYvsu@T>oJLkl=7yiARUw5T3RG3{sOzy#0tW#(OD`LyPoGovFC!8<70?S zyN82$ml)ZG=$2_(_XdNK5C&zD&!V;>^uF!szGDZk|G3XTPcIyem*jf!f=?yovV-Z) z>9VTy6@PG%7a90x%nkZkQ6x$gVE_cVG1?bUZ~r!=>0sH?U!GF=Nb|NYvrM!GQ>i zfB{q)B&k0(I``tZ#hH<1!?sH;9z3~`=P`iGdy0eHD92H;S@KY!F@ZLPZOC-ja<+{!CCjCbB%s%W7SNKtNe2 zQu08p*0Z!%jZ~9GrCozCm?iIS41@Sz~3X+eOU5=dyS1y`BpqYgWt zJt0YH%{T`)#%;IC{&c6;ju{KBB|nOkA!7?-+RHlK&L~Y|fEP(&U6&V0;%cS%UkFWP zEJ0{vtrCnNdL@9kjDR$^MoGba5IiY33ts0&k+5Fui+#7!$EPHOlvH*ZhXQ`=W1Z(L zf(ey0u5{htngK~tf3g|FqgX)lz-$635OYBQDH%pH$H2g~M(jMwBzpafGD&I`GO2_$ zQWNtT!@mgM4-^-?@I56#AClTikaQUqmvKVRN|`$Wt<6b_YA*_v5RvTxFs|phQVODg zzQ#w`AF9Cjt#k4?MQPTNAo&}dF$sqz1h};189HS+RK8aLWvtYD!=lL1X_fjVq}((O zywt{Ia*(y{c8{~t&&*RfQYo%|&f{=>@zQ=ACvJ=J0Fah*HwaoL&w89g#qflpETdG0 zA>*!PoHHW9-GDJBg&W37qF1PFaUO^Gd@unB#s$|w_P>Tl8O7?Q1#fj*C1XlT6xMn` zoFx9Q8^yU%Pa;dx$hDxii;?hL&yT`huS^oxS2BvayeV6L3YP&ITm)VmN1w*P-2?4ZCY0ced!7k3BxZr#&m{3|Nqge-9X$j7` zxR@rhwvb9A@<3h*Fp_`-fv7E{Rx-Y95aWJ~AP6AbD~ChQCk2)3HlIX3|zvy zu->iE(>Nn=#-x`QVIT$PQX8d+w5vTvxW~6HfeXd08i+d@Ge} z01-&WIrjmf@8sMva4t9*DFqQxkR&<_J?9;!3A3tcc0(ieukXiEJn9J^5_Yalr%5b={pF*NK6s(s zF~QN%V$u6hQkG>dBq&QQF(ZU4L@|=Om1X$e;}qrG>MN&%;f0lQ14eQFbIVQ}a6L~G zc}X0(jQRfK@cn$^6Is_K(P4`i|YtyM%V!6U7y zgb<>yKEs{DIpcIyP4T`0WK6hO<>gZ}2k2OwH95TyAQ5kfRtoq$_shQ^j(hDWR1=*R z*nica2CCe5lUbHrsMq^aaHej63loL(adG&9q3Pm6hd{ z1VwS^e4COV5JjM9LxQQjX$I`7Y7EM`L<3cnt{as_!a02H^6|sRyV3|OF5OrgYYe0x zbTU6cnzit%KG+dlOQEZc^*m!)>U+3`yg2`5OUHorYeHW1!Hznit6_}i8N5F8*{YvS zS6p2_aJ-u@F5Ot~Q4nMw(fV>#Npo~``EpfBy<4AN!qC0f9dgwyD?)wgIzvkui4oX7 zj4=jfnj-M?PT;HmKR>-=O_&HvF`j3%al_pAvAy$fmcPyo=YR+gFCET`#pxaEE=`y) zFh}r%j9~L=U%pN#iy`QsRyqrOyq?VA(b=U<%W5VQdQTOv*Kx&t>SkfoaboA(q0BjR z903Rv47bq@T~)~tT8(Y9!GVJ$L(;kCQH0o>#l!p>TsO|FwKj`g7nf}m`!4iiS0hC@ zhYJq5pNL%UtQ?h|3(rr#A@zONE%)zt;dw#eUcKo0f$IawwVC}ya1+mKZ@cS_g-<(O z#tKuVd-pHf{*`$?+)2|koes17t?6LU&VcZl`+QK=8jsqK;=%naLs?GF|882A$g;!x zzbFUFywgeF{>IF`_STKxb(dot^};RuAKY@@^FhbKdNJQj zHuKzSB^UK9#A2@InH{Ui#iK+YYO~(VE4@v-m|&XoC!0TPfzGU&=aYQCnA?p_3O%f-CB02G@2N>!@O zRcubM0mMF(+FV7xz{df+g7aqq;2{8bmh)G%fSF&E#>>COU&?X*o`)WK_o<(Q1$fo*$w}}Vo$S}U=kIO~B zAAij=-SipW@LtK-zpMdu0UP0o9nwLZZCCMU=t2l+!tsOp+Y)Q(G7o?%@pz(P3C z?IrA2-OTeo<*KvVthQ%ZA7J>|t#z4Lw}JymmM}KkY&U4QPndF_F`qC6^ZCns#(d6) zJ0AV}ZW#Ne4PzU|c6PqTKL?#|eg4yd#}3-<)VOoE)4`pz*}UjQn>FF!;Es?Atdk>f zyWQa+-~E3Nb6UtxC@ELCB4>NF|!3XoXKYqWjex;)5cIq#^ zzJEY;uz&r+-kxPzr$k;F=f-hamWK-rQW=g*4&Leb&fJ2&UfIV->rLwnVzWX(L}|jd zW?OGo8$d~@hQ>CtdYVf^KLqj2?tJtfnSB54*iDr?4;pRVvMk&9`&P$vyR(CSAMH*D zbq2E7j(fOZ{b@2eK>Gr~q4?WMVv=_%Fi?*0hI(;0f<7Ji;{xY8ZO zDy~&p$4$vp9jmx(;yTCAU;E|{;M!mO#m|pMS6}|}tGM=pGllb#mkeg#ym|hoNdjl* z1*d7gNm-R~xGuQp-03{xe66F0XBM7@QJ!Q?AA&~J%wZ^^y=6Vz%#Gy*S5cCked0>y zS-DcrxmthQ7}`%Y=B91Lb~P7yn?_t|&!hsS-@FSaLMS;C?C9ljDJ6qVQt|_zz;SD+ z3=@VihTuV9T{(P96n#MyeL*XVo{XX=Cje;?h!p$Okas|&*P{pnfVsXGdmeUnw%eT@ zcwX%JE(3x<{0ZdR5Z!6Qat-C|uD z;zLf`IdSfA-hgk9#Xu?V36KP;(K!{4pq|y3R$+u!+P#dbh6FQJf+;rDFYth`SC~xn zDoA=NZG2(X+i0_WtT(vR63&o!4tkGmH-x>*H*P!8{^EiAZ@=ODM?u_6l1qOo?EV`t zpO3pz2K!1)cCTE$@Hz#kxdPPgKzh{e+J)#eiNYNMV_JAe3x z|LIGJ=l}2zNBMvFhdPw{;}}m{Q^!f;o4)8L9ylMfiLpa@p64X;Jj+03S(e1}Jj*fd zv3q_EH_yMVXRS5;|JKpk+WrsqtueO$j;_(x_F4mDt=s#}x~1;moa((zha5G_ZvO*E zFsWR#1ME|EGpeT~LHvg0vCrp|JTxD9ZXCPbd(u2lpZcTu_~klI-thTogeZ1B&y7E7 z8T*6ppZW(EN6|-*IRBXA&w75~OdV&sy0r9LZOSH}ZptQK_44qVfpC^ym3a0|F>|%rG_v3IzAR} zQC7>DIk!7cI-lTtR{MWrRvVSl{sW)!`jl|RG zacQb^b@1D(YuA?69xWC})_v;m(7SZ$-g_@y^bZd^t^L!})8?Sn`9n&xJl3(`Qml$x ze?Awnj`K7lUF0hvzel2;Lhz!~DcHT4wb`$A?6*9!x3{70krSCg0QCz`xFgpp)~nEDU&G@ zUqy7A^GS|ll$-?=rT&L_bW^;c0wt|j%Oj_Xq=UXbL_rgMqiWW@4S*5_p9pYI+`G}V z^;Mxy242l@a*Xt&)?85lGeQ_*Cyf0Ru@5o!A;y-u=R%YMv{A++^ECabH2tX*NPo)M z_hX2O_#+~)Zlz35T7J_L$#+hKpLU++IpCE(shXLDw z=l(};K2O)qpS^i)G`liy zj&p@>;)cVV%sFsg?0kXqCC;}yKj?hO`6b6`R-0;dhFLvRQY}`CZBvxFnmN?Vy~T&t zI70qtKdApStp4Y**<_2Z3I}LPht=5Lgv@|FY?9Bq5 zyGAR^WbzZ84xSaR;ecOF0-;?F1Uu8<9vMYSMUgyFNg`#EsQ-GY-?`fE{Aj0x!n3Yd zc!1aGOuHSR)1A(%$)uWhI@2^s(rM(nt{Zi_(=<-9old8f=f3CpdEV+AC2%nWb&b#f zA}v+9v9d0ZkcT1A?I2cDO4r^e-X0n)O&e{Zb{j@&(?=U<_t(}2)?Reo$XfRF-pfAU zal-qQejmqO!9C71F>IaImpk9){J8V0&L2Dfny~O(|KI_fVmvxLoX-yrM`L(yeC^q1uf?u+{*PLF zp4LOp#@(~wNat~hsN_@bNdUOKq?McTw$a<#QspU&~X;PTmnZ=H_^gYo>Q+!oMohp!!YgIIQCd`H*w z+_2jExHc?HMp@S4yVugGU75!1&Zo*Fpz??|+uD2;G?t2xggxT!R?a1mb;it(TPtb6 z4Q`O-wfSGYl4mpHga)YE6z^rwc|cIaZr;Iz2#L1_pGm{Ph*8|{bm-rKtCaC>WL@-n zht?RwPG>axL9oN_Ca^Rk&&@_v)k=+G+>MiDvpX55sk*$YJi}S5wOlS1nOEdF z!;2}7*dJ4tGAK3tV@T1XIHIE6W+?#fwHfnM&ZDRzDSGX=`~C(w_;Dw83O^XivMQXM zonjSNF*&qeKltE!O~c2XJ5N9TZzBQzJ^A4If51PT=MIZW>DoVtTh74QI1j~o;z$Nc z-A2`LlsjGLb$OMnrrV=uq3@V-iA(vmEK0oX@}o~(yhLG`rGCGvx_P&g=fY3ZFa$1r zS?lOHq3h%cvQRaY>mIkcW2wiUeAnyE4NHBGHtSl#w+U z|NWN)PqcEqK^(_6QCgeq(*5^e%C%9_bMrVp|AE89le4pvqob3vvy-3pU)vgbG4MKj z7Y3Sqj|@_#4Q2|1fN9cOB3Kt1Rt&a3W?%wixP%7}YiHM=zM?r5B2n6PhjCdZksE7e zbdKgU*G5a%&6Bc>zv|@d>|}fHBOE%$abEa4x{0rMUgdm(^UcmLI{yhF7I*-!cbsO~ zES8O{gOm)CE;PTUCJVWcZ%IwZp}*H;FY0$VsFkkKY|H7SUd%W1&7s06Y-T{b-^i$=)P9*&lf`=V`@-dF zo~xo-WU^<+O&0UTdR;}z-{;0~aGR!~28-h`N0>8Ae=taa?rb*dc|MryS>Kbo)pI@9 zdTfrAb+=b`hQpI;XWH)(7|NJ5j3k4MclQQggd`=;b$@w09=Eu%2UNO0n^v*sk-wN9i~`17-%qnB43T)AFGXv*-#h;ofK=eeX`oGNm2MAXN(yX(J+!(pc5 zES!72Jk>*N3_kA)u{kY$Raa4GR9wf!=vk>`Ed)2h)qK&g=zWQ5wpWqMnl9NOz<>Cf zPUc2Y@aoC_a8eCMTA7b>?k0HIXlL5K*lq#+!QuHwCQ3z79Mk8$qLaH}6dd3G;Qg+W zxM#a{H3vj>>IufU>ju92k}vkH;jDZ9?E`#3x>jey;pu6X<)3Av$hS((x0S0oAmT;X zb{t1Ij{o7D4>*O>b0*G-vvC|S5N?%-MLR0W;wr^&e6nqbx)HNgsp?eL>~{OoJzqdI zWufpjuMDJhgY*CHDal?hqsUhO!=DjpS6qe8BfiI&-`hKw&ghBh{%5NgJy7%80h2?Wa7;>#; z;Fo)T5On*k*02?Z^In?by%?_+l^Z$8ngtyQfdrdvV^tTmPE%qa>6$i;SDc>%_eeqb zc%^&ide4 z8`mvcnprCa$=mM`%(Y5#=JG6(6NMy;+_F3~nm8c~ZyV+$f>3M2I5(OJ$)Kdx#B`G8 zxdGImZ?xe=QZZ?b$y%2T=ZvM~53JT&wOXzxxa%p!MY}DyHPuAI9v^3^A`;}<1vlDc zsi(x8D2ijLAf@3v3eBDoAAlS)YwtSuIWKmebKdPZqEfto(-O&FhnVI<7-&LQqG>)6 zUGt|w^QO{hrnq_s*)Wv!I*&y}MW!ONEaADI+x`o&Lc zA=u<~j5A|mrId)Rt0ZG-GM%KE;Htb!hk-Vlt#uF~rLb1MT1siG6hc}ngT{WE8QxJAv=#Fa0w+KmV)p^sXZVHSEXr@6Is&USn-gM_hQ@S6)Cs>-A&TG=c2#bZ_h{ zCA-~;KYeI&fS=_2vp?saOO7KPyl{)I#BCmJoHsb1hnTv$urTG7noshG`|^UT?6zabrE` z^#?Q?gDc$_K&Aku4;seK%rhR=BPrcIggGtdc9aZvDwud<@sGH@u+(%o?hN5d) zd%6nx`EgLc_|A5-``CVOmF}Hw9-6FfyYbW^Cdc*R-cGOT@2H|z_MfbhDtRnvC9Qk= zVK0jOi8ns7Gc&zpPTa*ce=pd7o4jUk|B?&AHvR|xa`Ps%ilgG_<>?ddiyrUa=v;OB zoD2RQeiP3-(&;!4IZruGsy3?08#=A{p*nW1T-5*6*YE^V#bW{c8WQvk&z;nxR$>*S zE6Q#2=eKXSw?Bq(m?l6`PA|=-Ks~#J)6|M0NOPq)kqX1y%nm8qDauag5Jh&f7pgFb z1b6Y)wPF&XjlJTWRlt@0JAR@4*}wBF-+0|_wcG3Ktvx)ra^>*AoL>(G#cmL~*5e`y z0v&dkTG(E1wm%$*wLCn$v8rtrhKh(Qouxq`Y?>&%0@{?M{bMq8hB6J_LiK07ZVd98 z)xLRidh+1<^$VxT`FmE=85bv`IUOt(^=|NNdFLGuJ+%EtIT$Qg*TrnQS}(r#^uc&i zEtl8c{ODuLrItZFVd_jBN62yV#JcISnQFo9i(6kg1f87R2^evs%ST@^IRA%cXNPEK zr`ZmF^;^9E;Pmd3Inn&&?r$wjziRldC|~Rp0lE0oousd+c-n8=bsop`pSmoH8lOZb zle%0|F4@#T{?U6T#Tj+jcpH?p_;yf#r7x>70?hEf2W$NQO50ON78>xGf;387RFa{(~sIgaCy;{WWA!|Ge7|n!&u^DQDz0LI5(CkPu%GxT<|?)| zK7+njs*6(Gu9TLKN%=CRzECnDL?JE2oT>Y<^FdFQA;TsA%u8|ly?jH z0U>szbR35}d>poJIkIyDKEaF?B7MB(R?u);eR1cI-GIVNwroc zNiwO7NoJiS#z*4rk^syAJJ1i>+zH6~2bzyY^rCT*14-I1-&hCixw-7cckbe+x^V(m zCrOrStr%l`L`dT9PmS|p%{U)}TXZdFfHS`1OeaJoDm_AK)+s-#1f>G|hu)+>kun&; zrDVLGw}TNdN0_aar6F0ITY|5HpjKED&>OYQuE0IKjimR)wyE&5)?il!Mh}zKBdp3? zjsQGzswYox+-$kc`nrCTUd!}jYI~Zi`C@ClQ1s>d(GrjXAaHf@Kq-MwEhZ$0I7`}= z381c%%H0#3G2)-Zsyl!8yT#M#Cj zlrv+s0n=I&mqF0(w%ds(O`d{$dZ=I}IY|QJz7OrxCq>PxW{#172G3}V4J8r<2@v?6 z58|#6uIm$b1I_>e#=Va}=n--5TPwO3v#xca2}2$NoGYK;x(q}Vt0?q+FfbF4Y?{s? zHn*yo;|6%faF>D~UM0yxo!9C4LNy}05Z?%^aSRQve__q(z z9e2Feb0^c+-f=rar+c)-Wvzl9pp^*-o<(4^?vC0FxkgCyNr+=aG#VkCL}7=hQ|kyg zXWNA+>;Qik3cL$2GaXV&CKT+du5~~yM`p?bz1EkA6q85cOEM#(pIVGZB(SryJsL-? zcE8&0><|RT<7L0qipEE~5E4{+QZk`66xW7xDqJZ!Hx``3y|;} zV1OXN!2sP31$Q3|5CoVQ<4d9|I-s+sS-D?=gs=T;_eb{f#Zy8Ep@eYsJ`QGsJxH_Y zGu)NXDTPtJN)))GP*UNW&p#*r62wQ6V{P!ki~IZPWstW^47>_oCTG*_b~=-MGP!`~ ztqqo%d;WENGP#f~(1gslK*L(w3;X-(egv%7#bU&|nHau!yi9cR}$M3$a? z>RfehbMA8Pb6(^;9yk5Hey#IX=aZdJcbszDR7F{DsyxeA>rGh|+ zub$g-71WDvMuHqoWHZ8IWeL?yC)L#0Fe>RB&#a_svV1jPtk1C6tNzyIlRr4Q{M2h- zo4t+6FG_>>^+a!xjJ@_@!FRre==Dr`7mIRDcy`S;(R3jPNcR-gX>kx)PP z#hd|e57NzRU;EnEjxZeJ7d?+N#xMAui7L-p?~eHA#V*~ZpA8?gZSSkB=h>gKC*Bo( z2hsU!{5baUcJX!LuloUGuAN%LSY#gIj59BZ9p?Bi+@iPPmUHI3#`yxrfm90#A81zy z>HAw>ji6rcy$jcyZ8KkO&(MjqaR^z?^Gr=tU8uz*&pho&FTcOucyxE?z8+iIYQ940 zL_Ml**!flOq0 z78JLLynr!X+@*@AF(mML@7LBwQYtPK6NI4=2A4{8-|yvvPRBPM-g{*<4gw+7I0-)z z6Hk*~8h<1-Arsv7dff$9iWujdyg;-I)}~A_*r$~dB`ro?Fz0tZB*+VtIRDa*cv5M` zIOm!RqD!ISoJYay(5KIt{X6_MzQbudE9ZXaC!Jq*e#iMs=b!HdBDF>Nbm^hfWAO^M z7Lk}NmSyX*6kMzO^%CX~dnx_p!l!REt7iw)24y#E;v;p#n&{*uEd24q%&kA$*t|E} zSuUt3vrDv@)lIpohmaa83S-IZbx3u<>e(iv3@KPFSFQ=d&AQ3d3RhGkez3A1{u;y4 zg$q}&onA=6)<737T)lSTbTq_ygI;g7zIbuHA|^ETyx(W0${@0t5`i(F9RW)Ilz0lQ zrG8j2l9`H>W;!-nSTIgRTu2F}NN56L%|z(?l4KMI9*MBDnpCl~#R8oW8EM-3x%5?B z5J@FWAe0CMmp!gUAcT5ic%vV?(KO@HXwo#-uQ$!)_Y1hZTnz?;!D@N=Tjo_M<5$>W z_p2hK3}g+%xh#z9X~v|IuI|KH>{>1JJ@^NBk}=L~5D6PbSy3s4pGyWNcs7b8%Z~bz zXO|p>J|13Xhuv>;xnLScR*+=GMUdaEIpaK(QV8ZhztgVbOz=PoA$UDff4pw5*Vssb zGPS;1HXK*$BDe{-ivXRJ5;U)x18G6pl`rD5Ij#ZqNeD&iiL!XSH2)Qh8NF%<%2LAZ zuP&qn%QGfr{+ioCSq2PWH8LjA2*NZCLTGK0+}w7X$rGbBrD2@-K6HXrm?Ttq8BsR9 z3ZpF{C(1!q8(f`cM44Aml7#2KjF39*@wnZgFbI&n>s=XwAY_xyU|5zE!f_l&I{pj4 zi2s3G&cb=K^C`}kIv;R;-*GUj7qc)@;Z_Z2pSRF@!n^G$%+}zYJH7j0pc!73Q17Hu z2PhY=sBuQQOIn%HDN3mCB)!%hW_$+wnFNP<~?mHmB5JZ!gHrFMgG6sRQ4 zGmn$p+1bDkqDWiJ^Ncg#u)kXmhPZn+RXx`=Uko9Ek%Zi58PaSfArFfZ5E3U45~wCC zDKE^XRq zP1dlMGbt?>QOuQOV6N96oIZPYrTbrwTPrHL_t$g?y=i^JLTOg&Cc4pOyIOE3)jkjj0C- z*YpJSvlxwL4xJ~RFYpwP1|$%*fi@*m+L*+dkB&n-Uuc%wYTT9oJQnlCGS%ug9byI5 zRSZmFzL+l#QC&6Un7O8#x1jEdW}N{cW60I(+pddK0)nwCKs!RXo)9|(?u~S!0+Ig!^mRjqA3nZ*9Bw6og>x31G^F=W#Iwgf|KaIJItj=UMW7daFcV=o8%s z#xRoi5&1N{{aU`2e4pleHlP1B-9JCSaGrGD?A+|nwb@W)z$>)?O?9!6qMEJ+jiA70 z=NlI|>O#=nQ>5fCP>eVT75C&49y3e$=;?Gy8S|H7xN|z20d>87kZU82wP`O-IAg3i z5L^bxQJk10BEco|JU^C61>!_ z@Uzq6k?Fy0M~8&t;~Tdf9g-YCFn4Zbq@}Sr0pR7f6I3B;Vn?G8`4FMr$IE7j)m4NF ziJ&3fNi(>+GRYmQhR8!K8CP|P>ICqaTXl1SO;HS($<|$!CkSbXJfys$A73`x<`S;@ z^xqyj0dDnJX8TH|xvL80rJ2?}H)FjX%j`huEcIlm+&t5&@8;BhcZ)_9uQmLi8t*=7 zc%5y7xUO@5-!FEiVbEkVvE9l#--O|Gr}Xx{QgWAxxFxYumcU?eH0$+@uSifVWF|>r zm>W0CwC=e%?{s+Xc8yMRSLfQx5~aId&iZ|pdp&Kk)Kf=M)NVyl6t&t>6bHM0S5R@TKb4y#!a?1fpq6GZE*mg{3lGL<}mBR%syDDmotM=Qhh?2Z7C-X;2Hf1=e2aEZor>+2q ztp&w|h#^Gz(8ISUIn+4HL4VcLSn%VE{qsNTZ8o?4rTL|wA^5n9K-#Vnt~axwmw&~7 zAN^b8xZ4F?09-F>wTSY)&K|LQKX(2HzkD+G-1(&|tL4C#^1eUmjGfz^=bTTA)CjG{ z>QdxxT4~K{FQuBKDLRtUFLN4+NoA2TOo!<}U?7k6&AO(B+FjIkbV(jfX-YPD+c*rg z>i2qOx6{6~%9JWw`<-@MiXv~d!yjiX&vL6&zAmFka)4Wt_?!__?qIM)6LvaEy4DQ( zj7@fSVwW*4wC2)$_fy^Z*9`}QP!UTGTWyzUapmmFxcscW;VAUt*eXUO$G*{SF`XWF zi;~i;-A;axu{4SlC)jd49=AQuo%Q;i!ZUH44_2!yS1(r+a4xeRvAxsN#W?kp@&oHd zQIPg}>9@O{VJ2q`7;@1InBzF)&pY!40i6)Z^5AE%qJ0FIRPUq@bs|}9y&Q|)uEga*P zV;ra4uvM9^SYCbBCp3?|Z2d{AYu}0ezejQY?ca`*CwZA};x-G1mx87Qq?BbDbVXq=x?YJiA##0V?uBdH?|=K--)nN9(s%#^0gS@v47dylQ z#yIcW>ECzJpmWs+Wt!ms#`*JEun~??ry}pNHh{JMeNZY4l!RqnNSVZdB+2t2CLkrq z31hy^JnsumJTGqzv*}b6u_o&FfGE0s`0&H8k|AQ6yv}w+)P3O=UX}X+eVq?L>ASDb z^SOy$oGyCJ#9B3NE=yObZ!cRF^opF)pj9j;Oqb$A5 z1VI1{0Bb`p@HGewuiuFxKuC^!GTH1M9L(xIAn_7A)9LPB)$eu(=YO-iULUmw!?MNC zfBO3mPmagCo6Yggbecye$A=CdUH9P&|DSH+vz<%MbIvC??{U7&`5NaJoIi5@2@xus z;xeu|j+mTaw$g|GAuUzRS~jbUhx(gsKez6u?8a2$Vz36nt|K>goCfvwS_!7!!HJQh6ir`+!~< zoMEb#-Fu>_t(z<)W#?+Ps3(=0^pUNifW65xtkk`4bQRmBXZk3vVyTlX7tQhv>#Ckt zAvQ~XsLG4nX1%EwtE#N?6)rQDvhx!m04{#f7ud)zQBllIBmi+y%8OE7B!1HIIb%sg zqyXx6+5zK9hOo%Pw1%~Fu@DPvcNQlgX~b{+sccmwAA-+X{wVf>8Y zKHvd>Q(zz&(7>2(0OK!%-T(}@H-H-nxZ&!C;$z0fPXf9T^~e^M1P?%lBqkzeJdjc< zTfX5sV1G%%NFoRi78376&A{+woOXa6;x8uvIq^e51~4QJIpqN8<*)nU`3Ad-(+-gV zdzqk&_-DfR(SBQDoAG!K&f2dc${DMO3W9T+MF~gF5J0H`b2y_{Es>~*0iXI=u!pmr ze&M(AulS&|a$aMzCS7GzJ@2ZOzH1sNEG=HeB3`tzP#99BssMY2o4qC0=af|q+i-OuG_dI!de$r`c_ww1x z9LI4&$9dte$iWAk`<<6LuXWz)e4+Cd&Nn#U>imTBtIi)ef93oSFyxMdS-oxMt8G22 zyE=MjyA+}psj5V(hOTssyhe5t^?h>oAs+=xPo5%U8$el*m!IBk4?&OA_4eyh4qqZQ zwna&}M{B0GO^pdn7U2hP*r&{whqPSkpy0}FGh>81HC4%&l4{^p+h(;(p!l`;)<*WB zjg)!TAB>gcyc!PkOiBYXX0P1_I^BKKrFl|j`EWR9jF?iRK0fewD-1}8!)_O7clJNO z*D8IFa}A6qSt;c^v@T0&l^PT+t(CH}YdzO5%d+%c&sxv*i&m@fUC;iw(nggntplaB zYURqjHVOuVFnovK?hJ?lWi}ZT6ysh?$w@!t$n#1Y8;15B?O_u8j7y?%m6bxYqc9A^ z(2qWwLa!`)N|tFgCMce8VZ*>`wUZY>STb6dLbG05 z$UpKu5=9yKpCp*?`+k|nSTo(eZ!zAP!y9vCrsk8emM}w&0`{qPFY#;e-*J zgr4WO2yHfxJvY=!p$DnV2qazK?G@fK7l)AmRCbFQLFMQr8u$KjS-4jOxkUH`!p@X!e#do!yFotF^=KCvbw`u( z0&Kf86ouqWO>(KWo3)6a;8+UOY=IXg)ik4S=Df{=3^ZJ@>36}oT|0I6S}E6-x=ZPG zY`>_i`tuy_KEkJCK*n=%4E&#A7ox*P!~sS&=N!!U zT~qfl4q~S8iIgC1j1iI}oJ8Ap$e6W0uP+1hAc|ZBJ;s#;;-Zx#N_Be|?^~~38-+X7 zsvkvM5@Tdd6b^^`^IqQsrx)(nt9OSx*5$g}>t{Yd2uGv9)7l&Ldr1t<7-POG8KkgU zrCJIBfHO}i$pD%9#(+4JM3PI!Qo)t9v2<+;NeHba&Lz;3Ceg;TIf=5~^L=7cG6@Lo zd4#e&JbyHdLSq<@%ZxF`0Kgr59+6N=NI-F^L@Y@u{t27_sf3WwMm^3Ai_kx8>JH!u zpK;$38PB&Ij3AA*tyb)f*JeM%qbn4Bo_7Xm z%CzrwyS-jS&wmjm!(N=kMiG_1>-xUulc-b}M0TfB6y*EX6x-Z*@e~8`E_fU%3(=rFHg+SK_0i z_{zg!_}j_etM`6L2xB}i2t3aiA%rm6g1jK`m>bR)BIH~tW36k9Qk;+ZXyaPDvlw3f z)c*voU+2~ZT4{XcYLb?KYZ#VK4jp!uze|olrtrz;DQTCDQ8j&DfN_;_t~Gl*AdbOX~r23tRjx20+NPlhrulg)(Vsg zN^XcLPY6#jGQ1qWo?ynpHB1<}aQNiG!EU^auD~*v@irXc43FSB*sW<+i;1GJ9$wn) zd?Bt9C|PwigQaGscF`Kx{DcsdB4eACNnHCRI6gBQtr1A@mIYgW2ZTg3HNZg;OB5@uqS@EdD-(U6iW384%H%UOFU~SVL|Q8yXhx3aI=E60LP*~>rAzI)fKtr&vV$anHDxyl zKxB**$cXVU3PUjZx1T3#l>q!wTp$)gNFr;k3sMTD8-=7aIF`87k7*;M z2o?k&-{asM+5{AYl0nF_h+qr@g)t$0zsQX=#J~c-&z*_qAqbN+;KW2Q7>wc|nG+Iu zKAnz7$i+tO`(SydT@JxPNur40`MwXMiGomoC5cgh(CtXX9Q{_Jj$F-39@cG)H)1^pp@s5-PvDQ^ujH?6}O_MidXT# zRjXn1)#k=>`I^=0HLKODm&;GXZRbDtujlvU$IkD^kNxYv+FjI--n$~^Sf|DCV8Q#H zXPlpKe%|>Hj?9)u^r-7H~ZzY zEV+=L*25!woo6(4I}BQT{figxy?AjLrbo$!z9;AtkRQeojXMSNYGP6mF|FAf=ifTU zE*uqm?Y07u@9kf-riil%CsH1|18cK(FzR)EkF9i(7g_WCe@_?_#s04lsg-Z;St#oY zBrb(-ht^)Z?P-noyyY$buDkH5QqS={&+~|geAn~+Cm-T_?x{V`^X_I`2+sCAz#A?$ z4DtOmw-FG+bK9-@-o8|EI4t_CU%ER3f01}E3tbn;w^05lm0=>o6-87*v z{`?oyPW(}sx9}d^a)jeLPD**uy;-QbDw}Wqny0Q@dFt@!_D}o-+&cfWcf8}tC;#lv z%1!ahbQ8Bbu~hp8cR8!gI|_4iC?NH!O;x=tePuAj}z}cB|c@>7~pth%%x_qi8fr zk}MmKlBDBXi^uUKlrky%K*vyh2wk=rC@+|_Y$%~ZPidvS&p159iFt39Z@c?x!qTfV z-~YPTTF@Q={@rG3Z?|ywwI7s)H9~kUf?XHzyjIIvA?&0g9--{rG=n=fkTO&z4wdR( zXRiAn%*wDd-LJF!Q=o5X&rK4~#rW$m{UqHNZsDD{<;-JoMt1dhs&i*cn(d zMJ=cP209tfrjxtwdUCynUAh9}VUYu2*xK3Oo8I^E)6JF{1v?&x`FnP!V=^}Wye_c6 zfB#9p7e%i1ye!oko}Yc@XjVV49t@(`8rO|u-O?YzLh3c#jINa12T0t40ze!Z(Gm;Q z+)=*S?jL1bZI{bZ199BzjYj?6-tNqr{&-Xt_>|I`C@jmipC&;-yL%1ZmS`I4LI?DT_zdU7dCvI^=OfO4 zK_9Ph9A!XNppmi$%0u%GFsWwM`pGpmOoFxx4=^k7=;$o!;+gekz1XY`*Id%t z9?TaiB8FXMp$rlVpqi;>asjfu%GoE~OQOyJI*t5%z7;*HaihLo6}pKtt7Wr3;|5Sq zUQbM}N}B`ToV&EGyF1Iptn)+5Rlv6;Uk=6037Jc}?AmP4R3lDMO{z(mwSHJ7Ur*1x z0((W^$?nGFN>gPj(}Lfg`C_&SnBu~9&u)xHC-Y9{u@`aSDlSeo(^mYnH&ttw;M*-y;2$n* z#c^sCV`-9>iCf(cqd7pag*6zH72;eIl+xn9=V=Ya1Se(PEG@7pT2S4)sElK!G*bo; zjHFB@@HuG~4^s#CTn_3F($P~uxJnZd39U^#5PYZ7NsJhi1kyvZ7(PXEVp0yG28{m9 zjtRnWHjF)~9})!pi}mrE1i*3|m zghnAE!9P=ZB)8maui%b~6KT&Y@>z!kNUHXbaYFJb+l`crgkbFMd!!UX$_%*V2q82G zg|SaP%~)njI`C@Fv;wfCfort8SvV`_Zs)zupQFHi_!P%!)|C?lF^&fx4MKV zWY%f=lXGTi#w(Q27Z2!V{Ql9+wpq`wCTgC`JToWhv%*tnBwFTgwOX#0o1Z2Q0N!&y z>ioWXiQLRM+uN%1EYHsC_RV~;S>|)oq$s`3o79s7%o`uj#DFIH-~hAS%XIgK`ZFak zz)L{1S}v9ggi3sGXeqsbiyfPS%oWDyCnp`g(M~jea4N}ni#mX44=WmFisuk z7G+9tATXAQkp+BDNFvU*jvq!CAre6lX-Qwk`Pa!r(QmtkjO{RndAyD!NG?htX*BS~ zoZq;s$aiC#mSh?RQc9Bg{3=86 zNC5xa^Q71zAIO8i_c{a%3K+;G&Xu}ILG722F{`y^Um$Fp-6B?$vswtwr9j-AD>zpG zQl+n6@jXWAUn%gIF+kbMbFDakE-{J$SkOG3MQu+B5gP#*pdEl9MvgxtMoOyYn?4NS zGW7jd{SZ6fd6$t&Ste=bEE@2%Og4G7xkP!j$)N|Fj(njGCX)*n@47q1vuoXT@xo++TL%aGHy_Nc z(VTt!t_#l-7w&q)@BjY6!Hd5v;yW;6PRqHU5t4)0T;Y@}1(XcDNskGdh~(lYfrjv+ z@Fm?g+s)6s{N)e;1q$s0QnjP;q}M0v_a@`0tuQ?;Ipwzh_jdcHPZp+cXMKO=)n)XYR36RC$$G@i-?vpzXP*o_-46Z*4#N^i%Mj>f6e2 zG<^o%?YPboMev;WIp69ysYkBx5|$f9miEnBRg*kc7bkuJy50o)<;R_2U5gA8>*kJP zCfp^7aW7L{WKr>T=h}|7=}f-cSDD%{+q~+BdxG1w*Io1DXdyMU|m$!Gi zB|SY?0VO9Br3g`%_3#(AebKYBRSp@b+p9C?x=f5VG3VoH&JmXFECP>WE%f=n#jRGF zg6&azd@>jU!@)@>Nf0NU)R6gK0^&zxLj*y)AC}q=C?-CMgyz<};`v2Ab@x5{2f)GpJ$FC#?JetbGKxwr0wLPIFFt8%lx!zym~kEi zqr1jGJvCD1QDhCEIiCt9L0t5uN~hz$_~$2;veO4oUgmtF^JUJrIv;X=!#bk3Nuq!PnOt4* z$U;M`-omObSnZeUWmpzvlV2fTQq2PMf6r2MZ>rfgtp9^u0_Ys@O3e?MZPhk^ImC1Z z`(uJI6F35ZBt`CtxI%bKM{3w&$`|~7fLV<~D~+-o>`ca4&zdY{D9cGXonARxJ+5rV z`O(r?t;yusV30;>Hhnv9wL2ZH`;j#aL12te?{-DuG-0AET zVFYWVIAautVVVuj{|6!MbmPR3ATZA_iuM&KsWs=sTqzYXNToebMN&kezvl%$W0}+v z;7kgiSKfrLbcKT$VyIaTg!RG%H?92 z7ZxEn%XB?!*4pVeeJHBcPg`p5D0q z^mH-?hNIKdyPrNi1zouK_`{bkv5^xyf9^f+c}|+C%DY-slgX!GJZ?_!y!W(WLgXHd zPEYT=?eugq!4bQ3>5&&-yui?GU;4y_3-Jmn-*);2gTBk$ZfQj3o3QhR+)$WTn@M;S zjb>eU&Wmq!Xnf3UR(VnOS>Vwh!u0HIe*DhU)A1X8=-hJY;)Tc0>N?HvQk7*ze{VAB z_WDc_vA{n%eD>DhWPbE=ohE5-(i=^RLQ9cZY>)f{z`ap58IU zj}LcVeDMOI)o!)LUe^yWn++xf+E%Of@sMQ)Ur2F$c2K&uu2GD0ts_Q;b0N%yPWSPP zmydxLf5*Agxz~A<-DCpCPp+G?%2!#QNd`jH&#Q$Q8mltv9!}KNYSFAVRji<10RfJO zj09V@Pp0aR-*eiG-dfv@)6;5#w|s6qe&Hgy*LhiXZB-ramsa_HP^77T<|fOsdUh!S z9^iNNn8=5{#4YQ~J6b+G+Nt_IgC{OrAo&IO5cxf#kZ6W~f7t1G^cJNW_6H2)50>o? z1;?Cm*Y;1L+O4vrUq}a@ktJYGJYdhd)8j1T9CYEr6TSB3^GA2M9(c|?w*Me@0Vis; zQud}NnrhRO&8FfGbG#}AikG*azz)zKR$aJA<}-R1<+W>P*B=ID*=%;<$&0L!v#U1_ z%H12iN3w(c+n=~_f%I$k$m4g^JFUH>K0V#pfoPRQL0^=})+%|&v-a`TPBk15UAXYX zsC)F@X`OZ1XWA06SXWD)3!Wn%zbr&A`>`MUu^+Q2dmUU1?#TNN4j$UycdiLQ*88k| zO2;l{O&N7A4sS>#Vzey_=?C3Jz$(Dsd+N{YmQ5D(g~ji;Y42LDvaEO!N0#+^tvJuK z9d1m}^Zok{*ICY3_xSkYFMTvDifA{>^XRVreci*u&EpqNnF@Zk(D@ar>$9^PRhTTg zJQ}dcXg`*MEynxp7TD>9$GdGA&D3Z51$*k;?mXl? zc|5Db6x<;-s(eKo%8V+j%?dEJQ7)m-R~uET+N{zDaGkJSbf4U@*(B{2W36_AEQJVs zLiM)WC*9gwT8e(JM1G?GEO(xsj+Q}j(U6G0kUa1B*z@Vl50O;;8NK-n4=G9X$!&WS zkJBRiBf7_V#Cd|5j^|WyjC(`Adw2`^u8*oW&3ut<)|;lxOSP#sbzUx3S5d1WHY#t< zgg*lGtwMxh7%+;wM7wPV1e`(Gkv)H);Qo-APhO30Uhzs@uEP zBG+B@ubyR(9MbX8$;Bv&!`VT4kUHm*K0HF6Q^j6cq1-{M2&L+?dUcjzSA98Blf^`> zRDE6eBUW!wSzeamqStdh3`aMHlZm~;)@hm$JkR$9mNH!~vK{mK_uYGWt|ZE0IP5NW zcZu@+!f}$E?8Y(YI9x3D#gEAEam)CYfc`DMSeHfFtVtnlZ7(6OQX>a8#)9>t@X*wz zWIf+xvJ}WgTPX8vGbLE8H?FfgPcX@)sy3^%wL!9}zAcLojhO5bfyQ;+TyW9A+mZs- zM6kNCX0BlR;woiYgKMigRWw1)iMVtP(4Guy6qkZ&0~avsqkt<#Ov8c;p#(?$`}rj# zNPQ@y-GGsL(`5q~kndf*rkHt!uZlwjGkU3HuprNC?$&?>3;e4uci}xThzDH2ME6+W zsS6g+_d_D@DL`khOj2oR?s$nNS>O&m^h)9!_qCs)H=VE3($W*`J*`rc=Uqc#JqwYL zOELhx%ZK}XMq^DVnlB#3;u*;IK`<*Q9m<-1U?@d5wyJ#2lD{X!4hpWB1myy=5) zdee@2J^mc{Kj8!Z7T!;K1b_SX*Lbx14NrdKH@@eepI4lFoyRfOD}6J@w)L#emyxhQ z<$_WJTIJ^bx+$TV%P>Iw%a^GY{`S^o!L8a7lpgGHK3o{wK-E=AjW$9Jik11Kny9A2 zKGjTZs$8nomYH~o$G(5jWaVmhhIL)4l|7CX8`SW+xu4J5Y|HIR*p{V$34(Fw9nEhl zw@!cUq?a|#)XfgtdCn(%xQjf$?h%z`Z`>XMBNWqKnkq!$pe`|hIP2}a>iiw09|V3` z1VIoKzm8R^H67*Y`Kxahd9|NsS$5DW3OQDJo~7*NFBe5ob5KQ}#wSnLycdSjrQiU`j_%iV~N*p^XVL zA>6o~k3mEf#X$U4RTMkM`aw8M1AufXeV21zg^Xcj4VWL~rjs36OQo>ZGGfAG$E~C5 z*Uvg-5degUTu+9mUT{M4Q3}RG+q#d?=j{DjnM#U?iAfMkPQ*nFt%<<2e1yPWw>3eZ zqAXdCp1#T}ikS1^aOg^q)^VVd2mqz21a|T;AO!h*u{ap_)5MqH!nS*t?;E>btfWk( zQHoBFJPxm(Us}HxIjkkl#8eXyyX@~5%I$KIO5TlqD~@L%d{{$_a}x4d#yNY}2BF_{ zz2Jq9;)n6PbIo}JpYVDCKb%rQlnHReK1I%2*$!&dvrPg|bn_T&R3&?b#ufEMCG!-8 zAE_&BnC19kL}3_Ve0q9%I)1*D5WFncXZy1}1Il7J>U1Q|j!!a12g9LMz^iZnzwW2~xxZQ63f{{SUUboIl4p!8B!8@dDa?8fzn zAGv;GR)cQ4@w!*X zQIsS-&qG&B$y?n&Zw?RWFM4};aM;|l-uPF}gt_l+-D+nqIGs&tG`i!y5d&u&Qe@fw zL7E7TkHo{#?yLbg&kHUH_b4e@fjuqa&K2jmAKe2p@i-pCb)=otM^3O@<&s--s!6q3 zHeRgf%<4(KDU5}A+3rIsBA7!<*xR=YH8=Z4z=8l{IVETP#PP{a+oDp2VGwpb1HoO! zgta#GIBNncgL6Zy)ACF!3gNnOk$GVmYo&CWCk|QOfM~q`enR0c5D;kZcU<@~TRf zIad_mVtBGLB3iiV6tzn`3%i~~D-qXZcbLKZD9eiw4uSvz=zk%oziyEak`%+i@$p~? zwA*!S&iLuYmn>JVf8TvCZ>8zkrT6A)wsUs*uDfpI^o{WMj*fX94zGYx$WLe!S##{3@0!m>l?u%0I)*%nwfoND-g)4!nKTyFSLoRmneX1PjF>i zBvMZB4Hxh6JKf3gUM$LW^n{`vxTf15k)|~$77Ur3%-9&ZeZnf*VVu)weV8Is8x`V~??CHyw z7`t@&>EXx1Ps`xmvnX$N%63oqe&F$Lr`*{V(Up4x`M@JYk3KlpYuzsQ0^b+CPO;PE z(b>Jedhk);kp~=79aJkdtk;9z+(2&lA?FWmWc$;XFR^cHmoGXM$Oj+YtjQ~J{__X> zf%t$X{~8~8KnC|-iHdDibh_O4zp-t(t^L9+)W8v}!#n_GsQM~G46&(AEWSCZQsq~1 zf@gQC>alFK%J%jis&;lB+uzSus~o+49`O{N;9F+ic!m&wMjZdaAv8(fy6Kq8e>h z<{+6KUpPFxaI%wp(>sWt>?DU5J~`PqY^Y9TR`uW5Um2wDteyLuFLJ)o`9bHF^Zy(t z&9(LfHK4nULm%;G`I`#tlvfWaVl1d{8kXDDEUjqj=em;0B{*|}h`TNi3V2=y&=9J@ zvoQpPJra9nHwz~2q!zB)OZT5U`@?)*&i(^%Qq4@%e38-9fEztL_}FD>Fb{f*_4#6= zN^h%4^8XMRL$aRL|JRiYsdpLp7k*~SQYh(qozM@wFpm9Fn2uqg+w<)X*foY7>-XG{ znT`>qAIG5=_~A%^di{#A-TnPeklJ2vzbaeC^E}VA%4)yYvuV)T-`{1d>h}mD_FTaA z;@EQm*NX#VtqB5StqEMMwRT;twRYbg1uF1ISuVpk4rQK=0$&ADFHZ;1T5Al_td9?L zm4TkEG~OE;wi~7{@7|eGhwL&xQUy3XGLV(*@s6mcni1 zCY3!q*UecF5{?musZ@D!baX#|;OHnXltdCn_g>j-M&pCJZnbc2wXCWn z>5j+!L7HAZIOz8RA4#$lK}d6Bn&XqZ?>fD}8Nk?u3wPZ$KW>bf?<|u9e$eY599&M* z!C*A*AxWxgxw?i{dsZKeN1M$R#PJb&-JP9Ym$7bdXQ$i4Q5-praQZLY!nflVz7xNS z|4a^b9H*RZ7VFJ+vu(D`db6(An{~ae?X!!#)#SF>=J!!#+3NF!tfYviD|cNA)%l)> z#t0}7b2A@THp_i(5pf4!&2Kio2|>lSJwX924#*;QW|VkchYZ@m2Tl}1=y z%ci>wpPdQscz9u$n+b zV=EykuSJsGtn}pPvFVyg>GMrAi1=jy0qVvj7s5!J(};j8Ldv?9^aKMJq!hhXgSCA> z0FyBX2WDtJAO%{(FPlrRb0pn`2hE~r90`CYe9l!OuDfL%t^LV3$z6N)JBLBoL7eo^ zKgcp6kY}BCc#SEoN?e7-$@hf}J;51SsR~z<4kMwJk1a4TT#{j|86{!B5j>y{B_pOZ zlbk0WV@fg|WtM(Q62e!~y-YlAGs7h_*3~@7L&n3HbI&s7`e_1>Ad(`n^l3+y0UEUI z=?g(bTp6R8u+o!4Fv&p?a)Ol2jA6uBB;0^8-)R|uP{Q}6B6}zK1kIQr;>>46t}8=m zQXd}nG<>pY%4;bC)yjb21j86fJB#=vJob45S|dPf$GId%fnIWxjJR@3&KcKCB}ysI zjb@}=D@pm(09ejF(v41QVTn>Mgd0l}Fv+Dz!z?mIUep3ZQsVCg{r;*u_58Sb zp5>Ew>1`;PP7U$|wNju`#bEK2N-@wwUuhz1hwtn@M+hkK7B0Qd3N^ z%7#g#`gs4n>o_@<@lt$LQIE*K<7E~gI#F9pt%_!it4wMRdR53zX>4@uLbkah!cKYf zdWHYmEDjvUl|JT5uR%=_pzi8GtS$pkN0x&qJ0c$w$cHi;r(39Snx?3-$b0OW?ZhLh zdHrtk%lQRt7g?V5ku9=(UVW0S&AOgc{6gvpERx20v9%$bn6I|0^=4MpDbRVL*Rhq< zx8)KhZ@k%F#WFAUq0Zo<0xby)p-`<>&kIKk`Nz)7sc!XX7Zau144F$cuhyISSsu8x zO3$on>f=t~5G6}d7EpMF9r0zLu&xRtp&PjmCZI4pi4y% zLdv`cv@sS%>lUJ4K4c3Ef|m>qT(8wm$0cr4no_ivt^L4t^E3(}g>p7JaS-HLrOo7i z+!~rFNk$0~u`DOW&P>h;LhO!PiC|i70W%_Xrb95-_*thu@jS+fT<`T+i-@ttU-uFqm8oijtzaj4km1D+z&FFRyK-)3R%+voCzV_FiLOiF{Z3mp8iob zV_fPm0|>7{F+~$@Zy3PRyc9%&V5Jy9J1w{y29RnUTEGWyms0qiRz&pBepWwarM8~$ zOTpf7z?Hgt#(*FiO*~`AWE@375R9I+CMB1dQYvFSSIWSZs<1HvV}2AL6mG***_~oi zXxQ5<(eyA1_~tSug%aB4NyMZY;sI&ZblLl|U~4qA3V?#OoTn9#dsUVSiIiSiV!(;K>atvmvQGbY$rz|z%-T}e5@5eJ5ecg-IQKN0 zIJ)fneqi0umg1rsrg0hVB9yl9j$>{+DW%|ou82y~D9m!kiTd3#>*oWnOllW_Qf#IT z+`xKDasgaY2c4^g;M}#))#owU zG|lM-@OVT*Q$x-LFWAFJ+=LX*HP;?T8J|%-;TQ(c|2dwol;=id<9qIEsQ~M{(Gd`5 z3X}=V7W1gk(D`Fxs#|V70-(PympsHzYz3 zNKs^|CqFv|nHXrTY6AfYb$-R93=?P?LvS8Ce)snMb#m~N&O4k>i_+O&mKrx`X%=A~ zN5=-Hf57lVL&@5d#>%Fg=UA`M+ZF<)f2X(U?3V~Rq~wL-7@9-Nl9g1?RtNh>hx>_Ez@2s#Hh@;ltGxc1Ov+Ha9sGV^bZa;oGU#T7P-+{SI5nAaWT!-NB2}c z&L`6=hl}CnRf#&LhrCoadakI-l&k z-}yG@gU+uzzuT|YmTs!NDn<93)n=tsWo*6T9bKQm6E#f>CR;3PLo{7h%h5NqJaNmk zvRoZO;5Go^lGvvwzXqxI&-^YXsom%d^8uK8n+eZ@VG zc0V6S3EBvvMc}MQCvVfb1Uv+i8iu?CQtM%3YpY*-_ zvGqH5zv)-L!ac6*!LNRE(QNO2=JI7`4&Q$5u}8mr4!V5#nLAu+wYZ;$F@Ca3PWKZS z@yXY3Y&OHzaI?8_J(&@g;CoIUIsWAjpHxS!U;gFRL#@+0@|F~>%p{5HN^kfz+kvqUA?wi4F>qm)rWt1`N(Scb%^7^BiDEL zaQI6;6T!xb1>n-qYBJ;5Pp&t@;K+AbqA^IQkq8XBtZeoJBAUOVybOAsYzsy#{(V5xgV|r zzbyFQwi3bVGzh(mI=Ev zJ&7mPmHMTZ6`zU4d!i_c-m?%ht}ef{zEV}u$#lnxEbGZ22=g!mOOnhIl>-r_X{2RP ziC(V8YNBQnJ+dy2s_D*7M@#Fqi$V^R7v}lPT_uRLyNpA|Jr6Pq0{`A@2Z|-#W5$zM zGf;=V&sBnG-VftEeq}fgckse5;vBb}sq?UN)A<4ZQ^w2L4Oj_=(L7CTl~3wf&6q%S zc2h>6nG@?xJybL(fmTKwQrAuAVsUaCyWWh zjC$Qkt9@{Afho@qe65IC8$Q_a-6U(3?DXKE)xxCLqbv-K;rs1&8pjFla$R3YAv`Y# zwLx)wavy-hY7*yVDaDvbuwFL|C!eQ$KM0iK-1WjZ3QM3>?g?YUAV;q^34>0j+BpC} z|F_oR;cWoXQB_5Gk#jyG;;h#T!l_c86r6i*7#QQYj`zYx@uPU&u};r9a5m0W=QYlE zJ0Eg>7kA>#jx*hs+lZ{{YJP!84XY#uTDvSwtm{m8vsG-ib(!xnTNcUU&>3{(E)MP*wnzAl;A{Hq}6fXbn%DCU?&E?nGDZ;JDo_vw5=t7Y zOi035j%aI~teBE4l2SnaDHDuKu3alt3rjvRA%qb`T62)VVM9PWW1jXr)mVP{6^((x z@W_`!3Um(-_x6*t6-w|dZI72o1S`+hh(I_e5z zfGlfWAN)7ud50x#lJwdgL7WSSJczBkS5+ZF3K1|Ou9YI@`=O9R$|!-*N-9QxVVsaE z4pvvaok|vcg=9=*z+!S0UPMAD>-$P84H+y>j43OH-;9wN1B#epz8C5yfb&p= zc^-{N1Emuog^*d@t%I64p>4sBj=LiG{6>a z*hb2l?VYN6Q>%RF71eB-O;hG&Ugm!|9*>1+P$X=9d8ad;e0}-5bh*>HeCfa57>{pE zzPrFU|CL#GeLTMYZ{`!1p1gE_n%;lu$xBb@=|;f|xA3-j(WBUzIt?})I_UuxN)QtW zca4YCv1-T?qH>x4+9qmuh&rD~Z3vt93B9Oa@3xOuO1+ii!3 zguUIfH^2Fj?S|;|!i#_B2eK$un)4t?(-cr#Zkl$*(Qe)E6xC$!w!=d<{7!!L;85*e zy*kquEtJC9DIq>cUI1$&g&efxrvF* zSf<|0UEF7ylMu}hHwDu6i~x>ju3}q@#ByL_xLG#~)(oqQ>a$y~RWJDA!H>sFM@?ve;A)_Ja)#;%DI$< zK>G^k$M}aJuBGomzbPpl7S-U!tS;lJ@+Z}lb;?y;LMx@pLMx>Ud`Rmzx!E6Q?whrKvzz^K=Dy^)0g^b5={1!IqadK?CX>e~2x$z8 zW16MZX?NO0?M}Nx3F0`8iQ+hp;W*?t4#>f$I~(Vvj)RF>Y&&gVizR+lH)EOADYiJJ z3|p#Pe4A>J?Jl~=MZMR2M~gs-_?|&on6TT4qi!eCMLC$YTOejk8stf@n_cd-&$4be zEt~bqp;oKzT(>)w*+p$9JNCMEZQNIfI8vjLI>Mpq?*t$85{OSv;{Lwp5qaKzKTePr zW7qZCo%QU}q3;8m4dDBS*LPQCOB+2c3%vb<6=D3&caA~T59NWcHF{t2g+pNYgv@NA5*&!hA}~PYqc!0 zO_y08meclZ7Mrs1r~%WHswt3$*LIhxXx5J$Y{+$L$E}K{#utL~JOt-P8~gUEXLe_{ zSBWr6Z@_hpRZX-m?U`gQA*NKY8^=}I9D>%Mu%xakK!_5J=Z8Itt%L+&wcZJ{^rlkjHZusp-sD( zw$hXp1xwPhF4{&5{&6xo6J!|!aVb5n6kR?J>}w4KLv25 zbz8<&jL7Q3^|T9oisNv{^~1iV2>~>-ipPD~ugio)_uBV;L0zxcbsdoBb8j@<-ye=V z?t6qK%>SEZmdM(cJ|}NC7faXL^%?gahjLkR#LD?>eWsL(&Pv(Yo@$e;ZqGiz$PYH# zl|+NB*A-4%W|u3vE}Pxfn@O!M7|1I3oo4eDI*f3&TrEd6vogz*I9Ce(LJbD>Y&`b7 z@u;o`11X6yl5#Mp>(SWr#^c$PLX3!sZxvCD7YqMaW=#E~F` zERF=O**q)CL=wJyl|`0YcW3A5XlKWrmUaHu6h)l!PCM)|+mOz<(t;PaYSvmZl6hrczuA0-9ytLYu;kazv4>>5O+xN zIeMk_N)O*6;KVBe6Eu@V`n!iCF10-jkl;F z=GCm)s5;vdx@?jyV?~er+n=R=5cnzWWWFEx=?hoRu3WitcIE5~UUGKj44<2y74LW4 z?%iD+r@oK#-+9ji54`7h9(dq)*qdJeddCrMeMH1u%8s+=IHIa^RmIJ!+%&5gakEKR zO@*}C)VW$Q*s4384H2sxA9(qj9)IQTn?L1=SDfE=_aDA^{owhB*AKnup1XIkee|Kf z{F1xxzV;0-xr)CYeDD*mo-grz=c~>CIseTXzjR(+dd=JZe1m^FzY{-w{_k-1t{7L7v&Zj!JSb6}#=3!Z>I@`u~R&6({^=4LA+eXBO7fqrv)EP^3 z^rPYy@I9u=b+eT%SVwJ})wZ71i>d2MR*IQALT_oi z*lN4VtBg-dExE0LltQrDo-u1M>}K(OrQTnB@+*2h;obADHLakZ6dInVKsrlVL17T} zfwf`$hPoE{frjU~TcX6m3=>K^13};$qaXmR(J}~;ezi9Ghke8|6GJI{Pyemk?RC3` zRCLw_W13k5cCf!~c9`#H%FNQw6FhzsmAmJSq!(y>oDQNU^ z_(d2O?iSP!d^=6&RlZWQ15T_}$T+WiFiIs$eDe8k4$=(B(gD`}EJJm4bkds5=If$> z$nz`B-M&&k@{+f_W%cGaKWS|;t9N#grGxVy_k)9aFi7v%U4{|id1UF}1xK1@gS1r^ zld=T0TkT=HMO2naf_Rv&R_Tzs-O3Mq|DOhFhVws<5J$a=sW0l=|8jFai;t_sO~=)vnTf&+?sLaWM*CO7=qEt z&`&*|=PCUJyQtT$W~wS>h;n4n4v{qRIZBpIRw}_#hz7ZL!A_x1@|ZQ#EEjdd4JG!L zjW^4@D4XSCt1>lHlSQ+Rw4@TvC z#?fs@vu$KZ<+he0%ti$xt5W%LRjkxXNuWl}O|e>@q9GH+;H1M+l}Zc?Vsf}x+?~%C zO9KKjEFp_0G0E4ps<#cMh~3pfE+#_4#vN$YdJ?pct&0n|U{=e2iM$tZ4&Z_gSaE6Z zFfpr(RZ%uITb816e?(K20)hSKd9BL2tm?8>rL0#*DeK8*RyTE-&3iGs$B)ET#v0pZ zy(l(CS=4b^2dYshjlKHV1$$2MnQGLw*=V2VUB#wel}%kXDqk&2bl;?#vtHIJ2vm@G zC6PHrvwF5@LQutK)<+f1M$MY}qVb);D2ZZ)nw+4>zrUHaJDEHUqyOp+}Q@*)39|FO-^XN397NakQeCK4X>h}MQk~}ZDi^BDzBO~+j3Pa zsql|^6!ch?$t<`qZW?%?!uJCy$CdUNwS?=c+~;C*qs5=toEr*9{TM*yNEC z9>LSuE}*Ow79Y}${}rUk7nY|%AXTIZq10OY0!+ogvo-_RsLqq2>oJvGX!4v`f+RsE zvc__2a}zM(lJEN(q)O6lqKtfABYLBMp@Z3@UN5uOQ&M_NxPGXRF!=DvMLzI6Mn~xY zP+I$L%(?cx_NBwaPMZ*?LPQX*w|lJ*U-jb1cDmg(1KPdbh-)J_)5dB~Ndd4DmI#dH z0ON0G3@nbvgQAogf#Ouy6bi!k+vAgCNQJ}BQB`?V_{Ky;1d^fvxlz~y0U!_~0SHkT z#)H7L`ku!~YGU2)CQeR_YXPlY9Y`w~_xFM%j#}sjK0z|3{E!F#g;X?z=PBh96k|Lj zaLXtNJg*c+7zR!NW0H|Il6kH=BF3d4rih66E(IP*<=<|&lrmyIk?TWAYYZ48=@OJD z1sJ0OAf&{#mYhR_2vRBm+HXl25`#pKLrB8}gh=9%QHTTbl(E01MlO($=W}B;BC9Mw zjHe-BICIIhM2ZVeDi%EK9wPQU4sfG|=X;8Q2mP4=k4;iAP2d0^@?up$%7a%ge<<>K<@GY3{f^o((cR<`_ zp0Jqufh0vB?DUng7SQl~B`Bk8h!m#tBjyUud`@La9cJA@5VQ*Tph}2jm{_93hD44c zFdo?;@RcM_XW|m2g>*>@sG=)o)AOGQIY+V=gM=|@ODh#dp>N~3EJWZk%0WUz!@hN+ zAkVv9r3vJ7W?VOBrIJ!|DI-XY5P;Guj-vJ-Sm>hYv@Ktpd^tH1ct^1B?>?_zevhQO*ZzKwQ@E>;r4Jf zUlnrN0M7*H9}xT1iYp6Ayeph?jOSVJ#~Ni(F3+$oizX88JIS+4nAOY0q(RuzEQ@k; z)}EQv>v}C)j8=k^lR=)E>p=n7?b-thOq3I$Xq4`FtT4GiIQGDstHrYMSMXteRDm z)qG(C8(Ky+spLYctXTp+sy6*v{%Wngn<$HBzFKb91_UwxK{H=4@Z+mV=Rz_sRz=8I*j!Gmo%{~|@;XJ%!V%QOdv+cHxhTCe!Z#6WE{Q}abl zl7_#K{y(LE(yljSxoDm^GGOSfZ&QVuEN1Shnt8S=1k<4=7@IGuSu@|@?0S#RrP#*ySA2ge+T*QAiwQkm%SxanY^?XswT;pJRT`T!SEaq~{TB^UM(P;l*G$PK; zWW2jO=n1yBT27}#%nbU|{mEFGSG;0q{LLhZk|eZ%h>(4A+SfV;{R}bYyViAKi4zP2 zXF;GdN15mh0+&jTtqZ}GEn&o1D1-%hE`fe}cMnhmX00E29$2g0?r7K*F3WaXJo%)x zPk0EDypYUjFOA|FNC*xN)(UGl>(r&}$c5%{VTqK?x#tTe761M41LG!vt;UEk!F`Bm z8ZgMMwnDuf@I3RYMIsFW_619lI40lk`J5t-90tnsq7YjBneiG7JjKIOU^@KRZM;t;){e}XV63KRJG#iaH8C}nw782Ih zO%YOhKREkIYL(;hV9!3oh(g5~c_bCy;N*B?7l?erVLT0`)I;!tGg>KsPcnvENRw7O zNkRP6KAl6RX<5c0YxVniP9U00j!){^Vucz1TFgIF5jI9o=O>Y4g ziPtK=H_r{{6#jFwz)cWn3*ZfomZqfd86}N?n0fh-`BBITNPvst$}ECXV=jIlgVw$C z{}Kq#4oiw85g?T$k~0WVFeZd+rJ!R51b&*vE%|dyc%J8SfJv*9kVg?yAjP7Ht2oxe zQ=X@P1yVgFiPsUNBn?6Wi*gxh?c|Q*JWV$-bxxcM&Rd#&>i7Hls4C-Zy*>R4{UC3rq zZA+PN)pjG*+EF}CXVtO`3y=0$R>csLy2wjlgt1&M@}h=8*DN=(61gK9yY>T=c~#~) zCj~>GfNdTwSBou=g^hYrr8WE2t&f$gCsk8!nrgGE7gd?7Ra1@A0wIR*Fo9gH#Hvy) z>gEY;Cn);JOf{0te#Xuy8MjtaStI?&*lQc`oHJ&q_ghty=;SpCm&qIEJE20;|{ z!XpHJdj5ZiAz#Zl#s(q|;LV4eF{NalB&h^v#EI8zjT0sC<;RF=4%N;u>MAP|L|g$E z3Wy631#ylJ1Fz3{7KjBGiZ#LcUwPK?c7*4iYS-1F+xI+W!C0_I)Yi%{B4)J#+Kj)Z z45sVg9;F@*F8|@o_kC|xd%o{aktnGoRm*uYd**OoX1P2%RQsyOID3dH@Xq6dx}=D4 zXg^?#B~o(oazJD`cQ^t3oleCf7GiECTR-0&WVT z80!Zo`Arj%REB$NKtHGf_l zk36OT)FlXtdz1lrnCl01em}KZtrp#CK8V2Qn`vV|fVrV&Gj$_1=6fOQ(@wV4oLLKE z#1huT8P1g;=KG9rWcK|yQ04)x=O#+M7n^78HMpwP1NvL79{?cgAm5k5_nD0|j$On( zXY8Cf*Zj&!5*iW9N@F^ngq@2N@&jnRB6bSpcchh3UKTzM$?i$yrK}gT6J&!ae2_!w z<^eMd-*yo=fBBbx`Ip~~G`%<+4LezuQMs;+khP5!eiV6(anDytVy)3-q0KLK`~4ye z!-6p>JYR}PNj~fgA*Hp9jE>2A#M-(^>Upkftea(7W^a4Jfdl6S=VKrH*vCGG5B%j{ z{^ei(C9?G5knt?cb0UNRV=Zl%CHDM ztp%7iT0tAvCdoTrjB&F(%`9H?g7Y8k#QDEnbQ7QE_|B1Y+4(}}8=P-*{s0c{#;4;a z@Vj_{G%e|IdIx(X1T6u(fg@x|7s${SJY6QyA}fo0h(oUeW0ycA*{KB=d9}KsvYw(kB27^* zn!Ke38cW^TqMzF=G9?!|IUl;T;9#3g5bt4=zu6XcFh+m zlz*&rqrAr?)ppaAQZ=<9P+Mjg`x~lW9!46oMKhvSS55u7VKRGqF<;cd+Orphs0>Y$ zf(6Kb=(7j?N^lv0&8oY8Y->uC}l>N8cZBEM{Ql4J6sT-l79D*DI-ZJ-JT zujJwnxvExed|Q>v)uNEWNlKOfDKSzUhks~%JX~iVg?qh|y~SbyfyMk}@37Ye2;WZ1 z%FGCaWD?8g?!9k+UlN;NxH!iC+1Wj3lL=4)Z3ZFW3TsJ1at=ReJ)nPKc%%3Bu3o+G zRYn`(My}Qzj9jv=*A6(PU@2KKZm7KhIM<0cWCf_k6g+63HSlR_FgA|@sda{}6B1;I z;V2XiEqj32mqEpNG*QsgK?iHpucw~NsDr@{K{E!#=!S(FyNDA2SMkI-BL?+M z!-aKyCXo968^u5zmW|A1Ku(2L1g-PQl5Id*yAswYNW9n$*NB!-Lc^2Nhsj1-7P$da zUch;k=NZ>!8Ukbl7e>H_JBgIUiOG^PLRTWo_I9uDj>f$gKhx$aO^YHG7{~q@St%8H z@U(Urpcv@{PYE;uxG46;PS4vb$4W6SxX6u7I7u#L+l$lP4i`T7%e^2T^Z^!&OJ%G$ zXPPq!-%||IO!CoiG~Hgh+y$M#4&1=9_mStz!tEc9hT5}`{lU>Qx8F9K0prPCcYWgT zGOGj!tTyzQXT&)ptpurZJ1Chw2O(X-7}sH-?kAGLATNaQgBi*2AdVSN(~c)WKjA9Q z&t5)f!hQ`KPj;pT@D|Hw0$@1D+O0t^1>#|BU7ro2C?e%~HV&bNZD=kHk(Hu3I_z|C z{uh!+#*yMo!dUkUlDlNTEA%^=kpTnrj={}LvmqDh!_&S&ih$9mN2Jb03Tzw3AZX|+ zSqKiqAkid-cX#$Nw1`V3O<`$aHOmq@a*`m%l@(ldr)afFp<5DHMr@N4ka)!Dkbw(D zsBRQ7oVzR{M!uBXD8{ui;YFmmeTnCq$a;(vD7z1R&G`jX>`6()lD!T|I!O?$iiJc! zP60uJwMxO?E~0=-KOlqdTMLV4APS>N#-$^{APM2Az;+CA&ABMUt})@G_fw4GD1MRQ z#6!#l+-_`qk+xudhbBy$S)-AJ=J7)Y#xzJWWmS~vo68FFi9C?@T&^@(8xIEUHnVPQ z@-!mG@DOt93UM&iBFZu*NpQx50NI(V2;z1ka^2OF{=v)iG#^Mw#AKwDuSf{3+g@_X z4*eEqwlDV0j!$G%KnI>Az}14&S)VkJUq4HY!FYTxV{d~7Gc^%Q;@PJ{NCH_B5;PvK zmW^eMMTeVpJ)`e+$@8@k0#dXO;wWq}9tQc?z7aK66O}LAqP={~YM)t@r*Y+?OxdbQ zR}8=X3LnI8QbSMBE9i~%we+oya}{OXRJE#x zXv(^&YE_l0nv_|d)$$DMa+_zlyo6=F-ptfurt+d$6wP|GtyHyHEzht%z-F~P!}w(16LNnpd)q zdFj~S-!48;P?g17Mz080V4pm$dBlWJTTCY+n(dmH^cC+-^Xs9RoXGba-fH9c=CL4D-!QJ;W{Ghod$ZlLJf^a*@x=`ASr;F;)FPAvOH;88(0zkrFRuNQYt)f(dYpT0DvX z@o?aB?gpF}j1`>kDAgYmPpz-dOTs)*1>u|0(;~!5W z0Ep5Qt2=i#eksBbVQ8)E+G7(2_(^-C8*#+ey6)ow0XYQNFtl*3b@A(3irXk9$v=ye zK#ERhXJ=Q4-A>o{DUmYRKWI+oqtbPm53<|Q7}RO^`)+TUSJhljodLvBh3Sp${O_|t zKH$Dhs+XRB3uztq4h|M4Cs`pxw>;P*Zo%xKEBq2%8?j4r01r2JYJw5dr>Yq7RTq&+@+Y5$cJF9lQ zG0i|55D#7ngp5p*Fz0gM-dUt+8*8J{WS((BWVfi7_X8B;rW*6-(A8^p~m zW1u8T1`o~ETg8IiAlMDo9^mbGfLAcr1XJ|sc0t3wtK9KJ`vf*9-GO`O2Y>VU0>v-w z?uJ1a)8;!;iEXm0KakKYTtiahxW{ zhRl(3wN|up;wnTRl>EALtBLyB!2LANx%nZ(dqKE-$r{w_?S#HCKWI4TC|lKJr)swt z*INFb7ydu3B=nX0?mPY47vF#X9d};ub+uBOPEPK;^RBxVOQE&nfJdC*KjBtJ@5EJL zwej;VIA8EteGgB7K0>)7#gd%Wq={e7;Cw7O&G#f zTPTI5kQS1Vl9;t+5o30SAn7L5ZnxV#&-ASwD_iZh3Tw{eLJ)=_3C6V=4*PuuqlF1XnzC40G)+Zdgf?LP{&1)?XM(~o z$dybvx}2QM=vks(>Jc5T5H<&Kcr*(SIec zt{Et<7_I$vQj zy#HMf9N9zHJv!~XH&0J*x-WXu@D-;wUH9hc`Dc!wj-DAkgtG^SPlwM8f8>GD(HFn@ z{xQym&xB78AAHjT!?XL3Zr%UpKRj~X!{!s7iJl%mgqIB;c+>EKH`$xk-aP%Ndvw|y zy7|%Y6{kn8d)PcQd^&t)ID962diY=8bpPl_?;n2o=$Yv0(HA{1I=lZ(XAc~`_NMFJ zY+mtn^vvi*j^mKyyzuYw0)E`taqe-PMh;QXYtC&^H>nnr-IJTWR`5rOnM=N==ZLZ8 zS=^U~C+WSxH-mQTVE^Gb4fIm@alb!&$KChr?dP21|4%az`tA1q{zGvV=oR;pet+E=^IS)IJJI^@JIc3tpE*1av5%I(i$c~tTLa+8--3|hNJI;2m>|Qy4=gXhrp8!Z^^!7+_A(brN zmA_Sq-?~RUh}$j|mtYP3#mk=-1%Kck|M7>#x0jvb;_mzEi(}$a$mC_^=Y8d1kVxqX zj^s-r8n~`fn~jpP%Y>h$Es@fC9(v+}lmqg8l5!wrBISdF!De%rl$L71N7};n3m?Tl z;Cbg0d#~y6MO3Y64-{R`&LbjHRx6kGX!RB#_vCVw0p8~UkS$v;*Bs*WqL$Jc1u6R0k01;KLKKyqA}4Y!AST+ac8jca(^Fq6;L z_6QUwok_n>5JI^|gSFbNHVLj=YY9c($-@A$Z5Y;S#B{II1{YuPXXz$}&SkCT*2|sO zI`6plv~?A~g;*8gE@w86*H@dBZ4$d}ME&5g0!WQtCQZ4jr`y${D64W99GvM%+zl6z z*PGR*RGX?yWZ=^h-MhI`6z@L$8U%R}gwnR!?Y7@se)D2M=yZmIXy1<_yqHH(yYTRP zMR{#|eqF_}Jo)-WNGan;N-5&+e)-E!PcK|JK3y(Pzo;`iKHlwgW=F?+oo~C`YDM$= zBEa+0zH5~#TAjTQJ)rG0ilv%_LMCM#0g2acUyd`Ri>3Cu2*W5u`d!0G?{Ki&I=pb< z*bY|f!C<`_=p^Z0EWzqb-n9Y248ZQiW-(R46vN9}p}};kkQ!%Ki)K@8eu$uX&T6|^ z*OiH5I=~|6u#0TAn}vhHV7aW>E&T(`!zQ0Vkq9rKW$ifb?i{v{tmToE-52fbPK7M; zgUBXQa36iO)v@$%A)hPWu3tys9_00dTOOcTQz@`A#>@nr_5Zv%yW`@gUcTgc(=7G; zAnf-}UsvzK*F@Wun(_UbkDr%gI#7JP4k?|Ni^$ zzh;X$KHbFIYDuW>OlL+fQWrc5J|yY3R>(Ej@Z;qQ#-XVYrnf_$J#%t2RRgh6Ppd$Q z05$~y|364M{ zvU5CFVgYLH`HHpLf=o%`_jES4YP0GQdAzy;6~d?PGUR zhCdjopIDTjTh#MyBU25F#>KWPyo38e3XdmTa7&gOEx7QcQXCYxuB(YW4hkhypcOHt z0}050LAXRZli2}*DXN- z3jrwN0*pl@LysxXk^o}K1bhiFW(9{5j6i`_<>AGC5xgceT)@|g^r0nqc$i3F2=kg4#CBnYuC{c_{U2U5ws%3$1g(;&h2z>Mwabq zQu$>Oyxp}yi;Qut>-u0|Mg<~8N<)GX2Gg?);7UM};Fi)2-Z?H5ND57cPOfAG6%?gk zCDV2(#qj9}S}e<=SWlBg{7f_)X4L7l+w@8$MJrEYrL;<-EHm%+%c3Zx^1Sl!)#mim%v%blx`KHVdyFq z_@3*P9u2}H=?_-x8&5C*-QNDS&2}^b-x-gIPEQ|sh2t=nZhVmqr^G3jHQ zl`dWR{3AcQhHIo;v;2DbvA0lp`K7VjpQ3@n+wRPSY+w6<<-d{gX3PJ( z>#um)e&^+1q7uLmom--h(GpVRz)&;4bfa%ZhID`2Os_W6m(nh;nbXJE+=?r&oLj;2 zd^K4&T5MIGFaLqnYPA~YwOXyl{(R+%IXEfN+FmcZJ+oT5QLndl!gU;XV#0A8XTo-0 zE=QHUfo{Y>yoe+C1Z+c*`5_9wd3DRLaw5fcp)Md5xD0qQvh|#QBRO4E^QL_*=?wj; zyFD}AAyIF-*KIK8Drft8s~r|a1bX1P`N4}f(c!3LSND(3p{lJWfY|B=rWRLyp))gVXz70oH$8}Ow2GPk|ckGm`fxz97i^UD#^6j zF?1eBfv4+Pfu$*DsjdkslMv=OiLJ2<^MhVhK>$a;8;@C|(#dwJnhM7>^JYJ#zRH|E zStrmlDP^2YM6=Q1G!$HgU08VKW;%`Cgj)?QU^;_7=%eh%Nx+}FSDJSr=ZYvvzhfow zEi2`8Z)=ua*B(vtJRP-vW}2oD5;siC6bk1iMZPbIVsS8;$eUtPZYZLePb7oIqC%4I zhlBEDw2j{NMWEMdNL*1()BI$~sWJpz>ax9I&n*`+t{Y}RZ|TlMDkuA|@m%?-kF`YqSxTz%*^S(YcNhE8-lYo$|ovq)nT4HAlOx=8c{1;D4>1 zeoecT8HS|uU)CHMnV(PPaOa2C|K9;!CvNEEgIck;s>re&N4{jvp6}b&dlIKt(x8>5 z)`xAqU|I%|th=sh5xHKHCaL36>^*`tX!bTN;$%c{^lXJ46f=z3fkEijOuy9*b+3VM z8`HT7RBztKp}*SQvE>Xx|DZe{I54`U)0+#ES8GPJI9jbp5#2A#pbJkD^ioW_Th+F_ zi;ixChW~EMg@g2Ga(W{3!V%pn6qJBVisEaEezb;B1(!(Tl0p=6MXr*n$_mqj>)5&6 z#Z#qa;c%jg&?%mDlyhW#hY}XV-<^9(lXEj zFuAfcUTv&Nb=jzXRa*M8F)2c|#)a~;SmD8?E=Td`MX2po zIZ5iHVW{cjt)V=QP1$h*-%b5e1!f%YB~LBoS}oLdy*RbHQuY0$76f*;P*8(iyOCrgra!PMZmW82R7;MDn+>sHoGj3)_|%Om5nJtmb(xm~R`o7I*oOQCH$ zPRe5L6EdtBEwLHrAnhtpGCjhk^7 zcH^@+iI?#IM)H*aS988LVH68R1R9AZIb=j4 zPMQs!5Zh@#g3Lo)zV*6&`gEYFT(j+ZrgKh)nGVef*9!}7C<=z}8akaERKhn zrUGA-MoT5hESE}k%O_Eae21~9on5}}gd`bBCzx#sr;ruzoE^-nhNerix)J)WEGLIm z;Rm7X{<{~Drg9PrYfd_@IZ-ZK-uUVs@sejmcgLrfY$0uKVxkx@x!`&wRU^YpLqp=k zV#9Uq!ST5gRVdDbfXK3Jn{huNvP{cl#{1_SpG_5F&i#nZ(R(4#!xmfy3`Ks3 zkRVK(BvF`{c@GFgcD2b}%)x;qsp~IgS;`53!rHV&|cvPrU_c?{XyyG?>$y#@lB;6!Q3Fqs6 zIX01`$N#$lznSx!xpe=vj9tt54-1KvjCXLbJJKo_d0}kLXU$GGQ#mRvI8e^lQ3rl0pkVbIX z%nYMN%k&o(mcKP-T0V{XrnS7ZQ1bmNMroijv3hKj-m#|Ft5j)>&CFbe71XD5^@q|{ zz#H)dYUiNVXcNodChF~eD;ntR4F-bKSE!~J@_^Uq#<)W9@^(jxn_Lj_P1NB>R%rUP z!zYr!nQFi1f4ICUUaM*n1y)JZWV(mZCA#Yl*o+%YLV;VBZfJpRk**C8S!pVB-3YWs zy+N9;5-FS|u~RqA#CqizdQGt`c@2|-BvqLLE045dmL|-JLgP{{8ICPWB>TQf^DgJa z?ju>#%T};_BFzHY@F_6@E@_scSazTXzDl}Yw$oH_Nv>E{Rgwzjl0>9Pra9!s!q6o} z2@=IrsJ?P$re3GnB#|W2)Z{BNlQgbDV~56mjoJj8KabmS03WqU>_sEUz2R+8EhaJJIuTT&@al!g+p9MA7u52!OC-7Lo&nmGdXjP+zDXWt_5?mE9PRZ2)|s`HI2$MWZ33`u_aEd(U`K#2}5Vh4^o*(q=Cx{66`$9{Yg|B3%kZMvTxrz7+|2Brc1MmI;g zeyfo^M>rGk+F+n96Y{!F9Q#?cft>X8U@9fsphRBtPJH-wK>N-}m8>rD5O z)*NRM$o>PtP7?cXDB5+wC=BN-)@sM?PAhE&*oRoeZDEo(8_aT>UixQDSfx_6b~BBi zG;0a{2*vFT?Qt!=DEyhJ(6ak=n&Ph+=fYr*tJ)!FnwpWfwe4p(G2M`aBGDgNyS3J7 z3{J&kBqdcSf>dHm(gTOIC9>mWNwblY!#HCW z&B(G7bIxzJOVXqyg!H-UBiCLnOP!(>-%FYPX#Fks2 z|7k%gE|a1-l4-T$Ws-A6a7R%BS)q?VSJD+(l3f`FsWFJQXTfY2W!b9<1v(n;Kt1ZSkjP)-t)$>tQHh;Q9rOcL9&>?A2ZoEU~-B#(yS{lx^t zRy`>mu8qZ0e>@DgjcRLDMV1xyA;)>VtONQTwCp6!vNW-+N?3fp-1FJv_k{T#-&5`= zin>Qt6hDrAij(?#>q$&LW*F~W@3SaA*-yjsF8gmxA_J*>Z9rs2qF8t z2gAKNl3r43~h`L=0EiZVLXtQAz1sB8*B zrlktR4I{G4rKv5dqByE-dA6NqscC7N;~2i81cEVUaVc^=E~Rm7CXVB9>c+|VMyOgs zEtc0j8OpgI(OG&gCU7~f&sD!<{YmQ74(Jn@@fXwV*-IHr#28Lh^gHx4*Q^oTTo7cy zM)I-@rpiBS*1BnF-p4QxluC7b)(F-#oU-CgqsS5 zNb*+BZ@FZC-dU_>nHj`!yEVV2H)C@m!x+u`VUk*E5cs}gq-ppOUcncCo#7AiIF_V1 z=C3Eil(_F@Mc0#LLnj_b6ScIUys~DtUoA!v*-l8YZL=`U{GM&u)RDnvs=E4^T_T&A|`Nm4?wEIVJyvMkH8EZbypW_gxHmgSn9n?V#Y^86B@ z^yef;F@Y_(95-g+2sog^wnM!;q&eH`w4~bl?WCPdM=BFe(HuqN1tyXaJ1dHx1ds#~ zEyZZ#3W;7bZv=Csh`ck??45=vL;`W?p;#**WA=o*a|3=x*t z8EuqteCXaDu}}&bb!A1dxTOpY3DXjaa&JGP2UrrkJ!xJRB z>*p%pzFt-DVDVoQc899A<_no3h)BKZrbk}9mh;Jwv2c>J>kPv%H*Pe@FpL*hZe;`O zVz&I#?$~84Svycjx^{hYh3WFPZ2$;3Cx__vtIN$gorr+c2E>$c~quNRhWbRN-6L;IYnepWLL4dTz@)FfTR6ii?_7tKIB3JXWy?9BO0a%4zt1(r<`%zp4QY@mxFq*T0Wj!%Ph8bV~#1l_E!OKj(dG0u! zp`%!Z3vdl?!Tl9bC7#q6toPMDo&?~AizGoh1$4B!)8v>Zot1kWd78Os_JkxUG>04cjg0anm zrb(<}n%q5~Xtl7cUNG8?O*4*7GkR!av&q|AD9ffMvy2+LVH!2cvdO6|qb+S|#oUg& zR;*~|n=hZ3AX>BL!S~J!fog81{c9NSIS>pq=4 zQ=vxR1p~~vv~@&hs3AH|Y~IxJ;JWH;ih6^p7q4DOX5@_)R$qKS^NPQ}=#eSkmx)v@ zDK>>XznES7Ts0mv)S;~EJokB5Rds2g=$8T8QF#*F(E8s z^xlGoOcyugP|MFTYJ@?ZODvA6!t5%C8NfI+2Cvn1vhY@E{#D8mWB}sU9QUm0i zWsc1|LgBQ!2#I+4 z$tS0#J)cn&jpZB0n!-y51|0pk*IJDE`!2n7u9%g}U6P(<#VoGIG5u>xs=55GlL<2u zFAJaj@+7NP^3j1xO_}J8{;0g}F(}BJjmRI;z}eMd2}wa=U({Yn+?hU)ItFcj2K&G5 ziC}L)sDmiN!z~FH1EV8{zYEj38pq>6`!al-ggGs7ChVf+IF2HY3q_I)!_c{;tDNg4 zv(-IGw&OTf(H4wvbmPQ@!_YL)92!}h`_b6&@W5E3QPQ=>$VfC&DlJS+GKoh5bD@P5 z<+4RZl4arS%LQ7q1F*s3<_Z+wa@YoOAX`jr98LZgG^_ZF0hGp_1!sjQdFk*;#a zNvi68Vk&d=CAwaYFWp*xWh6~Ak(r&QBaR!#fkh;-a2pD0Rv1MiK`1MV90ns%6j~Zd z`3k%iG24%0*Qr_9e@Xvi0L}n(7T~i0{{|rK2V@D59|Po90c9^Bx`0>$)E$790@^M> zzY#E0z}Nv8rvP&vumIR5U@rpBAmAJV+$!Kc4|poz9R>Ur;C~nhsz7iE2+jcE4j?=U zM7x0KWgtERB%6VB7m%I+3JXBty+Ae$Wd8~jOF(fiPmJ~?Md0>%;P#`y9lL-#P5?W)z|QXgcOC%l3W2-s2JU(lxcfM8&pzOu zZvgl11|HZ9>^=lMcpP}>!@z5H0eeDV&uQRw`+(QGz#FQ-8x8_*+z!0yG_d$y;4Loj zmb-zsoB`f85A59sJiHHhWC3{O4Dje1fXDU%f4>0i+Xp=P2H@@20sD6YPb~pYyTF0< zz=4CnJI?~|ItU!x3_RNfp4$$*@L}LRUEsYZfcLe4_nW}`Uj_~h11}x`UOWwa2*8Ja z4}AD};3KDjkC%Xdd=>a775JBJz`vQmzr70l$1reo82H2y;FH6^|5^e*H4l914Di1{ z2P~ZgKD!(ETnqU8-M|-i0AD--93KRZp9H=X0{^)eIJp)0>RI6H+kkIu2fn!<_~!3{ zZ@m}z_7d>j67bzq!1qhQ4^99t9Rz-K9Qg5G;3w||e!2tr`2pY;r-5IFz%LH~zdQ-N zd<6IvfM1)yuTKM~=Yiks0e*V`_}whqH>jh_KcUI&^w0BRisO>YKuc7wV%f_hcZOcykB2sFC~v|>GI#fL%XwLlAJL2I{x z*6jeTS3w)*K^wjS+E@Z@QbC*NLFXR>Z3#ga&4MmE2D(@UUHlEuCF?<32SHcNg02if zSDgTD-vPS%An2NXpleQouAK#4cQ@#kMbPbMKzHl|?R+oj&IQn2+dy}p1l@BA^k5bA z(DR_z+zQ%r9q4tlpx2!Ry?zhqjS}chi=a2}0=@Y-=q-CdZ#@Qj>#Lx*?E&pw58Ar~ zdSo2*=swV606kFx{lkr*C-;Ni{wiqyR?t&hK~Gmf@B9wvT}z;YZvZ`W5cKR;&~wK@ zFO)zpoC3Y)R?vG*(EG+g@4pcA{-dA|%!3YH2zqfC^uhI@kE{oMffgZ}L_=-&^4j;5gh`8?>8n?c8pfIc-2`qVzqr;dV_wt_wb&}Roh zUw8xPi$_6UPC;MZ4f@K@Kws;EzP20mwPT=fE`Yw<0)2Nk==;Y&-~T=62m3%T4TE0# z4(KO)LBF^T^o#wVUz(s_J`Z|%FX-iCpkM6#@eKXk2F4)am z!FHx#JMRX&6JU4!2<#pg?4HwL_jSQ`ErR{s=fLiN8Ep3~*n_u%J#+x|_{3zJI9bo@(BiNHB*po-W_RoX;_ZHZ@ zwt*e&f;}sNJ+~L^`D0)&>;QZBHn8`+4EDY%*!xRh?|&KW&~;!hz5zf?HV6O!WS9nl z7xQ`<7gl=c954th-yh>>U}vv1T_9unndCsbk>O{5jOiH1`^F3;_{x}z2>Z169sviz zxNmf6=lwCl3VLCT!R9o^v5~&rEB!ep<5rT#6m;2F#&m3Fo5l=GviFR+7-i+H^I&tj z^0xaQ*m>W*)qa0^velmJb=uRt>hQ+e~Bny7$%xs+Zk%$Afp@bpHf;;^mi~Z2PwD;GvM; zBAu8FYA!*;cb`OQG9ASVmt@SvRr{{-b?qMPzS)#Idh)pztTP>6b@X>P=NEQY|CgI3 z^F=|#Xi!Fk3m5jR*|Outi32{pFqDA;;pdfxxg(reb7ak)8BZvI ze-k2#-V(Msf_{je?4AdJ^KZdsmA*~i!cG%~a|C0d&0HSNnO#lD@ literal 0 HcmV?d00001 diff --git a/latest-tag/deps/font-awesome-6.4.2/webfonts/fa-regular-400.ttf b/latest-tag/deps/font-awesome-6.4.2/webfonts/fa-regular-400.ttf new file mode 100644 index 0000000000000000000000000000000000000000..c79589d83dd5569afad3a4bf7dd96d97cfd52e57 GIT binary patch literal 63348 zcmd?S3w&Hxc`v%wp2wc|p7)I2BaJj8S+X^HjwHwS#IYUct-MX13M9pZkccLP0HKIN zz(AU#q#>j<6qi8i@+fY|q2W~og%W6JT}o3594cOEN)P9Z6w3tKJHG$_T6@ooWjolp zZSVQrtI@aj+OM_O`qsC;*ZS7lLJA=QVnMh>?xIVFN3Xx~x|azd`*C*e6|cEr{@lkt za7YOEcZA3+yz-Ve{KiM!XM9VDPPY($wRi7LH{4iQC~m>M9|uhGgnPRyh4fDVy?bAC z$6aq-GD662pAfFuTW)*B4a;x%*ttTq@4>l`zUGFz=EaZQeMtLof9}>BUUSo}m3N&b z#J}8vcY5Y;yZw&oZ~pn$h4}d$LilR}Wp%kd;{H$naPRfj8EYbF;4vYV|5tUnu|K+G zEXXAs`-HxX?D4;J6z7ZuoadIcZvOkWI`eIJUWC&zHY}t7qH$ zHPIs8jy#3ORnJgH>q#Nbyzeuh(IelI~x3uoKg5!$FsrU0fUgtSy&-6kAjP=Jt zU*kKCipcOeJFW9ru#d&MeFew3R==OuEA~ALDy{Q!&Ix_3UX~^OZ1eND7FEZ1zWKZ= z51+3z&hxsu2j>*N>|=F)0oMTa=Y&3X@^r4%%VOWZ{tBC%N#u@s zt|GeZ_d4gfSI}I3=NWa*c@OX7yM$dIJKc1zbn@x6m>gYAR8gR?)p=ZCebtLmwGt7bJ>O;kL>O%D$)pu6+Ro_*8clE*Q zL)G_HKTzFY{ZRFfs$Z=>Rb8(BwEArIU#rhok5qqAeW6-g39ZCdk}Ii|)|K{^j+OjM z|H_7yv6USwyH?I#Id|p4l}lDGU%6)Grj>WB+_$p0^1#YNE1y{T`pP#~zO(X=E8koB z@ygFuj;#D*<(DhJS`Dn8vwH681*@~Gm#kjBdhO~Rt9PxwarI5BZ(DuO>b{ePs31tADoonbps(es1;it6y0C^VOx*FR%W^>bF+Ez54ytpFeLr-~as8&wu3k zzdam1+;@29@V3Ke9zN^v&ckOPzUJ_o4}aqDcMm_iHncXfHo11@+7)ZBTzli%y=#xG zePr#4wI|n>*1o*<)X}48;LP2ogeEQ6C%0^u#ts{cIqNUlhH&p zVEvnQgvS;&AoXV*vi`>UruDS-HR~(Z7kSG10^n0B^)c&@k$%j2m$lD&tva*Ux@yCUBeBC-_uMC7B9{k${sP-HQ35B6^Z z+#V@oI}RvCh9jFIZAfL*T_#dqruY|L2K)fqTAq504)p;w1)4!tV0H*^_}-9c!H&R_7_;03`mf>XiaV1KYJm<`5)(O^*VU;7sM zozwprJ;nw@7&#*<|ED}GpOeoTZh1uhK>o~_G-mMIWR#4Od>lDHBUk13$V#JZd6Y^dbMSd9Y+kj`~L2Q47{eHWMy!w9m5Vkkt*Ditg=0Ev% z`F32}0k}@8mXcRF*X35ZooQV84YkL0IV6WvIt%^!8S!PNaTcwj{ywdDDCZo`CiM|g z{SwlOf3gRg{VLUtqgJN)$8zCq&tOY=7J;<7MSLD9Xw?#JE8PG?Jz3Ld^>g(tTKt$l z)EIwHiB0(RQ#(D$pM*-`f}HgTuZW4bNQk6JiL}UwEVSZQ(I(nOhv*bt$fH~IfFt)p z#uh{oxekCk4~ikN5$_llqhd^qi?Wy!o58WSh%?1@ahBLAc8T5Md@(C77MF?3#TDX8 z@iK9>xK>;zt`~F1$miE6Is7JZw^$Hw6ZeRBi}#2{@qqYU@t}A}yiYtVeoy?q_#m+G zsQ9q>hlLae8CzOJp@iG8;X68k)$4`2W`00sdbKnXAn zKu<#11@s32YzW954QcFOZUg(=D{LS&-DpD#_BYwUb|uXa&`+U(Xh54o7tzpx{i|#s z|GC+QF6>`zLk{~}Y+yZJV*{z^RvTES+id8?e%=Pwll+KK0Q{y6MeJ|4p&$D@Y#6}) zbv6(;ciKQ4z1{}W{#`bZbH2d_a8l?`8iuh4M^tb(_J3l-dw_ntkAU_U`R6sDr9~bz zO&|{X_G2vkASSU#c?sY|q6)esfG3ISRt@au)g2nd4E8%Uh&k+cXwQRgb?1>8(j zK^p{>=KBET6IJwU0&!Rc{Snw+)mt?PzH?rK*oOV>8bk&A*J%Ks6xBBY?#8p*v0u<2 zSjKl~02dY2cWNLnsqWK2UQ&IR2Jl%?1>F!7&L2b?AkN1AAr0&=)%R&oIQan50B~qg z-LFBMgZ+mzfMbj5hXH?td(OrFs~W_G*nbW16wY6S{jvtt?mtBupz8H3(g0QdpCSFP zNMC~e^BPqCN00`PS5|+afqbs|f(FP8Q3ZVwAU(ti`UrtExf0W$(n+KN#NkRx1LTib z0gqlG{p`WMT?6^gN{0s0$x2=Wak$d2f&FM@g9i4U70@n0fDTsBHU!e%3ZDT$o{1IU znxN9a*9w5Nv~mFeI3ta%pbb`l1Im*X)QtcMELK=1^-*0uAD=*aKGt%7|6qj39m+d(h=7@TJb94ORir6~yXm0e2vMFZQSp0UCr@ zMY&f|UX?~!R{_u!#47Uzkp5TsJOKKJSmpBomHr*10nkFkD)3KGY2F8*F0uMv4fI#6 zKB55{kXU^J@M+xhAohQz0h)|h{fq|aG-CC$8dUmoNCTkhh}F+)Q0Xrq4S@b5R{vas zN-rS|ATM1-`>uW&>Gxy*7aE`~iPb}ZZ{a-4^=%E5&8y$nKsmDda}8p@5YMAN1o4O1 zqc0G|hp^uRxEkq4u?PJT#2;b*w;IF&Ar411F#p4S8d&$kGa4vQ4}%X9D1Q&1se!Wf z@L3utTMzHlK)HMvbV8t9K75S^%BI6_){2wIL1AamCt* z257rtZBhgEy)~4Fz;;}_LWB4e_OH|+p1>ZowDv~a^WU+53xITjwp~NJt^vp5tJtGY zt`R>^W6$UDocL?(pVT1GCTmL?1n{?pb|VP1)7n!S#NP?|e2W6=Me_Oc6p)|G=YOPt z{aQZ%Qw0Kekk21hK-njOTY`WLl7}x)Kze4hfgsXY993Mfb9 z;rA=h>EA~hG^Nfzf;8*Pd;U;?5SL>AaRqD(dH8V!Ayl5K7@T$0r`?#ODYhMFLEuVfP6`= zr4>*gkZV~5)UV|l>JK2VlWP+SbQ<+pyA3TUsRy#z6Lx1RNdE5FMz81+Fv6L+ELH`J<@-V^arqi zMgjY^T>BRVl(};4X9_5D<=U?lknhPO&;S8KJc+$jAjH?PHxvl5ggx2`AOy;L#H~Px z&tmUUAOy;O#H&DvFJSLiAkf$3k$?gL+LlMc3Ir@zc_gZUHkCY*QNTVTj{yGwA-;w^ z=o}yf+V)7B0wI8pBkc-=_zUbi6bRVd@<>jBK;M){dK3upH`o^x2=Qs`2NekRqa#BK z*iYq=jSAQw<&jMa*ze?#F$L_`^2nqD_Fs8qy8`MD^2iPaLVQ!mUo0z7bn=TIBK<>@ zAN2E!A1To3A0z!^`}|Lk{)wIbDbhbh`XKhtD$wbFM*5%a^UopuoSjDd{^DQkG}`wU zKeN;SiuAwg^e=&PfTF`+Mv#u!=~kp$k-i7}yaFN6|9&~3KnT#_FHwGge)i``{~YNX z0NX*OPs>Z>!}1yUY_=P>8Xq@)H56u2ktA}kGPlIKl4O9H+%l4H|?GG9`qhI zhs>+Y1Ln7V@Xq^Q?pyFZ;am2X{I~eO5(oqy2s{yZHW&zQ56%Z43@(L|p({d9g*(IV z4p$=|iX65!So@;B=x;@zjolreia(cldE%L5XY!Whms5A94yLb2e?HTnSga=G zx$(=!9~wV0apA;ACuhr|{PC&wsi&qFXRev~!RGTfA2_4+jNdxrFSm4Vxo^u0Ti?Fz z#%)hlF01_D%$v_#-hS2ghqnL8S@E;Jv14|}w{}kM{OGRET@Re?J$uL5f4zHj_czWt z(4PExkZ<5f2d{qYn(th@dfmO(&&_$~_TP}ZVfE#YyyDy&H{5u~O}?ARWjsP0U6KoO z3B1-6L1EF)u}v0ZnX#f-&cw`eK2z?VERRf+-`?@;?YA%9 ze)|>J)xIgik)+HeBVox@ZJDXo#~!OavEi}D4m~E%$L6v6)j206_fr!(x-2S41>iLj zi9*q9#?qO|a?CY~7f*~A3;7>@z-6Ur z9bJ}7@`I39)5t$<$+7ZehP!meYvxUR=Peet$-Jp|h2ms+ten@o$qXOKe<5X9w?)l# z$`v`Aue!M7Lhi1N@sxF&-tBI>vIjSNJ8tW^O@7duKJW6B3tJX`k(Uo4^?`0)fQ zo=K|<6HoHB9(Qk7x8EBIr;{z&tTEmhc6(gzuI|ZrHXBO?%|xobE$K2`sA$^gXc-Hy zyVYaGIyySLvMJM@>f9g?u=*tGMG20%d|J$h+iSppOj4^dzcfN?@WaJ*w)as!~}VbMV?o zBo?bJaVsmeqcW4tW~99p#~fW0i$x-}C2nQKYO_Kit4(i@ismDfMUqzKTR^KQM5{gm8{26bU=P@l5b9PyfX2dN5l#goF=xN*mhjT;STzrWO#lQP#;a`sX< zmlx`ZdET(#+@TUEf{jyw{>gfww_9x6Z8D~6w^^!s#U2iYLwjTXNMgq<>#}z*x3h9z z1m&y{Zv(YYu~r0-NUQ$8t;8ydD{-Pm= z9{W4ySgS_8p5WiLOWE31H08W(?=N7PFrR`4&|`-^R@AQDX{e0ND< zmCRGUksJp+r>9sLmWo8senhK!zbXs*ssI}kQLi`n-u|h+f|P~6sjEA)EfML9r-DYT z`|93IR?2Lq_eOQoS5RYcK#2nUH{Q!| z{+m?&6|NPheNhAeE!$+ylQ_g|KA(A~Q~T@NGntUbMD0D1j_aM;e>iBGGAHj%NWOZX zQ}sxs#g_`1abIh_>V9AF$tGHlV+=y{6^bLMCCH*q+r>#u8#d`rf-u-@GD^Z7&3RfJ z3jZzBbf}tpIq;rnJQ_Mj({|q5Rdk5_gVO0C+*gJ^77az?S2fc|RMW>OScuCDu92|Y zKMKP3R>R47;JeT2_6>zXwfVR|oRqhq6_xh~yofmNf>&`5GF3HFVnShA)k{$qIu+y@ z1)Yt4k{iixtTa*@QJaazv ziQOvWRf3KcQ$@GCeCZUMPYTs+r`UeL_gmy!1lmug%`m`Q1WApT`zAB;x6F9$d$KR? z3#MuZLozI9Qz3bKD)hkb22*k00k0J?5BT6wK%Jsu7CgNK*_Hw&ke@@{DipiHfSsi3 zb#h*DqD5|f~ z%C2#c5y~8yXo0}(>z&L^_VubWbH^xk`ffbtDWrQ^HW;j_5exLhTU$Fld_?y4O)8`; z9i#A3rY#u)y3GUN{I=s{&sc()tRm&ZG=OKL@3OscFTNkFZ+Vp zF7lMh5J)*D8QUU>*U@R^C;9wSi@0iYj|SH; z7l{WghZay~Xg9KuA8`x`G;Fi_j7G(eG=gp{_e}2GIXODYZ4~Oj&=mfLh9dYI(rHNl zs61nQd~(;W$?^eu%Z&6pK_ zU|db1O6K14Mn(-|bmY9fQ>^>mZKGoy5a*p8W24*lDm=_fvv$ywyodMt)Jb*Sz88f`3tU|*Kz!`$sdS+H~Pu4$!- zPQWit^d3HQqWt$CQZGFwXD9D3qAtTuT}WJPK~w}4pe%LN*QUO%Or>Q9r#niezWl zr#K{8Bba43q?B#fPISvzW<{tiJ1Lo4dWz?sJHYM3N|UP1GY!{nL!UyvN=l4r^;iju zd`w|=P-c1iyn0AR8|i)fXk_4JYReP?W3X+`7nV0Vva;{lfk6mhjSgO8;CYlnj@Hi{ zY-JzP2)Wi?aP%Q1toj}ONBa1d=J&At3KLp6j;sDkd(_?C;R zU>dLV4bby2nQ5cOM=YQZn%;cRu+(WdBVvIko(Q1Z^Z7?in6p~zkIE0aBenl6V-dGE zrrS$aejW_|yavk;?_7*|>-8refaZqxNR&-uyI6li)C$UiY8=Z7*1oG#$URhhR4PY9 zZ9ZzcgYvrC<2vW4Y4evQ(hdd@dpUn(x*tBWG0PSffb#TkrSXXCf*Vx#WMvCo@#<$xh ztT>M&5lnI#7pT^@eArznNWdVb=7;nwDZhBN2M+Z0abQJx{*^OPx% z3SQ$qig&M5~!soSKNa_4ScNEO^tV4zyUYFtc#if;1QUT3V!RY3cK6j=gk{ z7<%|IcA?)1_4ba;6pC{B0Hh7>;k_se`@|0+=Re4}7nB5Q1Ae2K5>TjqK%AGhQ=!&-r5~2$3Cx5JQW5BO&bh~-0;dUdxqzmshvROYy_nLS*)MA_W zl9-NRx{GW9MScek*vxjH>ZNz_LQ@{(GfkXT^I6g&@rfg{@cw}~rcgENLu`I_D?Ny^(M zZ2=+Q$o}=vLsmQ*{{7!)pLEjl4d_MD#~+V|t@vj?!yff1JMFX`al*D!x!Q6^zh@9# zCiEx~{b(qbh`jf`@}XELlK7E!p#3Nj3B|toO$R5iscinOoZ+VIrl6q*sVuP-Xro!n z^ag6*lI?7t+Cd)4;o6TOEC_;P{TN4XW!zF?1fCesM>j|T z47zM)OqznoegfQU5SUj=gfb05f+QO7^nZ_LK48h(oV4EI_4M=%KF!`j-FpB$F5 zJ2$a)G!pdsJl@f*6S;0Fhd=2G$710yu#?TU=F_lZbD#VqJf7)vzBQW#j?4heCF9YJ zXSyc)i@{*tA2z)ncRmp8@1Jy?x$#lxjq)s+O!|Btv#qtyHPrR!qg_L;zScI=1oy%Q23l>?)W{+ z;||=`ig$+J^{#LzWVGHEaC@xZ+hJN0p75Pl_+;wlVDME*!+*t{Vb28QWcYW|u1F+s z>m!kX&phWG69)Aow+12+SNeCt@}gUUuGr=N&|Onr@6=r(|K%}P@D>&Apk(Y4qGt<; z}R0G8_Y(Z9)t;e>b_2y{TsS_oK zWe+q1;;8q zYS;*!1YKAiH|(Cu=AGyCBq5B$F@JBNkS}k({Dy(zhK(BsvlCU3XHxm|0bTf+-v54#zeu^raobYu_1F0Q*T{F3)JX4c4#C`d zD>f1NB=FIYX=z0HPm_yfVqn(!9tw7BjS*Ns-F?a;0SPwCvhEBz61ow(5YCfEJp0U0 z$h@@EhwZEfbhA)fa9Xk<&}s{_%_DWX<;{bB|1E=oz`U33Us@;H6rn9O4+Ns@ZuWio z@AC8LIdOPw=szTjEutOGa)uli%omIpZK)cT?&on;IT{l%u^JUp9bfev#nocy6!u`2 z{7fhk>Dbf(+x8us-r@1i#H8oZuZBGx?m!@NV?6xTR4SD2=t$Ro?5OWD$`r{n6pYoX za9LnKPYRfq_k?0GWEP2hm6^%djMwweUya0Wj0F6y4o~>2%qf&g6{+Ias!opjVV#kT zV$aOi@xJp>`%%T=j$z-L1ELMd-Z?z48_cmgGp_Zzo1@c7(O_VWRD*#vQWr-e!^1TG zhlgqBH_~#k!9E+QlQU9z@$q@+{MOYKeEy}>mE&iQzKV{1>NR{VYj>NfT_qM1o%E%Ps=fAv321LO2-xIpipk<=7*9+ zA3vxW*`&a)$`i-Sh0~R9AN58eiJ7)|3^F*U$a=^pBQstDc7%7bncJSS^t(D12*`M> z4GfR{A{;SW-TsL57)h@`Wqhug(d&M`Y|zH(Mr7no0uh;doPd~M)%gQ{)Q|MdpB9qN zRqbQs{mymhW%y+jW_hk>q?Avqf%KkYa-^iZX%i>yPck>s-`~}dOaz08WJgzj|H%5| zFZGs6Teg%+y?Ks?dP12@zUfTR;|b!-m(&_ zT{(}xu*tb4+*as`DAf1n(LB2+H+-A-C*xCnu_^hvP&8rHO59%3mFsG0LSi)5w&%_P z!@+r~t*v!Kn;Hs}oxOip30u15tnk}lLE8=0(c7`9w~>d@+5JHdgn5Vj-9v83E{q~- zzt<8p@UnX0rsYxpBU>f z;8&d(%i|n3;0%b0nixwdo3_6$m6c>A?WstC#l5rmTbP?$Gj8GO;lO~~ z-P$+4b7pwBy^W5r;o%vbjz?kvuOVSpdVMZ7t$U}= zv^--_i>SGELuf!XV7V~T1y9BpsEV>fm4k>@bT`&57fSKKyw4YiBdQ^=Lt1xwJ+0lt zud_9^*R>^D6K$_^ii?S}IcY6L;RKe}>!b&YVJqx!E0^OPxRvGAnmus^gu(71O~k=z z`juV|96{A|TsB1%60E_oW>{1;XaKv~xko$W;CLeu*5i|%68QRS^(Zi!(eST z)#8%6ev#Cx`R-WVW?+bY$y$I$L`NM+*hRC=^Dyce{Kp7Xo@rm+PL&CYRBX4I(6*o;agiDltTFNEv#t*?Z!L55abyTr0omiPzo| zF)wO%l|)@{*=)N?M)lNfWecfsh!P^%;WUxTJ)pcKXi>$PbY!b?At=9zYCW{HQmyk7 zN_45FQ#63`+6@OaoUS1L_}mySbBL#L4v}gecO;oe4Q<*4E!7GFFCGcy>cnR$wp~6? zB$3>>ar4aPY)c^2bZXO#KJ|wF&d!i8opeVF#j&){orD^}e1n!%C~n#`lu9HcZV$Ar z6Q3;s?LDDDOBQd~xG|ZCczmwLshLgosdt7tJNuJvUwW)qh`N($_%W0Za@nBH=7YDi zh#4e^O2Mh6MoJzo?UD?eqjI z^S9YxR+Y;-g}RFsBCt*$GK>#dvO33Fsm-#3iRC#bHK(w_C+c~iUL5O$pppyFM8?G& zDE{?aBTMGNg0%5#(j-w~W=aiFN-?<|okoQ%@RXoI$=bHaqKZgULPYaCrA$+y)=Kpt zy^Y!fJO-CWp-AsIZZ9WI+s;mzct<+pSlvsH8@WZ+X&HSWt3qkgX@s6+e31w%FD*;v z+ERy{Dqdn3J#JiOV^*?fmxc{vxO8?FIF?pP38G5WFmgQ1WucTfXS1^6^8xwrIk6ug zY^T)U*%fkox)R}7CKk(Rt%eWu_s^n{kU!t)^YzCAA>PCzz6-x$qjr#45e~9lk!!^{ zaWZ@-#=ISp_<@X5?l&qjN=;Da2vulu7ZtVzq9X}yk!6K%5E}`Mq&CLLNCz|;ZO3`u z#jI&0;`U-PqhB?a(o)nus{DTs-NZNiLzLCW(03vQi&+PHWH<407S;#Ii=!Dkrew_ND!iKd4<$MYx;zQd$%X%_ zhE@FJMEEXGV3drU5Ufu*oQZFRM@8fXkT4vLrW!9(48&^=*7=EQ7vg+^c7g-sIOHdq zE2+Q$n;V&UkFyg8Sf2U8T5-0`$6 zx1LVoF|tY9B2nf{!#bf1)=!imei}O0M(7BrLxOrgntL2_vPpcRTG~!Ai-i$g<_WZZ z85T<(0tiYKm2Z#)%0&mVr>DutU}285CeH0S7dF9C+JHEiU0lq;o0a3ZlG@H`gK5pg zlWl8GRkd26p1hIbb1x!SW@oE#Tg4N-dTZNyN^j)2jn-AE-k{7UQE+TYuMmj&E()P6 zq<$S!wI#~-#(>BP9pV~)whRv=(i65F^%6P`w{a}1u9!dz?HR@}nA+yp--xmtq2Xo=ZkFzO1{<=)&b(c99Ycw=WPTNPX{G5Z( zF?Mogg#>+@6!T!i=)`2R?Ekhb(+1~+O_Pe*l5=37hLo)zW&5MiQ5ogVZ=&z9S!fGe z!l`uIRzx;r23(evPFt31z$xC`9E;o#%JV#2z(b%Lo94$O$w!>qm*gDP0!)ZXg(LWn zgj1?@s=R@u22xVW1hc7uI@l9RcmhqU(D(ghWW1>}fZn6vk@M)YXJCa5%(X|TH#wOj z#z!bylo**-9FAnqQ@oy>hD!%k=ecP#CLW><0qp3zV6Cf31#t54v>d?5H@sUa`a13L zrQ()*#O1dVK3``eK&sQ{OIUu_h}%k}OwX&-R6hz6X#T7djf(v|_*!nj3F98f`2uY@ z4%D``1cQUqPTbV=U@#zCS~+@~YYX_^zyT*G4+^N-0yBz6nI~z3eSb-Q5xD4ufALIE z2F0=N^eSo{^#&`zuq+rjk;LJPd`hcENyQ~t@v+396aGE14Af8Mp(YMmeptYA5tgpc zf|cBxpF$)17>U+jJA~nCgr)i5q)H@OTN4Sngp2XOz`#uRt};WlF)b3|+T~r{GXn#G zI5M5%1KsCt*>Y|-^Qi5|Q+QU{221pj8hyqVSJmxtg9D%sqG34U%XXkl%du(q*tF|J z+xp4o1k(S%{_h?eQSwR0%&5lCD+d*D3LU|jONY*|MRt$Hrj5M)@5)c(ABR$H_2G-8 z>o=S*fqa_J>%&vo;;H(x$0u~2_4E4h6t`}tNIJ1@YoL<=anOvM9X)+?oW_9G5Cr8^Z(j>dz}~V;ad;BfH8Ar$xrF6k=|3= zyXQZvAnV_?&qyCk!+!x5MwVFFlO-q1LUdTNDG8~Mvii8GEj#5$v^7xY<7UC0fu~{y z;8$EYR6D5Ll4-1f2rCxGrK(-+g@uYU%eo@>WsiH2dZ*1Zm+QL5X2ol?#`GeQA(W;d z0;La=eWKeyLKKP+{aRb1rlDgyDvspSO370dYCRPewUIhjGf}R8t+F`J%E}At9#IdI z=NshtN*_=$&@W}o^K^$VPkB(1yCBOcpP-vU4iMk?;RsD} zY@klD%yC91Y=^>p>~!n+crcl0Z%-tHv6vN(wRiOQceKaCRxH*77h)Ll3-T=t_r_8q z$qT29nyq1s*J6AlY{4@e*T!ZXUT5o8ouS_5@Rs`VdR(556|?4FA}+n!PpSvNm71?L zrvD67ev+fw;e5}~P)|PMNv8ZhL?L#iQ)btH3@R5GTZ`EyX>0->#a+l{4%QI|Bp!>% zV`*|H#s*&wJf1Yp&85{s9YLTekctz2Fpe8bG|+1_hG~Xy@KO2Z5!~9C5l;cDM97JW z@K7W0qmi1`r6-9gy0Xd5#@Lf&B8zHsLW;wPS5r7$bGq70QMqOm-K9OC45nrrpa8F4 zKx|_V<{6!V^?0tp`knJwgY4Z{bL+!gGYUG{ujP|qjkakC(eg!CP95yAL zA*%nVXEb+)Ap!1O(cHOiDC)?&gUh_AHoKK%QX4(&204Y%+yA7qbT}OqN_t>hmjKPS z-%{h%yLqE3Pjt>nSaR+^>8vedlwYKvW}PXA8`K&)AB7aM)Lk$R2S+=EF}zCB)y3?7V_c3m2J$i`FdUr;@$i&PZtsO?EKkB2qCAdXK# z-+;QtJJr;RCZ7`df%b{~y1wsWL?xs%w zK8A?4+xVPK4_r5<6&(E?pa*(22bGbp$CNk2%HJY0X3=(hz^j9y02Gug8-oVwR#LS6 z(}O1?HI9nOq;mi5p-9XsQQO(@cL6KE3FsVn;{Fqoxf;bC$gM=1LTt z*d6k}Clq=&w17Y$nv1H{6q3n|-{-{=Fc|tpfLIV?$Vx{yFrFJvv`2Ea7wUTPEK|Cr zYS6BsSgOsJEW83l-5ZIksfakd{(6gE6yKYJ(P;2)e;{CO$)9sjY?0%rtJ>>~I`$V(wJLS&0a|(f}*8O3Rv)|b)Hqmw0^^Z|tK*yD930YR^ z0~n$5xHqe8cR=DCeojaE7vg)6<29#u@wd9eI5*PoX3Kx^kiych}X8qA~=U z9Imtpx=qc&)rcq=xn zYDY#7f!lj^q#~?1ZB9GFK@kl9aYBIdc6gRY`$Ltx9J)EgJ>BJUM`ufm-|G#JV-8(( zEamqy!o%x}`yid)fy($p2&Zw&P}t)}yoNvFMo+yV8QscBDqA{wZeT4DDw>rpJ=N?Q z)8oOYdPnX1np4fwA-tW%?J*sL`;H82vq&@%m@2E4nb0J!sr9;!pb40 z_rDOQ9d(q#c%IHQp%fD~j=^a-a6C>CF-?@lyVs%g+Vv>a{Y>eaC9G!pYOeMSu2j&` z@0f4rOxaXZ*7UqhPE)gI_YWIpIRk|0yZ8`+Gye(dcD39e@UARNWkEVK*+Iw=JYhkE&TGhI)7>4xbrukX}O{oZuI*%UKn**XGS9Hk0YI} ztz5tZE6X%em173;KECcn!Anl?qUM5Kb+VT^FR#_sy->Y+{+NM4`nL3Xb5YJkN$9nz zbl@IvvJGki_!aGFyErD#oKFfklXr&}ATQ`YU~@L;B;1rzC+g`H}ObIIU4Ei;%SaT!;3)!Guh zzIq5T1(IU>m?s-flL**-^w_EZQ*lxms02jNz{jLo8xkk;Y} z&ni`D0i*a0hqw~fbvHxvoH8BBrLu-hcd}lz--ROlfp8!bVu@E2?r^%1; zbR=|Lkt)^otNEN-76|KOpx~@Uj)qG#I+t^>p}K+EM5D8Im32%-j!#6JM(wG9-Ls%| zBdTTd@V;c3zk0kh3k8UZO-VJOMz@9A7T`F7e`cPq$GzN?n66ZuFrwzh6-X z%53YDx*JC7PprLV3QFC{DCxm1_#TA{7xiP%MqPz#(th=fcGRNvu`+d>5|=M%Y2miS zI8&W$X%WZhatIJTkv6wNEyzDY1j@kDV3Vj>vZhR{5r^~>ewJ~doHY!KkO2D zEMZcg9}K!^YAUCXH?`AlZD$WTi-GOfF*FqN;V%?D6_xuC2FH}bo^uun+dGA4kpuef zv9`lBW$dZe3oDgsKX=_c&p@t{{*=}TW!()0dzhxqi`Q=sy-Dk?*zUw?0G@%6$M5&_ zL$3Nux&GaLkK42RlF1Sz+tfO(oLEj9Ryy9QBcMkNML&w+Lo6%!G}+59v@l=cwA;)vGly@@&w$Sg+eFS5KU&Y$z<3cM?H1S zjKhD=Idv;ug%;Z;4+a>O76zXV>pqBe0ALcGik;8!syUGDMi4^gx3s0%gSWvXuTN<{l^%TqeX*rTq(skbSJ#~0 zIVWw3eQ|27{SzNT2FNBB>+0&!JLe>tP3s-X9t&W8(^ia7DIE*RUoP0ps< z+S0Q-rVV2nbJ=6D(2nb`-+_tfkx1x7=Xz$t;n^W6hkEpb%;MYX1TN&8C*gPSF5dm( z88%+hl%wo*Ui0nQdLc)e7X~2Sn#W8!;RO_BFUHEdK(}mticpP9iId&{n9 zOd9cf1OA8=*m}1W4!bHwG#al}#^n{?mv536J@!oL^T9;IKl93nKM*>1+++GIxjz{U z`j3j%zgUxF@7ejkYftO-IJE7sL2QEmaS9`g70e8}8CFCQCL8^rbc;fWuoZ##VJd~H zT)Nr1VDh=f@?%h;F?@_k#>FwOiA7=38O@7q^BK*JAFG640t7%}20=hc8kX`?FY}pR z=xLb@bT!y@FUxq&H!)MIr)$Gbug~Y*xuL7aAkLOAs)^jfmm)t(ncWF;z#^aeu^jwMq zcWJmFryqov`?2!y8?GNi*44h!JY0KBdY9jy4@Re`U`<7XdG+PR`aBx0HL(LTK(E4l zZe^(?O|om4Coac!R8s(P4tQ<;ov)K-xil0pU^vNxMnlpzHV~O>V zEZLc^hfy}opxzk@HpfpwOvFRezxm%XRzyWoj&6LScDOXUxi7R+2VCatfXa1!ehe$% zyri`rFz7V`R|+*(GF5u0G|%7>-7UpaU;be_lWEE2T4K>iq&PO#-`CeaHdX{9jq>d2 zE(Y;6JcJb-BawKtfLVFWj+{7?QSG~IRNx2Yc*j}n8DIn2szNU~Nh~>;2J57l2pI_a z3u_3L#0~(4q|}}lyNfgH;JB}FE0=U^0rjlrNp<_ui2Sj_?Lz}?ocr81Fw{=>b0qE9 zrHk|fyy#8y%|jf@Ol=QL!^|lwe3FHGj=D=ri zz+*4^$=Uo;AHv6p3gx3Bj@0l1xvuh8X-vh+dKg5rp=N!lp(9aB!?q_?*`8yiI?+5< zwU>KrgxFQVbL-E@Y!X@h zN@eTyrR=BYbS88A*@9F$?Grh@-hkpCQLHj>5n}Og!WhO!@x`RCh;IW&;}e{jGg;;; z4ZsXmUtkCu-5SbR;G|bv(ew4`_N8Y@F&y+QHQ=UVEWpOq<3*@0c-B^{70%fR@J#mP z;T#-S(TIIo5#>z`Nj#+&p)e@Pq>ieCc8(mO>e#37jS;>d^++N=cuhgk23_l6S1A~b z$AdwlB^iyH-!L&AL9CzTG`%J45SAPaw6p}ip*EZK84&Uz8InfCaD{Q^otx}CO20n; zotE!q23CK<;57_AudFRMAIXi(HSA)k<(um-2)S6|n@?Qg6P$KRiD|(%vuM~n>C`5w zb0<#v?aO_Azdq+^B;Mxd`Url#eYW`^tk4j`q)ggkhVp|iq5nlOo4@XdR6{PHmu`MF zq1ii(o&=R=P#fRNpx9Y5isfZCj$%UFpnH~qMWqCx@RZ#wn(Cz8BzL%YxzHfGkn+Bb zq34<$EzCpUjbRK^DlNk&2QRsJh-*AUB{|5gPYzwI&)nwqJn8XVa`7jU%gHD8Dj#Vh zv2EkUmke!7IAfoQZ9|t_yphMX4|_}#w?E(3lSuR&SKbo8xvNKOxqu@uuH*H=8Gz#n z6i@L`9(5;J70j5%bS2gR4TSjWV+(iU3LLnkLoUnCi!biPuHzC+z<}Cx@*+be`@zLs zgNejo=OsIIqU&NHT0&NUQsHAkoG)<9R!=MYc>P|ls%3vj0sKeLK*YR7m;xF|TcDUs zk+C^c=CL3sYDbBz*rbUiZS?v~Wp#3kv8Xy_D1zmHKt}8Km~f;QQSOMvNfvH*yx;(7 zDjtc){g|QL1&35Xtq5k|BQN2we-MGX9#dt#pg4i)^@F1z45Mkc7Ykwfys>+T>pQXb zZho(#(=J2mf4s1Q;G1Z$r6*MA&ZUz{ujzgb?_Nl`q&Jc9<|aeYV9@Ky@g3VE71i&T z&>BndtiKzmg$z*=A?2uFv})3uv3}zy`WuG3C&nEl+G3p4N~)Xo3>u&;$N}XD(33y)tx|BRHmm1Kw6-J@i9Nl;o5nFc8)I9$hD%-|7f0BJ z0`Y$IS&jDQy}P$}0^?u4E4E%42n2&eFr$@dAkl&JF!LNwKjum#k}a*sKAA9whj(#6 z?jT=l7~Hyhj5&p?+&-dlAJI0Yx~2+{{DhPfy}fRE#-3m>5Eu-4%;-Py7JV~$0{Lnc zK7^y|x?p>jMu1i-C%}Q8N2O-hVN4MM7hz#oJqLy=oeCs$Tiois znvkdG(=o^Zeua#g%qK^9+ptm4&U^L&u=rTK&E;#2L_FvXZJ%(t<86iDKul1>^#+=Q?}~*ntWJZmR-K7*k}=DlRJ7 zMd}3!gPUp9@ff7DkJA>*?`AU}P{=@G_wwjr(_4Xwm^Oy*cg(@PY~%yZ(YNx5HNPmw zThbTiM!%&;Xh>a(kZ{`FZ-y0z41&odyVQeJRP3}BZM$*9_$_?dExqkG!pcF_x)Af$ zrTQfz@Dl}w`Am{XPz~%e>6F?+2&2k97;k1ra;Qr0Gi6NzYUEZCLnDQ+*x|ml{{9KNyZ7w8CgpZT<1H=e*ckd(OEh+Y z2kY5Rx)Uk?m>CJX&+*0MscoU4FL=%L*?7X=+t%)%Vmr2alVOZ?ci@9s?k-Od4>9+qd&UIub&gP3!E_W>aw$@BGV7gKfmnZUPNxvtt5O$l9 z&t@^YI%)>OfwKn@nw9^7<#t8(CS<@LddPCi$RADa?^DJ*Hxmdeu@TSMZ=_a)n}s zCZIlmenF~=1~K^&`&hu^bHa2qrA(b6+p{gvv&>-FU*v}p)Zl6~lTBtIiq6X9^ZoLS zDW{*QAf7zvV1f%{@!H-a-Qy<-@X={6!!HNPty&fXOEo52}= zfgvgNXz^dhUG9k0(we!c8*?$iLiXyhUPLJ2vtTM-sdk7jno}EaXq|xe)Hq8YD`h!m zl)~yeOI&~z@$cc+z2PHaj28K%MlNv82GX|N7EGD55scNulp&p>_#R1)fDOW{H-@Pn z#S9&-O14l%uz3Np7%ewcMCEU=m)THkgY8GIs~mzfHT4<=9rEAWl1Y~t3uH2!Kcnv5=5i*p@_@6>g@O98$(`q z@o=?kV3W>1g7iOF!tm24Zja=K0(xp!W<40m6W zFoUV{GDDo>j2Vj+vTGc=0ISl>l9#<2jz2_v8F_xT(82gC;!U@{s zG|PSbQt$Tkfm5x;^^LW1o7c^Sa(Q}ndfEDwcb(j{9bBIa13UP1+XnL7mQ+*~%!N~I zpR?zk*%8;%yLp=RdDXl#Q{sA#S3x}^4_P)U@F`@mMqrQnLRcD6%Bpzu|4hWFe<=KW zV&#}AWB4vIzDsJxjLCqE9Ps=8Y<~#r6Icg)z9;twga6mB?z&4(EiRU@d&C#29gKP_ zE@R6Ux9JPXS#_{=i^sgF_LGgWV^i%%va?kCQOQsTKhe7Qm!LCpY#tO3p+HxeUNoAc z7|NA6mKj0$Ft%!pY*8ybHG4f&m&lv@>CrqV6nzg_riz`m=1QIz*0(Q}9M!S064-lg zQn5NwRLPFwVfgahZpc`70AKah!9R+dvoF+?bK!g&e@m*ooOjFd$Cotkf#MGOErlR` zDR%O7r#dVDKm?e=>*wd9}?#P!s%9SX+b++ z?cFKYY!0HL=~2`PsypII;ho@oPbDwFFX5dA`%z$r;{;D==#5)>$X~wkL9cL2!oUDyx}&$fa|EFWZGp~cES_(d9iuIYl*>#< z61)N>x)Yy$cE@r7A3{nyG057>d-|~urOOqIyX4bTNU*@Wy1W2jK;bG0^v5W)c2OH@th~1vO&<_S$R2K5@TzKTxGc zV+Rp5)MdbA1v>D9)uv|RAgNUbHS7aR7>uF}>xZLaI{rcF5#FLgL=h+)b`^>ZD!}4t z24`N8JUj>r1V7A^@o+o|r@1|BQw~TqC z*$7r*L5ci+#O}LXerSZgV8ry80R#n{-<^nOIyP9zxIPwU2>6GAUyA%9fLr(GY9arGkC(|@B^Ey7n8=S_Ji z8hRQrLj0uSqKc?YKaD7y#Rm*en2Q&MJ;nn+;krdnfF{i+WYYgYtmCK?dU>=X_5gG? zlRBH?nH-NBw!g24cQ^6WK&&ja{Q`bfzhl-r!sf=B|7^yp*H6vfD;dJqG>6OQOT_syWayDr9vH)4L}8$>>N5U z30)s5<=Irjw% z(wRz`w>6BRMk*5u1hBxm+U_wn3=M5C^tRNizZ=~ z#E-9sC6Y<5SkG<$C_ci}I;yvdmoKStjx5GGw$KCD9Fqpz*fs*X4frY?SKUOCQl7vU zpim_gmei;YQAj)zi%Jqo0ft!tqe3g9ErlXeP&d1haJrI?ZQNfWdFw^(K<~#c4!39r zs)37X6^&g-`zWYoEjU@p1!UgTTjrYksGVoS7U+Tq5M8wA2@xoLw#Jhn@y#CTirdX=CI?PZ>Q<@sUx+v;BrMmQJ>eq=dJAq z_zjn9p1JlTKqC1sXPm_E`9$W_DsU?(`9Xv*DI>Jh5iW+On z(e-qwXmb%{tJg_mYpTZ?h|3JsSNXVnu^Mh%AD4@IOZXZ79Cit2e9_ipZ?I%a=_&jk z4v)w%Fh-bZK2qllEFH*8s48SGyAV`HWy0_?Iy}-&uyV2aNS!kk5D^)0@T$nB3ONS7Uo9asYT*(Uy=VMYMqHZ>k66|Ns zFI>nnp3bvM)-5Uf+6TH}jP`}xli_p_k=ZM;ACg zA@6N(ce~rlM1Ad$L#6sl_2n$4h`zE$^$%IeQbmdWlWrj6sF!R0)&gFB79+RVGeAsv zjsQB*_NYFHj@tBp>x=N6EuEGI)HzW_|4bem(m+^-6x(cRb8*PVI~;-mZGj zoWrAP`X|M3Jf~7|38mZGQ=P5f8PK+yuF6;gu zwCBm!k3yG)%fP7{yP#9&K91+7v|vm@MPZ?eM5dh2K=NRT_e{BOtY~^% zUIeZ>59J+exz_8{7)6nli=4rM^@9d%R@Hw{y(L&;7l|8H zJ(M+NvpkIJs2Tq>v+SSZgy4qGJQZ?B@OnM*Sg{Y>%nv@eurhhRTz8#mrALKIzyy&o zd@2imFpT5JOW_EY8^rg#TCiRa+6D&7p@IPm4ga4C+E52&*E)*0q|q^G9F zqMwdFbLi_yf2$`HjQ_8AFyv_s#Hky`Js~T%anodXIu#1HMNBgsN^R)t>+Fa!bLJoI z2n4oMA)G00#66}NX~R9~?#WFXb5_WMkJQylx!kObj^h(Z{GMYx;2VeTZsQ9YAm)dZ z1w8HbaXoSnP_L4BPzIs$aBLWnj+kPmx<`G9R@Ydjy9{3dzs974)Vrw;0y8gAAm=E{ zbN9Bw^X8cx-wK!Lhd&Hw=+^N`d_OYUdau6zyGNq6pG9T6KT-RIjRLtz;Xn`a<(O4J zJ|5W`3ch*KFgSh7Fc#k&3^8Mjt>Hzry;usJ^EH}l$^OnYs}G`%{65)bSfxYYqgi?p zGI-0uR6PE%j_<8_i53pzaXsKu@E{g(iv6bAKwZs z%o`0G(byODFc^p5)44a3MutT_`7C+?h`oG@IOkru-!_MdTIR8FGu@(k7RwZ~Ph z{Yg2H@c$GcL~(*#RBECpl)O3`K*(<|z94S(7JZ=}ke+Pmzq%_E$t1#IV`6eP7JKWv zL*X;}i$-k$2x&$Jkt1U|%Q(~|rSmSKztGpkZ@BGL6j0E;1sx=V>9Zhx6Lin2X}=Cp zLN*>`o|F0B-n`V?Y&cj7=JVKuNet*E7eJnM_ghw zZNWhv4_sHivfChK;(_6alm_+Mn{5;ke4Gc6NzwG<~1b^vzuXxN<(q%t({`tZOVN10#Wf zl*ET*WeMMZm8pS1DJX+unA?Un)EkdXM~@(sC!slD?ORSq@V{4$DbUifa&ms&Z4`Zx zYcKDNwcn+`DSKCYtn>0~Bfg^Ho>x{*R&PoF$1%M*%uv;OGz1}K2Ks^R+jQewPXeDQ zh7T*BilxT7$1vcO@0N$~wfp_Gxe^>Wr4rJMJZ4IrVIK64YWbN(ENj!&(>2oyUiB~gwvRz&_CjQevo^sh_zR++?{FSJ`unN(EG!QfN3Qg&P6U?Tp1H+QYk zZ5-Em?=G;5H~6L`N+L{2pd<^VNbw z>r{QERcp6$(zs6D)Q+9z(WlZTJ?Se=fAk!m&``?J(L)oWUY( zf)^r%v)_x}q}B_y_da?Sn~$+Mo?x)fKE)C$OgQ5gP#D&m1Bry5Vgonq+{N?3xe*;( zVN(1RP&LWOuB`BAeH3F;t+Vz1AZRO@6(k#Nr;Q(ImvF6|ONt*YzXZrrz@38vx%9$U z+9r4wZC=P*y759yix-!8Z=nEKTRv^=!xV@HN1n4>I1HWUG<3da<>_$kaRaR_4g?u4 z(Jbcz^pDvWavBr7QGX-!Lx^Db6LKB!G|!G8xgjV<4?v`dUkxyOw{*MI(lhlLR*?Ar z19=%xzr`^Dl3~}KPw&WV8W`A=k>3~nTEh#3Re7w2i|>|Fq`d=cp~3T$#em9BuDbQO z-1Zr#+f{3|%sz#=ceyj(ha8^Z)k9|}AI`#$9iq{mB; z)$D80MKO7ehi72myYvnW@toplv$*flY!E5nVjMt)e?Ew+pZ)cMrTcN%res{Xw8y{M z5M>pwUwfhU%ysoGW~fkTcPe!zl}gD4fVbXt%@|e!o7N?EYecGLXA*kme(}d#498K9 zLn-&#q{^%!|Ee+aI(RoY1B4R7z-bef5@@jvQMjexXg^-j)8HhdcX&9R9v<$6Z=U47 z$;o{`SeKqHC4?uOz0i~jRtFr9wR14O8jTj(GcW7M-k5jeX(vt-0JjT#AI#f7$Y}RU zjFJAfrJ6wL7O6aQ6NgXc6n8z z9&{7+pw@Y#d;;6v0y=wh#xqq4z^HF)f4{2s_ix2a3DNaZeeE>_R4u1mRQ_Bb5IDa= z(3pEa8$9-lE#ut#9i15xnLk2ILpx>>Ayf#1f5vxlpthJzYBOMPDMAFI!6F1=dQKg1 zkScxxRylCg+5v7_S)weRn-(Wj&9TcNQJ;^>J4f^{@a|2?L`>I1(TG7ob{Jsj4<5PS znIih8i#TX59^Xvrp+k?mwl;Uq@{SO-k58~>5tPIha#t+9t~E7Tb8}u^FOhB!06p#6 zG?>)&2sV2HSq=5Y6Y3*y0qU!d49|=Q1DRR3LVTrty50kyqE|nV^2?KFrE6>MW@cEc z(z^A;u>>#! zAa#kZ0DnIp=)x2-pcx{i8(MS%$XB5Aj`uc~dMu%J^Oc_!VFMB#R0e|p!X*d{G7HEPu^U6@51t5xo-iQTcJ2i48{`D^ zCqtnpaZo|$gneUw0L~BgYnq0IwVQ9&f(C+-K)?;|4~Cp%>FY?+v{%W%+s0Esv|bjL z$}5(J!BwUQwA)VJbx@`k!!hikjD&}VMn*;w6i@Hi zIe7JdvJqX^wrOxftmy{>S`a|icbt%_dx=;7i=V!=S3I37L+>S$e2p508tK`#HGbsQ zXk?EO>gnEN=rgk?Pu_;gsrNkm(6K}EB9e{XuH7ripNb_=lcAw-#0bRV;lUj{(`l|r zBnuZDK`n4l$8e}??`eU#pa^YObv>Np#H;g7&rV>WuUQX!P_$}CIFFXbYBE+#0Oj!T zBR!c-3p8h3J>W~k5Y(JF$ZeJOjadw~ZyfMB1>1YIJ00Y0dIkZw9KpWMfH4G#9ZYv$ zc2Y4y4;iZyjnyL$k4CMh59_Ff&j*7eLBecpXjc$^%dkA7X~Fas%;a!t?|NzPM5Epa z!y^u4mm&1x7qCvT6=#T^27>vr@^$53A-^ET@nPO}M}Db;G0#h=0yH!@#`%y(Gz>Ou-AcI96qJN}9m%Gh|mDN3Vm2ZnO+{I%z(EJ~w&|x*qh24J(Fd zJ%GuD(QA#mOAd2mcL#a#T0euu5^bbmCO_mPlT36%2RY!*ik6o7IbJP$e$} z0tx7XN7LA|pH3fzZirn=M`Y{>4X9(|cjFDTXAU1x)gyW6Su6HoT` zVuJ#@tICH7;12)AU*QS{G`uiwOsC@_?lRgdJ z5|%*BT7q?i9jmX+bptHHj0NjZRvR5w;rtGRv^eo^a4k|U%^&1Xu#zmSE4Wztp{p6v z@bGjX9&wdIZl6dzFpXXA?fc)ma<=6{EiW|BWxLePYEKZ}skC4Zg6^d1imUeBIx(>& zmY9kqJ&i9BpGw5GOibMB&1}8UY5#PuX))r5ySVsp816AP?{epAtLXh0&@Da}sSUkB zGxRi&^`q&{HYZ}06{90}Z=s0nVjO|b(rVH~v<=dIR}aKUQY)u0zdjP%~7nHj4d9(*sGeLSDgjR+kEImN-Ni9Xn-6Zlb`#T0Bg8dyMK@I8% z|DH|BI5dlvlnC)OdK4N|2+`RlG#GaQ!|^kqGcmp<=4%M_(^I}az!M{<5BPh@Qyxot zp)*}rX^6)Lhle+bMWXI^8BgQ?Axyv$SdWg!lS6)7xXbMj&8?-QOJL3>Kpd7i$(Yk6 z{N3gRwXsd4oe^m2&RSBKq$?j(<78Wac*PpX-)_FX!?-3p;HdU^E-o(r-S18|r@xD&%Q z7)U01`d}#ODg%5S!M1hP*u8Hp^4b@UZhny^&qqp|7VKg~Sx!lrWkm1wYrcI=Bm7I< zI58raO!hpHP&WmWT|FY+zZUmAfnB1{b|#kw*~^#k_s=b7aFe6uZ}_P3B6Q&BKV$N! z0c9B*8yUgTa8Q7}R_b&FxCN~cGNOAo!voZo&3iAj7v;Ww#(K{Q=%`=3aWwl$k-cjP z{bP`$s~X{v8zm2AJAJZad$Sh2%_7l1x4U5=^IF1dzG>X`0L-hX`_0vJwRPa-H9Y^g zQoM0IzeY}6x6$5OBCZk>oh$jPsMsVZnpJ#Z)4XD%cU<@(>%i(`*BbJ)P`(w@MmcOI^YvgIN=U1Stw-_+DTzHaYcQJ!#^@VNVz(49Ygkd_!Dq_s? z?+(Fd|D@-W1KQ}#yWVa0t}m=mO&AWu;MhkPE{@;C$0Cf~&Q1YbAQ#N{f-j5|?AhCV z@BzPu-gU)mGcWWUmi!a(K4}J!9Ng4(jGLXd^Z5>YsmVsCc@n(_Gr3QBUinIgy}3+fgN5K{8eodeUs_}NE8 z)K`SD(R*wL^y;9RNJJ{Hfl-Z9r+CfcwMqnDeg|H?`YKJ9qFVPO;n?|u+A7_K$LT_6 zJhgUQ9$Yha^K4trkHC7@YJjl*TFQREx%Y;#i}oufr~O`A*7Uu*v3~7-TJ!Oop&H z7}~MztJh$=p(q2CkY2$-U4eA{Wd9>Rq(sYx~EpvJ%iLSuqPjn_bBR9!_$gR{bsQtdJ-x7;f{L_ z&QK2EI*BV&upRAE%Gst;Zb5GE347N0oxhgtOZVqm{VvU|f?mh=CHVW%hstIdTJ?=H zlduC&qks}p;z|N*AUYZFZy6@+FlKL-l)yHA>=%YdfE|PrY^V<>Bn;3XZ6dfzq#=U0 zOxjMvl%ktxgm%zQ+C^yqtGj6r?S;qKeUzaw8ixnE37Vt>G)2=iL$h>{=I9nWM2G1J z-HIbGkJ2%^o$jDJ={TLByXYjHqPyuHx|i;w`{@&OnjWAB=^=WU9-&9+F?yVypeN~* z^b~!Ho~CE$3}q=tc`A@e^Rz&VRHSEViAq$a3R!R}@Eq01raCp~ES;nCv`iOpl;Tg} zjG|A|XW(e&r|DMGU!Y&7 z-=ItMoAg`s8of@xO}~S^v~SQC=}Yvx^ksUJevjUwuh8$)+w=$YRr*7E2Yb)IM(@%4 z^mY0p`UZWIzD3`rKc+vSKczo|udBa+|E2HHU(#REcj>hD)YxXX=aDif%sF$d>HbLe0$9 z&6>E^3BBU;R;hsB=S!At7W9f$FPQVeMKfEg2kd&b7HHUJEr`_oQle(&>)Fae$;_0B zc0Dj($}WU*mbFyQ)|P_Ba(2OtSItVMZe?s#u4cya#abS@&vOPhl3z6QOGzi?xrS-; z`)lU18ODFcE`bp7e70m(3ZO$FyKEZ}y=Ajf*JVwkPK7NzDx6~hR0uUxD& zY&RkZ-EHHY%?t`FJB`BRMCI&m=QkjXS3S zWWAVQTIP;ZF%yecqqYFfwgqE`SE32QWVvj9DKpPKildV8?0nI-t(qMbIZDL~l6}Fm zb`@PQ=5Z%pT}DhWP^4xqpdb@G?{y;PvV_Yv8Nnl7RP;?RDwswqXRAgTk_J86?Y7k{ z1cA$QcHOGR=Aj*B9Q7#7RRF6knQ^xVWVnOIvV}s8MHY*Rl?eAmw;K$qd(PkRK?cq6-eO!}(IN zDmsq)KxR-k%pB>yplIo^!=8<*wqTZHkRRr9CRYSYOe8(&=g6}~@nS`L(W*v3H@xK% zvxXC_aMyS4SF`*~kp&XZtT?x8Ac`vr<{PC_%n@SfjdeTe34{z@cS%YGh~L_>3>JzP zE-c4Hu6eZZqFInX5rRWfUnDNnK#IH>b|aOED8GNreI|;p?i`ix zxP-1F6F8LdUP?qcUq-xyBx5PtSXh*l6G4%`TRct1<@5bHyeC64XL1lEOY#RbjNV{@ y3`R0rwv3$xN7t6)qCAf9Tl<@qYuB|hW`RgG3!O-2Z`7)_qV0?a^&&d8qWnKrX;U8n literal 0 HcmV?d00001 diff --git a/latest-tag/deps/font-awesome-6.4.2/webfonts/fa-regular-400.woff2 b/latest-tag/deps/font-awesome-6.4.2/webfonts/fa-regular-400.woff2 new file mode 100644 index 0000000000000000000000000000000000000000..059a94e2fd7a6144d1496157ce0c21e39fcde121 GIT binary patch literal 24488 zcmV)sK$yRGPew8T0RR910AHv83IG5A0PN@h0AF1L1O)&900000000000000000000 z00001I07UDAO>IqhEM>n0Lp`wXv>2v1&9a-Acb>wRUtiZr9DJc)Ot80IcfC(sA`oE zcZawKK%}4uy zhaHI+oyjhDMnMoi4*J#JTRsY*qvDpqMRI?!nb7}A7JfEl1iKjX}NjN6 zDGi{&oUjlYNE-~~Apqv@O*MO`*u_BSD)ljxDRdzf&3pfE@&EoUb^#U(fW?ATu{>R| zARPcne?JzhT>ykYQr1A3mnmLKltTi@3rq!hRBz;a{3^8Zbzn4r$fitBiDIsuV)# zt&c>b#!cMu!JQv(X*c;DtqT7E;7Nb@U;N=U>a%j8l*(w1cg6^gp0ay+G8&R^h1U6e_KGIqMi%2!{oNE6h4p#0IF|48jq zxo&^Ec3z_VGKy)b#ES*>J(%jX$DHWTqI|T=C*b4NYf>nxD%8t|hn7{9YcA^7ww;%% z^1(C)8*8R=TUo03YRfNJ=u>+tOZ6yY)yCS>cg4cW$QQ?@zVlI_Z# z&YsCWmp92<RQUcFc)&u`xEqp4bbNy-k2~VN zcrYH0C*rH|jrc?SHU1X=j<@37crV_MPb;k}9V(qGT`D~*y(@hxwUz#rft68}S(Q1J zxs?T#C6yJG)s>Bv9@@xQnJUv|rp%QsazU=heR&{{O1Y6fzD)Sv9re6?d*4s zJ13p<;V_VjG-M_xMQK4xTG5&|w51&qy*iPvxWQE$q*e#d1kYXB$16`5R!wS3J@HFx z8RL$baaB&7P~q0q)qwWU{yN}_ci;Ggp3>{rRKuGZ;0$x-I;)*s|JB3K!_UHx!uP^= zco!r2rsR3RmmPfB0DQ@5-@q5w7snUV{4w8M+Ptn|40GLFGgr)IbIzPL$MWYM0kbQ7 zli2`ft(joPn;y<ni3V(h{GL?x!&pCG2k5q-d^4U;LQu(oZcMX?A|2cO+eWd4ZKmiI~Jbj;CTw3C*XOU zdVn8*=Q4Osd3Jf0c?No_cwFv#?gilP?(XdFl_OVXlrYg$q0SZJSKP1hQ18}umY zV$$Ezi=;oLzoiG=KWe%=-A>w{_NU&dI~SfVO_!2pkv1o3rPF$XmNYfZFbDN!i!ooC zLmHe0n~1b~>hEX1)GCekPQPBbg0wSjC3S|@)IYV1=28v%*DLiV^&~2kx|3%3nekb3 zYHPoP-XJw6H6=AB6-t{=a~AaO27X819(osj!%#!@R|U5x1&w|)|97c_jymbAi>|uq zu7{p_>8+2x`l(W_My)#aGP(W+7-*2eh8Sv?kwzJ9obe`^Y>Me-m}#~-=2>K!4%;TQ&v}Zi) zInR5+i(b-tm6=5&?3HGt`r|zlYEf@u?3;lYgeDk{3227NSdR|a2GteYRX6NV-LX^k zz%JDjyHzjjQN6KO^}#;X7yDH|98gs_sH$;D)!?wI#SvA9qpBXqREFa!#|hORC)EI) zQUh^X4Z;~U7-y*=I9m<(<*h1C^EFzl#){(6T&yZ~Z&y#HgFOY2lPm^s1&ysBc&yno{Bgviy z4P?)7+`E8&ngL;7%8PuC$>Hr!TvZNG~xs(5~pIAPMk}ehZ#i4fmy`mU^a0D*h5?i_7Yct zImFe>cXKPiT;g`HjJSjOV(tT&M?47T6A#C*ig=WG0zFiQ;29T#>JU5!!Sg^32tIc# zd-Dc7HuDxdhUkwF`~=j7U?dBm)Y7qyfr~#W+2r- zs?W0?QWK>1e&R~KLj;jP{2)!%Ko4mR(nj7rWDLmI{lb;09#{i1J%ZIBGeBnU9j?p; z^UG{O1YJN*fLz!&T)6_EAIN>6Kga`)n@34 z=*=JiJ3vPP6+tHfGeIXa%oG5#K&LXyOaLy>IY4C4#W5%bx*K#aFdKBA1LlGr0Oo-n zWSBz$=7Sz)m?HodfF5O-44c_7ls}jrL+OZDDA*;N(XR)(g~cT zbOEO*Bf)9PTrh}o0-T}z2F_Cc2J6Xspc>g2R3}@2s$@G*i5v{hkz?Ni&XY6AS-3#X zCzsFp4&_kc1$l;e~$ zqMW5v6y+SHt0*@qeMGrKX)nrMN(WKyQ$~yOfHG2)hXVZ!l)a+7r_2&rgECNLP0DPM zwJ5Db)}c%i*^DwoWOIQY07`R_izxj>Zlo+0xu5ys=5}AWxL4#DD6c4Pw6D`J7u-_jB;6g4$2epxdP?3_*{{4 zMSQMAxhg(ard$)Bt5EiEUjRHQQh<*Fo|GuS4ZstF0(>6u#H0W(1U&I6z@Gu0R4Bk- z0G=#SfIkO3sZoGG0z9cxfIk5|X;6Sa20Up~fR6#51Qg&O08b(c@JYav0k0+JWEB+P zQ-CLH6yWaxPu3~GKLMUhD8MHGPf`l-w}2;iQGj!RC#NaE-vR#UCeIb;M_-1-^;aNy z`l}GHzXtL8>!1MF0e|!j@%oz(ufGNH`r8n%zXPGu-vyU(o-)Ae(|L+R;<^e7@LIs9 zLJIIjz^9fez?T4@`m&VQ-^afkfFA=D;PwI!aRCZ2D1(3=z%Is?KZ#l9TPzFxtdnM= zH0wxT`W9uBb)qlqM8sB*2k)uZ*dEPf3c_jZkG`Ndxmm7!%ck`PrH?Vs3YX0$+ zt5&&lQWDlNe}Yn{a_wU;{P*94T;; zrM?t6kv?T13!X>gXcsRQ#c4LlHY4=~PO|U|9uXH!COw7UBMFfYG@j`$@qCL(PZ1Y` z_vY@6b0(9taj|g`uTXjRxt>DhSt2DlB2tp)dW6ZdcYj6<saRf_IbOZyDn#M@(>0bR2KjFQRalKivsLj}!E3JI zHA#p}i0!VlDiuR#8r99BrdiT3m_n(>T(4AQOu?}vM2@gzP0wR_#WEoUl`5WOnmXED zwnPIBFX1I9fe&2(Oyh_l^<{?Y@HHD&d4>t=_Vb)uR=(ievT&Z?M!!<=F;ohVSkJO7 z&hv$3agK9Q6S^*HaBss!4)PiIIJa-&lXwhW07v@XWQz19qeJv4i{j}d`WePimbP%3 zW(!GP?HVQP^{=-|hWlzF)4maSOyqkXC|R!YoaY#>ReIpPl3e(+{rSz?x9~b1L1kWa zg_KEO=3l}j&7wG*{wtA^97{svacnrel*oiBSRWyCJO&410B5!DPw7kFql~gBj^b%X z*@!Yv`qEFcG@C|o6a!gXJAHa>jf5|KoV41A?N*XTAZa*!^f|zGD>cz$;@5 z+lV89)R((xjnLUf>?7+Px}l%9irhK9Ckc_~&r3q&9$w~*vbo_}rRDr%L_w^9m+DT= z<>gURvL?W1!mfzZ+^&-TmH+xaDvcyrdy)v;nFY5Xg#ERejNy5%Ql>{$$qL$yns5hJS zA~Z#{;FUGI&?M$Uy|^g<7Lols=Mb+zbL2Q;3iahSP9>RQVc8doj;kM0su^=@PJd26 z{KhZiFS~lt$#3apT^@CxCsLAkNJ8X0zR!QX&Vz=Dwq5kcoS<4;b#6#P5s*nNNrX`aq(t?Hg!)XJZp?^D^Z4A6_>Q4~{w zbbe>zw3FSVIEvF<+$yS|iS1T0ZmUbPJo_t*T_aDgtr1-QHPbfr`>yRFZN(G4>B_gMF8+geDa8) zWClt`<6abRWWChKUtxCspRi|ZWiP+3qk(&#j*ocyo8DaZZ0#BqoL$rG?^JwwChXyL zOrQiFfJ2$|J82fh9WTl&&%pz2RB{wgW1{s^*I!f6zhe(u9O@g}<&WA+vyHeHF-5bn zm;WbQD|@)7qmf_t%J+*fF4#3T2@o)E-^2uuVE{Y(11r!Lj|$ut!qRNIv5RSz?%*1h z>G!m0=8TQEG3F@!pdI)Uv9%E{+HgKy;v9S3w4L_4sb%}zu#*y7D*Zr>WU#bCBq5Sm zrNFLMn`+wYB6fQzGfEdmpC&(H76Q0{Gw>h)?qO^)8GmidERjoMx3iW#XH`ox={L~z zpSF1>RJ%T|nsWpDJghcMu9o;Z=r+#jGo7`CT zT-RF;iTpgc{bgH4TXV~cVyfxAU7T2ezeL3eGymUG)1=p!>y?6=LCJI3wYxw4;uqr^ zIU>&wyb^OCc)(>P50CQy_8#r4fTEWft*&y; z`D%Qhac-G-$Hqo__uc8n2IrPN5JHH-+UDlw=IW~8T&!9a=QdXNLp%lneXb=fwalCf zuyrLIvefgQux1RAoj*Go6EYs1JwHQXR?hE^CygdzvoRU(o@e8j2gvg449R$O_Uvd( zv<#-P=$|dj3hvoM%MNA7pCZ^Mq?=}`2{Da(OA;b)G5oY7M1C6Cvq&Or06FX(`>eLHaT;<%3Fibl+g9q1-f`gU2h?GRv?H6or5l)XtGSe2uD$co?fm54SdG9U4F-z}yJLi`5O&7nm zTZfVm`S{+ySY_e-$G2M+=imIdT>rSX=FGrYgfl;itu&iS^6zh29+u8yBJ<0m--t(k zm8}mH)&ZQhHOO|QAI^V+#gE9YT6R#9_{zJ5Qfm(A` zfb;K{%iqrz*DAN@y`klDHd<+xq48C6rYK`Hyq!Xn^Z!^Xz$*nk{~$i|jwmo$p^Puh zKiZ3o$@GoLV_1R-fD^D0wYq|HIGt+?quIc(^&C|E<(2jIm1V!O)N_4jGHErNt;uBPW!oab0&Sp=Z53XDh0`=s zz%WA@NHon3RLp~_r8`#!gbY^hJOGO=!UJqHS67=2{7Z(nfecpeymMthP?(jQ z3@LfZ2I|Gw^zdN=AmfWB^UMW+pO-SqR8rtl=F(4*kn!kjI(U&`+TukkK>HL-X7|E{ zl=I0}Y6wT9i=Xq?f6ftx`E$u>6fggDe1;&%h%Y>Kr!5RIAFGG0!vHnqF-aJXV)Ghc zD7uvoJ;5ET{8Q_V24Wm-9bP&_cGz25LR?zvX{dVg;(875xN4P!^Ba1%JKBn3ym^fs zm+dVr;aK-C;~cNR3tA)UQtQ}HMx==WR>z)#6O8Pq>=JRw(w*EO_9gm8ag_OSdYol* z_j$%5_M$FTC>7y0Vy#~DD_p01Grh-MTJlg+J+&y6LP7PUG*3l`pSneU6%2z`Dtt4& zNB5=S6__l0vRo*fitwjI$JdRr$B`9FLe%Ts9p5V}n#L3?N>@cB$QVU!J-=xi@RudZBH(3Nk8i=oYu5mmk;}nLVC$>L)U_iUT?K3X z$Qx3Tg!Gqpc9#2K*qfy3gAb-Dg6VJVYUwZUe9Kz!?Fi_Aw#YHRj^`n45nXU*xiqmmhFMjz?ZeJ4q6|Aa#{xvc@A?M#_R%i89kG zJ@FINvgl7dQSw~%h*>86u^*$RMc=5pctv&dPvHF~qM3gZ&$?>K%MU#2UQf%vV4``+ z#Q3lVq|q33k0VA~$fO|4NKB2tUFzxcX;45rlm>>P9X$bo>pRt7@ zKK3damlhkqsoAnUa;?|{?pXQD>8>i1x}~aky^%-QTs-!f@7f zvJ5*aQd5Q<&pm%nFj~t03F}T;;dQ}i35WSto%kFYT+g{u0UErFFDLS^Lx8J~RfQ>l zGm_gVeXlbmGu<-gvyLab=S{OX8X#pvf{nkIW)_p&k3szZO#i(C^L-SrqiPTg{uYl> zH#F9m6qX2~cSVa-Gy6@-RC>?H9h8O6c|6T@~-wouCaGmRK!$tW4I zge4;y2Ju&vKO7=<+S5~GUZ$E#$ET+44&v|+6~nR&18uufsfJP_w*T-C+lW$z)k?*- zk(El!B_CY7Q%U=Axf~V^M%6-CD);-Ta_8CyiLT?*&~-IUWwmNgS#5prgRNDiSFJHs z(_9xB0{|f4-(HYDE}#h$0F*5veJRklNae>BfNQLv#L+{}w zjoo)2D;D{C9x8FJ$loyVSr^KR_1vO(eAm7QjqLa=@|Ys1bRW^yNrgNAdaS(VBGJ=xK_^%%W5`*pxLyn(q0m|3vuk;f>@Jhz4D#d2z0 zc%xn$jk0Vss?|#$Ay2Rq+_Cetr|sMkbnxq!^{w*0!^`$_iIilqIUF|X^~P|xxzus@ z)>bzNx?5X!zrrtHHrTvuJACsOC6Ng;pyBz1Is~u*yKp}|7k&m_0sg4&0Lj`rEX>#9 zZ)BtHx}G}(&C^LL=RRT-d&S%kgLpJfiBp%&DjH)h5Ctv-qR8iStVy8G$@#owKZ@s_ zCo;OXC@UrR3P&aFlXG~0BEQrfI2UMM#s_g_^SSCIUfIJFxnC%mS6zIbpKp2#2zfZm z=Shpeb<0v;?&3guk-&lU1WrRougRC0$EfeK)${E$_XRE@H>_eeio$IBxj(iXudJ;N zD$~i?y)!|!mVW-_k^Z!oe$o~7plTF7tk_zSskU9^Uj3O%CC5Wz{g0xv)OGCd`{eqM zV!f%BnpfyqS=vIYGoAUA3o_BM1!!>hRh+*nzX{g08;1U_cj<ell+Mn7mF{( zHzYV+{a(%}d*~rX8OKj+(`?r|C9J}xZ&}L_qc9K{M`0ky$V#(LkLNDPp1B=Aa4F1l zLvbNcz07o-J-eytI@?oG;9+jcd`9gd`Qcu)PG$u3xiVWfZ|+W^7UCY+r~vO zFa@}l|Nrpwpn(m24RPIkDKO4bpJhIy0<&}%8I4o11g$qZI2=oe`ctSLaD@m(l-9g^K{v0$o#7mK8I#+X-PQ^)5}(V z%PQ-7+4A-G`vJaIx2(o`gY!Ro-TLcPw6>`F%|GMTPx4YxX{g4Z$wou!o)_cNQZK(% zt5on|Sec8aRn|oT1^&5D2f#7P`5_!pkXd&U@iD=pBEIKjWtG<;9)al~_&_~ae|fj)lHwN-4NTOe!talopRZ68?AnSv~3~(sGB4^J4wL+e=-0*0W~# zIo)(bp6I3{9&EK+O9R>4v})%bUncY5@oKf|N-Z3gi_Px)=Z2WQCBk^MIXS}`-Nt&i zo^2t`&Xu&+MY>w-tQHCYP~syWUIYV3UIW#M!ZdawK_o=&oGI`Q=XGJWgnpl#Q(1!i${es6DY?{I(rXm4+C zZx6+ML(jRuy`%koPO}2qcFjy~Lp>%WoDCIaPygC%c7FcP)X8ji+!r{T&1NY2$FrG! z2ApHX3OoxSOMMw-aVewBeOyy%#&#&=K72x(Q$|@5CLs-zQQ!??ROs75GD=3jzpHYaV4l!u=YM&)e=+_Y`L#CAr3S}x$Hn}F0VRGtJGE|gd za%$8UUQHUMi|9M)@ywfbF{pfZ|Q5S^cvhhN^gL+U7M5kxyh%G z1_bJBOe>mv1fWjRT}m|plKyLKd?)uQ9Mr7 zYG?QuTkHK;&8a%I$8M**3ijJ57H05pAs<6kpjvGsigvqZ+vrrq3-W&!AUxEyp#gnv zzOVEx>B}}|Y{514XoT~Ge81(onCb!C6nc2DCt zGhTq9`bI>S%XpvRW)K3za+WcHHt(W6Cu9c)gZu6q3=ju{`&`@Pj8>|J0>N{*Mo@TV z%EVopjQN!a_0NPz9Vw6~3SzZF88>Zr7=~`8 zS{;v0{aUjc48~DJNED67QA7%grYL2`n4-LPzOIl)rOY`hHAN|q@#yT?(A}#jzfn+> znu3NXSLy_jtk3TojZ}XLX|FdN1l6XZs45|>tSFjNK+CFW%35C5*aZCB3!LGXz=c-C zi)4uqS>*=4z^z!=vk~>TC@7spyV3Z(y)VHpsdoM(XU}4C$<0mW$!$Jv*f{>v1GRStUfpVv&Dc$iSDVXexKywYBZ7?aER~|8|}At?wLO(Qh_&O}Yg$iYHPl zxOjjnA!=EOC|+M*^&FQMR9-AD3zDGp1HH6V+1}b-TXP+*YRYomt@X)YuQ!{0x1h;M z9GL}IYVW)w+H}4K7Q`@yotXr^G|};u(jdMi#!Vsq9GMj7M?hfSMu7_xMLrjZH}k}T zMNybwv(#2tfk-5Azw!=HwA%uYgoZ&E3okR7-+0)yhhFOs18v5;1PN@yU8>BTd+DSz z_NUB)@$58;I}tM?(X==4vpZGOOmZKxI>G1!@`|Y8s0C(1iCyeh92Srx}_mDoDgH zA=4~~!~+x`AmjrAZ|w_2u_A8vD|vIjhspiqEUg}Vr{vrL2;d=O_BKjKV%6xAjuwCD4dy*l^ucHu%Lf~~x+p?Hv z1}gJb&V)8_fKi-g`Wis}G{lpgP9ynQPw@#}%A$A&vog<67V1GuC)AVvEuwJ=1~@NN zqBzaA(HxkHU4M7h#2ZOT&L0@_B5w!EnNWOZy>uH3^rZZNji{H5l97jvT9$@UOv50i zrGvgh#_Bjh>x1W_E1gLHmbUgQhaJ5r_6eusv48Evq`5%O;;DEK8}9v0;Nis$R_2~; zDz~v=pkSHN8BnYmZgtrxq$~;p7WE2PF=?M7`ClzL5u#9MAc_$5Ok8ai0E9`gake{a zI&s;OoS%s@{~duBlt)2_EGR<vn@mGrh~hp*kS9)rfz-f5dfHE_ z&i%ptNaR8iQ{djwQ5#$Bb_-K{Uuo+7znL01wg0^)s>rZ5`$;xKOBCkb-mR`<+fFw{ z^jbKNj1B=hi*=4Wi1D0^_ORChaW09VhNN(c6gP?^ww#RP-3HWPB%2(KFDK(&d&sAnAK871I_pB4b(({G65H$1eYbg?!4 z4(-$v%%Np;0X>0G5QVWCquf)_OMB7*7(OG3(y0&Yr6>g_9@K7JJsT04Snrf|eE#`1 zV|?9>RDFX;FP_zeP)Z1G_C(mOm?X8?lh_jgVxKXd)%#m4^YH6tGlpHs8B?wsxjb?? zccbFa|EU=`AO(zYAie2#>UJk*Y<$^<{(i`p8^l5&4axgs*n=@Tjjp4|5t^}oSSt2X zvRG?Kt^(ePYL|AniV<0!qAbc)Y&-))C|%Mf)T2t6!(8mLVOAlp0fGJDD8r0=p${}B z0@G}Pq$X;aW+0e0&_xhY`ORjhZkcZemxtS#7KDJwKB%}WWj9u!bB#5IAAvENMg=;9io?+q zkn5!30Db`EXm>g7CDE8`Mq%AXnNrL_z|X(oF=uQgnFg2>j8Fjwf*Pin1DH-$7~|%A zs|o$dQ3_jon=nVCQ4rK>K>%aMxNptQu1#Gm1IKj%T-OQ8i&JZ}vzE^p+l6Lpe(Li2 z`sJzlRud+NwIB$B8nTm)g=g@(_D{N@cM&Qq64mJ!aTW4aJzv#Q#Xoq1?-8>=mui9Yb?&@GeT7i!Ajw&QsElq{%bWc!nwq;FE@O@ zXQ(dBa2%7=-Xr;%kA!RJY4jTJERN^Yz^8d1M=FarEu6B$(sC&tNWBf>yWWX`3@f8A2aS@uFHSKy^FzPlaK0riL)a?;10is|VR5*w?{6 zH&{fc(IxZ6PydUIS2ng@>g4X6-^SesGR}Z)g!Pzb z=qxYIm#$A~?xi@%6R-R#a9O;De*wv*X(mXLbzS&)uc{LoWU5?(DTvz5t>tHfC930WU0^Uud-?F?0=Mz|x_BG5nXW8I0EhAC83atKBn3`$@od6;WXC9tG#o zBislcLjzA_2u%l5N?HyVX*Uk!&kD=ZR}42@AFu&192sNz3oE*1iDzBoDRyD#EYe2U zk{yb9?#ryF;;Aq)*fKO{rtPKOCIWP}{SiPCD`we|4zMtF--lz%ykik6jszvv9n+Hf zhCFWg+SaZcnR~Wy0bF74na5gkKhwF?1{FBt@NGhoj~cV`P)qG`O8PdSK%JuWQp28n znBXDiv=;}j=n1e5DuRPOT2pK9W#8hA*{2Ma7yA zBLUl-vBUkDXyQE|_U*Occ3|5qlpvtP7~f_ynQ&k_-}iP{BZX^8H5}}L+#sF1W;*;c-*&Vn=>{!763o8Lf zZU^_#$Rc5YD2A=+H=-xe>kxt-9S|^zhBC)K0Rr`q(VNF^aFT``Kzjic8I0i&`2}ym z+(jT-+^~s4;Xr_kH~ZzZHwSRORjo2Y++|nzGLINBRj3owHwoqM7XZxBG*bu1HFQ%0 z7U%%td7<10xb3euyU(lpKEwbO(0l;juRlQz!{od<`TgEPA0^)x;OkA!n_(zcJ{-hT_-p{4SNA)V1Raa{^}6qalwqq{ zWna%2Kjw@v#*gE^u|MV^K)F7rLDE5Q5yB1;dJYDE&~x?1G&=@K=rg1VHsEjLjF=|3j3Nm2zcbR84N!T{r*Q5qs0LcKU-mn=fSAZ?b*A`^tH zI|3J>H_U_r(HWP+8P3&u*8|~Nl`~v`PzAm1K{cgZ*L9VuO@(7WD%f>h6<4eAX2_F_ zy%;Y>3up~JfF456(|jU1YNh&IcnW5V=mWJqv%x# z#d*|JHK@TBstL(ZLXrbH5EvHNZ@PPF66HZ2^aurnCLcbW3+1;Kp^51|wGJaRv?fd$ znT(a(>%{W`T-r&1=qQYpl)FT1#Djq4BO$j>8*vq^14ohZgE_(Np#Hvn`KbA1U%(y+ zS*b_?m=Y)377*TsWtS0s5Y5ECfPD}n0lV&TQ^1yCPl?gUj-knL`*R+lQhwWjUK+gf zn0dwG5roG=jx8V!>{p*=Vw`MKLRLFSa&s$973vxBJW{O^&m);0Dsyt}Y8AlHA5 z|I+&BP&d_~1dtCtC;{Z5GWGDGha`Y}=%I6afR2i>=t?}V9eZ2^(pgw=WFTf{rb|Dk z9s6tPh+uU!5RS~UN0prYNIHcch{VB6C^KV85+_45d!I%UH{TMP#e6`p5AgHn=jR>M zG)+hBc8;*RBeaW7RKXYe&WdR|j%n%-kE~U6ORb_uVw&p*8jx6>(wE9&5zf(1c>dhu zf?=A*tYa`{^mW}}S*O3n4BgmLF|>^>0AqZS($j%yjwmykY2N9tTJ6IaDYSrA&^hZr zIdAaYmU>mG-h?h+oa_*ofa~n#MWFaAEp#mFX{yc21D8@IGNghLi=*HQjYWm3Wk@}M zp(D8ZMfxA;j1XXBZSBz3kSdq669dFMa|aesKioBR1tG_%PstrzD_+bvqEw@?9s*9A zap_b7-GVLq?HWgb6yZvvkFV}y%c9es01)0ZwHQ3&`)=qn$6t4>eap~j(yi~V z3DpcsZ}oaDpSfXJsf3}+eAEZm=#nN6R8XS!SYf=k z-3G9AZ6UMtvm|OtE7`(e-?0I=Wc@F@dkr^LfiARfh!CR_Rk3~(I^~jE^`y_^-19Qd zGtc9^-QG>o8Kt*oXYxm$w~>O3bI)tHyU+AllRZEd&9zXDHqa$Gjn9w*Qu$KZQZd>M zuqI(_$r7q!hIUHDMF=)iEAWv{OsIyP^`u3qVu9p7+L%OPT&iGSw(lygTq(9yy{@*; zuVTFFaPE1|`KO;g?|2^PPJWB-w(D-s1I%|gE8(}H^$!HlK`F(gR-1zq2%zn@g0=D)N&MfEhvO!G&hoNn=`>6Kg{|@;_F8t%< z7Z5TLx2ML?3y+zUV5Q<%DNLyOSZrLSSkt=g z6BmeSk_#u=T}{LEsu6zyF#I%N?TQWn@Lw3~S2PSi?X`WCa?UeX4~RT1Nuaygqr}k7 zM-AgqQ#VL&G1EI`enMo{?y9GDDyIgu1hU7a2=z zxgr7NinJ4iD3_+yJX%HP(G7GPy~sy%-H-}Re*_vJgf#SPl4S#Bg$!JgXtFY$<9SYD z9p(`?b^o;MjUe1VAw3o8$-$k-BF?VDKO=&x5{Y|9yh7jOgs_LzXJ93IZSP%%2Zfui#s@$H?>^f{;e;HwbM(7G7TLF~>ClRu;tw9mh^(3M& zPJ=Wl$`oN4q3}tWY+b*j<0hMO#?DVa`#o-@(ljxY+YtQu_pX1gLl2S9Mqn74}@@~-`)vqr+3?zt`J~n z&@+s3umzD*>~`)SOXhxAa`F@QtzTY<7ea_gvO&~JL92l-{tn6QH-V2p5 zGAIAD=`=B;a7X%Xk{uE*q)gt4Il!@;HM@)G26_U$2)zz{5`7Q)3H0j-1<%0Zbfy$pt9#2-1mRVTsqfk?MhvCZ=Hf2JNZsqRf#D&3kE48iaO}K`&hn zTpO9OF4ZL2Swo~^WL-Axl|~Z5cBavjiN2#5WP>mww3}og06PG})iT@mecJ}#uZL37 zpQBQSb>D|sN!TM+dFHHY)%v*vKc#d`4PtTx4q%QoR{(tALuryD*MnSn<&!FtMcsF&gEvYz6&rwf#93AYU55_hnzu z|9kY%NmQa(mS9HA2#B$~`V_d=y9sG89VV8l>GIwp(6U&EBnsmk0U%a1VeZz=c`y|M zBWuF^&D_?nCdT&~#;u#536I0?jr1{&!olXk&0F)EffTaa2b=S^ZZ2#FSWUjwpwzf^ z^Iz-TAm|>mouRcW5BGSJX#>6g8w`QXhQ1_nH#nZDAXagNRJVpHdxeWQtKhcHI3iPXW>kXj*6 zV>Vs-@Bbf-S@%gzRzOMYje&l=SnpV9p<`h4lK){n+I0;h=LNPCG+l7eBUJA z`$BcQqR~k1)Wt*_-<*%WUmw|0O1s*1dQ%-0hJ@;`R2ZxMNCOfCq*FN3wuxclvCs06 zFE@V-$1p}i^x^w+z)?X&B3O$ys92rCOZn0`k{m7ehwrIIi`NTVIMaaozU9Qdzkw^1!-Sxl7JfMhkmrxa#aj{@*$24LQfK%5z?ae`#rZEkZ za1df!#|HchD+(zQfIPphnKjN08mancG~KWF?AdM-K?L1yZtm8m8Q)R&13VId#WzVx zY`r$JU4F!?kYiqn8@A{8egLZ+8{**~;s)kFC^bT@ANX`V@G&Mz)#@%cQbP|L5Qd31 zJ^OYhGienI2%vj@-N%9h0nQzrwyN_X4BKySRB_eiu9P8wYY2|R6POGAgzkY&%{%nh zVj+mjeV69%fe;k>Qjw;5>F^%fM1e+-a-@Q{dTB|r#2zl5>*C>lu*Ul;N=7zT3NJp3 zjO%qr0Ep*sa-LsJ+HG|s(wawI?=1Z)cxg1Ns#htj_o@3sbT=Nm31Qd<*l}^avucMS z2w8vOiRD))jMXcq8!t>Tv2OeR7`NM|yN6KFn>X>8Ul;hkS+oV%m0q-)bdw(B0tjot zkAauf0HBhpz25OszLYPOb{(L>(f0p)=`F%1iR0F4DmGg#iK2Rib0O=lOG@7F51Gl# zoeNJyx+eW$*$Sa;*pEKt;(P3SHxO0d@aTS`YIhI$I$!G$64#x z0LMZzxSmkThxT=x$@ZW-m`u+%B25Sf;h;AMX<6hdqG_CTX-oo*R{-WkvQ)_iQZDG6 z2wcrml*@zVfk(E)Q?QUt_}_XMYSgphxK_1ob6BAlAS2OP0w7q*x^0@2c$(nsqopDw z*IZ6Mm*Z~F@0$TZAHLuwfrK~jc7ZW&g-1FAR%_q3;>I7=9;+uz}LXDoap>zh8)OqPxgu~#ulB#b9TFVg)=s6 zj^g`u0IWCeP~VY$;3tME%Fz|{s8*+}7G?oFLpunKO_a5oqxA$>-^}YXb`;QV7lVqs zI|^s;w@_I=%{zN!#8YnF3aA}jiRZhq0<3nv+X!j0rhe>~P@^q|Kl;0hIEv&Z=%wH5 zvXHu`Ff9^T{mEXKehlUM4*?IWygt4bH$n0^9IE0ba1O9_&|{B$Sd_*TG9Pucpsm#p zgRW{9B_UgL`&faT971}dcU%Mx*BGmQ|6;`WLbT92#mXhj*Ir)Gy%QILP_Cqkr@3?2 zBJL-%jqCI~N7Q1!jFJ!Rc}Udy>1#^AbdP&WT>0rJ(vP|o)I{AV zVWx_B;jj#DH}r~4cvCRE(xc#|Uc3n&^>ASUe~y{obLiL>i04f6dk<~<|NrOiU069d z$nM^SuQQ#=JxN9yUSHR#>A?1ZS#7Kv^hJ|DTY%=t$)7+goBTsCZ{0FVYSViN_!x~?MB4{weQbcu5qKui#GDw6Mp>Xoa7 z(h__6g>X=CgKZr^x>dHq&5@%mMrEaLx7N$Q5ry@+Cy)s$%u55Lnc6=Cy|xNTZ~%`FxE8H zoNlxRl`yPZE$R751DZ?KAkwJf!E~cFsH&(%O+)uOmZ@vdlG2}UKy#^I({)Yrd@UXr z#@d?WRZJ)VfC=DmY#8kCHCs0hoE54fO$P^DjSw4afQbj4uB~eT=?cLH0HAtMpC_8& zeb`5@L?1-YqOU;+sk|JSgGjaUfSl24gLMlgI-O;)2!#!iFiq8tb%+=U0KssQ9iyZq z?Ja7(v?jPkc?vI4DL6~9F(hhbNjO>-;{FR`MUz;?xk1s{U6e*q?*4lKna6%zMqx)y z)4J_oFkHv7ne8wJuH#6-`PJ#DURN>17w6ViPPEc~UjSpg6R?L3vyl)MHR_eHQt|6_ z+H@G>9nKhtem~T3eQwR71Z2kOOrw`JFPf$&w>-%OToL+}Ne6@g`AkT0{Z#?)%!#b< zeNfZ1&X}e-n#OF#Oq)}KT8`&koeKQ8)vBCL0jBF9j%2*KSgdN;EYz?k0sK0hHo>%T zHjf10$_&8$12R>G+F8@GspFh;h38!n;d2Wqxcb}c#3AaU6@-GNL6&EOST&0&cM*om zn?Q~HB&JRx=teRnveKM}y+!CH_W^!E`?ME?^3&Qc{4#S1ixhx<5kmHHuX!JSImZ2__i=_0MIHPB`W9nM zj=t|&<{E;c43B_>p9G9Gq?aZ$v$e`}4o5hm5YgD)nP`W7y?g9{hzdinT4GGz(>{WE zT9HDa5L*Pdl*=$Ju-ETCLe$4Pd_cw#Fe^FQ+aZ`+JVWp`S}yr%BDlSW~SF3VHGwXS))*U@ilx8!1Ej1!1r7UK^O+Gb=Uo+TBTB3 z!ucWK9Jf|MC($~(j1Vr3;M$;$tT!a4HJw zW$&UaO1O7Sqb!c|QkQkfn(Xr?u5)rM>mn3a_JA?Ym8Ly}p80n{WWJHacnqWa!Cp%Q z2=0&JhhdEF#xKMLROpd&qHh%pjWjNgz?OxhA1LYL)K(vQ*7+y8v2@xCOK7n0@br)M zVNo6{9D5$;t7juKiga@(beTWX2{lt)KYRANqI%Sx@tGUWn0geM(b-kbJuk-UzE4Oy z(&?)R##o(Y$>KXjC7s!)r1y+{j{X(}i5S=3gh7GCdNT}9q>oK*P! z&vsXP*F()D{x{w+jFE}vaN6v8G){}=mK3LqPvIWQ@T||w(|A~?aNRPghjDe6%3|no zV;7lX$-B)z6+9bg`cu00Wz#&fj~Zqz!9&Yue8y`&)%5&@meo65$?BIojG3Jh{6_r%-d*)HV_K> zfOHJhF{@urM>!sGGItD$wzqNvr1B4Yja;8rNH+SyT9?)Pi!#|pY_ zI%LN(L>64$pcS)E%&(#x-9X6bG9~!ghERix&}=H0fJZaIC=7GnBxVw&XTa%TQ8!BMZt?@%km_^1~NhZcPSAuqVr>dIh<^;12r z2xQ(s??JmX6u?As({_fvv^z=M>}|+i>na@QS0z-Yp~p(hndv2jMuQ{tT$xD4v?Sqr zL;m)q7ppGQl@J#V*b!;xoLYz6Rc7-hT~T+#jUQG#uTY+J;66MT=k?4wNTM!6L0l#= z&C_@e;&LWWsiBdin`(aXZxL-!^b?QneCKa%y=!&p^Pm6x=O0Z+p2o;f)@;S*~CLR!>M+ujCY2xS!-MQ)Nsv2C%RJ~-@X zBE3(pD!2cDu-B0Z;QWqlb2AcrdpK`|aY$`S5Tj{E7Q|$KT$3f@L#_Khe5c{y1z%jG zBC2rXp7rZXk{KJ zrTPro7s4gprSB*Vp5ruB48Vn-x!ke$ z;{jGHE4ln``I|>S8?u_=*#1BGwqw*RAI4buh9f!)CyOZ+Ic}X(>N?ShnVD8oVgP@J z*OqKr)>+Tvd@Wsw9G6mFcb!O06(<)u!ZCb>Ct0Vn4RYDEeA{ts-!hjaXW*kXPl8b_ zZvfce`mHv=#&ThRCu<)~)A~#QLr(riLW2d9{|vLigp;2!au>i#PDdZ4S99#QA7E?) z*cczY-F9l8g#MEqlgXV9rc=}DDhyEVKi-ac9JnqX7Tccpt`9rz>3)hQ`<+fZLvkd51n*%D z-!Fp$Zr`0l8M=TsmzFwI7$p!=$iO!?P5CR3Ak#Z#LLrEL*OT!MqqPZ6WVNgI7*J}3Csvd{c8~sH1 zbi*W`RKj%}-+HQiuwAL}WNj^RtCjYHPFvkfTgUR!1zQ>N__AwA*Y16PFT^fmtdTDc=B9}Kp%sB#&8IS?H+mUy=_ne zk`m_Wltn(}J>U`nae|1l)`0*(3^#iPQ)xnHir?29=)>TrGfIaq~nm)jLUnLk+v_V#p~n*7O!TVCUCfZCRK z8(!<-C%KtoeQ!E|lY+q}`xPX+3nH8J5MBiX%h4C4jy#0GoARb+lE{nlseI@SlPNfw z?84z>H_PBK%O<;UFxlOMql!37Rs}KdcE1OqvKLE@kl0a^oz+d1>M$~yu>zoh>q191 zC?c)V;fD@i>n5~Z>x{;Ls?9FT$LpaP=ebJg)|m$GbXdU)1Xfm_&ic<_4FH2dsAgEY zuIrRwsK-f=t!%BWwlXQ+V0O{)-Z)Xj>0<1_l{~h6cDzlKTdt~yvOfqxc=>H)i<(Gv z9b*iXQp*AWVtL#TLx}rZTv(o|eU+QKpEor)NAIKN8hSb`zGLL`#igIXzRQW` zwuybmYjK_MT~{OAA^Z)~0q!Dtqvtsm17Mcpd9L6XbK$;)F^;jOb51&%%ZXKcTU&&? zT8D5~)3H{Wi|}+m1xDyG^jdD1PY^_1RVtBJm7ffm0LT|R#`$W`i&dU1)<8=8)s0Vv zQi=Trj0#`a7oNP7}B{f{c(t?OZaz?B) z=frZ!DnP$coSj-*BLM&HO|pO;w*RZXQ+GL!E-XbMb|5*_@53q=!X&X4|Mkqgve}Lm z?q#(O;d%Ta@}mnYWKeR{KP4`q$I&Z?kaW;1LDC^FI)3`S1mA5@s=3u_r&F!E8l_gZ z-|zOg=|6+MQ!@;!;d!3duna>J;ycH@duPo~p%J=(uAyD@+Q^5&R%CO(8O$)hwH_vM zK1`x~m_(e!O^t z0$Gz~{}A#hLMlbK{o;&D{*K4-}%1kl8 zv^4)qpFaaGW5(Qu?|FX1WenT)3vY`$|Db25+F(b;Dx&ezj;!I3E{!8V{>9!QhsJg< zdL`wZc)co+ZpTIkz&5JP!#(Fs=-Y0ctB@?O$c@N!qtk4VMae2AGnki}kdky2@2u*}fq_~HI*!q?ZUSTyv7@+X88FPtaDcMN0l+kKc+4Qlv` zB3GoSgvN_K=XvPv%~A0{;m2VNL5O>{EZW#7&Z6WzkwObhg|x9XDHj;_(OFH4?AD0dx4Z+sWgr*y~WXSn9+j?FotnjtwQT1DzQC7j!i?rHqY z^A;PM_nfED>uy9{jOt!AICjTIC}3={7C{ZX2gEAM>nVTxG1GjE8bq6()`&r2=Vgf8 zHqG0_AidhWzbVrh(=$!esG-fxX$I9a&C>PX>C7;bbRb0YwON1`&_(nFdJTF%S~*?h z1D%LN3GEIK0B*yM3PuD@IxRfQ)nV+vz~tOrq(w;|ikvrl!-{N`##|K-n~ShI2G1s_ zs14}}ny0%cix?;)pW#y?429rsqv>0iQhRcE=D~-qU~J&ct*vrM>8$6Q zbk?#r;Qe$P)7+J=ZEiiVav{EyIj&AERVN;4Nzu9otH*9N`n!YznNl|WXZDIrCD-S(* z=F}!6xg`x`|9pMY;oWYxUS}579hb$)bg$Rc2(g|=0UMgWLNtRA?em(xYW&^d7AAx} zp8_@}FMj?3xo9u!R>`geJ2Nb>rVJqvWkZcl<08%z+{bXBn2cMsD^~1sW-npGz+TS) ztT!m=y58;Tx=!mlV58?9rWg@}UZ z0BO0jU@`wTyif0XVyR4Ui$<^RaM_0;zv+fbj390aS9$Dup&_+aIV9hCgl!`2?FN2V3J7@ z%}NDZTaK(WIKU!T4Tf}UCtnc4diC{{WnB{t-yaQC6`C%W`})YhNrKJM8Ecsb=?7*A z(N1bHNmV6g(N6LKu`7#?Hj~eH1&vbB%;Xo<&`U@`y4i7BL#VT#MqU_QZ*kuiwGlTs-oXg{j7zQ_n$B+OX5PC+P8m7<3hGl zx1l~~LOOX2A~@^&oNGgI`!`#WOKvKw3fq}X8XlHqO_C%bwhaEi zKbMCj$4u;E4kvICH{s(6K&s*_8cR>18ln+bRUM=E?Ea6`+nnSD;X;!clQpsHjm9}8jHrk_ zT#{vpe{CSLeEd2x`&YmSs#lDc;*0oyd9zY+lHY-o5iYWy6AH%gs|h_$-~PJK z5rt%}hpLe8%*&j_ISFuOS$vdrD?4Qr&&M0Fi*Ep_r4RRrh?rfm16oDXwJFCgRNXA? z0me%RZjVd3VlG-(+9lB;e86y#OGfgEan7TWWb0tuM#o^yi=|fpjJN{z0L@%&Deyh% zvq-S5xF$)HQ|_or@K|U}>IfUk!(&;_DiR;AGPDP&R_;~mIO&=iIJsB!ZMo69J(pA~4k1MMziY(0#h^BF05!zU?7CUUK`n(Ek?6 z?8V-fstRJ&otaLn%WBnJ!kiU*TbzpKBDMUtK~gfSa=7d}=W9V#%<6i-hnz>_w#a~c zMKbcYLPzYWe)>BLg_y74!C}i>WMLye`kd-Yi>qXVqB;H=ot7v*vs*z|eA5kD%D(CVOBL%EWOJ2&CkaXJOJfJHTqYBwNUNUm0 z`zG*x*i90ucb0e*s*A6Yv|E&Bw?!%Id(453_0>tIkO7zz$*9L=G=WsGZkAjN3vxyJ zME?-AEoF*_B^{S2t7?$OV!V-3X@tgNv^N}<@(wT@?tM}@sFf+}c!e64&csfnKpHWy z+0agm(%7?Y5_>icab1rqP#&gH*X_BL^(u`?y{3s*k&BV?==1sG?@SU!7K=EJI((q& z)#_7Q9XOH55)Vi~I)l;0a8U?)b?yh%yuKPq1kiF=bc1$eM>N8Gr4Jg~ZAH0vM|8u` zBTxX>wD*MOy{3=8S2wND5W(rFq%Y;yf2bq4FJ6r!|M?(>1zTvxU-tlk*aX9+ueu8+ zjyzCQ|G!K3uVa@D#|idD2u9;0hBfTUQYjo4__$8HNpe=0E7;XzpC*Vj7$gh=; zj7f@VDiUK#vgh@G|0f45poPznljK?CYZP>WZqe7#e@P?Kj`Tk1d-7TNdGfCOD`v1; z>^D5dxA~j+U&M&GC|)JLqChDrb>%_I+ms)uIdwz*h^A>z(cY&0ET9Dz0^5P71$Kjx z;Mw3O^j`fj`X7v!7=JN`%twWk&;vq03*XNgvwqQ0@Az7zH?k4AC%O}TQ|xT)efEI; zgFVmcJl>^r-Rk;GT#H|f|EPPd`%`_iuip0-=e+Zu{>LSrmiR%kmV8?B7kgil+MBvp z>NBq6*4=y38w0U{FAUZOpEDF3ddcv@@Eb-VBM%z+$LJF>G_#fYUiLoOcjd0RM2 zW>;t5HFtXMd-J#Ef3R?I;Ta2WShN;DwRC*xOUsqz*VKAzPua)!E$#c<{=)uG9hf@s zse_e+A31dR(AMFB!=F3yu%o48^w^7zUqAl86E8Tqb86@G$my@0x##R-&b{Y+>%!Q@ z(8V`hdd1~$T=~=0$6dRAonL>^4d=$6>d#v_xH`1zdy4lq06>fwAb1z*QeAiwqk9h! z#POs3HVT19w@K*7YocH_3qQwL5{RqqqVEbLoob7FF;09W7(7+0s*n)#L zyi}*>2DY#Y2R@o;Vgu`Fz()pW;k`?fdgDH)uZk)P$RLM2#!*HQc@$8Fg8|gixjr1s zzZSQRCN^JLeCLt!C*Yuh3`)TTjlugWzRX-f4J~-^Fp87#aPt}hK37Kt&IiR8w)oC= zy7xv4jd$~p`LEhu!Qj0Dy!H^DtDN%+qodb1^%{Km*hU)@$Rdlo9yNaBwX;l#tcuO& Lgti_09|HpbvH4-J literal 0 HcmV?d00001 diff --git a/latest-tag/deps/font-awesome-6.4.2/webfonts/fa-solid-900.ttf b/latest-tag/deps/font-awesome-6.4.2/webfonts/fa-solid-900.ttf new file mode 100644 index 0000000000000000000000000000000000000000..e479fb29349a12b564516c40aa529276c6c5e12d GIT binary patch literal 394668 zcmeFadz@9{`~QF4_r2B}n%Re`In|u@G}E4rI?q%l?Fb=>LP7@d4p9gp*(w#{9g+|R zMG-=X!XShYLI^X{OhVXu&pu2^eP7po?>&d!-oMZH_xtDj$L~8mp8IvJ`+Qp0y4Tuk zCK5!{f&8S%bISO>1I|C`f|H0ucZ615c=`D=zVqCgPLwl>X!P8FO}qZ$n&{Oth^|>g z_J~naFFJpU*I!nNv}-~6R0LQ@Ca;Fw07|D`K66&^olav>-Xfx`P1B}dc)l|?Z5z>f zTX0-+`T4VE*b7uw#HS#==Zf<$zbJpgdF5oU-%6x@pE3RFnZ9Sg-B0$0O+;hgi)I`COnJd z(2lMQ>Tv0LkPgT0mhw$n(}uXM+g=kd&!^M>XMZ@}Pd@a`lPH&!^R`pOBg`Xxi1VAc zwzeKC4)?W*gWYz_bUuGPKWj}JI}uK;`E}oLtn>L=%WlbM;yf}D2Sr*>JiX0JEYYV# ze-g)9{qZtcIW6nq_}YZA-4=g*{3$+me#DbbpVCqvr$?AxTfR2wI2>(l2f}nd6Zem8 zNsNV7zb+%uM~|+X>)>!|Gj2H6<#M?1zmSN7a@o3kqEzXN*J0Xf+Qec11cKvjF!BDD zysdrVeE$^AVLsFw=dkvhzWH0)MH1S5bi-d{Uf+)=x`z*=ht=nbXsn2QA_)L@pBewTI%spX^Wi*r~gt9mzhXo z)U?aY3EL)~4ol}V$MLyjzoFs&wUEfmX%qO{*gi_;^4s*yj60{HIBfs#&^A4pb;{+U zeQpWW_*=`=^FZ%cyB-o4z(C^0u@so=*Bqx_0qAUku_jWE5@Ta9w_j@_FIc@wpt9s9(qXOj=zh z$JNB^LU-9W|ydT?Xg(p*;x* zHkmPR2}4}GKC{oL!THW%KH~$?Ud%g={RVBv$Et0(j;DA!j4#LOc-Sc42b;q@JkFry zIMKI6J@Gt=Hnl3(?~9iS)%E$P6VfNzt;47WaTLc#Wo&cV@%Fj?Yx$hV*V1NgdkS`Y z?XG{Em*-Ezb{y$5^(BtAB214lr(xS4PXpC9x6P0YWy~?Pr1K!1C(##OhOZ^ArH`V< zgz0{AydUK!!eGbyZQ9K76sN6?FwT?6r~BtO?ffUZr3~cL^_sl8t!>MRr%TmoP@X=H zm)owLTn?{`nwGZdvJgfZzlmpCr-8Dq%Y*7LfBcwqyY-qy+j(tN#p`X|W0GEjJT^SW zywBEaNRJhl16umwi`Ugg^;}RqJf5~~-`b5?P3s)S+nea8N7u*YpbW%upEdD5@_EAT zN|HVu&uKI*V_=Rw(ie~SAErK(hp@xO$0L35auCNh*K;`S>Y{kLP+iu4s__w|eT5lI zlZX35($|`wk9quB%4gdg^IVwzQEPl6oz7R&CLC$ojzeP1r4Qj8hdFHvhwHIGT)a%} z3)+^^wl3HyhaKy7bNV=X%#cRlIN@LP{hrRp^^vaA*HRAZZd)I>Q~GpUb(^(aW5N<` zMS2g1A66!DSiGNR{rGiVi8@tFozy1W*v@#n66>zDY?LVz_;tPx+zzBqTnD-R9ov;n z@jQOBztZjYQHItUT}J!%p*%eXT<&4^VaF23Lf7Ss*OAb8-j;Uou}_!h;IjVA=hqUo9qdt9Xo+EIaIc`}ui8{0*8u6|7J*4xtFYkXh z%>SQxbsv0f!rR*akiK0%INleB+LQr1-ge#2`1o)iOh5cEKs?;^B_5{B_y4DFMfxFa z%ap!&y_}D9Ssv+YEfc;hBOcE2X77Y_y8MK;?jsXrB-WNkr}M|tC)DK2ZdV3}`QrFd zKig0ZY(CcgOoWk%Yu6VJV|;Nnb{>w6|85)ZH`fkbKBx1?^FZ}n)@b!`c)RhXcICId z=C-so(FUE*r_&d;YdgwBn$|X%W7Bq&kF*}$FU}i}!*P6B#^GtmfT2<41X*rCzoHek`Cssj+kC z;E3IMDYOF-j;-8Dc*0zbgsGyMim4m*qMq(zB#j z$pa;ymHbljYsv2=f0P7Eno62W_LclqaGoN-ZQcIrcEC$s;ZY8v^FHoEIXS^?Z-|)Wa z-RynO`@Z)l?=Rk8y)o~8@84y(;VHAq?6TCd4rOU&>1D36jIxepoys!HvdXf{3d)Mh z`j?F=JFaX@+1Rr2WfRIyFFUhrO4)s7%gXNWF{sDjUXEUQz4CjN_8M24RO_hiSUa-z z+S&!R%WEI3T~oWZc75%uwHs?ctKCuiQ*A?C|GLULU)^bS7uH=;cX{27b+^|2yY8X7 z)phIYHq~ve+fw&s-B)$r)cs!9R2Qu~Sf5(owZ2dN@cP^8SJb~+zp?(k`j6^At>0b$ z+pfZ0&+mG1SEQk`;n;@J4bvK~Zn&=D#)c&gOB+@-Jln9Y;iZPH4L>&QZuqU?_l7?j zRb#itzKy3ePHddsIJ0qK|-kQK-dbPjY0 zq7g!d!Kd?OTNZ_%+;5QP#f44Xb3b0b_f0lgaeU4G!P3M z3?>Itf@#6@pfi{e>=?`pW(D(u1;N5#*I-GoEZ9BRBiJ)|L~w9$Sn#Oealzw*V}lcd zrw7jpP7YoaoE4lGTok-3xFmRQ@V?-(;Qhe|f)59u3$6>k5PT`PG5BHd)8LNax54j% z-v@sT?hft^MuV~7zTp1g!H@_ghtfiYp+2GV(9qBcp%X)6L*qi{hNgwC3(X4M5V|om zFLXy}ap=y_U7@=}_k`{XJs4UMdL*4&D;rpBhLv1ek>#a@ZM9(yBJ6MHB2UhJdTr?G9ZFJj-tcE|R{ z{){!nBC%NPuV&et+}xo#tvS8f(d=&S*qq&*+g#Axv$=2cQOzedk7*v)Jid8y^M%cq zH(%d;Q}f*Bh0S+2-{1Up^Va5Xn!jysZ2q(P(4j-{+{IKvJ*gj6{$CIMf6znQ+Cz`1 zJ*)QC+WY^(L+^rz9@@@B&xeP8@c+R>7ypNc{-ni2zYzD($7>ILZNm+5552nKd3flT z8$NBQZP>#ey5Y~p5ckl{L4t=)g@<+o-Ej|{t39*_9=bH{p?d}U1uKHXgFblZ(ZO->&}RhC4ps%P z3C<1Psy+1GEgpJB@EPr)Ukq*t)&xIk@z6ViwZUJthi-<4{wqWwE7T#B6Dkf33=IkS z;Gs{3hdvb^`bv1{>)UzgB`qHM;ZSwxsnGiW@X&9=Lw^w31`qvxi-!(|BHBaCrqmV> zUC}gAd*}Z-i^KhyDZ}`s?tI;rj6I@cu{^Jaiv;=mGH174XnUNA8V08d({6 zI`UHFmB{Onw;~@zK8b9Ld*~nAc<2L%dFaw;kLVHb&_}lO(Bt8u&xMDc61}9wL(hud z2oHTrbRj(S@@RG3L%$OD(C;4Rp?`?hYY)9Q8ek6{JrqlWht7<9=>oY!jm@_-&(|KhroD$gRD0;BL*F0z=FnI0)&KYB z|Ii3{dU?8eii;veKNf8(swsM_=!K%^i&hssS@Z-RX)G+drD#skfTCVS-HURHl8a>F zp~6t%uZ4Am-xPjaxViAP!WRplFI-i)vhaz*y9#eBTv+HYyteSX!qW?nEBLG6mx9j< zwidiw@K(Va1y2?{UN9rSHvgymhx5}GjDI+Z+XAu?aBK&Z+Bi}UPIoly!yP_yl?Zq%KJQH0a3=SV1C9te9i^8 z@Bn7q4CZ9a#^(+Ccvi-BTCd5NiLk4|RT)=i%*ePxhfc%sWf_-dT#_+WA77ktVa5dr zJ3pf;V|>PG8Dld}&KScn2sK6ih2KX(~oJG=I~ z_PJuNDCcr*cWrfTa8hHPki4RpBak4RrN)^>ZEJ>gg(Td0pLHU0ublB3Gd+AE|R)Ij(G1Cs#*ThRf~3 z$T$x;W6maL&>3+4>HNdF$JyYlch)+8M9dcF8~EG=HiFl|Yt9YMSDmjo*E?TwzUX|x zxz72#^Ev0U&Na@boKHGeIafL#cUC(ec0T01%Xx?MHs>7YZ0B{(tDILlr#q)PFL7Sz zta6^?oaCJ7Ji~dK^Hk?J=UC^7&f}fOIY&89)8%wJ)19eK<&;k0B*!7gLC0T?W=GT!afBQJ$1jdOj@^!(j_(~`JHB#! z=Gf}^!12Cgv*R_#tB#i)FFKxeJndNRSmk)a@u1@Y$Ni3Fj=LO-9d|fxcPw<=;_y3e zbX@P4<+#pqjboWpSo$C7yVC2^e@g!`{j2nC>8sNp zNne_NFH!oObp1A+DFe?u*8ac$msFW_RMI&~nMw1L#wEG!r;~QtJCi!%AODmmd6G(# zd`a2ZU{O+T(qwys{kr{%y&>ra{F`fkYBOt-*4mtFh+S{rW#5%lZC_>2x98Y1?b-Gf zNn?__+E?J;Y4+}(y`mf9;& z!h`rX+CC2T&a{6_8iq1@+L=kY_MW8PN!fPDwooG318kX;j@Hh$tL@!(z4f?Njen2W z4fbB^MD)>b-DBTiooJ1=23Z5FIo1$s1KP0&y_|`Xms-0mc&PvOW8)3X|5HC|+y7Lv zh4(?#bDYYm2JnjL{ge3YqQjQJ%CDXdMQ75L^6Xb+F(A6nvEVQR~%JaSP{r z+aK#5wLsm@A=V)Eh1#htAM&o&R;9X>OHl*(NKL`>zVE0$2;EK({(tGcNSq@AkFgb5WK$AlP)F)SnRxd(n{p@@Z!~wI zJj$m6Dx@Ov;8{plJPj$uGixuEQFrQrCnLS6H}#<-s4xAS9uc>R+r=GXiMU%V70bl^ zVuh#{Pm5>7TCqvIE#49Dip}CZ@xJ&#d?-E=AB#`LXJVV!F1`|9i*LmDqF(G4d&Tb} zEFzL*vdoe>GEbJsKJp0JPY#ylvO*pyhs$H+D0!?LEl-dq%9G?Na-2L(PLOBH>GB$R zoxDNbBKmvypU?vf3% zQSO#M%RTZJ`K$a*?v;PbLkh2ZD_eC?X)0YgRF=w7xvHBgQ{7b$)l>CWL)0*Jq^eX$ zDW5t{jZ>$p)6{r1L7lG7ROhJ4>O6Icny#)^*Qx8(4eCZUTivAQsoT^twMKoXYE_-8 zSG!b$YE-+`Z|YC0lT~G%Z{2JywU$|rSSzhn);jA|>mBP|>wW7(Ypb=*`qrwqezl_3 z-*&oPYJ2TIxIzrFD=3}UK|jl^24$XI@$)O3I-mq}0}}KEy?_n+0qi&_8B~HvAOoBq zCmRZ1qRD|?7$+ABKcndk#ST!@1lVX( z;N2$E74W{B;XYwEswsuSTWGkS*hgx-Q1^ksC4Kn2Kgowds0Ro z3;o6*?uNz;YB!MwcL0p|9*Xg0Y0zy3u?dPY8J+$BY``|3N{n0%6$Y6FMf(_g02JfNSbsw^3=-#um(u~e6k1@A z$xx5M!q|ARUKksdcuNd6&I@lhgY^g$W6xL^LvL?`wGN7Pz*w(B`x-2azjuhiS_Q>< zz{m+u+}kj67u08vb?vK~6gpst6WZQy;B_Z)+|33{$UT?d_P;2oRyJcGIsdXYig z2EEuIzlUCBkXSQb&I7c~d4RU(zy`c-y?%q@w%uY-jnKIU)c~DmP~4aK22~5a)u6b) zIL{b49g6kA$U{)98%A*0Lk8<+XthCby^k4WKj`BI-j8~pFvy>wD-CiFl*b)NZrhUv ziF3mHltFU);eQy(YhaB*{sw*8!0Xe?V+6GQB5WY{LfL|AWNX!CSY+qw*#o}pfQ6DpYPpou(m?~HYl{Sj0`ddDh=AUU;}O2umPV7 zWvK>-rnEP#!^)uNrX z@QZ~0)jnt-PP@jy`>NWt209zM-azmrwXYiJTNp+Xy;mI{1KxktU2fpLSKW;U-h0)} z2k0~Jz3TpL;Qdw|`djx9!motlJZ5;GRkzN-d#t)m2Ht1YZ8i|bv2Kfj_gHmb8hDRY z_mzS7S#{qSc#l>0yMee}O$M3`jT-1C=s^R`A*xR`(9KY+^ZKr6n;+W8Ky#tP4KxpW zn}N{R`V|I3pX)J43}L?OF-Ht7guZ8>MbM87#ASQ}K1CX=v3jgsh89ENFBrO$Xjh?u z?t(sVz+DvWdI7wMG)tfn1KopPrp0_RbT9N+11*If4@M*WJ}Bmlq5Gkj1BS3J8!*-l z*TH@Oig9ef_|Zd9)WOihP%aD5BhXa_dK8NJYM&<%DIv<&oxPv{6808W9O z2R#)`gpKvyh;yuQI&7S)jaZ9~Sa;$~DEi)rxe{kXF*l9>hJ88oZh*BbkggGP-H0&| zbD-7WG1z`6`qTI{Y^;;U=M4gDqj9}K+)5Nc69e%3VgVFwXV|v~EMOxXbq10Q>~rwA zk^)YI-vM=lPO$HVW*XE{&@6+%JO;82auGBKbVhp2X`qXNpDzV47K}O_nr{#fK?@A> z8fc+`_sIdA3jx$4(C2{9z}J@m#x5`l;TxgH8stq-jstxC3LFo{BF$USao}v&A3)DB z2+V)rT!Z)wI@ut;gjRuzk$yY$Uk3L1fvE=e`GHFed<_a*3a&u)vUdAxwcJO_9VfQUeO4uGB)o&zA@KLU4xyI{wloFDnbJ}A%4eX#$6 zE;I0cIKXoQ#M4lo4m6k2Q0YpfpjE`%QeZ7^8e#zuqK4UHqhuzv=BAbme*03b{bf%3Qj zRSAt6B##-71&~idn*sVKhZA9_DVPi!YbBUs;3tj2RFH=7G0=1aUvq;F;6(T-Q1mmH z0sB;FM}xWqnrRSwp;-p;J2V&MBmH=2fk9wggM|h;5$XXLCwUgM#K3bM#CQeE5YBDu zZs0vhu!libL3f!=SB)zAkF66-a{c^*cZ$Dz-F z=U}gdt~1Eh&=(8}>pA!$cnN9NKsSJmusL51_z*UHXYdp7DeTvvI}8%(g5Mgn{T*z; zPqc&I8}$0w3HwKczX7cUSkscr{uN-o$Pb`VgZu~@GibXR_CBQf7`orUf8Qba7dVJ; zv@1ja^DjS#S|Azrm(UI%4fZ$C98d@w?F|)!KCtVc13@|LKcGXvP}nFim~FE z^5#RU0oIGcc!izDRsuN$GgX#^%S_yrM@IFv(H|ALlhHf_~oY$f6!A^u@E<=q5Ugx2o z4eTdFzZ%qV=!t}~#3=aErU3@A7Fq$Y{`k3B(?oDK?9-s<8q^ukDuX%`dI7i)X*lgI z28ngtG}j=xyaiw((oBLbF|eO+T4hjN_M6}>q?ruGm@|s&dC#EEhkj^KT<0g?Go(S? zO`jXo#n2xN>R-@WgPIC$H1IW|3FFN0w{cDT!C$a1gZ^!>mO>91)a69sB!jvF>NfEF zhPxWn3}~rA;XDg>Gw}KhV{F5NQ2td=j0>Z#fnrUBhrzxUigm#7_i>ya>MMiUz5(8Z{T#FgyaRh3bhANmzV{7kJ@gasDblv=LBlrn6$_>|pU9h?A-3Glj_7g=?5svyIS)do}FQD)*40VL|0sRpE3$(w1Izb13 z!3h5yT5g~WD9*Qt58*-RNCW%Z$kE_#gk!Bo?gbCS4W9(Xm_{CleFk(TcnbC;D8?xA z0_@39^gn`rTbTRED**koF#i#ZUj$=kT?Ty%yayZSMdSnUG3;xh7^?`z%EG#hYy;?@ zbrbX(gXM>QXRvO8?lf3)p%}vm#?Zo=jMNz{tjS1&!CCMaB zn@F?bzv9J!J zIIkJ&MJVQ#v0j4C0yxJltdA(>I650P)=2bbgN3ycy#>ri_-jziQxtPzy#ZZjuxg;o z0mj368_NAe|17MhD7PC}A3(X?z}f=kwgT%zDA)HY?2n)u4g7pH`liA91j=;)3u`g@ zF4zqFGbon@tk0o*3@ohCC?5msOK2QxJM6Cv7V9?#>ucz@28+Xf09d=$4ro2Vxo=^O zM|T@6tnug`gT-n0g5Qw_d7=Rjf?W&UZ?KR*iZ#qw4bVg8D?FnnBP8 zfc0k&f$lZfJfA$@z^;UbKoe}taV%o6M?iUwfz5NiA7BpckwneXU}OH9ZIF!cQBceQ z!_S?X(+vDfvKjNi=x_&YU}L>By8!0I9u4hiuup_y{WfDB?317vcg8*$T41opLNRvD zJrRBiw6DP)4?W6YPk?e+z&;&1#$cZT9cQr5gmNBWPlRF}GWJ=}3k~)p=;a0*>$UlM zgMAM4M!;+DTqxFE^IX_f(D`5?>3F!SA-q(pH?*9WA{|lgxXhofndCcC`khKT;o`$Tmpzmu)@Vsx) z@cv&keWW3a%lt$`@<-@44Pl=zn!ePKnh)Ks;cJa(`dUNQ_0VrMguRDo`d-6(1JP8g z;qzKF)ob`!pTIpA!+UNC!sfL_?j-lehpbR^fnEtYUo`W!o97){R+d^1rf&F1AcBS!VhYU z{SfSj;^8Y`7b42i#?IN$ib4A&G9uGNrS1+CMN-2;j>%=c`P z$7;8R?|DTypyB6)A{^9^;x!W0khl-ZeTbnhj{i$T@ZtOznIX{#6&g}!K`jkG=Mj+< z4GGM1q=SaUN@$vfp8<+Ux`rgiGUCwiU&Iv=%o!jNf_Bu9cod3p1pGaMh-7LA-yI0t zZ!&~un2cW$RG`WS1xd$ z${722*vDgzxL+q|$a)!iqK4G7P|OV=>v`xH4c}jg2<8%ywHb;v4G4Q_5gD)H@3TaN z#~Rm6p1a5u8of3mSHiv$>3@Y@qalek61h%8*c*$;jT*g9A~(V2Yx+6Rxf=dHPDFUD z7hyax&ym|Tq_|IaXvnI9F4mCz6ndYA>|>$JG^BW*x&JFs&TUYvKfrHUi^ysX;TP?*v8UTMfVC zAtJwM2;U`%2+zT|kmZ7Q)sXcss8>VQKxi)wSy+QnoI8MjKS4yX&Hz7e5qO5c zkaZQ5^9+PN0E%%3{H#VqxgAGiUT_}Z83IGr+0a`wr2c>|(C~K(B6_=q@V&E$-l-vp zIgBpR@UtBe#hd|>Jci3OB%g)euOU2p5x8e(NUnuGs3E)uC88@dg!@VneN01m|3pL| z*O1%^eNw~ELqzmx4XK}@SoeU$mryQuE!G(P81D5MvXY?dG(`QOFKS3)Jw~yP0q^rg z^c4+R4(J;ivIj%4&H?`xhKRncA;sgmRYNie{ZvC1&+|47SsS5WXh{49t<#X?Io+ip z@c@+LenUH_K=*3Mx)2)B@co^L{-q%c;}omaXx|t^nZVffu=yHv8WiONV>iHVh}(^@ z`CecibhpOX7{}Pp@$fycvG3Apa1O?PiQB)z#yP0N(dHQ1%=SKv7tnrbpA)t-ZfC>J zMw-8&T{MJu%)~ydc|dqBD)yCV$Ql9dp&`o$9jPG;=fOU#RlxVrV&5?uvT<(h!@328 z??=SGu^LiWL$Rg-+3*SbFt32@3@Fw;Ae+m>`2$E{4)Ep!-;KOAP>d%aiMif~@dTuvfMVVO zDQ?T}8j^FMe`-ix0>yd&WTinv8WNbdeN7sY!=U>#q`rY-%>cr48?oPbwjbv*;AdZAKgI-*rJy)R06$w6``^~+XQ3E9{=rp_!svA~QVapx zkqaZsPho7t*$0S{=HsRRL7)bIs&Fd)91*-_>J0oR098b3DF9(<*pa1I;x_+pXFj3&y( zaqd>4&OT60)TKLEk6#$b2WVG5@)bY}(Y~TFM4rWXs~hP{`r)n1wM5-#tjZ3TZ|Kh>ksgKko0x|94PJbP~!KgNT#25{;bzb`qU}a>gwsI(0VDX^Q~z zj9)=CVKrU|oI!NP61)tE{1Y>Y&O-d8ZA9l-MCXnpnvD4Kpy%x&s@jfUuBgOId!=9t zUf3H1koF>!b@4R(0tMoyqEDAp;YB?+UeH5%m#xGvMvNi49CcrTG}C9{7aw|q^>_&n zTr~wR;7tH1e`W)IxdHLluEQ@fRO1&I7UP!}P~HtSM6*+fZbH5}=|negCGsQw7C+J4 zT(F&J-V&nuLjc;dU>ecF8CY6-iEi6~7u(hn-GTZSk0H8qE74tHqJJZ93F^BC`R+yC z_n{5T5Vsuqz$T&x(Z3agh#rRhXg{KA=;LU|6VO%b@lx43qNlbItzJv?^ep_M#7?4T zr{WhR!1IV-hxiu|{vz7+l8po5cSp95bX*OHK45Bvxt61xqB+XI-*~yh<-!5-`zxiqVB*s zc=G^tH6cxSBEI238>1lB4^M)SXCK1%k0$yH`3@{4`Wtl}1c%m=pe-bDUJF@Gg73(a z;Qf-}tx|BDx`{*wTs6|xk#N9MI;W9vLEX>{4_=+)G7rlZ^$ zYe`&%_FN6U2IXB-OXAwCB(6hWW+Bb>(@5Mfn#7IBH+v6WAVfVkqYnQNu$aUx$a6~= zFA%OIF>faE%Y$P6B5dINcxaS@8$fW|3KF+x;$ejc52VnZJCXLTEhLuA#s<6^F9#xS zDe^Asjt4cHuvu9`;z1m*$j4@V2Z={84%O+P0S~Sa|2XnI0ahY>)d~_%4#I;OKM0dp z4P8?T(6%+rxN)3MVl9r>t|sy96cW#&J zW!Ntx>=m@*)qW&!?upk>=j*)z>fKlc)|1#&i3c4>|K@hQFjzyP2IEo_Ao2DF67Qg{ zcTmr}Xv5}wfO_7WK;nI*|6nwUE!|0ch%_JWA@LFF{kVa|R@k5HCGqJr5}zUdGvxj3 z0A4^@OyYC2<%=bt8Jp1A0BN`P2B_<+6oB}z5cf6O_BHDI265l`N$ki6%Sn9e16xRZ zw+=6#Bkj&wJX}OMKcJi+(XJnBa1)I@KcT$Zt#}a;@%5zu_O5Lt8fM`k!4TXiq5VIP z0jOioR1&}JAn_~G{Dv}q3*bg=6j+Lzv}q*%K>9ym|CtHa;)O=EA=nR~&Jg;>Ph|9+ zWixi3C?i&d%R18RL;C$l_m>+W?SXUn<`=BMONf^TlBt;_I}8G~B-3H1SK+}i)PX!M4_-_}9`{~6T$)d^Bk1G?JMf?Z zcII}HS$>k)EAfjC4S3*_3&ObBodu94Zzsw8tt1Q50qQJR3~*dn3D$zWB#U~3)g(Pr zNEQzQi}BLqX55f$BUxHavfBibUbMrD^2(Nw?2hoB1dyi}%ISqN`jnD9VkXJHQ}J@+ zXp;R=W`C41;2aPjIdBmk#G#Bq>q!o_z(lYX93WXf10YSsG?GJ*cPPplhWKIY@W5pQ z$;wT55V{;URI^F?kZ0r$l1EpOJO*t&2Kh!oN9`ebTrSDuk^gwa!*|IOYDk`lxRd;N zNarJYaymd=V^ROu29l>#IfDUTTDW z@n+lv?Zu`R{k;@rUxxITy8+sE#Riho5k4K|PCr0$##EA5qTH(xe|055+L@zBUb7ev z&QbogYe`;*a%XKLc|H1d1LAH(_>EzbHzEC;6?g%14@v)OlJJLe?hKOis_^n7>bZ3t zera$k$%Qyxl#hoL6G+~k0uX-36q1Ykk-T#>$-B_Tf4c$FF4;@+Zj^Zs>bR$YF{36u+;DOWY) z0r+Z?Pa*Cpl)HKz$u&NbPcO&KatdywOF@9-^C)v2fzc#iSVHo}8j|a)NWQ$3q<;_b?<4H}og_clKyphJ$qz^2 zAs@>AxF6U?a_cmbpCHXAsN<6`;mZz^+fdf$L%?d1Um)F=6G(1%gIbbbVSK)xiI*2k zL4f2Ae)YmbgHlk%2=rKHL-Np<&uJ$RWB;XM)F6KQ%40c%P1MjgF3k?J!Mp!_5J_>!(WseY&f zdnz>m=>{Tf5b_NQkQ$7<?Tsrtt7S14c3x+VK%83OTh+G>$`*1 zq+Uk&D=SF7inJRL|5`5COlso&Kqc9N<=JKo+y>fM>7HfNH05Bk23 z)Cb77r8lV$E5UYBA1x;JG1~I+0aEZw>J#Mu4CQP?{hyD<3vq);eL0F0{E_+!VPDmd z`g#f;iXiO{@a+sz-&K+Neg&zWLjdajVKcs1T}rBU0;#%*r0U0kEu?m#jz-kIdmE`e zDDM~4^DFpmHmTpokosdLsXynF3L;NvC8_XUQc({nym6wMH<8-6fz)59^8m{IyAoeU zK@aXBi)NE0^T|@n@kL-IS;-T~N}WnphlymRZ6YguGg-KovYgFixpt7{t|cpD8d)7d zr&6*qdxH&RWg$I$ij`9Z_L7x5pRCR^$m)W0d6@wD^21~my2&cSvByVNG0KPEuu5Q; z1jy4x?)ngV}*ymWik*D`Ivick#>xkuK^<6|(Kcw$p2~hSxq=Rp; z2BVJh8nP--_YkyU=u)zVA?`@ztwgy;p`4@E;Xxnr`p~wK-N`x{WgK%39)zRrQ30}! zT~F3=$d7w4YxEMbPS{JEw9RBquL4MSWeQnW zZzt;-lyTi!vTj&P){Ub8>Y2Tbtea5(oS9_Zyn-x0^4)^)x$DT9H=C^aDEroNc-TFJ ztc8QfTC|v~+oqCr2g9yS+Aj<*O7l?fUHfZALpf2lZzMZP|rK# z$l8pw?}HDJZVT%C5aoRYK3+-IR;2wjldR8b$=ZhczwnXuCCb>2K77@Wtglhd*QjR) z(tW#@tnZePg>%pP0sZ|EW&VizaerggqTTh#yK5#{4Tx*pPS)<_WbGk*NsDy9Od;zx zl=mCz+>0{zqQ2h|_NNB~$O_CLE7%>NywFavn%0pOag!BA`q*r;nir9^5B2Ux-oH@( zfvIHuy_&3p%gLraWXoK#)jG26exQczB!KI_o!T92BD(|r@6og>u#N0=gy9*b?bu7U zb2Hhl`DDA1CSwfQ9X(`s@&V+{TugQr%FL+*Tgc8u8J)+G-34{!AuN9>*#!+`7eb3B zknKTuac_V!agN$0(6SX|_duI^qTHU%WcQv80%Z3=p1woK?q`wRA9)5KKkhl~K{aHT zqa2)%_K@Xd4@LMen>4BHrAcS`&pK=1OJ_|wxLY`! znZlFlbO^Q%*0MEh!GeQfeAFxu>G)XSZi%i+SfZNnBk@0^*3xJkbRrK?r$J@?y(L|f zld>{0bFv2xs3;$tp~@@DQ7;;RcKA|Loz7JJ@TVsyy+eoe9P=ssCj1Z?6?Cvlbjr-^ zB+Mu3LZ53z4eC0bCZVqEoc`G^F|Y#VqhvM*%6SGFaiTG0tI5gz<&L+`|}WR2m1;y3m+DV z0sSF~7zFd^aEFJBekJ|-m-Ocafq#i1%*kepL;8yT{o2MNoX2ZmH3mW|(a*unOzYt6 z+pjQRC`+d0_E#c1CpR@ zbAOeciwq7&vfZgu=gb^r5H_~UJ?4)E@y0BAH(w$xMJO!OPbx2Xg zW=2X>a&l@?db%UsvhC!gB$bqw*3r!w(nJz+bB27Kp#&KSXA6FC-uU%;?E|}faQ{JN zyk1qTRuDsi_?tjt&RWPFCTIi6W1uNW?#>-O&l0BsKM5jZxuC zE$!5?W2aK{d5GE`akKRL_G5hyZ<$-Ow3CxuA~B17Me8J*q__XgS!-#2b$E!UH>!0VaY;Z+Va(}FP4#eP84CX*Q1L`ws zRG&UFq18R|@`T9C>yc1FiD-X|QN;=Ti4@wfPosA{c~``r_Dc14ZGhm8>B z72H!SmW2B&VVBS0-0^>R%`G%5v8Y$&QQdn;(c`w4OudIFD>d2qR*n!kIbB6nZjo8u zg*}Dr(fz2(UW?-Ock1MRGq-EkoE)sx_5}J?wq>Q`Cs{fQPASUF6e2UTXsS;5 z2wJVL7ot;^YZm9=oEh!{&XCOctj;>Q&cWcVpfv;$*CPOYR!0f%;#TqC{F6k=Q&DWm338O=*Ac2c6Qi_p|jFDk53HUa#x2`Su7UhO4FVhiE(qg zx}+w?EhVdE+|trr>)M=S#r(Bs%c$`(&&W9k&w>L4b6*-xF#_1JlF0r_1;IfPh z^PW^8a1BgPy}Mh>sV95(@{P!KII?p(ch13;pw$wWaZ=8*w%1T0giM@?NB8bypY&Uo zY?C^>ORLp>Tsz0tewMAr8b4v7@XYZEiH@6<#r$Ra~Q5Z z>4K+^uN&BV@LDgTN}7np@8$JV!kz|a95y^W3VL~C(Ug@}w3$X!ivY|q50?HI+;6DB z<(H3JA~`>ohKqY?w{lN;w^AWWa-B{-^6`<=nOh?K?!1m2h3MEZ&zCUV9#6t-F@(vF ztbL0Ut_wW27M(fj=S`d;;erN>u1hO5qIkcs}DcM zUZBlJxTfs^`ziFrZ~D|JiAUa%xT^4;-iwZ*e~KF@k`L}nbV>NQO9yNIhc0oSu$KE_ zEe|9g`lPR$+UuCvYrRqr;!8*QaNYu$rJmT-BTws|ejKkJv-I+Nm0@ytZ=cpP-{o#u z$tG-EyZwI4Vzs(<%J0%o#D~a{lGiamzt~DFSrdG)=0EQRY9wJFe3CvJ^>v(t(NQ_VT1SOj>+$Mqj%vp`U}eOfw?wO46lz^cJJISn$~CLUU4(c3k38 z!+C4kZye2ikstG!2k*qY5G)RJ2f$myffyC;OLtJma$Kq^oj2Xj`*kq?y3D3dsvv{1?#vkV4ycmR9^$Zm7 zx!2YW@`#q?lnhQ>)LcUE5wxc(8SG0qAwTytBO|{+ce@HdiI}RYmUV(2@=B|&)^Qk~ zl7fPauKhS`O^Zuwab^CN>wPu<6;t6Y>CgA2@wrZ1Z~1CqcFt`=#0mW?EBp8D%b(aV zrFQ5r>Wni+^~PUbclBngLx)t}`*RAP51(eUG;(C0q4?>2^wAyCdD|cNp*%0SdUx1* zo*7S%{SgmO#=w#cec|8(;vQCw7%;G)P>8~UfdfY9k*Kb!O87uwhDOK5(;s>FDU(vl&jSx#rp#G%8anK+YCR`6Qk zY~5S;%)BOWpVTd}8i&i4on|rL8MS+YGF%k5zgiq3Q=9n|R1+VY#8mnHrV9t-TGMVl z=zE6vRqV*tdz68R3tBIc(K1ESV@gWgp5W<&-ft$3{pg5k1>X?2KG;dL_pr4f_<2Q^ zeiX$Mq3={NH^yo3lx6TE2Emmk#x~x^_{|xPbcKgnZqVWlG+q;_bPL<{m(TsgJ-$n} zj&94?i;7kUSs^NLwqt%DI-+fVM&8PI&$uB=S$vzgRG;1*s{6iX2IFA!EMr`DOih}S zcuL_&`}j`X22Xs?$a_)K_k@?}CE$4QpLU?XKIE=3CDahk8}oS@UFYBXgw}R$DFHHIkU6l*W_C}WMp)>H9n--;!(R}b8|;KB`2{^ zMD`Y^bIZYBQhJ?PI)!7g$5!5xWuvQy%|lKa*fyArd{GT!>IT_>&_t@jstTOGf@$f@nsty^|h zR(7{;y;`kWQBXK&@R3Ij9#n{HOIy1NDZM>=dQG0VRXu#*z@oy!qJaa4w_2FWc>8>4 zUl;Q7Nbr-@#6>N+q~(!}Y;%j7Wt@4!xm20q@Kq%qB2*byo>=NtW;pz{HHl{tHKyJg z{me5D_Z?Kh&phGX5|8YP;RmEAMTj)MtQ}t@5_OBt zgGaS>baCG`xL+de5jETmk$FZMGIHSQJ^vST?*b>uQQiqwWIeLpS?{WTRCiU^bXE6s z&vfe&-H8nF`uB20u^vUkod}XQ-4p}NC%7aA8hTvPs7g8r`yUW*Vd?QgTB~x}J z7)s!fl|msN4xgy07pGxcRl=we0Fy9$TH zJt*T-zL=;DdxxMr1qFl7YpSc>!HLVc{+{=c ze@Q?<|G)S;7Z`1zk_DU?op=Cfw%f8rAq?ODTTKjUkQmQvQ6x&^+K zSZoi}cDGJa1scaB|EM0{kJ2mxF(vvVKcXIGKA(T%r}W_d)HnEqCBFDYf1mjs>Ls-f zXk07Wg>D*4Z~)cAxi}V04*&WebZx@{Z&i`Evh=EKJ~RBBZU@qix*2Jq&Ag~SP8)a; zzEo@(0pkp$92j4;s-$6$m=2p0r>Zb#CZQ}qKn-NO$FdaWEwj3})4HUXZ$Y7kw?t;XiFf@h``!MPa)DerNYNFN~K28Cw zhVR5xAdyJM-hk%wwv$xD&zCH#v?-TC3LV@w!rC?~NXK_#%6|ExZsR)+CsJoj=@Qx+ z06}ouTLD!%9=I{^QcM=g#n9QwYDLDs+o3SJrflJFU`jzRI^Zoxk@-(A&dpZ-2qxAD zv$+hk#)|-2uf>dwUr9%&Xqmbt744Ej>X>d}g?#U!!m&oz4UhNuuHo1A1I~^WU2uN4@Oy&rdqTrc1^i$elHdTCsul=D$y-@{;S~;p zl3W+K1v<^w1nvhO3Qfrpeb{@!$Wxv*QE;tJEREP5hOfb#1XdW)FKcM`a!5Y(=BQ!f14pKTh@{ zJIud}`o~ejD~BqjQst1o`e!y6byW3KfXH=lnikmUgBpvU)cEE+97}%0fg=3-S>yMs z{KNeHNHX@Qat2J|k}(P83@@qH(h?1byt<}tU11<#1IAym9zrbYe&9WUpAP)IYN&$> z5|wH?;xIv_?>yF|$zPL$$qw+rzOfFo*Lqc*Pz#Xd*IR`mVR#0!R{tIW4W+tu4IrNU z9p$tLeSDX{!PfebjnW1dROm+hgCN7rp@Rv4AHe-&h34KoX@epk>mKbKTR+-4x_%ms zJc1Q=y?nIe`yf8N00q-zN6o$IaK!BpI@YCV(zcH6EV0BSn)_S6Ah&kDhR?A4i&486O3dsiDD?|;8kT+@}eHI)0UlyhMj11 z){11DpcRSaaB?IRXR(BGGC@0&Nfz+VSK84;DwFY}QO{u7!&2pQ(VD1()_@C*#YU~k zrbn$YJ@`j9rQO#UrmyPpAg~`&Q_2BE67gg-6^%QwY%Us00wSSsHWLb^0THFr@gQL$ z|AB#Lr$fa=CKEvoap<7n6_cM1Mx0DEn8}6t&8VfqaT_c_Dps5hc`pqA4<$Cr5m4Hk z8~hu7N~#+*D4v*XP$-AbUVVe6g5k*qMcGuZK}qND(4`NI+5)&>eZbsPPs!Z-p}rK!1ye+v-I*ISW^SyviO4d8#rObhdCrpGaF-l_F6ZbaS# z5d&p{LMa+@oLn{%$Ak>mXY6!78;{Nu(zy)0k%GZ=EMAO9@{p7vR$CE! zwxygxC|XKoYoQP%#L!|DnznR0kq%--FUIXcBHgf}r9vnaPZpenJj;9U3_o}DXhD@L z75ZQB+9!9yKlO8HO2JVy!xZdzF`3TAv+;C3S(-{DGiXjYR4Iib`C=SR$tUfQve+az zm!LWLTrHc@7334?G?ag(8D-fjZ0C!KFyu@$x`_TjhagYq4&c5hZw-Gv9A24ml1XPq zU*D@6L`Kq+31+}Wp2aF^>w!=+Qz+mmoEJ23rJAhU**C6$IJ1(3O|0)p77DdCi`DITlG{0ktPa6!^; zi31qP^LZ%lqjoYJerd1wB}_%w_(wECRqJ{Zb ze11OORO-wd`0-={HXi)vu0vAz3SJ!k)_4naTmEpe4g4cbdWPd)Bx)&4q-&y6Ux&}EQ*Li zF2TmSZ=X`@@T=84$~uZ-nhHjv#sa6aOlD&K{;3min!+q^scj8Xeckfk47bOKUN$>7 zJ$?9^RQhl%TTI?+$7A8=#d3wz^(*dRuU3jF$Uw|nn^7Q%&~zYuhHg67v_LRtvRP%d zHZF6jy$sr@#Dh#d$kfM}`q(J%y>}gkXQQpgmhZ8f;gf#yr`i{wte6T0qus-Y=N6;Z z3ks=N?me+^B>skCGM0P$P3yX6HypGJmUX0%jOTi5vFH+umHDZ~sioH8D3sxKukr5=UBezYp3M`5d>B&JfU>d>gu1E9ygI5 z1Sj-Dh+%2von*$^OJM@Tat-rqWCKZNf}8{Zw{%@`O0j4=6k1Kl*?Bujenu; zAO2V_k>tZSzRUrYjRmv(QI`5bGLv_PZC>xqrrwn+USr2o@ysV!w$B=^HK%x?c%7Z8 zyn6TwwP>YMdC9*mjcFE3IQ&r*B?A%BQLxV~NJ1AtiC-DGci+Bu8WTNMi$%CQ5buN` zz|SJ-HK&5m=+jEb88KIkuWlxYdij{k0qg^sAKL>Se=$1_h~fXxof7I>11`v7MKYqE z1o|>ZIh!@jm?3)~nDxIj2CF~+nQ;*Pw^ikk(xAp)|4CEVU`OYGfZ>0gOIT8_)HMHS z;9TGZkSPhvw_+z#0Dysh!nn}@PMXiIcvT-Zxas$`Kx>nJFvb10!p3wM=fHvZu zY!{4c5Qv1I0@ZOJjvyb<)}qRVZZ{oF9*n1w`NErW%F4fi2(xLGmbMA4%wc8xr*!`= z8W!XuoXh;Hn4Be(v$QK`=Xo1P=HN_m=+N0hrjvB?$y6;J%oV`m6E&kHstzwq6;9ux zsf@mEn+ivtc);X5WJ)ULR{nV_$pt^Kt=F0U^h{y|&37&Y6AP9zud-<;lLVUOGs&gZF`!0Xdvg8TihvC* z%!An-fpx?=YSkPDZUi{L?kI!x?z+JD05^|z%XGOF)iCi+tZ5#k$WfLz);DbYVkt5| zSfb!ieyj@_7{c@# z-D^713#o@D4_uJ#ILay(^H4{}vauM5oN}flnM~x1MT<0t&y$J$o{xrn$xy95=A{^U zYZ}rrBe%_G63MIk%w#jS%*@>M5k@V)^kfXU#su^}DXgnhXTw54W{0+3nAC-dq6|dz*5AF z(E^MJHV|E)7v%K}{OC72jh43C2wM-rMD$v*SO|2g^G(s^$1lz0jP`z;^iyl}e`GLd zqmMgiaSd$%8E?Dtl~WUiJv5y~@R9OrpU<`8e)#V%U2?f-jJ07t00ORG9xvbo?g)Gs zVU_+e@HZp51uPEOSK6HDflS5cF&d@Ze}yV}9~=HK%~q+~DvPB8atm~upbutZEVLF{ zkY>7ks%I76VTWb5aMnoYQJkagG8Xa)-f`C9AHRbXr5qOLN6-}gkI##!%oap~zkI}o zB2nDC$eka-Kd2M+Q%r5VMWrkjVEHLS8_@$uQ+e3(V(E9MvKa^Vj%uTb6|+#xWFxR- zG!u#QwL&RdDnahWf0luwh$!iFX?%)4`zidtpHC#3Fu_E!nIg91C5w$JHswK^kfdBL zA56m%BcF4eY^hNDezSg@HxWjje$R4iu8wR<>U+Lzu1q$Oiq16I>x)7Oq|UH2{qfY@Zn}_el9!99fs9PjHVQP zfnUbQ&?Wzu^G?Q^ihjn>6Jj~L?*LE3fg<>NG1!TqU}tkx4i{$Bzx&}F9{?Y zyHS0=GxDeBiv?xXg27&{RLZSgFu3?OJC`dR`Lw_2dxOCm9EytbM@qSz{Weqb!W!S~ zE!Aq7NF>qAW_xicNBws;VXkejq;Xa5PXGZ0X9XzO4bDrKy~3p301!78$x$@`9_#>J z!PJ6vxzz*+p;MTj4+iJws~5gZW=XS5zqb_39nZ&J^S0rVy7szQ{&+69e$PrcdJvKk ztcfZHOAbi+Y%Z%lL_SD!Lmzv8HVE~>nSXo>6u{Zfo{XrJWxe^$mKDcxL;cbM_HAyX zy|a+!ZUTN_5XI`Poh>nXE7&%tS06Ir>qHfdCTSIk7)6w#5M_?GrrRXTA-wvb>bq-l~Ncv>Wq68Ly&- zzRF~#cdB{ryUJI0rZXA!z{G1(!DQdge`kGTkIC|3t-(L)5%KTYbnM4CC|q5wTt11t zqTysdXI*8-zB-e3ob=3ewVIAZ($(rT_%!FK-%(G>c-<9vZQ%QWV_0!S6H?T4zocn= z3!{cP$>t(wa#gtMRRttslJut{wjKFW7S1=h+}hzgrl;>XyoUZI;@K~yo{8UjCTck1 z$2<}ZUFoLnIE9U~XRp0(C7E2g?%K0wHwunpZ@y_iVtTd2_|jfjBBZda z0y{Wxz*~WNBj#sCUGZzUF8Yb{))>I5Gua#gFfhHn6DA6iRy~t?Dq!4E1}0J4#%kd+ zY;5~i92eFaoZBliiArVO)WzTFCOnf6(6KvR7i5bzueh?JF;mW2t!W7PUb>=tLfC{DYq3fgP1mzo-*8g7%~ zCaKH1E18?u*DdSRsXOmHb;?pTpaV&Bapi=|>26&tQR9_%O6E67MIis)77Hpnmkp`Z zBA=yOF>5tvS-D=$Dz-u@DSQ;ZvXDN0e3RAlGrjeSrv0Tx97LR${%=_OmZ@G6N4NM1 zxg=vM8?tlOf`wvbtB_euq_H^QJDFN}H5|5*qKngV64%%|u#jHzqg{!W#Wm*&FSgk+osdehIRXEoJb;` zQ8NRv((I5U==t?zD_{S}-AAnjlu5+i!euEr`-K;jVhL0;*xueW9K^L72aG+3ZC?R@ z02$BM_R|?Plf!nK;_}J#YG95G<8?dTX^Vsy3-d&BGFhb z`)i5l)Y5WgYB>@)m~o0HqnY8yGSTw!3z0}ReQzk6t+%uJXfzTb(!$x$`OqKu`*nTx zVep&byWeNWz>3b|FS@w6_|jDB!0IG03}a2Q1%76CbapYRh#x7QF)E`R?t zqHEGwg7Tz8Nwp3nR*M)L&{O;a5|hNzFd!Rtf%KGv0E&`-px)f9RJ^V6B0|FY0<)*LwilH9nSLV3s?+X0jFMwA1dkmT;3&es=YKT$goj(EAP{^O` zzQuA)FFnZsg@jcSa2SzU-|&9f<<|9wp;@%@ ze=;At;C%FWv#G2pbz|24eDDA9JH7bF`a@Qp??5eZc%d-WAeplc)Jio9@Ycfo7x=ln zHM??RTQ!@GrhH1INfw5fz0yS~gS^SWjqPYn9;~ zdex)mH|2cP$~ZIofp4ORLACW4@f2yMb@q|rQ%-4*K@za*_pneD{CND1GC0wRDr2#| zM%xPB^eYgC(jYwD|M)+q_I_zJ=oQBSTWEMA!gn+9z2J*}DeyR8jFlHK65ItQ16q0s z1{w;k1D2p|pa8&7Agx)lfuhjN2!lu(FPM%Yg6LFsneH-}csFj5;XR>iUoU+Z{5+bv ztIh$?2lQ2ZL*lu3Y&J!FO3lV_BMuno9Wo|L&7@f>Ju^|NZ8Q*6yy+=}D>nJ;sfWFK z1?pIBgFQYx(Y&kg#E#JhQ8*oaX7kx8l6MO}7yY#ncZ6@jo~c3L#n{#JCTQ4OT;)08 zIKzYirXHnvItVOjNf-vYXaJ4yk6GN!!yLqDU>0_W1DD*lk96U&4vx=Y9B?E|JP~Z` z{^9ZIwYBMUTqm{ZGm0%#NQYI`fp9f71#!x$sxT*>`>5|E6QRWW+Houjav6TJok+yv za(UuY8OzE%VMiY`?9Er41^jIpn@PJoa z+xrOMM87ORN#JyM;N5{A#|(qlNipH0lakZiZ<3yf>1Nlw6z=|l=PNvKnXAr zCw}^j>$eHdu>fE#OjWWU+-^daiz_c!uBKfkd{1E^!iWgZ{o&&POVSZT@!n8WAs39$ z)k(47IYPhd4>M4UM!}jyqcJ$H#Q-J%ECy%ASoBHd)L^BuN>P#8-ubD8Wa_a}PDbnM zVa&08ENVZ&Q5yaWe~f~!?*621N+k;AA-^eK$2gGLu30^`#ke$!p5y}l447ut3jPvM zqqUvR<7Gqy(dfTDngySxF!1_Ev`DZmH;>-t33P73+x|OPCw^3mCfi6pI?)bn=~x$* z(A~2D@~tg${Dr}PMe1r_N1B@o%@uhI=%3!ek}(djKu!taE{4Z|Bhp~OINmtmIyqp}Cv zh)82VdL5;2g*756Zd^j+Z_L08Hkmu;wt_nr(oO|jId(K4uhwq&47KbbT{AZ|5ua;` zMCP3bOZVlH$(n{ljW5oDHqS1yF6Q;j7R`qq(Z84KYcH z1+73{1)acE^a>Qc^c3>83u`NP2$C1JN_nc8MzP!|jubTwcZGMyqk;Y0s!)M?KzA3d zb)Q7gC3G&H5eG-M9{XZ1dA;9uyKB0Q{c_c6YjGCK(wyly;b)!$#`l#Dz7x={4Gb){ zdLys^tz}Q&051BpH6s2b8DDf4%ccIsR!Vor7nJXqft^4#JpJheeDV(e7^VNO#4^Do zHVLnBSO+D+@RRyIzp!qb`n4? zN*k!_uX7#BO7xCp7BY-ZLMCwBtwO?O*sv$j)Y$ps_)SJaR(+GYDV|(-=R)$Ym2*p6 z1uMn5xnhC-#)Za_^#he)IL>1t_kE@wnx~G!)8r}9>CMku*8JmOIOAbdSwv`zY__m~ zI5?G{ir>=P?*Z_`qCX>afP=uXKo98R(P=`20P)BHAwxO5i6LK`Dthyv%?LY#0R|bVDOidLSI1F0X#DTTm!I^2Lre_WsDY22-IC|m2(Tx<^IKYX~pkU%$tw(VE}jOh$k>FY46ugAE`o(&9Z z-9k?oSO*w{#AFwsv&d{n5OnA3nD~19V271_X&F5l~2x zbeLO0XpgKKKD-AcwkF!w*X?tEwtM>4{n-x$Te_56Rs&LBS^enYI6g!J=|C}1gXK4% z8lZb;u)7A^V5th#x0vG)EsA*12>Xl@9W}T^J@hj55R9ei*WLF`r11Z@IFBRhp>t{g z4|l2y^~~O8jI(~PoP8bQg5e*Y^wdM!J7(JNpi$r>T^{2H0Db*cvgFve6KPN_#67pz z!-OmdE3!Jt21MXssmLtNJaJDVig;oY9J1gtfh=<8THEXDgWwWNv4@16Dk2>yj9K_t zI#b>bC#hEdUo<|ricJ!X5-1h(!zLt}> z=CP}S@8zM!&Q4F|^;{Ys7W9EaXb62%b^B(a)WGK)D`g3)_3&4T{Qw#eq zyfNvU1|$d=jrLuVJdB5H)h|l0E{aq1004-^vH)7wkrjhuG_pxx6sWVniqL^kUu!PF zZKcx5P3*r*f=`jyfN(kEoTfq%LH8IWAv5y88-Dv)6<>1faO^y`9NFh%VcS_E)_p2g z$WIw}qp5r$_9+HhSW+LMD8;|ySO3y*$^k-P4m zu-pYKErt3ZKQH1X>kR6uk9B1?HQT)jXknIbW13HZkV@b2tTtH!U)B}V`H?56x>G{~ z&W;39OB0XQB=B0S=!VJM8^}_Om93cC^cHY_&gjSt?rHB{dP2}m0M}jM;2LUNMKpHW z1`2zZ9z)m>#K4p2*VGZ997o>=38EaZB;(=P*G<#0Kgy4p<98y2v!pt2V5z=Llmq_m zMmbWwG31op-hA=GA4~#sSH|!^n=g!@N}jETJ!)InqjocVP_Zw5EYc~6CLrp`W&Sj0 zq~TiJWE+TQ@*JqoyS#Avmb-Yf*?J1m`BLdbJe0U@`5?eGpEDV$bef_2^ur@9@+IVI zGPz?;oV*F0pv-f);zV9{xS;a6x!7qZ{X8e7K7t@sAwECkY;2rcU2IO@U~-OzgR>@m z(5|vp@=Qtl-z!FYhSTCT=5ae@P2cnR`K86`T=rZ4w+j7zFoLzEhP7oI{LhWXV-@&i zx{hhee?&W1H3U>^B8P?rdLHV6Too2<{za-lr)En+sGV7bSdB$p#ZTYqk3r-Qwr0U4 zMxuY%K`Vh>Y`H9~O|jU@^t94}iI(_2>*?+reQhgNv_dv3V6c#g14U3Q-HqK=Xts`k zPo;gS2Rgpu#U~Pm9S+7~O@XpO8#`$dX$esmHohLt*0c4VS%@zu&g!|Y0{ZuC;<9-L zHv+m64Db(gWGIU_87o~7*BbLxjpcj89aS^3W*=~G!vgz4Eb^isGEG9<;-;(=1pEOZ*XGxJ_3YbrE-Kx(T&v!-UOQoQoEbsq;w}{g!)|Q4;4w zJR1&#qtZQD&zitId9e^|ZR>p0H1754=If^c{AbG;1TY3E+52=3>%i+}7re|osy%=~ z;4a8dO$bxm1W#uY75pY^vo=U`>H=J&@R4!=sQorfM)n{AvdLf@L)axxTb&4AA#YO76 z4;=8P)u$ztSN`GD+S;|t2Q1Z&XI`+AFMlncUsz~17Z&pP&!pb4`+Z%P%MWwzAUcc8 zn=yr(-^d2kg0>A;>Y!Gjl=d0$)Ty~jIoW-KhV$m3Skc{ge+{k!c$LY_Vg?6kNZsHr09`0KxAGP&EG7$YNu!G$vgO z6H|6b!&@~&{%GieF`hw4qa|3mGg}pV`gPd!9GRI<#Y@lEP|p0}%QCMoTH*gw^=T&@ z%2mCWoDahZUqmT|jkMcNDyhn^i^sD!A^Z_W5c$z>_fCZ`8(SUv69$g{uE;oXNV=Pd z0`*q(Nt;??N=$sje#les(%|xwG*~n4BiZYH-iAR3Glaq9{^UmeIiPg!zG&( z8SuqK;G!p`#lDH`XNW4L0!g(qwxo+pCvjCEJ@)nuFZJi~bZyL2kisUR#rJ_0q-T&y zx3T-pJrg20QtQy?NG~T^6M=cnHJj41OL4oRBnGQ|3Uj8pVu{KaL)6Q-B;|P8c2#!= z;`RFBpD>Z&cCyf%PaIenI+4q~X(Gvvdv}_N z9}SZXfIA$RoqB3sLMvcVB^^QP;OuOihnXM>Ofb>VdEV2*7P&#U-P`VOb`4DW{1KFu z1){E$dz#OBp>g0aD}rZP`cT!4!uATKUZFdF{o)z&r}`uM+PUe};lmYfzV^WYcGS^H zR8`n2wd8h2pFv|-Ul3XJNJKYBb^5xxQ)k?5{ZNucmyln)DPV5PdF^eGK4D~MT3p83tf_}k6%p@QJd%3E2aA9$(8q1ug z@3eyd3`g3-ccvGVEG=t`f`Y0p#Gj2FY7Zky5mP>;n;T262&Lwz`s6A7TY8^@>Qe zuAm#l!_6zYqANyJSox+Ybxno^K6U={^v%|eFv6&7l-QPvwx#B6sb^d2 zyy$n^ypwf2xqqKc)RBT22IZBy%qx5 ze;WvI{m*+=pyrE-NAxB~)3leqA%N~Y5uRQwqy#Gd5i1aV%-Gn?TGgpju_S5@!6uL< z@kBYcak#*PgbKEuI(zPoZ#;Jv2gwz{=Bng)9@ifYEnHP5Wj`3TW6rr(zxrGzX2;G( z6N%{bi*cIFHtHIF40T!&I44?;6G8lng1UPw#~lk@U*b2Ql(!g2!%C%7FOJ3P57CGy z{l_?LgS+dHzjN0FbW_N_2nTv$5cQG~Zo~r@+rQjXG;9%VaCinjroX2rM7?K6uzdnUi79{;q9Z}@b<_P_+B~|vTt=i33YSoQy3tqj<>lq0 z7r*@R&9(WDKd#i{AD>^_{J8Y4r+Zh^KfNveCPE%eujnj~F`?>ktET3A%&H;6Y)$C; zz@q+X9X%Cgkq~Z%Y=y}N2k$Y3O~nl;(y*#ghu$Fqia%gyK%zp>{c75dL_g#00L0-v zCqEO7*y&$YuQD4Bue(2%JrPAP?h~7OYhkUXQU{~qmCX}YC>%YJjop78P$m%K+(nxh z^S_NzcrjY0ZKrG+r-xzcKu*lmI6W4Heo@Q;Eb1!&o(@VJI#FI2O=OWzp&VT{~ zYunzEI#8E8PW^3+*S1;Xwlx1__0ab&7TpPpG5fkAM@y?O`Sldc!GK3)CD<-Im;I_YUFSAp?t!@Ty!v!Sa!&-ILdkLe$T znH$-yUO8z#>1qgcuR6bb!PGkZQ}ZDYj~Umet>b*?8CEAALwM8Ui7~`+C%!bicgl91 zC<T4F@lw5`2M&Z(Jcdva(a5o5x3XN>lw0;v zXsMPJ7FYobw5ne6qd1hR6J<>ISUerQbz_5|dJLXix@cm~OCDoaT^dEbMHMGfUN1Dt z)6v6;qbq+zd~Gqo$W!dXEWy}I^jWPHm{2l@wTekQ{%CS1yc+2&Hl=5F@HaXRZ`j-R zGjHArAYKalw!ziwh{>c^)|_jI5k|0v|5&p=lmpYm7fklaz0L}G|63^(yw#2F3(mZr zG&b29U{MJihW_tnv1D_V^hL$9Aai4vc%-;aUkt2{++9OqY60gyAh1`NaYrW0TM{|I z3#})WSbyCNhnr~&d-l>^>PzW#12GgF^=>yYd|3VF>npFX3>(qt@gk$57f;ys37tBw z5XE zxa`|tj0BrXLx`Ze(wgO<_cLGzwhzSsvwhNHsA$@w(4T8c{XG?dcgp4W;QLjr#^=NH z38bLOmDRopQk>VI|eX+`&34;)`SH>$P9;+2rgjn^6zdm_m}NYL%&I))de%8sl-#NFysQ;$!;IYE+ zx1ht@b&M!LG(n4-)Tsi~2#wMD3_B_bjHbn0!99TvBD$X{d%P@H>@{^D9gkZKQyQJN z+$Ew$_l+8NJ2H{M@PmNzhpI4^8I>!S7o#w$Gsr1x3D0oJLV<0%h44g^8WTDXS2@NL zzuP)zzBxS|fiePk;M$O=pRb9{VG}+Kuf_hr2eCi!gTl>X*e7){qaDmp9*cM-evE() z(L+Lh0usOp^C|>+Hwgu&Tb*B{^US#|@3qTTDML;Hmx@VPrAuD&IDCQC*WoLys@1lN zO#dfzwq32N9q+|0FX^iTY=^1EmkSnWkK8Sj2PR0YBWzOJ@GT=w=?!QGnsaPm!?#oyx#O@Q*TUyKs|$h+*a`CgrIhqJ?Yh z1^$u=ws_SngJy+wdTi?=oN93DsXnp$WaSXt>=2#Ankf{*kp*bKflrwwjZ{Xhi>1*w z(}zD`SGrM#v1Bc;Y~c|5Y;LA*Pfr~nGH>AzuWz}(uhesHV38+))CgXd^yF#cYe{rx z?clY621C-U$>iQe=m)JablYAcz@VnYrk##$xIh)3etirh+_9)20* z;nCB?**a^fizvi5db)2tR>XQkp>3C@=>Bofj_Kd7m|+`q=O+0DwK1_ey{11k5gqKc z^O5-NAn~!}eaYkto9`06m1*XmPecJbnvC6W8+_;OyOOEri}s4`^88q8VnN!sWpxsJ zL^G2qO^-1!TM};&2uIl#`5gn|$#$A3W^hq)YL05Vs?t%?1?*&t$z5%WCKYEieuA1W6qWz&K2ZJ#LTzGdvix-RewVdxW1LU)~lC;KXZPH5%< zVrrSPfG_a?Vi@hMI0CD#Sp}QAS+Gt4n4Rn$oCX`;anvf@(VTt^cYQnY2f>_G$^}Eo zh5G97>#OyJWJq0xgc2JLKvLkb-Fsm$iI+e|cjCd}?`MMuAA1l%zYvV;pri3N@D$+p zmcaXEy^=9D1~rT={$czVB=}BYqm9L^jiVSSLGa(Y{xzhMAsaXufim)dCoD*@jS^6V z!iT=HT3|c=b<0gTlUv{5sc=igJx5&|%oUfS4vqkDWv+N88$GtVhGPvFDN8LxaWs5B zTPWs&%lW7ZT2{We97jl@P{^`Cbg_9Vk_sZ|zp~Of9M0Ckt6|vOr)*pc*Zk6u2O-Xv2YNl#K1`< z5w*iA!9(8CIJ_VXO%fy2^XqH{Jgvi#h!xA_k5;EjsbaG_J;jjgur}C{aO#iroAj#- z2k}lM;*_J%S<*h_@)vc;G@U>XyYi_0ckRt!n+V#CjyBg)Km~DuIzP`$jvCL^D1+KS z$_Mn}8c)q%IS8#2xJ?4wHp-5Xe5XQdYiEyFetAmct-3@SGrYKMmCpAN?7k4nS^e(v z9pUghnkw{eWxe!~OwLLEaA9TT_+w@j24@9|Ln2C6aiAvzMZh6c`{&EyLNSgVq~-8J zBdE^x)_S?zi|M<4K+O+gceQa|WX~+)E}t#B!56_J?P1Itmp%!a5YHfmh*i5NoTbbo zt<})WF@+f}1cF#DpD9`5WGh4~{45oFvK<5`6PWYA5d+>T!vI!4x`5z%s#J2VvE_UP zN0-7xn@pC0q>$aL8j2L)ZO#z!LlTawQ~eQ!xlmLNQ0G^}JpHBIaEgeTwFo6bHA$aA z6v{_Z4!_6mW)cV9_fo?XNuJ=P^QNZ~e6 zg_eLfP&u&vvBTMLByldAJ(q}tvxgruCNS;0*t<>PumJl8ldHYT_}&e+6-=qy9t|@J zU-(hi7nRZf&ZX1m82wLJjnO>qJr(FVSsVp~cfCy3%iJE$dWmSehx9pwI9Jz$UxI@h z{*AhzJIRrRwVCi3Jvn7bk0-!Gg*9E_RNrg3=aeb4UQ{}4)TPWNDg&E-a5Nc-yVslO zi2!4~4-gR-GXBnIu#WlqQ^0Ak(jPXs&JGxBCU&Y}M`E#oAb%J!y6A#XU`<6i9n5S6 z@txh`LJjn=P4;KlyOi190naqiezXf6Gs7pHtxYr?eOCc?5){EVd>K)V`bSIX`vBh` zV011Z65DG67X$BtDogW@v|8dHTPu7FunlM`dIdpObPE4>HPu5(f*PW;vXg~d#YH)T zwoOSTG}J9+ZJ!oq=*C(n7QwH8jwNF#)P$PR}yjbIQl z{_=@%Fr2azz?@(lL9M_%r*M+yR490D$qoe*b1{1bLG}}^qB9Lpa2dhnH*qLYIuZ@0 z(vbobKJmC*daAx~&FSYq|MWErb>&b0Oe(}RJe&ePJJBqN8sm5I{GzxMvZoWVGGf>x zD1!~D;~QmqqXqMbWfihR&hXFpN&fwCB^?Q-a0#V1YjjbS3)a_=*m%f0u432pmS_x{ zU|e*htz$k5GT0WtN86DBts+*O1-qr)#59j7;t|Iy=<%Hh=fJ|?L8&f-#ubBE#&YZr z^z_l7k#j8g7WK65tEWoS7n8}asVS-}m_w~v&DBE~vf#(VtS5x}xW|{(n^XE0u_??gd8Quuz?;&}pGBHC-Zz31 zWy)%x4j)$XAe!d8{55eDy)0w5KS7X13^D{=-`9s(yfQI0K|V2U>A6C-rO^=rx{Y@C zj~{5LLSZ2sc3rXi{eHW(hbJ?z3TMK7pzt`_?c@E58 z3iI6cIca+-KYmnzAtyw?4h~CpT8Q3QVz21|4mYr2-XC}kwm{9PG=#^nX|i>_P_-xx zK6zw&T2wzfzqU5-#Ns9{+szV^?WMgzkL#H%XP3Bo13H`d#m~0>A*mG75 zTUd**JR^^oQz{SK(={_|jfxzgbe!7XL}fOzU|7(F$ZSQ9dpb^?ZK87Eu;`mM$yh$y z*V=xC%!oBrt`YA!`ch-<85;Oizn=DWrk}3ffi&z|NO1nxYyi2QU3$97Y9JdqxFizp zde-S_7~!!dvbBKYg`tuql2fx(wzVjMAwbj^y{KZ`^_0b^`Qo}7F znb35o6^taT2t<$W7d0P6q}3}nqwWZULNyxj-mi!VQ)2;I;MewpKy;6FS|iRBvjtAr zocnsd;#yo5CAa9Sm)+1~HQ63%6XSvQGAV2->Nhu&No?Jy>6a5~#iM*)>XvvM@y3_9 zK2}d7BIefUQ~2!W>`{J+OUKb!-Ha=|fo}z79=;^g%#Mg@U}|x*ugeVfqS-5mn;wrZ zaq*wMCLYK7B7Rb^?epv6%_q#(Zp4xHb^#lOC3xWD(G3di?{de>bNH0h_DF1QXx^tr+t;KVOthOAqq%3{O??;6r^4(9_vV2Ds~nsJpB)rfI`08u z!{=k&fQ6fZS+0g1{;+`wPGY}lFIe?^J>8X_|0%g*WcT{+?~}YidT)lgI|5i{x^xwA z#5nARV{~ednT=>-)Md_Z#v# ze3supd`qIkly1S89NU0(^j`9Gi{uARi$lwvlb!Pz+?||sLpD#W8sN@E2E>)WS~{VI ziP8$xX#C-5VMxHoK`nm_wRFO)1TqgTvUrdGI^u!uHgK}(xyH)DMloNSg&TD+i0DYo z;_8w0dBjqT&95IhRA|B;LA!8jwv;b64jydebGHgqqcCm2wvn&S!7c`9UYe`o5GQ2C zqL^3dC)^?M4G!b#otwg;&?zTp^Hcm}+`+*xu)2mq2yr>(V6TywK!P?RT~;hLl}vt% zwf|Pw3gc9SWMQYxA^mei2ZxF#h1}l)$X)q_H1{@A7&khO22KIMGvMJbSZdN-tWgAx z8`b7%Xpokl8?qDN*^ZE?lQV2F3@!u?;Z>i`77E$woB>z_nkICyz1Ii{X#2mDf7ZhU z13&7#!pl}NZn@CIA;QT5K<5B*Ry-;5?$O;N_2?BgdH9lK^4Sg-#sa?ZZmb1A9bzo& z9}9dW@JWo07Xwo-anxIRYZ(@}6$LbIio8?8*a@l|`5bltP6C-EJV?nahO-g88(6;g zDdw-Byy$-iZ29P&^0p==8(-Jc{A+reCV^PkdRz^H~m$N#R(XzZmgL9l}-Og$4c$r(y@13n~v3kO(%!Q za5(*AQ7qr2bH*>a0ZO{$g_2%p2pA|>Uj?r!&=CPc37)IMYRXw#f4%bPm}?iLC_onxA_b8u=$-V zpXdAdjx+`f2^(0xRI$wEGM0M17M8Wn&u zmUSSOFC<6zeBMkJij~4tVL^T?#X=I%@(cWJ`pbU9*8;aq3R=1Q%4`*nt70q5_m!-# z`rEMOzghCP_1^V-ELphT3a!(#84WzP9gzXvkKgj)r;E9hS?Upu+4?FQC&a-7_u_DJL1xC-Y;ot&MBeh) zYj~Y9ehvG=cQD_}h@W^v0K1f-F%~|Cyd$PJa1c8ui^nz~ii4z+(FA7Fb)Zn*V7#xe zc&Z8oC&vjCUxVEV-ULk@Yzd1wk)IZhBVC-^p#rB4))>I$qQj^v3#adZ@h1CVol#bB zxHqG^EOadE+939ozJ%AwC;!V{@2jod1+NXB+WnzZ2hTd$-8BsMdL6)!H8B91a0@9x zV_o!nn0jJPGb6485^;R#_{X(P{_TYm!fXRKI<~|-b|tovKtltdMvk8NM&W)h*`rZi zR?B*j@v4zjhJQoLhKnZfY31Dn3J9uXf8iie-qWjbfAL*`9|`;#`l!#OG&)S#VI`iq zIvAuU7?3#wC5uos(-PMtzHf{ zR0s6U09XIA9*O*pkAN3!pHSonHN0c&)$4V?ExO2r?JW(&{JpW*-}%ESyg#oW;CU$M z?lpP6o_F`EL%IrcKUVZjsA>E&qrjVa#-P5}l*Zwt;qj{fZY(Z?{ss;xA8VvkZMFun z)xK}(vQGsj+r=-%ban1n5>IEaZod%G^&bNAYLS{UBQzAQofnoCfNhFa`2wzvIE zQ8NwiL(FRWS$RPZ1?%xfxQV4-Y%qvsH-bOiI@T#TwfC zV)xdL$t?y%3O!O&`Fy4anLLxvo4vQha{s+9!+hFAQsUElGzWEc zb^9h7)Pp|7)2Nd_h(7gI>jCw7@CO_4cex!hBA}cHsI-yFn6dyv%nk^0_@AN*{!uo> zKbJD_vBxCPvM%ob^1iOBt6=KFXozJ^>zgsr>Mf&s)`m0I+k!*K8Ex+o_vYrAbo&0} z)b!%w`ugJH^i(PwPI-69NaVNu=ty^vboxw_LbmH2edZqrlR4R})tmeVf`R_Z?sEUz z^!yxlPQ%G*ZhktM9KXBSKNHnDnSt?IhjWuTyT6des^!X!5S0elv-?tDx;aS~OZ14? zxKgC1K02s_UcQ5Smd2{)qgfcBt}d-f=$1!jpBSgIN-`-(g|LQtAP9z_Y#2vdsK zwoH?=%MHl>fo0~>*gjT@sIBXgEeBGF{nx$;0ql1?1dipNz}sBPO_C_M1TE3X{agey zkDv;FA_UeCto^z#WCG9_(RxZ=0FFlLzh|psvFS%?{~1jezhs*BDH0~Y;>xe@@T0|W zFovF=(v)zDU^IXLJjNvMrvj6@5mSzZQD-FlK;B&!rLAA! z)%u8$4mhmU#hySs@tmFjH6!X6(Wk6C2r@@fBjamOKs#pa^#teyWbAD3 zn2dr~188ORVzeinfiI=}FGnI%$U&%7``Ua0{jU#9Zw7ThBay!r-s*Th{J~TzpTb$n z$t2D-Mlz-O@PT6cqtF1Khc+yb2aSi*2%{!q#f51YD>B%v$v;^%xKND$=^h)cuyh|c z55TzHw@P=a(o`n@l6+*aaJ#GHXkehq{R?^ARove$07xfP@#{m0vfnvGM`{124X}mozA^(e+gC!!6btjsDkUUFzQ){;^bu zY7rUo9em{-h!6Q0CTeoo+`)9j^xOfiFhh?i*Qy;tss&D*7g9j2=E+hh$H^)~R9X z0c`YI(%t3u;Wltr&#}XNdAW1#+MZqhh-douQj;DY+iq`n!NpqT=?$&(Q`yf;Q%71W-U`^I+!y69fL+~unAY+<*j+u@ex>PyxozgGt3kllE9 z|8s?R`$BMESB&QljQhJ?JSXtn-@{@rn7$C+);gvOG~b$e#x_nZQ&5m25!dPm(Qyb1 zgdGBul*2efzG34S-i;PhVF~HVBZ$aJjif@Jh9d%pXjTxb25VcVhDm2w#HA<$x;U}f z)d{L5b6P#Hx!K;_yt28e9w7O`i<@8&#Mx65v2Fe1i=fhDWS6OE24P|*Xr0iun$R^| zE}=4Qwiee>)Y?W)U)RT>`@=Av=}7-0*h#`>+>ggU!`tL+vNmmXfWYH z@}^~EEQeZ3W@!O3awu+)Im2SlpMZf8T$HO&70@JSi2YnOi^%KwDjlLXX!RVkkQNSk z7Sh$?goSjak;|(s-NyrbAJx1&0mi6r7K1*Rq{O@VNqqUh@SD%p#+FQiPlb(5NgvMC*W3pRut>w-fSGX{1PZ;)x_J5XZa97d*~6K3c_<4Q_5Q)Ua- zQ^pe4bDlGnWsC zp?Qb~on|c`j#|YMHrUu1b@O$ZL^^*I=cMDao*S1bl2rKOxI$3vOprLZb4g?R*1 zXsM*arQ?}UysUCdrAQE42NS=(=H#=9#WST)Y12wX@!4>pl}ls_iBR%TE|bX2hYu99 zIoR#vk$lHV%oH-w)O;?J!ik9aECvCBjqn!(#F)~2)0zZcl@%Txq-@rp* zSfHGD36r>(W|2pgJorg)nPZ+JUH_AoS14kSJsEpP0$jNaxM=#cW(5SVi?IY{#HX}j zi+-)Pk28f@$J$7jx5%rKrxeC=)5kX&2GUV53YeQKCNY+~>E-UZYQ7(Squ@5jk@#|bWuFS{#Tz^y z4zuKh5iUfUf(AY9*!~R0)|0kH6$@Sw)B(e&Ppgnou4B4UKos$><4Ydvjy>VtG`G;i zld*TMn%`_2uz7=7PH$M*>>LMoxZ?wj4`7=f7%zLk_2v4?s*4^^>ZK;R(}8?WBa&&- zrpg|9eHD6sv{GMf2)xwsDL#Dp93 zKmCyI>@_iVG0p#<4C$UUV{cA-fBO5$|DTQOzP$NA)6nkE$p7<=@&4R@bTof!(59b+ z75sYwuME7Ie%AX^ViPiLjMk1()UhX5M}=`jov2?ovhI>ifnkp!H1zJdpthI6`}Ho7 zD3a;d&SzTjvuQr#E|AX+O(l;ytWlZRcNjNxv=;WD`yA(U9t!;Iz9Rr@Ft{3FY_P+3 z4A$T6D0H8N6a4PM`Q8}9*UdjyYvF*S1BmVIqP+KLrE~qo+|=6q2cMe(^JKwE8PLdv z7(!^RDcAHdhGq`E9A^Z*8y-~8=^O&L1BsgIL64^qPF66%CtGXr5sK4R*%ZfyM7)G7 z%8}GL zNBVQH$$l0~-B^6fz*_Tx9@I8HU~GBGi+dJ;iSYq!8&4*xbH`$_V{=sop-MmSx>p=2 z6)jJ2_?KB!REZ*3%%S7QS3{H35hF4Jze#|`c}f33dbw{2h1!u!eR;XrEQdnnW^;MD zo{6;YXnt|#;PMjnQoH9Bb$$Cu2+ww1+Ix0TY83J+^Nf>5rcLR)oHqH<1Q;dWttSlz z#>^T{kpb_%i&f~;J{0#QV?82(zL+T@cYaxW()XEB`u2u4?C8Kut=xK!m!NSkYKEec z2x~IwL*B!LIRdI`KHpxM;n$MdkmV1C+@hn5|~Eb_m*M zq+pkP^TnH%zDaV~A2#;)@pUvpzKuo%0-1eyws!>{34A#43xQt=d^zy7z`qOp8Ai|3 zlxWM_)swC$5;T3Nje7T!mE*5p@e>3}D`VzTh|BDgxMlzPWF&=>(FPTZW z_f)^^HNvN5@u7%MY2eHCmEH83)hD;2Z!dbup4YD}uZMjv7e`mTjy=Gohjm^Sd#hA& ze~|;;$MX?|TAaI0pxA^mj902LF}hsGO4N)3C!oPK%G6ebD%ET{8N2vuI2L5?>EQG% z=QZ_6HXEtG#=)UioqL#n?{p}hNZ+u){Mskf7h}nE_7k)q z^MVSZzojb=n@WXw;-}OFw>s2mzCuqk?IRli+}5D4zZTYOcxLE%jHxWl&<14@Xsg|z znB-z$7lH)mN+-=$9wq2xxxvH72|EX+3HK7itnmk7i2~8qwopv_WFoKIrT#d@ZfgB~=V z>t*T}ofzvM=uZs;F=uJXiT%~VgOSJ&n)gQ_fnQV&`sW(@o&;|QECMBkP96=$$d-eh z4Wb22^4uUt{MK6-7!jTYkl{H?brd;BAzHpjCxG7uW`!&5b~MDWz7=iIX#9WK@3a-o zU^FqNDTMn=a%d_?rxcd#B8HkK7k5vWkfnb!t#NSq;LGW4O} z4Q#k=_P_pGfwf0b=ZjXAT740YrM3By!msHXF^#~a+#So;@Wo5W+kzC3dzn-`lMN-m zho3qgep5d7HgSNU|HDA`l_Ue_fIrkLXigV134KNsF&M+nkZ42zUdA|)uub}Y4Zx-C zM;t)JXg2cK?Euy2S?X5fmGIZZNzFp28UoPBC$O>Yhwb?s&%BtQ<@shfemHt^@#M+H zQxaSgp`NSrJo^G6oCtn*6x4hD@4IJ?J;TssfSdER8UxYrI;?HtK#sc>dEPNUeCuJQ z4&Tb-)A-@VyKppzEB9`?zks~|{l06hd!_-~(tbq)p?^y_CCT;10o+;u40kx-2iVCS z3T4_-I3qblyWwM-gv^CcZs2e-M*2Sd!}s$$g_hyN|~`EX=0 zW`7XcoMN%cY`9X_ZHJBY^vqEt(MYaFRr+MaiWdtJ2vD9^B1YTwl^Tvb;q-}7a*&O` z=qIz_E_2x#;1mfOLX=O3pp6P!g$PcEW`KZEX;&t)&VVk}1FJZLgeRkMPRYIlu0)_J zybxk_f{0_ue>j2LjUZ$%(j#8qj1ncg>*WejezunBoocz9EHBWr%rc<#^&K|%+uEr@ zF5XV%b1`*8E@tk&VG_C6JznnmcVNICWSl9Sf#Cw)G=jghVh{hrW)4vxof}n^E*TeQJe?|BFq^q?b_pH#I@=2iLTEcEF2QqVFa701?>6GCnX- zw|s?x>wLusI^S*0m&Oy0iZ4gg*Bz@5pOrEqLO-9eKDBhuRZ>963yDTuc6(vTB))=- z9PQeRR~w5-OXs<7=JO|Wt>#7=9=4*;_*?8F&wi80p_W?k!BH>q44htS($v}K%a!ym zB$F$Lt8?K*TH-GZ|0b3C#1*36+O^SGA=ug@{@tYaor&Gn1MO@>20-s6tRv1cf=(2~ zk5VXSCOtg31qiZ${zde65KsUquh#49X6p~aVz)#Ka!P@tG195}zmoZ!{YWtQ%3Lau zdhrchLAbhX-t5@%{42xZGtsn@c`d^(r$U87DD}(3o7JGEhu-+b*LDnNt3H>BCJMtp z42HVVbk=!sHJgK`<7hIU3C)Eguge1K?o=NrSXSZ3)zt7NjJU1|S2`!RSr0Dm+A8Zt zDR2#VdBiTvOHa63I0~m3p^F=I!?Nk|1urHn4TQxAD8!5QI=7;+RGCB#$noqwn0EST z+)bHCBwid|hWke(!)603k)7NK1}ig}nF?bKlxMOtm0)m#q4Hrg84iB!J6RI&G7i#B zAtZGwMVm_>84nh+=at21-^pYq6wF%ce75i)9`?4jzV#gte#n?wRw8ccIFQ$ir>|&V zL{vaP9D);6=_(^ZLi4M|qmB_#n=NtX=A^&sAdm)+46)S_X$k79jsGa;SCZ8kj5%Uz zzTWHaIpEVX^|mYrdhF>$gD42vhlbj2kcj;{xu*piB1xYeeo< zxr$yrO*GHZ7*c&EI%Ui@PAu-qz&)lMRvih4QheHO?-}6JjICZ$V@oN8!@{||R?5Km z!cs1od|Ghq3Cq!N|2RYg`PE zZ~zETVR$U`DF`bl4xea&kA%l(=`dq|SKY#t(g5P*|-~;qBlQF`|z<)A%NdbkK2H}*B;=b9kc@ZIst0hzdZpd^a zV1Q7L77C%Pn!Lh)Oz&tA*Vo;L#sM?dWjQj6hCL3JQ;3$Igm!qboQOy?!?TB0Ej2&C z5sHMvHS3IKfgf0cpB1QyTvFB&Z+(B#i_8%Uhko21xzHEurr?A9V`X z;m#<@D!w>f%V2x!fkUNY(J==g9XxX6a40nVui5FD=^*|)h|$RBLo@k&cCk=|9!frf zYp*w|dJCzZe!!ot!RQI@#+p;}KM}vu9jgbP@dzVd!~G6iArVY_YHD2^hf$}DL_~Ft z{DYQZmq@7c*?cY(gG`gVtC(}*hY#a?h2mYga=97{X7kxwmm3Q+bA=-J{S*sxGYgI7 zTi@hwmHH?)M-|u8*<8LGcXGweO;*#*=d$VbA|9OzI!*y`Qu3KhzES~IaDu14sXMQ) zW_}Sb=K>AAFRB|UMnqelH8^EXvTqdaARVq3!Oj9-sq4Vepn|s|2V&>4hlfzoNjL|n z=6>06vXqYWN8$gh6YDSN^;DqmLjArtB2rX`U#}vDfBZgOqE-vHKeHb0otmmj+~jd7 z;BPn8xvedCY(M;Xw!l|eNg@0Ahc%pf+p*{iUJ#9KgY>$-!R%`Rw1j(PA~*pAW46KJ z%44h=^>S-NoX{I2x=c{?&SFt2BAgNFTlI=fP23URO(ZK-wph^OLK|IXjW4UWC*S;gtvs|;q){C;n5DrY?C$^(NC5O!rRNa zO7v7|Pv*=~kyLr$Y}lMNj;}B1uM5(-TF`Due;tH# z@n~0S(7G6zqGX3qDXK*6dcJaLq+XSAG-P(7yy#5eJAw zAQ&+#U+VKVi0o3HV20At4nd)YcSEsUgjwliwKjYzltd6Of97m`_nYdM3zUSM)qjK`AXIa;}C|^F@=UF=2z>}(@qqD zaB{6vxt9N7I5~I7JmmZ(Ke!__n5VJa&GQqxO}OzxqQoItqs<;r<^~PrvN*ulU5n9Q zT;FJ~w-GV~_=zblhqj2EaSHY#oGzvb8jmLca!^_;hu0f;VzJREjKML#YZv?)IIlF7Yv3IL7B?la1QO{M(T+B?(f`|*4h-=w>-Ce;! z0K)VYlY#`|9+h=A_7&^__X+LlZf(<({xz?y;jXWsi?L|Q;+fXwL`0l791kH5RN*es-X_Bu-OivNqQqFiSw7bEnYtGL&V-NX+Oat98UMEJqNm z+X`k%7G4+*_WIUVYhj~oicD64=wq#gme&_AB|B3_&1~x?=qqqU_HJ8SSodBkI{^*X zR}CX@s)$6VLNZTaX+k(`;RvgitIGv4fap`6un;$!;aK(BBu0-lbz0@Y5p3{}RjlBk zCQKi4S!hoBjt~Wi8W?1;?S}4PCjQPQQknQHe0}1yMyyTsrq*bnfbl3g8+X!)vpw_5 z=-rwDcJGurP%35rU*g^bO0uIo6V14}?>G0XJ+rc_vX<_ys;;iBORdh9)KY6Ngd|!> zV-k>s1Q->@2pPtZvJDo-9?C2RJRXx{dpynZa3A9ZgMq~MvGLf$=^1alH$LN<$D8w* z@j36b&GC8o*`(h0{c$7j&8o~+w-!$?H{)(`BmRi^xA>RWoBg!cXNtumYTK;Gl}8fc zxLsogO~@6olM}NyRw_5nPMk#LQD!)++3|4Vk(EZbLCR@r0hk28bc9DwKri@x%pd8< z5$H3>(i9?)Bm9)06YeI7dfN%|vZQ7>lPCPlY)cZhWQpPiw=L%A5_%;OV(OCLkFH02 zTu>2V3bVO0Y6YlF>weOf5I3Bk_LF;84R7$SWAOrVjz$7>$cZB_ZPFf_oSmH->SfI@(+6Jc^g!WBCjP2)w)Js!?sVWSaa2c#IY zxU~H13ENIs6UsWVa_8a^q?xIntW_%rV>(i))=pNDcxFkp1}RyW!-1(I5$qT@!x2yM zBcH-f>f+{|D<`a6EEZayt;dj6j`hV*sL!s4&K~_USLy4P{#z)zj^uCWk@GC#6wDkm zaDWH>wZdU2Wjl~Q;9B}`Au!YQ^z7_3vIV%M1M2E)>Gv&z;CM(`ZS-q;>~CjYnQQI! zP=;WQJT@l}3cz_pYa7?n^}$1L)K5l%b}fA$IOa`*=l47s#{Ri~=e75K=$M0;LR^uZ zvuo+};PHm8&8Ql$rN0Bm+5pVKon~G?bnSf{I>H-HQW-tEy1N);*si|d6>cxHy~|{L z-O{!6ec)JMK2VWV>C%yx6c)qMC%kZ240Y{!!SERFm;;~$eb#fotGenduv;tmxZjUh zDl-fiOnox;qeR?>l39YKfa;1+j<6uSvPN;Cr$oelj_4*qGCY<`hMz>>dG+P9iPJYc zbb4_ie)ep9Ve#}sH=Is%1Bb7DLvxAx=~8v*aI{GT_n@^?(d}&CQ#oGinQr$A18unXn`h2gs!~~Bzj<@EJfeO$ zOcbYnRm16Pw{Je9PI*kTJ;i8)t4$xw#<`qvjcA&1R<#jx;2ijY#{rijBkMS%o|+vB z$J!h~2O$=j{_tKp}Q-A6HVEk3PIkHwTW!)ZHu5y0Y4fFb^n99&s$?le?%Xj$VOvh-QSZp z00;H`y?r^xbgm&f}E2MPjG~!3nA{x5H7{5!XT$1uO;GLHvY!wi}#s zFqt|4xPKZ8M%SZ(v0!j4;1quQV3*aeBLPV7U-#?Y9yrTD_fTDMAB#jX2RqmOy#PMs z{akQN-R}j?zPD-*uf7Ui{SnL_*{wN&&evuUP?PfkQ4NOYJ-4nsA=BCL%F3xT#fL$usMNU{5JwN1&%bV!lAyjrfc|X!q#8|1>_iZS2IY>fWZDjph5+3T)@g$ zP53L?1=4I%Woi&|Ty;1fuyqKUf#r*ZQU)P{iHK!GeC4GWvc1F;(Vxtv*Qf1R{8wJc zzTfL;blxay8Glz<=Mb|pnTqX4RrrTha%FvFd~EECFJLcE@7-8>?`9hs0Bt{&>GR|b zJJC*vUyMCH4M}9TO1P74wNlHdeRQ_5OCq?-)_$N+*tf%xB2*CGtwEffg(D7r+B9|( zrW`MR@*TF_Nnyjs!JiJV>=`2vY;5>{IxhBfS+w8UoF_CDcKs;|(7QzWJ^#)pSJ8Z#Ht?uP(w~k3CbtaUbWIB{ZxE?j2T+nKQ5$`eLDU{lf&_ z`-QxuDlSfTOCL5dTD);vK96*S*|ay?N&tFkPJmo)2fByr9M9{Y0Sh%X(BR}|6T`)g z>T>hq%#m^sE(pxelJZ;k%QQ(OPR%U-%Jq!8o-D}gdwml{VOP|wYvLcqn*_cN=1Wv< zL7D5Aie3Zi4q?FpRTmud(Q67=hI9l9=+wbY!_oF{GO_)nwMX2BhX;o$s~~p}5Pw7C ztigEtlZlD(nUU;F>k6uQ-XLWz9W(qMohfP03Dr+O-4afQA(S9eGR_-VR%S>A9>m@e zB@ALlEm`!mx*J$R#B*4AV5RG-kcM-Uy zKaIyvOQ;}Zz(gL*ontezW$xRyT}t&OM%(e!BgH}7(_$=kg9JfB+D;_zZ=z)k}C;hs>a%oUk4l;t*43|9@v~HYfiD4gN{GXXIIoEDipZ?q@{5Dh-r)*$>koEM#YouRFBH}ePI_q4u#SWY68?vb zP#Z7Iy`%d{yx)P*OR8hr7xi-o-*)k419sYz(^1f3?wFNrx*T- zmUH7%CfOMRBC13)mus$GKQ&bwRcy{hj4emp+T48b*0r^4XmA2K!#0hmK(thxoEaHW z5w;4J-l3G5%f;*;20Lcbp&6~q|nrNTh5Q!J9bXRItuS}_f1JvxE`Fh z_`|s?_ST+uKhTROA$oc5Qb-l< z&}YOK=?WW!YXwC(;s8F|hw*Pv9;z`&<%FUOD&3%khB3~iSY^phLV-vND;9PIynEW> zlLdI$Eun5~@&%6u_j(&Tr$P*>js7&#D!A=l4<1#&8H>Fkk-Yo<9I4x8MLcgK)zx6w zhHom{38~Hw2Un}GA%W9*Ws^U-`|nOB-oSS2c}rTJg_amnXHF)lv;QFyfOp!85Uv>? zap=~byN>?503`{0sO3X@{7j^&kU^+rk`Os2$Ue+MJxOuYp4UC!?((O=P+bpjFj^zoM5*xro4>@sLfDhlc7e(NTQ~~D%{}ByH^I;z#NU& zXHTA+$oc;^Rh9{dh5T{-?a2vC3DpM2&uhPz98C_o>8f@r5fpcE!AC z13nD`Rd24@_Qx;r1v#i?*ObOBRaT~^6x+bIz~XrYGX^zFg_v<3N7*)p)67SV@c!eWEc{D?8?K&a0kPz(Xdc%{HHeFj9$!$kxR-O&Yqj1YK{*B+TsS0u z2h37~qzPP8t2ZM$oEmI@rHK*e?R_g#g({@EU#akI@kVn~a$H6Sn$0HX3GvZB?zcfl z>lz;!Q(?bY3t?^MHbg_@o>OhKN^KeP?P;b6`SzfU{|eH0iE(SISGX-1kNF!a98UEL z5BFe)W~irV!^+3IVh78TYAx^uUWoZwQc~g|we-8(u@fVmA9&;|j5tG1cOI`gO`B^yoVk8&$+?%d`kY+uN44y?uedzsis%Q%@1s zKQ+amCg)SB^H*+Nd$%@Edrw`NC;9sjpO|9zrug1@JZ@xq&fQVp4E#F530$RpK_itz z7IBLV^o+^QX+FHI-CmH99)0K)hBi6UVBh4>Y_lkbMw3N30B`ugf3@JO`F~pf)|q9jk0*ZHp()Kb)DO z_cAW7$X_Y&5euiz3H%}i$!&8aYhWRPaj=`QR%QhTsFh*nd@wtDG&R-dOfkl zqoqU=Z=MAF)OhH(LKDr9hk%@=Sn9PB^-_LK>Pb>dP9GQKj34)`xwv5uAJ$p zC@5F-b5655XqUu_7FoOi+w4{Nm!I?9gB`Xey+9zB4bmNWcL7a2hZ2`GkRB7H6Zgq5 zKHrrMzYuG|20k|1?IJlg4oXY6m_Ih>xGX{aQ?!H>KhG73Z|Vr8Eu)s`_DZVNZ0Juo zKb=FBZ(hR{^|&VC5;sX=UkcA#gbmb#uz~u1$!ZE24|Cp0P6`WNk@D)wk=w|ZLDrCv zHz@m8weQv{_!dc5FLIwY2&#ekBsLTDSE>~_d0=%5R%LZ9Jk-F@(pjv-VV%YLa|GPy zO5OWPPKhMC;oOb>T?h_P#kYs@cA;R4PuAfpjfTKeVA!lo8wzH?~?8-(o^s0Bly<%i^VP;)ZlxI)W)5(UN zNat1;?9^O2G&(x7FgjwXiFdv#6x~>1hMdiCbaU#A-~br#oSE8;hBwnlqY3|D>T(IU z$9%u#`yJoce1G8kW61K*Ly;n^X*@+e;u%P_{4c~Lt!OU0whk2#SgHb|-f*u9x+?u& z))N;FVOcMT<1hXsvpuh_*>M^QVcuu_v*6pOeWd71RhN5%iRk?Nb`}Q*a?C_9^Bu^g= z2W}`mHo{pnGgT@EOAF!H!l_gwm~4bXGxbtAycmfrhRdbd*-)sF424st7GmLra&R;d z)QLx}N-$L`SN#6bR3Nl?!i@Hb%6!0&7rxPJR`=H`2Y&lg#W$f z*{BipsxtS`*ki|LXG?E}UuUj+BUF^pH>+g5h(Gl&X8DBeIi$lc_zf;=&8zEWtW!Bjz%xu2Obbj;XjaL;>(f%-iF+MB=A~M=P7kS& zC~HdOiR*ry@94Hbu);?AZ_mM_{+$LZiC{KIjM}WoeK`QHnUl6#^h8GMUcTY<^2i+% z$f+9pN-T<8Y46I#qp`0*C_`0`yu&YVy^~Q*d10TQJ5R)Vevd_{noa%Ce43M{Tih|% z((0F9-Xm<2q8Ih-C_dr>eA$M|>GzLoKulNGLOv ztYvR_>25@wyYpS6*aeOLcsP*=FTXpU&&R(BWxQQh6>li)ZW~(V(MV=QxhM1f~!4@uws%&jW zBWg5zNvVz1Qq8iXj!vW~+C719k*B{>Up5_1{s+Xj!sj?U%^ zaHR0qj75rqpgfKO@n`R`wZ4i<1%d_$mCFO~V;rG#U8B;9H?&<_?_Kc%bUuN4&#STV zaY~XrMmVANj$?}iy`^-50AkF8SeY5Sf(?}8V>1=zSwu`R=YGWd_S3fgoX^X~VN#~m zo{gxwy%$V{tVTT!VB2;@AAcb;GLpIf#KKJ{>jgbfW2w=xX?ZaBSB3h?n-&lf1dW*< z8%>SrC)D}A8t!Kmf8kZ?cCVn$%*cY=S>T(C`pv~&-KbIT51Vlt5j=!lHq6Rd8Kfd0 z3XI||m#}Ezz4NkGg>}69Kv1iBK{-8=E1<0zioR>YfoPc)lL8uKO!>4p8i=npc@fX^ zkor76yx;VC67TAsS&mfsJp^m?-tK;LECC0JBbLP|1-vH9m4J0iJ`v5W z%<2oX^2BWg#Njx@L!xkohuiv|0saU$_=EA5e-M)H&*-&9)FJo`!$#7fw@CnZjv&wk zwC}oD_m@s!b4o66oP-{pOwKYkD}aHx<*6Sh>nK!>r(h(pc`eux{0zpaZ@}T?t-l6l zh^$h5XN)mi#`MvqED5lrqh)_A4(=nY3ihNQScc(}w|)pXKN$=JEq`>BSu-+`;%!CZ zAfgv2zfR<%VS5Y)C*cshUJB{d7&J;#k=gu@hOBeas0qXc3+rYNG>nC)?<~U4P)5CTWjNQ3`dU~3t*fuRuSO(CD8YE?3(QO-i?*z z2vh*DUSq)fFcxukwLDH|YZ*Y3%L)rIP2-E+2n(t1A8v`muD9wvUTco|IMsKuJ5y8N zj_4?hk!T-(Rpk&8a(6{yw}L7V zd+{}l6ZLzJ4AJV z7e@5*-{H@WMnxlfg7%}LZoApk*Y)wMONh)AX5FtXU?wCBuO;+RaboGKs`VX2&T(p# znxoOt@93r(TEIE~62r?u_6Dyz)PyPkZfg^IFCJchJpfP+2ww!eZUf{Gz{x<+*g|y# zUU&K6N1pQ{f*h8oMiP{yoQVOC8$^fGNMs2{K+6nk20Q{afr|rh9|D)g)kG&i5Bpxp z;m^;caydH*ET?X8JU$=GhCvHWi9pnh`?dn9$1j&*DcR(_RjX|~FM@$?N za@F;v*Gl*Q9=6#RfgyeH#K=Y+y7~BBNOSEaAl;TB8h<@}vHi7%qx#0+eYbER7{50% zKYI>Ry}xt^$UERyL0r7H!0$A)-v{_+FH$1I8DWOvQlyV=_^rfvh>Op<6ho)5`S$u6=qn`M( z0}TVWzyE0}-n5!lcK%SM*a5;;4>B#_;Kno zq1J0)1%?ZU@VA*>2at~L>}JD}a_vs1(bFLG3U}W$2tvcAhcUHkbSk2)?^VOXjsu`b z2qE(ffH)7lJoz@{-bXbI&^3)wa^wMe8zF*Yyyyd>t7a4QBQ*)Kc`cqKyWVNxAQmp7 zI0=eNq>hyn@RW0=#C}zz zKNSc3|H6RQatFaJUC}ik3?+SNo;6<_K8>A>(kurFdo;kz0~<9`x9eHCau|NBT%l{BO{ZE$&nFzjdCBEu<2Q*)B3|} zWq12$?WLyXWt-*~DYV|UX~fKjM$#~+9eK!znVWAz5-l~v-0UFREB4#SkI@!g@sq$0 z^vEE4E5Qk*GI4B;~*LbtX8-_V3*XPGJTkmK_CSy+^I(T)I?%%lr zWSl3AHt&fur)Oy};MKjRLN+jUb zK82Q8p}`1p>s95{QAyR7Ohjj!>Jda?<~)nEgBqULnLj>mQ*yZEmCeL$G6v7&B9vCy zQ+Px2uC0EM@3M~XrqXxl$#%!e+*~eRE=|U8-lrPHlEj+yJ@+;1Upjq!Cwl3bH|Qz< zx_Smiz%~n!5v)uZbhZu?6!aAHhO|d!WH&x22 zBYuBfwKaHRlcjPzH&;Jlz?DvYmtW&oKe#IC?NB0 zty%!}N~x;g&#VLjM;thJ#Kw?&oe)#|bsTH%ZnexPS29_Fy#qj-S#d8>9yuenAy~2~ zwY_bMgeh2UQx+m3apa4)*yRzY=;An$N7k_6>rt0?+xvsWsn(}QVCwsv&x^RWjM(vN z^iSJvX!+qZ`u8Se=yA9d7q5uX$^d=1XBv6s@eM>xb0-sQV{qaT)`9f7yhK9fG{qJp zE?_F+Av}ro$!XUooW9qHTY7E{m)O<6^8&@5M@X;8ve{}F-^h)PQ;qJ(@0XA{|i!4+zV;Lk3?N1%p>elcA5%<)&_wJ2YIW+L;)6}?m| z(yuA#cJ`%TBiO5_l`bTTGmb$9oM2a}^dtSES#=J31qEb%lI;KlbYI728V!SJ8suW| zIx^acB@kZJOKN=s8N{&iC{z?nZL9F{G@AUel9l~=iT=iRTmRSk=SId)l#8!Pgd!x7KFBT`FhuW46@Mcb5)*o|yy_E$KvDovB`{2@#Sdr z*Ep}9%uXx@LsGMP;|IJfgKTe^smHgvT?C&FZ^lC5bcL#bN;({hZD!?C$o57YFRm{n9H{M#9&jKy21Ugc(*uqO4(UKHk0 zLmk*FIY=t(zs+QJXa|jhx=h>U9XWK7U0Ii4YIE5HWUF&xqLE%8sSB%y2C+M34PDkw`0+?RFeE9DB=Kx_|1Fk;}UEC*Ix4 z?zb<<%Ke;cWMNDz#2z1Gu64LASx1b0xu-QBE;c&3x^k^X5+2?G1mlo_M6k%J1dj=0 zA$U!;Cy5k6GPrERH9pbXh?`QEz2y4-=d+baz`9@CO04D}NNO)x8d*#pG?RE(Z(EM$ zOo9g@Jt9aZJ5tC-oAc~I_VXG~DA-Dj-Tl0_5~a0>ak_!vYu8-tcf2nY*B}iM;f(jmjT6qw--r7$1JQ z$Kbd6Hmnc89D6*y{T;52cl+M&`-tz8zEA%zg*}qoXhb6cNz4fu13^E1S6{BhbCqHG zq)|4C-7Ha7k00w@d0CdzRfh5tu5-o5**seg7fqpPJh>-P4Kv=WA5=sX2Qiqg5VqKW+k+;>GTlC|Y ztzRX~BRMtzl`I;#6z!Avg@`}%SwxyiTGm^@_Q(7a8KfGn-7R^*2P#<)#1~c_|J2mP z{Z9m=H)Z`Rw=D)E3rU4g;E@qz^*cU0GjjC)iK!`ncW=;s*`dr0UR=(;-|f3_UA@l@ zb-r681tp7x0$4C`U*I`>F zfGFG%b z1MRr)$Zt@`*S)3)l^sfI<8T*vEHoZyxQisILZ?2P@khR(c0h?6U`+-GrBtuu%$MbH zDuYyn(>X4Wa#q$yav>62yluswy(t=e;{FezlIIOcsqN|2_n>D_I}sbsz{B$Ud_RB) zbUz13h*m`=w@h!3)1RjZUK>(~_<}&R7Px7J#Trham2ux^(OD^curBv{7w9q8JAq`* zgmp~*B$6Y9dFY{a06$`-e`Y7Yf3jebpuP@^ITb>9Iso@MABGC?;DRvCDZ(cYYghk> z1$Jo)TI>s6tsrojBj2ynvAs9u-{b z2FL&vz90tF-)epCda3D5Wy(&R(gHC2sw1^c{NQTN_N!WLlOh@d{|vA!d*qf5(N)0Y`e1f>5V1N)V!gU@J>MSnamVgOLu>eq8$^1q6J&#@+hIa{ z-7p(G&xR1?!)x`imjZIb=NUqEfPe|YzD|G*o@20aXpw8-fhv}lV1BVW{s$@>fXja9 zrGU@ynKd+J({(~?@bns(z3BzOZSWi$lFjMx9DBK8I4srEO9I2IFo@SZ{|3YHD$Js* zznett!8;^6nP|_z9Xgg8+R|lBjF6$U)L2k&M9AJbHI;*}2A%zi-eXh;sn?a^wh}0x z3|m%kHmZWb@X}H^7>v#`Gw{jRX;Rm6e)juM6{(`Rhg<3=mqVfD2w2+S%vv}Q@Q;-J zfk5~q{8qv$@*nVUM_0ff(rnMW2ZE~TwYhJ#z&jRw6TTzR;huHYW-opo9Q$jU$*6%v zz$P#LqlE1`7J@yy7#(L1-9<=%if#dp4?KVtsJGt{dNuwn+!1^jKW#a=LsfXseNsAV z!sz|xr&&iM{u}Y%=(vZre%csZtV6A2_IW86=iza^fM|vfa|ce5>qIXcjuXv;Mjae3 zI3!W4m&7_xPT*f-t_G_GL~ax&mdvW-qC;kN4Q}&J91{4^!Y?1bAHLCO5H@0WF5`O|`;w$fz43wv832U9K*9%V1+rtBgZ*&wg}i|2pi*OldSzywA-w8(fz@&0?v{FXi5-s`qJK{9zSuc699kuA@p^;983V+Z z24q8n+`lxi8y@T)>(ZTb%i`||8)VrzK!^pA;2@u9(>RJMKv!6N2qHqV#1b8!I64)!?Siv_NkBAG!toOR^8ytd_-X4Oioqh| zwy`d|zaEWpS(~NWLR`^fQ&WKlu^!E}jw$H(X-W2cK}8V@LE^MM3>vk88At7o`2HU_ zQq8a!TJsC;@1cIevVqV`;n(*ceiXs~^0xq0HX5yp>l5yD>&^ti7JTFb2amrC3qtib z-M{w&hPqj4UkmhSN%T+KY9hFZJx#FbObjyYr<$o|K)69qMEf%6XHlIFa5KZ75j^mbEhDiqhQXx_0{3RWkb==~HaF=P-X)VF1DE9j?1m0b0wL;;^g?Xec zCz(@1;H*kzeqquA%QLye9r~pungT3&%c^->G(sn&Nc6T!AdsD|ZEW6W_9*Y$+^9{% zvcqp(KK!Px1w*OjVe%B(ANw`FA4W_q+1+Lrovel{HCdP|P`8JKOtPEoZ$2z}8cL#j zuhpq%151HN@VwdW=gApxaaiwoUz?%~m-*`JqMI>_dUanm`|MLD-ioptR7$GQkzASC z4VZj^zv-jflJ5l`Nl!idcd!s}1E4r1?5#T`m=36hi}~Hs>uElXbT3Hyf+R(!)t6$i zhw0dH^e$IU1CJ6@nWUN8n z8f0?10b>&G?Jc@v@}s_=e)&gbxZ-?m(DuR0F*E}?nL{-Hr5~DZ%bf^rX|N(>(3(L! z7w*@+oNH$I=xk8ytYZn9$Xv9;!Ib}J8=~Pn#y{6s4QE62cr}3IxM#vBJg=k9&&02X z)go2N#CAG*6iWp`bAt6&k+Xsl6_C2d^1yvF2Au)%)fgEXn2klqs@^+Q?}@7H5((Y= z&|}67?h?@pk;f%-<*`?jNAMbtJ?HCTu8P`qunAB~ch7UZG?9| zXt#H%LB8aQcM};%5iiNXHU*?nbg#N4FMeXgcSiAejqlCA4*;TIQZz7000oSIsgTD4 z@)_}BhLmGB@=Ga(VQGKI%|z6#@rD8*mf8(6EAut%_vgk6rM#+~Rp_;pou?OCO)cX!HiO*<%`wjp{P4wDcGGv#jm3 zKy*6360%bsgT1K_#2I^8!d!1F9oLt`k%2?M#OwgVfHA2l=4yov9~;_c)hy`vy`O24 zCh0?)MLE2^)so#(e%uoXxTW6SBq@_arOl!ox_^V`(Z=+1zo)LCjpM#e=w9w1zLbnH z;ZQ-miL@a)1|NGiVn0XU1X{$xf=HVjobw0aB<@&IiQOOJ+|=Mk@ymYyPB@*;ubw-1_NK)| zV)3T4=gzI>)9LWe$D%5LkT~lB6@ALESWmsryvcnr88eXYzP{doyZ{e8Phb6xF+KfS zGy_FGS;E6gINmx~Y_DSrFcEuy^=>2P8E5-ZL<{0HgXprX;Pd?YxRq9OR3LEl(%@8_ zmS3go-T$slf>kDkMZ#&gnow#Xh@26V@0P5yoTH9=CaVJsEYEnKe4hU6m5kBGx)r8U z8*0?AHmX0uaB?HLa5y)@@N?N$#^bL8ppdPL)1P#Kl z+=Unefc#A`YBq(B@B*L+4_7&2)Y`q#Y4Roab(U1)tUuA_^N+nHW(9ghf4b4+)g8WE z>D7>yN86mAkl~PTME7|b))h`plnh}n0*po&=oXj=^c#4hjuV4z2@{E2>@od?h}!F+ zx35hvDMO2N&;`0tjO8}Q5nw05#3L(^!xQu9ZVx`XgY5LKmrOlwd@mlO4@hoflW((@ zX4-GX7m&aq;OX0gh_(0Dp}k{oi|nCT)=NKGVyDnSjcQP$O*i^a!B!{}-(YD&0$m$z zqHIL>*5yD@v=MY7m&;IyOGh9ULcT49BXB#!rCd$|QCL}XAMpxtAM&kpKZ~=9#`*dP z?WpF82Se?~gD-nOt}DO#$A3{{2z?Y`oS);mhTWsknm5vmG)Ey%!>*>+0QL(GUV;)G z*bFor4h?lDzIDAPMv{4|Gbvi{%C-fgm6c^OU)0<(i^WO-NJ68%zdmcw@0>W`%#8;` z-AuHuiS;5p6N%R9+9&3S%E=qWVojwp!SSSBlgN{tjgiQ!b+C_UM17L0hX2(^gN8=P zBHHUadvp;`;NO&XnW^yRk)|5$AaapSQxHe8?o3%5m7Hult7*BI6~^iRmreZ=?tlh_l|zE>x7;%s;kFUXJ8WsH!Ri{ue{-1 zMz%g#OH`8xJQR)MkgO)w469WV7pp@5T_+%Ag3ed3Ty7od`ogjY-G}Tn=VxWI41t$L zW-Q?D{rAOmFunMDiMrXcZre~Ub+{)Lkm8Zt_;@yv$c~T0L@c8!G&mFMOVrn6M@<3= zbkgUAIy&U^v4ed*kw1#19B?>-`xR(@h20#u=p=Kw{WzRgqA|b|dss2>tyX-W*~;6I zbay`$i|_BpV<4BQNL$lKy+-o_H&~XrrlY`05Ef}fihp3(3RFjX6|fjzdU@wo>xI8d zrgvr7CHuG^JYIyR(wfn*9mj*ZaXAd+MioB^gf_!cjahE55WI%)czA)C)Cg7Y$uCoR( zHyYIA1nKlj>+I^Fi$ehZpWTUJriyAFzV_VUE?{E0vt-cBG1oQ*bf!0NTEGuk^v9=| zJq7=I(*YeB3~}|-|2CDpm=ADMl-1}H%qM%(I#tp>hrist@@+$RRojRujdFL7 zqpWuh)NXkXe72+907x8txZ92m@gIcNU+5{C1fC?kZ51FHfS~G-^cg1FG!u2>nxCw2 z^*={^Rw-YWO^P<#-C=w9zx_P-WhY(w)jZfKh;^o0`>el$FJ0{+qthX+7y-_E06RP1 z2OU|9bDS=xj`bwzhUVbFNr0}c0D}om%a=^b!ZXSnaJPmS-F3w7 zuA@F(yHoQFPEDm!;Tgo$3Kj}MxFaNj!Juv9kWjztmfidRTY^-G1Sj!+M9`&l5i0yu zA%x!~9KPN^@r$YVrCu?6*X)mkbA@<19WUf4fX(V(eN7>OVlZ*y)TtBrLLdZ3{`#?F zb@*z9LI@6F%E4f$_slQZ1w-QscK1K?qe!}(Ja#OBHt^f(_nV1;Kn}WGv=ARGiU>m-x=RmW?2OvjcL*b-Ga3kggXPGWeuPj6FwX(co7>RA2 zN9Hs_GeUL&CBn8$JzUzTgAd=RPpNviPW6EnRsSxPYEy`c7Ww~$K(UeLbd9njC zp9t@CZ(dpHjIXVYzw+kUiHX^pC$5-P%Jml(dfr0+b;Q~`V5#sZe5*j)DS&DY#Z46f z0>N3v{YPe%rYr$0A&OYNCY{qhedC6#)@&F7g6fo;8XGbj2=EWpQszP;MliEC? z>Sd!&Y|FKVt4I{BP=&Cm*zVO!a^c|B?mo6B$p_o9XcFMFYsd{^P4%=@N<#nh%X{aE zz|OO{Z$3=~*Q*vni#gdf1qeF>uU?LZOY{6xTaPx(!;7`~QaEaaER2cfu{bA>AVZ?; zXhO{`%#}FPh>}Vq4$0;Lu&E2LzbO$2A_9C4A(eN6nQZbbw%1-$92+m>^Gzx-$BM6s#~uEa->pE;d?#(4au8}TaIS=jq2@9G`%=eL z$13sU)RmQjMQ~aLHBxpj2p`V9bFEm@miol)f17?NT)tqydZ8SCD4n)DWJx=AdI_`G zzzN`TLvm=?b(BvRjG2`BQZ$Rf;z+SZ3>rj?aFb~tn&Id_>W3T7PYacsa{nEiIku%- z7bktEkZtsSCq^LFL{<1C(Uwm5duV~Pn2SOrJt@c%39dp4FEFMv?%^5C=e|fEnt8-a zSV<1UxTJjSf!1HIj^^%m@byCy1_|L#s^)`>i~D_(qHhxF{d{ZEI`R%BvTH>XDl^T@ufEEgO;lWI zHLo)503zWZHH>Zn?S9brdfz)GN4xf56gxvIRtk=WMf~?40>N(QT#w^;o3klXAT4QzpYyTVJE7FaQz3ib{yMQI|9@NmfXZ~NiHXrSy&@QGM1lcp z=DxW=q!I}R{YZ8lsb;1F_x&sN+R=##>(YKTvB+2FMBZIYRC{d1z03f=vhrF}XV0qF z7wPe3G3=B~g9_aWugo_xGZQc<(5LK^WZWXIy7LrWCi8Ye^HxLxK~arC8AgmDbh*wF zrnPM1QqFSo3g{c$7Q{0er{K2FDq1`2$M-Jr=$2pUjU=|; zqVb9*O2tW3UzseG;u0_iwcm(cvW#A9ne4i-jw5wEU?h7plvtKA1MWFx|&{ z`ZG8JzOlYJU4c))CN%Poz?R`%w1I=^ylGq%6n5I*v5wlIuK^`Q9a`fJ>G{>(S42 z|A!ZHZw^>Rtd9VqKFvZr3x$-LOC1?YC6fq-l1?U(l?DkwtRup@^Qe1NWFC(>*AsGeE-lK849DP zWw#cC@VL;8O@7LpT~I>GO$#oly}rAbyrVsAmB7==Gg^IXrsAC-dwrelHv3NdKHHPM z+mO9ypoyYggoxHg!lddmFB}aNFQn+q;90w7zZoC;4{LNiC#O_;e3k13r%jlsy9StU z>7b8HOTUWaXO3>oR_oewnoF(HY<7oP+TJiuyChin!p_&>`+Ta6+(>KSWZo}X`DkM% zo1cP75TX$&!H5np>o%6xWczw!&Ee4({Us)C0L5GZU&1+vBIh{%@{9^N{N?LnMxZSw zF!Au0^H#VSPRWf!yMd@gbBPt*Q7-MHgwOEU|s^MC9U(nf95ts5PPl8#OGT@D0sdP5HZc0lkH#CyKnjRl8`yzCSRHgobV{kzZf0%$7`9y3g zI2A`}d^|Q8ti(}7>6{KNbuKYcpPh^&1Kae`spOI43TB6uL^Qs5YdCbAb!=CrVv$oU z9S?@%U%4iKC`oyKfn=GR=KR7$d~%`6uT0L?`^&fJkXq*w?_k=!MVa!! z-IU2ZGCKR9{RO_tDt5H7$VDR@UOaH)3;bZDHz_GIYB@jWt79+lA!HuZQ3U!7Cu$J9 z3_fvL={h*ZFc!aS-@z#igir-e7zidI7uVh#kCv{l83RV}rxzmX+0i z?%w-~TkLj)19n95|(*tB7#(A3_86%bL!O0sfe<$T9eW7DMp=# zHG`Lub}u=uVLRp*;pG3tjD)O4E%zK(>i9i;oAJeG{sm-w@JW`>#vbRBreDB87;a2w zHd;`ZoMWV)Kl>q93iRyLE6}z3yd8VPOfiRkBoMhcoF8A*-6U1Syf`|MMR;ZWN`MM`ag6UX%sKL>rb0$9*s(ZX6NnAEP1{~b z{}lK+SO{v&WsOn^A#u2hGr`(gN%(T;|Ddog$xfEGgaIS5q1OW8!2NU|rOGyX(Aqpw zgQ9af7}SO@G$?7h=*KM`VihPXl3%FX$B*53Y-R@Ozfq@ZHCnA}ViYiT)O~vUc>gu@ zaY}4_4)jhG8>=NDG#u{(%l)doT^>XLw#m`V%+Nghh?DO@msRU3{T>Lot0a&hzWW+< zza3;DoW`o$VN5RiSYv4U7lEF!1DUC=W@;d=Fuyrj!%94@a!AFE!iL8Nop`wccZZjCRxLN!JX`=efrm4|zKkm0?KEUhp=s*uwplu1mchH1??|WK01G6~hK6@f z>=lNpv0uUzqd*ZubTFcZ5dihu+_?`BI(!v7QTn~peg#HfTgo1{vY9DFp$<)Dvet20 zw9qV$v0U~F~#G-yXLLR*Y(2xdZnfD zv8HuL>02

    GY!Fu9+A_RM+$d8rfWi8AF$_u9;}G5MG-pR55R$!Q5$>m9XYJ4=U4hMl_fC-oXL_SFP1b1H)e& zHn1#_B{WQn(4$x75-^yu6Ung!m0cK1ChS}~k<7hS3{$PzO^ALGnM+1DS{u>iT*UGR zZ>ovBGWXWoHJlx3rsZI2-T9`zr;ln6iBJ>x2vTKNSUqfAm&uc~m&R`jL9G+r*oZ0= zZW>=|lfPNsMJ7dFhD|MbHj_UtRe-bZ08R?O`& zC?ac}D6IvBa<~^5<_o!M)pwHYxG-_}&7_|bZqPkfMb$_r)fZ#-`R;d(Ehm42AG-T; z-}%2eUy3vXPzGvSH1>ZKx@{l)yENYBT+uwKS*K$6jkPV>J!&Sc>GgG&xY1#>oHjH z@*eDXWA)kf(AlH>P8qar4zR#?us)D%S*F85mUYQer=$5?tg)91g;I}?SZO=kdInzO z{_^9=U^uteP@m(@`HvU={ly=5;4W*Jnbv(9L!~Wr z3kp-BNa3T|>Dqp6Vggcbc4DIDmX6=KdcvaJ^^e2;+B&g%=Odj=W^!_RdJ=g*-O@5| zX$x(>z%_trAn>Ix$TalrH#j?ZPy7Crq4wD50CNUtI9?$+t?9)WV9UJEDRAZm+!+{C zXE0V0K0#A>@-kha^~w}^$@(p#tU)sF1T zk(Yv3UGEb^OiczGpm?rU98}``kmvJUF)@6ouL)qRQm|^C$NWUj%vA($S;ICmbTMJ( z(8K^?t{l_^t4iF7p-r$EWc%%s+uZEsw- zaM@e}&Kb~7Q3*8~E&c2K_`;s~?QOf)2EsG9SEkIL=oAK4`)Y5j3H&l1B_nB4N#GXeY4~C54F2&-sWwTN>d$@jrN?Ud1El zZ%bp=H~vrT^eA5ibv98a=ga!q3Q#v)4|QZn?Q$Vx+))q_xx?K;y~?Tj38$_%9z0j` z!|s?TeLyV0rJAm+UY)LJ16NT~Yqhnu!YK?VE3)_7piZJey@oCgB`c!s^*kuA5bt^0 z2-<%Y+J{@R2?BICyj9(AvqgJy7}^-IcV!FY`}T8}6@5cnd7Az7uiVn`dwYm=JL`sUyq}jGGvN7S58>02RXu+ z5J=|(YD)j=u->}I_geH)G`1D{;`r3dyhhuf|MdOYl2BLJQko_fN`_{k{PL2F&HS}%g2Xie29mwAT79v3^ zcvevQry?AjJK1&p3*`nCxOM8u`AbF}vIf=YB412ND0Hnqpu&XucK%|baGszh%m#HO z5IbsFM`M9-G#E?7Qk(g5B9P7_hj=QGDCaj*v1BwD4SSx7CI>zh3q-?LyvakSiqOXWDM zH{#g`M049w)#)2f-+c4w8>Yd?ddg2fi(vI}u$f!$BI1!Y?4#3dQ0KE~gD~m?Voqrk zLcPQDM4VE-!(pU)aoq+-ky3-=L}8=Vgl#i`?3jXHDn5UYX4HI*0~pbzS(^RVVkq2D z{4!4jHCEXv5ep#Yim(e&oI;F?5pUOoOQ>W^6RX?z5Az=1e>^`jnmhKG+9go=-{8dW z+@asX%MPi%<2#Vv!HZN(HBva1%N>*b&K|BeJoXs;t+mD+3!h%pj`OkPvO+hjg(@;+ z5h>IJ1JpKtxMXKSTVk!JIbUlll(9?Qzf-B@)qFG+%|9*2)n&9YS^QQc@`!26Baz6r zipf-?qcY%JI~iMPG**_Dc!bAW4;MQxu4cS1=rUi5M)fNQ z1Vx%{J8fU{Au=5sMGIg~bKME3)V8kL6RtmYD<96gswX8&m9+^Pbvll2uGC40Iop6@ zYN6T_iBW0(us+Y&^nHLTJa|(i6tWz3jsp<)=7Cx)M63Gy=~47>7*J-8vrg9ipg$CX zj^1w_0Gs=Sry`(KV^2k#P{5we7A(pi1SNj+Fxl>~ng`5=&g-hVMZn{*6ZnS9B7qk% z+>)oL8Rw3fncW6U?wmo#-5G&7RO!to+>uSueXhfShrt|nrw2<^)%T%&@15`J1zxmz z`*^(qU?)7H$mwy|-O%q_5DX>|a1W;o*KbeF=7`5yv9{O7;+DX94Qp=x^y&FI%kK5Q z2M1d%FE1P2%}V=u10JN&`{3)_ z;&CasR|l`?F`%a{Sw#XEXm{P+PF_AyA{|t|S8Q#?Q`&4E8dB;0^z^B%^XIotQBMoF z8LhOPif?VbLhV|sC++NnW*_IkAs%V4Zbz$*wa6Rv?oNy4ZF8EmeetZvN-sm-70tzs5R3>&-lrye-q7j4}bf^V@pfp)Ptr< zr4)6pdT-{xK*#*=ATurjm%mEd0-l5DO@G-@*Wv3}lG^XOuT8jW9@UMk5A00IDz1!= zD>Xj8!lurb@n@cmJzdS=PZiCvS1oImirO0vc4&7{x78=-wocu0%c-q-_NltOytTEw zTxGxR9Gj)L+3XmqPcPhh>jHjo@j&lCG!RIRE=$ISdJ5XN+ySesCw))De!}d|$&w|E z^ni&0j|f`Y7+q$v=T^=r(aj5-$PyqJFUdXG=>ZyW9)KcJN^6+xL=X}dMHj>!qPOEB z1?m7euMb_l(tky7B{W)&HMeM;sOxI*;3&OB$<=q!4TI&@W;YtWP*8nWu@D9|;D**9AlT9GuC9&v(8XJK@(l@n2=`cB0 z54Gy`T^c>?j*PTuxzPRh{3u#+(P_oS+@6<;s82kzu)e8*UwrXN&mJzlK5mY6nfiRp{dRTI@yh^69 zzM&0UuqUF#<+u02N_SuRjHHgu#SUi+4z?*Eo{7hJRKG`RDdBCQ%+pt{m`S#5=8({p zj>Lm0pa+>eZdepv(HX_Sq?EY zZh!}@@x)kNTP22KrrSk@fLPVjR<&o!>3mAFz8)b6Xj}_EEfTfHwR<@mWvi>+%MxPzs-P}|k0P~gaepJsbeO%^-uYL_j^`?LP zC`|n*5AS1+wV}Udyg_4$O8P|>TPyuUE5fz0t)Ts&Uv+-~EyO_=jx z>>!vT0IPW~-F~O-(Ef=+WKsEP0poab>+#-((FB{{LX92Wr0w@MbfNFuBQl)?l2`lMj2Nneo_BY3Oi<8diEuGJb-E2CT7ffm;TzbIvm46SOxC zSxNYIOSjVaZgRQr?s9QE?fNaM9U9yquE^$b@xypA%W3jJ$d-$ijk4~7GDFz=}i`>#L=mq4Q z=u3t!>9ecc;}yuskXGmvhIeMbyQ+yEbaZR@!&BFC z1sA%FLtQ-Zis}FJcB!wo!}>R1tX|akb zsBNsmDcEs6xcaraTMd{7Z>@7=SeE7RaXrAQqJI(OO0d-M^GoJUCo8Y6hE~(MRJ3fs zQP+ZFd+5o>&GS|ftdKdugH};^Mc@K7(o9yF!JNt+-3R_i2p;#+{=vr+iN~T5?Cc@q%44P;{dPoNiVwr@%2h{bybi(!sh~Sm3^=8jbE?XHg}S z@k@w}ST2{8K9W9=m;9Q@TcbD;y(5H1QE;MF+H#JNR4r?As$L(9rBbo6dVOlrvewt{ zyKjBnQodu`+Z&tJM54O6vAuna*LY9!iygqF1JBqa*m)EeZMby9sau8!I}E6qJh%Hr zc%3`-BJq=FKh9h3@Dtq@A9T0%+>*#hT812@ZW-DlI9&0w_$%y6@2diMHbHcWIFqWa zYPDW3MAKF}TByU)PP)?+5fw4GDYH_s+Tiz>&YaoUpuOA1#+frqY}X~;mw4S&9zmVz z#Q#(OsJ`S&LI%AXaAwx~w8ADcxxXMcr9{09*P}3ILQiUEi}i+_sm9N z>YLeOF+2G>7#n1}e-;f)24eBlLy73c*Im5J*f1&|OFgt=NNm%=H|o&gCA zG3@{;%s$eA)F|f2?Pg`bqJz3+h^#>I#)z-Omhg<4BASXKE1eN6tXwVbw*w5%2 z#uN8vVr_ha-@%$=J{|1!OW+P3Nzf<+id_hn-GppuFz_U1KM`+Ey5OR0X^V#l>p9pSL1{>1e5 zv!DGF-RVS!mpq;Q;KITOA8NKLy5s3|3=tor`G)k@k>%6CyR*O~&>0nBrhv_XncJaG zS8#$Wt4a}T5lX91q>`!eZhIU*mr`S+=}Wq)ILg@22i(yQu^Nx70TrueU5el@4hXfL z6j?FiWTn$9^$NhRuA&Mh)qC1eUiGdy^Q0xTYzQSANV*0SD-Hwx$b68o*dQe*3ZBAP zgg0bQ$tW>#XKl^a6VLw4XZ{#NVRz#34liBHQ1s8e4G6bXoZ;w*#F%?}p>q!$1I2LX zGF;i5{TY7jGv2B98Gcp{*E97T_&Mv$?Jd@sw(Ze*4Y3kVLq*x^Z3o_r?lOx%|1u+7 z9o{pI$a20MRl(>@6>}T7YSr)ZAR*^+eZP>pl#eH;y6wqSJbx)w*ypTJO*2ijpNMMP zos!-YDtFRd(RT1T^AxA5eA@>-3~U;aa8Ps#1{=Cq7W{dK$S4~9^rtmW?EEFqJNo2P zpVBuwsIP&(P_KjfW@sacBt|okN zYtHYnLOC>swE+=r(OCZ1OYs`v;>Qvtat50qUMJ+iIT~)zVRWExWalibc2-lipNk~H|p(vy7L;hmhZ zx`9yxhPOo4oDKj)jtWyqe1|9uPG{vSYqYGZMy0GS69yC>PKUhsiXSt(Wy$z3C>;^eD$N= zQ4~GV)hoy2=9At1=da%VXyX8=J@X}soh^8}%wWcoWx-6)!VGx2nc-sAlik0sDM~PB zmXA+61(oQ&ei#<3sl@Ub5%;E#JB3>J@1JxLkXv$Sa0fgLDC0r;&YjX9+%@vCRb^?`qM(dU3?17e`ptnclRZUFCj14n%jKp8XvV_s8ys>p=~-?ymA?d zC$q523x^T#fs!IkauA!ntu1dFv|rzxnaSsZ2qh9n@Q`GQA}K|gBt9+N_|m%XSwwpxI`M5&hXwUooAiU6$cLqYZeW-ISy<|L1G#$aCrqdPu5WA^MO{3`ZyeDp9o<~qL;C1TZ zO?k@Ht6M{uD4*?Xt4qFpU_T$iEU!2lOzy7IB|O~cTF}u=(MQ(drMXAAn?9VkfHWvf zDU_gEOU#WcLu>~VS`t?NujoeWV`CV8g?_EL%g1rpKhV%1_o=W!#o3KuaLk4wC*?w8 zha;L`l4Mb`2sY(50{*%fHHV?10vHUdVA)yT=a8&^GL@R2u?wlxXrY+KR*Cxmd3zT) z$+EIeFwTv~jd$FL8;^YFjf~8Utjf&F$jZuBJ)^20)eSTx-9T4&LlYp?cJoZ^9&JXE zm__B`2+1JW;IJe=-4$?jid7y0YB;(-QOB85W(H-~Ay?g%(N)`VcW`Ha6TSccckYR} zkr`QC_}I$sxX*L$Ip6utcfQy8zR4;0KV4coJvFJU#l@?4%z4vsyO7L`X3|JDlTD|` z(i!D>tLjUs^!%wx5n9RVDQ|jmveH~%$G#*yTVHRUzvtdNy4OpU`MKF_dNP%EDwTXX zGZT|~P8?WGz?o6H9xH2pNEFU}b<<8LU%ZMmk7*8dVRybX)F4SFU>5mPP?v!+$1SOE85UyPr?ER9WHfE$TLKD zg+WjdRP~C+zaoM(%CNGxxIn}b!VCM3@Kykv^*T4;TRZ%HVzO9-b;tC6ZSiDv_X`W# zbYRZc;VY}VX+Qko(FxvDkIrNTdb2e^@1Bi&)t4A8pxfZ@{nOqww9`E}IQj8wADB~g zgr1+q9%j=&Cw=#`N#0E+UvO%+U%;9I4hx+;khzzG?Dc=>FLR*t|#2BLU{E%7o+i$NUAjr z1xMy&Ji7RutKq_yo4Edz5=9&H2SKMF&i{+EaFe#IH^14kGAefVU*r*5_lAxi2`V?3 zU*N5w?+1oZ5@8y8U-g7j(g5s#CBo?2b``o;v-x`<-GGVxD^z<^01RqIVRj%r?7)+O z3fiFCZN2JC5^-XZ!^k}lcPrHA*=ikB|gl6u&5xBkEzbk@|=jnCA zlds4Zd8gCQj;fp;^QA!9;O6eV{&T1QiqpRv#KlqQYGGuOv`M~)CUi;MhAYs;LXC{1 zc3)@&1I;7@)HjLCUw`q0>JDj(FettzRQ;&yfKL&3AkRsjT$r9(@s#^4k4@dnCT=U2 z{zBf$)JKwzxh)rXS686^dpp~ttkTTVsV1|vesWI1QR)BTQ4dXK>&xQ4q~?Ab+v2E$ z)C)xNp3dN7f-MuSW0KXEiVhZLaPn0;8W39w6@9_sW&&w5A( z>q}lk>pU@}O058-Zh8)Tg+g;C?29mR7ZjNl5njOy0H`8sRo2`-KQCsRBQA4XP-@C| z8ruZ~1IQz2M-cCN8gCCjr{|o0u8|MFZG*kU?zR{ZF&TXA_|b=eljft(^=KgN79e$1 zpa%7D;LOn-T!GF3UF%^;Ve@sovw_=qj9UY7=R;Qc)QqEkyqHc!PS~zH+C7LyG116$ zbq~hfV)TrPokDQcpeeZ8bCU((@AA&5%Gs#no}k^l5v+w{4!ypt zb+CsAvX_d7w`QQlGK5hyR{DJhk%hh9rPuc?$bh@$l@9wLyy1aG-b(xwHf^zmOQH>} zwk%a+HYSq+oHiyr^bo7~?lI)B82?B7X7|RdJDx14y+Se`-%S>}yK-{yj2hu%yvG8b zns!LU9qp5i1MwNWsfTeAZ(OlK_w^Tjgf@QmyaQ!JU!U=L&YqT@Jtt0RB}{ABJt|q` zK_YRo`}LE$1PYpaYTn!f`Vi)!CScyArC^R~p`Z?@uO>oUL7|PI12MFH9z*?Dpz2$G z?%tuT9nynPq=yIN(8pe3&ShE8nKw+l7R5t|b5AU)x9MS2>KFBwpEXWQn4+orrKReV?OgT`WH$6B$7gbxHBWJG z4N>)z!7SzK>S&9sdegQs%kfl7HhE@`YEl-G4=^N ziC3XL$A5M;4t5cYt7czaqBL+Q{M@fuPpdCLVt+LBFGF7jszV$Aa#P;ZM@&K;ser~% zp_bD$jicc@{s6RXWgxwtgH-KuYaNf2JFvt`oc$i;DUXgjyWipwI#EQ;DE{VhtNc)v0K#->_5{|@@ zxe30NOXA;pqYrKZQPFMl0`%%#1Gzz#)rKH z$Ee<0CxQfCZ69BCu+Z^u1sgC>b@xky06DI!0uX@h+W-jhM|25T=9ni75+|^A)u{HV z>nDceZ?(29`~py7vs{5Z05=7U8fK*a-KcL{jrAG0K=DskP%hmJh7(iuSi+70<|-bJ zq-G`%8}ID1;R_cZb~iTeJ-d?1PRua<3C1L5Ct?ULwz2Vm8kh~|mU78N6gAi>L`6x( zAR6Cz#nx70y9NzOB^E=JlVl2fWjLBhWtYxfU0t=~RQIBfhz+JbgFZUQf(gyeKy0wQ zdwQ*=yh9YQke3vItNOL|i`yo=~qImsEfcBS2+&*Ncp7 zPOodHnR7~g3j&p1^j`2)O;W%50==}gyPxuDtnf=@yPCPeHT|^` zZN+ZG4!Irjw|I3|L{gmph6eI)KA`XK~>5orUXkP%Adc2%G}dh*j*5ZQf|JKn$&U_k)dQvBjC7k>VVL@zpNG8+67HVKg_WcK21eHU+|HheLe z$QRuF_V(7&V$scF;m0~M{Pv%n^rkb>Y&M*mn>(G2hBMk_83mAfz%4E=ZEbJo-9kQ* zWG9B)p3Y2rCo|z__VnCbE}YH6E+X<;NBF-gC?k7Cenw+5Ss`J2z-(bu$TIy8F_5!s zuptuU|iZURmSUKmub6iPGxm ziIeLK?szK*%My0N2mJ+wt_ew~R;W!vFsh2EmDnh@0SoY@zN=pUtlN3Sv5V2<=-5UH zTM4sagq8A@AK4|SMgh^ou>)Td#(T-)#@J{wTC|-dq}me2N-lyB1{y9Hp^mgrI0sNL z{^>DAOE~J<)GLFoX)>EwTi+v%9AWYtgj_-5%~)Y(W@#YMM!)ILDhacZNtc-S75Qm1 zb7f>29Uf}~;PUO67Ukd!G)Z%P7(zA5?^mPMK#NFSsMrPTQW3p^T!>tNItZAi6+(U| zoT&~mr`rG-7a0TVfy%Xc!5@L5CljM1FS1AnN>9Fkz03N5z2_ne$y}L)7cF<#i`$S! zDr5B1qsf()Qak_)aH8!HE})Lae@EY=8aM<}@YJJ^Zf+S#ZDr*pJFmsu4#r&fb}nD8 z&zm=7Q1$E=BdXfkeDu+$^o`fDPt4O!#dG`#fhTwb=+sr1~s&KIst-XtwdrP|Us-s}W%y{T)`zx%-_ zt!$y_AQV}tkhPYV@4R?rB{e7NkZam5zeQ5D#gwhXVHnUE`%%criU&MC>N>|?pS1&& zBrfow_cm)}b^spyQaug(6SW9r8+$vt8ARJ>emvcmzZ(cs^Z!4CkT8S{+6K)C;Gw4Z zpbzk{>HCesze00^7gSxF_j=bP?>+z0zgGOom44N`2uRcEdu;a4{t2I6rPy$lWe~b%H@K!syMW8!Z%kWA zSxp;hUxoTX#({!@aKOhlOCcTP-=$fBwO-nFy!VzXFv8^H@-A&e(}sI(4FN#&IJge) zz!7+X^@rX=)J+&ej-Tc{_|R|8XViWeg_98op-I?g|>&y8>$>ORN;D%wxSJk5UJGnO7HB zQD1{S3th*oRITtA!}xFVd*ku%4TpE1au&`nFI7HtBT;bR9ER1>DI|X9smjvw`32`4 zcmwZ!sgMXfKCeqWr5?je=jzT=o$B;-mH#*Op*roob|YV3*xFpEX>&nsVRLH%yPaO^ znKzzW*z7-jwSHZ{qFE98m5_H_rZ=FrC6Ffs3k8h`kcYWQ1mD?JD(Qs+iB+K=617EE zAQpx?`@X2X4X3gDqRNVwD!iTiToL@_hSBasmWSK{ynN1QN5vqKCQ1|in$ z(K$=6RQ`GexJ7GvtrwkaEg<54Vb6wiYHx{r(`jp=hbepZB!5o317J1LO?lvJiuRPf zuwbkBg2)#lU@VwyJKyxzslHLVP5h>9TjMumT7M6+_B8Ng&wxhx($KKbJZv!VKM=5n z3=0TWg&Hj#N2V2Y9%vo*L>Tr|_S#Z}x~;eMm*`BR>ymlU5gSPwUK-2TIrB*94H%6e zrgEHnXbVCHD+>4s&Rw|8tb)Qt5!$wayJzZ!fKJ;Iw#3>cY{JC`iJltmlicR5=s$4SB-@5j}p0Rl@3kQ;$@ff0#Ozle4 zu!+f~7}fbxmGbQDGM9?Qu`#tP9-asfwlY(Lo|TpYXO>EGEDQ*sK07P=G|_p8u5W>! z9jd3iM>Hw`zfWlBS8+l0q&}PBsu1HD{D|_}KFvtHDG56^c3e$Db&-6dzh}y8HQm}z z&mcUO7EgY#kh_sD@)f>Z%-_freo#O8pMUTNXQ#Kerf0RDucQ73>**p^s4EgX$_EC! z51c?qr-(4zYGXK2! zC&A*+1}nxKqg;tUa9i-2b;Wp~lbwk|f{MdUCYZ@17NIX1qJwh#=Jyq|cV=k`>Fp=q z-)VNlhf}O7d<38TJF~_A)ocyc+sfP6GU18;rM%+tDQSP?xnEOXR-b1CMC6!f{0U)6 zIg5qL39cAFxm(@wSAn{EqdPk4e!=sqi;LA~U~)ldn`hQ8UtVi8zA(44GFPkJsKEKW zRNB+e_TWj8z5z=AM(D)8z(St)3?kv6;4_T|3x1(iW5FAx5)1C>XM32p$s7C>*RZ~B zwhWkV1FFJeiqTdkoGed>z29SLL=^X3>;`a0R$Eb6%9(qgJNDPW;g04fcoil;2z3*?oN&a=~#rE@l-_UQ zL~}uds1>;Fm{kxe0u_O>YJ!3Z)eKI^wr<`4r^$|c`u@=fcKf;%L1YOgCA2}-%?q+DyMuf> zRZ&A+&4%2<-gNj?%6=B{Wpc17_)-Sx5nX5+U64yhIkhTflxsOVnz$!!zsEQd7(4Xy zUcZ~gpVpF&M1$}C5w@M=@eMd+s)}0~Da`Y!%H&cnMn0UqQtGbFv)M@2PQJ>=x{f~nuG)m*GpfD}7pbZ07GgQLDvbR_nUq+} zG8PP=?&yfi;?ekvk`dJy%VgtLBphbV@`XrL!40jcRBaMk`~dFd3dv;L{gp&%oJG)N zIBzT1;ZQ6Mxl`E?B-7=Q=Ja?x0i5HSbRaZIVmXSI6N+J@h7?`_(8E43nzlsY-haqd zoAA*O!z8hXC=K>0nN5HW*%{$jG!$C;;E;DAf8*equMZF*bAe~t^%!-ugWYYjD68OI zL!ETZqQ}+v5b5W0W28-lYlUc5;n^lrrD-4f{+gjJwoN8-JK)cLz*f~ZHlJ?0(`q$a z{I}m~9ULale9eb%N901rNk{A{@YS1KV8P8miqNvS+N|4^Y&NUjo^#z?Et~DWGv^kw zHTB{rMxGeSWs7c3y*-<)@ud4soG6zMP<<#OGF%%x#To2`y9(V+a0L{hgu~ZAF_abg zeZc*}sKNoPMiKkCwoauEkX!Rr13uV2{-HODJx_g~baJ^|6Hkqf+FiS^-Mtwkb!t$Y z*ZWO`lQG^d=BNj^>A`LJK36`wk7X4R*8=Hr2PiMs76Q77b+8ngfb>m07I$PF;so@l zy0vxEg!;Our?id`Xdt>(M4qA&|B!qKqzC=%t;yP`EW_Pw%H3v@S1=gh*S~Yn7qt3k z`i)@lI3fx21EXL^s_O7gvm-Tm!^A;$m3Y-ee3lS&J&-N*UT}^g25uUjlXNYkLGif| zZI11GF?T3XQxtET@@14w#T%T2b0*h~mDdVtpXNh z(8(hsAfw?!lK|mn|2{;!_xI0{O;E;{@pnS}UI<$RSDBu$_k|GV%#N5uc?rUCxVpU7l>0-wb`ZuI>CZExbsR2T$wWaMLThq20p zPyyl?1Wm4FapF^B|DnkE{l}#b3T8n1`%0N4c+vsfs4u>XuJj^8h~x^>JeB_bxmKI8 zjqpRFj1oTlw%oN967FHDW z&ko;VNabBVYxqZnvOoeAnuff(kN%W#0S?NJih7V-5NuAMIJHgns)SoY1WX6?V)s6t zV+8GZ`7JL}AFMc;+(8aj>7VPL5w(Dy;B$$en!kl!P1YwZ^7=`B0(XSPz=&gyBt98P z0wkQ8Hk__Q$$HEaT~fb$lNi`E0&@HrYDmQsaPYpepBZxZ8K_rH(S9A&%ldEc4m@4p z6K|kmDdpK};pp83Wet=)OR)M?tXA&xIl;I1Q35SR4pcio*$V5@VX;7*lFaPH4WVx`hl@EqyV zvEw?Cz5A}~Shd}Z!bWR2iKQ$=q)p?WZADX#%4j7~A>C}Y;5rsh$8D7_OweV4dufB` zHXdTnK&#nI7tn<6&GeJpuANSoqO1v1n$`Y#AeO9jc%1r>P)id2(YqdBh!k~L#oG`W% z$U=Z!*0eUA*(OIOB$uNpBTdNze#KNqV}m7cK_+1PX*JYij*zupJPwsIQ;jkl%Tzq> z<@a~!;=GUIGdtqr7K~kbs;*M0#ci|qWqT32w`*@4`MVWz8+H3kktbafkfbGc0PKCsMNVbL5L!glW;dhL(@UffNc=*=L2?5ThExdqdt%)BlTH`MsO3=i>kjK*^=aNq zyv>M!Z%eRN)*iRv^!B!MizhRS-=bvStJqyzV}%nq83-b*OQi%axW1(M*}o9q41Y z#|yjr#dA-a2uf%u*)MY4ShMRQxC`#P8|#ZmK0(g+01(~c9;=nOPNTyBYZa0gc|LFV4GnUFMm2+x(+xYs?mxR`cPOIO}=sbtskRvY}<7$8R-yVmpw zSO^tab~S^gqXrRpwNmt`77~;hIiH$h;sP<0z2+Sm_qJz50gn#c8Z)~7Q9C-L01Fy4 zIoK!6SxOAzx&(e@tUFkCJE0Mb^Go^}IQ2M+aU1j+h+oUw{!la14BBiCoF`SW+R=K? z&G-I6x|?5v#)x+?-%MN%vQDf3|97~`?{o3r;hCK5={z;EitgxNKC?V|N-7YzySv-{ zCWr#>f+T>p^iI_t-)G5gW5=BCsGE&$i#PX3K$`q%n1?`B|CTPgkL|XHDwX;%&QY%C z0ym>edMn0SSPj5}N@!}WCc{0UjKGkg?w!c2iLR}4^Z`Sl0Un_!eAnjjGHL-})$yQ% z5tRKv7&vrujVd5Ltb!;U_@3Iykq+*+?^_$x4S|OWgtYJOHwZ-l!2?YCr+qj!sJL>y zrlu4Ix6DyD=B9gJYe1bfXd~P?QoVdh;CUeOprJWf`TBk#LTmhpmw=L=Fw^H=m2jz| zva03bVF1*ncmq<+XtOTZY(K*E_|`aI!z__-po2!3 z9(GdDM$gWDKPH;<_*3cBW7y!5eJquJ>Ty|ig&U81n~4ljMq>?WTqd#U!HBWL_Ue5g z5)1;I*Is1a&gJa+2vU$}R(@}j`y+f*XtIt?Jo`PpDoe^mjH&0_XsXx?vL0%3SE)}A zMKvKbhB^!FNyMSTLK3CVD`}$42#^g>i#1^y51)Eyfe2u0RT%cunbQ3ng##JH)>d4l z*A7g3nnzBIx*YM!%GJjzZXr20V}@U~{$?~1e=Mz@`6OX+aO{ta;Q`hl>esji4b`JY zxhfG2fsTTSL{K1Pxzg-?v3J((D2G5sP&*B2Ie{>OuJtXbdJoalk3Y_Lc6RuCvw!OI zx_-Oh4?DbFE^&r<#inq|=ovF#?=_~sRjJ!~cOYmqKhj9d@c0upBZ{TILuA(dfCDMvV zxhm1eJjtW zB)fThF3890e6>SqsB88%8RKfe%G4uQ-Oj#V&Gawm42h<+k!M=L-i7A3?|&fQH~OT* z-w!YrN58*gX!k+yWcSD}*RzYjNG8 zI0dU7nDoi>JpbkzvYu;5ky(lpfV~<#@1Ey*tj9IjH*GmK!{-MqmnA>Lp8md~Ggp4LCl<50*J{vRtD_C0ACxry<67!xwcy>rU+Sb1nEk74qoypI>b z{FgWL!3&o%0B)cXgasL}zLu~y3|J{ARYsbgNimq*sj}?pf9rw@wusAku+ir1e zEN;7MFK9LHwt`pt!EX)l^Pkm)bwz5vI6fYa74^OD*Yy|m*MSb8Rg{?sGtfV6O_oJn z_&juw3mg81Z!T%7V>WCUyS}b>4L*7g-l}%!uwk_ZP{kPtUj;{oKU{$m8cc9dABcxf zwec}|0bZGfs-apbXH#ix>7SfDabj{Z58u305fdn*XCNXJnlQjT<%46p^hGC=Mk1q( zlS-u0MTd#i97J?Wr2Onbx<>C184FQwW8ix_bZ6)lw}eAXE4{$^;ZNH{>IBE%9t^LE z<}%^T%*?4%Gc!2JMU!J=wc6NNl956F7GU_^OgNTZU>vy7(S;m>9!zP={M2WIWOfh? zK^QECE^sIC@t6kbyr1tt#utcj2nQIb!k52>Bj!$jeA&(RU|;t-lpW;f*x^NaFF zP$A|iU~U6Y&_*zT*W|R#-2!;ak z)C?r5^Vps9iqLmJ>;3l7dqY1K`pM8|Lce7CE%Lu~T=OOsU+SNyw2z+mFN05LnIHaS zuuR~kL$&>Fo&;L}KT9w|!jW{$S+^O8)#?n5;JP#PF5Wfr>8?j)lb=8h-_G4Y8-m(a zX=;c~g&b`@vfKNZ7iigyp?3rC-W*!P?zeF_@HI{IzzuZZ(fnYj6?z=Fp>4gS@8Y11 z$&sfnh5IWsw~e??R$hdX%4W&N_QB0{(Drq>ZBXX`jRQ@}MR)m4XH!W}u8N>!&T#Zo$T zqFBgepqOu}(AY>eJ~4qkc%>3j+oPP5&yE!ek$56r=zg=5$c)WQ9D#QPTy_EFgD@zMMNXB?ukTS=-m4uD-pfNidirI@YUp9 zRx}#YynR#TN&3`_e;vF;pr{;hV6P8xiA4)2mY|Ux9+?;GTLJ;Jtmg^*gnlG%;7wkA z>Qmj%_W**{b9z%p{wttkzbx5Tu7?9*{$~SGu`vqRW5nECM#RRXHKKQQ(ECHn zlGYgVUUVwm$r@ZVUK+aoyF4T~S*qJ)Ii9Hw&&2!Vp@($0 zq%2DLwcw2R^;5vs_5Cy)@h%6hfu8|nX@CF+2P0|V78F{f*<5ZZPQl76e|QI4@!xUs z@5|-hmv^$+_q{KheSbe0NI`j`Y_)AWp{znAk{lT+Le7HKsG*+8Wq$|`B)K2TW(&@{ z-sR*+g6Tn!@Q=2r3Q{S>Qw7V4y2ZJPiVv8!TIuLgW-HpHgxvy&U@s~x9r0C$t%?CF07;QGcYHZz}E~j;}&955kxPM#0 zSR=fG(2jU$C-hM0wU`se;wh0b>=O30acF2i5+Q}7)-wabs3B&JL93}~se3Pr;*$I( z>Y)y^ToGNJsOn(wo2<1+RR^trG<^@b9eC-RTrc^5_bd7c$BLep#0X7&*YX6Eeb@qM zi#2KJlV1=}__ntTr-PCH-}3PB+tym6Cl;5g^>DcQVV9*8_h4?VaNsDoV2@#vqQG$tv((cPwQTkhe9bAJ1s@2h>}J+U1d zK23$4V2|4VL~ghB-RwX9$EcBdo!}ik2IzAe7>hm!qPu*%ow_x&j_ts49af#=fTa8$ z1^aQdXU~O>0}T3H$6Dm6kA$WWAuJ?oC|06#;ohKe@L(6msJig#+N{{B_NRryjywKD z;%mCW-}lZ!;l1wIC=%{HvGaZJy;HsGPkFV2Tp@($Fm4TNU4o6D7jaJg*O1upv9{l^P?8xW+2FnyR z`bZbvdcF>pi!qiFy%yDN-yT-RRs=38T&Bo7_%HY%C8*Z5%YeV1ggii-Bwa0@ywV_* zzvmwQHm;mpltAxXdt{C1@YQ`|+m(~w>Da~xx(`OOGcoo%F&7RW>K^bq_uQ|lXVI4lAURp(O1Tv9 zq=t~Koq(VkvSYuP#~!RgVdc~-#>QT8Y6UBMDw+R9>?`VocVIs)uHALl-iSm=O$HQ(&TY-Q#Y-Xa0fx263T#FK)H1WBMisFqG# z;^)cT_lWxqdEKX1vV7p>{a~sQE)(R@JF-@+g>+0$IFW(#p*M}VQc81(j+m>WX}~EU zVU{%Uiwc{G4K!q0Uk*6*d;b@Rd3L`Ty*!yO92D}CTVv^>8!uEnI46pTb2(bv&!+z! zIT+!C4n%yUV5xo~nQI|ja@dQwwd~mV%--J2_*izbuwJm!#d+wOCN%vTaU)(h(fv-( z$k!f4S5~2I(;DWC6}L$7$&wq81Mez6aYY+-dtcv$}7MS z$-he%JHY?%w;4XYAzvfyI9}hkiP5B`O-A{+C`O0+*}Toh9)$-`1?!j5Y70VX4!Lea zGiNdsqd~*-zM!0j;@N zZ#BchK30LrIxaCiB{cUUbb%-iBzKMTXD@ifIdwq4%V!Po>HBRs^j=k&D6$(ausaLB-*DY47_5PU`>|y83($X0IEiIktpY?;V_0M*K z@m>yGcV=dOerCqSKXc{_N*e0GgE0JE-;InDf8*Qqm^A>^)OkXUKGF^yhu2?5 zH0ShQDz%r+3|&v(WM^-HM%mY}OFe!qhK;%4xj>9)`(TDJM9$aH7S11o9^6Pshyoo% zF;%ZJcO7OjSZY|$CHRtTE!TlGncCojhKrS*vaG4BMaD{R%?;k^Ho+k&(e8H-Z~{1+ zR05J*R~rPH*=TBJCKb*0hHvoB?mn3;`EfLGJN`lFtidPG^e({2vQt$GioU(A_k|#W zT-)Oq%Fpn@|B8`Kv`ng6SO=FeqJ4D+0T;;31TICCwc1>u^Az|Lxy6;@UaeYe^U&66 z$$J5tR!Acv7aq0t0{sBb<-J7W1)^{qY=b)kI;^>5&X#=NQwl^CoVA!FAY)Krx@ffP zw`_ya6|fQA-#*$1QHBay1?KYXaj=gHe{Xmbdno)>zMaoExz4VKxrOaU%F`KvWvw6-u;Q(ZZLsh8l8cDF(76gGvI~H+T z1xe8XG4;`R5diPfNcqh0R_*fXy9lm#J;pNoWsoDEFTuW*PUsYMS92m3YBK9D(wYe9 zbeKIej_)B1cN;*LWC3Cs7kIrjqTdx^1+k)oaciAIB3Tu7pg3_5IwMsXDZ#7&PGtw_ zhce0crqe&1NJlV_yU&Ug)-<_3$f)%5fi(0?ZyaEEW6g`(8EjdMIL_463K^FRNb!^S zzLevn-X6!)X~MRx6OWi;W~)jtISJv=mqkb6M~rxZx8U%7<{k(mJIp~1G3AW*QY;L^ zx$U(NpmmY0XvQ3UMVFrA&Etstm$o8+&<8S6bG%D~K>zwttMh={IC0) z*2!{$6di+3`^os@$(WtHnX_Zb$Ky#cg#<|>*VA@vG2*K$oSO(q7r}0IYHynL1vruLjGG(vS>Q2)*!hna z>P>wX=;s}v?!vurP_fd1i>IoW?G5x>48B-{%+68-H-XoEzkBPu9D-Exr3{4=#wM|s z7E2qmb0$}&lbuY&kw-D4yY=gpU462vJPuWO+>fFY8MoEk?1taU<*~|X$LZ0I`K4vu z`etx_n8oVw!{~z!LZ-N#XxV^|@}LRxNY*iIt}3jzyeVwI$gyDFR<506dY?QpzS ztH)AlI~|LKqp4ITnn*;;sf6rPAT3_5%@o@FGF$LZ+Zy2e2OWNzq;ubaQ8*k=L~J{m zE)_=>3pqOy4Kp>FigJ@+6mgXBNqip(VxU8_;Ao(1H&rZF7O@u)ag~t4EX~b?VVoAi?-nUx*rsZ=cXNi>J-(-fo_>M@H$y)Vo`*o3Xwt)ifx{ly;tqFX2#lRYakb9yV-67d|E#O9MJX8!!FDC!-u6> zAup=7hA;==zC7aZkRj6*IPLSLAz7vH-Th9xz1Nq6hMoCKT$@GMd=a?#hEkGAN3{KcTLqShdencYjiPbtpLA@4WBaP1OAqtj5lSZhV z%M^>I0!}VjLsqQ$I%JjFZNO>m1q>EN+(d`^7q^5{vACc<90XMNcXfGn$^a_a>i!w- z-OE@|8dB!;`5>j^C^iM3Hb6}t4Sm;bVN|6r%J%SS{qJz^nOo!7Cv}evh16HAfy2JO z@(&04>fm`XEUDq8f7`crA#{ZCCpFyG;br_t=*^%bw}D}OwDOlt#INny<-fgK&j&M4 z!@xhZZUb8SmD|9!7v3JA2H@$>$vALr7BNsBBBrT%%t)DT6sLQfgw=xxSZ}(Vh+_~r z2jfdtLLf0weS#M$uOX?&Dkh^LTp)abiLOIPbE(EF74@sxT*ItSF-P~f(a=XH)BS^6 zk-B77Sw{EXJK79qoyw#Wt0g$9&&|DqL4dWKWcTh{P(D!BR`O?z zg@7cEAA4(pAK}NYOwP(z@U^LOMSLpty3mL1lYlKG{vNTnNan>pD_K0q?NUab^CiC+ zxnQckWI4ziKvEcl>SfgjzaFSfn+!+K5<=OHQa2Y9^{bh~5RI9&zLCp5FL0-XEui9* z>;aA^#`Adt)Y2*gmow0>0inW&Vf z3`}Pyukb7Un!uL2Va;=$me@Vi3$fEa3#dH;=>(`90cAC=U$6*Z{D(C-s?{%|YY}ZM zH8N_ui14RKZlPlhjOGy)1=cW_C1Iq0&D6;JNdzplPoykpP>S6@X=)7h59;H*n_j5# z<;iTe0-xe=Dmw!igI+Ao*yP%LWAxNyth$!sXFd*K9q1Z(@Ofe3Bc)HGm!NH*Ru1sE zj3tg=Xq5Hrk(JY)w7_5RKdgImq*{`GNUUDaHG}2+JN0S$+w0P+4|fT&iia3WE%0_V ztYL@tLIw$krZdy(Q-@T#fQ(W0!vEALa@CU>aNQp<`c=^Xfo~n&nHyv+G;v>1vMUty zke~Fq8S^a$aWWt4{)qmVdb0m9$Y3E4W7T564)2&Y0(>wu6qpNB-_ge3q9M|+z_$+X zgdQ5D>RbJf9c}zAAnfZm)g_EqT5L<2f`gC%1;Zpnmtdz9qmueRBnVh*Vzo z)Byp_fZ7jkU%LlsZX?$GjExDM4u|b^XLNKbJvBP&tlQx*_|LRGGYo0V)o8P}p9%C=g13|oLFX-5aHC+y=Tk!Lor z2lS{aVfyo$ zGM1Q3`8B3Ljc@2T+UP@H|JneD_&TmE5EqPV2{>ZoRY67=gC>g$WC!T-K>Wl)tToV7 z?z5*HTP#Ow$C{

    usy}#&{$gy?7CUpW|;VTF80+@WXMewTJHG7VgJ?b65b_gM5B! zYG!6?Dvy8WO#O18zWD8Y=ed4Z+WuK>ar37tJdQ6{!U+Vx!XF?) zmPK??SOK&;q;`NDqp(Qo1hlWZ7>9Bv>%8FfDqQUskaQ!RNJ7&piA;4eQtLt@nS_1@ zPXDJD7Ai4h=DuOeDXAA@BneOC%}eww0D)t0Sp@N(Vh-6(=#)r*itz9zI6h<$8IYf9JbP+pZ)0y zL*r_Og{v!C*OM#|NDlK}&w~{_!@2}*CZsOSudv|e{JQWi!sFmdo6}T?7CdB0c{j3- z&`2iK523(IW4mKz)g(2B3J>C@)zsZ;=EC8oCexSZ)+vmy&q+{GycQ1U%v%;jRJ;Z_ zq0$H{$2TJU))P`4qIYqJj!=8lUhw0x&v};RfAdl`_Ll_MU&gAJ{P!%&o73~P1f_ty zCvZH#S7?KW{a{nz2&0alJ-v$AWal>t01PMH?bvV8jNH;P;Pr;BY0j{B{*Z=$a2)AR zatq4~qGXZ8B^4KLr6q(kkeO@v<;Az&PNcoC@Q{hjeMD_CH`3=mZy+t8O$|TA7%TyfUZe=1K{- zn9hxjRwe!;JXwC&ie%D-N;VU&L<{*!RH;Xk(OD{Zdt!(?ifH(wcO@=q6nqhQn83>O zV~KJxR#;d#BjFAaj=PvnxZ~Irj1X_L6BC)MNqU_kK;Bbjm}8T#p=(f2@QB?xlFj;F zp@f&FPn27+OZs-~1U;EdLHZ(04Z}f;LBRJvAjLkP?Z6iivf;#_5up%ys|LXCu74bE zP4M(2@efWmXj7P+8F=Wi&k>=GZS*;Ytl8(NGo@A(Tayqt1q{SmbzPfB)*ydRhRMk4 zXrU0z5!y^Vr36o^AeK>0g~0g=dHU}zuD{{H`j2l|zi1sGvM-tMxK1=1Cu{D+PgIn* zjvdj-B=$wGcMmEjAfzReF!tn9NzZGxRIBN=TH=|TPUW3FWOqo5i*8FzyY5mpyX3ml z>ZwuMC$GWU=xAE+(}XH#CU3xfWg9&S$m=traDh_)B~ zD6%JnC*K25N~VhUkW3D@_dWZ%G0PfzodxfwhkRSt_df(@mOjtm!k2~GcA1dAqY9=T z=oGZir5Hk55vk7GJu4q%GEaiQHVV;r zMRh9iXaNEInhVWFbD^z~NdKx|Y^6paNZA9aO66&VI!pK>crBVTL7aJc2qcGUpZMWj-HyWq%TIW>+eE5dp4rqydLSk zdjdO7v7%0VU@aC~`+(@h(OKxup>_8{r_tLt82WI|0v7o(9jl9E@#+W%gFhQ&R%n74 z8aSkOrM8J&2qmhTVIC#n1$bmpl2^$c0tR64vDJ2ClPYM?5Wr1VtWLuL^7#sJOr}Oh zE}AOzTRmjCt4lvCrMDmH41A8i9 zmqldIRP7$Vb;iOD=KdGV89$&xjg(TUJ(epXgLERlx4pP{7j{`%g@P5G1&5YOIjA2t zytraRtVoQi=X6#TNBK^q0A}ANzb|}G0bX3p5&ueP2HSuoB7Pxp1bj7s`HMghfR1t^ z7999tYL6eP|KQqB0wh#cWY%7OZo}U6GbL+M1Wp=xR{DVqWS$)L%WOA)b+YMBx?{h} zZnuHpYaO$-5T+qLM8U1`PzaQa@lB~fQ+cZ*7J*Z;r{{(&cTXmv2PK{J7t z15XCZ2!$rm7RpVs-{KGNbPt@sW_FKrTz&%E)^7~G8`!81>du;^&K*;+M95V8yc9Pa zT}j|$Fs+*}@0pXF3fv963s`E|RLkGoX9W6$c@E6P3_ngb<)SB3ns2QHg>NiZo(Juf z{Q4Y63AugJ6fqavsj1o7sVR3sB<^%N?sLSRYR@7zx;v|XyPcLstxlE2;+VddwDfif z;;GHT)sB9nCAPc+@P7O7bFYCqi?6dp_ti=RkXv@hX1w3QJK`U)x>4a{{An?XMl_H! zPBX0To3gFKuR>G~cnrN~@J{`Yl8NNV=*S4dzUbpSvR2r2%jH})Y{_wYe6}`IEtjh^ zwb}7#HdD;!uT?77^7&#W8&$7y!&Wv|F1xN3mgDXS!pLKx8pSc0#!-Dob$kLFAoEo@ z=AG*6M@P7gBr$4^zj-&fRK|$O&DZb732Kg%W3h4sU&GDsPFF@oMt<4^m+pn9{m@tgK;D|BEoE7 zP2mc{Wd*o7C(0LvzRR+U1x2qhS~3%xZynO&znnaCX0kq>&7{JWc=^FxSY;;Xllgoy z&nZVGS>ZV-#_Z87<=d9RItQQ6Ce}OIi4h&aOiXt_IGIu5+=JzKC5&Zod<`Bey+wtx zaPGAF;1XbV5g9L%xogGnNW{q{ob!xEI)V8eL-x}2#J}aNUZ{+-gy+O!DL9TclTkb8 zp}GX(XP_O^`M8sdRwAVbb4Zn#yFcznEd?oH11BmwfNP|u1BoNrC@oCjI;$XNj9bll zYZQo>@m60IQROnK)e;sZ*2)o=!K2Z=-!-P@$KvJBmE*6jB}Yf5KQ}!xnyewhxeo!^ zmBF(~m1op5C2ihI5>${3{@dk6t#KcY+MUqYsL zE&bL!$E6+>)V?ju(F;@&JVKX!ZsUSNm(95aMZZk$I$zeFZm?`jt13Wol=p}papNaB zo~%@Yy_bzZe(!6^N`+d;_Benw*8Mu5HsB*Ejt{-QNE8&$%I7C;0gCGr`MkyI+p0+T z?FXU3`@L%|?{=`7cEHn827v$q=iUA<0ii7!U==Wjg1_^SeZTsl7*gKeYg?u23#0I2 z(N%w{WJO}o|w;naX;*6XcFEt+n`_% zgkCS)1GaDbo;v;xR?&3k>8oRGWniq(t@-)|kZ^Q~j(cn5tAytYa9vRF@rkf(ETf%p z{GuW(aM0V55~(3(g zR(=rLvfVyl1VmOY?>2UFYKJb{4dKX|QhTnQ16#_l$*o;hi+A}WpJ+CHJyjcUqhAxo z{D{o1`3~)KR6)=CD}oKqgs31CzS5Ilw5-%dYw2(Ff$?c@2{EK9EEgjU$R~ES${USH zu^b1op+A8@m?R*8SogVy9%f@7eyA9Um-msd^3|`#wXa~Jwc2-S>9{683;$h@g(Otk7Pcil4mB}KZ5?WJAeNi< z2*+ZznGa&b5wcs+mixts*vX(bm%zKnAV;T15rHkw#G}{XQ;@v2&}bxZ(x9VhrV%|B zOH8&>O<6s=jpV}6ckdvT{M~ksCK%k-K6N|rh`IGBn7J)`#q|6hI0XgdGNd^kl0mR7 z5KfHI;+UT9_vM~pXEA#uJ7SN>4k(VTRjCLKke}m*QRXn;z~t71rFU`<{THvr<3fTk z#p3a6*Yqpc<{YIipof?A5+X;^ms6?q8W?4eK`q9U>%%kIS1Xgq)-npbpJj~zWtO2WH+IqE$l-TRPjjD81Il{t1KKqw`u4HS{>Jn) zUq=}f$meIwiM|b(V_TuGk4i$e+6Y|^y@*(d%|Y|^O>#ck21q*zjVANZ#&V89uenK{ zqYSN{MKr`!%nsXRiR!qGSwqzwg0vO6R)O;4F_5;d-qN^qsd08>a&qMCBWEn@OvWje zuRQX|m2%O^@H(6CU!OXg&7Q3(HFHWAVA0>UQ@RfdhJBaNAt15!#8RI*$5kVgG4OYyXTT%bnv6|sbHv9CQ4$>w^ zK8dNF{w+iXb5y6`s4u3VV8ZT%*y;;%;96f6i+rAW=s&xZaNVQ|C1lP5{eAp71DQMo zg9-Xi_8EQG@kBLH01^TY;Lw0Gpo2;i;U5t31(cj6z}UTThGL{uesew=OHKD9hF)yI z`5+#*!l_FS1UtXJa;o;(vSr^xOur1(|38{Z#iIE)W0Nn$(&-HeS4t?VDhoqwjV}XR z`YA;(sH!Lj0 z+Q$x744#KThl~{G931wHJA6I#1(T%$WNddgxQg%H7`WOCUftjo`{?=mw@;vtLP@o# z+jsP&ineoqBCO@^NJ2x?}Za~7>#NLs& zg#JnBr(nO<4%WaD3S9y7AwEt^qux@rc7)pz2X+H!fvxQ44eY=gUS-|5B=^c{CJ}0Iq1Kc z@cIq73(8udWY;oPGt}y+N5>cBi0g&%M`0`U@0)8pKM3B8#a71pZ;h?QVy3tpc|%_D z?Q>Wj=^t?wSbmthVza6+*+C65gT&!FFCja?2WQsN7oHI9%auU>!aifsJ40TUCZ(v6 z{L5dSA2AFck1us6%;kjpQobXX`yz97%p+}hYV$ICY9-fu4X;PS%SbtG%DLcHOd&0U ziLP)2E7S`yIGiZ(xdKIn57q|vXh1N;!n_J4sov&ih6E!adO`FS-524RCQ8cNm}u&^ z9PAVe=TF)8sXSEeVdqp#so1F>pt0AQnY(uGoMoLmca6t%IFZQB!_BkqD~nRH0w}GtC+&O-e71qTS zr9N>LYn@f7jEz+a@L7vr9gkeFt#EkUYi!2i@z`d=8xMyq`$A;=6a6qE{WBz;Z)75F z%sKDG+(@Qz4g2}xcgJFPBZ*%0#QHok@Wrpx-BZ!%DYt$F7m@mz6VIsQTw3lNwUOJME z4HLHl7A>e-JmL&~hTnC}V{8k**^g=cZx_ZZvtHG*s@`m6e4!MBf@5=he6t)MkCmvb z@T+b0Pj}6|<7{6}|J^{d0(bR3u>;II#_sgs!Ei;W3s({B7!(U@MMsLjI!|j6acmQ2 zZz7zQ)kI!d&#y^54quF^!DfQ3gKZM^7N~I(|F>~IUW&$}W3T(;G1xIm>&vx;^K`#BzffCVKZkly02u@I34BZT_X9c{=Ba3cLTb%0?9d=2 zaCqSsNPW=@_{lVV5U_fhQPPZcu`!@XwT0^0?v8&n+5tapBJMJ&qHY6dCfCNELoCbg z;|2~MlPBJxU%P|J6+M05msvFIYp@2LgZIcwVMX`7m8xu8o)f*w8Q8V>pBGvS>sqL06;HD@927Yi8 zkph8?&&hA#yk$ta9~lChbcWG~paJ;*K0PrxIdK|h$4n-1uIee} zRnH}`1SAvd)&`s><$(^zB)3t`hP5BG1&5M7{YgpUKD-$IeNTmcDD+I|(;=7*hp+YG zH5ybHU~aA|35CYcQU=8?CK)^t1X9Q|64tq6L&c$`x}I{)C$ z2!FolNBWmfIG6lG`|z{gb+dPdSC6mnh<-uXlqSpQ3V7HL*44(d-u1v&_K$tS`i7So zsLkhjs86hgPKmY!=;=q9#F@&S*9gn?6FU=zfPrkWt6<(ZH-g3=kdPNW1+~!k%vFuq>i>~#r zO20kEh+<4k->E*XY?Aa&|>L(uKJ9ra_Dg_>!T3DE#oa}ymq^kZ3%G;Koj?UT<9CSN9=s=nn+Z-cF&2g+AeGp=JxoDnQKeMhL zSRDNX{rCZ_+@H^>C*7w`(Ki52>6xgW4SRGIeyKhCUvv39T>Vt)Gm|Tik3A8d_D(_P zgRI$l?CI|dPX(h_Cax91CteKwdgw2SaR5+n$)+~|+5%JO(VLZ{_j0fTYpGZV3Btn2 z_M-=DjrGRbwnf|U2JnPur|IX7KmEq(ZE@Pi^P%^*!;RJAS_2j8BC3R^ z+Im!PjljZW6S~3UOkLO!dc|E(uNaBJQxN-{BJy4Is& zO@fslsnl}*cADHby3bl?w;#?~A4{gszvPaM4G&I1StoNLmHhZ$-26t)(z|b9(0$eA zD_Q3+K9)`)WR~Y`Y~0~kxmO@p_{YC3*hM$iR|V_2wLoB|S#7#Dk+vNJJ7(x4y?SDs zzq|V4A-QOq)#EVm9uxwXc>P&FX9;qh;Is{*2LEg{x(R<@7wgq!P+xIT=A7#(0@uJ8 zB3hI9#uKd6`EH6Yj1jmwb7jrI<2BVbA;3Gm6Y*B=WtWnZZo9wXqkjh7iite#AKOMT zZjQ(90m0hW>kH_!<_mib=E&E?K~^hr^|}YbS*sxI3Y~v>Y7c5_IP}oVu-$|+n+;nF+U}A`hprt`c~934ja76F zpzn><>9xkHF!ZQoQEjf-b+tvQVr=F^%)ci#O+!JWQ`g zdi73wjr98$<}R9V^BDWAqq@(Ya}(*}`Ti&Hsfl89lunHLqpR?*R*mb|+Yf-riOGET zFyF5rQ@!@9{$Yc;lh{D!J91LVEm5L;lVR{&#C0RHudS}Hibf3f39@9+`U9IGLG*Su zmu%x0Adk4dg7U43zNvR7EOSKWhw>gff58`iLAUT9Ip>alcaWQ_HiK_U2g2^ zo4W%H_MoE}$kjfjLH=||RUHq-$#=65zrjzKBhFbnAA1hT>3)lE;#M2~%Q@&rpPvk# zt0uq0|Ah}f0F9t-bt7~QGfTM2D)BHNf#V7Y>Mz(n`iekF6ecPIpTUgY>aCDS-<_VQ zEYi_e%2}t7%SW#i4WM<3~1zSQc~*sEJLchS9!Q-CO7)X>uf2O+z} zd7E`>-|kzyL~_G9E_&yhXojdrDfRb#%UsoDowy`42rMDM(BFqHwAS!HQubV6(A#Xf z9vDufedTf5_&1=d;7ICW-xu9bx2>=mJNYQdO*0#USGfN*BzXdKfeeF5(zl+{RL<($YW;Y2=Jzmfjgmtym+7033h_fPzE$JV1o<0MQ7$ z42AodXwikAv#uoZv0LW3urVJSjYI=R}ZXM#OZI|ESau+Mh)uOOuH zOcf?`*^!U9!0X3E2BZHjE37;=WpzWrq5~OS(RU^I5JKNexERc~hR#wfQxk}zn*O~s z{_~IO=K~jV(}yAV7c}7U_9nnk?5vtl>lp&D5W^^m3NN7pSs~FL0Edl1;>5jP1q%mc zg1#mM8Q^gH&d|xO{qbI)Q9!)2pYKO7oD#;2CXaCd$asadNR% zaFsvNJl50B!Qn3Z${dn9xGMKb2z+*~O@iWmY1X!9A9>`rMwK-J8fL>vo#984Ic)yI zRzO5|3@hyLW99saUC5^4q%@jN6BlKEif-X*s1>>{-j-kvz?z}o{KH(M;Eo9fGdr*S zbxNB>z^nlkc@B9Ffa71xZmiD0C{!T>A&}<~ToAcimQQ-pS2h_L?jR9U_Grb)Cud8g z@I{q44>6-&gflSn&gCC4%lfEL%+ z7a_G>bG$~g-SC_y63#{AND(krv#~d!oQ=h>Av+zmUV{)iOQ;srFCm!DYalwS@>ndE zEo0Y%T^k!$7QAh5s?lh4RG~%`%%c95*Y0>;r|o$HFU@~lfv?NkL+=L``z#l^FOfC{ z{fhRQW&=UQh+0HCLI-?TfCqqlgL{SzJ1r#%cD{LLQ*4WUHVm~O<(nQEQ)TTa6QN}O zpLJfl?2catj<#rA58oX|n1bA4j%@RyCf3;YyOK>;E`5H2+}T9mNXV5U*Zn`~J8srH zdW_$ZoSA7Do~to4lf1(>5$NhPyEb+IG9%r3wA|-&x%@)4TAn<_sy)URP{>{wE2{2) zIzy&yovV!b+DQH~T;I(dxV$eWy=%FJEYuirIONg-0E%A_6dbU9{~mq#Q=$>*5r`YDOX>%Y*xv zWD%wmuD8eHOX8*cr?GO$N;vUUrs$+nb~XXCubar)*jZT2q~cD(DwSi2G}1Fh6LBi} z;t5P3E1l5%2JBHE3XLGcb*KVZ5Hjc}#RhQBt!Ic+n=73MYX5#KSf37`#{O!W~Z zFwOt?=k4f&v$@o&oy*2gTF%VlQTyjfyH)WAqSlC=&Dkf`?3@$xtmqpe;e@JI&(x|O z$kM1+t(~b>RlM}6sBL*;Z_cR9%fnO9(0wwM`rjif)Kfh0U0IcVQB;kL#Wv>a$!Idk zf=Qgz=Qm=bBj}UKb2TdQU${CzSr0z`kfer*i6_BnRWi&svX+q83CGS~@>9?Z?&NY~ zV^dROV>$dYXM2|x79?3F(_X5Dg-b+g;N>R5_`Ye!*3y}3D)#1sVGULvpp(@uH`G+mEJ>r_*7Mfot^57j*Q&1WuD*Bm^rgCIrZbhyOr}F_O+u0Z0x2Yf zU{E?BAwYl4V0$78dgL?SR&peVLRV#7FSD+?>bl1kcNJY^;ta7HZpVC+Oq%mV*&4q% zA}TN6=G+|V-44T>0fLYA0WVcjqC3md@?rk9*9gfEo02)xb@EPwn`oBN76?~!pH|EvW#`;!ZeapAL z6X( zMT*~YS3hF)Pu9S)^xSfs1p<8skY48aph0)_)=8~|9W2w+r-aHD@gHZuc#1xnpmW>v z`5IPDr%1pk4pif)pYAQ5T4}1jy)X|u=W+iDyM5fv>%4yLI>Pe{+YL^t`7(aQ%|T;lP2`9(WYe zrYB)zKNdeXG{Zu=Mb`OHL^3ed+9T8j&v|a{>NA^!YSE4eUFdt0y7yMQtLY3vSeK6{ zXKtcb%*RJ-5|{?Iq}pgazj*4}v~Z@p-$%0j(%$g|&`l;kTo^-?34BUEnIA1ygnAwS za@cHYB$^>nQvvNXjTP*B;LIFNtPrU3Wjs4lmqm|q-ZtX{1< zRCxk|5fs^*#}iFK3l|4dPF0t|6yoZj56iC1SQYTXG7Q0BW^`-=KpW73)Zqmy|i{tyPC(j{7t~> z?r33r(ZFgX%v2eh3t6x`JuF|zX>RC_6WVDvt!zSPtA#5C2!P4)X+O zSn_sdiL=I>EGMv+!aybknBxExR%|z%!dvVZK36?1fR`^(Pct-nlxZ84>!rMD-No*u z{*vsMpeZ1Hl}3+pj3fGb-i&1ZdC#C(LSKcBKyIQAj2bX#wZz742t`(`x?}?l zv0htQTQO`P6$)q6@#OmZB9SK1@Qmj13~?4R2NI#L&XAlm9~nppelZ+w67y>^4v!*- zND7G;g01g>UNpO(>ZA95N%~m!B!TDmfah;{;gw=K3XZnGFeBjVCE5r^2C`NS)KTwm zU9VmlG^n2??`fF#ULxHx=W90}-zC%u+dDB(ez_<~dL8zkeDWsQfK9T50@z9c(aj)gfe1V?47oDY{c!!f&EhVpjeolTUwf%WxO?0V%xZH>HzXK zZ69N>kZ!{Z@lLc+(|S&Sh2oo#^$NV}G!+-_ibd^lF6-l(&ozw-#g##-t(8NZ+d8=z z{_B43+7E7;2X&OLUbRx#F*k(`W`+Br>o8Rw^N^#b(R4)bP(2okklYtg1OgzzkgK#3 ztWOzCpqGhAIiR-FwN^hEXTQ|4PORQ~|E;ShEGYiEPe1{v{=Uh!Hre`qNj3ZNHv6US zC;29;(3-=aIP#Xf&v|vz#&4ic46NZW(m;i-ea_e0r+<&UY0E_K%gaR1{qptoOWl3- zo4OYCO>9!2`}>Ih$vuh^6e=az!E~Y{(d=Ssw$(gCKsc;ACiMN{19}>6gt<$8Ap` zoY4oM)9d41h?@J@i|nW4GA=?xcKu_rt9!+tE3Ps| z>q>9(anINi_0!igTy6cR87_{$DdUcDA=eM_rY_;gvBaA7l&z*}L0`Opm=p9>0V1i0 zELy={ zd1K$akwoj7ctPyyt#%u2`=8ssBxBY6Pi$WSI~-rc_+9CAUQFY@xv_hZZQE|ZLE+KVUC>#&Mrmz-_heOtNKlije4Ud3QOXH1J zDV_GRvRDY-lm6*xI7i~6e9U|JDda-qV>Ipb9^+W}M1H2+GYM>S0XcDy*#$Zp@-dix z&bg#79Wds|5^=T@t?n+OQEwjeVmF{QF z74@0!XQBKKs=IjXB58n_x1FDMneP!ZITzF|=EYuBUpsn5r3&UrGClTi&mdt<(dgJmt zM_b;za3350J96llhnminrBM2+LHM1k$K}oDJu^^m#;ClRRpQWXo2#?4&ZR=CXs-2M zCEN)A1!}mD7(bOm)LuuVT-5{#%vZo3RcEXomwrI+0ZF)s4O8vKvv4(Smfi7o_vl4% zzzN0!iL;Bi?R>8T8KvXNBenzb!>(iBI)`3}A%+v_0-rFjfw{#PHRx?CyYR$v$*6{$GI}10&AVc- zDEJrdf9P$!1J7PXj-+*M_AToUs-<)1mK=w}GB*4VxC(YxdBD=1JBOXv9o>V-P0Q!b zE#Jg&b@~k)4`1uv8i~w|oI5vK(`(*ody`R+ztPqg))5~J{>AGV-G}O+<{sOLW+%Cy zO#1C}=WPG}^>+mQ7%dh@yZDu63Ha24scO^N&meEH!L)agg>8pS4UG?ooRQ}yBy-kykUTjEet|4jF# z{_&DHhr;PC1O|Wc1U}AoF=Z}sY$nD%Rqi9uH9f#aSIMaj2Qy>Sr)qO8-IX^a%Hy-U z@Tvjwjb;$4()q{rtVF&$Kzp$sl-Vi4-7{!1g%7UhR5s_782-ee{m#w9k2+^27$O~T z^|+mm!5t$Kee~?BGSnODi~ZXy86PweaW3noV@regBz9`zzFSIHcvKxE{?< zo>-cQ-ioBRiL<|vj3@klJDB{PKYvbd;E83py)BlJNrS)8qh}VX${H^QkS!tC^atV6 zl!*Ip(&OE4V@8&yB@W~Kb)UEk2E6N5GVGgY>IjAxIs-2uf1?K< zk>FxwB6gP-TgDh#Xvzx}$0QvJJAhDFk=U;$!C`;I1S$G$qzm$` z_rS$FJUp*5@I&;ovUbRd{RHb^T|a~U0O{XDpM^)p;^_cp7*mSbB#bIzRQ4)tAb3Bl zoT3xtQV!VjYDKGcF%IbS1_399@!|q6fX-}7gY`iWLr&fUx*o!b=M|j zdbw#vjvh4NuW=M9vkH_AOhZsV0C+YXnAZ3KM_zEi7I|?0zxDs_GQaTR&vr=ES9HS-JqCtWg8 zzYIDyaUf@GCK?Te#H_OvnO+|*rBfhcTDESh7G$imKtWtG#(jPp^ouG$;g9IkMeQFP z+Wb0rkh$zmS59oJUXg(NknZ=r;bby=uP(!NDDl{1-5+C1^%bZ29rQ%qoeBi=Mgg_| z7NnEd*ZowUd@zvu#YJjfR z1_tlBZCUw?R1#Pd6LuySncAi*yzaEV*;L1c7D1p%Y7G_&x8@>t<}UFTY3(_2h?`E^ z+i1HF+63;HVm60rU!&pL+<}dL4qqWUeR2^^%hUCUU|@NS2+@^NySERm?Tlf&d*BMH z&=VX~x!dlIB_1VT!?kpPHcfbzg~xPa$W}DXF*H?HmP1sLDuEIw_Ue${rT>4NhDBqc!v`)GjEc4?4qejS~{ zWU`%INe-ZQeqE;V1Pm4Knie1FOUK=T&$0FVceKs0ie@w#(~Y~nHtRTaViO00Ko?c& zsT$YYgO$U^k-nkiy8H9!AR`OwVgEc1-sYSzE8jQMKXi`Jem(PY3w!l|n18Sp1I|U) zIXBXm2gG26T^!gyQ;uDxQv}}U>bnM-+nqF|*zS6*QpskJORG}rmzqLiV5s4nq8izI z+UtE9&*Lr3aRBZ8Z)oob?BA5Q$&u^L$raER03-Z_4WsTS0}r=hnA@z(mLq*!)jcQ~eNdbAq~Fe=Su=HIU04lbZrZsDSDMU=LFp4b|>+%I}FGUaN|K zwEL!yI>nKO`p!o8Jxaar1a`H@x<8$;PpT(QcHgy%cwaGfXTlcv@jU-U@cbMfxBX25 zc*FX}dDBL>ror(oY{_`%hs+r)B^}O_-cPn0#s>IT@m!;Oy~d>e5^OeDW5-9N@ES{` z=8s5ks3s5>fc~Yy6tb)bSs&Z(ljEslv#sZG;1AapP$2kuj!g+(KG$lwM&Nw`gyeB> zLyvs)_|{>R-D+L7r_XEC{7(P2z~A@rLU83w**52kgbeTYS4-uk+a}W^?8hVh`=;M_;O*=R&WJ zzFz$c*!8jQX^PK3BpsvybCh3Vg_f0fMv?Es@?_K$Qju!IE4~BbbAw)(lU^ZT_y=a* zd)2*qa@WAm7Ssr~Ed|!}m3v>sZ8I9u}9lnQBN_q-p^_{x{^ca)L8C{ zKKohjdU@)p?%!(WpRc*ag8o&9zBur64za6rd?roqWiBu)S`ex=q;u#FvuKM|g@L^D zq?Hg0pstbe!PBA%q*#b|QHSR^-tT_}$Cr(kMyyePE*C0#1GZH}{y_hzH8K><*x_7s zW`2Gq`T$eNL<{=Z>BqS1m+rN*xv)QiUFLy%^C5rkmO$W^oIjMmHxM`xh#==-*7m1T z{;Dg{ttk|={r<|W}f5V#<7}VBc?P& zjuj_}1Y62Kpd7MR2Ahi#5*LZLh=$}34j$Ut$jEGPCEdW7$fi)|0RV|A-nh0lJ}$d@ zt+ll~$fySOyy#=u$}^lKu@tk#=dJI;$_>nfax10Tx%HXMa4QbQM=?D5uGhWp^l4>{ zTm_PhjRZ!Fk+)2*VG1-s*X`=R=3ZTh&qHvtx_{|}Gpj}wP6O3<{Zt49TgpHi4&(tKRx?AEmTNniw%$ve&um_Jtu zED$IN<{NyE4w?a#F=}vtKs3riOE{5IH}3EgeBcaCSf=!p8Nhp>1fSa22{wjpACi=T zvPTPA{aHW}{~=LocKW4JM=$=myD+^$2Bdtbpl&vm+#9=|N_MdmU6kvGD)|_{$%=k* zcsxXth`q>!X3)4JNPwd&%^=}gfTn-%KSDcIh4nG9pI^ z&1T-ISAr%X=ALk(k~)CG3`TdrrG~iJVRSj{oUaC_66ZQvoS9$V5G|HoX)tLcCW@9k z%p2Gl3Y4I}KBEIuIX~S_Tc0~WTMOgkh#NrPoS+KTSI!F*FQy398Y+d8K?qU3a|73o zE+Ig!`XJ2ptk?O$SM<5_^K~^cGS4sIOR;kc^RkRD)U$~Im$kEw!0!Y~*-Tt-McEy& zAn+!$vHp1Dmw51Bd5OLV21RK_2Q3IKV zo)|HlR>UMQ4obVCSt32yTCd3wKEAZ2R%NEFGW@HDhL%pp;&6q@M(@CO3I7c!#m>#^ z{&j!1l#f|zl-HJqfK+1T0?TIzji+nCMe}nviYD=XCQx~T7~lz(eu$+X>Ro&CRZBDw zsq!sw>L?+Vabsy|Y$|BonT>_g|2*UmgdfR8L+Q8Px}xXr!knGOcxR*GbaOcroZ({y z)-Y9?sVxO>oWpDo5hAL84BwERS96dc5%r~z2*T7hFp zT!9=W`l>*DEd!|n=oh?vY~mb&*10vWka&{_Y=m{qDG;ca4|Fu^E1(3Qze0 zQV1ne!xuj~;q#7G*49ppjZGl&w`C!DPBMCP_xG_MDcAjaF!poi@vnr#Vt+mW6T8pc z+Da7mCVHZX$mm03U0(rl#h*#^pxwOzbFk8Jghcxc;R}DNlZcLIviZfau|<3nhq1+x z;o%Yf7Sbt%($qkskBF3+N*8!9pUsq!Kj>2Tvfq!)99BFIF{x=Ih+f$6{Kf*A|<46SJ_jAt$hzpDeh;eWuBB#9^d^56Ry6`$C56%tiRZZN1`UT8E zEVglqa&G=&zpZ)Ix||_8M!|J3wFqx;*af3q|(R+tr8#T{!6jnm(kzd zvSHQKa~L>Yf=v=$pU&k+?hGT+R1AE^9l=O6l59U~EhlpCnn@$5L~(dHb2gZ)AKcAa zLy-i3kV?}+(pY@(op-@}uZQ#iZehB@&VjxHZvgzSul8s*by{Ih+2esiK1b9KdAmW4 zr*;{u5DFx~ZnfFze3IYz;Z7$0D<#qRI9pUo|0(W0~@EMi70j zocUOU{33B7zm*dY%F~V>K&XhzWW#k?$vCz#!Dbr`NQanYVnf_S#3X4M8U+Z}M&d)Z z;gJZP9hK4@Z{9|{M6)d_tE3@VbE&kvf13MJ~nllRh?0~-B)(?OG#CooMapc9x;Ey zGl{>If#qW~IoE(7$M%+m9o)-`4++2SP0;TQnz96KTVW*&6j&I5~Z7>0Lh z4Ldr|z*F-cP)uy25GQ6VFxT3wXbp3JaRN2=R0|DYO4Fs>z@i7AqH&<#T>;8}=nx|E zkL9@t!@nL@dv2Nb@-={wP}(vhR@O!b0rqnDzesJU7JP2__uQ+!x5k?sL;mX(Ftvb5 zW78VOXdu9(v%3#qCLXlJhTz?hW&;PiC%5M4w1d$glCpV=&+*#2eqUb_DvWBhy$un& zv1*7xQlKngD))?z9Z|`ZH8Z=tJv#$F49;9e#bz;{or0rrn^!(Hl+2A7Icy}C9QqVl z;GVwyQ_7lJWfiPyb;?qodfp>EGG47j{~f#j@1m7|N`3xW_W5W3Y4!On?DJdxY4uqg zH&+Mr8GG|xRx+$t(KK;!!b&I3W>782TIM?DdgeOjeOb#az{c7qJA1u_8FMdYy#$Xe zVlHmCi9+>k>sPO1!30;YU&^4}L$(PIe3zX)$qSwvz#sRsVHn>#ME^rSNF^Q15qe>x zBkZB^u3Aq)G&mB$ws2ILwhjB+KgKib5_;m0;;tD1!;2t+mN$oe@2S*24{D@b8B31M zQlX{Ai;iYv^%3f*zBk*8zd@$ESA#m82qWIwSh+Hy=p(YvgUWfWWD3eOs06zINr0&{|2KcVD7%4sxQ0ZVam3kP-s}lJ!gt;x0E4jS0VL&;9+-*12xNaTtK1$EV z88-GUQRPFZ+_8KDuRo0B1i>^?Zb81zRU8|se)wm*f1O+ynb8B3OzPj6k%gq;pE{5Q z=HVs!kmoJTpFq&!`XX3DF2i4rr(AzpQKV@rNGPuIx_Q{pT=N`cdK!;}5~DK5s46wB zuA^Smk5^HtIy05eoA77({FLS2o`R3#*eLuAbsTs#HMI?uujLQLu)V{vd8zSO$ZxF( zRTb*G@~R;O{e-+^!`hfgM5A!2K-RQOdPw|L%KCU)ak{o_!aOY3ri;U4YIvh@v-2hS z>dlRfVKp{99kLU_;eLqY;UEOOxpRh;pPS38aA*h?xMCbiM1Q;pTig)ylMj!KU^|B# z4J;i%=Az9J`vf#6;^OBxf|bGmXFkJIxdRZC%|5cO10aJ%fkUejjR3z)`~>+j4`I!EQPI<`4LIw8Q&LIndvX8B^oT;zgnPyWiF#64XPH8&7@jJF z9A;z_?`5j_*xMN@7H8Z?_L_IKf93T)mvl`bC$!U%Akq1mZ>+k3pMCf+q?9`ySp&d! zfZ>l~Jhfk;p4uEpGN)Md8<^(aWEafT3%&|0Yap;BZ^g1Gk+qXJRU(Lt3~dd6`?@b8 zIKWR$-^V&9!owB0ZmsLvqG>(=SR%(vJifA>4(4(`e-UEsMMpJv-Pbh3AhkSjY4nULEF|JTuab#)Gz&hX`h8BMWSYrp#rdx6EmfO{p5R3qbjx@Kh zwY9mmumG@0ETFu#wSY`=uESXV`djwvK~#X%G~sBG6!G8H1@&150mj>XW;6cnP9j0Pk6e%aM45w12P( z9y~N#Q%RpcfITM>e{|?Qu34N)`UCoUWccvAW?l|Y;?xrBU|p5r&jck#nlruqI}=0B zl)^%-{ zo8NMH-Fm$jV@c2(-kOMSZtj{Ky3LiM#u52y*ja#`HVK^^h#M#i90>n5E}TyRrdX_N zjrsKvVk=UV(8csb0ai9!=bLbC7Q!5g$SQ$i#7?f>@y zEmXm$j3>wrz#sc6XT5^i(8A1X~*2Tw3DZ!VkSSaB4`bb-n!msAyk|Jn$D23$4 z+yk~J=gAae_wziC&A7l`NOl1NdV6mcF!n7y{`%Oo0*J|m$ZP@Ffj%tijygk1t@(DL#g35-{ny@`^InVucsn}o>a}^*nBiF=53)T2?{V$|C>afPecldbb2N;RLa&bMIzy_9R4sOcYZCIdplg0ypw_CuN6LB@LPpDFfe5p znBNsI=ttsNyjja+K7A<^ibimVFbJyXR?v?xz!wv{D3lE@yWhlKUsO#!vhv;)=ld7_ z(lj-f{Hyn>pTZqBYbKq(@K=faPAc?k9t7YjX4+JA&}IZD=r%l z(9a|ymx94dkpwtQXUU*_GlmtBtAN}&n|Adt-xy9XMm z6{2WfqjIo%@JnYd8jOkT{*j$NX!j}=qgrE-0-jel9MeJ-@At^{QH;38!yNAtJW=N0 z_3LP!g`)@8A+B+fYNeL)SJ%fa0T!?ypyYM~?3jU-hKGo8A2ZRuFA+%l{J$*gx!rF6 z7!sk4UKouflCiIS=Y{VKgiePLm@eTBXf;F|gYfSA68T%f&=aM!o9*@+<4ENY!%NZV z&+`>7VtG%Gb=l#~n`kXyLGyYAXp1@C7#6Td*mU6ViW}6p0HkZ$w*n`mD~Md8`M@t- z(v08Cop;UGI^|+9JZ;cuKRfeOq*!#|SOeVdK6!RIKxRs%M>z0q-kf!eq*ErJqsHJ+ zCTVsb?FLMG&R{Jy;VTZUXb;#Wa2-H`dn3q;i@pWRLH&I&xW@(6QL#j1@6gF-gW+)S z;V{Rd9*h&dmm#tibAhON4q*xEaWMHGJ6gxlbM?7HZ8&s-?+Fe@ z|F|x|ML|l1sOx045Dpw&#&jT)7`Z2+67UmbaD}Ipjj?b?^z@DM{nFbX!QPF)Bes=A zK1*?`If>Y27?@+WM0fx2LBIb&JDjyzdiGijes*;m#VbMNPck`_6!PRy@#n8;Ob1Aj z80}6VMqs*$8v1zbcV8O_y!PmK2X43-+$A*e=r}gCo0`)ujvuBHtwp3HO@-+8weFS~ z=s0_96IE@{(7c!?9+_vYm^E|Dc9}W%65|1;F=+0bjA-s! zXjbCdq1QeX&K)lPJq?1`|En^Yk7DA0~zvRVE|b+d1`=Y5}tAMyIl>df2!^x#bW? z0Ye0b2x38tL)$Y)bo2-!7@F%Yo^3TbA1w}OD;^ByntH5E=-S@hP%FW^&KQ0X>-P)D zeRu)f5<^VL{BrQ9=RU-S2uYF6!XkeYwy#FkeADC1I0WQ3x!nKZ+LsVzth;PxiQV;;PV z+di~v3yg6?jWPLRGpzJnZ_M^1W<;fF*6`)Xc?}sxuW>$d+1!IEx5IzZZ+ZOz|0`bM52#Ou!=Fe(uLM_o zPdxdF8DF3kg{Y6npgcxf0T@1C^BOkaLWfWDIERgVcCId5iv4~aN-2XB5r-5Z@Q(4 zt3C>uLi%YDF0g{3GI#b&^$h`pe&Qb|ObQoCUo%l%S1kkbdWMaQnasV+b`NsOs&2r) zt_P-W%+Hd9zGJ{h3II~#_AiQ0Bz5MVg0v0|G)-b#xJXYh5p3TE{|Cdh+W45o6x(-g zY*^OVc&#=}ZDi5y1h`t+Y@9l^C{N(Y#Z#vmn-wI1ev8|&k8LnH%BHx(I@7}vzy^ec zLqDcrRP1Wi6^No1c@W^1d>&^GC{av5`X0jD>3y%eq`SNBqq%cnQXFz!Gao~p>w0I0 z$GYTd;PuwNQA?8bFZT}S>ZM*z?is!~(@P7%S|^w=LarB$zarQM(EA#^XpNM~(boD> zb~Gg^H+r~(-XfW5Y8ARTto`2fOW}$e2kagkxHy8gnRG_W)=;63IPXAW>5qTTQ$CEh zh?@mNRp}Ee_<5tTYwGK_U>CfMb{q`_A>%?BnF52E`qB_ zr^j^~5}5h${_$OA(}dyD-SyZ+K3I=!jQt3Bg?Z@4ZfEYw!4~~Yb)7APRe=zvIpSOu zAcmcCtGVMd6*VAT90Ew`O4AIUd(XFy-`Z@-Vs7*3Tp664=r)z`F=cYG$fM6nC)0E- z#cOW&1sV2^)Y*}*c6g=P(bW%1X;M%6>deQ?f9Lq4@k0mv2lwTWrm@#R)Z*$x^>l-} zC>u5x3Yi&7SxjY#YvBD7xcr~7vpRlp#2Ox||4=fT$XL5Pc7Jp@&287Ox&P$N7w*J< zYp=5(yO32so(RbH?w9WDoP0+jgAF)Wco)$MKfXgah!ZwB?a&;ywUr7urjij|)#@VTnZE=0O04bvDoG6#@8T{d0#9YB<)VmwDbN}!?Iit^ZjXH(eKm`*P*-8(dN z@6s~BD-upU8?evSrjv=ws^i_Y6N&8V`SUm4G#`!5-*n^o^Q+lJ!rt|s?S7!Tv9Wb> z2`MA=+<~Jxj0-Cq5tj(mi_B>V=XwUA&Qc!2v)iXe7MR19YosXae()p>M2Ipb)G_Kd zm5;>?eVix@>}X5&T0}c?cwwxFftSf3UaH}X0-Q1iLg_ddQh?kFk;_2Gy6(O#Z7U9n zJO+I_21I-7_pbDs*Npok6B7}Ce83g?Jb5y}CG|Xu;L488)&n?&2B^o;vQgM%Q5z84 z>49@gi_~eWU!od(ap~Ouu|Ftk6eOahb9e$z@&#uO^cwl*{H@vs<#yX|`p5FO>dVAA z&<}8RSTouSh%yYP2NooTgX5erA}>}j1NLI^s9#8>5S!#aq|xoEQ)~BiKk6t$Yxg;~ zscluy#N*ibd0&nD#T?Ztp8a~S=-d_uh@)*9_;qi;d9F6Ai-W#?`RGNq7->|sinn&3 z>*HO|(W`7R*LKi>ry?jlD2Sq_QvQb(2N=az2C5(}(*P>KImT#;I!xJJYpYLZ;!h=` zw?qidoqk6ppycpyoiMw4?5cRh=>%nUQj$sLO%eLDa?^+jM*p zAr|OqqB?d$mWn4V1skcQcD7DVOsGOB z6ij&ILA#i{<^w0AzF_=(#UG2?BaoK*<*5HpRp6eJC*yD>55~QTV5n436B8%5cHoJn zEbv9wx*Nw9#dyqLIUf)DqNhe~?H6r~8hzL`uCOJ(8RvJ?qhmo8DC2XUk%MugQ9~qR zY6=S&A?R4C2WZsDOJQ7WJPOkZ6JDT%VaeiW*^)7*7Wuw=sw!~PN?{?M&7YdPrxS?; z)WY^>Zhq~RVnJCqd}e!XNBy=_C>H$k&_evjj4Mcarpj+_^J{k7tX z^;BYxPsFNywN3e|X9jCflWykykkH?B-)X92G}!90TSz!03N+n!&_{o7ZkMso@fNIz zHw*Wtg;a3nWNo2>6b5${QT;K?pa9?ua5TQ7h^T~_#vRy`s}=W2jp|Fh#XwQQ7Q;38 z&@W&BxU;%`iLARg_$JrD>r^btRlektNgoSH$7!Y4^zGKX-^yRfTmJcdRc84z%k6S- z*m}2bGziDvgetm@B9N5TWs>NmNOEAMeJ`V=)^79oGICcU<0ZR$C*jP94+aA#`=u*u z(a`wL&Uh%gCY9)R$Wds1x$N-I&}Mc()7LP95=TKZdP324HVLeP08Q=W`$Y4hM-EB= zjvOb-DosvLdsRA>?PODk+ExTqhr{1GJy`;2jW>5^p{rGCD0^oQ#A|6NP&s+(CDm*$ zG&~&2WviR@*M_lG;#*Hv0z;^-ix-ebhWg%Fy{{Dv%0Bq7lkkx^hxxwaIGzWbDr{K% zhg?GXA44t5KZxo~Td}@cz;Toz7*8o?99qF?uE%h=d9~)X{u}HAJ>ZRY?;3bn!?&G4 zPmI@YUmGg+9xARYHStu07gk?+Dh^rRMg`2T*ay}m9<#IRk_kJYreiDciO~p}>9V@(-s{3DMr9qk*tUgp>-q z|Dp#wvuHlXT<^WyhvWs@MT49xRqWRz5>hU=xJRvf5lF~--Md?3758^uQia2*zN-a47VQl?vQr`irHXxVF8CC*tJw4 z8AsG0ujPLvIdgd?`G}t}hY-!YkV*#LamfWHvsiDgZ69Q`l2ev7l?0cEScPzo3Pi$@ z%a?Ho1Z@!E-0qbg%wW_F%fJ0cC)Ep@Ka)>%_->I~Zog4evmA zRH)cxNR)?5vDIYvAh{a5TsBrKl>p#31@J#i!1r7t7M#JG_RQhZrDS;e(xvHevZWGr z%oXOXo>0%Y7XkUN1Lzr;@9-ixFp~>|!7W@_0k~GTjd!7AUtX^`MqC~9Cg7MO9d$0A zM_xT`hR$&Z%4!RrFY8wDhyU~$udB}C^TW>~NUQOq-f!;fw@5i1>euQ+Y4EuVUv3o zvBF*a`&{~47Kfu>%gWDRe?6E8_npuA;(=$o|M6Lt5RC9vr0ON@!J$5|sCfs`DW?%LVjZ@)N3i-tUKFmFT|;jQ zb`~OJ6+IL4H|UHHKp@6l9^g*$BC>*pAnI--x{`D{XHs_NwiC@ri98m^~z@uk%c5Y~B zZg$nEv~NTumK_X-60wjsbV7>p7;+LJMHzg(^*y=pWnE*hu3qiJBk%yZKZWtq(G-9w z^&mOmAfi8U>N-abLZs*0cEG~#?0lQ~?Mx4u)1m1*AAR&rAQ-LnHosOp|6Zf1elN1e z1CKg#5BZZKxOc?9hgo-pxIzs36l9Gnq+hTnCJ;L(0G1#~H41NlLKj-K8lg~U0w{a* zjpQvRIyB#QKmC}0dH(cg;nndTuh;UeBJ}1&aOLdZxaBeR3w#vsf-QODDDcTdClvbf zV`o?96x}->1x2$;zUWFdxa@zcTl7Pdd%P+*bFC9%SbNyX$PN%!HX*05k6A>kKx}|1 za$$CjmaYx1D|7$>N(%HKm_UjVEU&5%TpCLSMxo7_ zg;w+|b{cySL5FCC{m#OjjhrW-4X50p37shfS`ZQp2}4j4{PZpwJT4~!nGn|XE4Wm( zJTtR_;gOA(B5kiPRJ!%9yLTo}ZZ~hd;j7m0@Y2kZ&v$-u%D!p)1LK8TmY37Enwq}h z=27p->FMNTF|f3>Tfcofl)3TNTX(99i)%k0w{|bwyjHsDrk4l&)%xtDKX9TkSq|9g z#(5Z%5=*zfShjE#G?jROJcEWvWR#tb;}{{_9;(%{}v8@v8TFR|IzgvcR(hJTb}T zX+zKf^=8DjYOa9I+2{{AbM$p}^~#$^W?1C0`+-b*YI6+v#MOf?YsbvT+$EcSO0!SVvC?XZTUKa zjLZP85~`P_eBZQ6U!PIsqgM*4U@%p{NH7?Rd3ula?h0~T|r>3o6&zVw>0WHR&tk7N8m_96UPYDk=WeYgsxl%DO>2Qq#M2I2+25+x-h|DZ*yj1BwJ4m^U;5IGh81~bz)<*r zRh$jszCScqw7y_{H=jwyeH(Z_5SrZp^1V$^#ApMRPT4jW7eW!DOUDt4wp``0uP zH(E^-EytT`k1j0KYIbnwJ4Y54X6F3nj+#X%PsFH8$IkBF_)Cb7ir}qv69Q*%??A%0 zA}q(xjFvuM3kHWr7Z+!wP&rXF_nPT!HijsKsc+eQ;7ib_qJP@X#PO$ZF|`AlCneAv z1t$*v)xO2>peFAPriUH0uiL-)(YB*)UWNezKGoOnWBhO1j$(WHP1IIvnl8LG`>-W` zR~Ns5o*0T$<603U;zF{(7v}19cd>_HA8r%={HoAj0HwFt>nN^cFA)O%wq*Pq4);-jp>4#bv;*=juQe^Z@3O zFXgF>Hrssg&S5G~W{J1qO-#)vi>iELs8}32p^yJIQYuXzOqNO!I13^(o|Q;f<0FJ( z>(qr;p67Z<$FfoA`=hW=Bpd9kmE`dsp8Jv+xQERUe8o^&wP*USlGfb9!knd#zXFyp z`fc9^Lr^#hy*|I0%wYR6J`)IJ5GEblweamoG|aE@s|YEtYyX`2$fmi{GT-QpJeH5( zhYsei15WCq=K;^_Fv_C2mJy?d3c3<0+pE(xvOyd){!x^pWL7Z+-qPl6`U?ZUfv2vp z;02k3h0_ZVtO$}ptaM>uXggonfc8pxk$|o|{8&6`do3^QFlCz7wV2k0%pl^LJiM{c zzayUexBqz}tcu~ppX0atm2hHnWE>TV#%^(Pa%^_ef^!nWH2bqTB+Nt1=y-g1Y;1CJ zu_wEsdi~_&?4j%3HdEYSlQjOT=kOoLOLURIr_}x4h&4*RzqrE}GO&MaaAah=2*!+X zh{)J7NChHLIOV}=f@eg^r?3Tf+=I{Gl1rxS`+m2Z$U%< z&KK~P(ZqIUJHZ!! zpkGC=Kg6Hgn0@zNT-fP3&8u!IcKD}-{qD#IMf1D zFp=>Z3gZ3Me(|is&XQK+q2xXvf`|P!k!U2I>@Cl4dpRN?JBZ)hPgs!+qidI`yp*ODq6_GRHzpJ^E zK3C!)f6_+p_B-wNar{V!Y*nA{6`2D2i=N}^J;b0cZudTmw;R6=&BmoaJ&;Sp42M%M=-0 zoSmzd`s{syM|3L`Ihh&yRl?2XLBd0#T;z=GyLajCxB8pXesu`%%SsspUJ2Ee zusYl-e874RxKY@_JyzS(hUI0Au~=O>e{;rh^pwv*zetN_)T{G8+HjGd6o<0LqGjh7 zZsX3dyRWqN_Yd~>+Zs7G_0ORbF~VV}UF(>+R9D41;ousP5;Qj^BTw)l9Ig8yC1zlcs{J`&tq3yAgh=;b)kCN6v7G>;|BYt?Te&=Ib_OlV)eOp%gQ1u z=$wzD>^8P=z!IpQIrCuT4h7~ayVMVR#4vmAqfz0QiE8zMzf6qU>>tp^K0Tr@B;_3O zIJj0TQ{t*R80-V~HCx*rj1aHUWT;oJrcUg$X}kEFj)BX3H8{)yFc6lVkR_XBi6Ehg z7WgB3mHa%C-06j^`OIyFOzFlf&fqBcC}Ycs$X(>HG`JstcDTX+04J9Ysj~m&2Jv*b{9x3INS@comi5$;F?ks@n6LMIhoOtcy_f?;O9*}6kCyvF zy9^PLoRjxtM-plRP+rhaY>Rw`4A63$wybDYN9t4=M#;gCejUB`M>IrFuH`ZC8h1mcc_Z+s2cZzC-7xSYnaqHp2s#u~3KCSk zcdiZfy{Ff;f4FO{33{W)M0JY7k`PN+%|3m7*}1Dv)PCgDe6F(OM39JN~;yv(2P>%F|i zDz{!U!h_>gcM|;Fe}s1@RwrYWjUHl-LH55bIOX&&z(9oHy`7+shBQDt&|%6*V8LP z+)Cltm%`CyIhso^v677{qn%e9NvmunHucG7LTq4Q70`b2rtnsU{r|||I%AC1vi+pJ zW*%C}kwYWt4CvC>{K6lWkRlH?wIQfHhi-skGFAvepJ9acxgRC zmTYe3t-WTmwF}#$=1l|+OOTXrcSc>KRv=fmT~+q)lZDmck6hw3XQgVdK|dmPXu^#H zDbTYb<;nysvG~uFRIpU7&dye=rOlqq*^@;!TagJL9BwK)lN63BSsfisr&8(B(Q3c6 zr>k@chrof_m2E#6xifD2Vs{|LO-fhe@W6zxDaYpM;=+Wqqzs_-c25hSVavKE>*8Ta zImCoMr9&7*66n>O-I2%&eVD6+-i+j*3=%MpE)pV5kX8+r-KPGSFR;A`IO1y&@t%LzrnTvwYk9};{KfBwtXo7E4)c0xbOjnx7@6SM-&@vo-` zCA<{4&6^_(Bqqj*ZJ{Gz5l{IefA@D0|6Cy8Pdr_S-+gzy@N@#<>*iFT5k;Yc1i^pRAC4c5aAIjh-7K^{^-dAS0NB+l?v6yewhyP=- z<@qieP7}_1u;Z1Ou~)XRAFikowW3v>7xhU zY0JwePcAQOTM^3V*QSO5=EzrvI75iCl}JD5e#2cZ_uv6n9bB(vChDC)CVP3E%y?+) zImhGZXq;#udWtK1(_`E49-#yK!xz4_q^|DZX1)898FzxF`?q#=H>{1j|I5{#NUPY? zoV4)(yx=}EYMdi{ak8{_fQ`cTY_^o#ozAkv$Aw?>fTKX`zhc2Xcea z1A#H{1BAXOJ#;jb(OE}9!V5xx1sqITCZ#+ro)u25T z@FE});=<)ZzECKGu-(3Nz?%w1t@M@T*xXL1vok-MG+NuP*8O5|7eL%n6DRLAksR+m zi4RwjYST&w;HNpl9feV!3Iw3}_XoU4&4>*dz7!l*l;0Pi=PTVgN5OI0QJ@Vxe7QrBX%Gb)`wuPeHW}IdMTxgZ{kuWk&YEv}+8*&}7W~J4Cbawxxd&aKtGgHb zfk@;b7a??)g&t1Ta}$8q`Z_YRtXTD!7dib9i2iMFXCsqM4fW0B9gEe)DSfj4BMX!7 z>3&S9O+LsMU~n|9dn1!rXnbqv)ffo)?>h6)RL?+9yMt(pz)0&W0A!;HCm#ahL6w6) z4^J_2!&nVkJBqlm8$A|q9;PVJWLX3L6{JV4ul{I0UlVW3F*YiP*G*mE$)=Y87rMBc)g{NJf=J^Y6KBt(hD~VAJ->Ws4w7)#L5$ifE6KEitK*~Y>ZUu z__VD^(!9u*=vTgQv|Nq(z4B1^OD}57vslyE%<4F6=md{k%#^jjr@TloQdu9|IBtT> zr*$c*v+(|VF);LuZMRBemeG$`W2IL27hbqS+GYvv9(vn{urf?Ckx?&|B2G{Yz{Co` zLaUb%0gw0z=iA=OsKu4Tg)U(v33&-?g>Xhx$LLHOaL3jFY418IQ->eS44oBhSuQ2Y zTEr_Su~@^Y_-aNSWLD!TKuIuJc7(V@sVwHbvqPB&N2C&j`@E<*@6F~zwUGxi#K3nh zRZ9JLLWo`ximqmcXLe`s|8QnC8bU;qQ2&di%F>-|1}d#R)ZBD9dCIEc=YQRLOnnag z9Cijy<%qhZ{|Rc(xQtee1hTCpOf_`t*n;p9~UfUt!Et#z!5_WrN9=IFBL~hT3O0yj~t- z8SJirSJLBO@}6f+cO;Xu2-%t%d0h8zCke7Y5!~D~VOQVgPp3z=ZgHNSj*}_DAsh20 z5;^)f5w~Md2qRTy9fQI~lP65B+J-Yo2)HF{vEY(FBc86HMBC?dn90y<5j5Y(&SY6H@;JI*ju; zSrbA|V93(4G6DZ$*~HJw1Km#PqbK z-c*i+9v{Wj4V|lE>8AqU?Prr(Y_0#h11T#!Sv@zDOpH7pij?;hBJSLA0^_!P;*Maj z?R$SPSf8C89a>zRO>EDel+}pR&sW6acFdQwjK<=-`D1J`b9tV9`q#&+x{b- z$35>CE*LXPZV|IrKoSEHxOfKjoo09Q;}&^9acdYwOiryp0{sMKlsU&GDDxaY2k^;M zeF7*TvO%NcJk#L7;6y;Ak&!Y#q8hty1#T0L;|wDdk43`X_?;P~S}jfIm!aAbK_*mbm ziOA}WBay%;hztnPm_IV|<+rW8nxAG9)bF}`*nRqK{5IS0>Xja!(t$OcUQN(U14t(9 zwe>Y&r3p zg&pdX^!x3=$YL~Q#f#X^Bzq&u@iYdW5=`6q-3W~`C>jk8f0Lp?3jmlhs}m9lpHHfJxeQdTatKnhr_RHFVTb&X79 zZh6wr|7-PAfZs)WU`!!;uk=yI2+lT+7GM1OMP!tDB)v}MI1*x>NU-AQN% zvf$=pt58|MZ!C+98?Vyi-ZI=n_p7l~DEwzfuFfQ*pt}x;bl)2AG5K~D8o}O06^%hP ztRk30{VtA&EfcCWG&VLol(eaB@eRTr3Qg ziut7aVDC$)@xc9Mj=Sb>sCQ06GI7S-Sq@alNjM)p?3CjjC5%pBKaJp^@S%NKbYpCF zwMp3QdQg8^r-IU7(q*-O-}ZLwr6GBFw1WVOW)@qmwZ_jQZP`m~HQcpK;w6ZcqF53VH#APS1vYVpwYj3rY0e14Q<(e# zF&OCvU~;f4%>4*tP^lnX0vs7b$Q=tBH<|)#7s+Cv9n6FvBI>DdENqu1CrdU8@mwz6 zRL3~X{O`#aoc^M*WCYR1voN^8=#q`%%4JX=IhMy~XO|s7bk(D$Re1*O6*`m)8QnwwY{%YJLQJ4@SSdYwh#&LA zxoS+MIJK)@ck8Dd=S84Wn(i>~!ZV0au&lr|!e3)~ylizZ!L`r_Wlu~yJ{&aNbnqYk zJ?|{#{PFR)m0N;OqHmU0eh?+^95#0CiJSNM_|k*C%&P8F6IAYZ_uNFg!{=kSBXzky zetS%6fYYOQPD;Y_Xr13Cyd+)x8h)WT#Gh(X!Kwc05sd5=CL{)89btM zZ-w5l;v7Y& zy{D_v$FjRZ2V3Yn27ghvTVcE)mu^>pnQnRsYt+(tcdNCK%Jwo7yL7tu#gpWvaA?6? zfChi|`7MB{s`Lp&1qj*qQ5dzN>1^+43u#>Tzz z%}f3d_|?-<=+{Eg&opM|q7S+U?ZN2WY~wSpZ8qIwRFG(QSnECgch81w*Hl#kvFn&h ztFF=cZ^(Bjd4-YxEArjyzo-B2Q@xN%$~`u}oSv9iV7y0FE}!`JuD|1iRKj`|UkCBs zu7W>$DSSwt@cc8ZYT5B8INMRd3F0Q0;}1*|fLp7Gg=-YHQ>0p3i}qxdEbtRf(g+|i z@Gru+xTFGMH@Hm5;e#Q8F9d$sbK*SpmQ1a7;zX^Mi3UTVV6=B816w8eFAIq`qT0s; zP|;#N2JpX>@_v8*w$(9^+F7|G(Rj%_A?94Yyw((=>nHLC8_}79ooUkPJ zs)1c?03bvH(a7|m->80R>HhJNP&Qlbew;l*D?d9Zg4iVvG=^O4jjGchm42yvB|J2; zI5k~FAKBw(Z9u>>*fpDa&QKZFj_&)Lr0)&L#U>lzkvW4Hj1EfQ$#u&@B1s1`)EfqH z3Bu#hphDuvKS*vM4i1BaDaLrxC&gWdN!LkJNJ>2i=PM@7S>a3~TA`n_R93n_)Y{vb|qF?#G1u)9Qp zkzyozf1y+=@ZWLc?Hx-%{z+!jX(T&9l!!PGrVRbPApfu%jQfKLe-QaslaT--KL!GE zJCZ~;RwfMek6|B}&l?@{2jhNQoR z5!~4=@OZhNICdCgDyVAJDSD$|4?LM#+Lpn+=yW-%W)B``XT_;0zc=BJ&aAxTe)V(A z?;#mG;$Q3M>p=33fq+?e)?O@eIu)C#Tjz9&kYq9%|9PDtgegDp_Er5mD>;o`6ejUk z9n@Z5IiY18A{p>;S_Mg{#30+(i(y{#U7<|6;`83%P^F))kB_PJ$h6str>+a-jK$Nz zrCM#op;^jIS`U{JCH)7#|4WFZl)l}B{;elr=L?o?7y^=5|LHG1l8Ro4;{VhM5|>b{`)(48 z@v##~U!`VznT$WNW$d(D;T!L?3BUH87Xqn_Z>IZBy-yJ zQ9C4u>jOYFWK>jGW7>zh1DwAI=?*a?z^ywj|kt1!SivTzn8^{G*;(FoQ!1;c!cPn48 zibMCS^~aICDjFFb+X#g=#zx_yrQ(l=LJwQH{D}vy37`v&%_aMedLUVuot>U8_Dk#zGEn#V?wy&P8~*(C@%(v>)Ee`yr@f{nLom0D;Xmc#&%1wxpfKXBu`xMm{lK5@ zEH9J&QtuD=yZ_zIpZ{UBGO?&x? zKr3VKV%gt4lzAi`zlBvilG*JkXhrWP@<&N_-%M~dY5Ry~h>Tvu78XaSl2BlSw~5r@ zpdJw9E^-J~S7TtW5^3(v%VxP#FI}1R$2KYxMBnf8S$mfE{lwuDm5rExvNEt!uOEkZ z1MI?BFi(6uX2s{`xA+SFLmgZ5b8##7@h7l6ocNc-X^gM30AH^+fH(ZI=f8@)N$ZXV zf9h^~C-ASAY^~h)ifJP{fdWnbfZH{wfdG$_58+K{kT}6LhYwtbVw<8EO=+|P)EZEg zaqRF0SVGN%Lft?^K##>_y#>Aa=z$@8rKo+$X_AKogF`Q7^6Xr&q^qErdD-Gb>5Z;G zl;;6g)R$A&$CxH0s(2B#AKw44=iUv0AUv7hpGBy_;@r~M7^2Zl4S6v!Nu$^0?~O^{ z!<*C7$||V^FCx$pBieMZ!8X+Zaz=meTrp>b!7o}?D4b#*=~Ou6Jcp}eql@?BuFvuZ zf{}DAg(%ecFQQVhOgID=0Ne^^;98)c@rS~h0nZ?NB)NefP?>C&MhrQY-CHr-CF;cWxQ~2uq_KK9S#QJWIP_!wxG|$$9xDe zs_1@@L~3kwC>l+|`9Yrc`RQQ&qc#GD`+|W`ARGP3@~MbFXuF>pDMZ8JXh9!o^RuAidpF>B1^WSSfj91Jpb;SD!i)->5C91h z8jBMm`U&anIh=R0&m8iurEE&X8V$V5V6yvD7*XT+tq$v=;Ld}MyS8yNJ z5))I))Akf%BWkk9wMs!m-+RoAj5Ad4Fz44hQtJOB?oGfWyY4#CI``JSxAuKiNvbN9 zq>@@&N$Qfi)mydgcDL8aZO3a9D~=u8AxkG?oWu!<$TB1oGU+&cv2g|>7+_ci(qx4I z9wlMeGJ%H2BrM;|2)_3*f%hKq%=dsym?^*C?|<&Cx}~MPgyHpa*K<#u^}jFg9vG?K zD1Nsi#Sz+nq+DF4{tjbB+1quJ7f9B}F;&O#i25H;kY?S!Q(Y@2+sKCuZ8!_=j(1)p z6E~o-3Q$7Wsi~DzBpkFqNh;{$nfLMZ&dlq0dLRQ|t-B~7d@^?ZdC0{)6M^oIcXg?^ zCb0BeA60lIAyn*uU)=_O3SP>a;+WK{M=mz-Wv>=ZPkhs$ZlFp&1%MOHrUgF!YO_A> zBCZ>u7omK`-VvA$hLaLg$kw>?bW-Tm?;!r8m8{ifC+qb>T7{f&xmF(?8Xd{soFBot zUMq*4kV+To^~qWIs#ypgSu(6(SiK!a>Cq^%6u{uL`!5Lzkt=|RjR+3V!T^1!-U9m$ zmG=^oOh+5(Ji$#&Asva_lRg(Z3`v;JM zJBoo9sKjqF03R)=1_J3Zj9zF=!5AWFnC82YY4I0M%q&>ygWA>#W%~j9x?e}IzEEQ^ z7AzZxx%Xz~J5+}juXWqDBkMYi*fXAQKArNHW!2WRJ&$8wnk%jbKtZhL4US9)`Aj)F zUF;-1W=24tH!*AE9kt4$yQ6CS&u_Y0HUAE)>GD3FlJWpXES8-lphL7(Ae7t~q1at8 z%1+l2wY@E2efE{zF?A>@nz~-8TWn`Pqw}{i4NCYs`1MYAYoyWFzV)vvE1Hby{$1H1 z`uF*~@31E@57v#ZsoG+?*4a-Nx1;F>qs*!T_#?b1RA<|Wpteki0tyaGL4!|5@PlJ3 z=6;op&tobUdVV2TA==8S?dGka?_z#g-Aztc(xTPgT!2A_yG?)$->jc@zf=+nyY+{S zRRNTxhu785keU~UVE(#9rTY&upZs=1Aq4Mr^Q^Sg{k>%60zdhiBcG?e(S=I#$lnCC z{s9IC^qcM=3gMdr?+!ePo)FFfXaFwa;B~@sSpvyVPD?oaJ!;rEp|~g_bXgFk`a&%1 zVB`)vBm5?!C+>>HjEP?S z(C&{XQwz3({<*b@1jMfyQ_`(qRLJOrQRJMHL zuDeb^n;e3;wKh^LXOly#J9nKZXT8XzETJw|@?{$)vEVWC6;CsF8+iOge%Q8QQi-_z zY8XjhDwSLg^yDkxbdZQ78>9KCW0)Hi2+7niiYN1xMpA^h$j1ibb!FN^wIMjbCmX3` zIc}I2A>K_kykC&A9`DJyDZ@MP41A1r3};VVPa5A9NKZB`a&97d@|2oQ=2_H6B^ve< zUh#{(Oei^Mjr9nmkWNt@posU36Z8hz*l%lejG5V0;`OmwwNwUz#=m{Dn-0Q@{{HM= zrB-KW41S+XmP=Kb2BTK&HNxL78SlhcEHM%{)+g3np34-A1JG97%@+_hW4YyCat)n; zrke|nhB@dYmoyalzs(R0-5*7?(@+RH>Kos`5W&K0!u(Rn)uEGDbf|Arq$@bS?%=bT9XemTB2v$S+=CzP`ig?OT>dRA40695)&uKTmNp)6M> z0uAD9Xtqs1p%4SIUsynCX$GkH79Z&O|+`9et1oR3w)+uU^Xy4x>QzJ z5+X}C0wpML$dG#lRhGHiKa|gZ$UEOez{dZ*Xk44#T)!8ododa12Zo1-3y+Px&3h{_b_c76F;1Eovzrh{`%OJO zP3TrIA1k?*9L}S;7x5{kT$%1 zbKp#F`m62uKi)d<&A>Mr*Z1d`Fycke^In(Jm$Uaf{+4d~`>_8i**LKLKuugfq?7%9 zJ75p*%_G;`vxoPcZ_hGPw`gae?fXuiXJ)BOZuxtn+naj+IdxfG68SN#r$9YS0##$j zxnSD6IR(|8EBX*8eo;c~3YbzXAs>=9qH~9}Z_x=T;fu6__2s+U9B3BlpKYT9`lk+F zV_*b5u#ea}eOuJD)oYoi8VbvT0d-s}ca2``?p)Mk(Aj1EyV+aw1IbOuEAJ=x)HH;9>M8^UMUhIi!%~Z_H9A4=mdk&8m=!o zW=hT4bDhrIBjefJvq=47fAe`pibjK(eMiJswxgImD+C*p0o>gl0k@1Cv z<<(yY5{|k!0Q0{E9u09iK-(>9bpTV)h=T!nC-CziC)s{_^GJt|lCc7bfxo1uyv5QG zSWU7+|IL#f;1X9CYlTAal{qN$MlW4hcIO}-d1Dxp32j~NM%bjpZnW$|xtz_IMj~iU z+^b9Jq}Sk*PoF+-7xJSX2?YR>%YofVCG%sM+b_5)=uJqoQmBqzPlMsJRxn|hnJi4n zZEHdo;MCr6=FIGDXao#;FJvt?sNM?!?1gh;?&pn`+*`I34+je zIIUXh0a1fU#hTfAls~@3JrE+VKuQkDUurOCN!^n5yt_amdm%O4 zwbt=8zF=B01mvcxO~)Xp_hwz2r9ZrJ>Jpaa%&YFggeO)@PX&U!qAdz z3V%UI^^FMR0CJ~x``-MtIR1uc2nfx)FOBegW@uV+7O@@jF{C^k+mAK)`X0I zYu1{qEiBbjVR&*8ES?z3W$jrgN?=?v zJ72AtYEqfC>in!2t(dcRHaC=rSIKx)t>7vp8MC=~Lct!XGCom(|L8WZCdMm4xU{N7 z9B(1>P#0C^$D_)Cq)&d>2*)kBamJym;yvVma3A^c3<@~8d<2#u$?~tkLfLetQ}Vl` zBlCsA{0OvUXjW?4G0kZ7*UHJ16_4bzcFg$O8U9Kx$QX51?^pQRl;+9~Un zc<_^AXQ*@ba%LD124jd>(fY%t1fSx5?yt}KVNcwtI@E%7w>^iXzxzA&dUyYA>NCPn zKj67hZHDvt@TTWH^}qw&XZ_2J>nbQb-L~-6@EYvLq|*S!-BJWD2H~In2bY(sLg*|4 zbez#}2G-XpN+756;C!7LZ-5*k5igj|R_!{txZ}YVqJo{XLLW>9i$8PAn*3 z&xnx6Nbj>UGh4of#pZ1_&GWJo`L!NTOc?Il3xG0OyNPW8BxDu1FT~Rj66!@tJ;b_0 zYnkfmhrG?u-R5Rco&IL%{;<1Z+|7XK9xrw?fZxQRLJ$u9(7C{U@ctr?qa{roK3N7j z;0Ri$r4)4rG^9fqB)wQXI$G@Rlh}Vv$cDzwfGL=k;2}>R_xKAv>If|shAc53R%o7k zMHJQT7=iz_jpZ#tdNwFF>mO+N)#c991A^nhHvk!^&WYNIp&=M?o;~YbIsEbN4!2p2 z2nl+u-ADXFXPX6h@xmt$e{TYR9~S&Q2_YULwb6iqgK9hU?`pOiO?W=GftFx!?Vh~F zoNub87C5(?uKTVRbCGlm(^2^BfIbObK2~iabF^VO>+9@*d-QA_enq`*CX?g(T~KP~ z&^0Zt*K_B1Y#}z}uf|f@WVglRJEs<>__&m+RT6U*3kK}2ExBi8u=3IFqhmIAP<+YB4ggN9}6b1#Ax%sBm0)Q0+^ZgtY}FXNsH^A^cs$o2LbTlubeGWPxH0677;KL z0^UY@RoiVXBTyt?H<$Aqr+^wdea-39$B!RBmO&eFhg;rd6S$KQ+SvVKOUUyMC%V(& zGVoME9li2i(f#UZtPl!i(hy_^Be0)nyZ51RG6{`$aQn|LY7PA_UwDErII10jWQMr@(C3 zOxPOI0RFPtW-EBr?XJ+&~lf3xfni zNg?`ZkqVYYA@F(Mo?qEvyXAnMdp8%Nku*XFr4#WmjLz(E4#QM1jbaqhj50 zgDHafQYs#@l@mr_lr&0|B2r@ivD(}-R!@WR!OxgM8daIFtFj`|(OTRAxWfD=P1uY& znKa1O5W*TcAmkVgB|1`z*`}1h(2|e+bI2<`DLP03g+8B$aFHI%$HldwXZ6J&oen`|?tk^V&d>Q@sZX%LNsVqc^yvYXKj1OiP5JJ& z@Za-*f#(4QurelcN8yWxhH4u;;MSu2N81q#2a~C%Q$s;e3cH26C;B28Pw=r<@VnPy z1B7YWk%%+C9I)= zfN&Byt>*vA9m9R<8+e0p^vl=i^?H!xQ2f0_>z_v`Fpu;Amj^uS?Wg_@>0NcVH%UsM zA*~ke$Z-wU_ne3Cz}*vo^gxLguk7f89dXCTb%$Y1JoqVV*296I!LtI+v2R@p%Jr}k zQe5U7DUM*mr3(=L>xYNil+9rK2RO7FoI5cBlB@os^}&1h_M%KRzJpgWIYh4pUZhf{ z@x232HoOODI^h|77)ifO!UpHK#3)@M1L6HgKhkI>nF#McnwbVuPU05q1CUYg0=lsN zQ{;-7no6bg|Cnyqo+sxCueRj?fTb_o$J!?nC#$qx3KWsT5UXqUgS;Tgd>Ppi2 zmmWO2;=%5V-V?taEiujt>}YQVRs0BdsT&?yW`teUf#lFb(rP4cAyV*q3XHwaC3iBu z!<^%rB0j_eRD_|MK$I4lR@Mdt23W6G0>ZG6zI>Y}HR)dQu3&VY5bRT9H)jev`}YrT!u?#Bxlp0-foA@}BN<-cP9%uheICAzK}>PBYQ0JTOYy+nPdk~%Wj$O zZ_FnWjKq^%Mxt0`i#@d8Y1u8){SCCugVXTX!UQdX-KleOfOoNyH45H!@>A^|4KJm+ zSkl~2SE6Sk^8HQh)B;RojV8OaHQdY;~hV*ts5o{JA>Vb4^ z0rcU3*@YOFs6R-i9|_+HBXs-Dm6)?0jZbxd<|1Tfl!#p{6iU@%_)w1o|D3w`ZQ`#^eE>l3FRHfc6BBUPX_QO3T)EU3ot~~%4K?0aKD-EfL(F8Y#sS`1 zJiOc(S4I`VG)5t`%H>Ms2AswxChGoTpaft~>i|oKBM6)vaYFgr)YQmuD!CymLTHwa zWNLV1YATmUL0xQ9f7LI;IUs$d=~ORMZ@*7X-6xI%A~-I}kn9oy38ZAy%z;&m&V#9O zFfah5YHM}~MrJ|;x-=(5Zd_(q5H3)ZUD)0B2NY3(@oXlO9oNUj>fGFlKfFkLeh!vM zT&69qKYG36$XYwvBNl#z}Mip8{C3qt8vfn z+fEYuTBk(T*!Zj3?aQ?W+xp&j zy3fX1Z@7MKZG3EOd~NOe{#6Qv&>MHh^ezu=fB{m;$JN3sHogbuwGc+0YJH?utI7U* zXTyE6u&oZVc-vA*+1-NQy{nxK`JQ_+iS^I|46HIX5G{b~THS`dz`^kT(?e&a3DE?2 zEJP~i3H+!IHzye91oo0Kz7|S#|5YlqmQSV-1ADq$nw~0`r|^FoF|eUDYlXs@bay+= z*w!`|t|qZ@efNFw_vLsW+<%`U3@bPLO>XF{Zkx-MZ<5&f$w=sIEs&ITkA5WD80V5wdHB!y|!_HA%i8nBQMj- zks}<7s1~%vLq8Z3)eV+pr1xnaiiLatWTB^akcjyJY=aNfCZ5PE?~U3(qj95;TL<1? z93^&#@ULaWpL`Qycf39DE>ELCwTIpogm`En{!RcxlImp!)`62~YQ@D11Rsgu1L!^w z$*6&}vOYP8)z>WY!0+Ju)m$wG4qVkoW6`lO5{C7;BQSjpADx%Gu`yDCWAjI$XpSA3 ztIOS3G!_nz%^}4^IDBNzUvBOQ%0-XPjfKNMH-|sa=D3Fful6U@B8Tb`^EvdmUXtP| zesOb3FFOQHmbI9=nN@dp*1dc0yzXsnmqzEF=6>eFBN&oiQ+X@C2;@dJC&k4Gn`}zR zfPSRMe-yLA*?2^SXNOA#x zT?Ke=&q54ifocUTOM>T5e=$~dJ%zL9RO{+$KNg4bE9)=3N3n_ea|y_J+VaWIuv_tBb%`+`~LO8JL!FLep8g6fD=< zV(hkRH@R=2e<4gHd`0j8=%e7%OrflXuH;P!j5LqXrY8?LIOwVPMUlH7Ul2?ou|;KC z)!c6DuA#-0GIlr)0U;B=@P!U@7BVpSzzdZr1VPP6->lxFs!$<0H@eZml?ykxAOE0P=U-Y}Nf9GM99Y?+d z%e;psbc-Q!aL4a4Etva^XVt@G0z-@7?*z``0PSX=z}R!3cCku@a@TGPLXxK~5#thJ zdZR^aDU2k(1S0HyZ7BOxIQ&$0NG%g$c7H#eURa<>-@*bd0sGgQO3!|r$m8RZK~x1$ zWOp(B3CKy|wRga4Z{h0o@Wi4_1$0QGsZ9_tY20YsF|yYnL*czd{}C_^r0ZUsO}AmLy(&JTv9t@~OyUl{e*5o8OyKLf2rOY$&?EJa{njFG_sf@~&42PxFZGa48~D&)o` za6WdA5GLX$4%CakCzMbZzBCh!Eu%4%`p>DMhSAv!r4vnuE1*>3(V0Q$k!ouAo5Lvt zcPy5sTwB4^mP@>SYp#&4RMLg{OCSB{M?-c5Uvm|eN<2F7r#iln&o3NbK6Gfv4t-QZ zD1GEw5t)SOAf_OLgIqRCXHG&7!VIlDgIgWa-5Bhln1k|_sh5i!WzArrVC5ikE66a? z7(ZdyS-72=wr%DOJHjE1sMkk0$d7u@e}Wr9b^{lOP#)dQZ4w(oaD36W5Az?txeNeP zxXfDQS@$n%y-#|T)Gp}{8PrYdR*0V%w?lF?ftsZXKqDmDp>ZC_k@l}SF=;`h2(Jw& zNJ2?RToOzrVknQ^_X_NCS>M?>b&6JL_(vSH?VfRIoI16!gX|`{!8x+k(UAfr4MC($OJrtB zp-_3QIuH8v`!Ck?>-BJWOJ0S;fBicBXp?q}(ynczgL$fg|8(u~INbPUM~EDb zIp+!_P+=M3KxnssXJ}%{M6uj{%ZMeqsxEuV)k+~BZ>Pq`TZ|rROiiCSRBvPo-Sc)z zT}&a_(l2#5cz55;W9OHm_7#shgqJ3RXYvyXJenI1zZ#ln=of$KanBm*@n4G39Et7b z0eG}KGzis!(Qz?px{-%H(jN30uTksvYgHTME3<6Bv z!}d5L9`ek&2?zE%$Nl}-=#8VP@g%|%mXDXmZr^p=>}@q{yN-YB?fBoQPg$v{RQ2ZS z*!i*2v7!}oZ|k>@mDWmD?A`t^xBXuZ)H%@a3FIri0XkLEBK!ATZ&&yD^16>eHsM`e z_HK<(b9gvn#tw~)IEx21d2kqh65%QKABeDFSB55Iri1X`kG;sz;9lMqzH&rM`#_V? zZv;{q*{?LM6nzpj3TdRG0{bDbokwlUpXLrjXp%HGA)#hX_oGJiva_QC`##-r3+b2I zqN$@cJT9xIpS#X7-7VpputzqrNAl2vv;)Ms=P`2+f^*u#u}hO+iG^h;q3T_XMZGw{ zHa5M8kg%0x8bHX;^Vo3>yS9+6 z{)&gDMYBM}yMFMK{f(Hoo;!K~d3w(K-aD^*=se{;Ngez|_Jg+us8c+Je9u>jT@Mbk zuQzEPM5{A1J#pL~B7Xnq^?qf@C!*1b2}()F$0@PwUvK+w{MQ|IfF5DTudMmR-WvUt z?7jBsojS;d_G&;0grkL;qsbe1aYgJ<2mGjGLS+Af|T&}z045# z#i>~v?~lYnQ{Sgohd!!}Lgv0^@}9>d#X^&Q^Q?AhWLRY$h&k}su)?eH=p*SIE0l`E zZv)c^_MEt^`}JJ?=4gBsG2?<2Cw9Gp9L`T05q|6detZ(U5GjjD>swLS8~x%oRKka$;IOFSk5$`L|kFqvqU@; z%~kT5nUF2k=&aPSO%rsn=f4LVGS`0(T_8P6l;FAT@s7lm_GQvfUac)E5P&Q zyD1t+^s>WrXuvYJAAr#% z0tV8!pm))&Nt6#2S)v7UGolKTH8GsF?Q~d0j@11;F2|%F*@(+j%WC}Nf$VRhkAfKEVfudR}sM?pzVUwtiiEcT+d2_UUc+Ep?iQ}->Z zqGQU70mKz1G;|t*9D-ym>LW`Ab!r>BI*is9hv;Wm@6YhoHLu=$W0OC;o=B{-vCTJb zg81ODa;Ai|zStY&&F*upCQG3xK1ipVS2j1jHU`kfseRhmH`{A3Yn3BxNhYbBY_vgr z2{ST*Hg5LX*l*o04z}$VH9S+8=zberZ&38;;$Yi$c3K?qEjBUW>{<8M1CE2weHm+i zKY9z%rP{Dm=Z!`KDDh8n0MN~)ZZ5et(V^V?pOh*{{t`3 z_deB_?_Xj^5PFl_7rGg!t$5UcS18V0gjyIN0#B?$JZv2`m+*y4MI&z{aUU@S_RRrf zw7mx|U^q6DDjrQBeQi3KfcJAK3WJn9LOdda8g_g-VVcytz!@`}N&=rQ~7&&0ay<{X3H*0%GhOGk4E)2*4!}j#KGHk!$O)xW- zKz5{BEf!56b8iyY_0y1LYDbTAwDv!I_@E*A4V6wKMpe?8ssi{XMn-@)qqQ2ZM~@q7 zmS1<)GsZe=o5H#Z3SUL|bR(>GFEBsm?j6;(K>|5rU`_@QgNsylVGwE`5W3w5aIMLM zgYoYgieT`#1sCAXAg)$<6@B_qB=%bIl=tV;KziL(^q#wFU z9RI#-^+n(vNJU&-Iz|ZKU6ky*`tS8-ig0fb_w4S5hcLSWQgd zOKsbJ%CZi1zhu}K)EBgv>$VFl{Cy)M&$94S`F#8Qzg^O=7TcEf6wH-E&IRB`#;@ht zV;h-Idu4eMRtR8!X^YTH;w-9qkxLdqsycOvMN5uNEPckBpxFSH2$b+4V3r1D(`?{v zZMOx_x-GhJJ{-Qhcmc7APaTq$wB3fOrbd%#WU~la$+Y%&Vvo+Z-CjTIj>3i8SoMEi zy5NLhhlSp^-NESoSE9artyXfJQmvL}jo4$f|M?wPb}=hD@ec=n%+rfYgM|=vgNBkH z0eGDNiX{&wHz2#oso(*+$PTjSkYA(6I<+0CcHw1w&+*cYN$W3-r>pzbP^5xKhMZPK zsJ3MsJ9c+#YI-Dv%pZ-VrDNLQP>7(fB4jlb*;u94Ka7-Ph8c=wQfc}{2BA%i!A;*- zT)OE|sKlLd1%a>=OIx7aqR~gmuO$owg-#-ZbRtc2Lk|=nwcq2*D|Rnna1+UVVEtc#IX})wu9{{udEtlP5Dd_X=5r&X`pHN+b8kMAjLgp?&rAN^OnFxx#-*W5TB&qqs9YI>Nke|9 zQg)w|^Tl=~naQuO^ZRx&_ZCf{=;MGr+Bi6flhEz2d+X7^g=}FJp{Ex;A&?h@3S3oO zDx@4H&_Q$+-NvEH+SFEe?Y9V2+@m>J1hcOdRFhc}v%nz_imCS!Gun|DBj2*fw z{~_jC$CB0{$fdCM7bC6fiIAxDFgk(lu|M_U69`^o#>?eetz3?q2xc=jH8s{bXe<@f z41GaI%|FqfGC}?fr}1Yvg81f1FFxY7z@j?1N8)-A^1ZOH$Bdr1v@P>oFE;B=qm~al ziEngt-5VSF-1$br3Fo_=4|mjL=cj?6vf0$vKjUxAXTF|7#){2ONADR|etRoCg92F* zFA1a(%%gE4lfl3qL35w3St(&1ARSHEp{c`BY#anf@xUzx{H3SP2mQUJ`{@Xbt|AE} zC_)M)xJB9{2(pnHk49_E*U?N-_gE8Krc|ViL`zu}s@pcQZsekNypmm6x#7_Grw<(a zAILc2X&Zi)X*f^O>(YesAZh| zrV2$EHY?ZqJ_EKEj+fUe!gIs)vJBu|3Kdl%n8?kflL$0yC6S7%n1HYX0U%QeB%89% zw%d92aRh&0w1t$N9xC6qY&sWIQ&XK&N9H0HLR>GUgNR|-AEf>j)>z-pb_UeMYhfAj&cF}Ks9_xR z(4o)5_ru#;#s*9d&X9x380CuAq{GZny+ugMmt*FxROHM&aNvq8C)LAIPs&roxQhSo z-Qj9HoPyyQ+$b{0j$qo@+c7gAi-y9fP%s!zLsL{AC&~+|r{FJaB8P$zNoOLq5gbSO zB-^%=iKLlIsH5p_`=9~-sF^?raRZ0o1v3F+#z-WCq1VMjDHtp|IQ$5NFsTsI%wzyJ zkti&OxJoLGtOj`uIV4!o=_oOOE=!~Epb-Y#h?qtW(~Zp0$V8^>h?>7)wm1t^Za&K$M=)v^^VfMwTbS!#gDjG{qO&yt<+K^)k&!&#>nOlvnjDj(J zh2tRJS`f>JtBu$S^k98jL>T&&)t5ge?}IyNFgrDMcp1(xU}(Z8&YnB||GvC2_$^2S zBXe4MKNo>ykOcRmy`G}iyL$eQ(|20`gGSLt4%pUt(7ieyGN>0F1%Lnbuy5Z#>z|xW zf8!6F`+fWUc27j-ojYVCyc?H|7@%1e5F7SGSKTf9&;3!=IE*M@M4LjzVRaxlcfNt} z+!z#W2Mz-T8qgjsFTUuq#!DEqpJPNXJApsQI1bxr1iDToOQj^Tx7p^_u9tu}?n~GS zzO+HT0}Ab+O(Qh?jsecZu!r+`B(gzFZn?eMTc8sya}!!=2s-C=_-y>!03u2YXhF!c zc8mO%Y$`1RYHG%l1GuzW5W@&rYZz%MxSEx3g>=>m6p+$X(-oju@#X`>&gxME&TK6r zXm>v7G}g*5V@i%1FShWa`cFvy0{X*JP6Y7;ZKSWKy68XMdcrUu2P+oqiFkg%mrVT1 zHjkY-bBr6(OXSLp`qREy!bA*C($>{ps%zlBn+w6-Kb1WIoPoH`j(DkE*`j-1+8hqf|8DqkHt?#Kv43b|rs)H%Ii<7$bf}@1fv8r6 zz>xcwuql2W7}8AJiPH(&p$9@DMQ4G;CxR)+|ANph@eKlvI0;102)?w9@XM^}bOM1$ zaAy5hioaIEPoQ4bpEmK{{hi(8bRD&a2lGbg=zchcuoUA&Kue=o+LRS7Jkc-~L&WDR zxO)j>)V(JZx(6a#b9~&iEl0T*_PwFdy$F~8(gx+HTL|+OY?%n&XPGmwJ4UcR6U>5l z*+LRk{n|%Yjv#aHeV|hQdEgU(1e8|k{s4-C$_8LK3=m4tfS@`cRkXl{dsyFTFd#fL z0E6sq1X4ln7)0ro;sh;Xe#Z=^@&q3eA`lR)G_69_kUI<=iA(+fen^N-vTKb6MpeZp z>gSWW9OARbhK67U3vnlmvXmX2GYkijIOF-;s1urwL{FZ+{f?XP3?2!1R&^ZjS@-j! zITa@F>D8m&fZ?)6T1zV-6`q}+A8&-i>F(#v;86>^-!?}f(+NM|D8u?R!bIAUSM$;g z956_QtU@bGi^H%245q{3#`ygF?9HaKjs#81P|mAiyl=22)|XyQ&>@$m?Y`Z; z#&4x{4u<#uo(O&_iyT(KXdmzf-wDALsD3>IoF$}l!!#@yla1xxOH7#d((Atr97MdU zr?4X<$IqQTfhejb9Fdm~Pfcb2-ZIjY7Lew!qd=gKjo|=;2n_IK+OW3eEf87-(Pa0$ z>=U&8tD89>B)2$G9trkG)|dsAh*_DikfsgV0n@Z5D&X!EUR>>+$9GX@G6zZ0!-4M) z{Cwa~plKJq3Gte&URl8$Mcxrd_D7RbJ1^qj2dBBY9{BtQV;vBv`(8zuEtFz&aldv>Wwa z<>f7%$w`GUyB$zL$mlSVWy1{f{^KA3P8`1<`RcIkvm;>ZKp}k}=uOZ{3_*&J9||V~UxQF{IVv@<~3k`>ho+(Q}wOY3(C$J@)$Z>ErH2n6Rr%zXk!wZ!|{$V>*Sr{%>PM^NhPx~A!%zJK* z6^6qIK3ksiID^44JB>`l7hmF-(JU5ni$P>PL7dJ<1K8U9F30XM;A|fT2iWvbxnS^P z-}T`AGoo;y^^ny=>%m}z7=AZe?|xZZeo@LuHWu1YPk) z?kj`)`K6JNugGMgAw4#sD2)F7avI1Y12qW6H(&=m6X?KF<$EPcEcHJS)DEWn&hPtF zT#dxnQ(RQ!tGu{O?y2A<6x~Ejww6xgv-bp>1OLliWv~8L9T}{3d)MvYuf3$Vu55P) z2FhEAMKKNA-#cKb@NU35u8Li@(@ehF68$gG!8-&S=>pIhcy)iLd z6J-$fO~Z*dVrj6G7rj1vW3c00crVl?meYD}|6p8V!U4qnMtxFm5liF}$A)Ht7u{iL zd9}%f`@*$J1ayK#aSRgpz{G0PDLRXo`w78Vq^v;*A;!OW36fQJ5Ih7Pbyy&WSJkl} z8~MhazlwO;>1ZMoUyX%+H&HrQrcuOFBvO9+sk2ola;9K7@BhZgkKJjVDLGDu*DQYcz}lI4to6>%_EPz|_xfdV4TpMcHs?U2uWU*J8MH4=DvsI4&GVhu3~ z@ji%)+ zqBpCXyoe*tKWrH~zC;jkqu8KQ9Ebg%GVk2pstfR2sn%+6LzSa%PFk3e8$n$@2-(NG zkRyF}w4F3SD!DPJWVnT74m?@LavGqa&8dU9RFjaa9CGj0|Ar!s{ogS7W&e#Nc)}04 zq(7a10$AN%5Co0XFME6AvcAW0qaV}l5(fyHUmP$y`XZ^G+xMxj>=axPp|dQHjg`yr zV{#&we3=HkkO~rli@)ANqqr3}abEX+1A$=rKbY!2UElYquk1GDF|{oHnzJ=XQe62o z`EKSZelYM>YCC(`k+40mWq=zskqKBP+Xhi`J>Muk5YmFCpt^W54-F`%PXtM%msdYkbdq5ZS{=id#pS>z=>nQ_d3J}6) zV@HwK;_&KGYk9u{&Bex1)1`_Lx?#69%3fZ0e>p1x9$*b4T67Q#ikJBcbR*RKLOEh1 zSqel%RMJ51wD%cAQg#LDSbqHFX#jsS6p1<^o1w9h;t2i^Av+59U`zzEw7_=Pgy6&C zykJTo(vzpqJ@Bc%g-XMAuA(yZ+xWac@R`7$|NlBi|FoL7f0T)Z^asn}A5~NLk2C4t zooY{P=|!*C051F2fzJiLMlWnZU7j#W!@H$b!bp&ND}-)EL2P3026X2_$*b<-HaDqk z;ubV|`|NXwHM!3?%{5WyE)*dNFxZM0p6hqptym`fTsHI2|vNDuiFa0^1!{CVRHK_srYwwtAZer=Aq-E4#VsQ&u9GPD6iEw34sH*$O5TnVj|d zsaZb-tFLq6=sne;p)iu=4k2>DcMlOlPV`@CgrkpZeJy{1&He)tJJOZvFmn8FVmXAxuV1246||Fqz2pFw*2{|4YRIDiigDv)2A zcpE&;;258#x6(^nQzWXt3nl74gErw4z`hg}>TeE?%x}F2<*Btm$t#9o*EpP{Pt8jk zhdq0%X!j+8B5d8Cy4hy{c5SU6^@M?taf%qVbC7Co*9Z)3t|3nhND(PIC%i1_@)OV( zzY;R!H@O^>&Yu8BO~MY%Flj_W5OR36F>u3Yh!7xr6cgux7|$;=Ws#N~7is%eY*UEr z${_{<;*%ee05WiQxuRjfwWh}rnZMb;%t|(Sm~w(DzpR{fM{QhUtjcwUn%v%jzKSL^ zNOsVDt{?qgh;8uSW8EDu5MW3GL9t{Af4r|?OOH|tTn^gP*&W1k2Y>j*CX4$n^_ux@ zqk}OVg8Xz1a-k3kRGeUrX^w`GNb?@({97t=-jt8afG zKh~I-7?v+l!|=pJ1JNj*f47EEE6KRySo5G%VQ16*19jX2KS2O zGpInW3t5jii*Zi_@QGmq^y`>j#PBeiVqPN;Pbu~1Xv}ajPXC@e>C1X&Ivs!PJwH{c z{OWr;H|8zGXo{C#dt*oac%_nz=ks`-NT49g@XPL3-gEkf*Oo!U7*_s<(}Qp-3h(Mg z#O*+w8PU^JF*7iP7c`FzheQh0n|V|lH?lWnZ87*o+o@EeCK(hOE)g&c7oa5uy^&#p-|DVmSWDh84d>RIfMj_ z!s{R+iOj=0$BH(B z)}kFW%_8#21`~@`un~nZO>o|o3kI;~v`@rUEFxAtKNfC6ib~r9sdD6L;2=10Hca?1OORO|i6C{#-P=4nzCyb5km~K0GS5F0KVG1`79Uo&q8cu6a|Y-P*S}WI%wI(1u~UYe7;AX`*gX2dBRL zxZ{1<=~7%JxEo*B?v0e-ODJ(pk|{iJx67tKMr$5%51O#PS%w_&4D9IX#oO;uy4RyL zc*kWdZ^?q@kH-p}iukHDMMV!K1Q`YaXHhq5b+)rBLTi6L>7Mu6uTwmIVK&l)FG0|X z23x_X6`YO+n~~XMFpB3#_;kVSJ^k5!DAxX!h+;uqAk|ubkbDs%Hos9=oEr*T(frIz zK5B)B<`xSN&kYq8*R5D?W+sO?)qHeUKlp0@3NGq;hhd$9dhe^!NV~hubTmrTa+`>7v2l#-}SzR@(9v)m6_T?lrVBF5Uyu05Zh( zHDT=6H__&tB9>19Dw|VAQ(avB{v)Z>e5O#ytcNdEV|Ux}XDpB0<2f4nkp$9IS0l5= z52#-}^8KqUI*&Zn#%!e8{dOktBax^_wDD{`6SwbmVirNvgeYK*~**}F%uSHFD-#hRp%P;)b=2I}oOhXX-`(}3`h^m| zMkSd{_tU5%5*#(dkp$}b&XafX`&$zURKcK4ZWWaDc2v%Nch{3HucBq@OM&Yo!bF=G zylQ~uz}_*ieT3~tlzvQ28#xg)Dc=XCumByD1?&@RE?{IDX*=`oX)`mG4Zq=gOSwkc zGz*>6-LI$P=OZ!nzcl{o(SI%Nq!Kqb<<}@5V0tKv|4$j`(FI+KnK+=H$>tE z26ViCX~=d`k>o3*<_9Nk%0X3x>Ya~8&+X>r>ZOs>CiB3jn7^`)*%UF$`&!9`3 zFl>=-BU&vqHQ{>$IZ=gu;$Euteq(vljn??68_@A}-u<1EC1_lc+WXR_ix<7kB8rZ! z4gGmX=aLSGd*66<+!+}eonqq7tqpHm4$_?C81u7?c5jjT2j2n|<8c6;?zMfbxHq6azYE5BTZ1njEcF4G zp>UTe1`)x5k=~TGI>2G^ zIh8!Kz{v5;l8nWYV8D1(ci+9CoVz#hXIsb_w?5X(ProUt^9t`vmtN0d z_J7*)*D5etkHPi8MHd1Oq5mGp1w}@ng}oz-Un3-sz|?^t*+qB^YvT?n+5;WXGUIEQ zfnbWo|-a*zV7~RB&KfmXzbAR^bl0b^>d?nD0v&@v#%JLgwv(2VkMJ^ z2Pdl4kT-NLz^eUHa(STy#_n7(bc)n?FgPV~lS-vQ_`(>tuMT|VsTB@#{}Oz8B;NE+E>qAUOSLfbM|jUlBc9hGv} zozYi+m*U@m6R=^^@^;{<8N}6UW3Qe9MR5~WD=`-7IdEis-+Qt^dWABQ?n=`nh(mPH zVq?{5tS*Cl!s(d8Ee}YJ&|F@1icb{Nnc!h7o2mXu$AvjAD*6+wQft}Dq>J6hKJ<9> zLy=<-16Wjp%?=^7MmKJ_DXt${4Dqi#LIHqqE-G1Kpd7!j+(4Yh zNMrfBS4NQNcD!-o_)WRoO~+3#b6Y0z%In@59~#Q8O--$3k#8P>S?vE$;H|s z6wn3rXZZZkzmH8HI#X}A>t_y414MKEtDara3dS8E4&a~E9Qwtru4y?2HWCsCEhXhD z?(~F|c#Ti~U^rVVq*F+*e0ZvEsQk6a*KTC8qs7ds6Dn11AIfDB+^U$Gq$%3a@^HDF zR_~c8sdy}ZY$cyej8{_2qpbo`kf_9*6^vQQU@BF~r{a~yNNFvR8cL=heM9_nv==VE zt{qeiwprS0_90kye2^YTS#61iJVX#Zxh8oWbkRkM@md=U-lRY-HoONFDWMWnH=vY~ zYIKe)kA|f@py<~@N}0XElLz#aQf8B@ZxgUe(8H!cDFF2yg7WA5DIf-jP8VvrE7k>; zLX^7TO}`mHgYGidZ-n}ikQC1cJji&(qa?1Z`!hhOl>TI&DhGb7<8Q`51KGV^9Ui?* zdD$XkERir4!h{Q-)55c$VayQk>nwm}#SI~|RYbD4x;lcjB!U5He-;ZlnJkWxR5V73 z7u5#)Gis3V94mrGdj#42I)68mFOPXW9xLaE_=ZL5v1PCS%dxtoibs84uhp<$k6>+` zLwn*8!|%pL{rx&6vRL z2w3yJf|uEH40vbJ+?v#46mn&POS9cxM2Y|SpbNJ(B-#SNG&kjH8EeBNBdk8Z#R)eT_ z-i6RC+ahPsveg@}hKFBNycx_5Mg?8RqS{|7>GFCWMY_vbrRBFa>>Nb?UM0ND`hc+@ zU{^o>MlR5BF&<@Wq|L~mxSgi_91s>dPNQ##-Kd)&U)xZ1As!?53ina~ zqsyJ>QB^xP(Sr2eNaX*R-gXUuy5Cr3c!o}m^cDYm_F!ENi%4W{b{~h%!@enkPvi0M zAKkg_)w|v4Y<4=|_N19&BORz2r_=^`;d#bhQNk_=6eZUMK7&h-09ott#vHWNM%VDz zi#4){EB-}szR~0q*D|m9gmcI8K#M%7m487yC!|LhPUmvzN-dSLPM&&|3-CJA^^g09 z{9}znhZ^~8VHjE`(?oET$=ux1Vm0hI;p*bjYWoy5D&@BkjcnqKqmG{usHb!Jxo>l1mK~B!>=v#YY57O+1rG#w;jpuCFODu2U!n zJLgxrUmrG@`t(;Eqh4<|^WmhC3?scv<=q4pM9aH5Z}4f_U}+Iy%El ztRF59Eu1>Fx{7s>s@D&_Hjh*z|Kqz2N8@a5-&}tAIfoi*rhxYPyHS! zkn|HnhB5R+8btQ*Ar)1=WFA7&*b+h#AzV>*#Ixz>s9%MqX%co0w}PH%I)scOdh5#s zSC<_TEl{q&aE4fr@Wi3;hA!2ZN;DYEPTR5p1sP3&{UVH1_Z~m@d5vC;fIfG>(|Nmj zT_P4!Rz5#tnxmFVCQ8^*<#IU_T&b9UXbzj*AH}FA&YB}@2<&>ieR%wG;Wssy)5pu< zl7$~WzOphMi`#de|AnD^E~3InLTw$tDSS)#=5?h0Lt>ht@NFF{5;BH|!P^LK+kwn+ z3oG~h2Vo<+`Y9sYnFPL;KROEtm(KJR~tt zIZaq_049B=+ZXT-07dw~87xT2fahSDu69U1CCuvR;TD>R=B~SBRqOC*)l5+Ox1EXp z^C)QLMx)38;mBL?v#ZmGVh{TJ_F(ML^y<$daluzV^dY1!R|?t4*+ypaQr#R0|V9(SV!5jZjgD36K%U+Q+a1j5>fw4)E|?0tt`&nfmO1XYMI=c)|1+ z@IKz)+;f4*D0{pdr)aa|=c{&%(HM=mW1~j@>gGL9@D5SOb``_JT>qo~6{O@FdbkCj z_c=PYWy8--UfEltVa)ql(yR2r{T?pu_qnVc+NIzxWUQ1Nh)7T~Cj?FnVTmbB(7=sq z1mVKLKnnv-A>w3{>Pz5F7*fe;r!ssr54GFK2$XO6qr(-WwQvfa`H?sSv&2ufT83H& zr{Y><@pSq4V#~B`s7@T)Hd~9wA2@juB0eA#80Q-&Pd|gj-j=PWbGFuE;Wg9f3ZI^B5BV+aYa62+pC>9G-k@j%Cj#L!8pIlqW!8^f>#tPx^ zrx30-T!?|(4qB?!uP9{Q<16);TOx!KMHSu4qSm9u*UyzwF5q3aO+S3Q?XA1h#|R5Iz?LM&~e-slp|`W$2Cq zp5|jnbW#jb)DQ~stycwL6^u5!7kST*St1$<=W;`(Y&JsU#k3s?7t<-5)g%gbDqReR z;PQkwk!-d!l*@$?D<4hZ@31a)iFIKu(w=n(vJ0Ir{rbRdk~5ulBnZX53NKOd9+QZq zE{Rj;`gE^>t*{nd%t~0nEDIP73FNB7=e>xN@+}p@RqsAy^ zsKyBkxEG^FG@5Bd%BAY(tH?zD{tYr0a@aBoA_BlcJ>p$K8nB^qH{Fy|4>^$TuTNx- z>TTb0k^0fh#5!dE&O;+0+tVYL`>uE)Po5x=sVN|!ofU}-wTTsd4gIHm5{_kAyp#%{ zH_}hfn1~a;j3}&-WPy^WWQX$7cyABZ>5$NE98I7x%vEW8?REo92kdP(cPd*UXOlKdp_xfJc3;S zXsJ`CQ0$ZKNxo|ccLHySViQP&)E*v?I)P5!yEZkMg%v?OLv#f6Qw7z(Ld=7k%hBM- z&A@lo)HPuPDVr@A<%Y(Qtb2AkJ%nI(w~Pd%+dmjp*6-Gw==H4z((J$#%tl@yB$y2S zeaN((c#^r)!>LH9;uxt^`2*!*F^Pl{p#uN^DdJs4BEe8TA5^~_xv^-5!r?P>4rB$f zbr>Z%^+t0(al8b(XE@K?7NCfGx@yY~GVqR?X09@#;0c>yyp#!W~1%dNb~X zLxuNZ$IB1goZIR;3!c zOdnnR!ye-Sa4?lDbMw=eU!tpQy*+G}*P=l)bTJGwrQRjrFd(nz35;RD;9P=-YE87; zsQ#UyT)u*xD|@Y_kJ!bPd@l4(!kQW#`?uM=+np#xm7xtxX!Ir(jykx~pvTh@<*9db zvnFUsNY7!vh~3qfoXfCD(jjzyLH(bxR4VqzG5mAScR2_G+|ke^0jUXotx12V#R1+=&5V_p33z8;Dg{rZU4E>>1GYk(R8b;0fL!_Llc@HKsf0&t& zZj3@x%gLq~$~WHKH+f^Y>|2C^fz z80kO_y1J8`3rMWA9cj9euH!${bahRt)!DrXNJB)9#YI?fR(lJRJGR(iE3~y^rwf6! zkv8s4L*rPecZceE?5uKC(oD4yi5BhIFM1dHv65BII-Um+$^YH2r41upN0>!c^5v=| z6~U+YE}b6pF7#vIwHow8HSEU}VU-d&*N%MSS z=1{bO9gcn8h#s0bq4)bfPdEF9t^KP_KNBFD%&S*|AM&bxKG3&j?O(0?`5W>P7?VxR z=WF`oAsNp!Tfof)X9jHtq;=p;hzuwOS;SJsu4^q4!zqvz_-k>3{&+tJt00{cW~?~livaQr@$t)GiVCjs)1 zA`qz{vax()1Z5+6Qspux?;A?m^L#dv9<4SfXJ=;(!{HZZhJhGvShJ(?@R?yJ9Cn6} zLmU&e*KS~Rh76Py3c-&%IX&SLE2TXl7X8|Rb$Q+<}9Hg-B?KL zLR(@1j-u<^B*x_6sk88O$6p(7x*M#5Rdp!Izp0Hvx+WYunxX?G4|yH<%6EZoWz2Q# z5WuRQj7d}M`5A_^gz*%TLa=jn=3Ss^1X)bve}j*ao@e~Rrv8cAdaET0=izHbTWyYH zzkm#kiGQ<{NZwZ4K>)puzd1D)Iz5_zjT5xmh2M*TydN(+we>smr_nyWW&^M_mdq-8 zg;KXG1FcW`=+f0(iW*@+=)fBhkb&b^?b_k-ViCSvgxo5h5CmKqO2fhjXt;Yx?o;U@ z1TYUh~zfzaEbhcnCgHtNC)%|1N7x@0S`(UVl#qlpdmh{(v8Q1#v!w3ZPLcbFb zuu(@t@VWDv?sLY3{o5UIcj@H61t}6T$ zVHjfm(5uNcy~R`60bEi|Z>JCsaPF2J&;Lp8Xt<@t_?XRJndY7{0d;_pDQ(g-Brn12 za=mdqUDn;x#{r`mk6_*B)xP$o8{}-iMr!SAtxW!l>S;?yc|OQ61=qrV-N} zC!2=@f$~Oy+;*>r1EYW($G%tlj_g+NOB97wj!fN0k;f_fq&JO&R!5ioRdQSO?g9Jy z`V_9$m$6O{9*KR{*S;M(V5tnY4cf@7?4jPof}q#a$}M!2rm4iu;cPB;i)ngu!t?&k zD`h^Gi-ofv>y0i(Tx{lcj1G8?d|(1}>-AWbk6@>NF!0PEMa~8Z6`t+~ogx!yGrAO3 zA(<87KL84R*DO#JpaIqdy%M;vDy$2;=04n8;)SXRP?w^5<|*1x0R=sU?;4)^^n=8+v zj-*%}-~&{9Pp$}Qip&_niu({+jKLBxn*bv4l~6+2iSz*G-tI)>mPkS5lq7^5N+3Xx z$Tfeu_(BD=EeKYsX6rfY-A!?M?@i14z~o)w!1mrjC4_P6J?dU|mkcWG3d_z4d#_n` zaJsLy6kuI|k994mPnJT5RUGL}e{VMRqZHrvr?B@{KY|1j^=VLx`f60yNqxRA`j@P# zwX43tw}NlJY`h!v$O6{EgNQBuac1F=-G{x0@yFJ+$vpG#vOcZ42~IFs_)Df0)JrSh z%2PIm!ic&vNxLGH*2{)hpj&98fWIt6a*eg4c&ja`I8d_S+Z@<0S42R58=+IqZ z6WWY)DjtU^SOn6B*n@@f#w=>69SW7qP-L=s`|Y!{iKtNuk9;JCApZ#N59N3&3`-|7 z^noB!oyoI!ES-vkjcBq~n*_Inpjp<$((QRNX}}U+!CBrkLjU)~#6&n)DvvbRekvJDmr4-4 zMiY_bPp&O}B82?A1`pOteg?*NkN{mR%W|L;$d0E#%}3 zGYH}$NcsXxW*c!{X?b_d+wW%9lzHiCNNq)_m<2457zx}Jld1IcMyGe46%Q|82GI~ zKp`W0V->+g$zHl_BiU+_HxhnSF34_@)8ye8Gn5O!v4jf(OlctHLclxED9mg zsNcjj%3y)~f4sd3m?YO-CzvlXBO)_0@`${z%F3fEv#RULs;r}{4|TSV?v~V&+On>e zg`Bo!TL@z}7~2v?raU&-=4`+i7-L*!FkptGF)Rj*$7L9UhnWG5XLrY#VYg?$F`pOj z@`)M7pLgbSdVjzFi^$CCLz0= zn2QhKt&?3^4&;jD`WtffSINLltrOLl9t@ZdhUrU_(T_%@J9NvuF_ZntOuQHIWPl+= z)K5i|P48oDv|VK&JYW}+$)?m3))obTtoFTUikNPb3kWL(c1w{`u@+$=V5MB*7jUmw zZ-pY2Vy&)Ld2#GEien<_Q*j}`zj)5h-d{8jJ{*s zR@MgrO*ef&zz|tBZkrs!S~|IKezFTDV)_hF!{nZ+UCVxfd!~C+SA_-=FCy`B!H`W_ z&?NAh3N?oN!NH}Wz>#c5S70Xe4>e5bgDd0K1}*-^X2Nvq(! ztKyHc24Bg;dBLek2@GCaMr4WvA)H>y3nMB++#y6-G*5#4m!m zk1AMekzUtSa7&nJxK>!M06Fn(XyjQtScyN5l_&I54=G1v*O(+L*2;E{oGoJXCYB6G zkz0J$SyRdPb<(6LEK8QdjMJ3KAWiufbc8cy@)^vIjIPikS{cF5YE`YUuv==M+3=68 z#gnxbl)#WE!pI$LbrqYL{r-0!z2#(By&TpQ)wNhe3y3WW?QyG?jISNLb9*}*;jp_- z$p{xRo{`0*SkXi}8;N9W)WHiQ{Q5K z%=(q|OgYYGV`Kg9KaU+f`cTxi*4NJ-UrsqO7E6TpCdxe_CpJP=chwGOB#GqKv-LQ{ zElfoi0Rk+G7D;EYMG641%15IOo4X+wiJ z7y0@upRiov`qrE+RfAZ+tY6Ln|S~v^qc(t^^f@){Rpq0pTQ%T zPv7YA{Bs}T6WjS$Yc5A`MF!FHy$A~(t2BhNODMEQcK~2T%n6t4tODM|yy3rnGK@$K z?TN1PqUr}T1CmZgNC+!37fyEn-Pt*J7=~vl;Ow3a)*D;DhrkEpz|fsLd;h=tqLB)BH@&?ETrbo6!LN+I zMw)tAHVwXk&oP(F2_AhT#0#O~eGckjK&- z4;A;O9%=Mt;YIjsnk$jLM)0)PJ{&4m9J)|$_G!id>z^4Dp9M5%_ZP7bBL5*xf+t}R z+&$sSzRf|oz%CT>~>M$&=@2> zk#ZFBP6iC^WbYY;@|@~Jz^x2reVYgVvaInwIY}W z)^tR5Vw_g^*)XaBqS#(sE-@lbwFJE&uLuL}2Q-@a_FF@5Wp$J+hJqoL*M80DDoBB% zNt_vA!bfr#riZ~tQv^aUcKi6W^YR({IgC|M{gV(;be1J0K`HI74o(q#{dB=tAT0E_ zkv$Bj{)cZw3gJ%(jnEl69B3S;FEa}7#UW*>Mj%if7=@_#-5%P?X{)$&#WP?uJKfmDoeT zCWZ`PuC;ZtNPXc9YZ4z)JA9BYkVWbUp{?)b3+NE1ZAe)|8VdK8h5> zh1Awos!&kTM~ew3efoiX*epHPjd|JJdE8<~(y1gCbu{XM5079iQ@^f^IP~mJ++gyT zOd@WCQ(iV5i`vmtGMzFbV1kMlx7=j9@BrL^OY!dMOg@KhOEEip{(RPsVb{#%GpD=p z5?qKMD5R6_*2SXw*-Y93Pv}7nkH{3;OS=h^+RY40XCkheT1_+Irac=GDkx{`#bc(G z&R{S@X~@Doj4kYz$Qa8(DC_{C0>>1@Ul3RoO#*iK<`8yl0Wtts0BWMd17)HG13=na z&jIL|6sg$99rzF!k;)E!C~(@QJC`oz!uxrzFk`qMS7Hkz2yPWcnSeNrBwx(~Az3pK za5_*(IVv(^AtEVnL^_t$i5POWf^6xlGc(HQI`Il4V54?yu~v0e7!`XFoNXGbW-Jvo z+JnK%UN#uCjc6)nt{RBWM@q+9Sh>~OV$8;EsZ=HIbPY8#vx*wG@FSl3rFS=Ym<8gj zfDyMxe;rXdK3XG;n#S<-L}6pO!ZRYb#9Hm%?!4>f>ND+fF&>SY$SSlE4%gr=ZkiPo z*F>cR6}^3NQK`kn+c7mUM3I#%u-6!|S~$Fc97IS!6EBwAGj#;e&+qoyt=LHv-@WD+ z59_j4Lcbw2kzS#KGCX1c<_`5e#yA@QQ{aX^l7Wx`-sxH}RrSC;5m&4Um%+ISFb6{Y z*751;m4*9h){^-`I5b&eo$QWVFx+svwY!_l*4OQ5&wa!PDjS}CnyZEfC&2-i?rdVp z*-sZD=Z&;wV$1_V@U>=-=FNGKB^ZQCj!w};p$e&S!t-XHMOrF}qUn}V{K1aT8i0KR z0xP`o;I^mtldSGyx&{i-M%fJ62Un=LF&np25wq-?#Y7d;X(>_R;g=-~Z9paXjDvB@ z-xQd-sR1#`$ahK%uq<`*=o$}%atK~3j|Z|U6=VjVn*n53W~1#Gp2$drRTv}~g>en% zQY7*w#wdo9crP~U2eO;ddIbRtGjo=LyBJDjM8qz6U<}}=7C{7bA6{N}GIw9LxYI>? z&>|e0^Xqxj>dv!B=zMnG&w2?VhmGKK$nUHv#C>3)i;a?pC^XIGCVsWLRg98oT2m+f zBmVvp+YRIbw ziLjL%TxD}Nnd&5Q)HNerb?z+w-Wm>Hu#4&ZMWrt0(?$Ej=&KK|dXQeiiT4$w7YyS< zwD>+8q)c!1K@2nZa2Xv35XD(-){+W6T(W%ix^uCa;V>23>3<)WE2 zwy;`uBe1C8qRpKiRkx(*IzqCGf-YE9M4yxhWW$56I!-Uf(n^4GqqTn;-&$Q!EQqYmUain$R+Q-5rW~@ig$STwv@pHkB z`h#e6A?o58u}E-Hs#0z=$`#}@1Qt+kbS_(}6UAU>qFM#R z=OmFd)Ir9(ZmU(Sm9ld$@Nf%n%e31W)WMl~D%kODD5Af}(&HVFX$ayKISV2!CKAjt zLhytCggrsF6vUX;cH^W%VT50e^m3+M8VyRend?P<^~zvj(8GVFdKoiX91V(5GtY9sj3M4ri@qr`vdv==kV(T3j<~**--x+>&6s+{2|s&78b3e$Q;LrM9Fpv zKAZrmx$$PvZ{vE%lLF`@1IQhR<3M>WS+{5Cyh%}=8ki>XAlO_VRIe~&!%4M=IS>ob zRl&A$NP+M`&&>!404SKqW<=}n2Z3OX58`4(8#LaFd@=F&3WdNaao#=q^F=Z!U{nuNdcX$%g=8uQ|edsHp z|B8(GNOp)g7^V}_ZrfUlIi-*m_4>vlnfmK4y;0kL-<3P24nn~A!i+x_CHxr*Bnx6M zh-3jvVBpcUC_xJrCTRhNT`cXfRu+ne_70D?cgfqj#hxcX<9e?Mcqs%vT z_UnJ8C+k4}ln*axBP(&Rr%&$h>+ik5o;Jks0Cxo62iF|K3)@~oDiD1eaO%>v$4l3> z{13R|UOdtsesf>{?!JG>uYd>AXRsac1$i-FqtO_?=$^LYYrEh)Vx(Rb`cAO^n0QCF3=_vpiE@+Lc-hU(jvG>e+lg zTR*1$8jyyp-b&ZwVkuS$aNB@oxq5 z-9|(ktU>4><=HyBMjl5wq&7$G8EUP8PT2$50ytRU*YpfX$FJbLJ%s9iQ%b_@Aoa`;Ux!u5z9SUpa?9bPq<480wF&GiC}NDE9iR4B1= z1Tp-;k|~Q^2SWMNaZ=~7nf4luEn4BdVz*X}B~3l)odo0|oKLu6eWh&`iVK8)LcFdwigo-&qUlPrl88dDg+D}o z`OlBH_^3l=t$qo^DCxiqQA@(l%|q4)+>kK9vhz;xA{MGv))?XjPlNUho=(;x-XdId zqO_8^$`wY0>`*jaD1xBj6-21u)rE?9o#goje3RGvoS1YEJ6;m*ZP5BfIdCXVnJzN< z955TJV#bw`kaCn`xZDF=MnhjyP}jjpO>=?$9NAH@oO)4nra6reN!LZlhRy?R;5Pv@ z1OT3fC`f}OCmWpzhYUVeLkTGe*1zt>YWHxJXrTZt{6Y4`a~fcIhW+puUGq*n2Rs?s z1|aI`Y7jgrSV(t{i!^u3F4++Q7+3S*z*S&FJB)DXxU@S7?&N8N;^-HL1(*B+OwY)8 zIzL|}BML2apkodjsabfLU|wR;R0L+rsGUewG#jh;s~-F%^+dxz&Yaqq9q+@Kz+$Hgax35p%xxrLLyrLt88D3JLb`@*GL=oFbM&}i zd$>}XsbC=Tm6=i{ELXz8BbQDf*IzR0dB{+NTiI?QDn4Q+<0%NZQc%^l5L(LQ#|bd+ z{E1*c6?5_PPQ0A2Pfu3xX1-qw@oxk%>>1P^LA3w~RuGbQwg^>)qS?(8HU#PA>2pXD zMivJj4R>1Fy88IS_IuuQW*HQyq~;&rURW%vmE|+<8C_I236%*yb5A?=_#1yc(cipt z>&V7E&&1jrN4D?eNxjjud&?ATnZHFxaTnaN&2FZR^$Atrf|&zhzF5ga{NfUZzGsZW% z4*k>Ub#VA~zo}=Pgi=p;=F2eJMnZnakNrCg7hwK5809`+Yf66|8q6R z&VSd6+M7=5`MCx>xZ~$|6K`IbYbxkMzokA~&v&&ALRG4nhoHg=Wa2$M`?^F9<3Yq@ z1D;l-Q=9;(WJnUmA`Mw4SwB#WASDdmPhN<=Sv;ZFz62WMHR;wSltaqIu=ApNrHSfs zZuIGda_-9mF{VDYRId0gccdTY!=iJD-vXuudk-V?-&plgt}J~lV^47l_XujK_W|!n$?x&y4((<2=ZAKM?G# zE6tX>5D{l(=kG1Xwm zzdpD(R*ab$6ZwDSs~EGakBcYfEEIQ^6I&GJPGvFXfU7ZQ#S0U!!d?U63>^`~4?8>l zO#z%+s^ElOH|!Lk*QcrO2)V}`6}8Z8R2LS&BZ7)rVN@{yOD1;g1JSWm=(7wvHrBFe z0%qvD?=w9(5}t?}LKy}O2QIkI@+*LNt&foEey=c^&O5fw&?zWz4*CPJ zv;9L|f1W?H^$yMXeecBIh+RMFyX*Xe9eMyu6PczGS`7_eY?Od72@4!EKn6bTfCT7) z1;7FM++-N`0Ax^?Aq!Zkgk&(;Qpun{wgwz*Z~CGQ-o%?b-Gu@&t1WapT48UvT609bOE*oklu7- zs{P^asplGLEp^}&UPk+03_z3H%<=?fbdbjyK)J+OkE z38ZU+O_Ybp+1yc8Bs?g5PLJQ`z7=IlN}AO zOi$bBr?L^{UC{?>_L6ErBX^^FCVg_Q_%dhzvku(Wm8)V#q zW!Rn9uc((NCQa0(Sccf8LR4$(G2f?DNEaB|ScYje5w(MrXh%tkmN$Iw$+v}pDP|4x zEU-tU51WA*u}(!Irx2(Fv8@mJ%{pruvj_U}j7h_-@9YP|YDWLY+e~wSG*Jpn{6^S3 z!}69*!)PKOpLNCzZ$NV3$w3H`8EmX5NN|7^=ODK5IBIg-h8jAk$}w;E*NisOlkFh; zr{*#Md|>_b6#MfAg#eKiJey%D9%ybVITo!zpdVnD1hencW>k0ty@++ zo_gcaM5&ZG`o>f|Zf)t7T~}IQfi}0d^Z6rBRIA(n_(&dqNAk~L{|8#F?MLwUa17#o z{4vxB$UcUi;<1O(f_`2B^qPBxZh55LKDxYowB7!ZR_oE__Vb#q;?XVJ1*l!BXFCv3PxnIE;Js4Aho+{+i{+$DT(I*@Lo!* z+B1Jz13@(hE_pC!u^|`c4%k-v6WdNV;;4QRi8s>I`&5g+wlx2pUmofuF#Pg&&X-<0 zil}(QG|wh8?-o`rKKd{0^xc`{S<`Gh6OGmkr&e=}+GLM#9T)u&`Iij5l9mA54}@L; z%JUj{D1IAjF&7yKLPkp%PLX#ND6@xDmXL}WX(nxQmC_4#+ObV9Ybb5GqyZjK(d@K~ zT>wrO;A8hunuW%te%F?EyFZ-hGkm?q{%B9_jr}kKq%-=iaBT+3mv?6^*R^JM)5uMJ z`1pGO8>j1M>h&|TKbCHKUNh|t(KPgsc{}Yj8)HSi&#A&2ZJ-Vszzm~t3r2YK$7W=` zXPRjP^S5oJO|!Qi86R!W&0X5WU#V6*U#p#%n>$gfJz1+w(eWg__ zd@wd!D2E5602~6`PWu8_nv+WM0_t6EwH1OkZ_$v41Sb!M_-H%~t}WJQvu*$} z_zbrs0mMr*G^hkGhRnts$ASE@r`sSh=i+g!68E;}4)nzb!MDbhdP|tm^p`O6KKQPF z2&)CJA`&YW&8$|>0$%WerB#}Q_Cj?hjdlyoY@sS&SQ{O?al>8B?`*ZT&V4zeM7nuFuM^O{?1mTgH-lV zrJ#k52}ZcvSYb}b1^ur3hrSjd^=DQZU4($bkwUavhzp(RG)y}hwPBry(gER|;o`~u zdxpbvy>s6Py|+|)FaN7wf%br`xHp%!@jN!NfZ=o0j8v%L<8M>7jBeGfZP&~c41c}? z2dwe8QOKBX`&Qn!@>GPn(Gwk%-Q-i_&U@%RW3y2Da50W@x%0h%p z!m)%C>a0P*f&`dUTKh_HP>Tw~p)%Rjdu|WJ!`t7J%6`wz4ijqa>@YFb^uGE~EH~PN zk}C1EAT-|ebRzoh>#x*z0hTD^YJQhBx0oY?&j_|&gNo*WILCZcPwPgw6!4KNxEk=u zUY`x)dL}OvC}e|gNhE|MKobSXWRG}2uLErT)ufvyMcpU@urDSOVqd@?H_^l#&M@gO zEZu!D2HRLqP1se>Q8wBM6Q-MZH4+oBfgqWOzM3{q(qbv*4}UHp4CypQfn?mLZvf^L zx_eSI;XCwlnN?S-@USeXCgO}E)`uy{eIWC0etl2oDSo{=^Nmnii32-P#OP{(dM}U1 z1q1T0kR&yV__BO6VrI0_j=Pqf;q-v3 z&$#(xo(i8n9ai44yjzPVw(r2-b|O~Q{v^IExdN{FZqO|F*TMM<4B1Qd9MYN;4j)EQ zVgRrjx>^$qL_!&h@|N8RQg~AG6fJ}JopfKC-w*gYUw%M7I0kXes!JSO{_p9pl=^Pi z`4jitlYrI!si&s*Uy3h}>IF-)kxj7MM5dVzdZr3le(P0{MAB|HZPdkEU7g-1J{R8B z0PRCvWUxc$2_rbWt)7AR7jkyGF$CqqCIFRf zwD%S0Nt3zfpC`b$U+GGLhyoeW{82x2BJI;(zg19k%V)R`<`?G=< zkf+-KjfO4%RjASYW;_>dRUm~ZU#jJUli(zUu_U1(MxgEy-*`Y_-!QR!*dhvAirTER zwNCHcb!Y`=js7VEdO#o=>CP5>LIn)|5w)=12Rz_s=Dd`K%l97DP=8q#d}!GTKwxhJY%v8(VRf|3QP1B2=41=FEI$ekOBG<-zx!{6>>7T}Zr zy$Y2!`epxc@T+tl?BWN|IBVI(+Av2SLT}!(<`JOepgc;YP*1!f=yyEvzPxgP@vDGq zmkjwB4(M+2q9Uw_N*9~HuT8r#=K;opL)!dWuLEaGC@3VpwP$pCz0bj2A9>_5{-cNV zHx5yfHapr^m!JD=admG`;$d;)-rj2Qv(G)RyaxPB19UhX>Omc*Qt5^VE07P1-;L*< z119usy#ztbi0FDqqY!P(_rOOeSa{;>FTlwS-~#d`@5#xX>UsDhB3ncY@xaF7;w^B# zup-f|3m{=_6f9T?6Ne+kL7_EzUdRLTvl6{m3i(8?A_{;RP%~g1h4k{-W^3`(;v)Uv z;?Ye*wY?Q&zD#*9+W`@{&;s!A-uW_IE!i29R1*&%hjhMBpW>g6;~n3Cw+zaMwSc!} ziY70NJ;B;gcu`~|bhF?X037t$#tvQ)*I+Q)Fm$3u^1&!#q&ktX*#Crp z@oC+76$MYqU}pR)HZ~9(7kw~kfW0|;){Y)3s06X_MaSyX!QDHa3`0OJ!^v`~;i2=` z1LMx8fAr6%m9bY}Y&3-*1>iiZ_8_-B8G0Jj6T1CzPHjQ&Kv_f`K=jyyPxa(9;GUu$ z03AFf3>#ehd4kqbgp&XQD>rTL5On|v!pfm3!#sfx5Jq=lO1`9DWZP06mpkog{1)`V z=0@~LvYc@fkzB-$(1f&=ug|u>KRxL6TP?+?{W6_0>$Z9>Ko_I8*+%~3dBaA9J7kc* z+Su2t=W?6*aOcG-%Uui+akPR zEG5g63||LBHZSKy!%aDus24+oLB_J&;$UGwH^6mnb=rVm;%u1eAvPedfJQfTz@-9n zo`NTLapQIuuA)r;he=^qlAZym304oexWB%YjNTiD6Wx#4(I&$HPuT7%AXll*y|Khr z)cz5~rG$T90lrrf_6hjDs`ZaxI3N!Enq#E?S9~Wvk}{lMQ|iJ#_EK59M`N&ZR!f<^xgtX}pP1ej1C=HF5^tA^tvie8Z(Y_CCk?4E)6}N28a~qHznb)F+Xb zwy8576TFE-h{yrtP$-M(i-%OX!-m8bjb&gXJEi_bvG^MxKGA;`#Xo%i#&2FBIDfcA zsD>{LHl+`<<%;^9cznoG8)n{wThxv^YL%^1nAIWbN zQOS_nLLzKxiSQrGmFsTFu)A2Ar0hWUCm|!Zx>JWF^n?nn`95yYTYw}2*;clTk*sTb zo4I7>C`2}(-9|cZUV6%3o^#8~bN=p1G7?5+@+2ZlBX+5t-oG@a)T;S|$g5gFxNo6E zA zYjIe#BFrHQTBY9{G6b*W+(4>^J(z03?n$)+=L5RZ;LGmP|3+$1uY(d`2~r1zxwW`e z!%cvnv$)J)-b;=2T76f%MDo>aqM@2kmEq7bJG<5zj4pv+vEbp7L@AEc29c-0TB=6X` zK$;~R)f?|#80}5=)ztob16nC!B@gz37S)DECgH4DF(+EfLd)%(&nBM6V~3$b$Mc*A zbO18W_o7bBT8)7es6_U>11)&A_CC|(?=8+bFaWN`v*)23a^@DW@gmxob9i z*Br~?RU(Da^W3VmxbVvU)JEsy33MPc;0V7B)rfp)R?^TT35{miMbYe;sQ4;^4;9c9 zddSfGlS<}f3Qf|E3NJ}0+I;UmL8#;p0UCl<#L~)picmS~ySe_TkK}QPdYpaW%f06) zW@=e;Z*T8+r2qg$OFK>fV{UK+>o`x~>-*Y#<+iJ$^A(jyWS4ljG(LO{Mni;^IuHwL z(A6R-BmFPU>U8u8qjENmWpuBoF& zdv>-hW8c_XM!~}9@)nMKpK;&spo|rJni(>(+Gj-9jkW_N5`9BauH<^pksGwx!fIlX zlHtUFOBQvXjFE2nYPYfEI$pR3c=rLv8vnU_puhEYc<3OR$)%YDJOd3J3%-yutmF~7 z&7pfl56n@H*-^=*#GWo)VxRbr{h%(}P#+Hc=HTlQAn2^?#$wD;=3jHKn~!(4#2d}` zf#4`3!;(+WCC9=tyv?EQM*I&z71}xilxQ3YPlD?NvV^OXJs`B}Rd?5Gu@>TRM3c&! zqOwEGU>r~unko1T=hzG0ersR74R>1fVL1+I< zq2B~O`fTXWLw}*#pi3e<665n3!FD6`ibQ3)1v5E0O?tEQ|JNmWmKoJ&5rY0VySJtS z%`ld*aFSzto{-TSRyPzfIx+kS(%e$VP{u~Q12I?;#;1?2UfXhu_y+o?dGh{iuUNhI zijTkO71#A~-LH%g+SCyB;&{7%U5jt>KG1NDEyeMhMjPm>4%DX*RGK2)!&fvM?j1hd znw;T@t^d!?9#yGfIz1#tduiWk{Q`VxAAyMr^I&>8OE^Ms7S~_Fn$faJ2y3Pr8qg99 zRt^6smXI-}vY<`wtwVi`g$j~3bT`Pb36yrInk0%uh8c1|0s!+K5CkY~fAFov^`D3U zm8lcq@VETAy8wjaEF_WYF_P5#?vH+f(YO{uF!T5d_P%zlv9j@%t=Y%Jh6lImpM*FV zeXc#-i{5XmjAiAfdXfHeqd%J-1iUjMG-2l_o^YWnyeBji?qeJ49jhGZ0NG)DuYOrT z6fuC>DSG+I6htb4WCHmG)U6F7XA|cEMez2{PS$sVllvHvQ?L)F(;W7*8yJ4lyt_(v z9=uGY?$uD3lh9Q#x|L+|=jTlGNCo4BBHLE@=p3Sc9m_Pz(R{8JffJjdE>9;p{KWZ< zWbPvRL$5EttK5E|l1Nx~d8x!6k4kEhFMxLa-49sSktf(AdsdJBaMp+#(OCQy)pR-* z13RyIv#Y?NKaHFRZv!v?KFRg84RrH%X`Z_+c#OyKbNosC z@_E-iulL@zn6=~i^-S*mqetP=4t;IJ^L{V*(Q#3^OYB5?4jyBgF29Vi#q(QzP6@m=Jr>mW(bjc1(6vq}LP0B1HmxxY6uVHCNz_CADkTb~!uW#*Wr5~vG0pdrHr>_B@ zc<4;v{wQ@xhWYJY8CEb&Aks#BCY~s#GSNZ{)xk`Jmbv){EQ`aASFIc4ar^wexvXv5 zsrGr)&EQKxRc>_6QmF;y1zD(L9{?ZtQx1BXiG)#0{`>Tx{X%`!`1SEX zh%9Xku$GYxcwPu}o6Fd4(`k}S7}q6`7$H-F=75;yw~J7{5JSo0WZ z-`;sy+JBNRt5I1L(I~u;HJ;fXTeF z-Vn9iq)(c~=z+|u0T}=YwCNp;w;RA%3!ThUGF8+lWvv2Aeov|;(bEi+=mq{ebPI?^@ zTYB_M$#jrNGo4fy)9T>K?=2+~Er8RM?kxgZ_FB=sN$qhNHVr<=hidz-G}HOuFlxjY z5UIDCH)#*Rs@)v>@Zj<=E053fS5#8a2S8iyJPzcF?0 z^4^|;9L!YA0~%I`?7oxlQTDJ(;zrGGeTeZld&Rw+Yc=u2i#ONNiT zQAi^nup5m+5rJ$Qa)BP^eWRYtaO5;e$>-Z}xQrxow;wqITg(ix%!k!28~yqW>lz|s zK(bP84M@Nc?F6D$i0XcjH|b~*Xro+mqS1Wz9^IX!x7EA<&5pQ(ERjc#ELkF5? z|BSE4xr_C7eMKoA4a?cy?r*@zO?Fiyf3frtA_3^Wm5>k$8Ui52KZ!#1L0(M}9^hFF zg$;IUDqr^>rV5DN1qIJOjI0z^0@cYQGjO3z?#`NSu~g4%_{isDR%y5AWqK_nccyD` zv3*>hI#WliT{WIP%)#!ZA1~x`iLHz5kR8940@&RBuCS(@P?b$>XH%wX`XQFZz^A!d zvAo)%o>RXzNE)mA7-*}opoSngPS{Q%4}~w6C>AL<+CQL)B@9m*U^U@rE&I5*5vHa5 z1aZ$Lsp=`Poc78er64(aUJWHOH1%JQ9taIrh6)uIgVgXuh5718l!%0Xv+Mz7(>4yD zDX8JGw#p_C@z{csKBGPj9;PP#{j{Ah!s(DAt*e#)eEjPRAHh<9h+`ka3&NJ*G_jfE z4r-z}_k>#Q??%cf(~IPLmo$G&M2wYWEG(Wpxwv5X-sgJGF6%X=x1)b)yRQ53k7Hv) z3GROX_q#B5-Kb3gbJVQANS>r<5O=Z7+CGDzDq}@?3mBrq3sjS@d8PMfVhxT}na8t8@bDvd| zMME0{2{Y|Wv__DRJVkqz0FI=3k@K)Ske`}7%Aa(u~zYsHQPkwH2k&Lz$w8MHrlltU>8SR^Dq%% zm-97_Hpaeg^S{6t{y__CTcM^p)HvSXvU*+uZn8F4v#Ql z#zN+J4O~Lb$9F;3xlgzzKBG;IDhpZ=g*m1>B=m}C7V|ePj=F@UB|%p>OC9w!Xooxx zfSJ7jL5e~|e6-bq|tBZk1KFTLT~U}ARE9xdbsn!Y=J5T^EKqlK84D2(cV)Y z7!|$1!_HBfHVRAm^2zoEAOz^&AVC%#H{n>Y{A*d#2G@{q9b^lD(?Y%X5oX$DA*=fvXE!Vt>H^}T;`0mAV6*F<&J|x0-iA#g@TpybT`{b&CaG8 z+0HXT+o&-unH>&&g?tB4)Y)}aYfw|FHfRaQs-({=H<$1NxhsTuYeD-UKDiP{Bg;%g zc;B)p>qJC}jh*@M1~h#TC=4zvgKNlu*8}LtwQW$h-RM&FoCjM-;-LsoifxYm4!H}Q zBToDhqF&obf01oiMyhXFef)q$7aRK4SfgLQzRfb8whX&!Syh{t27BuF6Uit-M9ZY@B@oDE zQtOTp&F+}=&vagA?_ zBfPDUyM>mX@q~KJtS+EVb9xhmElcJUVQdGi3djHuIWJT1BS(%`bX?bottU)77A^gJ zwo=YLlFfbub;M_LsdbRU(HC!x$C2Y(Z`CI-EE01FZ|3z>9+#CqlFdHC`+mN}n2^M} z^f>zUTVt_T$K!fCy-s#Rzou(lfpo(!gi{5wKte8Ygwh}r-fQ>T8k>oj1xiJoGPhFa zM6Q3YoM4mw6OA32ETx0n!o3n(7KTttq2k&B+6HUmHtIaLMeDxU&XgP2SXL@ofV zXjJH5wVL)QiNqbJR1Q*8F`3HsKc9fp%G5LBB6za^fUbW;1!BD!BtmjzkVw^tE6#WUO0AS*0a3@O?t#S~lmN@F21{Z*7_a+XVA& z|HXFIe^_s~qZk6C4V%&S99%M7JK8Cg5xkLyvlQ&_-yx2;&>+YZR&vM{Q{jlVnTL-o zVtVwYY_j1X>q;Y@8NS#q=zG1smME7SBG*S5IiSj=PAQjj5d;dB5KFXGhmtTF;{+wa zsmssUmC77{ch2#8* zN$mk}ig-+NjNvkQN=+xq8#flYcCWc~XDE;_g9g}YRw~J2hi`*-l`|N((SgkrGi@uW z;~lN`{x!$XwhuQ5PNb^IiE|BkX>#URR`AR8`SZ<4}20eSMp@ zcTpZZfW|;)YK3I-(nm{27y;zKg#ojQsgQaX*#SE4j$4Z_X=svJyXSybVe?F|8_)l} zZlks8-ILZAAC{Jrzm&7D+00IwW+$hxJVQ3t=V!2j55SQfhxf$GAdS5~gcOU6as(ds zu`pqUZ+K%5{S9Ak(-K;C^8S+nni{L=-5tRLq94by*9saj3i=E#e^XOxZ|=bVP#%Wo8DzPDP7Pe%9iG!b+9Pg;FSdI(7vl>@M!?(ul-EoW1gi*|SZ{R? zhYw79`2VPD+3`ZLo~@B79sOU)Y|nFE!sfdK2jLfP7FsbS|LvlN*jqyHhfbUFpR7%8 z{e=xS$e^+U7?V>6SuWq4HYHKI>vUCL7QXMiCDE#SAE|XoYf}bDEZ`eFlnxJLY7(4) z81e$=I&Eaadx7%CrVq zx#z{zeQyXNNsepZ{~qptl1WFM62e3c5Amwud>+RU#G|Tk$@#XMpEAO4d7(SiemZU% z%gcu8rgBhGo{OoC3ZINmSdLo{Kc0%#AmyhD+0^3}67V2-(m*qIlkXn}dw}c%;WR2Z z8F)>&)P@UbG(^BY-2BYV&-uQTB0Dk`Xy)0vBTaJ*i*^>li+ zSt+N_-`C>)Ew`OIb9C$aBb962dB^bqm{KZxu^sDC#Hda!2%LSiGGyE$X=Kx0k~M0h zv1n#4U8>p1a3dVIn>F_quUNCIiDWb77VTy=aqV#{k4WNHEtS6DPt=*_DSUSM3d2C{ z68z>^9}_faIp&WbL4HcIoSg;T5H4j~GT`~^u0?-UeqNwHA7?8VXmOhf8NK!MDKLfi ztgjo>`>Fw)S1gx{g#`YBy`g@5y?y?^-J=PWK9vl+yGJivIC?b6%-#v?_bLT_TcI-9 z^I1_n>YxGI-;eaL1&?lBk&uX&h(w+-@a^#fBe!?Lhobz!S&N>TXxz_${52S(PA(mZ zhxG#n!c;hZWa*?)_}!Z&Lx#pA&P&Dsf!o`RjD*;FvVHtH1pt)-3qVwQAMa zJ@(Mf>RRn}&{{k7yWz7CHb$Rs&&*^zFEcaKp6pG($5@4@K7Dr`VKI@{)z(&b9y+!Q z9q-z2vbMn=F{y$2G;7boU&XINtM?8~_izG^K7T#H+_|9!WW<+;hM~(CgWyG=``c;; z2YHWzFy$T((FN;p`vAH}3vx({%LE3iz`??oTs6L0Oh;JvVA7#s#Wgz~{6`>UqboTF zU?*daI8%M=NhQuD>;>UAYp{0fp%?^78}y+_#a#Zwq!WW=5@rYjaxYTKVB+WT`0YJ6 zEmxSy+@1RDZHvX(Dil@vLLac5P9TW~35J855J-Q)YoytLUq|YTALP&RXTu2G9sisN z{THSMtI>Zu$2_RSQ%L)V*oo?n`cwT3!=){~b3E;$ER$YOpAq~#V61-@qXuZh_-Owe zEz=S*>0~C<8XOZDo{1jA*Xrr7qLyqBAFpi`80zBU=jYSpFk%gaeS+ti zv5Wi3w3kRA@Mk8|bWxd(I!CyIHDY9mAnp~5b1ROs&(N7On-gOIW#lLzmVqW98w)0Vx1#eBvX#2{@J;Tzma>Vl{iQ&Iq7tpwkBF znN&+h?mTz#;<-DIAY4aF-mTWL-E%UW`WTu{YB~M07?OQ|R}AOcHmzI6RmF!o1(CmG zhq`oxcw ze2(CIIK|sHF_$}`TNyi;)tWQ0ZE>k^fCFuaM!Y68>7u5B@c<&5sM&xe#;xQ7gZ2i^ zy>D-CY_zLl&4$p90%rC4{6@R8MKiE*Zgho)3w43p!6j~Gb1>chUZe0N&tRi^ybY0Y zv(zrJ)5t7pWg=FNKXo-QgA9Y6;QJi!=YOB46`9;ahNiQK5Bgrz!*ZD5pc&wG(;RG(sjS)b)GJ~L;}1h7)G`Ux90iUCLD-@i5CTAM9W!Q21Y}6-*o>iA@AS1O?Cp>q-}@f^`^&p z%pe35U(yHqqNMPEV@3(1L*&hY55|!SRT)EVtzxr`HFr4PoCl7#jJ3{UtDESYO2%1O z`WGIAD>4Yl$rvMRDc(;8K%n@VvECogOEB0z-n{u-Hh9bRW6U4c(|NOZNP&VYs@Cl66PP)W@!4sEH){P z(jcM5)SY(7RIf6l9i#ym{S+og4M)G8Sx^tG#f|^tEw1z1;ne6;(df^okxUddjHA}Q zx%f|DU6#vEtiy=lYi6oe|x&-ny*zX`Hh?0a8KEi{~D#&M3Pkee-a!pzPr9a)&ED|M{{*P)ce^f}97-r8 zX!D`?a89bM!fECuNQYH9ekbuYj4Tl9~(5?d0Hyw@LA zS0JCRAo7NJ%s~q<#d z{sEp(MKV=!r@Rt|#fV+e6HJbmN^T zHxM2f@3F;oOBMCxVm_D4FY4_sZ>iMl6~suRut3_WrkuGsCspNd{N7flq;KoNSni(N z+B)aLF5lDLm*>a2iQhq{kyU8dnd?W)+jxDt>B$=Obu38g2g!#Yz7;=P@Z|tU!Ihw) z9-uxMH`d$>S1QYn8@Jyg4h;=W*&3eLm|a|4Sv~@X56`>04~N7l10hn{luEP?$~JU# zb+yj8?~gSmGS8qh*REDkh%#BNw&ywn__I#k)0^%s;3uemI3KzQZR9hd_hN?0m}xN= zcpguUm*lH)VdJ&+mIhTU=04tuf(Sk$?Y40hujNbgeD@k~VJj+xqYsP+en@s8L}PH^ zHAAS9GdQqY_OC(1Y><=yn}(y<112l(lwhwPt7c=#qup zvbn8#%fn6Kn1Sq6wc7f6tp=ZJ#Q9GT!ZF9F$FuoF(kkRzj{Vf~JaTB~as{}vB7b&g z=?CL=!?B%KzQCF%S#@Bnon2@~y!s|HD|Xbelf7O#jEJsSEmNsvphPz8aJtt!c}#Vf zqj9tDMVbp|*KomHr&C41Yo$u%su~@#{r-5SP+LF=&84}yvg4{(du}|7y7Jh#-d7iD zg^cTz=jN7BJ#&f<&VUX+jyaMNlN>#%A|m=88FEM#@E9AP4LCxc!qZZ~$8&IwTVTx6E(Y*U=KNal(pgZ*>jl7fzq< zb|{yVucJefZlJv@QW&G-1|@cPBi(Cc*%KSE-;&t?$?Mb$hMzIUAWQm&Re zxs(q^&j;_X((pLccSs#_()xq{0nZIwL^kc4d2S_Y_|esG=E(=-im?gVVrvWmS9E|6 zw-DuX8Wu-z$A0W7~^O%>RaIO zY9vPgv}jr=MFkK12in=3ab#(*G+zzcs(nC6%ueJlt|pw-XlmwU*uEuZJanRda`p`; z>nH24=SH1C@mebt`(M}%Z~dQ(t@ffdSUO@9y`ar|SuAoAWrS|oT5}SsDf>9;$K;dg z`#afA_BL)#quOV7O2@tgpI2WChP4GB7JwKa2p_6a@xlA97jVRV$haH;qQo_|9>zeB zBh{JF{7cj;ptnReEEIR~W{RGN%>J=#wpv8kKeU4}Bw|9^fY+Vso`5MmzVkg6(xks< zllv#1Xy@{39^T-;AM?E84DQc5@!$@2Djh^i)od1>{v%`cWE`F&JEO0r+_-gWlLseO zQ7o#v)0wAi36E^w$4gt)??;y9Cy=%Bt&rt4Rah6y0>!a}q~pzW!bKs9ez^!_qxQAN zy@!4x^9<(&_>m1C@#K&4U&-bdpAOBwH-M;Sz?L;$a#XpF*P%WMN1M zI}*R7v}(-HJ#fvJHmVUlYU7*U3fsn!W)cuEY9XQ#qK#`e8V>jZ{C|MMhX4O64=xeW zT)KXH&9(mR?1GQ4{rvVo+t=X9{urX1-^47HI@(5S9`sfmiagGAACZ=vcj~j-6af%J zBSG6tcLwo6u4MMsAf>A&iR1t@0~*#!HKZcYPNdj@b`f2HX#5K^mxos)<7;BIY|`~= zg@B#<8ARXZBXg)LSM%ItwjBK@GwVbNZuGH8gclt&gk3}Vw_Lv7F3i@8?Rq{34Gnm> z%Li94%QaADuh(rCNNPXtn7MkL0X5~E3)jHBFnb5QESb4l^wL(ZmREWBA-S32Kkg7? zBlm5bS6vtmFYvl}LUI_fR*obb8hwqedl%$4FYGA<%6A;<$;tbD#`s>uGLdt-z!4^j zg#?WyQxsf_Js5j1M!?6w#MmR62I{5`j+a^D#l?}wt%9zR{Q}u0x%t^-`Ppa7$@}v0 z`26f50`p8HXH1+u%ROYhODxW=&dtYDsrcOdD$X+zUfkt*q<%ErIL^<<2C7lyOJF4)j$)I(NO7dI)mt>gm&~l}h-b6nkS|l?wM< z6#Ym&RK=}7v$NGWR;?aupj;dM6|Q2v>;o5@0VpbrEz(kO1H;Y|l4{szL$Jj6n zh1svy3k7q7IfqAsqgnOV?C6Txhy8c-u{u;JNdI0}52J*}!QLKVc|s!}fp5+GLm$Qx z<+6e+0n~sD-6uUH`sX)gB`|O}qjiQla*R)+c^5Kpq!Ze0TkgVafm8^C$E+$)bjU^& zji9}kOHl&353;grFMRvN9;~?ifdR9{g40^t2PMmx@<2rF=GB zt)WN`f{Fp^+e=I3(Ov2$3E)2&kE6z+Y}Dn76_3H$sfHwtCY~n}3p*B`C>cRy2-2J( zOE6MMhv6d^Nx)GHXbMk*RML1DM2y*F1v2S+>!D-EtkJ)U;BlN{DxS$TBNaRgf{DXs zZD!?(Nh@W%WU1e8 zSD})KB??}taL=L{=_Qj3qi;-Wgw*dP8S1xqPod-$m?*=jw)_3%Mbm24x(R6GOs@nj z-92d)>3QD5|1+&s!Bk1CCfD`{;F~MB4U~-Qfzg%xzb$Ve)uFUPP(<^$%{4d98{tbR zf)PIPXhap4H#eJ2rPh9%3cBB3Q>xkARPVRq_ufsyi%hN&>(%EY=BY~5>2|iQ)5{-= z$3M1w+S=}P9q@axZ~UqHDrlJt{Bl0T?3R#GMN5Uz#1nwyp~^}QnAMjfOJ*5^=|3x7 zJBWhu;X21`aFx^WfeUPqLf?qS$w5o7kmYXe28*A-Ez*tLjhL{b^=xQyo>{3s`buNxs*Yf(Af@Dk%_Kr#S)Ed);KGL zoyZnEA4qkw9uBY`n5&BwGZ4u_QFu@$jPOV-m+Hc05QgM;Tp@Y5LJlxFCHq7nBjlmALCzh8 zT2=r;P_!)!TCr^+CkS*il20Vx=Lp)+-vjiymzS`PEx4i4*wj{o(cg7%x#fI1-Pl=K znY}XlXSWX3%+C){s7JW2D}m+f3J38(HEx{&g#Fw*jw6G(@s4K_v$Ly>y)(xk=`@6M z+6$!TJ>jPsd&kZYkBCpB=*Y_8NJH~YNThsbE>$v2egYmuyGeFSj;A=RNFKA33RcLg z$4#TVa6Gcn=_DFnu8sn~p7QP+9bgqmW*~fzt^y$s{&hBYe4z`s^ZNQbaz&(`s+Kbk z4K%9u2}y&`G~p{M7{}Ah;sAbCgB^ryic%OfF?(S2puVQMgaa(_cLz`WIvdB?L z0|Nf{&;bWWdS&;4Gsu85!+wWfzUK0&du4nS^qM2}W60{rT#lz@d?+7c;_$)D85=vA zh1FJ)Q!pIC?HCT}n)Btkb{C_;B_X$cp<`T5g5y9$%>rDv?Q;u-%sEstN~~^ets)JI zeJ)eDdfDilvr(&Kp@L!t(OB~8G*SU^T*F&!Eica^IN5#nC*#k;!eixfsLAC%8^5qt zc-BQ!^6c_5-2hQuVPapwJ88Ly>zDeLTj8Jj1oUCrvxSNkJgAm{0~b{7hccxLBmpFD zDnbE<$?otD`d%U)gUwVdkW#Q0Q7hKuZ! z<6Re}xRd8D*SHtZhNvS<1Pq))jSJM4?^ zBi|t4fPunbz$HNsF%qnEY(NeC9AG{z=uR$ZXC;%Xvti3}b9f!TSo(8ZBK&`O`qokg#m5cg zn&+)E@k*uDs#M~2-iZX=21;Z3HA>RDo5P7#M$hf(G5cT&;#xfM1caV!rv3zM!pLL( zL49sdL8KjtZc{y1Fb>)IMi+))$*YPo*()2_TrS&qWfC{5YSgla`u?EKNg$&V=8uO4 zsCNaQ3ve(nB>Snt;MH@{;`~UR3VKVt>dVaaL zkc_{<3v@pCd6j-c(#dDi7v++RJ6MMdTHU$$;d8f~yX62f-bub84K~P! zqe#SigOgb3EzhSe>PvQbFYGou7e^l%-w#+5yNl4`8LVVtZ=oGrn_w?AkO|p?r3~c8 zM#BEUlH>z~DDrV3z))0P+r=HxVNBo5TS?-HO2Sch%sxD%pFoqJ<9y#9W9E z*TDt77^ZgA0j{o${&jGvR^2}$fP2#o>U`2>rk{5fvc(3hptL?wgQ7+ADYG6{{!USo z-@fQ}x0a0jJViFF^8QvA|J&KXfxa(?&{s+Jg?4`OI(~`)m>0+e3>u9K!wdK)zVHo} zuH&j4H1-CAAqKZGxbd424yLYd3}BF#romvK`vqufJShB*3tQ$bz=Ka;Tr7*qJOc@!OWY1Dxgq^5%|7u|#vjV4Pqq+%B3#so+pV<0O1F>Q7H2s;hXk z54RA2eAfxQ9GBR=IRw+@?#ABQ!?3vBFVW;Nbz2fO8B(e2MKYTgGr&Wm|7^hol{`)r z!U;ERC8Ad1Fdm~}#;DcGXHHB40HDXMW|4m~`5zuixrLYO#8bma%777Ej3%CuH0oiH zkHiV81&+rd_n=Uizn(v%z9F<@5s`F<$IILXpNO#mlGj!cd0mW1cOHa5RBaeYEnFbG zgYX;{hGq?>BAswao9c?{+35iqeZQOg=?!al~T#^HiH}`#}Kq!we6pAcs`avuGi$@(fO-v>~(kCU7XqMCemK&uG<_Z zYGX^3Ht%wBFHz^CRbv&vmA-Ojlvxp#jtiY-dOJ;M1&hIC}2j5$e@jVSOBg|MP~90 zfeTcC{Ea#!)qZ=Bfvoh%V#AFcZ)}78Nfn+_>gW=w<$IL+Ii)^9$yZ~HJE{^DGL@99 zh>(PSbvbV$)0m&R%s33Z1D%iT{zr0A=N+tlQA%~y=xgkdl5CVegw1coNUaa&CcAj$ z{3Ll9#!u@g_tZY3k^ouIQTlo(stH)6j9P6O7GDpw#4t- zy*{;#f?l_{`T_W4wFd{{1gaavqX4j9+Ff6-n}v33F%pgy4I&vXpu(u+-}q$-4B6N1 z`xFKm_+x+mwK%f~izHgsQO7hNQ4+9&R*Ztai;*C}tpYDsK^ylWZJiH&F!UA3T;kva zKSOyH!VNyY2Gh>9d->f6I9L&D3jHuw+qPsWB8?ypT0`<#?gJP5Rbj{n(_Bt1D2AuC zO9dCv7rN=~VhwUEH@4QPc|kG>{;tM=;Lm7E$M5&@=8ZeU{kkgz#`jr-=5TN8H|tJ? z{JPZ=IpXiHnO@XJR09+r&T^-n?H%2U+wC@M%3w6J6pxfG5z@omn7L)VD!~IhAB})NAicB@Gis zd)^1{VI(&_b_~f(VQP^5J2vMlC}e}X;N)v~=@gt$!_l=h#s`VfyH~NSN{@(o7>D-~ zBP8r-0qK|&LwDiggqRJ5pe=-L$f-P?4^-GpC82DScbdKhj=b020kAl9hcU%#ok2E( zS7HUxT&@n*?IF^WlB#P*QKmlNw=h6}CgsNOyLfKfhz$aQeVTrjSWZ`XaH#!28 z03&@KvW8tgQZK!O32}j9ckAxmrhB*U zPWRre^8Mb!9f#$#>Hl-0BY?xZEmMGlBz_Rj)>8T{U1d>RMz&anfCE5PTm7*popSNdAZ zUTj>7;Q*_mCI&oyw{HM6ZH5UWWsy?dMk}=AL$4*VYFUG()lkJ3$g;gDrcp zek8IzHEvY3eFyx{pB{ii*}9*P5H(jmh>nvqZ1ndH^MhTz{gPR~L@XVjn0)JJu|NfI zPzB;+jy9V0(WlkDdse4lxY0ewue#Jz!&P|ypJOJzpU(2Vic8+H7+$uAipxP+YV zqw!9d9~@7b)JfE&d8&Axp1?fa0(!WVbpsk1gPPKGBUlIcE;~EGm}pRG6J`YJ8R(iM z9$3}^VskJP@z`KvkY0hFu~7+Nkg#n`Sndk|R*2r-)Vl`G>2N}H;4{cNwYql`cA)`e zMSZti5wk)EdUqWHkQD$oZ%Oa}Mw)1t4+Q%4TFFWiMC5Yvtlt$kR6M{ff#JoTB1X^o8g10BmZqNaOwD9Uo?@%SZaIgS2 zquGTo^YW~K`*ria;Q%Z#^Z*z?;q)A5M0qof`oQ8gu)c`{b;jo5E0UdCVv$3E==eO? zN>_y_Yyxo(ss2^H^P$-@yRrH1y0zVhqrrpE5Wj*iG-yxgzyUzR6M*M~oe2sZy=Lti zthmBwAH{Nmvc-ENu`Qj++0cCNs{Rxxns>^obb;N?k4FQCmKVF%uEWN@-G|cy2cP+` zL>x@XJ{xxTS2BlHZ@7UmK*@g}`{ z@4n|H5JPQsUbCsY+Zr9cq>BI*F(zrbk?)=8M#$r8@lxy?mFnw`-+yq|&OMjV#tjH) zR+o(W&$S6(iePH*^ z%;+B2MO2-+KmF{2z0(!nwfdS&25Sk|?b><6;JSz~&3twq)$H=Z-ax{}!y*wTp z-Me>GjtmX;$NFMjNgInBN95@E_?}Sb4x^tj-jMFda6BAS-2hW!lJKma218}Rlr#b7 z17mE&V1bOGK#$fu@*`@r1H8ZurbvT_g5uK4CrKn@-o|O^15O=d={v&wsU;~UsUE5^ zucrE_OR5AMPbZM%7DiTF#w~z93-D)S#|$9DZdwcu@nxnGV|dZ9fa3s*7afCVoi#ct z8NGs7Gb^WP{=9?MIAEfu9aa#G6jO9DMFQ=T+)y=PG(32jg##VkW z<80RT(x1%nIm={t?Oi-h1 z;Y9R^(vW7`_ayG{SN9T^Y@rRZssC4dnp~a4_X%>LKZr{D1Coc&H{2_mHGS&bGSlqd zS&$gK(+whqu*J4Z!4G;i=9%3y+N$WnREL=Y0gwNUn*eiy|EDFy&;W+ z-5LrVH@YhSOm6J9`>jCED3dumR9w?Ncu8jOxqCC0_MICIgziTh5Q}%xCC5!A{2UwX zUQ-MOrc6V*fYooO1~28SE;$cZS;_l_0^XW7pGVxM!>DwDA!;m-QhhBsO!-!$v zp#g!BPD4C{aD9{z{KWtdA80H76(ri{D=#al_gA6hs@3bxT8oIpE@2u!DANQ zHn1VlLwnA5##apuTKGK6+BNV}2JEh6|HdBg^FxM$mwa=jm7v9Ko&ESoR~X+TTDJ;K z_peIElKtxvNjoN2ufqO9_+IRqo*pbi9qvgaVEd-J)^6y->eaQk8B#XK1Pwg>9cnG5 zaVJddPRQgt5No_0u^GIQ2*DR72$W`^5zcl%!p@-{MHiyC&~qmcdA52Lb|1z%4y?bx z2Lo5F-oAZ&C>)AJ2eJPPZrGVvI|3K{u3h7296Wpb4$IoH{p^EhjPKfo?^=jk@Cl#b z;O@~K^kADaJJvMmkmPY6Y=bK>ihfkqI>DR-F#j0+A%L5}edy$fSS9F^LiTVZzz*?S z_jt~m=rsFJ;NMAf$Vt=VxmYM3ik)jRE6UhegUXY}gD`P&V&b?raP-4}$2s-@nWbhC zUdrxB;5$RVgT1pFdgBhvuZ!thQTr&$g~*TpG_cj>|frfFtNl)Te#(f^&KQr;bhnoXd81g}TE z17p4)<86FA#&jTY3;qcBl^`YawQ8QV-a;gtBnXj$tq1SIrsc5|-v!Trz;ThnmL3^< z#~Fut9~Zn{Dux-DN+H%YDNf+<@+iG@;9`2B{kS5LTEF?sVGUDkXw&#an%at{5b^pp zH0{lmlaRH?F^ULz1+5_%1Y5X(@@OcqBn^)hP9e^?E*>W_0He=tgL*#KXvY5mUNgeNp9%_%FKGT&MQTkahtM*q~0KxZ%D4-Wb{rzaEV*K7WoM zB1HbrBu33?>X)+xb}Z((%mT$6C{N;roptybmMv>{c-L_gqb!ES&-?n8kK^-q{a7D2 z*^h7f?8I)taMWRQVuW4*CYI57FTTYV_j9ps0lzTfsqbyQe~EM=nf!dRzdxCHqn}tl znGmnRZC5QH@9U#qG1=F50V3vr5omn;gFfol8;=4<#@@VM4&H4ny(8D%y{WbQFuE^p z`uQZ?*Gu5OJg3sYNRNYF2RgQ59K7`e=wffN0TJq%7Ih%gnx@HgwipoQ#==wt=Ytf8 zQpVpt<|oDS?+f&Wu>u%dIvDg4A3WearGZ*9*wNf1DUQ#pkvKSPscWJs0qydmNy`(HD;gL2afP8=2L@g z{((C%=7&jX<|FVn%v1`>g=M~n&A~k^7YUK`RXg$e?wF2? z=r$43(O4i7=)9O7GD*=Y8-}(I2ID%^wS4yjwh(Px)_E8ZXK z3awtZZuO4)u7$!C?T3e$z7vlPs)L<~ybbn5mtXT9=1e13?%ajVaJ#T;fr`Y(*#A$y z>Q!i!7y4GphUMHw<} z1DIBnycVDO(%8Gu>~*#SyDo^p&Rv&Wdh<`wfmrvgwSy?tka);RGZ5aWU}PHkW33r{ z7zxvThuJg@`zxX=yKcVpl3hCk%QIS=sF>wPVNt5(e7g0zrABio9cj(@bM6H5;wZe7 zb&U2PLoS$BaEsu`YjH8@8`Mjz0C3C!<_XYOA$aK=>O-Xm9775ev5L(ci+2EJtg*7| zgvxnqseOiUdb!)j#Kh7rID^6;4P*DnT}w-LVEwkOX?$rD5XJHd!Y-_Idh53J@T94$ zt*$OB8t%uAv0X_kg?(r3U@VbRs*4tMNO%S5e58-gq@w{(XZRyJ#=k>$@xqpG^%!H>{6@u|foEZ{W4WC&GnFbm0g; zhF2q&#uvn?1{Pmn@s3P(1rT_Ij^%Z~9u~9%R{1rEjhciuFxye&FFmk_!i3lOQ)SN> z&6y+=(lf|*{^30T zy9o9A0Rgr0Rs}771M7b?2j1jAS!wNU;HT9enu_tj(V^qvWr=nGA%wA0?KsvOk{sODZ0-dQOA`hsYB7Cm+?2R#B5;T*4nKx?@?(bS@j86VXly*{dPevk>*mIQj$lNr9^^w592*5g?PDU?`;{W9C^&1G_TYea= z`Pi_2_hcdf9+klV0hkkwq?kCPC$veaO`)DMl-lBNg}Ftk#$Jhu#B+4)vT*pam`-fn zx)1H4-F;iPCPe>==D20_2}~4kee*u_27CE{wBUPyypsp)d1C?_j~|odShBhQIZ1SE z!LtNvIu4SW#P5=!NlKnt1**hod5E+P(!VI1d=xZaCu?eS!5 z4#u#{(N%re{^=~d2A)2?{j9B_$b@;VvE?VVZoBl!(`{S)uK!v?v_%h2jE2J#SbCJ) zTsAjZN`}{t4$ybNkU!bFHB)nwfd5kf$WE^EK_JvZNWCf)i>ZD-mtOZToC$Cw-5vIag;+UqU{E90>p z&jtR<`)Lu1h4noRWp@C4I*@Fx1*XtWscvFN3`a zh&;%gKy7%bKq!F~%u^s-<(!QhBPvoQ7+%E3#?M`iCEf(m-*@B2ur}qL&EnSag2?^v z7qqs2vTYbyZkzYo1nu^6d@=SRklXZ8Q#>*r91%}2h>UsO$O60@@52DI3y!RO^A&G4 z+1W;?uba?2;}k9*!uK>epp=H6#+#Mmzz`xWIM#~PGE^h-K}?PK6Mu!>m;I9@;r)!& ze>@&tzAhR{9!!R!YkWKEgr8VbZ>1fvVR%0xlLAY?j#W!?`3=!X7C2mg&+0X)a5%MQ zH8!jfJ(J?y;m%`zQ}lZvb+|PW4J(fmv#qrD$Q#3vKqVLmMj8pM zzsUEkU)Kv5usa;?1{m18ZharHeb%SV7!0tsI*Qq2z!D4r_Ypd51F5lC5&*aCO^B`R zydFCSoOPDtJaipC&|}-3lWS62#{0yTgEqWK?x}JM71I03SZ3G7V~0qF%UreDx~q^%6+zhzqkEt5d;Q!)uE;#iEk@O$8s2Gv?neYXytfdy+IyJ;$So5ad=ML&mWcUP}lU zU@6$pn9l@Nwqy>lfg(NI1#b~orgw_#W5)+pV&GDmwz{j z>=Nd*>7a)C8zhJr4C)>Z;!w+p6Lhb40vPwd(NIVNj%d#K{gj-6g){o4q)En>)~L4L;H9si*c(EKO7t-W(g#z zZ)A+(4FD~reD>L5djY2!PIUr=J4DCjU+*ymK_N}iD*9RWB zX^(lYZh^JFu;rs5!q0ds&2u6Y) zSx_<&U<*nMX1QqqA3pM~v*3-z76|vEpY~i5MMNJJ*<$o)B z&VQz_SNm+hy!B+;k2j~hpOfaC_w(PW>m8=I%&|>*yypzz2+cklho<#FthNA8`LEQ~ z*np^oku=ow|3le->o?OG>9(KAd(nIUo!TP*r+zbYGRU+>xCeTTchw?ZLp(Q0$|tF+ zNmIhRgvndnVdFRI}CNdJMAh2vr{-Z|py z)i%PmX`+sJ?xaL)0Ah-j?#6E*h;`DmU||$-NvnoY=JrEDlyw$BCUK4>^owIHe-vjqrkCjs--%jmU7t}~`XNHgOLYR^OJQlJ|%me#*{pr_&e98J&%&6*d~VR+04>ViVFhNk1oG zh^3B`*tF5fXq?(aKSa~VQ{s3gv&7ptkWId+H`zn&<31CJ9`M3q($6-Hz4j8*IC}X6 z(I51yjtS^jlfbmMQ*6~fOlSyHGb5pxB0CH^AybP#r`=L8=+!b;9BU1zInvh%z2LPH zlYVdo{ffTIU9FXuS_6CrGp$wWVn3f5Jlz(~r!@%;5jnMidj>5g^xzRjF>QH&1WdJ~p;}TN)p@>rSmo02PRI ze{s{^z2~357je1icp|R)whtXVc=OE%4{qAgr%;gIES4;&H@3B9%bvYM2)@Dog+qJy zY}sPjP<5;?q!4_GPk>9@_Z+p*3q3>G}XO%9es!QWqt8ZYj0%uZ=}C}WLNfO z4$*Of66y4kUtuYo<|_2+g!i9-&;Nt?D#}*uDRefYBGB@AvvGW|utAR|Vtj}Q6l7{> z5$+Y5uM|8TXeQBleyR9vw+i+yUloraf{gm2VHW*FbU(b6IG0a0_i#t#;u2XWI3USE zigb8tu(w-1OodVds(7s3+`0dUT1l3tH#IOphqeU0vua>KW{+pAu|jV$MHBUUCOgyg z=3z#_jU&uR2qOeYm%GPrZJh>16!)+vr08gVEUTGnx@?-M2CoLgInmsRsF|8AXv;yaJ($2WuGlxP{y%J!$4gPQ*I{4yr4#62d7U~ z$RC{`JSML2Wgi42dTD(79Mhg;Qri5C9H*hX$Afq^d8sd=CSK5uwz_ZjK^`QKMIr9N z%85B%bh{x2k5xB#HYJ*LLA+oSIP~Sv^IK!KT2yz&rHPRlq)cOK()+?~%B+IIp7AY+ z#%Au&!n_#N6s9dMGl~L%H5P@idnpjFx7f^qY{5omgW6ArOY(=UB z3p<+W$4&3~p)7QllfRqfw>1}Xpf$DUHH*Do+c)Lv=oN87v-scYjt_fjfjzpuDHN%1d#yz; zEjo5vZ}8Fx%->%4Rr>k{tv(vgmx){vJ@bZoY+ z^3q9a|EhAakiUA?NiR54!)|)M?AFqia;09(R?~%Cxs-ODS~^?Gr)%YMajsC?c|o~U zyYGVImgk-E3#yKj;TEoSs%`f3Z~IS*4%Dqt~`I5tIjP~7ORDs*;;x_Zfm-=LTftRdJB9e zw~XMV0)mvTMwpV*k?vT)aSEXmF4FTj>vq(TsvuQ?6Q|gbg{hLp6*-iYkaAICY8^YVwGh>-0j#E$UF7ERJC5=yejR{N&1bk4`6}uZ_?k4fCfWzm+6jR@ zicQVN5F{{){21=CrO_@eA#80qjo{+eR?a}}akQ})f4d?2{O4JD-8gFcPnB+Ys!YrE zvv`vldcwtaCcE(8wD)$|wrAMMy)%C=?{zNPs9<7M@!m5S1?tT$XkjZqkLkTrpZ0%$ z3(PLofglsVjk$Z8f11|SBH~#nAi1%QIQFuz1@-|HA#9}_5s2Ftak$44A_=^0P;?2b z=oLMpSM-T~u?nj|*NCg zQOt{yD2s}?RaAv5YN9RG~h~Hve%|<#GkRh?_b5=#NWj~#6QKq#J{oc#6A*XD`G{h7#X(G1}kk1SR1WDYm+r(ZMKH3E!I|Ro3-8A zVP)`5sa@8HHEQj)_E>wZF>Bn~XYIERSZ7#gT4!1Ju?|{iTNBoOt#hn%t@Et&tqZIR zt&6OStxK#+t;?+YS(jT^U^le;TUT0FSyx-vSl3$DS=U=PST|ZXSvOk`upVeV$eOgW z)|8dA@|I&wTQk#^4D*5jw>rK`j z)|;)jSZ}r7X1(2dhxJbDUDms;_gL?>-exA_o>%-PZtdCkBvp#Np!uq83 zDeKeLXROa!pR+!1eZjiZ`l9tE>&w`^1zqkHi{n7f9^=IoZ)?cl^S%0_w zVg1wkm-TOJ*}7YHNFlL%2zHy$5@5azN^Dy$BQlDu%Hy(ACS+1}$&~DtJ+fE!$$q&? zu9j=$T8S0ca)V6E0l85Q%1v@eZkEGxi``DpnV`B-_oe4Ko|e1d$Ue3E>! ze2P3KpDLdwpDv#vpDCXupDmvwpDUjypD$k^UnpNBUo4Nym&ljOm&upQSIAe&SIJk) z*T~n(*U8t*H^?{2H_1EXo8?>NTjks2+vPjtJLS9NyXAZ2d*%D&`{f7Z2jvO*A^BnX z5&2R1G5K-%3HeF+Dfwyn8TncHIr(|{1$n3ZqWqHlviyqts{ES#y8MRxru>%tw)~F# zuKb?-zC0;^Ab%)-B!4V_B7Z7>Chw9zm%os|l)sX{mcNm|mA{j}mw%9dlz)d_dmdo;P)uDv4u-OQp5oN1@3aSv+Lq=3o#Z+8%s)R~nL!gxERz0d$ z^{IZfO08CF)LOMptyddVS`Da;YEW%bLu#`cR$J6owM}hTJ5)yPRJ+uO8dba19<^7E zsd2SW?Nb~k6b*?&3ov$uX7pjZY#p)7usk%(vPhGCAP>0n0 z)s^Zhb+x)iU8}BB*Q*=Ujp`Lu!>>SgNX>J{ph z>Q(C1>NV=M>UHY%>J93R>P_kn^=9=J^;Y#Z^>+0R^-lFJ^=|bZ^x+zNo&WzO25YzN)^azOKHZzNx;Y zzOBBazN@~czOPQIAE+OyAE_U!pQxXzpQ*dl&($x~FV(NquhnnVZ`JSA@6{jFAJw1K zpVeQ~U)A5#-_<|VKh?j~ztyt3TX$%oEqsSSAq?Ku0UgvK9o7*Y)iE8{ojReDx=W{Y zx9-usx=;7(ReH5vqu1(ndcEGD(|SN})Ps7H9@3lju->A#>TPgnOZ8>?e)@8K zg+8S3udmcs>8tfM`dWRRzFyy;Z`3#GoAm?q1NDRSq|WLoozr>k=xIHpXLUi}qUUr` z&+C#d>x#ZrSGB8ax~>oF1${&>>ZAI>`XTzE`eFLvdPzS*KT_YOAEh6yAEO_uZ`Y60 zkJnGoPt;G+Pu5S-$MjS6)AZBzGxRg{v-Gp|bM$ld^Yrue3-k;1i}Z{2as3kgQvEXh za{UVZO8qMRYW*7hTKzixdi@6dM*Svzhkmnui+-zqn|`}~hkmDimwvZ?kAAOypMJmo zfc~I9p+BTQtUsbZsz0Vbu0NqasXwJZtv{nbt3RhdufL%0)L+zJ(qGnJ(O=bH(_h!$ z(BIVG(%;tK(cjhI)8E%8^$+w9^^f$A^-uIq_0RNO`sex=`j`4w`q%n5`nURb`uF+| z`j7fg`p^0=`mg$L`tSN5`k(q=`rmq4-)(nbZ!!z}Ln>R_Hmum79kRoA#E#lAJ8pN{ z2|H+Kut8||Cyo9zeK540a-Puf{~ z%Ffw&+p(wZ8GF_)*tbNSBe`OBK3ju*7MRVtv$gC@BwsGV!ppdYnbPUB(ooTH-ArvZ zTe6*7>)E0kovAw6np5Sn|BIGsPiN~nrxr0knS8ZS>7FguT_=;9EzFmk#mqvXlrJwt zO(8v4w%A*9<||G$TdP-{OtxArFJw@q)V;E}UJ2By*+S8&2B)$)JZK>s@)Gs=@KpIo zrdDv>y3>tU%4cTF#eAVOlbLt&gQht5_0loc=Gv+a3vRlHd`pLORto7uyKYTfN>?+|oO)$5L8wo)rs zBE@=X#?5fuSj8#R({Qi2kxDUJ@*XW(sn1vZR||2*EoUoHcedaZ^X6*h7D{u<6rET?H*8D&bKAMsMryW-vcHCN& z4!9P#aNL9$#ah+DFwj`WTMO!y9+RzCGSk)ad}gYe&CNNrXtwTRc#BN07}wbvr=nAZ zTCQ9uaV9=hF4i(d0s=}H+t^gOJeQ%vJcc!v%g%$2GSzYoR2<4-+)WF)*=)6jAuHDB zOA(Wq$(CxhD5gvWZ#z}aR`YhQSO$6#&6Rn0=RqiFgEG_Q>RggjKun66DkckFHjMVl z=BZ-2YW=8FsFwM5fqcQmNG*o)Wl$f{c+e?2hqLJF)O4Xp^D9@V<}j>B=&2*q#d6hq zz1TEpmd+Q;u9FW>SF@#@d4-vJ78&zCa;9F2GijnCUH}~tHbh0^mBb7yWJ{T9y#m~Z zZ$DSSc;h)j;0C6NSg}xZ8oe5x&z7>Lw?jm-ndxlK3C$PqvUFKwzD!eh3VAnN$`;VY zRBV^ZwY)POtYk~&Dp*gX;s8c6=R!!E-Uu3^tydy=ph9-mTsH;Y3Hm|V3?cA?aXlvBjm7OI(f zjAPGKwlq`B<{eCkOf5U-J=Rot0RvdbIh`1G7sFSnI^aR2TC8nWM!Xymrgz5UBA}|0 z>8Ws~Sa&n?g;L!Ox?oBmqFTUpa>RjcceV;Xk^*HPEfje)9FMT0hW-qjiDIN?(07@V zbEKB?GkB)FlLK!qSCf93ThE!2m;+j#&6INv$N}uH4iY2YlmKagEg8b1M+@;ZFP3w2 z-lWZy4;S)DGl4m6E=+;OX3Gtl#Qe?8t!gi8ku9gxHWcjDrhBb2Q=2Az2J<_ctCk^S zXpFns&rs!*DPmkNQ!C@?Ym2MfdBaS#T*t73cRAJm_ETJzA=c-$gZr7L^5vRq%3NkY z)HXeW;XBPT!(GiI;~|~tn%96=Kf*Tz1H=?FH>WhF4!;^6i!L$D4sw*9pQd%py9xz( zkd~aY=p_Af^(tm4u`&NSLG>kSgPz^5g%|RkBs4{Q8WPO`2dvdF()lV$hFa9ia%Z3x z$a}h6&Vw-?aa{1@+#I-rJyk8_XB?0t{rE&;M!LPMb)?(Rn349%Xz-$5n(u5+Vg5Qb z1#)!POw|iU@_N*0hK=90=hSoj5G$@|d5jfT@Pot}?MxT55Ci@l&4c;5(oDItamq-> zNF!e_H7_H+8E>3nrlP0nRkvp6pg)yMVfv}nOC_h+2~7q97AW;pwg$!#UC%jv*iU6s@!csG*hXAv}3vY6vhi|5#5;YaY~1sV!6^j zR3Ya`1#$sR>1n50UF>U}@hv@;Y%OR0)ZJR*KTm`Uy^t2l>;%kZ~cgm#w zirk;9!MP0;&U9@O%%0@9;c=u1bhVW*E%mn*wRB5+UGA6m28n65S7K5_r6PPJ6_VTT z;yi8}LdiTdC6dA8G36Q2k4h;p+J-h(~GE+F?8J0Nxdsh zvas&P4c!bq#bF21p5}>L>tsa<1+_wvR6FErPNYET)d zxFr`8xV?-t<$5L2QjdEn2v^;$^+K(T=1c;rLQT)pyou2fdQ=HRSX)E|FNfX^LY2=f zIK^VrGaz_+LP29|f_f_MrAebjU-;Q-d1}h3F8VdAMcxeXD!YUVcAMU`J94wlopPtPY=a^}x{F0Wb&3HG8pxT%o;}@9DatHcv>}3s&_%DX{ng*u?aU{G} zV32^HqM3fL0LpfumaQ&kDlm0wIMH+JiE3fG==hz`sE(m975xsaW*1$*r}+{LEkCFW zu7&Bf-pekauW=j0vN1zo5|TEv`n0E6c5goAR32KV109HFX6qz6rCPB*Gn1Whx{Mv* zjh6~)sO;sU|Kj|ftJ=$2ZgNU(zD>ld&1}?5(@kR-Qvv}X?rVh{paO;yz)SsP zzC7cD3@mBGU`w!+Go|T5ub1=)0C5G-BAk5GD>eWEI&OM87TN zX(dVrU#n0xu-861cm&qUeR^8To2oldqeg93saZvD78>PWQ-Ry*BOc{yEejYZVe;VM zUO~7_a&Mw&Fq@)R^hnDS7S}^mpxJiT6BLUIds%$c4 zwv})zK69(hLT#Lx^-w`r)IKc)KqfG6s$s(WU>CtgPC*KTF%~?r4a~5%sORf$A(uE+ zY-^@WMRQb9FDN|He3HkQ2NN%mg`N>rL$18>|3h@VFkdh=vM+C#)O-36`^NAA41a= zKv87%5Rj%QEC=U{c^k3`N^qnGm?AUlkk%IF^gz>jb@qRvtc0At=M>jL`LiQ*j_>RXpbvMfb`5SHhhfH#SNkFwEh zIE-(|Lla>}(c}s-pu}?v0Qq2HIhAH^8eEQ`WIm@rev7&S6w(3dlq&(?esoRL`vocn zoYT)xL)y6ovki!ja~Q}KcRchPlvd8l=KdPtX+CE8cnP3k`6)>e#R#p~kY+N!o}us*-od)(JmTm=VuV*si$m|P8wz{(@^ijYm)K%x;38HG{op~)_RFm#=dmNbVuWH2_6den!6 zNeh>A&Z4F%=~55S69FUxkH`>ZdEgQcTN^ALs?FBtr^qmGLQ^fZ4C(c@@Oi>)JV=`) zLuGc+uETK0d@nlC?={a@Unp|Mu7e*yvB?4W1Gt3u&&#UohB0QuV+)6!;1u+9pmcz* z9dN;EI5cWbf(&P)Fq)BPe}z{-yGF{ zIOVFVV0SNu*dsxJ3PI(S%3=lp`cFp!cdpl6P^765Xm z?#1)uV4uR(gAOUX=us!=!la}jQB$~Ec%Cq1a@t)yrJbot0w+;}h3%tuV6dfj?N5+L=<(MRlSr7r2&O5V# z8HmIRgg1Byo2YS;z=qNtOxqwx9o!T$pQLN3>Qsu08JLFgh9u)rQsCf~2>X)2BFuy- z%^AL`h{Z=-xNWL#4xUqxQ+>*$Y)E4aC7gaokAm-33UCjE=L^&riT^}1X_|unfpkfb zuwlp0z@VZ-c!QzV0q^0h38ivsDQ`OaESWl~`;;>7Eq~to73-B#FXhoNo`AUFXBqSr zLDtN?T*wdj!7z*6f}8XcKvcla@R+_C0k-E$JQBw?PLgZTqa~JCMmqu=3&0#)%}_t* z=n?#?%!B+^AvAitCQNm(BJ)}-7Y)BR6-ZcN_PI%~ev=_|ww2T?U2S_5o`LM-;~}flg%8BHXa@s&xy+CpoXC( z$Oq4zK%%i(*4oNw=0LL*WQ=qpSsGzEXY3=dQ@L$8IcK~sN2`?c&JwL~-gEbI4{(8x zKd^aV8*@;?*i-Z6aw$T7dMKf@5Ni_tg7%K4M#CNf!W9yRS>n0%zzscrAZ%1jbLu&D?>%h!!MnFEuBkpXY&41(vfM?jj!R{^CT30cW5D1zcOKr*1D2xTeQlS09gjO1fsJ_ z4#vuCfD!4c8!-fLoD?1bw-iAf%}?3W^%}&Itko97Q-u<6QCrvMviI9q%l8}e1(_g zxn`Ff@*Rau^AyZu6FLE%i{(DOE#m_QGm=wIo<^onit;f7>9UYrjKYnDMiw*E&@g-b z3{)q88-(oOoZF~FigKr4$2$va$~3^@KW2KCDIWSnA%jkVE)*?Kk*Snn96Ii05a&W( zqX-BZbD22(B58pDr(F_UK-Mr-5yjC73%Y8+dQ#VG@n)VLh#d6ZY4mfbk>d~vG(v#| zWH*zx>;(aN>Lq)jz-f;{&xW!MW!kMkEJmApHG0=%0B$%yd=OB8VVDaeY5+}=;J0dr zVX$~s4|RFGkz;Aq%(AW5%(8$A!uF-8m&E|oKk`+CNi+}w<<=yMKvu3x2C>Hy**f(F zBpIm~8i*LrR>IJ@kd}>nqFw@%t-64HvK&6(qs>0}yHG$V0YmW2fZ%{J`UWOYlr@Wt zK@b8^R^Zie!Z4l~gm6kGWGa(&iy2I4@Hk8vOu8V|$ir~d)NgK_raq7eOdWDu!g?lj z&aCudZy3h}o`&cTHN~Va~xJ=SNLtMk8i6aimV1yPZL1%&MAZTLC%^qrV`=!%n{2 zGiTcGVhUqMC5GZS4}{b(c!o1On>Zm;SK5prX=%jT{0`$l6<{Aubz`H5&NpjwZyN|N zNqP?Wa1nD2))f+f%S=hwFdHgE5Xu62XND^feCh)2q9bC)h&KT(<_v||5&DA@J-uSShe9D2mWny?`3X zAKW9KY=9cRMQkiA%wT}9W@KP|RoptHa%W>o7;_Oux*r=txP^~}(;b^0RGBYjHO22P zLQR6|f|hF#;89j8J+nM!)K0(%043+iXYM~u(3?ZEQI9$Dq>hoGMaY_ohqWtsS@@F4 zUG1j8z8kC`0hOd)uQ^Sbe{l%UVss!N^c=beRv2*S%QUV57fUrum~n<+U04<{jK4y3i~L>CxR&al|mM>23{0FgdorlSYd@rcCy3{NDDAl%$+Ps3W^ zU}x|%6=Q6{X#rP_*2v~+vyu#YpdF!RGvTEc=Eh+rI!1(`SW zF;cs8Fj7EVp$0VVT-;yN zRLf)wka6t*k>Wi`%64z2^;<`X6hT%&N5R)`n9^FC-~8^<+#*;QwOadN<% zvt$Ef~~XUIEv&E_wDI!(JALBq6Eks zItJ|Ck4x(DbB zTt=N9X&T>xlQ)tF_lHKBs~M!DX;K?_1HLBUQ6>#l1!*I^3Z=tk%pVGccVIej#-kzQ zC~g%yajwPQR48hWxv#lXy+UYNk&u!iIZ5Chz{8Uvg0aUb01V7Ir(u&JPQ|cMOf5iZ zG}~fr{Tcu8_Yg(K^Y&V&M+&nU+cq7HATT6Dz$A zSy4{XrRCXK*x*K$7#$i&Ed@<_UT2TK7;QqNUWEM2?6bg3o&`Io)u+7ZBsjrqPEp@N z!A+*$5aREen^7v$>?qFCz2Rs^#~Vs5W)UV}UY(O%Kgn-k^5&gdYfuwCt-2Ec599TS zlCAA)`3N3pW=P3^m$EwNR5jEjFdV)G@c+yd#Lt|$V-*Y!q?Ung>mop}1jjoT5P-Ro zFA)nLpgTnw<^m)KUQxvg2^Ur!{6nBSc#%uF#Q>#Bg()s!E*E1cF9X^xAe^+*UW{>Wisb_N~|HKvW}7WQp$$3@&SI5zZO#4}64d2oe5 zWy;Nl+zM1LGBCj?p;{z}!q__sS|h)E(-GwLfp_;h%{tc!yKIaSAMEtFrn^vp9R`(# zLx-V$hYVc41pMqvT8~5E{^su?v*kMQu$Vdz^6vnuxD|*^d0>kVn zIL80f)p!Sjhk3161Z!l#YZk~Cp@3D+6aRG*4OIa#n!t6cV8{T+$n8f3)d(d#Y%9cR z3j5^Kv;r(afFe zSS!Kaea&mYjE9Fkb^!|L#JDJ!?G?ZfPyyX2k4||SfXuW5Z(@DM3G*>S^Dt_X#uiDo zX81HBpx_Qf5C@JvKXm6vUjTE$2!XLr!^@1e5$oZ51Is1CygX&I0=y0${HD>F%_~e1 zNHh|IQw3;bF$gyd52+zo=%XlBD(-9WMr-KwvIL8g7C~ATjK@6s7AzLnP&M1t1`dJB zk5CW>FOy;g(~k@w?2Q85@q~T|lW8+BgPm@mDFcz3cd>Lfi!N5Tzz9n5limXD4SqY2 z2N^hmo+E`nORfTrHjewxK*7JoIiPa1FEJxrr7T!h~*Ua;CR$#ru`| zRzxM4_g}T5+^0D0LRWYFXMz3$h#M=!C>Acs#6`FObSsy@fS6Bi`7CtUkh@sQ&Xgfv zqR_tRfiXq1ASmc+2+;x!*@p`l#cb4zL1IWEh!@B#LTie7`g(?O5%UtPt-+%(U!i$X z%ERkIu~>+92}4!J(h2e&0bk-(6!ntKl^2X+o!>nI6d(PYAD0MEv@z>2-{PdfFsjT7 zQ6LYv*#WY~8b!`MZ0vkOhV=~G-ZdNK0f`^P$k*qvPz}H@#U&Cpj4&e(p`9&|BC9ce zl`llYU_&gErn^Hc)%+px=Ug*F)raz$|04@A3hdB;h+Twt(B{c_+?Y%!{WNj94bTD7>GV=ktAHXHx z?G<=U=z0ZJex@D21&iuz#cF{!dymV(6=G89h*z2e$(HggHKB-;Zb}hMz-fISK@e<;pd1%&gIZ9u00$pRGB0nIH0 zzej=2k9$go_e_W@LZjh|L#|jF*+>N@AUGAhO@!mijm&_P9)fyb)Cv3X0|=TWbQ1F$oPOSfsT$nfJRsxT zM>E-||GXJwT7xNo4ghea@QLQS(q>s}FvWq)XtNb$B5sB4ax&jTY}n4jZN`EG!fL9# z2;`8~p;Ho6nRiGV;`M)!ZRj;k)2d7=6?z0Nv(jP_TpO^1gQ1|G0H_qJ&9HO({{ydx B77+jd literal 0 HcmV?d00001 diff --git a/latest-tag/deps/font-awesome-6.4.2/webfonts/fa-solid-900.woff2 b/latest-tag/deps/font-awesome-6.4.2/webfonts/fa-solid-900.woff2 new file mode 100644 index 0000000000000000000000000000000000000000..88b0367aae421f51483c07ad7ae3ebc5251e48e5 GIT binary patch literal 150020 zcmV)uK$gFEPew8T0RR910!jn`3IG5A1-LK(0!g<61O)&900000000000000000000 z00001I07UDAO>Iqt2_XKkp#+={zS{GKm~_z2Oy;v+>(+3L~tGe0JeJPVMK~%4^I(2 zX}x#=R8>_~)s%#Q$86gHpsIo&{p4rA_|>+{{}J!AHwf(n;$Zv^OmYtyzsAdfpQ%uiLib8| z6S2~(xk~sjDGp1KR6A3j^#g^HzoP(8L}&f$s;)jjNoF$#*?aak$ipMvKc1I#pl&Au zO@9WMKt==ffU2ndL=);$0ue`geYr3spjgg;5JVWv03C2Ovvzl=kiVy@lX(k~2g1Sr z^G`U<{=E^5MjDVTX%xqC>{zjs-6R_;wxuT7G+rtk?zPt*P>y)7_n|%X(YBwrN9Y4$ ze<02=rOko!^JL}xzvoufy>%^Je>$ota&&Ry%T$WLvh^O_pq7FQ!;FO@MI&!vanMU9`T`lx9dJ5Lh6EbSL>g z^%o_-k?!9}AHzR9uR70tD6t&NBlh>$%G^qDilBxvTq;0-*cmDqFpViHWC-el0JUTo zYPLg7t3rk$OBAIDaKT|3DjES9BGW*ID9JV;z!>-kz}$TPpX#-}w&&QeuNL;T4Hz-( zQ==X`;<3ZrCo=YkGFECsds0SN%7{BSVMo@yT1VW`!o$N~zwW8~5+@Ps0OJSGIv5kU z0z6+f^_{FA09+HDWCCaf8~Y5mH@dYInx0zXGEG5LG)d7U(*wfu-?DGV&kug%@t-2z zb5?;K28WBs$7r{zUW=*jCNwGJs{XT_>K?-1A;h52yS*VPnmpvM2yPutVI@+dkq(EJ zXoPUU^Z!dt?z3pq09%%NUjjo|AbM{`;Lk{4#xf{XkyE;M``8{kZM0Q(YC)y5%2d*{ z-jts%=7JWcuwDQR4h#&Z>;J!BrS<6`3D&^kpGTIE`TyMAG5`)aOg+!?sO@v^74CcY z3GbP?@XX8w^Fc6o55dem1atSe_rB-m?>#ejn7Ic)caI>-0{~eO0FoUM2y#V$Z1!4Z z?jC|i1lIZ<5m>7O0N3gOL4nLHA*b#+0z`2EU{*Fjvg!jQ*ZKhYr=I6vkCFA2Y%NRc zuVri1TIoFvlGKvWVvFKe`?W01=HDrb$Su146nc%fskY}+7Q*6{@NZ%KE|>JVjP$Z zSM+ypzIC7mAv!EFt=i1&?e#CA1`N2!Quj}6zP0V|%H3+Q5Fjx6#AXp4R@e1zf1eBj zgpk;>ENy=p1YJL3+l#gB4^~cmW5NFc;CXBBKd$|(a%fI9V2W4CKMvRuYC{9(@N@X- zoJ)S6Fk0n*)6r^I*~I$<1m%`{cinSFpi6oLob9jc8{2peX4WyCtHQtG%#ACTN%jtd z9v7++dW{J>s&_yIB@n|;HBhju#NSAbqFAE)Y0M1^C~FK&R{mQ;oa?%Cc;l7Vxwz5F z8zrnYF6EhyPJPh?(@dt!$B7T<3@kR;w!80BW1nG#-}BWdw1#&CPTP|6=%vPoyg zMTAnm$G@cCPyb}q_SCw{cuH~1iyR+T+^=c)q4JfDqu3$(MVgZItowN4D6`z*eIcM3iK3LbU zbbf8LdsTA4!~6YMwg^tYL5=m{Jq$%{_`SCy_=NoYoQ~w13N(@GcNX*m^+kuEwkCMj z&c}ehV*mFDJMLG%1$kif>0M<{K54MkR#`7HVx3chFNNjj!wNkn2W!{I@e(z9-xzss zD29nnnR&<=Cif4A-($^L9nxY`x@XU%*8Ak5x+`_Xo&mvJ;&|jG4h@KVve|B)#4y)UJT8YnGBg5D?YRVaCbv3yiH5fY|lDWFb zcYnV)>7O2klM{o>p{>+f5G!6m9tHW9sa8r3`mvGyC_F13`}&KFfnma@WPNSrW#4pW zlfem}x}>%DRvBA-5bu(^MILK2;-A?Ul2852kN!h`k_X0r{d&$BKc|J($ny0I<@xoF3EapSD>%?)Wz7~vYIEsoi|+D)e_q*za&5N$k|GPnd9a8V$_QEaM@cs#V32( zbXkGnV+uzKdIoyCYZ|K;xgeopn!g&G;@~#s_nC0S!$$ zr<1er15jhV`RA-jC+psbf_1r927|d$I)DD{+KKe@bjWiqUW!pE^zLUVV;d{0Q`Yl} z6^By!;h7Uh49 z^*fpE`}@i}V+N2>U-*c8@uNA>u_ouQRx&nzFwm_` z?^@Hzo-5%WQ%7o{wXqDpb4l(9--LhE`T|q?GpOxh4%a|C*xfqVGbMKcGj{nG;yVEy zaNxOM>tnj>-W3PvnB)H6Yo_0N8%8gCJOHWVHozi6QB+2IbVGOaKu`2SZ}dT5^h19P zz(5SbU<|=f48w4Yz(|b3XpF&FjKg?r##ZdaZtTM`oW>*kFj-ALliyS}Rm>K%!|XJ> z%pP;ZoG@o?%r3AC?IOF_F0o7Pa(l|2w&!D6Vg+1+OXl*rLawkY;)=SJ?v{JyUb}bh zz5C?8xS#Hq`{Vv6o{cs{$D&)&?dVVRmtY(dn2ed3o%v}PqhrJ(EXEQn$x5uunyke- zY|jqt$WH9cF6_nL9M18az{#A-X`Id(oXJ^S%w=53)!e|1+{De?!mZrK?cBkg+{N8I z#Irod^Sr=|OyVoP<`4enKY_$cGD$9}C5@z&bdpgrOBTs0*(AHq>c2HrLs(x7cN)0e9`np^A^olG_hz?pTej2S$u6j+RyeY{3^fG@9{_caevz1 z@{fIzf2yUmn%2_h+C%&4Af2GIbiS_E9eO~|=y|=Y_w<22(s%krzw7^yI>bWpP(92G zYr^qxI$Q`>!_DwKym?aK$^IvYo_u>+?I|9rx*D#J8|;R=(QbiT;bhsgN3J zkQV7s4i!)ZRZ$J~&>C&f7VXdt-O&R*(GTM=0n;!YGcXggFdOr*5KFNP%drCMuo0WE z4~K9M5AhBk@EHysU+^;me#LM29se;pV=y-3Fdh>!5tA?}(=aX5F%vU0J9986b1@%_ zvJ}g)8mqG&o3JTcvK_l}1SfL_=WziSb19c`IahKm_wfJ^@-R>HChzeDU-A`S^BuqN zNB+j&`6vJ4e~PH+imi0Yt70mtnyRIGYM{32qv0B%Q5vnunyopSt9e?eMOvy=TBCK^ zq8&P><2tERI;}H0t8==bOS-IkdaNgUBd@ReUO!3tO@Hb?i)wK!jis|3R>(?NYinyA ztg}tDNjBXU*j77l7wxLuwEOnZ-r5KA_LKc>|Jwf{T1XHwg)AX!C=!Z=QlU&JA1a2* zp=zieYKMBEL1-NKa0HBn@h}Od!CY7bOJF6eh4rurw!?1N2S?yIoP&#S7aqeacn9v_ z3EmJ45s&~WkO3bsH`c~F*Z>=1Q*4GEup=JCvv>)w<3oIcFYqmTVE_hWIL2T+CSnSv zp#wkSSNwth$c3^|Au39x$)bu>m1PGaZRqx^|>u~;LhBg`|@BO#-n*W&*VA0fEV*x-pE^c2k+q{e2mZV1-``B z_%`3;XZ(uavMal@7yENChjKW_^9TOMKRJ_;ng8f4I;YN~^Xhy$zb>SU=n}e=F0X6p z`nsL&rTgdsdaxd+C+oR-rCzHy>FxTUKCaz#ppMaT+SWg{)9Ew}qoPsSsAV)T+87g! zNmaU&{qVI<9htf{_4vLs(i*0IinE zIzF|p;P`3hw5!{V?1Ao$SSgl93rPE)VfQ~R1_UV$Hm>n zfXNAmJWDJP8^mU{R~!(BKgk(!L0l4-#RK6gLPfMl73pp9MVmqio~+AFc_<$h zsv=cFTPPJS&61gWVe`xpok^W-lkd8Ndi_2iji4E{{I+9YJWPaXFas9A5?BsvVI6FQ zEwCH*!T~teW?sNsaQ$_HAOfNx8Peec{Kn!~2kT-(Y=q6QId;TOco@&)6}*Ly@fp6t z_vnK`7}~m%d;LB3DpZr|QFCg&uE8{d#?mC3LNnWzR?uo%PaA1F9itO;p-n#P<*_h{ z(ieOqu`_aVUM|4JmaW0HxgNLvDg${KkK%DWlV|gMUc_s718?SSyoV3BkAI#o@fE(w zcljy5q}dnd(e&COBi9 zQO2)7F%@c8s9K?-g;uf^YPnkPt%ufq>xOmBx@;KMc59=x&T3@UwrW@b~lqG(J5~&(fCECv{1kQ-xG2l}N=>_LL?5kALHz_!Gb5SNx10@g0b7@i{)lhj<_F z;%&T%*YPS|#`Aa(cjAVZ0$u`mu@?be2zUVicpl)no(*{B=Zxu|*5C9LPX;^*@I+7W zc#ox6-Dtq0JkldP{L^lz2YDdi0q*Y}?(WX+V#*M)B9wmG*Mw{lCsEdV!nv)ugA z-2iZX*8^M^a2>$4UCT9H-PHhBbrrysUC9+)$hn5=j8qg|0D*!+X0L{}} z&DI1!;{c5XG$!_c_8$posD=RQtKRCR9_psf>ZtZ=trlvkYO1O-fC}(`{>Oj#7XWyh zmwBAqxs98-for*jE4iGDIfoMfj^%LnV`p{(*pyA!hz(hv^;nm6SerFija6BhEJ3sRyKkz-@@dcmo5g+iL2v0Cmgc%}C z7h#$RQ<=hKCNY8WjAJZg7|kd~@*wwfANO((cX0=|ax<54F&ATu>;$&6CiT)JIKJ7ZDLb zB~(NOltWpRL@^XX76iZ_K2Z1DeRuENWB172clX>?cflQS+uRzrTEtCr!^J~HL_|bH zBQ!!IG(saZLL>D5PiJ*WB?d4xsi9lc`}^pP&pWwt{{=^UM=V|0oRGkK^}NZAydVm<7$Fva%R zFuTM0*cRJluh^<^HXO2>;gHP>+rqxEBTNhP!i+E@%nK82hdpOc*b|{bs1mA#RLBS! zCU(V*oSN~jQ0p>#;u1iQok6e?ND8d;`gTBhx?L$<>X*a2&2ZETOtviCxR zkP#|a!BEt`2$e!%`^mhCVKq!bPFrM)?5KTaANk|{s6Xm6?GyXPcd!+<%}iLdAHu$#8dR@p4uW4rukei8usW7-qE?Gt^08~CiCmtC7W3ceor$+njouCsyYkYmwD3G^69$AfHCAn?I7wBL5n?3L0eGd9cl$)aqK-+nJc%1>VhCe#y z#G-GY%Rrxy&p_va&H-s5tw8QcSm>B-T&<1(9VW9t2kGH1!pxOn?w5N=4#6B96ZUM$ zZ!l8Y@pR-5Fx&A@Og9bO6mVm}4T7U|CL1_F=Gw_$*A7AP7=e7`rvL>hL}7|hlwuU8 z1SKg&Y08iwNs5RJ(!`Xd9ObD%MJiF5DpX}FnQpz?;5NE#ZoAv%_PD+7kUQ$my9@53 zyT?7~9&!)6N8F?CG55H8(mmy#cF(!z-3#s&_o{o%z3$$2AGy!mH||^ai~CiOEK*2{ zNC}CgvQ&|3Qd{asU1=zdrKPlz*3wSeO9$yFU8S4!kY3VPrpj!YD+^??tdNzmN>Z^T11O#TFYuVEw2@{lGf7(+E5#56K$q#w3~L<9@aYTgLH@v z)$ux2XX;#?uM2dcF4D!iQrGJqJ*RK!2l}CYq#x@i`l)`VU+ee2fFI_E`=x%D-{Uj= zNq@>;^|$+n{KNh+|D=D$zu@2WANjBRZ~kA)X&Dw-P- zzywtWn5YuKB$Wgvs}wLrMZi>*0ZdbAV7iKd8LBKWQ?5%VFfNi2diL@bI_ zh{ch5VtwTOhz*brBsN4oh}Z~uA7W$V{fSMG46&G!^kBnx~1ckl!bM$1pPyf1!Di__xSk4=p!rk;Zc)+>_em zc8$@vVL;lSb_ArEXh%fz9_>iT!_bb5JRG0|*FI~E3~(vGchQw<3C7Ze@_?rCkHYrd?ATw-(FqyH~X9ppDxS z4348c3x=gV8-ovM&q4l->E=YBjtp%t5yZ-C~%NZauoaF%R8QbVp+?x^w8x#X5B73)Z8%4%Vl;5jLQ^ z6E>u~2R5R605+z31;flk_bzNg_eq0I>4xa)e61+6(O?V8e3S*}X+>ES*p{+7Y)9Dw zwx?_dJ5Y9l9VrJi*okr^<*4~rQBI-3u9WjB7tFbeaxJh21Z8yrOW4-Tdup@l=}r=Xt_htkg^IGlbVID-B-IFkNC zIEwxrIGX+eIEMZyIF|nT7LKETiT-6APyd)MarNbw|ND)IG*@E_H9}J~)rMKlK1yKs{A(5%o;Cn0nQiE}>pa zy$P36Z=v3btEqRW!L`)8s4vcQ=c&F-{TPo?JL+e6o%#j!YrIGO7WjzzoxbcpIp6bX zMjrAN!^jLX;zx#=8Rn*p$1oqm+LXx|)@9g)vM9r540}_SW!RVD0LsP;2Qi#X*_`22 zhO;SqGMvkB8RYvG(R}j+@OHr;QmQjng60t0?0_AaHMWG%^tkkj|MyyP%MR}T7 zhuC~BTf`QiyhUt@@;0$mLwTRrhS=$NpopD;k%(PTJ|}h^(}=`w#9ovyh<%8oDBlvt zs6`3lc;X~V5+@7w9O4wzbBR+?ekM-SW#dj~9?LJpnZ#L?Ux{;wb1A7UZbp=+rgHG1O4kBgZ7iI!4`0jzf-1U6&jW zv|Y&wQ2s_v*rNQMoP?Z$x;{CjP_7}TYEd^Kry-}KZc0wC7UgGh26ATV7UV2~3CLMd zw;^X+{@4QZn6r~}QMV=MAs3|XNG?n+M%|rUl3Z#Iy_sBwT#33bx$-h*J(ygDT#I@L zxemDj^=NWKwbaOs$W5sykeicRwexw*ZO9#|XOIVyhfvQWk0Xz#UPPWqo^m`^a z2IM&nZGG}w^8D*li@XrjtI3N{mnSbqy@tGW%z7<(1$ia)I`Ve%F6s^BJwm;Oytidr zoV<^`pL#3#2>JLi%ozC$`6~5p@=fwB>Z9Z*pgvB1s@?80P@f<_*L7%qz97G%K1qH< z{(<@o`4{qU)R)O$Kz)t;Rh#DD$ls~2lmB8XF(LVH@_*Df>F9;}E`0?0$kg}gqtQpF zeoUX3J{9#-`n2>pj~nNy&re^7`Vaad^hK$iz6^a;>QD65Xi@)5Uz5HT^?&qr)X*Az zUHX~@8LggL$mGrArgkRZb7U|Jxmywn|gSQfqAG$Q;#9mr=BXrhSaMXVk7Fq)JKlTiuxFcO{q_L zC}K0}a~`od^(E>n#1_=oBw}moMQ^GRqyFR(+f)Cd{!Q#a{a2!#N&R2M zPUH{~JCmb|*oB;2#IEF&B6cIE>JU?rvy*e4olA3WI>cV&eB}J+;L=cO`cxP9gV{N1RUXL+(eMK^~+I0}GJ{lZRZlI6xjo9!{J~ z9!VZUoKK#>2FAtYN#x1ICFH5(nZ#w}+2lFI)#SwzaV>e7ZeMg0BCaFv@Qmxpd&&EW z8_0*qhtCxE$w$e@iCf7h5pf6kv`5@YzC^xE+(o{j4UBupx5*EPd&!Tb|vi^%KEhHX}3`}rrklild?7KZrZ(+ZE5$@ z9-{0(dxZ8BWf$5rv==CQ(_W&zOgVt|D(!X3L9};BltXFni*gw4LyvMe?PJ;}lp|=L zNtB~#Uy5=x?Hf^!q5UArv9zB(%5k)RX#Y}1IBIS-sC>S?c{#s{=}W+K^idbCJ!MGC+;DSB99>+AkWl* z@i2Kdc^>fyc`2zB6FFMSehjNW4vcN`6MXOMWR4?~`8- z7$1<|lRpq2l0TEb5+9R)NyKO5KMnCY`Tr?AAMph>95p=g6*VFq;u~soYK-$DX^n-5 z@2Lqz{6J0W5kFE>Q_~PXQPXL__=TF0nu++8nvYt5_?=oUccbXw+%c>9olA zpiM_TLp^&gp-(+ey-1shdKuAXrC#mOW~1Js-aWV4r#`2?q|Hrzk7)BzKY6tIsb8tz zXbVt(NVJ8h@@NZFe^Gza7NHN6M_Y{E=tF3W(}$H9a29&1oF$zZz_bLi!5_csE=MsM(3g;8Q0)kD`D11hAE(%`|cMS?(6E_%z z?}$x8;YVT)g-CQ63j2tzMbU`8j$%1tFQZtV*xM*pA@&)HwTZojVpC$>Q5;V68H$sM zI}ODt#GQ`fbmBijaW=6^D6S$p5yjQSKZoLh#6CdrFk%IY#}j`Uil-327{#+l(FDcw ziH=9{0>fR1;V#0$+~pA03&ksmdjiF4i5^Drdg4c+coXs0qj(!Brl5ES!J~Mm-Y-9i z4?^4|6dxk4I*N}F*8s&wiT?=2$IR^>hv+91pCI}f#ixin4#lU5TZiH^hIr=s{0 z(LNMkCcY+$uMk%s#aD^{55>2Lu1E15;@?K`BVym9_%ZR5QT&AHUlczj#Y7Z8BkpPx zKPN?d6u%(OqxcQ+$D#NeanGXoJMpVf%tU9SxR2;}6#pKj*mzZPJ0(D0qa=u*kCG8T z6s2-RSE5vb=qi*d5*JabMBFndRVF$PrRqd~pj3nCQj}^E{f|;D;s&Bro46NHszY=G zN_B~=g;G7@E=H*}(WfZ2C9Voe?TCGdQYT`kpwx@F87TEOO<-GfKy4eNEB{5I+{B6NxQD=_KM_LFsH#j7I4k;>)3QmA3OFT@CSzP`Zxj zMwG56?s=4M&^DQ*+abjUl&U>clV<8J4yGUZ8%ByL)`HwJxJVLC_Sp}IZ2PB zBC}ImyKAiYpP(FsZKTtk_=xCHrChi@SPb0Pt<*hVBG|G_z8k301rP;u!ew#UnCftfQNrb zF#3Rpe?uTY0zCXXg4qB({0D;hS-``8BH(ub55Ghpj|V*b3ISgOc=#QH`53^%?-I;M z10Md6V66Zi{(@k>9`LRM?JJYJzJ$5mm$5MS70kG=V#a+9g7G52yS{G5eFHP@o0xIm z!i@Vi7SDYLbtgvx?VFM#i5aIM7{3BMnkN_*;8Bxcd<^g?C764FM<)p82=M3(!R!DY zoh4XvfJf&DRvqx@Ji*ujJh~6TcqZV{jRb28@aQIj6#yPRm|&d+Jo-(7@i%}+R|xP* zz@yg@%>M^GdI!P$I^faA2<8~@=#vEVR=}gbC6HGE9vu+O7XluA$@0Q|9fCOpJo*-a zJO=RS+XUmSfJfgY7*_y~zDFR>13dZxfjklL=!XQ;13daM0lxuw^b-Qv20S_<82=7< z^b3OdJ;0-165w8dM>B%)OTeGsMlfy%{Q0v8WDD@;cM{CM1N`~x2&Mt}^G^_rZvp=N z5B2)cFCka}$5R~R6bzu?fr2i;9_B&P&uNtev`UkzTUJF`E$KujflY0?l;v3-j2`^p z{G$(k(D*?Q>VNb>51xG;g^aKrNr5P2G&_i-!0pSIXFuJ(e0h2q`Q_>5%YGDPct3I_ zIJtYzNjkp!_yDfM353uF2nTtQr5$R=L6qqJW>pRYqpZq2%WY~WIvFu0C1W<(PPpsx zB*k%9dJNwk5N7)rMp1|_IwAh&9v|QoCvX!y7$DS1p%wP4vMLjuXodaFM8^@eSCKlu zrcLcU%d)Jj3;bq?EAj?Wgc#1uo!Yu&dW>V-TZuWbHE)tz+ zYEydyGn(HEbPG2No#@baM{OEM)J{8jwu`;ASENOj{|&QoEYVegJuch8q8H<2RAn_B zRAql686yVv1a*kvxpJeoxPXR9yt%?aoyV_0O~11^V#{Hz z7B0W6IEKY;FT3dY0LM6i-SV}vsZ(2FNw#d@lhy88RqmmEwklg#RNhm`*dQ=%&ua*!)N zw~d#Af@DFT;70w=Yz`3ic<){(I*Dy+(=5;OVP(_8rm;?RQjL3Od6olNUq63-eVy1Z zJ~`27AT}B&st8dkP9H@=Rd^oZQ1811e;%`v(*B1ApHm z26Y7Ir$&33?(f@uMmP=`b4QNg+#?2U&Hg8o?5hR$FSp$YCvXxV>?my3wc%h7v!G!2 zhDo~A`*kR*EYI>k=*%tF%1*7-(^s#YAb8@pKDrJ4k0EAMelbBzk`C_AwW@lT#gj-* ztXzG1^$xya82VqB>vWO?x23}tKscz1atHfj{W_4^H0|Ws26iIZM7K~U{j%DgLfRFt zGcYE97&EF~thciqG0(Q|+n#SIVwooAoKqs2_iaTHVidKW5P9~ElCd%4lVBLDTQSeJ zx3e4rziD#Lsb!kntS8Z=dxo*Pl`+}IvTuS7fUwMq)_wa7yNX)p;AJ;4tv+v=CKsIB zW6Ip%pk$(U4o2;A^7z1$@fN|kZJ8#Y{m@tHVw8hitl0()KLGp|#$==o)t_7T_X<*xlDooH&z1V*Z` z-klhs<~q)S!#U@MVgJcrRICrAe1A6PhGA2|IbLK;f{_<*)`HXUKmg40BFj^Ico?;* z-Op$tJ?9|L^0F#Qmw7gpRlisC3d_7VA?at{uUo>yV?uoqOP!)sx-tZ)hyv$++-b zL}!Fhu-8ni^|iC*e;m`SPQK4YWQ+^%o_+9p0Kn(t^Pvuanie({I9mRn%)41$R^2ku z2|m{{O+NcM+T1iP$~m7+1H=GFoO6m#^Mqve`&KShl4yzm*NgB{G(pmOmV-Bt~#6eHREKsY5EiF*S7KQp`8>u5hF`u z)h(+m@Ah2_EYsvjXGgoW9Y>dBHa#8?@Q)9$hZE?-2*ysWgI0o-xI5HgWwhMIe&JxZe8o0Tu^>(wq0$$IOi`_*j>#|xG>+tY+E3lMr~nX$*i`P zQMWOh9uG853E&j&*&=nUuj@o7gP+b`x8*hu4rT^6JFp<-Av}#VN%((!mS=f_`Lb|B zSrSr~H%z7Lbz9q=4q~UXy>;FB?v2KY^@ZiWmtC{kc7)XJ<4IZGFanA5#-uE7xS=fL z@Y3G&+_eEovb3~*?RdAuVN703H&1XwS&}Juv^d>3#tAf_g+#_}YM4x|+{6CK7p*Ec z&d5}StsFSU3D#h#od%0Vt&1TT)mEC2Wj>B0z#wfkVIDu6F%+jJmVSc3q zNt&y|Pr=m<$LovJr;Ck^;`Hgl6@t0$`I~P(zow*AYv*si`Mm2gAzUmrHj2@x*x29& zKl{v?^{uU~t+lgfT`pY4bmQg_V$RnrIRu~Qf_@rgcL(ckySLeIE+Q^A`KMY)$KVjwxbXh^_wY&Fo=04-*R;eznQFF^XTvquN0KouTCXbD zk=f*nmd8SQ##6qTmaPIo97M7g7zWf%I~#bdOIamazWwb|YiHf%C2kP7FHuu3jc>AQ zci&yJoWxibp#BRf|6=y-k%tpUhh4`wfkl9@OfXQKsBD=X?T8*9f_xI6og6}D=pzBgWnKGre-UCN||5mgDX1*GdjnyImc} zy4zhn`{oDOTpSE{c7}t+CVtNB$GV%HJel1W{P;SP-G0BdxY+9Vci(8Ovt(2EoQDiI zDa;SkN$V~lYEc}}!vGWQi=;Z*x7TPtoQI(#^sDN@)a2tS)=}AaZ<@ciEIAbwScz4pneX|(Gd z-Rud$97OM?lhxu@e!g?3rLNk|yIXGamF`tCyw7FmWrmy|mWXa1IuvP=NFMQchx9gx zbN`k49Fmn2yO%Cqy3|QDqE0$Fqdbj=<(InU-*9WNoSxb}u|f_esFO}7(J}eTJ)FR! z@|6PZ$0of(_lu`v`4&odB1<#rM~UH;b%}eD2P#$8J5X=`3a?|J--^8?9XvaSf*uh&S&EgF~l+F+_J49($pQpj>3^LX_k9v)6RJ9W8rb@X*W4Z z;@tE!h5hSVmU)Hggx>Fdj@ImoX;}i_Xj+z-{TD84L_PZ-%!Fl`7Wc$#9FrK6*|%a6 z<5+my0?bjs3Qm;QzwTzYkSv5I-LQcb-sTi+))4IW+nErqhv@vl9|Q!wuEf3m2Y-N1 zjtV{B@)i%#VM2KCeV_Y}z4fi``@6gEd+&UVsl>%B^4Yjr=5y?@?Fi0YgE+6X6yMi6 z#Bezm&gIMaGS?uEBRK!}SEi9~@0I_~xo{j}xS#tRjFqbcEmIjSR29f9jw0sp*>`@~ zZI~Fn&_SO%9j+9&(CR`b`+-58p1XEEyUKjc}@V$cY4gy&d$&RuPSo90=>dyXgf-R%@1h^OoM^>vc4HPc*U3D+~O{g@PPgeUAx zgyP&~16NTY+)nz5&qlN!+^H&ea9AaAf3sBqBx6gjk@fZS zgXaXa`{>UpUA1YU;=>0(mD;QMc^Dbt6xLu1pv!hf(qy+a8m48?v2K}c?#+OXG&M2N zw7WB*`d`{oGD5~goAWkiUu@KBF!~-Cx{Z=&zs$_P4RIq=k7m9`|L(9{7fVTwqr)sKU{-qr10SCW_N2s z^nC^cLbN8>Y7GWw9Lve4L0`hEI6Z|A&QyGQ5-dQQ5OBhCK`sY1AK3yeuDPxlQASqun$hASw-h*QfiNdsyaKK1Ly< zND3q(04T!(L3+Jb8xfm0XXx&U6@pDfY`1#Nn-@_jTqIlg{>5#GE4O1t<;WGn9ZAQD z@#2pC%-iSvcmXE;N-f0x{M#>KyVZLeBI)*bPplBKa$>jNCHVGUtBtdL6f#=keTAs{ zd?}IwN65GKnH^vl`Xk0hei-6^1?dlT`*=#)GW5_kOId2NnLhql-uJnl@T!!>B3;&Z z_x$tw|9JpGbb3bj>TLiAFAxUW`xI=!S-3Ym5FQJ^SwSfWy0ErgCpy_tguJ|^`sU?^ zWtDX3u&i_vXK8X0=9M2G%JN}Zz1^e}d@e%`>8T)dGlQz^_m;j}Ko?u9tF1-)m(G6d zOEy9wquHpfFi@!W9S4;{$GM0~p$aZ3M5PebrNbQ*&i-M2PV2e#4_WEmSA`Q4GCGDx z3OqUAKy1vPjJw_sZSF?Zix_{k!ZAKWA*yF!_#%av0X%Q zR5Wo5JN?b1$fKCriQYqbG@Xm3gKx%d0Xs@?x{E`;{V4yq>aY zmWP8pw5sgmNIPLA!xbO^SEH~ym`|p zvmYT|oQdu^VmNzemvT}+tLy!n%d9EmUvAy7{N)B^mS6?_K!fj!{Zv3)zC#u-y6pw$;mv`f!W{wdr4X0 zJIzQtrc$-QcH@XzOARW+z;zmttohtmh9Q|7yLQOYB8bGM3puLDbhHTCrWwbM*=@Q( zAf+b_BqDZcs2D%gj<{Np~Gn-O4jug$CICaSK{4w|&=r}_9 zP9FCVj5`$dY~MZu6f$bvmmKq>iO+e8Qih?F=umE14;hUlT_EWkY(otMINhUyI0)o|axj%8I;eikWAv%IW!_+EY0=&0wOi0Hl9WOEO@ z{i0L!ii4{(A^Iw!dzzeH9xn=mxUTQZ-@HvBD*vINStI{G>*+e2f2AjHUR!?t>`p{{ zS10KDGP5hL?@M72_kpNXRiw1A%<&sb9cm3kSo&Ft|$@MfOYCanvHZzqv=s;b6lsi6M5{*&x|)ut(Tl5BQ22J%1sV zV4S_Rq0L%NLYf<+0B%5$zwlw_p20ot`{H!NTcm_iLhF>)-`>hU_8g}tiQ^FYW_>(( zWrrza@*fnU`jFJshkR(q&>eU?|JaFI%x$&Y3~}$t-1i0dwh1Dlzr$JlgJf*>>8qeC zEByO2KFyxv^kxTZMggcj{e}mSLJuAePlVrqPXVCIRh&3sHeFE}0Q#`OuvZKRRk??y zyAdn2NnxkSGlg4-PN=RezIRpT8BM57cd+Q24PL=(17HhB9=@YZ+D8AEipC*t;G{@&bky!gW>_2PeO-pfSu zy19j=#)IZ>sD;hB^m+K3v)`$C9EFUY@=z6qk{N*tLx}`G%l%NzdfX4ylLGPVT6~(i z!u8*ZLPq0A3M3mUzCJ!n{kcca9)VpVL^zJ5z#Ao^VxE$S3K8lAa=%|~Ic&l8a05IN zp49)fhUt&3up9Tb(#=y=3-okt7wKG|?XH-}@-(ry(}<_r@5p40FE0qAKKrApu9I?Y zORR)HL~$liU2IQAspPh4{COg;HNNyZF-1RL(>61V$&bB!V)Lgk_xL|3{D(7C6o79@ z&rtIKu7Uf)1LfKDOn44Jl^q}{38fYWCfkab+Ell2Zf;3qo2CU!2WK-u4GA}eUslVC zw*IjVBx35MO|xA39*`T}lXKTsl{ENCSMV@;Y7I&FWE3*GNc74?GKT&sM3td0nIYb* zU)G48YrS{nfmd$e;OhS7;v(Yu`i00JaussXmPRciG_m1!98DOL*Vm)nd#eLIIjB6% zy|>=`u6yV7t*ruQ+joppScU80e(+#;G(cC%-$^apEH(?Bq?+0}W>wk3dr~Gy$)~{za!g<^=YDmJXS9wR^W%7hR z=~3sTMBn0PbqudVR0{*Ce2)L1LZ7d!3HCMGkJ~QpMxz!Hv(qA|@T1{A zq;MAQ4^R!Nggq|0j)hI0>=T5p!YqV^uOEYRcK=1=%oh(hBQNJ73rC@M|DpG*u3rSlrhRBC%f9R83&+}%-E@QGy z)lz3r`+|1O^fPS=bn*w|W-G@_T2d)JDbK~3-{lfq_dKnnH|7tpr z!v%n_>=&CY)NyK4CkD<6H z75q1R6~5=Y{4H79pQ!=Fek{>R6f|z{Y+#n_Do>2%BZh-v+3yu4O{2M!ck;n- z5UZn+#b7l?3WE^6@jlXjGAf8!zhaVz{Yw#^StZY zkq)$$5)qL|5^l$j9<_XuJgRCUPn9d<0uMwoHnBnWe{+sPFj-k(_Ezj z9krh^;P1T-PS(wS4(HF2jQxo~!seP$h|OTJm1k>fYlP6OUPq(m)Qv!E8HJJ{oWa;W zmOlrm!bpC(3rpVveoQjw2fjV-QI?`R*QYuE;b{Gz22HA5xXW z!3NT}KTP*h+M}!I!!*tfJ9D;&Wug<(@c}YJC+)S>#56~~-$NmzY|k@AoD&n@k+Jq{ zzG=>#Tx%!VFbU_;=5JxoKb!%WWxM-8((1 z&-5y4+y2V)DR5VMMi4>_u2*02ZJWMo>$m81&-Q&g?}VXAh?-kgu1LgeDN%z{MEaqr zic*At8-+-;&zt!bx2wg-xgV*FQN66*aNScb>PTR=B(Mm;GwQ0FX#aEIsgN1*W8M2!G{dTBD#T}d$%QkacTRfuP{Kc zetCm{j`2%him{S**esyt)hSFcroVaxoeh&T@=e^CnI5#eFayo7u|FsMN|+ z%w81JA;%j(TE^o;RBWbqjW-L8L)gy^sYTny4tDJ7SE{yy9cYr~AprTriAR z<8}{dt8->p6*{wYq3Zud>4Z}9bxTpfwtLIBluEZO_iP(f#rnFg(H;z@5xW%B^be%3 z3&Jin3^zG(;>gjirge`VIdLL!4TJ8!M4_$*Q^;NSb!UL>;ihxwHiUq!!>kycd_d;1 ziWbS{ula#z*;~<20YdRU8=SOZxD7yv^aw)n?l~A{q3>ht_bH_7`3H=#$}UjMc3#0W zAfL{1eleKFwqGi^$5Ycn!8Guk0b0P7q5?&EjxfwZVLF?JY-Pd8P-yP>`xa3b-GUya zt`o!4NS_a~{CW}lrr@|SN5EAQa*z^$D9aK_l93$M?OnKBM!9l(3OYX$TD@Af?2d{a zk6s8M9kvX)9LiS_B}GrUDJO&z7~y=m$*dC`oMjKj_@rA_3x)cdr!ikHL#ds2TR2Kz z0|(*tnpdZA38tuzj-%_)t67TL^OVRVqKD?RQPY5SoP?l)NhI*-=@^#E!?)+OV?yI5 zcuHp7lxqN?8aZso*|+0c(2fD*OPbdV1{iN{UbwKii6JbgpsLqh$Bg$F?fC`=;D-PD zE2x300;npyiU~k%O9Z{;8ytAd@S=^eo!i)pMbLn1-`3=7Yr7XVH&0c`2L&}b=0{3X zsB``RxZ#gZfrX~kkqV%`_^N0OERou{iGdpdcFjeVW822qHp5_1lCTEIS85)^6g5yc zFWo@m7N?OHr;!&oX0U~EgBTFgBzC&Tr6`TyM?8rAu?Mc*XqJ!jE}S$Km0ZJ1vsab4 z9O0WEFo>#{P@YC=yaVEii7$Sc-ETj8|0-^(im9?!xB#wJ`Ra8(Q%yykP9}7XO-GWs z>;Jdyq7SdT}CHocRIEyuAnQsL?bark1F95Y9_U)Qku1xH~5|R@Ad5_9=F~M z0Mh+GLs zZD+%*L1+vu;TiL)Q4O=~@#Sp1DO%({t%`@7&pRxxKiun#8!jG<(axdciem8rCYNX#it( z>Bu+7cpYrJJkAYl?bJi)G`gkNh(zqsj-w{^Jwjs<7z=)&0+E3`(e6RiIMx_uajZh9 zTJure_Zz?NV5rrW7YZJRa?#a!^?qgE^|#-yZV(?|uGIiLe_m&&jgPIh+klI3#-cCe z|97cCe6fV_pBEp(&T_2=imEO*-C{IfE(4wm{jeGMfSdKNLany!;7WIW0`2x%5d(kU zl-gp9%?l%#qL-sLptsjtAZLJ}FLEIS42Il0xFBA@JsS=DAP30Jrwo=M#6>>AtuKOj=vrXm*ak$=HheHSyHbVGWHn!x6RQR^u*F6$l#=P z$Lcf=hIsI2%cXJ9x^q@I=kwrW*fh#&FPj7y{f(mY=APDxcg(ID{*5yCJps7k4(gF7 zgA~A}-0|NISUCG@1a$6FlKsPX^d04m4-3Zs`O?JF+jr5e=uz}5=*6l(%B!w{=odUB zOv7N3D_@@EGm~pArtM4|rKCHg>Vztl;-F-47svM1pESu21~&cHe&8LqdFo>v_#QuG zU&U5fMfWlG^$JsLg@jS@{NT~Hw)`Vy{^V><7av916p@X|)IKEy4ec z`-47BBSV}uFK!L{gCY+zRqPrNOpR7Dj=Fg2#nZ`&JdUDAefXA079vG+IDir=Gu;CeDn0Dv;F6Et@*?eGyM~Zd6i`~|9L(RHIMgsN>kn22{XsviyH>?6k)PqMaCMvLZx;dcKn-R*gVaT$~3{z&5 znkJ=8HVr0U0V9-sjC~me`BHr9(@jl(&bMVbzYfs3`<%OpI3BUdH5Y}ftob* zec(o?j_1suaN}nfd*q#Grz;c29{IqvY=dSzNTJSD(=!y!G!5lW{ zop3P$el}HvmT{H>87$fDl%XInS~5b_DZ}@b;zQaGLW(UvR&-q%_uM-@g>j*-X#Axn z!`YWoLshyd_!48|{zPZ~WL@+pXGB;T&z|2{dc%Uq#j98^se@e3sT1fly0%h7)+mvc zrHhCyxXL$BZ%>ZfU5MXbHi)u-CP9?VnCHnmx1Ti9le4c^@G3n-e&NzeiGGbc-F^z^ zVR-xF`o3QO!TaUzGjh^pwH8Evb)(i_{^fJ$@-W0ShC&Wgw1P5p1f9%`qO>R6jI~6! z+ERX4D*`udHua)33DV$7WIz)~*)B(Dw9gqkW2&mA*(a~0tzQp;8wHo%KbJ8c_WSwx zoXq?EyuP6dLlJw0s%k#9;6H9_S_b)W2oIB}>q`{;x;a&;^t>v|c==tXqLObiQ_z?Uy?xHu^zTYo zmQ_i5t($+#vwz^Stck+c8RL3kbO~+Lts#pL!$hUJN_jcyfmcwAs$Qak(fp*5Hky=p zDE)e|EVJQ|$ujhqABS*ufDfYJ0S$iSY!7`=5k=DN5>ZrMEJ;)4j1^`eAWXLIhme5s zs|PrT396uVZxv=LrE&n4yT|e(Sd(z1(&{$EEP$rwY?%0gC$U6ot^p2!GGqF7N*(Vp50G1Zjz>DwAsTe5Jbk!{uT~*f@1+32X@`Z86)ryEF1JE#H4^#Ro7Tqj_&`7+H5Q@H<~KSpNkJLs(EvG4;@9f zpoi-^Z1;o4N^+@+e|iDKR!*JB>d1RdpNW;g_ckR8nGaPE>$AOPP}hOye|lxp8GFmu zqH0HZcFEXB+PuG8he{BT$2nv96fN|I!QY%7amGgYEyK8k(O5EC$<3c6+dy(${FFd0 znh@SkXig)IqR}<7bcA7Ic1{$#OcI05IJ3*H*qFHEnV6OlM_xVAOGO4G+t7@HbTy_) z+~h?$Zzm_)5J`cF{Zp9U0#DxoPYXiMFFyW*I~s2N@5{Cg1fD(#(-|7w`%U0!czT=% z*2OVA4Nr4)CgADGff5J~>b%TMVS?V6JqBa3iMo_TBoc>g3JM-$h9b>!XeIDH-^+1Nzc+cYI}#vlk~m%SQgknR6d?)Ofqh&F{9rB*({|LESRsKM*)Mxe#LsJ? zaELA-t}g;PGA@AxO1NWDDx~6ugeEn#YrC?%t8pFb1J-<9PlG%{*L|344NVr_!#DfM zF4x~NsKj=ts?r@MQKJL4UH&w4og(GvVnxSgpB}d6paZbkGK|E0u$DGM$2N3$3L&q>deYAzHM+h(?13RdB?MVFIN-$GzF(b|xt;X$0V3&1i zmMewlC!FDOH7cFc+N#MOXRIHfXqtR6f7DF|5yIKD6dd)YJg|!MC?`tbk7}8rcpY(G ziTRqF^gco;Pn&d_((|7hC(#|~;Y@f#%ErhS; zRAHbfG*^_@dSW)4LSI54IT5YUY|D+WZuAR^a)u1bcZ>rxh#%8G6d|5Y-}FDyoX{=vEwqE#O@LlA*va$Bl;TIBug6_uby6 zqNr-CrK*ZDH}?|4^DW=o;=Eb!^^VNXVdyL7bq7xH`;OCWw%g67<9v2`bQ!d4lUvGP zKX+`So8=zezLE{M&1|+4Zys#c3W1hAdc2PBE-^1Ov!~X$X_%O1nQO1~n4@VV4Hn#J z&?Qn!89VZ5Lj~EnVCiQtjJnJa=})-6gdWC2Xl}s+ zChEGwrzk;N=(z1Lg{6SjO8Yio!+t0*+s`;i6G91{S_|Tni%ZIL*erN0=PC<_7~`R- zpeetD6^c#3vis+`>dES9&e$ESz!XK-lzBDcjJ?HDFy|+CG>Zl1gCZMHBS9PJ1Yh_J zpl+*$y;Uc{#RLaOSlWA=lV^*Wg`eg1`eUTZKM&fm|uWu>ah*qxkP)}5-# z*sD3WtXB^|5*AwXE!X9E)+Sk{A=4j5;Zja2)I|F_qw3v)5$CU?qNob=Ux|-(TXR8` z()fPM;yh(cRqwPc&hKPQRbOpcoWFYbEzjQXLv3N9e=iuF`J3U7nci3FgMqoibx}rfTK@5FA-hh`A@(_rfaGKz~29ql*IB9 z8|1>L-k(mPs_85@NA{WaF1VYgZQC$e30xA|FL4O%hM93=J8HTosK%A1w=CWzdU$Tn z+vkkQvMm4qODt3>ia=#uR~12xh8t7NS;jEe8rl>2X|C17P^+(`ovAheGWuL@;6`)H zx~d2QrF^N%#5Q&}z}yVzBvUzUTaUo93{3 z8%$P?wobQOw#>BYn#CD|Qkf~Kwoon^G07N?0!3RT%DiQ{(Nt%Dw;^+&qBa}LdqcEt z9BZ`;LU;^g)Ili&)gJdD1Wf}-SFlBm*TD}|UVyVN(Z$Ukv?C#I$L%d}$V|29FiV-^ zVnSQ~3wStm&;!_w81rib@`YxrSY_r;!88C0#ren5q3Gq0NVRV3=S`47mH%8wW)b46|_aLKrIL2%uS3!*Jo| zf@y$R3}$~%spAxHrqDl;QktIV1HHNEIP}c{KDyuLSRsViw~cSY7+%!LEL%a`j+=k8 zB(H$5qvSjj0>;wW>DujAmBF$o5lz=rsmW;AuN9beB-S`%PDy?3WD9-S<&4b{qGt@} zj6tQ^b>Ks~rsmz6x|_%in84qS9@m$_3+3()rrh(835@ZN-H$nUZ!=5vnl#kSh@r}2fSbEN<2%W*-T)c8Aa`-04k3Tnljm=CJO_DVwg|f)g%o{ zZV$RS|ALRPe~6hw%STP;o-Dy|?=bX@b4-eB$^ygT#0ar4o&d zA)FkbTPndQgnBN^vK)@;5VqN5Q`dvcI8Z-Xl?i*$H=EFA6w_wxN1RgPtDV?}bQ&3W znqTLX$e)b@+*1d#@mZNr{_AjW(BV!;U&E|*SrYO3Zg}Ba5r&{I$WnKA9gC8@Zn4)K z^)V55dhHB4FIj6q#R$PJ+k=+aO!S!+fX0{z!P5v}rwN-9#2DY@$=p2%;#E!C^Uu?`6j$v#u>6Q4-u&wvY-$SG(Y>@1=xbzLo+d(!j9`hKj%3KfW@Tn9*>EamBQ*| zxjynvt0VX0#w}>{H??CcpbfOl+6I8CizON?g)j}I(E4@m9dH0&Hsp-e;Y~J#HZ6Rk zK$U;mLeUD-z%uN)-o|GK^8p8lfC}AGKbVJbXJ;n>2&8|bwWeXFPWHj;f-IkB*6Sq~ zu^M82P{mN{L@U8(Htl^ARr5uY;k6^qSTIFq z{!Z!(x6%{{(RED~I)Y_dIY7v2GQ*Knz0yocZ8Xn2WD=Xvf{>kvLwJI<^$;i#QMuD zz~qb}`)L%0G#wB+^RvjCu2=Gf%zpPP4FKbn48P#~6&ye+!%`TwKcnZV4 z#suOGu)I+Qadh9Hs7V>nI18kC*2OU5R{psyNo|fL3G=>PzRr2XUB`geKVI$Z&G4m% z3lA4&ku0xyf-DQ(I;HD($6kZcn8FV&`g^r4N$uIha2zAz9A0lYE{`~$y&2XXE<9Xl z-kzo`uX$I|ogM#MQ_!QFpl~Ljr^R#YK-(T4_Yc4Iej@8*;Wf}-kA)tV`2kW2D0hwkjl87K3|7Nf4y+0>x&8K^@vxd)IaY2h$N7Od3F zhG4GJRM~sYaYex(%eq|3nAHfOT%J$XHv#4HypN8vmRKIwW(b0o3=^W$Rz<-emFrCk zvJ#()hr=5}FuF*m$llXE>g`gA074q_BPj)qw*Lk?fp*dD=t1;y^g8r*^!_}LN%oj5 z?WI&1-U{ZZWB~2wwzwTinENSmKyW+}m^s`=*aObm4OY_V-`Dj5S$S;oGV# zL$%rmQu<+uK)+gr13dG(BfOk-zmLa!^d~Z-iO53&P5>dhNNMRhA5~>pmet|>yu(Wv zm$)-OKfG}s@mDfVt#mY0F5kq-DlJ;s@@5|r+Hp$H*IC1CxGAjA!}x)NJA*B_Bovcv z*A8Et*zvKHwY$DiSR(q#rEuAGCDQagV2qm`X-4Qs12E<1oIQIZEo8EqF#I*%6H^c@ zyWF6q;#`=kgQaHgp7W0KV?5Lgh+_*3&veSwla*wJ+yROTor4}uq+L!{0%MLlixax7 zx~5ttYtSqR3zn*J;r_PORIAXy7_e!n8bHXkwc7~{dzPwcs#U-_M0sJ@DW)h+&^F$; zSJ>taYf0kvHpH$j!PjZ5e$iEiuzeq_6XoVZlrimbO}oDRTKPt->KcDdnZSmxYV6dF z8k6arntsfJ6-?Sz@+0-UwRV6{L~&xM^}TE#obf|N&b^~b(sdO~W(#HqV1x$r&84z8 z&052oRJc0>Er#l9ffqM&;9H&x;SbhBVcHI?5OiJedgKp);pN66vZZga0T3tY-Epc~0RorsZ802}rs+#T)P<(oq>L$_$a?lOshIJr-0mc6KTd+F3(fz~w{y zXHX9Sfuhm_Nfea>0~S>PW6UH`98>jsf{lG!5VdAwarW)SMpF~vk}3*pHkD-t2ISWZ z+JOPGEFUPs?7NmASjIxJ2v9687ziVDb&9US6ukny8!>2EFf(#@swM%?VmIjrg!Tt6 z9nTiQAnsG-XlK9n1A>VcwBJz}WHT6>b=!h_S9}q9_qv5CH&l+vboi_Ebqx ztRj^Z#kD-o#`7){1ULsU?=dU@w*8L9sEh@SBMnBDtP&7J5DC?FRTTw61ZI0fs1*u2 zfV%)tEd-h>Qwda^NFp~FldvdB7(kSWuB(~|7$ib4vz^sy(bv6pwN&J~EE7$qMAH8V zRGmno#u<|YQ6hvGfh-_|2trp6@b2vmw2V@eqq90Z`M`^Zlm3k32T989 z-y96Hm{1SEu{-rWmj*)QB`ZPZ86bm))bZwn*z28GEBtElJP2VQ$?(nE#rmjB#6(|Krtaq9{o+7)B5o96Sv$b11f9D-U(EmgCw+-WOTGKRIU2v^D9WVK&SrWO$MlFKO^vu=hHt za~HzvMrM=Y<V#fxCj34YKt3t+oxK)y|Xt&2+118T@u&5&+_7bPxnPWi=r6yP=+>Ec;n z1*Bc)+@@zZ%lFO6!LG3lbNryWZ|<)?oS8-8)&M&C1bLo=u@wj_ur{8#R^LnnQ&dE! z(QW7fw1-}ckTkRe5XzhGyudcY7brp51-z^>!iqdg=IW(GwwvHX!I@D}H|F7=P z>Y^x0MAC_A8nT52$^kf$04s(_ODYRggKLIGO^p8{kQoEPA*v!$(U|=eeBdp3Yk?C% zFK{CA-B3|fHRNId-$`Q;9YG_jp}}vI9eR<8_M42}bpwb;tLVj0F}7F;_((>OJ$;vY zluTx0evrmZvc<|(`j{Xqw&a)roz5c-tIBS<8dF&yuk!R6_QF3>Zjpj&RA?NFCAWLwF>9l(}Zz89*^M6 zg}X~phIV8eBdWk!J#`)-r@>xEwqx+G(N5k%ioEaA77jC25gcrkdQBuMAS#oqaXTVg z+_428ulIU&gDK=1ZnpDYLsZ3t+jCwve?QonuW*EeL`6K$E`aI)b*v}`QJZ=NNn3w7 zd1~Jc$0lRE-;dxod~}mz&X~ku$zAai=&cMT!rCxhm;U-5*aEv4JIQ(16G#P zY(q?_XN5YHr0tm2JC!Til;{l&&}51mHdT7z1+8Kwff>p zvrWG-hABF7bwXwx)|Vw*ik?^NgZdTm1EqF+=FzEGBGhWHVrT%)QV=m#6F*27h6Q_B z!;1Fv4DC2dhgt59NAQ!Dw2{Az#@#Q)um?nOZmO65AGzHl__|tD)}MvO**{`zwv8hH z-Shtyw$RP!W#|nEK`K7Z%Sw28)8;@@Fc&$ySRi$rX%kkmb{tu>BWYD{wk-Z22^>jd z@#_idkPU|KAhFz6sHV_#NT{)csEmv*o;iO2h3 zz$zbo*8p^!|1Nu`FsE?LG>n2=?;|9J&p9lR`4Ycr)=4Y*uaRwp0ZsGPsdEuKgBAr$z@ zUcsmS6(8~D;G58J&x9vAoN9qrMp#+eQXIgIPRk@i$ZljhO9Yz#)D~EfG9A~1edHJ7N)>0G8UOnkB`u7 zZMhXp3z(2l(DKO^rU^pOxf{+C(WipK)S5qv3JnPP@z4PC@dOy5`T5ueTw2pI`}8hj zyY)FLy)x6HSC&dQ0E8^Hk3A6VNIFDwPKUeS0LJcKGka@3x~cx+CB5Am`wX?n2}_enqEL5L8ufdy6ZBI-``;2P;c%TUT|%L-mU9 zc+tW_<+TxT9)TI)mqe_%kI~zj^|mH$K(-X{7;I(ETDbH~fxpV8TEnpR}n5kl%UV6;X1zZ|^*y$d~yei0$3Ne9I1-U{e-Af_!gcR_N0w0$8d zo1y;8*BRX9k_ElBl3F|Y89^%-ujMgBlzh7`d%xdSBglv&wyCus*EeTFW`gH%lrMFyzkpd$B^5#x3aMU>|NSPt>?Y%{z!SW_I9k-fqnNv8x7HI z2(=oWxuFjaGU`4i8#lY7uWUn$!bG|>vxn(wjKi5dw2$qgy`cH==5xvFzr7J*zr?i? z^OCk?a8tbi2x|7;JuI&pQ*>SNxobDCPVviNid?N~V-tN4JR_og{GNN-U(`(#)3 z`$|zD0qT0gONAqwGHi`&IfvcB>@h$7>xlu*ZTg+yfcPNanqc3@fn{-?TUO4wWd+#( zK3&J$CGL&`u*UrB%b0z?tho-G!??^C?{mh=7|$`s)s_v{T!*za*DJV1xB2)d7d%&M zy2V^OefBSof{&{Wrr3Wx;C$t6bxOW=Bk7|@QJ)Eg$WSr5~;9p>;l z5b6d=I~`_H6yO0qcLEUx76lBr#Ib(K#qz%(rX&Kixe!u8QuYi`6x!Un#WW4;bq%U8 z0Ao*)1lmk7J|aK_w;d56THE!1>|-4mCH_}%MA&?i&4m+&^}FlzTNe2ro%b9<4v7X$ zgk@RkdCNf?CYt0`RqWRb)SRc4tOP0jaiKx`N#z56d5;OUU6wzdII7OW{Hng$zcMoV z0%5K-EZa6zjT5b3SU_^#ksXwxJJH+F=i^f5Q@n@40deMmaF7p-nhnVzS_kb;ri!x} zPys{6lk%&cwga9^5fouvPSZ};A$79Pu-5?kJJA>H@2_u)3hzC9?z6EVN<^YllqWim zFvbScf4Aq_^oTQ2@Hg=u?lrb}1eTWB$+vpRBkZNHBmC-zFKzKCR3g&u^FPLBpp`}J zKmiFZeB0$hEB%^R(XowFiAW6!%k0Ge;P4^39=!xT9hH+c_0ap`H<$r%LuR|6Dd5ti z<4}fy2YkqK;h{uorvnT{i31A!x?)V01Frfm_AApNWBmLggVPBT4!k2IW5RQEYry<6 zO6Q$UK#vf|a;*@yKi2Fu`Im0bAJQ0SEL-UMJ&f`}RxGtzgF&lRD(aG?FF8RYL7p3ukJKbpX^K!LOx#BmQTpUh{nb->=n6nzkf&4Y*%yS*GNeZVQgl1_76M0I2hr_4QBHS#keg%NyuXR7GF!8zuf z7a6>Yq;FTYaq`?l4RLPzstIyWfn)kh6FqBt1EH55%Xwf=nlet8W`=lf5Q;l1oQXYT zoRQ*P5cQ%*%Ov+0K3^)p0w?c(O>Da3tQ`|1qH%x=-;XA81snjm%>tgKT*J%{2XGDB zA^`I|f43U2D~;NXMLaS4vE4F&`T|=vFwWT6VsDf&9`(?hLbfrcxeDWltDK{cv|q~- z%L`KOk%b%ap_o~R$Z68$q{+8Bp(JX8nkj%;Gz>sDn9nsCMH7GqD2evucm}bissGV2 zs%hlbBj^!Fwaev3$i;cnM!9UO&XKs2S225&t~J)lhGF1K{p}> zC;NC&I?U*B8-|;(Qs-~r{2vxuMC;M|K!bRJW-k`(|HQ73$JoCBe$jMpbIhXMSp+~w zmHw9|lYO4|4vWLOF*4w%Z+?GKV2ogj{Z7Zn&lWXRmQ}6jYrIh6y!1nqU$(S-roc7- zf(@4519TB>J7IXM1;jaYrEx#$+s%|$0%my>my5U?e9`>CrHyv#c}oG2@4x`W^J;;u zI8G2aj$#Kj&m#cs`SHwb=Ga77``@`0+XB_mG);33KzTE}M)SoP_paD9J z&Z1l9^9?Ky6~WNZEae(0C`ySgjcQq%Dvygxm`Ya56fmW)L9HzM09mrOfX>}N5!f@e z@4=>`t=FP6S>Wviu{hVRRes&i-tt zD%Ve54tF8wb9uQ2B4$7%M<>u3?`r{i8gGIJ5}gg^NW|oN}Bw$X8P7kbJ0`76`UsZVNE!1SY+{OrGA#)A3z^hoctyFROgc3OF) z%bRC_7t1ApcqdENon6tUJ1si8(R@=6Stl+3@6MTUbUypK$~FUJPh^^FbTt1~X=Itv z9o4x0M1cOx;=bN%U z;fdYd{_gJO-CcM>nJw%lXdgnU%?jyP1!pXWu--4qX4t-!!u+3LUv({1)lQ30qrYzC zFh&UrJ(*uL{ew)JYEnW6ltB=+uMBP5R!CZ4D%3X^%E_2L7hB+w@cmL5VZ)hWHVjZc z?Ip`v)p5YJswa=(^xboH6Lg@{xru#@&S@vbfVqF+b9Y)PlrQP|RRI7-qn+6Fo>1ZbM^HJC+b1 z!fZ7Ti7+RfBz2$&6Vrhk_!(WZ$zcyN703U37Z2lKY*5EjOC{BFXw#&YNPSO`MJH?< zf(*Vd5!-IJZAX%PA7sI3hmIs;&nJ>aO}OT$sd8tPc)ln*hCwCQXS!row3ewvn&}ut zJ8aguK^#x#}Oi>BI?)XITJX`&Zp5a((Yugw7U98Hk&m=dtRnt{PZOJxOvo6hfmaVrvg*d69 zmR(cU>$a&IrtDYpW9(>JYy`%dkAB&-`ABZ* z-<)?VO667qz__)dD5|=qC~K-}Q*xdcicvIw&5A`M4+!x!m|S|1zuo{dl`V;BVPd9V z_bM763_GT(RWJ;ws_NmWHM`c7<*R?z5`vI=bPBYiyg|PV8saq|9+(TF`BZ>EFiXVV z;DXluX|Y*{sNJw>LOhC1tB3fGwF*M+?f+-ZZHnMx^UXYrCI_3KOS2>)`x?QzR4_Ui9{Tk*abGw~YB z!FBYPqPx(;==JD>Z}7`E6|R+Zn2mFB&6x7F)J&A)V3Qi@L9drQlo3mpT6TZ*LLV-s zi?AR17>sA8v~@p1v3MN*Rx}|xs%Yfr@?L5P1)}nz1miJvf#8-*EDpiIN{HuIyYu$j zRZ(D1nU7kW!_?+Y?Zi&5z6`w$??p-(G3`Rg{m|=AF8PG!=+ecFPDmId!4S5C@nYb} ze)=HxhS@L^DE>@dXom)X=Vwe2Dl(&|DI|%v=hKWm!2vY2S{pJptW{MFfIo5Z?iJs| z#^`V4CoM3-f(!slYio;Q(WMX}M#kY&t1yiEF`+ zT6tleEyPfn_^p4_%r~0 z-mbrG4iv@sfuSf+D#s7TT7r&k{;r`YiekL}VWo16kSRlp zYlFsZum82^9q1$I7tpVvZ=l~te~SL9EhFs!bwG;0+ufValthyELa?Gx-nGgL2CDGc z_aL_Mj|(9bO` z0&Qaln$;8?bcz->vwKwc8E5vMX7ZECjZRCRVtfVr0ddDJ`Z#B4#RLTXa6t~)iF*Cm z6Hy%)8BNq_$rOp9x5?>-a2d?=-=ZamN+mPHy57`jT+hfDzT zk&>vW{8W=z770HEUt=1#J{6|4_VABrp5m$^mL93zw}&`vhH!xGJMEyu=oA_$$3a7r zU#E#*Cy43A{5qrpDbrBLjR7PXic0wM`q-fs=8YLQvPe@A?^4bS^uh zdbS#31XdqC*&Bxy#dS+RQ*u4oswn!0n>Wv1Lu=>=I)iRRh!Quk z19^mSKqFX*$rF1n9pj?4skUQ>UqU^JX_9||5%c)otOV%ZH4bRo<*o{4k8s0&m2Gh1 zl|k(S1L0;guJounRsZ~#`6oD*UM1n+B=omVD}w0_Y`_5J0L<8Hn7OT}s$%D6W6xkF zDRi40Z}1E;qth|iKP@Z}@}_J-{9QHWff#FOQzK?sE^ZRi#M%iyKY(}kBCP;L;uQu_ zaSGiyRs$Y!@5E3MRDtHC?Kn!awm0@@3NoI%WVq$afeo)-r@E%8fi6>LYtS^)fu@ z_$0b)gKm{^Sl+bg7DeTHfW&O=1pUoZRCiy#W z$Cf1#*DJ&e+CbiDZEUnQ0XBEgbh%o`q9ogDYYs0N!82BRTt2j7DL)2HFurS>g}ao< zceWrwhw7So?J~gfwW_9rSI@NWlnD)D8SLi1Rcij&(tAli{E@92+Q3=|t$5e+SRgWB zNC2~v}ZnS3qM|?pd zEk@rDP3n17Nx9-R&iMQ;Y6d>YV^tTRckc$5&nW5{N%})9j%_<}4`Fj_sE=+$4~+M_ zMr_vydWS!E1rMH=c$^;&zrs?wKFY`Sxu+f?Q9GMU-*#_TS{WLmr)kekpkjN5kx2Ux-PxE%0e8f~9=2 z!Kc}pRj1nNJrAlupaspE=MkKV=J{VoxjcVkUw3V#uRAt_Q#M@tQiQ;3 zq>Wa;nKFPkM(EyrHyc=xSybmOob+G_SJx%*J~(MSn?j!Zl~*fgYo>@ExZ$kw>M{zz zNY#)GQw%!oi{TKA4i54VX0{(<5Uzr(q$dI!jmxEhoVWXM6Bv^nP(A`%v*Drzlyi{V z{zilxqp18Ub3Xy|$8Llb1F|4iu5G#pYr4{1caL}CqNB0XOEf_sA=Pv(dv=npY1wzv zJON`o&lkA}Z4``CtO4Uqir%|_yk>#1n}W$+3&IyI2vd;lygG=h*a*-(I>v?u)y?y< zAc36-{9D!V`0{#yDW8vxcD$O=mZEeQ`v;D*VaM%+*M;WM|FS&K`avW6bUQIX8E(^7 zIMtnSCm&kizo2Q|dyX9 z5Hp!u#`&sc+g5c|b4;-&k&BkDX&2zVF2?SApg22wIfXFgV+~T)!f#@qZI~h-9YROY z;|MutLr|uw;o6{*iGQ|aqUh^J#tCSzWjJk9$i2UX-Y|(P2o%dVyasG4058Wj|xEc&Qj>lyCe&akCuJ4E^ad%ClM!F4m;>OIz1 z$UjH+T1IPWW1}aZ?27NzHDFG4SOsP1QS^HBUi9-EP9-Fb_Otokrcuxo%x6&-Q+)!v&OPZl5tw1G+hF`T4ON$ul^+Y6+ zbWI>hI$(_FfOEqTd`*SGkIMe+bIiHUhZ{W#!q}aqpHA1>V?KMJChir29$gN;Rtiln z%X3S<=NZR?Abr^jD=W*QIQx-RDwhO75Ej6;9Y-uXj@5EK!`MqEmxl^QgO-h>DSKi2 zknBOHw8w3mpcQl+A+XUjZXAIyCjuXR0>x;BaagAZV&R`2a4g3bSv3gkJ3ZS_mX{UN z^6s>QpvnZxv96tun&q17sj{qkZmrym=C6IpG5+TbXAC~3shT(7mhEJUVSBr~o^2?Z zV_SUSX{xp<7={}Z3XW+yg+kyOhOqgA^AznY$;4jd8&%1O$Ld#G1CT(dYp17zm!wgv z_Ayx-bU7PVx1lbAFs4eTLuUV~VOaC!vgx$W0mNnxuFG>kV~4^^Jg$J_R%hR?x(+Ch zry3T9Qvb69Dc>wsv6CIlL1z80T{t-!$+}Y?dC_vKH_ka#b*&dalI+NQ!sP3&li9={ z5Awfq@7wcGlaj!Zg4&&7+>RPSoCvS~HGz^Igq$f*6@0((OAX%_s-Q4Fo6gh!M+^0O zqfxIHV5%s5_E_k-0InCp2f3nr{LVWy&P|i^SY_gMds+Q@U#FC{gKP7w$>SJi!|Z69 z66^n2dic-9^k2c2ZD^WdTmFHG2iP?G^MCp$Vtn{Yk_Vki#nv>tQgMow1z=f4ga~3+ zzYYHjzKG`0jp+60)9AO+U+b%xc>$3xiQHhSl9>Ogl@pp-O^4eMYYcbB$8ktR+Bj~< z3|HqwP1=qDH>wo)^FsL*v?#TzOFB(6YRA$rTfF8GKoKNWzxV2`P=)Oe8@>_92=elx-Uvf2^xo1j=1} z2W-162r7+kxkBMgF*^ic}Rx@>hh_|FV7d2ZkXL z0-zkC0+!SYSZ2l5WV?ayY+&F9Aq3bK#e$?(08FdwX|e-Pd|7)-JWoeve9j%oQf7aW zLH{ zixXMFg8uZ}5|e|IT1(N=wfC$5SG{i{ui>Vtz~ca4*q#UOK9mt;s+jcH)9w= z3_MBSvpx-a^C}Xbd<8G-0Q4y!0 zR9XbWABnftI6md2G>K#Tr`c<@U4C%$j=9UPn%g}D!NZf51m?Oca>jNUyY&5qI?tPeBZiH zkeGX)<@3DgF7^;nvtsMG6PUntL?*Kc(`3%RREZ*YieYwRJ&u4&+;$~!#x?e z4{97&w6TEhLhm1=8@i-4~=4I({i9=CFlM!XsU(#ma0Mdey4P7Mr|xC8X@w->kLg9 zK(Tn@XsQJ_A^9%)gwZnOI=h{+lW2^pD%;_uijC## zT&{z2ea|?%~-U;aBfRWdAob)6{Sk@XFw9cMhP@SdAKC zQPYZHBU)`V0D3UyL)>zoiuE~J5a@`?t3Mh)_`Is(?%LVG3U(BQ4BN3HDN0e(ii#pB zaeGLPEdD7wXV7>Oc=(^6Zt(QuJb+O5Qanjp@cFL zv(?BJ1yd2TsbtIOJ10uv>qoS$L$_{q7t|w1)P?TWt%tU>nYcaoy&i3Tw4rfu>!B^} z2~_@EkELxLx^**;(3sa)m~RsGpF;K-+m_IAbSFYB=8Gn^`z$}$gun-9I~u2(q3wO4 z0~%+ECj_bPLptg1gTVnicJ$t3!(kW#6btP=a1D*YBOs)-=BRTE^9E`TN zF@)i8cxkEq| z*I}wEU!)r5ea=A0?Avg|v*yNSviV_~DdpL3f!CfB_#Qlf=5^X9$VL$wq8&SDkOUxg zZAc|%09mpH^Be4?1zRy<&_o`my zaD)y9Cw2W#6({J&1lVpVT!H}SZggU45#z6VL+@(V*9js3T(<}sJkqzId%pA*w7-d# zb~-|euD{w>aCWkm^xk#S=%%uz<0ubz&x6!R*ZM9WNz&=ocAhq=8()Jy^H~A95TcZlvyu#wiK#)jt z$$3$1j|Y(zgMrG`G+wzq#(4fN&pR-w?8`GX7~@AEhLa7G*;J=?(I;PHnp5t%Gn<*w zVj1o^w`v<3aOFJunOOVib#3wk-_-^OSoRqn_CRoZ=FaVgoOsq>Sml~f{$9}~^%pbC9s0X4LgR39yMF5sJr%IBG;ZM3?-{y}A;axcD>OX@&sZ}ejqTL!_F;wPg=bP_9 zsO1I&nNAL~NXCLnmz5tWq1b`u^_IwO1({(i*sMA`$Xz#@{b#hO&+N~1eLjkc9ROyL zd`Z;*L{+!U1*wjynX}kkYPAah_wNb6iY==K@FKCxQ12?rAwx*2)1ge&2kbCaR&iTy zuY;sn@ukjSXMS|uCv*y-K1X@Es~)6|4pjdPp&Wu+zuY7`ieQd0^B~UAs+avvVz%`$ zc2O*Kq*rEZTL_J;$eO|(3#?=y=0zb*9se+(F&vH^aK?H+-<~;R{5$W0*^8X9(3h!s z;Cbl_r&SVi{hl_)2F#|APdHuD|Lrw--Klu zKpQ#EC}~mWLe_8{ki{}y#VTG~yX9n>+N3qSq^1=?WPx966^kGfDb)*maOgOS`6K|i zMEj;MisHVh>}cA~?2COZlxJY1pLWnZ+Llg`a%5=nytFASN`OnGP^-v<>^(iz2$A|I z(9O(@S}F*C+mrPYH3Om1g}~|>VcwsYGS}v74{o+vy7bqt5@+3Gez1v+y*EXKw&Os< z-P|?Z1!BNvb{ejeou49Y$A|l!x3?exm}b!r{i10CNDDRFuJ!HOcirE90vHB7WT`|7 zLHbcQ&7x$!e8b}&Y#t-vo!LUVJ-mIh2S|rI$RkRMt3W-SM^${-z9*SQ+*7zms`McB zq8@EwH(VD@S9<`4q$tv&6kJi;{ssYK1cveLK528`P-zjqs3;F<`nj7NjMgfQU2mS; zNF-_+zyoat!=zHuh)N+WgmGma=G=5n*B;_wKwq$8qfRCu{0umbPv&~8)1UF;`qT{$ zp5%dt-pDUK!e0PT!@2SZ=32wCBO;c?c0{?Ke=Zomu<*kNY?x~e%ey%Og-9pIKd8cS zjoRc!&VC4u(KF}+vLDH#8;TT0$o{Z=qEz!cC2Ge}EDKR2(P7RoqiNz@xRX8D#1A(* zw6Mql)O98Tr>aZ1^tp>&7p8muR|2Yo9gue6FxUmZ}ukvRd_hS ziIIanz$AL#lnb5MXxC6sD~G#9nCcoKqjO;e70^6dMcY_o1EI=XP=!WHhgm~|nG&6$ z1xN_0o%aWxdpE#%`_I;xU+t^t9IwLhDz8u`JF|<9%xL90?q0N1?p`z%?7LjGE;@#4 zU4-ZMORI=k|J@UYx0e7)nZ-E(=T-)mrS5RoRCc&aRLe1T6w~E9$nV9hIy#CF7;KSF zjWUsD)0}jeB{Y#D!oMf^Nq-O=Ve86ZWedRUNpi*S&#L$voPuFq&O3QO@8ke|TatrW z9>|j22Y~53hb_t7*fZc@`q$WRL&#m}oXA7??J-_lLWAs2kV2)Xotx4qZFq@wlfH(e zFO2FMhN+vjAn}s8_FTARVeq}ikEZA?CudU_mkKpCAJW1Z3r8bN)bkIp)$lzS9ptpa z<(5NT#u%kXbrQvRUU5)-^J8&gE%atWC+oKu+s-hI+i}vw9H~;TBl;T%d4loEMNa9e zBIH0Re+zD>-z>&BJg(#VDbL1he`**n0Hu63)>TD#dmosYIDoc{Ip;RNg67c>-GtCw znNC3)%B~=)rko&r)hVNK!@q9@xCW)tyuP}6I*$>zQLD#sS>aqMR2{uvHvYCx;??hN z*~MjQrW9SsR2HoNb5Pl|_{J;;|-5F<| zclLzA3yop?WRb!~LEk`zi#@a*M|FrY1EMU@R6Ru9AdAAKR1hTXZ!q(kbjZ1pFA$x} z^FGmac11HCA2~Un!U-Sl71U zBZfw82$LG@*TT~Bj&zTXLCscl49e`XV`%0BR_1WcI=SE&s&(?X&2)DW{Gi!6v9{J| zz;m5W8_&lWOtVlh@0qO!;$oxTP$hI(B|__qEt)-Op2$2lX>$>!<_&uQ6NsO7%u{cHzbT3=Uh>ZZ-NR1-+#KK5E@*OsY z1SHH$HQ~R(|4*owKf<9Q^bEep?!a9KZ{RJQ({=FHvA05}Yb8*aytzr( zJh)EdP&K^dx)%E_LFuQqwb)hQFGSV1lpAbQE#Vu>mW)^=4%q{AV>$%*B-UAE8g>e?qrM@P{vx}((}aRX$(_vl*a5)bxpadO4 zC(yO%JQ~H1m&iXzwCNzCpOPU5KE!R<2!dOL;WE7O&@g*r0bnvhz>km=7_;WtS{^x?(4Qtjt z_F?})fE)g+iEDbEsd|*Kl~5BCW!KFQ>Ow*;d2&t_g}T;`W|x*7``-juS9RauQ1%VO z`SY@E!?r)-{sn=oLjQi4(5(tGjue1smLAO6B$?~JA?x8IJ>`=mY3yY*(bzJ0TK zJGuc_sZ@Qlyk6fhM|N+kJy0HmFS+HNkwL!pT1em1Bh$R*rqNn?_Lu*I{I9~vp3x7r z_snYV>FGoN?p(Ki1Q~Q~=%|1qFZ1fOtgL#D-&9)FWY8pYmE)>U-2Gt(!o8DLgK5?G`jRe-tYUc&v%-2ThXnFn+~ zQx-;Jr31xA_%TLS-N~jX=Gsqz*RoXbN{E&Y&EjqbBAsD%&uC zUJfnrcndN{8&@&=;g*Z>`NmqflNF3@!fK-X!Zy%HOG9w_sTE||PiAf-}kp;W|Bed;`6*}yh+V~w)4 z`gT2VyNv1E^)*J<487fW_6O0I(AUs6(eI+~AQZH>0U9z5i2Am{ml@*L-f@_v!(=7! z{J_iXfK_}0{?$Q}VxU#8Jmuk#P{8hrLM``bi(G!(G5be^teNy9=Y>pH1&K^{z+ zt|)ls+$mB`r4xcjq%JeJJ;b4`!+8Uw(>;?1X)Zx#U4FOr+V}Z&WcK_0{%*hD?{it- zTBPEk;K3q*@3-oKF9i!!S=ivRr00aR;-EmgvfQPGpxq`U*9qkt3kqE*NF`BnJbEP@ zN<1orf>7cj>8_mdXSx#3i$boG@4bd4y+-GK)ZQyr<0ZnRy0DHy9Kr~>7SJ~*D*-$6 z`R)hp_R>ZWCE+Eb+oIVT|TtcFP^C?j-z}-VX6zSbyTK&!$fp^$7_W99(tbJ*^8^)3PQfc;+ehsE{uJ#+|wP&4YKY8IUof1J*%9X~?&R!peK90z) z-$68Fqb0P1ZbrAGi|D23Rp|X!iy2ih#6MJK?$wxJ5s(_g#CMmE=_&083MB5DPoM0J zfj3N7f6N%9TnPmXs_C5-wxx(Fe;pV^zzBu-*?_}FOXV~Hi(@Y8!vMRA{-!$_Fi6=#BD_SpxrfHht(r%xyibd}^?SmP@=_AcF4m*TQ~ZrfHOjqV5^ITy92nx$VB$@lxo?l~)uG@ubgYi;BD>3i0d0;O3k$XH4DCf+!J?OPUCIqjc>BozN7$>~e?3hDQQZ=2B9lUIC?5w;e88%|k)qRIVJ} z=G+vOyL|?lxJmqH8 z3bxJdzD;OATH0|0Ka?#k?kY1;Rk%*5z?^(_aX>J&O^1cFG{7#9I6sT1G$SKi;7DSs zx?%w?zhtdl=r-W3b;ky$C4QN&^1W46M|TM}aO4L@kn98dD@k9;Ixjx%Um;g7=)g@j4S@ zO*2eG$u-7H*#}4z+jeY1*e*y!Rva6{12xMs&B`fBQWQ&1in^xh#djagtE#8_6I*Sg zhek)~{5K>N8osNa=JY{Y6m&$4THXeXkelJ4e96(pkiN{&^>RG3e-J19-YA(VAgvU$ zSRz!trh3^)z->ML)dlDMRgpOg_}<*xrKl*W*U9tk6O741a7Mn)4`YM(w759((08`z z)K=yybT<8x+3PPc&G|3ujPSrWv5t2CFs$0&mApGsfvh*>g>dP8up(Gu4b7?KV2q8 zs9*l~Zlasf{c_BkcP2wLal9d_5M*7#m@lD-NtUcllG`g9na+hoXwhZGJH1rU1bp!8 zn$M^^xO7S!o$K=K^7ZqfzWP_I_xN-CGv!DSubqcJHZ@F&Bzoz)TKZ-AwR$b<&>bh+ zrwWBw;ba+ptbY4Pf2+JCuQ`em$e_vw`ZS2yYUyu07{wp6R%ci45C+8X#BLTaf*wp26WKQ3jM zeane{nvCc94k2~Q`0(zKa-1@HJV)PFos~(j`ey43Ne_(d>89`Nj!_c$*>YJS^mFsL z<5uua=U8)XSrT!(IY`FU>Xo6O+fL=#9KNpqXF93E_ zRhRC;VnOZtLIv(>^{^9;r9@Wycab&D#wfq~O*qia7O2b+AZb4E&NkYGVBHDE3i*D6 zPNg-lg(TV{*J-SuFYv`6c`wl(!5vhY7Xito?)=XNdJmof&jGj#>z63Xhjj=uuX`iC=e|`KKZZMnz@ESC(C6 zU*|Umpm7urG8^ZBR476cH)I|P;^F8;`vZTFtki)9eh`cBq-j=`V==p&G?6u3)v!SC zfZa9=v-5(Byl`s0;W`PAiC}NYIqNz_0Qtye=(t*s1g9R@FeeCUlv%9Qf`aOF;uVtZ zZSbFHSfH||tM5%BUbygv4ZA@U%`1=~z3*L$t(u@HWa;ELOG0!3c!D($#W`A&&R?vd zPK=N?pCgmbSQij~f8ii@OVMu)S`wGn!QA9^Jp-ARJYU}=gY@y2nuTKD*{J8xSQn*% zX{a1~WvdcB1RZjk@xnlaQ;|E$yIcgt(UnI<2b6zt|xj)^osFFG5ietkV zzJ3XeHP?}^i~*p_MDKG-^gg>I_#%{BAs3jiXFz(bV1ry{z5E z5=}U`mbbBsM?dy34nAT4HlC#(6e~Z6pEJ*MtLuQ!V5JmZ%RsKz3 z*2_)d*F`VDVAKuJ?`>+cyg*r&iJnMeLamgZ8Z~K< zUCElvX7VgB1YSG=IeU(d!>ax^Hkd5NW7O@ql6Q&&m@hK`n=gCKYX!d&L-8jV<6{Bu zyYLMlYNzBxddGk1GWg*X7jg(T+_ua4GqRj(T!#x-3eFmIuH`#huO!3gF*gAWZt6!E zlY!ouv0o24V=uF-U5Ut(4?Oa&ZUdYFisAx z7k)Bawk*zf%d`I4gk{0ydmWW!4Iq}r)!i_LTc1^a9oe0&S@$5`u5aid$1ZBzo@gbo zqLhBQn9Qohz4bl!RmzJGi=?l@z!%3NjukB8pl&?hFxv!RaX7tj&*0SRt(AxU>enGO zz!&k9X#gnJE#@o+*g9mSu!uMy=P^w{?(9!Lcd2d;EjuQ@wp}n@#hjN&ZAjy0RkX@a zJnIe{gE8__Tn`;~zhr$Nl&k3)>Y{6HfMsaFi$OMP9j8`1<6w@7I1y+)4q7we?GiKWHO2yZfOy5V=!D{H3 zTKU{P43Ia<=j0`7K`N`;K9O9~gt!0nUZYFcLos!HHj=}Hb9I-#;SCUV37xlbiZb+s z{{Bo?duQeYzt1=!@9Gbnhk-A#NoyC~A6Wk1zwbaFM4v>Tp??N*_Ok&-f#f|uZl_9r zlHn+W)h;e<{joZoBOk>I7D&aBG8go7E1sV#Bht-)43j_C7Si=r^vYS{JLH_h)Sw)C zLG}!L*BHiNloPMAU-W_V|NA?vvv8>C8qJ9yXql$zG{>+=wDaW6 zI9^7ZHpzn_Ha-=^82t=mFYv3F(8w+k;bq5Q^~o48PkNA4#GEwkI*^TDwgmZw;HlGM z=#v3rKHE(jmQ%Ev&Z({>7i|6GpW&wlf_;=p58w2D?0k@^p9MdK2{NoU#M|gP{E*|X zLmxvLfKU`7)ell9TAB`^cvMmL9KzUtkSYRN8w=u~v)dh&MBI?pi$h@v$};Ae83p2n*YCJl0Bbp zS)!0v#Ka0S*~?KP9e>In>5LnsPSS5(95x2-wzac#>w5XLIL-MNlQO67pS4?Fime_r zznvsoiXta2R--Y#6Utf|5Ss|cdyXksq`OBHCPx;X*@dpn9(s+w{ZDyX+hoH3A9YxVKl}QjT%}-XDpOq_*uq8T7wb}qEd93 zl^#&?8dD5_%#mWxT9S9ym!wIF2lo~PO2on{#aL=HkR;mcQAuKLNx*dVUMh;dUBSU7 z0D#Sm@AkIAJNtz6Wgt{)rBoD!VnGl^y4sQm1^L^|mJHVm=Hp$4RkBF0FZNM2?rZ-@ zJDKscjiM|nSd{vVc{JIE$bk5BAj!t)H1&xasz9CHAMECv8RPKkGvu~2OCx zBZQ!lERvvso4|2)3CUYOZVD_w*!-ci8^zmArkHELyGa@v&G-ed|ZsRR< zzHPE)nGhBQuKgfAfpkj8iRT8+%y{bZvog$vi6e9D{~30UTRH2QChc0*QmdUZr&tha zoX_9T-!B*j2#-_Ka^H0wJLQdKia!==s{4fCx>5Jw0o6zq!`l4D=U^BG#^4%P)n?}3 z-Ll4ckxpb~Y59Jwp7f!kskdlXA&<^B#JTe?4Y z0R2)Lyw9O>r}{dd^_L<9rHpL~m^p^QOdzsA1X)AFT2dx3oWv0ptd<8iF@&(t&3aq_ z!zctcvTKNIP^m7~scCv8(|{8(&KbMZ(@YZ<+W97$>dtyp%5z5^dK)i-3u_8jj*|!?nF=04CBIu-Q5eB@j&+Hf^Y%J zpnD3lF^mhl#e>bc-&jLESsvsP5R%xfbsQ184yn_G>>Ydn0tt`bR*g%;bbRv+n2vjr zC@R0Lh@#XRj|r_U%!eV)Le^V_!*G6~h6Pcq^*FbzUeB^P@2y`Sj7~9`0#KQq3PsUw zM%mg8^OWC+8`iR@X#*DU|Gd6kXB%-axdL+^uC1CF*GyI2N=B(r`F++f?!Jg$2bf@y~BKtSf4LaSH= zC>C3-ViAnRP2VP`)4BWjGyf49Mv)7_!KNoQ!GOFj+jO6*^7k9?RYkeoOct4Uo?EtU z*qB%U+v|X0u~qRxaNX#&F4y7qulnDdG++#?{`7waw9gdKXa^L9Phs1^G0w~H^EaV) zf9d2D!X0;dmhQ;rPP=uVP57ih6P|e8{H}ff8R{vkXnssiQ_6nEkHz^)>37GFk+tx8 zYGlKnfRBN{Z}c2WUNjR>8D>cmFYH_@_qPbh#rsM3lX8s>A%tFfYS-QRpeUTa1PD`R z)g*lIHq~Ww#V01H?Mn$PaQfU3yP(GuQ*z1+Xr5N*FUH<+WLp zAy?#Kp-@^`%9s#2%LZ^D z(@c7as!5OS2~~=A%T`B2=Pu-BJo6o@X2z?nJ9uGnaRDzyPncC5UegLk(_?|?(`0Ud znrI0Pa_ftzRiaI>?TgyUckFg-3w#=^Zge6KK9k|PB>e2iPb!?HHYTd7C=2nc$K!iI z8q_cb+%|?+j}%2!Km4R{qA02w&;EIg(ay+09AQHAk=K|s@8K|u7Ki*T=aYP<$xLUl z&fF5x3wPz000T+rNOE76I)bEtB$ZwpMtY-`WeI76+w&s~#33O=5!mD1tm@-$gGXy~ zNJ{hg#_|5UatK|6ZuSL%dgS+x4>pWE6R|MKyE)zj<(3r(haNsgjnYglx{T%%cf0jL z6Yza1HlPEQP1O zS{Xk}8yE>|^c)|e_xg1CM3iQ==bYO&s_Ko_NmM^bc29*;JJCK(Hz>*tPEl7~#N#`? zh>oFa$6XYxu9-au$y7rd09OFo%k-6@^h%xF4TL7LR=Bu~SZoPkU3iU3uF<1Z?cs^c z2L({^kIFR&a++eO|%!A?7yRRNONiB*JkL_yMh~wz;-HSC#wLu-7=u2sm+JD>~woD zzbBR<bI6zCGJ%|z0Ai=OHogcn!^Gr8=PB~L;05<67G`kcThL19_j_V+9N;Y|Zki^~4L%4Cd zXcNcj>Y$%0eVF48+<`|Q{N=wo(#lmGA)JX$MeMYDNrUP9t^n9D0AiEX@fhFjx*3_*Qbm?;!*qC($?!?CV);W7w=Hg@{QjKy{w}QO)x~BIApenbr4qf{fc=_o zkt;|lfG#s8>j3JBJJITwY3rjcbRBvQq24aYe}oAE5drD%1P@rDB}7Uz76;A+Xvt5> zOx05+VDey8aLL`LB5xHzjSFxDH$tV<+tGzBJW-^=xf0;S4&K~k1%V*u<| zTYkj4pnT|s+%o`GF;&%6R0ybRXE3a0;KNed&Y!8D66C96UxaBgQY0>~81@SCOPhUZ zy&lGRsx@@mwi&Z+TW@G-bxQ?eRt4S~tdw#GMQ zrwVl~`QR(Zi6j!=@pugQ2V1f0dwv_l_XB|c&!5<9G~;&W47UMv$2m}O z2u$JQ@m0hX1|e1t;47Lok&W2_SJZ<8RWTss@`V3Txnu4_UZMyA8UR?&pDL5O=QC!_?BBwS9^e^ z0$9##n~;JL^yTyberTJz(VV@cVd$p4E6cmKsT&P=|2*#tS$IBJVa9YcG8lsbR-c2f z=!R|9YNl=IC?+|-y`Rs=Z5gUMnW(BU(d2R3Rfu@FGtsyrQSX9rlB39ilovda;IWt2 zUDw>qxo9?%*?SvbO?J_Tzw6+-b>u~#UMFcbq4xSjHw%bfo#Hh)C^a83ksV4N_X>7x zEJ#!6nLoZb#O1OMjOO=nwd8ZVS4!g5Bd(~f*%=VOHpcKGvJsVrKbIqQNa69c4Y*bo zzx(I8{+uh)_owGL9Oq+K$AT|EC9ttsnm_b$==$Uvysa#!t?F3tdmeh^cpr@gk!Mx4 zQ4gQA(x^d-ob&?(TtQ?mNgZGnH9EWU!y2%jcr?5%FU2wpw8I9Qo>oOxGFb<`>2Um^aFj8V z(z)V;6$M=<&f!JO!uPlt-G=T$FGFL5F+||i7U)SwF``EU(x)kF5};3#r6j6ul-m2Knx^TZMBY*TFZ^x%`t~*L*Hgz+4MX)Ds;bcbO!L1y_=YG+ z+RDbpiY7@SQ8hhYT8ed5BScfDMK`D#gPG7T!)y(rjIW7ADmA#QIr&iF)oF8KTX^XFpDAgZ95T4 zoH0j{L{XcZ-rRWgtEKI2=~Y}^j$XQ?HXm*1Ff4dx>sdcwsM@cn2$psXv=w)I?}L#QQ25mSj&u+XPT zcBAO3r0CdwQ^1A$j%uQ0UPTVq$*yGC`jPuo^=`jh@mzPL8ixAM_Z`t~i|k4|w*gs_ zs2#meH4Js+x?ZL1->t@QtdPIz@erNWIIyK(1v|#d!?T4Dgk8T52F%uyl9{+wx-~qi zob&I-t+a5VxL?zS0=?BR%&7%nO--XG;GibYXTfhv^9!e}Qgh*}%-->_+xM5m{s5jp zVZV;e7#%5&ql_r~zZ(cXf$VtN{gHVir^hfwtF|%n4^yiEOn0AO#ivM6OoM2G+$lXZ zN#n?ix6_Ual{9#eDgmxE`n`C$K*JtSO3pdkQ92Y!)nnOILd)h1lkvIaD$=Hr2hEIE znEktDaUTDYkFoztvBbF%z3%M3bIvI_VT(p|X^XCHO3sOwopU2Odq=payB*=&vSRG} zIF_70)-FQjc=uiWfq<%D8WMFI-AzF;s%b^H&Gjrzvpg5jojaxwFTW4AQ}G)OU)TLc z!>>5lWD45|{?T|pm|hwtzFsEZ?M+KthJv>+NN>7xmr6+-d9*;uE#y~K%U+$gQxMCx z4aH~E$j%{*Zs~RpN|8Dvn{W-!y#^w$>me*L+NnSc?s&uTsN_T)lFCUPM|Lv44?Sh4 zos@S7wO|r#9P&6@w!gDOxN)dIvJth@e){=r;z&w@C=Dynq6YafPTC#uUMe#x(4`bB z2Dae!Kx)sg+N}c!yWLx6S1w5hp>{R?*4rtf`O4z$nVkcWJzfET*UaG>9*su#haGzE zD*wKNRK~NRv4Bpa8{|TSt#CeREPIKNOjW=uTN!K#Hk+a7g6bZ^KMPbtnD_&kvzaX7+1c>5>zEvy}LN(ozVAYM{vE#2O*~n+RvR-M1c#2_EAonN5veV}o z-)$qy;&NZ0zd#27!YJ9&a+C%;^>_xtx~_CJco;7=TY z5fypygfmw21C2d4U~uDnY7o>8V}bWdcCgQXx!=g))fU-kGOp|4A?l(vbRD__J%(Nz zS2dAvEc)44+@|okVE_pl%TZ~vZT?^YExZH`L{^&#R5TsyX$o)_&YdT*4)RIMC+qw3 zX3J-BzJr%{?46HW)=w`@9Ja$b-nG_?X8(9C(iIs#COX9MpHCuy*(PL{5P%M!D|21v z<+7pcM!D=82H@fvv8N8rmz7}?o5Yta17)LSyu$H#&w>$*H@Qb+QI11UtMc*)hD2`K7>77Tukt*+;#i!6Ms8S!z zn^9jMe21o>&;pB8J7@)Wk#Q>MANza+@n7=9ro4INcnFkXI<;|@hYk}1!V(8m)JE~2 z**6S;m#m!wb?b>M%^Rn3lQI7@J8%U4siw!2M<+B}{m~zsh(XAMoc7;Z@F#;|_~pW% z=>f3zdE4G*FE4MG+r5?Td#Tw(RkVZ-{j$Tu=qdCr^m7O~9f7F-P%nC?Zicm^_BqI9 z_fj^lFP64J)e?l1fa)C)WAWre@4Ra0NCTrdw4sDiUt!so)mK!72XASY2_MrOx&sNlz}!oV~(54dLHEWGdwsO&9VN;sAL*$KQPjbvv4gqUy|dAbfpC2=PC*rPmW@pJ0r zXMwMaaK=8nxZZBW*q7wT6^=$TZd+R@CXn*k+(#*T4SI)VJ}DuMr^%dQwwkRDdZ(j) zT07CH-G(M1NE0stTvm9|$Ngzw6@boJx~MJOn&GAnJQVi*Mq&bD?)L_w9)74?o?onV zUXPikDBjfI>~G4S@MX%o^gX-F|F6Ne{UJ_Mtt0pif{SH3!5Hsc6g~vyNJv7?K;5^X zmwO&qeqag0D>i)8$!PS#Zc`mLRkDH$X&B{F7eJF`BAG`sv}Bl^Lc397D_IZM1{Vz(^IhNTpbw*+ymdPG)RiQmn!B2jnYf+U-&i zMt+4@GGrsuUV@-@gZ&Ge-qdr7rS>i63&Zr{?fN$-p(!nTZsB1sK)F0GUzmoS$k;)# z7oNFSJU2M=Gjz*gK%P@seU>QLQnc&UMhK3v zX0z1KSL#(ckdr?`H$Su?e~48a3e-!pONfE!-Oo#k%A}4E!r1=mm*wq#u!__Iq6dz0 zJxX)XjK^tb;-QxBfE81AE4HfX)WwTc8!^IHOxI-FDi!OF1%P?;#KU@6!&hdiI`7SX zdd7v&|17|Mx2&l;f6{V^bK=wgPvIG7ZJ}3ia6j1>eRp-CqrUy8^8xJ+!Rtia!_Hei zeU7{K6Pyxxz|K1HpVo+klJwORt{JUObQ0~NJ7r>_@+c&9@NAkQtC3g{M9y)%8jZF~ z8~e;@Zh*`4G&JuL(_3jFLyulq|W@b z7pyEcK)SiWO=mivJR_YrQ+@7(vBLRw$Y)dWc553n%X>38P$(QS2o}Ku0lji7ocRs? zGX{SMsE(HZQ~LTh8|dHcn>s7YNy&F-tR%V?g;4s;a7%!&qV>JFjBTq=ySm(kKsJCzsfuAX~GFon_;U=^Ysh($PrTv($NcRPO%xqaC0?F#4BJ13G|d zYNl<50HNs^nrRw+{9@u><*2vtVU~H{9v`jr27zd_zsJHgC=5MBwU1@+11S_A4vlon z22uwo;7mhxAY?TfLENALzx#hdwG(<_-kSU`IAz`}KllK&KLAs39P`gVf5h{D{?ARv zfj{}c2kxUq3kZQ<1*;B7ehhDsO5V(H3x+^v6Z}Z;CU_LfxQgcO4L;CnQoYLkPcE#D-~)-)NbpP`Ic|)Y;GBa~6R07GJO({?Pj!O=ZWAGgWim{}6XOeT|q$z(ENOcql^W@8wQ$J6mR{~r~yQy_))P$C`e9x9YH=wF?)nT(~eBU%2qY3m5VW z7cPIN5S;Py{|t=tkKCt}kp47MPv5A-OVVf_yhbWbbwwB9HzLn-7+*zzX5+6E*N#jp z5={63Uk0WyLPdnY_XaLSUI>&i5)`}0N&sJC>?%@p0DQxT{}J^EeI5`1hPO9-vh(rqcPrRdiuxd^_I%_mtA6bEHL>2UEWAMrJ>BGp@J&1W7I69%>t_%krVh zkyGfh>U?&V#;QTFhB2j;I@*Lkt!BV2Yp!NkiR=C0TdB+kfs@thnCI5DpwJoJnJd&R z3$F~$o?RJW%c`2H5&^oGZe^p#GS=+%*S4p9O|12M%`s+qeT68RKRZ~#cx7;QK8gy+ z7rey|iqQtT7Tx3fr337Wu5^9^9u`>DlnMo3a^9OYu@d-Re=0p#bK@Dd0rB1@OB3{U zAre)gpzNd(XKXygVYF#M&L`)JPAWur%)ivXVhA*E$NfE!i|w(Kguz7erCu8_cvt)i7ntGL%^V z`jE2z;dN!ee!i=k+n1-wVT#Tm+$;qPV-z*R+|am|urw!SX9CHm7(h$75&g>sj2i_H zWE+4qn1UWw5k=Djxwc} zw}~oG!L;sU>K#&%5C7Iv(RgoB6M&kc6$4VVsRFMDoNCGRAU8e8<3^@;`el_JzeO{r z{lb*KYVGmBvRf=UOAo>x@mrYh(?7zCS-x)LGe4TSMF=>Yo)3P_THY*jC3_&THlk_8>K0;*o;>TlN$GE-Xb&O6 zV*g4VptHx{AN1FAL+(tSOsGrUmo3!P;RekNH`yLF;{ouA&CQ5J4{N0?q{?@>Wx_gn zPL9fiU|7MmryVdB@P9T-y`FWcCKOD$wG0O37w;B<5E-z5*D`lz@>Hq34%~>*Iy&pi zc~6ghC6Ia&P>b(E*E6a~-MvYwJb!Si^T@Mgwrv89&Dje5?7ba)NKwnSg)!`d*>1k; zpVZ2VM-xI`3^CaM)hg=hM535oK?4O;klMHyq0w(7*&m~#QjR_E>|&<%kO}>MU(Jc` z`wH5n?*}ZsBaxnb*WPZ%>K_WTp}eD?S-uOSCh9Bp6Z*RHeDlQVrVGGzi_Nv$QMP&F z30DngmjP>gbH}DpX_j>6{lu0Tm2A7jEt4JtJUnT{wlc#KXlcjocneojslsaZcv6rK zFj#UDx_*6Kg*z5SkZq2lR?QEZl7`Vq$O$@6=kSYH(>V#Jeeu?0EUj`8;67t+O#zuj+}(X`7l{edE94G5n%;dx&_1tZXu`g(01$vKSTpL4CwbS0PPk z_c%j8Tm$S6DiwfAW#AZ^F<&SY3iF0$I9mArU<#xQZ6t~No;TQ`iPc9gap`9&(?(Z)srf0D(3{~QB ziO-Q2zPGB+KHMCx2NQn*HCFECVL8ApC>GtaLq=>Za>FCRr4NM6W4 z`zDu`(*LDA0O4Wl2l240({v(bl~==R$5GNL2E`;@l4U$26MWSOjklR3wBQ@9lE`3W zcIK};@@-lLd1~tu?9V)nYleLvgE7n22{18WnKhj&{E?Bj@c{F>>Oa93-me45sipco zG~v3?Fp%~^&LyW*bl+y%mz?s4ynK@{C7>5M^us0RlHp*}g54Fl#Ca?RQcA4Ov9i%K5HIwV zOI-J_csQV>f8`-kBQRf%aV&C1%)9x!$`wTI3svcz+-mn?w24x%ISXmM**-;3h zESNFRW6zW>Hnv7`Dz+y!p8G7NXG(i(Yw#`?z@2^Q);N(f){k>)8Xo<>ANtUJ4*W4m z=b+$5C7}^P_>+k3MVnx%-AnO=0c(B5OK9}$7+V%75{dezqt0=)VxfFonwaQl9x_eN zSc7!Fg5Y8>*BbVlR<#-phwMsC&;b@A=p*BN4A*ST9=pu@$vq+@lQIu9 zu6LQSm4njeTj>M#Jd+PLX~@d5hz_Ds?|zPzCf2#jT8ho7gv8whYPjD*tQ}!pxQ2Sk zkA~N2jXdur65@x(0OfGnGGD@s#=^M9n71Y5;pm&OXrJ^yhR;-Vo?%>rUgms0!*M-} z{C_m><46Hu3wiwo`7WVCh0JYJ`=XSOO%L<+T{6e#;Ut~Amo9NL^q8f2GvpyVT`%Ko z_wbJ;_+eS*cMILeZT4Ri+Qq9wsw&n=GX+|0L ze;f6&SqO%Cu`cXK{p_wbVE>sNoE9M@UHw1jKi)vsdgz<<m)aFRuj|(lQB*&niXyqr zZ|pm`9KtjcH4(NVmi0R`{^^m|H2Bs0s_Htjb8%7*W8{rLoRho8Yt1=d4{zaZ7zU_z zZd7jHDBLpvOv@Z`J~Ay(jz{(RMuj<7(Ktk&SFy&Hk~6v8s64M-(rA4RBt`+^C&vhV z}uFny!PctCv)e z=Mwmjs&B@+-B&^}wR4NX?5*#Ev6JMDavaxd13w5HM>TSLd`@F6+uU3l6hf*u5g%Ml zL36QFL1}k?W5}>gq%}Tj8K(R0ZH73Zny&JsV>}$*(8))~=6r4FA~Xt%!Y2oLWXMmE zloe$eyWbZ#8gc9~0~^eX<3?kQ3rVqaEv)zPd_HiFhr=6s+uK=I)U;xjZEyE(7!L8J zHf>Z7TepFaY1>8M|H4<1j>_oVxRL?NwMq{pJ#=K6eJvXE8)Z@(Q{tZVtXH>aEPYw< zecSi#+PyW)vS)wF#G0t6{8o)Uckgr8wBeIRZNO+lJ+Bdb#HwPISOAt|OtmLrt2U*Y%-kV)`|Bsl`{?u7`;-Yf7RKGJv4BvEDDTJ+KV)>u_`##+ zNnqRFo<%79n`8VkSdQ__2W|Ya;aKotSFi+Gd+*~elePB--YT@8wmkk`O=j+=@!8a6 zc88t-!5HZ|z5rAqh_`TCH{(Q>!XJE9e)*UGAmgXiriNp|AM@V3u<+hnN8>Q_w`ds3 zM3b4DqxP_G;EkNIMgnr!=|Z6ZP$(2291*&!Ie?FHomXa)3fK81UM?AzicFlJ5f6Mr4)l$r9#exb?2)R=WOh|iJ+mRwuS9S4v@7oeb4FBA!5x=vYjPvSL(o{D1pf(ww8Bz2gumPbX?!E0 zWJqhfWo0psGqy7rXi9>o6m76_D*28jI}COh;~!GO`eNqB7we&-puGq0+xyTO^a<8z zmv8{{un%!|@VBwV<~GTr@?OA@gO(6#!BY;FR=R9_K)5!sskQsdX8#!h9u9BV?k_hd z9v5h{`!0Y+1X_Q)$yaJFe?Mk4`_GqMWnABVk|~T)^o}yfQT&r4h=^v-&Eyl(0T;b`v$Skftj+(;E#Rx*u3ZCu;!(%*?9=2h@&+$a$=*% z35eG-o8Sl3O0>`z!m@oFWCKO33Vs3A=iEFQtMv@n9Es^(P6L#41TW~>t zT3JYo4_#9GEGSScws2A|6v_!Uk-5*Vrdm4?X$5Sl+AN%*`u-G<4%zW3ymnKY%Bim7 z+e{PjFxzhxi+~pv=DXvLIK)!5O&2uP`c`MBI%p(qzI1?|Q^5w0BspI~691|h@)vqs zrHWE+!$Z#+Y1EQFiNHH zB8;Hf$V%KQtrGKV%e{5Key-(ge`9VAAdZh7x&BBdNoCWl<1;S~sKKPd$q$~W;CLh6 zO`@+x&I)@pwZWN78lj2(I{PH&^DWckyyW?Y$rw~?$;$S2vQn!8?siWdYlU^0+KOJ( zHQlzkrj;}u!f*gzRkikV;A&d2UJL8hYLKU?e9;R|)BMoR>0`48yrA8h<62eK%pkBd zH4FlOwNmAe0AF+5Lfd2D2u`95;aR6l5k-}feY|ly*1&;%RGM*HSApD8k>1~H*y{J^ zq99NuNezN%uHWC9`?qP5q$P_mzl8f>+hvt0SkM}cPV6|06^eC+1(-k(G#gP=(X>hw zHJV|LxW1DS69Ucu`UK1rHFg}PH5#3|<6x{XwJkzpYNb|j=(A8fwTc4)|I-hlTjRFC zrSTb$xik(W9&fRl#Yvit!%8&w;i~JZqL>1gO}Yz>y%##`Tk{=k#kzc|4SL&$ixt20 z#&i4&HhNmI4L?_=+59`IWXfmzxh4;0>0y@*K*J{mkd<~{{@`{2x^IM8? zW_d30YPhx`;|;W)v4Ki{F($z{=-ZgdoMGlYdF!-e4hIcDf%bhp(ZAr`KvR^z)co9W z4otwA?dueFoUrHQiUey`xnBP!cJBHS4P+J~Y$>9hRffOOBWImgJrKPt{9Srz5aKK% z+dXr|w&0Qh%KWg_Iu$T3@sI>AyVGBf!b{5*g;3TH@E_VI1GFI)(CUlLB^|T2l!4x) zrMAq1%sBe6fK;j(Ml1w=vWJO^CL#th<4J!X`Sodn5zel_B2#>90K!9n_9pH80H+8; zLh2}(yIYxWOj7QTzHhpL!@3_UMb!13g&i4?deeZ|55D0r3)Y^|z8qWG25CLjM*EWs zDTEA}6dKwGAH=G;VrVK0oqDYof^(%)uk}i>hN&!ov(jX*_EE*sd(6-wTFxMR0U{sN?yduU@x3Zx5aWD3|B= z!gX-_;U%-!+!(2hw3O>LUqQltf=!=gvPZpT*ev3x5zJ{Lw9$;)vEa1(-W=ZZ2S(Um zZkq73uBM9f)NzWl14(iq$R|NVj2` zMKCD#XFGl81^r%e<9XXuLI|x^HPy_P2)CvIK8H<8YJEJNEneq6ub3FR0;j#dzAb?nLmGC@V+0IBO&!gW3q8GK~)kq^h|(OD2~8Teb= zE#;WajChUB#q2cEt8w8C`XR0iqs)l5vJ?&jEntc-#jVC@OWVzFr^VbkRJQs$9*fF? zUInBK{f35+NDmHF>0zxK+;;$q{KV}t*9td_>F8F~^@+niFiZFo*n z`K`jK;wS(Ja!O4~1RhRAi?=i6+*<KQt4py-I^As$8;f{|x3T>1sdid+ns`K&6;B2Mz^HhPLUjX|Z+K>UM#7MB4O9@wh z8$SUrpzG0H=vUF#IL$(7NAQD<+i_bBKQjD*-XpA+WjFA`dd1&{uP9mJR=_&_q3v;E zx*BKE#>&D9P(f3rLa~V%t@!N0sQH1&rX$Z~ePDg`smV$f#Od|*NmWS{1xZqP)pUu- z6;)9Tk+^2XP$WqZMM?89_BBZq1(7I5#dL}2HKc?!bHAPW{)mNlD27Z4rJBTaxoQXi zf>D)qCTWxsDjSNcsH);BhD;^-_0wc2KVKg7xW0R9!<66FLD=7G%)Xi^sZQ6|*6h@m z(GN$y4=bHRu@DqJLWIQBL?{_V>$)BiwiZQ}fhuNPJ)0Y^<9^y4z#Y!ij@z;CU1llv zpPvvDMu`9bvLLF>3Rim@SA?y@w>iV%>5ZPsD@|1t7=S=1BQfCN@J4tdzCN3ahQcHP zfT{|BN=N{gZdqGn>(LxQK!^&Us1!hun93J7PYniCmeCU$g{xEeHGc1p;vxk6!OqTd zR+0!pwqR%V9t;9Zd4bj7g;}~$-i`P;6}5T=J`h>XQ#I>ilAb0=z{!Gn%C=2_AM+;= zjx80O3zih+CiVQ9v_Z<)Iz7NubY6*jp$<>ORR32OmTTArLqZr|6WSl5-9!8|sh2Dp zh9~(zRdwA5i7Do6YAQ?ue_gBbR^dZ2ur2rbU_BQMuItBPqUgqNCo=0=GszgA#B;C&d3=LVimI6`OX1-2 zB5?5P`)^be^GIj>pgEhe@)KJ@3Z~9^UYhrkYU`Jv6Z{dAIKZqRa(!f_^p-0EeWIMV zO^@tOTLVUT@J{T$qx+KbWx%}S9H!BS^6#?SE%Mwjgv$Rd@XpkJ8i>~lL002-9F;L) zED`e(Fi=T`POB^o4h`AA{#djaI8}sj8})Bwbv^IY7x&RaLd>tyZ(?dw~5% zkfW~t=Wn8%TkWV|TOxciYFnJsn?%bh8aDK8Aj{Z4u`+0cwk=DVZkQ?>&jD1^&^1Z6 z?XWReIpJejCJ6oZIM(ac%tjDJgZDJdTCaC(5Jf?MfZybkVmv}d6OtUbO%Ipf6HXrt zUB_qGRR&AFgk}-x`J_nNE*JMRRDkpzw5$v_8e1VU9pgp`euVPnW|(5QAUcES@oEEf zOm;Uo&<8O`5&?cL2P-lM2tExiB_w3VBXbDd11ws;n<&h(9cz7kZL#Y*ToeT&EV}GJ zFNAen;#@FVtrac_3Y>g55r`m)+;O{$YwPQlV_QslUB0f@!v$TC_)4o~2%JlO1C$r3 z29=>2#K49@6aIFUOBtvJzs9m*4}Ov8+_+H2aOltj54^sr6a+$yQQ7IF3-iT+$2#z7 zgAk#hRA2wV1BVU)E?+RXPCCpR6z3Pxj#C~Xgb})Wfd93IhvK1Qv>8>ym14@^CWQ%2 z0RNe7bUjO@gKN0mq^_hCOGA(GJ{L660Dv3?UsPt{^7jb5&lPuADk_po%|#v)?V2HK zX#*Y`x{pgp2-hYQUeoSglgJfWTA1#~Rp@m&jzTnD(?p5b&F113vkl|$<15e)pF^m5 zI+HeOn%?sL$TKJL+X(u7n&6${>()|oI!0bYI=3APri?_AOl}hc(hE}0$5QV97L0y6 z#RryWeR;+3Yo*lE4k@WZmO`27kx8vJv`LVhL9i}WmEy*wF2TD5GA;FepoU=8!bx$+ zu7h;$5Gc^R)IW;sk>_X0-~b%Q?6#JB2@AweUO@_=b0w`;JG)yx#RgG-|N78DIT=mPW(g+ z&hGTi(ukj4xy-X&?;jdc9q{!o{u4##f_a!LOt80gQDD6u6GVO67MQ{hn*!IBr6on@ zqOso3vAq0n!k4!#8{*4#4Ai&HF=7_9ZTdC53{kn=kS277gynb3$Q2XUofRj zjtv9EsWOj52m7S^7zz~5I^ zItp~r4ONI-b(N_a1<)<#oSC?8ou+=`w3;|s$+A`SK`}oo2_Zdw-|S{=u?1KlMc)WB-e% zIJc}l&iAJHo5SA7xn-TgzK>6FuG!kM_KuBon-#~?*N%^~Um~`E`t8bZx=ETH+ViudK zfalaWp(K{A+i1t_I8p22pUrfbB`^}cA_!j*s;`yh*9yYktIW=!rN!VwmzZr@ma1Bo zX|q3iRj{~psAE3;T0wZNEPvHzt9XaD+VNHJV6eEf8Jn+~HXJMzHS)JRHtONMLo4pA zWu2(^Ymcatt#x8+cyGAYhO3=y4e#zvXf;Ruv9^SU{R!S`H3||wKvRHwUi2^^7edd1 z!4~xfN5_|zFcD-*&PjCL0O_0rSoVVBOG{Y!qGvqkR@^TZtFtS}FgHHNOH0QCPsTv5 zk-%7|(m6t9f#9X3T`VLi8+Tq-?{7hdHYhKSgtRV_>{sFdbMsEy!%(Qo9+OaIo z{VhYE%<>-GmiExwWQMDI*TkhD+ajrxnusJJaVE$jG&S@~U|D9af5}kb9k9aL&cV}; zBCElcqJD36kpG-cMiDxMkf&(5T^()W(u&?e7;F$#Q#Wow)XZ&rVV}YT=o&~MnvNSj z4KU2Yz6>2b4nG?;bg5PKkK6~^=}?e6Y6MNu9XUPz4-?num>wJ>c*($N6x(i{@Sh0? zU1lPCo&=^f!!yq(2?mR~R2bx9g&5^-e}-QQpG6sZSzig~c7*H+#m-E(l;>kd|4@abvJEYl z{DPYip@b4LmZ8Q4@IZvtCf=~Zu#W-w_`*WGnk-@!|YBDII***@1A_L>p1Y^5IJIp4Fh`Y=&6=jQrl z7vOz|bx?v3bW^D03L2S>TFvWZ%P*#BqI~~MHcTO86xGgNqJZO8Vd!Hi|1lz_Md2uS zqR%#+zLgW+Y8#h)DOJ5>*fHH&{5x-b>s)(EmxCIn~j=BIyPes@0CIWC8UR zlmMq!S7WdUnF)u&9!NWvP_}%~Ej^B63X#&;1HyXWcjDOb=e%|OdI%Iss_Iv({=0+6 z{c6=$)t}4%qHwU)Zdeo0E;h#<`9m&kLs{27@7*7H-1Bt(=kkB(X{L9!CJW}ZQ67JC zQmZ2Xrl&vwIxS7S$Vz-8K#LB${5qE(=f>c}!r`@ca|_Ha2vdHXyZ_B4E#EfIuOjQp z__YH9Bwqb(_!@jE=304*|HC8=CgpGHxy-ukrHtV*r&x4e2*XCV+jy3#DwE}BlOsox ze*cA5uh)vAOF^|7c;5cBFEqN{Mi@RT%S=_-v;BT@p!hR7=bX}y z7&{vWVATbQf1)3ioO4ct*$xC6t4MgzExCGP&xB3WKAAbiM*`xbZ3?f{apS~ic_#^o9Q`M*&aW) zE0KAaV+R$yx5;0&H_+{nvVyk9r?|bbhgQ8Fu3iF>clwXvP}Gi{wk*PvhJjCAS5>2E zE^zLHH0WkYvsJ-~iWxIX2M3dVysfLM;aIBXRI9!TX4DYlj*!xAEz@`4pDk%#{+Zz4 z9dLyG+25*~X{m-|76Ae$DA;XL%3n0yP_I=~@KV&CL1iDMp?{e3KGWJ*G#MEh3lrI0 zA#OP$A5zh}6o(5U#9@RPmTu5k3H)H)lYh}V^q>um{NU%MYGX}tT_uKemnBr!d`~@* zZE%rO<<8XLUheDuW60{dB*E7tL98{_xa&F~U?Ta);0CZDicYcMoX9q~BwwS5&@bs+ z#-cFH9`Gr*CrY3NK@JdAxsoo|X4r}%JKtnpS?4mQgaTl&%;8EBV4zeMG{2D7%Q8L* z)g&ZhjG8WP%dbf+Gr3g!oZ`yC<$awgn-M|_1 z_^x&3jX_6Y=3Vp-a7el*Oqp(I&osQmi&F9WXO%^1;b1?}BHhgXDpM-iw`e*V z<)hJPG@6V?2hc5R!x5B+MSM~_9js=!0~Ga*7RX{6@Hhb>U_gI#_U2~**FIc$xM1+` z1TE$~0_Wkv!v%U!YV!!)eQcgJHc~=%7;LCYw1wm7izew)Zb(Vs>Hi{m+sG^F)Z=B! zIp>rTm1fTfd&#-+OqUsls)bB*g4&F@vf>KUC2Y<^r{tV-Sn3@2lWf!w|qUM0VZ_;`yp z70I_yrVrTIQ{nt!K$mC80@ab-S0Ou2Q8*7i_mT7Lo82&-W#e(74NZ`VP(VB$XnczM z8WjaU!3RMc^OCRe1AWBc6Q%6Yvw&K)@Wmh=q$~wk5m^l}znZpx;5a{Uuwmb*51|Tw zVEI_kaGId^3;q%;?_Eyz#^q1x`lpWHH+>)P=bEu)JUE%juS^v0g!d}sehG)#2zrIz zHE$4`Wajr9me47vn&f3y5Rg>pUO2|Vxvo&y0{rZ*EsJRk+lsC$PcUXGx~_Pq#!Mh)SZ_l47YBMJ^=i#p?ih1tA=eN*c|TpDmaM$ZWNYWyb>g?638o zrR!G>v+#R!U+ZOjoBFkh+FeaN@+)f63;k899mMTvC-0POC!W7<1n zc`WPL*!k(`?ybbD=+Uh#M4TWd#rrdS#18-tRS!&NK zTtP&|K~=l!DEzJKT~&d&=Ver~ zv!u%|FH-~7sG>a2g zy_^19Uw<}PgpH`2hyG!k=FmEySn|UKkb9BE00jn#9X2wi{mys~08D$ZQmKoVmX0sY z&G|uXzFY>fSgrVBWqh`O-L4^Ih7xHiM3HRF&6%P&NGf8_pQ~)|Wm_z2_J*xF__Wnz z&GFlOjQzLiwkGN)9X(<>mI?%fG2ldJ43uZJ6U2oNT@$=b%`jj^jSO=#NnHP@eXX(7n`EyFj}TkrI>3ozcpai3I#k3V^;J zIP5H!B6-evk0=qnq!U}Iqz}M|H>%`n8bouMRIe}g>tUEY-&(vfID2X^AnVZ_XqsEn zRZRp}X)S`!=&B~NCmZ?Elgt%<`$?iqEz^5)v%8>o%M}ai6XL&#eCs(ZEuYl7-OVRG z)1tB{5&herKy)(#*R^?yWK=|L88nbHsG~pdgkPizR>Ml8#EPKrlMq>nzQ)s+CP4SS zr-2UH$*U+2rIPl;c>0KEIvol?J3X-%?1ZxkjKZ1LsaCtt(SM`nBJ)RTLjW;5M{4q{ z{RM!c~=MWGd5cnx*-HYK69vq+Gw5XNGG{UCaQK3=OA=;uj5HzM>WzCO6A>L@my<2 zZ%#Tz1>@8k4`(@sCyH1^5XhMW3?*)rk%I}ngpJ%~!bMIhpafkfg_Eu&^}++!y&75c zayyi4MrDb7S0*H-v-N|hU{&hYlgEaD;+%9ei@*C!Q3HOp) zHP|dLmL9Q$et@LFV+n+j5kMJ}krQnQA)oF(J=9DeeS|V8BhxZLR%?uBS=YLzk+fbv z55s+=j>6&a;6V|3a+N-?r%Z3|j3#N+BPaSM7}Olrp}QkPh2HuTzG(ZI7hk2R4{`wh zpsKyT= z6$j6u7y&x2w0Bn04U~Md0QO!%5biTAZ}(LR{?>@s(!UB5$h-djL9GMXhq?Eg@;mv} z?ctwBJ6TyUe8Li$O00&JD&pVSrDPIX*Rh8 zjG6^`A=(}fCEQsBd6<2E9Cc8xy)e`AOay=?Wz<&QSo0yk^aNIN^#p%PpA9Ijg0Hoc z&mu?X&~3=i0yzu8rngo_UrEd^iBaf4X3g*&oD85;I526fX;;<${+F-ey$+R2CD);} zTykAX%jMTA_cv~-OsV6RN-}lK^tR&^PugP%QA82N3$P%Djk6X zkGkg~)kzAr!07}ASTOFWh`j2T_7lkYFM<|db^|<7HVon+gW`NVb}o)C9?Ax|fjhV# zG#Y`=RZU@5wK_jvty)Y~RW}lJxDjhQV|XpX?JMbIAVgm=!%Ep=s!Y{f7hoOcYARD7 z&pZ-!hqBV*-}x1E9Ni*V5^(pROsr)}ihlxdk7VeA-fS)}H=DYk8I@$FWLZlIvQch zfW{@v30D#HnRwYQ4(WCes>RG*PKpKwb~&YrfYsAf=w<2TZcf%Tf|~{xEB94?lpoPB-fswqb`XWLOj-B{vHF3H)0BMi4X|hw zI<#@dI0(+jPla0z%)XSv3A`lrO?~&Zg8@=ZeX8XJ@=uHgxcpVnzyL9ud2`_%x~7Wb z;8S8^YL~IwZaW@-4{c*$_Psf@&LVhGAVi<#QLX$VFa-ukbnC6FVv7xXj7=G9j3bO3 zomQ0TIWALDAGCxl3<$+UFJp<&Q34>k172JDN2c{2!+4Kna{iw8aQyotY}yjtjc0zOB%>C7g9NQ#$mp{VY|{f^1$zFI8cOGP3EFsk2e%n@pI zZ;QNy+UX}SiT%xp<7`Ny%<4abcifl-(=3I@hCllJpOp4|zd^5q* zAB>~oe0Q-C3qs?SA6G@eX)N3}d;j5nl`-}2)`;Yy!<{y8{pu7=V{p920NK5srF1qn z_8zr-2~*srqwxD0x$%83AlTr8YZC!8OI+{Z8K8I3l*Kw7JWm=^R7bb!_a!cvp$EC} zg2zad>K?su6M|s_0N|s=62;^`2(Z(;Y0_?yDTGGN(P;dCL+0t(ST+4ineewh8mwYmD& zz1g6D5tVGr-V?w!Zs?f>MIites+T(9%ZPO0d=)3#?H-F^K#kG1dBp2ix61ng-- z2_1Me`~=Sr2FnmpMUkV1?RdF`#bw6qPY=*Cgjm1Qpba}}8D@hiXmx6;Th}$f9)8+3 z>~SUnwBd5bJ>5r+vF_omKISL?z&z<;IKM;pA@D)lFiGY3;~DLpoQIZDifQ+l=ywlZLa9v~g2^`xH{6YBe1o3|bB_m%L& z)s=q#xV8hO&Tw4J_?_pDe4Af%MB)n_V82Th@@$D=n8oB;tC;V!^UTbWApB(DzOXX4 zd!OIHFTe}wGH3vO7H7l#Hysk4i+KI5tzuSTRh7I30J1_Dkfw|U@wFKc5`w`er z6(Z=H?e{%w>Pp0c0c9<0BNNL*OANz`#|rACZ|05>vmvD6K(>$&H@W=+5&- z!j$2&B)^{zQ0}n_Ml+pC)dX?YkTGKBJDG_wW@~`S*Yg7j5rl%9KcSU*5&l>}m|Jpx zGfdgFm zU{B$hmjb@57$ZYrLMS>BFO{47`^|ERSMAiMn%gdwl*-9npx}t1z-ABi@LTW{O83fD zcv15JC^YA1zO+>t=%0Wmj5tqNMJzSbq51ZE>f?1?lISCx5|VoJH;BT*%F1BSvLz~r zrP3jZK=W2j(>%l0{}!n7p4zT+o4cB(-P!~q9V(SXfl793Fj!ex5X2kiy_68jAElC{ z|0OJi>lVycsOJv9qiie(H$A5#SOW14K7XEb%eu(mzMA{gvXt;s$~n;-j+*C z$G>ay<#Jr*9Jqv;z7=LBk;DTvBtZ?Kg7!y_KpK&ra}Jt;4_82a2FcW76L;pf7Mo4m zAB;7{ar{#+_?}V33KF@%0%v;4_glI)8kn~5G+j*_#!aLA;Z>HP9NmT zsj-JTdfKJIablSXWLO3Lul5=MG;8a2?AVe<7pUfoBhesuB>x z%Y?f}a7k@8Rf%6h$J~*2*PVW$9{XE~pNW!ce{7TTm;~ zPtP04JIrJt4cxp2235ZOi)0mW6&a$_68Xjr9=_IElyC zME#>6{{d*6edYG_7j1X*Ka)a#m7}rcw_fC15 z_MwMQJ-jxybEcDCkdSE-N(0#)^(HpShpb%K*p4G^2j+)D8N}AR-p;vYy&aU=!?Q<* z0jM(X?Uu#)+aL5jOekp_Lfp5Pbn;e&TKt|!JR|;>Y9@A94)g3$trX5RVy5mm9Rr8x zU@%&af&c_l18ibZP=kW!4S=HkcNN9Zm3L~|yO<_&#%8Lg8S*|HGC^1+teen3fFOuM znd%mRXqvTJZ)?7Tw+uyLuTxD^eZAq-dMtcIY$`y~mVgq6j0vx(TU^>&KHJp^_%p;q zCCODytobia-e)L^xF+fEb52o{4qIdKSJy;EG2W+3U%`p(NCvfqi>q5W8B}r_LpQ;X z3D-G-!f9@)H+G`P7ZpN{%LXONMOg_S2xvU^4UzX$LkG|ebx%HqG-WuG=`&lLG}v{n zLrC$EE#+^rd#|W5Y85mUL&{o}F!o&P-KS97$9bGgLk)c!Z}nD$GS?gQ2c8>NZ->w# zFkh8(wy$o%Y7-|vj6XISKyp`pzr35N6o$hcDz&M_D01I$H~9PY&6zj2QM9lnig#Qo?mPiw;Q4b?<|^J?V0~wC#-I}m!#B@WACpYr7#{%uw;2bGf=2}- zkAqJ!pLWBA>7JnID=OFPswN2%(XV&C;ap2z=3J9knXK46jP&pMxy;)O>}pvk`?q3N znL=xBD6;>grBbkB>c(DbF9xiQcyuw&`k^`0NAKdWBkDf{j7b}`*a?XQE+-e!MC0L= zREh>8D2eOz(V)K`gK|i~n_MICcY$2?5u(HkVfJqj01%Z5=!T&ivMkDT&Dbzx3V;D) zO_D@O({w=sN_7$_6axl9DGQ~FA zJ~`Gs*s-gYf~zTFRBqf_#zRCY4$%yHQ%C&h?$vXV_8U;b7VIu5uiibp1|}YO0N$?= zzW1JsAUMC%X8C+U={?%m9w}u{GZrS4@T+lE3FLI*LvvUYTm(Czjq7tPDT^aT&ZQ zd?Qzi;vk{%HUv?S(D*PxX|&=V|E1>F4Q4Vh!9!>BHpj2yOjcxo*SU>{8BJpPg2K6S zfdMd8)0G?cdK6t#834QR3}bxkG0M+`0#VMG897RBdZ(>MFVIn=b+u-$IBv&veMK3m znyxSaHV4c1-Pr7p=hy%}7*j_d=7R%_$JB+GoC6_P<8W+wl{t@8@3{!a7VLLAY2`VM zJ;77ga1)Fu4=O!yA{7JJzU#q3rZ?#j1MkEbT2P-`+>U7$L}@iAIm0RQxaG(h>Ay~K z-41;Cw-3$!_`|TghccS|GiU6y>5K{jF}bAFB3_Crzo$O;Lv#k+gy~?V#JedbQNM%(%6<6#b+m z>Fgz?TZdDO<}CG&>+Nj%q)j<`E|ttX#5-lFOAb(mjM{|hi?Qlz46M{4)X7re#g9wA zs-Ltli@4^tDH~?YvomRj$6P;v-{MA}BZE)CRDNHX$;WN#v;Ur~6AA|mf0yqSjVSja zU1I~!^7TcgRfdl-$z;$lfLD{|sg6u#cJkhwqigC&`ITM*@qs_#9Ij+2ye>5IL=x2hG595@D^Lb zZ|SM33AfHqt#9xI)-_RhLL{C)@7dqxR;;#}v~g`@yP1T^_F~F_K>* zd866qKc_5_&(oQoeiuNo*qW;zRR_pJz~E=#m#S7UR+T?K*fbf5W8?H#dY3C*Ktp+&|UzD=)8(W({yM5AQXpB z>4(lIMbmV(P6Rr4tDFb6t_vz}YLet~9n4ZSY4?lERnkaQS5?JN3X^c37ieap9`tlT z7Y5;Wa$+O{pX5|%6BFgL9=}4wTkth4|q4r=~pA!x*gK}#{}z|a$mC< z9F=1`TAsaIYO(Y4+&Tf6nLpkOorY3;o;3tOO0#!giNef(DU;jNPVwr+t%2^v+)UcI(SOxAZX@U>EcRLu;mUa3Jy)C?d~bt zrCsoK6un##qH#Eds$lHzskudN7`%9!$D%6huaujErU@p&2-k${sr}?`Bi|?4OgmEg zh|THxs#P&vN0#7ZIU5Gs-bZK9{Rn~Y!XQ$y3|{OYJDtq1b&)76s|>VM!3>$6QQh`L^3+DlhvZGF?~xwoH(&2kfQ#;l+KfSpKZ8SDDl% za>Zg?$oK%B=8wkx930GS?zZ(PwwqoBK+TR0wpWr`bx@AIRd*hiB}wDvnUw)96gvtQ zG^W5r^^dm%Z!tvy0j~_su5>y9(G(24UX|-*`gM_GRq24&;QK1rh13WQS#BE+;V?Qh zvg4*~!K5Od8#C$kcAKnZd%e-8s$A)m=ugRbr65E&Fd2Lt^u6YQEiIv+`dW4>H@q$M z$c*)XP`fOmcKirIUA665JB7Y=U_K8$^K38- zNL$;h)&#*QwnEI*`yfX6MdUEyCVk83;#a@!1(`2V|(4d72~ia zquR`HD+xn4C8U@g34X>{m_;(NO&|q1l-vfPuQ-JAJFX?hPgqLY+TJXV&?!)@5}&J- zcp_a#B7bE6PzYD;RgRVr6^DVl;#2bJfMQ`$yTOmT&*1YWzpWYy+Wr0}o@ZuTE)PYs z)EsOCwu1@VPC1g|^(qzr-40!DwLIpyno%k?ctC!6j@x(_7In=IxGn^eZ3Pm*U5Yd( zsjA)-S>O~E*Dao4;))Bdahq5BN0{#tyVF^fB>7eHgV@!WQ#MTq>;1V(ML(rjiVCI` zyvpYslzTKHiH(nGa=gI#I4>DMs88gfpGSQK2C$dq7a2kTa%rDPHl18b{{iC$Y{3QA zkx;S4mlr%M*y~Xctp_Rs2Tb0tFj#G_5QKVwLB>#pAl)vmwqRu&A}?_q+)+syDLIcy z29Uy;q=i1{68K|595QY$ZA+mmVSwW z#qv^_@_Ybcy1d*}l;-ksIy(sFWtb?6DT;=wgke5{Q67#0lp%dhwak6fQZ@A`uX+S^ z$64fj(Q)eVs-mGN;ssGr%>17D`=|zoz7#!sDi+~|JKhV%{s^gNiwbITKi`!;_A#jp zs4RU<5Coz64oykGh$Kay->z5gFJWA|9|M$c?eE(DhFb~2A&woi3US9n$;c|in(uT5 zL>`GK%k8G-pt%K+^9AYu6O__x`O$$dlA`YGi;6__4|M&NzG?nrFeR5Yb1wn<1UaWf!ipv_C_A-}b1Lc|cl1wcnx=8K zo1Fbn)AW4(x)|@VY} zs63wrQP%PUZ;rImj~>HE)=?E5sX`TQ)iEn^311yNb;I{q|pTazIzn zdn8?#a$;JHong!}Np3rB2SOV!nl{EZ#(D{`ZOE$WP-5Dm#fT{~rfC}IhNDJ1HpccU zwjuu%aq|-k`~P~-R{_+0ixJaxO~S1G^=|hBwnc2)SemN>sH$nmswinP5Yu8Xgqd-48_?IBf+xeDhd%;z`aqaVM+FZBcY4wb5I=M zT^0xHJut0|^WW0Cz4TjUY49eDFoc6)p(b#?Y)#s1U`r5AIVDGrz*skTAnzN7ZqoZ8 z6dF{B3^cKKobn^3UD{nBZeCVCxQr1cOSjbCPAah4`9P8_F@9n2K&v~>ba#m!$2d{H z<2Lwe9B!ZXmi)9mw#~H;?S;B(NXMzw=H_ZOr_<`LYMNXtE(-1W^QVCQ^0X1dDsMYb z8+tn^QXg(iNrz{3m0y*LJnrQ`#^IMBm@NIE3!RI+O%KHK3LAb1y`4@xT+PHt^KQ@_ zU$X~u_lW;iVM?J`C}{78KFzVmf3iuSO_#lVZ|RW+qX}~ zZNmgGjdpy}Fu~Qi=o8|LTzA1V9@m0GvB(tV$3OZ`Rw*|crbTO(N$b+nzljli!y+kR z={j1G><0?^?=^2jK!YrHvXpxN?D_18Xl?gk0oTMff1z~Uav8SU3Z z=7ORO28yy^%)O><%)wsUn3Lr>qy2dO*6_AYtzNGI{K5Uv1h1Xw(5Ql=OeT%jPE1~U(F0A5kY4&V^2@`qFq z)J{+~blMo4Nnb8CLdH0gV@=a(-I7g13PkU`p)%RHL1qQW1)yA>uLQdYKO&Y<+RIc! z4jtnSre92zKQ`~Aa6@xgI2;9D_2h%w>k0>bdkNYl+5e(1-yJE%Lj8;NLQ#oqWmp1SXvCiN6}F5E3}^{Kuc zZ0Cf8-{SR0pAPP&GyyRy<5eUmGX4-6=Y)x`KvtKm{_()lnWmhvY7nFVh=KUkh?F3x z`n>(ZU-aeD><1;;$CJ#%`6x)c$BQkik{pc(Dwc&oP-g`9>{%D8BCc9P0J0adgN?ytTq_87ShdyJmO9;2Tk?IXxHZM9Jgtsz8CB`J+OznC%>5J!3~ zF{U>b?g3MrZ{L0Qc8=fIvM`uEF9=WGci+j`YhK2(KG?%r6I>uB-mcmYa)g-opsf}F z3U7W}Q|((=MFh9h!PFI)PZ>{1L@!5IWYWl;^N2fqT?#4I7uMQouVJ z2(nUvz5GFAUBqM?Y~vigu=kbg)U-&wEc>q5C#FRk4Qg7XFS@=A(slpyH_xu#dfT_x zZMV9j>`zQE@4njrFekn&y2t%-tcuXcJN>Ma>X>iu{<%5BF_m6VwH#wQ)$!im|5;tN zRKq!X)G<`+MR}k+))n=zx{~ilL#Mo>@sgviJaIf^T^fapj8VMfpx>ifTukD5SHSbb z(D!t(d&Sl+`uL(OYnm*t9k_Fvl-n`Dt)ngqA$1i$M&D@zw7QB*E; z`h6yeVqaoR()-e#VrtIx92nyv9m_J4WqDVYy^}OwaNyOc-LdYH`bvoB8}wpHlUPaA zdbsM62`qU^b8DH7K2ChIh|mHr&;n+^umK0bdZ{j@?ssk!=MxyN6}1bpd_a@3eBlDh zo&(=7PSH-SByGB2urQHKMQXtil>@9LLU#^qI;y9`EHS)|(4(Wcy>2RG#G$xiS(a7N z8Bu+|*Zz|rjWGQVMN#Nn!vHWEb5uz*)c`P5O<}UklzTe*8>`Ff8Z4seO!xd!$@g@o zYotEdSX7j+z8Mh}BBt2IqOCAhlXZ=0w!6yvJ~QBN(WCVOnpmzXih3+gvmtA}qix2w zyon(8YWmluB|%gHR8e?Zg+ODmGd z0h$}SzOg&8d-Jans2?#J{n5sO<){l96P!)aC&Y|PsHNU zBe(9@l69V2j~p$Egy_diHi0UC{2`K(<3h_0C@=Ki7_4S64B$UZoC`YVPaHR02gK)O zrfA=}V1Wr!+k~&E?rdbplnl-olzBH1@k^@8SbtlAqrd&(3TV@0PHT=T(8M1TtGQe>{iF9%5sm8U~GI` zn(Y-D!Zq_;r&x4c_TzKaKb}{ZVYk{eC;9ykSWO%IRR4i(^9jkS9~!Hx?~A56|5x+K$rG*1}kMu(98^tTMfZsi&N*JT3g&6cAbN}wQ-*F7tnEZ6GE+6 zxl6*PjoT3&EgC3u2=P!TTp1C=ko9guCg)K7fX0R-g)rbp>d&8&DZzG7Dg`zsR6bP} zw0alsg_N%YIfaXTDq}-rbG7%&5JW z-P^ITBt>7@N8b&QOfr9>hlM(t!;n;M@^)BXV1bcI7NnQyL~ONLwx?DSH)Y7oT$SjI z;*UgVUBj3&R`w)WD&7BgB|;^yyr6Xd{T;MpqcG30?=U7dMCa-yH`Ar4bEt%I=};$H zN;eAy4a)tV9ESO10>_CpRE#V&L5Dl&VLijs0@bK7GOyF0n%OCJX_fw%tFA?J&f)mf zXH)P~-MH56Zq_(#d+k&d4TSh4y52gUu0yLnB*hyX5@UNFC6ggJ2Hs?7K=A>?;V)!>qxo5fLzOT|1#{*ep9~;1~`a{+(czgJ89Kxe$Z4pm>DXJ_6xhiHbKk4 zg&s!}fX$H%Ue)keERFOu;#VVTRThR`n5@8${mjuz0_&D)>?-qPlOd0!eYkr3__fy_ zKfXHPt%fLD*Ijq}?bltmrI4D9fpUJ!b@}Z|+#h7wpx+)flc(yMx^;d)LN@>BN~Kz@ ztc>rwcDC6F0m8=Sh-I8+DIT&x^+QhH5(AlWaEP_>gN?^@|0>U>yjRz?=w9?H^fvS? z`V9J7dEYglwuN=JXNVaRt^@B$21zi)~vnlt`7kmMrit7M#(Hdi%Gj??D15ZL7YJeSR{I3Iv zuQ`W1^x_j(=++@ziln_>Gh^H_vRD1vQiy9^2mhw`-IC)tP6?kvP3=LNj1h8L7^c!B zxCm~J?nFT;%w|;ojd=_;X3x!7Tej^9~m6rNamQlxXz6H8CRryeveh{d!$PxfG~)2Uah zvmX>13mxAvwQ}&}(i74g6`x#s;#mEVr=l_}#t;fQ5mV?ept-Gb(L#(iDajUpzN;zR z*e(9Nf(4CRrtz;Wc%}2I<$4VOWLcMI2s1d2|^49{T^rf{JO00{38cO%`+sewGcak|fJeCJ5N@VYZs| z(|)p!`-6U~ytN{whJ1K8P%nCpD^V&j0v27L z*87oW{5(x)xA_X`XN$HO@ejHTHv8}Y&nfP2;;H|T#VqjRJ!LF#SbIkNvdca8!$qF zcoXF#3fC1+R#X}*oU7N#nc%?eQtc~*T|4%!;<(1nDZo4#Ai~^?W#= zhC(!tP%C9d=tbG!_)6dflx1*lD`~AZo-;D;EyZXq3i6)-W79G z-%^!N{@dlhG6w$9B@77fJaUwq|MqcB?SkRjUvefkZdqgOlj&C=Sw8NP>UlKV@D;&F z@~nY}*Cmi(Mo;4e^v-9FeUEI&JMlJ@+BJM66+0-l%sKGw0T2LgFVT`fs+koe>Wqrg z#U|e>!7RF<{LnMJ>Bn5ld_D^WxT zx_q;@gcE;1*SVKD(Fa1!byz47{U^n67hPn}zH)>(PHDj<1zaFhHk!$4cyTVPAW1r*-&)UemBO2jmMT%&u%YOmh$YTI{;@iklY=bM9UPu4&MJ z;DPatH^w+T4q>Vj8R1szck${oW3s5dH{3$Erj4%!tJyFaq=VD|Zc_Ra3VljtE056y z&%0p7jkzO2+a=x85I%Tls{u^^=ZBWw`Qbcd&NHxe=)noGi@u~vvkl43LnS@iXD5T` zKWH;DoS6xxv<}}hdNw46zzv{!;)G0p+-k^A`V*|_wIFY6lAX@SHAEL$;nb4kLYpfG zV9y{r%4jSBq+S#3d@OlrVCLE4bJI5T=#i7#Mds*+ZE_#|8o{4QzhW|@xYO@}Vd$pq z(xdHl+tkB9M)=)nS7aJTf|En158&KoJfGM&bT=puA|Az^ddrC+?b*)mPRD3`eM0-)Sg)iywG1q$+kEloZz6H%- zx*>SxbJAKWGjJ4FYBgfEkpn1w`y+2xuH+G~R{- zMt~Lr(^BRtd2@lt!ZTiOA=N}x}|E} zH0^4tb-@h#`M4jNIa~rs#!xOrR8cga6H@X)6YxF7R%=n08I$$JR;zdq7Buingm7O| z6dIMvfMp3TLs627ps5xogj=dM3dhqhoQ}f~(R{{7(XEl-6g9AbaxB3uSaN9$loRS+ zW<3CPby*d=|BAO7_z~kpV&ClYaGflwNGQkyhHRMkE;;1~IAgkxo~>|Fu&kOGZq<~pp>QMgyxw!xVF3pGu5_F~sG=`p3*?CJvm3fV{Q*vSUkE;&T+!XpS=M=0XwyMrOOjYfr zYIPTMMKZBg1pq!UmK zo`Nh`zW%6$U5r(!+%8Kh_NMgBy2AtFuY{(i1ISDbnkOE3;6yV}Wd@*YX1L-L>@OAM zM7}b$tB#HC2^z<^VX1Q&lxx zU6oCSJ)bZQV@YDN1z^cck}%fvs&D%cDxebx1wsiBn`5=|&P)1Cqqx;?3iYQE$l!@C zG^rcLRe)E_=u8LoT>5vvL8bF^hB}}Imlv?vyeQGY>V2nx`xXfoC^zVQLJgCISb9_x zSj72$M9SXt62)QZr8?+03pJqRIaU3iVow9mZhQp?evJf$l7ln)iv`*H3y&^z!Sm(}O$MyU2Ry=$1E7TWFQKgmYTKY{PB%%?m!xxPI8$Q7QAi$M;(A~KH5GwTDz5e?B-G%jt z{*X-ZWq&0+Bhi7 zBk*ev(4XYE*y_jSXGs%F*iS|2F2*9Lx#h=yW*UNQ)7NOoIg>gf~1T_Dn(pBQM)$A z6G_h|Vks2Wj4dO#K3*!;A6>aRO;bgob$5i4%%w&Rpmt)LOQ@ni>nG-iTZ>)A#<8N} zVfZ%BR=SH@!+B#N?G|dc0JmSq0xr!ROUjCD(HwSGm8mdA2-!tWG#FvfLi`3PG!=oOw@B z^Fbrn&jI+=LIXgkZu#TlV#0Ml}lgkoHymfHv zKs`eHQ9n=rA~s_}{RGwuPGk)|Tqr%)g8sV0OoC~ngBt*IN?CmoW!b6LnlLHOQQCL! zm+3{9*4ziUC@Hit@6DMICaFAH_cgvq=tKX5T102j6Zh{u&0LbHIO;i{q)WB?^^fmX zgZk3>RE1i8u(s%?z!=vWUNU~tu3foN|Bf%4LG^SvtPrSq7jc}Z4fkH~Ql3+!*HC4O zmPgMd-<@o1K=MrrKXV)R>c<8lJn^B**N=S7QuQ8G)n$D}|5p6y`)Zm%&(JS&Zdt`@ ztSJ^0GNZCf154 zr^U+2h3Z7?2v`zbF2kbJ^>V__d`{{2k6&0RZFa{HIM;SL-_-#rP)Ctm|s|4UYHLGuRQV@L$il! zC`MZ&os#j+KIpwKl{&2Jz*$tQA+2kmf=Our(QFJLf(&DN`)T-i_QJ|?2>B7_xx_F7 zs+k;ZMsky-uV|Mgq;W{PJIU1TTUu$QTNTNop@vaiN31#3$VE8bS)bv2fwR}?GJ~Jw zN=J3g_0c=c>=S2`73@l!qR(t8b&y)l^D4uTGTY@(+Kzl3&0kpd=62xRjuvh-KG)FS zh%|f#0|(!7J}EujbkQnX&>aj>LFC>RW^_aC_Vb0Du>;2VffE|M18?L|mCd2KISrgz zf5MJ~dzXZ8X<#ShM&dXH-$peZvr(BXS%;vg1Y&BKtZ)_cH!ZS~v>b+R^%iqe`|yeO zElUEHeLYlPjsm~mZ_BGF)W6sc9|6ffKhsszDwW8xONXYGNvYH_9LU7LJdrUz zVizp%0F3h#UNP#}xw@%eyJnok6Ntl$TYNh@lrBQ_&UVh&<>}3g@%Z70{zQ7WGY2upPvuk-ss}y@r(UN@VOmgEP1;}L`T=dZgSy5n-{aYc{n&w z(5$Mz_8UpI$Sc0uowZ8 zb-84kC<%Yw6M|`$WL;)}nZo7OMq^dx3d8HFPrVz*wp|no(LO5Lwt$(!2MKOnYPY4p z^gaPs7#7x4cz+vpnf)#MT;cbdcn%Flfm>4vDIoUQVr6>9wZJ1 zKuar3WkOWL8Xhzr;SKf>@+jll+z-z_w+}o&`s{!hE<&v+O_&)=jLoq$^5O}<9kLQ?m~4aaAU^~Ud<^CX z^G*iaE~EIcqt!5&kG>#cjpeG<@f%>uJJvtrS0Mu?I!dTD!*C8qfn~sUiyt^iOtDTO zdqzk>I1AdMNla(M!}ubK6xCO-7Rr+tu)t%J7JJ3=ci}jgpAXjdcXoFAQIiSE-Lh%Q zcPj#GM*Z2-r{A~phWT@&X1^ab8IAi`$C~|RMTDv&RfsLA3-53ppgucz-Rnl+`%TOE z6bJoNhSl_a_;0U!-MeQ_fbTc&529yMnO+>3CcPa-TY%w+MAPRah?1A#ZKETq7n6r4!_pnCK;OJQA&Nr)0a+zvk02pQ2 zskJxbh2}}~!$w%2-!)x9IiWe4{{KduDL#OyH{~n}8iO`%lV`auTHsunv4LJsd?OF(rm3mg%a7gZoE@y-RX``o2%mkIjd8|0e0Bv38>*(8ruOJ|Ujs*3I=ij(Hn}7WGSBQApbjRU3)*UW--%E3?;mfxdBf#|n;kw}El1xq0;L=fa zoFo}kZAzr-s|t-`FXm7_Hq3_EF_Rxn2+MdEox6Lxz4hqjZp(A@+U71N=S-i`GAXA` zjF)G6zfg@df25T83cF|Py^ro+#3CVogPhy~-DOHV`_B+u zBGHx7=cPME>2_?G_<`bdBn&j!7_ZWWLDR0qUozuJcdE0QT-!emdVIS)N<-t`Ept_7 zF3EIZZmzVWU_s}mp|0&=PBZaECNsdZ-m_{+&H12)s~qmtsBgl_JqOWqnWZ>Xzk`CH1V5b>WH)vye@dE??Hv!q4|^p!SNTQ@`@{ zPw5(q>!Samo>hVtT$Q42$-OfVg?J1jG^bbL21=+nV8mWbKzFIs$;Q-^J!o^lZl(3Z4?zR-{?l1@Q+(&RGRgU2VQKEnP5FPpSAo7C~t))wJW zaphBT%-QdvkHHU`i@{k>rOplF5LHKe7cKxFfAbioNJJ&sB%Y?#uo}@&(UK%_;2Jqb zGo(RafbrMWY|g+%t7|#v}E6B>k8W0+-kL?DJJk@X7cbru$?1&^pJy7WeE5mG@ z&@W9yBLBdRf5DnV{~nn&?8{A5wDw~71Tcrrjp$j3G@?V=ZTk`#i_i~(q?@l@uYJ|D z3HwW3n!sZ$k%RJZKDLh8`h|`n=W&1HPZKRVw-p6Dau!_&-5j48&U?Lc>iY7ImJ=+i z*Xx6bBh=^2w`}M5neew^{Mj^}=G?OGJ~NdoBG$3sH*ny3HWOGqzPmEN-?Ny{46Jw+_XLAeWW2CFj0RL`a}gY zUEdkXUIe5~XyPM6idyj4&A6R9{>W{9P15a|srl`CE8*f_bt7;$XAMwoUg-d4`E6GK-pLl2Yu9j-Gd?MwB|JglJ7TBSVOnr0FLYO<+l&WJJjweM;;D$T}x z0?`n3_^u*~(w#s@$Ic`Fm0v{Ld9RFwfF?*-oPv`a!w}KswzHL#Ii#OwP|1@H{)HPOdI;O(1yu&y^wd4vU{!*bvNDJ>!s^R|(mOC}qVb}lg z`mIWVq_bpL-+TJwuErcQ`OUuU>747HuuzLX?=a2%_{V2IstrH`ds#Iw*5uLs|G1B% z>ybo3l#Pr8o`O)29UUyvC@Lo)d0rSbTc8Y~oYF}i^q%=Io=RPyAbX%1C9t<&vxw7bPSO;#Q7MsK06r%0-l5ORypB=0mKr#*@Jw-B;IR4IaiAH}jH zMLaR`Z9oWrh2IlzqEoeE>)?HAXfo=*3b!F>U1iyS^J)(~?vssJxrec2Gy(_&V*3GU zj$8=>ttV+hDRScNw!&9V@(f=D^jxi1=+t9j@%8#C9Amml9~X%%KMsCQnF%>B-YHwc z^vQ0?Ii~^foLgPKa@Sq1Fq(O&1^@N+VXgQ&FQlJPDbqhoM1nty{88HQP>j0Q!s-#o zG!j_mkj5^LpFPZ00vc1^2Lzc7IaLjtDVem-C0g@~n<fp|+r5ME=vJ9|as$Di)){8p$t1utZ&>O9dmCkRIzszJLOrHEH4V1`3P%EN8R-g#;IcEA zm(@nhE&F~9F0b`Yu6^zRr`qW@Di>f1F~OYmqlDQe<+^ys#}KzSeEj!XuMt$4x`n@7{ui!7IKn)vcgz56QdZ*I_$ufB-V z$V#Yu3VmH0HXN)pE0qNClePQGc*HXa*|JVM9u2CY4?Yff=Y|q72{<^=^?c zshn4=oe5a9Nj6|}$rHb=9W^QK=)5oZ+{7@0_lOX31b{#^)6i&bHCvYFTe1}BZL_hU z@Y#z&fsa{qL1{mG_hOp9L`BV<3xQ}A1@I9vamt1+3~mIpDx-Fw+xsUA-ZE5*Ij28< z5!KCcZr;2jXJsl1OCv4NPBjcPK$#Xnq;C6qC-+UM`U5v8c1WL59t_~*j()+n=>Lj# zInnRG)vo8WAAtQ`0!?dIj#9Xo<|r#9Za zjPmVf8@15w=+*LpQb)YrX20HzE+$M7O3}dtQIP&8v9d(&u7`nyNu4ibMkhn*8p`)u zSL1dBKkg8x;>pr%Dn$Ydld%8#V!bY5Md7+E1M3e?;6h9efWN9`PMuBBg*!Ra!sdRN&2COLh@x?Ai$f8qy*C73Rm>$g2xl4+!!_iT? z?LHJ#Q4_H$t-hM`s}oTa04Qm-N=bn1_?^!pm2qSp4D}LvtrRit4`S1}L2=Yz5W*DR zpjbo~oZ2>f8b-%4mdERm@WAZHVdxwGT(v9kw5!aug1EII9T0);uNG+VuAGAowz9w6j;{xr$pu4PT#b5XO ztJ#N44Q{LyW)J*!22q-QUy!8EP5$@aho&f08>_1;d(HkFYMwXyv8?~bOSb(`Q4kM{ z%1ML_wkaqmG9kRmN|urc113v?`4YYARYGM00wKODGng7$-EsY;T5YN3d9|flZK+Z& zSC(qErNFjK$7lJ+p9bGCEjw7M)s}qM2~`93XO|>NB@*Tw1I;O5&M~{pFy~m3h%DLb zgyDoH3_6x#~u2--!rBD>+6X*;2@MroA6>mX6$7#A(_f)Sa8ysGt} zWqV$<>-kY?lrmJ0uMEdVItHeg2ER{NWYr7OAE^&MLj3x#soIVPL^ zgBy7~BVUFx1!HbF4u9Q z+K7gbnv=4L+5(bm!M;=5MT$m|aHWGk%YZs=GIv+&Iwl z?pGO&oiLL;kwUt^qq~Z(iM+9k^BJ>E*t7M|1dC;eIHakgX?kllmDxJde0PHPTGi@Y zK3A<;<+)b3+nOuSUfw1)574~ly59Vhm$Oi~`d;pu zeNrav?_NjW%+|#;H|fVV4JIE!2HD&&R!|^gWVDSe>W-d9)u@h{=jAt~?~)IwE%hex zM(>d@@Y6`ee8-L1{?lm=hT%W%hhV_wZz~WSyggjH7v+R};`3o|vij#eUeGXn1wi?; z>qmDYD>P%z?+Ibypdi^cMCj80ZqzFD)hn(Z~;a|H|AF=DT9) z7~)r;#)nXqw$R@SBW^w6DG!=<6{ult@Dk#L+ir6&{>5XFQVAP1vo_zYRBGQWK?F_Z^2$etSLVWBZv+k* zimIBmuu%ca;(TIRoP#G|)3PXItvmbejqMoew;5fm_IRz`U+w5?_6Pp12v_QACyUR} zqhz277)pFz20igjso)WsHJxI<-!MxXBhZLw5U^!UO67}j!>^gT5rhp7GmbCx_-vd* zgc$lD+3LP^nC0+F4MamXYreq+QNE}q+dzx}e6R8OQ(MRx`~2)*&Bcmud3=+%;R&6} zqS=2vyNj+t529CcmAg-ho@|lcP{%gquU_hcnp{kxfbw0VQK=jc&6s197?E=2lirCL z*lzhg;eu!2`z;}~V`vDa4<(w8H6e36FTJxqASP|mZE65)U18yX)pp~`_cqg9#u>zk@JWP@TA(a!A z86`MT2|)nkb0;epzm#@oCpS5S z7nW8RtDXYN0tNX+rw^jH>&OTsSK4}eFVz48PNy+KuSQSd^HI^rr&_$(lYlru)jOYj z2b>(5BJ=5YuU*6drw{(#l>*HGkyl;vs9eRiL7;eR3ecvLzK_@bzvOMR=En&1FmGxx z_T|`R^0oyR)t;_|(8e zZ^BOvYb+Y%aj(n_>yX9tCIE-$Qw*Tcrxw62+e%}8ZY~HQ6Xy-O zi}}5iAh3<<`wk+M zv6R%qSnT3Eo^EBqP)#S2aE^kF^AW*;$>R7B2{C)E#WT3#ApkcCmGOONh3Rl7B>w~l0 zLkiYNXoG|qvfHtP|5xs%pgWWsrJEp|?vj04)k2LQG4_SNQv_ZxSxSj33z{^xqXoS} zy@BNdLE%rSN?j^j=MU+9QQ%eg#0@uKK+HC0FI%T1cs3}XZX(txN6v>_Hm>LwH_6sz#%Hi@_@xN_6Wjd zIRX1bJ|X(_n_om*=sI+d+?ODME{bsor97kVBx?jl#Y#t$)XwP!*eec1iGskB)IQ|3 zt*P#_r8=Tv|ffl^uHgi)?!+vS=Ku3Mk;B*!gt5E@!`9m$)kyDr!@()fp2tx}0x z%?zCl0?+!GC@7|dv1KZPr~=3;Az(QDrHCXovg)7#W=#;Bo4P_<3XiQ``2Kkm!& zeU`H*9UHc9{@~~xnEcC#63X}cy9OmJmsM4^)~K%2KR62nJ@pvG?Tu&+3};h;K)X!H zGrYb>_ccuiUDtLQ^KVjB^?HLqZtehd(m&|)P~ViPE_P68=Zd+gmG!WJrTvPB+t~Xf z0N|bH2Z>|gIu0kz_W`6oL5TBxtv;qT4>vXc_e>gzio&1p&ENC?4wm)}E zMK^Yw=k^)X)P2T+S_JJY(D9<7p%OwRZR0TI`OJnG9v!@Ca17;B|9H4>vuFO(o8VUl zZyGqIqT8|UQ~zZ6PjE#MBWnE9o8BXyB8zenrP))1c+kVifK)txELo{b{xZR$ugNyV z@H-vbUIlYxtvaQm`=^%mpk;CXpkJ-}x-~Mv`s|qpi#lUi@=pJ(4!?4M{GuJ55$6wb zZdng{-V1K|mrr{VW=t> z&r~G${_`!HNn{!bJSTa60M{L)VTypG1^xzvv+r$Rmps^uigQx1ZhAb!3=Z$85BOP` zZ|v*sT(A?eDq229ueCn=3GpOgbA#Oqq*o5>ID=0Vbl#9_x#_$?Y*CWe>>w~6kAI14 zTJ=;_H%$HC-gxSba?4KgQxdg2&R@+OLjf z@;M;cv-*(pPZ!T-8Mpyf^#1)GCA@G@BAo;*2U*EpADua`oQ zua#eaH5(b62B>e=qrl5i8e4|2zumTmg^7;og9PmV7w zVY~$wFW~kMm+eM=8F*>wc)khuEi!!#Pt$2OlWFr}kTMq;AW_Q!QFE@hoX`cxsr(-C zajo0k-0XG86fJn(j!q{Iwu;ZR;&mrR^+r-Jj3S>&Z6fe7jtd!aW2Kc@6>S!&Z3D%Zk6ZpTc^) z98VMq?W=p!&%A+1n;!NY;zRiNo7YHxDIXm4Pd6XHRd^0rIs|k4zU@f`5;bF?y$u10 z5~xR68T1p+DE(HYucq1+jqWMx52>osAF9e;TM{-tNC3zbU`hbU2i66NNVnZ45%`3n zJZti+NR|O{^VyC_MjB8GrU{gSHX@=Z-E)s5w%3Pnw@t4i)Z!lr==xfgq%BFD^#Mcr zl*A9S$JnLR14 z7j+^VeUrFL(9fBmLzy1ozGioo4ySpsRv)lA>=`Rn7K{`rlY+qk0$i*K_W5#Cx28i0 zF{xvjF2F1bU3G)Snx@faimbob!H#sh4aR#ceXl&EaW$JN67PG?h#6s-M*UIT_`V{8`+HWqAk zlWF6gr~qob=?rbLZeC-!aYa+?W822qw()TrV>@3X+#J7870)*!#RoQVprEQE%VH@9 z1e~ZHKrY;OX*>R(MCbfM1(!?xpEEVX!MmnrII~|U+XIhv?zv;*YDB z`kkGPr;ONXe~q*MFN`}*c{Kax&Qh^tldR3Ej@k~CrvX6^NJEvJ1kRnIx1OlP^pIZ4 z{8ilNmOeO6?R@C9Jq7hMsx04pwr802FX}u?QCc^GfG>$!C^?auTIfiHGyn!G)O=K$ zez3l4Zpxoq=eA$TR?^mvPINub%Afk)36%mW67a^5rI#g`did9`MA>Qxb*Qj4{2`%^KYh%)Q&{5Jmll+puIMX zz+=|0(}Ht0Wx4NA0?J-053$!=?I#B;BYa2jiEFJ3h``f#@ZHCkmoQ^G{HD$rURpl> z4W~=K0U=&mKCUyyn0|bD$*%3o2y!#{3nr&Xhsw7K;v$NV_w^W3>mSuM>BJ*D26+_GTGKW;j& zvW!;JGSt(W`El+ze4fvC+1>aJe*8N3#jt4SP`k2PO`(iLP54w;|B3(+ODjo$7F7b* zOOD3;WSF>Mr`}Fivx;n!=LXHF2{t#@j#ty7+gLl!NIx?W-hBz{9hi+*w@u~1=NlV0 z698^tAQ&)+Jojo3icB^HF4Kay(QDbzM46vxNIIvLP4MThN|XK?$juuY+z+VC1w&@V zK&HA_5Qt09Qg88^I2e@cQsV@dMQyWkcD78p-)IgYm?G*uR)38i)L1agRt2AJ_LE^2 zXHBIV_7AdlOq((B{2+@7m4<=#3354MJF=i38Vu^gtcS1rSFW87v-3AcK#mTAMeh2W z3pYAQR} z7#t-K-FzN`!p`c}^M~;jqN?|ue12;Y+F$cYNFcg-a5Vk1>E>YT`NQ}(Nym%RN!9JM zPfc5Fg)1UG7svheDO$^OW&z%=_~-JqDT$KVvgYN-ud}{1;srpl=G8caPNPTSVvoQ- zB}}OZiJ`HCCNTm#?sgrC3sDxR*LF_mLDX2FVuk!mNj$Iy7`D`$hEu*Ly(rx`OWN}= zS8WUp>|^YU<};?~W9;JwV0(3w=R4|3*uU&!sXKwND`EeVkEQN7l*{-HVpTT(f;ojs zi5Gf~3iM!_LTroTO_myf%?27biK2}We5cIW-UbmlypL?m4ZFOmvHH$VooTB_kuj%0 zb*2=C?Lz3oC2K(fnmX{oIEd?DDQg3LvceWo7p~s>8&DkS;KjLiTL9Z~r>SBxT=p}X@GdtiH*&L5BpeJM+%v9Wn3ay?cqGtZ6I`u8b{sy?#OASKS&Y#uF)2v^9XTVj*kb7$MBAvIk)d zeT8Q&RTIw54$cXhYCXGVU&i>C=OLV@*qeRM!<0}f6TIBLuc?+yzx-t?TdF2X_2DLG zY!=Ud1QN$fU1|$c%M$y*rA_438wj5X>EK3aLwbT4LftxcW=%%8U0#j`9qA5NgQ6e` zE2=7kJT3y{RRIr&H~fr^Rj*LTyLtCe?7=HMOy_6QdDz#T@>?v0aus8tMJZ*BoVUYI zT9(m*Gi7T;IsW1aY|no71fuzQ{1?K@QxlrMG*$(2Y4b1TmbHK;x44xYyxjErzOF;H zwzycUg02UQGlt9hL@Qy&xcp6uz5nnqouAGh@F1u}i{2Yk2?EX-FH3uSQkgUMzmKa~ zL~wNqGx~LEEfvcnI$9nD43>y0QL+NfI%Ysu1iYr@CuPAprZ>e2+W-hd9o0sEfL zDV2$;sFyCOib`Zkx$f;=zHja`A5Tz+PDz9uATH*_lQ7@z8W1$HxSehz`@%N)0lnwR zE{#tFG^8+h5j`DG9g9 zfGUd11cH^%8I5Thq%D#IJr2{vCb|apa>KiewYz zfKtPhknlbgF%T#Ppky<%?tStev@<%}{u9r>cd4xXn>%?e|N3LuAl~`oU!VQe*JV@k z&L7XBuu$c}-k$We5I!x@_}ZnFfBU+Y-#CB56FK?%?7x5g{0UDqD4Y$tLX`)Jd(s#4 zOjKjJnAMyF*ZNT{N_B*b3a^hKa72CbuuZSVZ5l7QCxw)k)@Bp|lm)9YerCyd#qrvm zVHbfh1VQ|#aS(`d`u4p?VO$ZJZZHOn;8i9lI%kSZvBLPv6~-6#{UI}|XU4qWDLT*= zCG!iV(!zYwti9h6&J;{#g&8^%*vjl;t}6oL3bWQ@q4zg;lH8wshXCpGykU$i8)0XM zs0&>JA)@yH^pc$&LD<=O$#iYnE-gB|)V}WJFTZXoXQiP0SmXYiTB~Qj$>&voDveVb z?;WsPC;Y<^8jvj1qB!*-;`mA+1r=N&o@$B20*YsXq;+4MF*a>d>{TDb9-~ba(%<^o zbB{~C?xD}Qm_98CSWHw5O?hza-`S7CMmz77X_GP!KUVcHZB7~co5zn0767{neYqeC zxF~9aNbX6m?xz%eEEvm@5kl=ktr*P^OlOz3Dxmy4r(Lv;Zj7JzZ8T0Y6o$ARw}hY# zn_UV|5V^ynuq@#=!Q!UB}-=-Mbv8%~l$vIVsW9mXb2E4d!#20D(TEWZ1wqASK{Lqw?MRQzB8 zP|3CPvVQ?YD2XuY%#(6uczv>hMOHj}!;L$08(aC2!~cTI<>k(@D4v*W(W6^$s|Tlh zJ)1(SefVTm*l4%SxdK^U-tC{;V$PAXXLq8drPVKRynE{8YVqjNTZt6)=jSA{lFfyL z+S!TC%_U>`?3wdI_=9p3ZEl{fhG!3LHg4NIF_)%?>)jhq9Bhh#y0*j*tgIcksci^7_$&Q<1!268sWrsqD-a?&+edYB`O92PZ`bI0Q)(@$21g z4Y~AQGl6l+a`F`_&o1v?Qq7caSiHMb9wx~XE#Vh-vQQm$&;V_qV{rI{uu_jBOVB|g zL(r!();NnO%r2rah#*GbfM`%O3f*HOmjxpx2>OX0`r;>+2BBWI6rW|}-c4!*Ea!V$ z78hPC-E&Lj7Sx>-vEpHj9Ll&a43d~?&IJ}ce62}x`4v_E^~(vbzT)yMuM6GETOSxh z>467IFb+GoGAM11D`-Q<6DLPNzuiBuDQd@#%1GE?y$F_jO-ZEde)5wJ@rTH^!wRwi zI(IwrP)bha62P6-ZP|fvX-in9x$K(%1d zVp%R%mzFv+wZ=+;+Q_#ew_R6P1;M&^?e1>@hA;j^Z+k&Wo6)>ihsG(J%l6luf-^4q zBL!UZ8y`_I)A)LhAne*3|lU&sCFVQ$xG{A=GINWrCr{=l| zaR65UT&{zvDco^P1A}(^r>0-X-tJwKM6>19mu=tZtDSp2x`6IR4_K-Z_`fX%5W0?W zfe+E79dE-U2EM^4bz>$ogn8C6?Nrs^gRi32h?0;*ZcRNHW>F!g862m!IKg1+J6THIX+Joop80tLnR`@c7o*$)+Ctx^xmkGI;rvrBVx zwfQ-$X#iN1Tu)UcjDhoVtu{9|Bu!}R!MXW$uX#2$P16Kqnx@(JJP&qOZOaajJJPBV z1x}2s>A*uTK*M8Y$)Yrf)2?ce#TIH}yb?0K{w}9I)1r4QSH7+5URn7zE=%Qn!#KL* zG-vETMI!0T?v}f(tEw~*iKIWvRaF{X%0-&y&i(~Q$Mia_9PgCCm)@j)1zg7XLtIrQ zW6RkxL?Y=Q-Hq%YR%3aR@^{-{0LFpdB#Q!E;~Xl(kJ}xEDN3+&H5WElNuLUQU^-C{ENs;j$>J7;tmOonOfh0Y}u#*S)LbL zD)7r`d3U-NGrR6HNt8+@NtD<=-$)~GSz(|P0*iQ@qJ&bAC$>VU_*7+*G(yl9U)dPD z;5fUFCcxjB^9}9!f5)$fU^A3dc-m332a$IYpw&5SPZ8}EQBlOoN#2ZdQI`kLfWRM` z4_I^`W7%K~!JPZhGYDU^p(7iGqe;Q&_n((oQDQZ$hP%6{;PuvCDUu#%3Z`I1oQ2mCa1L?AZ|~CF7yqF4__& z5bG5c7o{9+x`IZ7u)DITO%x@NI55~W(8_eX(4EW3tCay^aZ|#nrljHU49c6;ASbEz zs7K0wY%-EX5TJ2EUJUHohu4@hdzS*B(2&gC474T7f9e391ey}6>}&|eCU9e#%gg#W zQy6NdXk9;UL%6a)1Uxs?&s3t#3r9D>GkSxN_2V(}gM)mj z8u|H%38vX)HPHeNXA{|Y*QaVIM2WvST;wQ4{?|w)5)E3h^=jLv;2aYVvteN2$JC{^ z<|-JGV`TE1LvfcViu_d>lxUGhY1utAvw$dy{51;7tMhvVH-m%kL#`@7za}dRapl*k zEk=jF9if2yRasF8j1GRTrjYk=KsM)iyv?D!AE4cNKp3o!iVVo$YJ?vvbzIT&nVBB?3JxyJ2pV@4X zHQhr25sr*&DjNng;Xg%Gt+=e&WlJs=C{jzzI&)E@bshe8{tgGu{V6nB=uw@^^RNBo zyT~&-Ei4nR%b(I+Ckzes1`%3V!WM`96Ni3rhDpJP7a66w!KHC>ZWey>c?e_)YEY6biq4;N__B&8@h#VL>JIwXp9gjsAS$yq62{LBp4LMSVo=o^7dmHwan*@ z5`v1Cq+x17X@$sd*V%;Sa}t=syvMns%DqDsobKZ_Z1q<`ng|VcCmu{DP(ja)7A$~? zy4m#_%eFk2rMlePy2Y>uJszy~C!!YmqhM87GYsPZ$uVh_iBw54UGAwQ_Q@V@N+CuW zL$Z}Y->VBN9t|%|&_@ea{dY#PSJmOmVMu#SwO!t(cAPdCW^K}8&*vYuz+781;YBmJ zb=|T!U$?sJs;VgK3zK#Dau|B%PPIMdMt&9yvo;B|;6ppO^g8F3wSH@0F0Lzzs;)0g zQvKH#p2KDxmC-!P@bS3at5F9(qX3Hpt2;K#qy}XfU`KC=(;*6NBWU0Ui@8kOpsgw2 z(v{wnSbR0JAJ6`$g|+U&=H^0I2h#+7zD>gxlz+cHV8Gp6RP=8tTJKAIUHS|*vnT?H zqRhl0Hr5uXwXsoa_LDt&7oqF|i%BE@6`#fCky*K#{PmVE*Sd? z=&gZSxp@5T^)FWes@27A?ZCX1bIZE5ysVmj&QWb6FvApPMq{>N8l~9+%>o*M9K;o6 z_Ky|U28k;W2vEEmW8o-#=LeF}gAii|Eo}CWm5zj+Q-Z(=4Rga$_~Q>GqjHKuW0uH| zq{7dy`r=Ppb#rC8PoolfiS`odw`xU<@wNWi10gWC&LwB;nSTG-)foWC3ue9)`{F{M zF&<7P;d&!~h`){^Pogys5elr;%%@{;7Mije_@IFtOo30!La7mXNg<(O_I!XXXm|~z zsy1kv2AZv^zUB~CQH0|;UK;q}?2?aRjQ!cOFrJNJJR5gl%QX%8QAv7KHcWT+TZoo) z&@^gyE?&U?+M18|@_e+ro5vGRO|@pRzI6PDGc64-5-C8G=xUgcaNP>c4Bf|?f?h-} zM$!Cy6cyL1%d+A;e{G~TMV-?IBd8~}R1HiCp1eINrxGE>hfPfNI!;CXT2Q57@HJhA zNbW4Z)$f==B{8~#3Rh^j;#Sg9)-I`b3i79cs{{iT=iwX*yJTqwg1PKQ%Od)~G$kCx z4xk1O{6BBE#Cf%eEMr^KnQa!ID4I6YH5jBM&}hNQ5i^$u!6HpH>v}yN3A( zrlO2J*U>TzA7=GByNT<%v@ALAdY2364!)!&=dbUVI>1H+%zhWZ(*fi9_!+%M~WwT;#z<9S>f+1cdYN8 z%46W+jsN^x!v?*zaGWHoU?zz5)u2&NW$^{dsl;hXLYSdln;TG=wOwmag`|;eiNf6g zG;GcFZ-ldPBotN-aIL|S;mY((Jb!9{JiLZO7>6)6tM+RW&#U$OHP0i>y@u8jn!9w# z;SZTJ30k8chW#}5HB`_Z+DE8GqGsG5_*;>2%@7Txp1?7Jx;(r(=9lM{_-EE}Gj1BQ zDHBi#rs50*!S-0%Pgg-Fy+kJH*O<0rA@PIA+k`Y45_=9Bh)YMpsb;IyTyCr>CGuMu zuK_@~c*@3%7N|g|>NwbA0svJU)xdzUO+^a?ZerWUGA$8-5Y=_Tvxg^%lp*1q1PuGq zM!~aQW9iJ%(V6AC7a0dY`NBQHG0}=`gWo=Oj+*1Ig>%OMpyk0P3K+tw<{F3D1QMbmFq`AzjXR8V}+@yhL*QqAnEiDM1H(^yd?-eaN_S6vPDUz@Lq4i=CVx-kid&LYP8w9gWL)m6>$_)vP

    N{f}=hJyVz~&Ig83lmY%4(j$SFN&j#sZXv z1%IP>qw7GE2)O(6wJ#(|+GwOfkTx1=lDtGYj(jP6Fx2h-+z8WBqnRd25F|<3 zY?K~U>cL+RZ##KzjU($0>n;AhC9gI!OjC0^jt2cf-&U!5gmFsz*AY5;ao){6G3}2y z$&HOCds!38-PtEV!{*Ou>}S!LkPkWMrEZf00V(cZwR4?$`~PpLf7UP zfQk?WVTy5CZu;-fMLdHi2O%6ZsC_ym45xekMMb+R?msCcW@=WWG~YZL?!s`?PYk(}bkTI9 zm`o?*0q>ft$x(L7^KJ=}BLi}t2{#p%14 zkJLhbUBz>2k4v3h21|%rhU)<9!&`(nAWsX?UtFwLR#z82^zk4~MsM6}v(?9ux461m zsV^?}g?O6SQ-Na{?z07*tdfMV@@5cZM&=}Q6Mc!J{M75MkQU~x?V4Qg9l|(94 zjlmdDR%R!}h3jAy5ZW$3WpQ5)(>EG~X%r#PnvFE_AmplcGq;A|`Q>i1uEep}OZDS! z+4ms4)r`rs_lAq~DCGJ$Aq2k(A%qCowt4uT{-DFO?bwtOm^TeqZ|M`WP^{4z#Qn~k zZ=W1#_uj~@y2QC#!^-)x?*TMV)3Y_8E1adLn*g3)K3@rMJs18vL+*9;=wwZ^I?C&Y zHK?xrbQM50#TLZP0eu@|LnnrOD58-Xnc3S^ zPoKgig6k%h;sgxe#+?E_;}8NWjw9Q^_+*HlxY8W5Py0DLceE!bB4RWT{+Ej%m-wI6+*Ra=Biw5MVlgS}B(j58iIx!Zw~$-fDZ!cy?)~A};y~ zS=>LWdzpPRR5crlEHs(iX-^uX(X9TCsKF-%MCg5JepfiWduPYF?bt@M2}MZaQRQcy zxvUAi1vjD=ZG=ReW^}8eFw=5epJ@JJRMom!C=!0MP^PduR+8#h;r)15=#dRKK)=j!DSolIV|6i(4`n=sPE?eCw zUX*;w{N=`SMYcIyp&K}+kRv*mIO=z{xkX%rV<_C+;?$?{+4b!{sOZ{Y`Lr{`M2V>I6pKI{Z2%g$%*{ zOqXEgpR5J}Y|mc|1;(6shM>BIEdTi~4MdRRuz*r%cGT)!`sa=@;V&{Sy82!4ufhM@CaaW#>!ZJ8qWA@n zWy4p_>Ctxq?*KYm?YXsS-DW*vy}>&*!1|*!i<;qrwCkG1NA-$kVgdWcup}O@-aEpL zKLEl|lu>cbK#!b@2~0T#1q)Kh`Dc4=SQNuyQG~XTQrJsyaaA5{$H?YIq>}CQ0S1f# ziy4dOc26ix&W;*QkivPOLNNRRM@VQkM*ZF0{w}#jDG16C*k7(x04kMbtJOWaBPA>> z_r2nVG+}8$_@*>B_v6vZ%9bN&uq6)GE*0Abn5Z^Q>}6|>Y&WR z4g{30`$kbnkW{!3;h%IsKH;K);dcUZAD`>8wPKyJ?vnF)43CCsIY74n{!<14kCZ8t zdNEV@pYvb*VzVZrD4&0z>rYV;0|XL)ksdn(7wi$*m_Ms3wTOQp7j<@>5iRrgC`$q| zO_qD!_bN3Vol;9We-ZDGz&PoPqf4PXf9Mzs0FnE`-g>0AG~(io9eZQqFat%`SC49H zk%z-3({Z{{`1!n-@wK}a--py;j54K-?YTzG-*OrMNf{tep6`1KY*kiLOF^+J1C$k} zLTahX%HHa`5Rp;~UDEc&v|LWpayh-`*MJ+YaE%uQf$IR`VC>6(^UrC5G0{ybTqcK&r}_ua z+6uPi0XJ#c5nGpPx5)jo#;j&?oXyM%xPL@1)1la!a(~gdFC1w)r_`456*xeEQAjsIIktaOb3-F3~)Buk;5)`pQ!g zXE~-oWK~GPR`^^RfwJziD4v0j(pkV^BRunZ-fO~)i+2Ule z6$6KT%1fKz2)=1`=wuO4j$#7gS+eB+eybEJ|Y(y9gG`O|W)4eCt$@I@D{cZpd z+)Vy=NZ$iWM8+w_X?~f4@4n2pPuuos+aETXAf+>Zqa!6W8$b3mWBk_B%nwO#{#ELnRqCumy~V{Tal=QME-reXA;)Ta2kud|)6ghj36~tG6HB;b zXTMDLuek{K!F02r(4s2HWT(^Lu}vm8z<3dJZw37zoXFzyR;ze29nAj(PU7lJB=qk4 zlPd;h>Oj7yE6r4*KMizA{nbdnG)qgAo(q7Ek&RKo33F(30nA78?I*&O=4I*@`PG%I zS?8Y)gK^aZYf#z0iSk%^V8ZaGgg`Di;W8 zN&y_(HIATE+HWu;L^BL;_sjp~?%KMC#2t=vhd|!?+U{k@yrsnAxL5z(v1(msomE3Z z-H*~;h7N~4YTb{zJP^Dd!Z=M%zoAcKyp^P=RJjP_n5g}tOL+;!$?*Op^=Yn5H`n+r zQBC{BB;;IgajE+CZW;Y&7M*N(mi^NXR7zw1*GrYkCpCYvuz)`H7iZSj&*B6|=l@!N znfg<^)n26%euaGe`20Vz;ot^GNc6k-Il&Xx`x9|LB5-Oue zQbL2)0(B;>Hc08bm7vQAKkY~f?bgJh3#}jZ2b)3!fW;8ee8UGEWR{kGAAE;%WG-TNV^k>jrFJ$AYX z2Id%gC%thm9k-bmPn<52-u~+wQ20kSUq1q7oeHLen#OtoRI7_8J#uMi{Oq%W5yFn^ zYR(W))4p6Q#vfcN#Yhp15t7QcZQJ&h-1em;7&q#x7Zt$-Q6_ZY_@I?iN`vo&N>Yjl zCMrZI-0t5Hl`6ad!72a1gcQ2Ya@S5yH0ZVSrcsiV(U|ufrF(7J>*SnX*}I zBZQD0Gg~pq1Q9}WC8ZRa*+LM`xBQzi0RGp}RPbEQZf&t;QQI1;H}cn2W3Tx8FXU0- z7xxOQ3M~70$%)Da0M8EKPj-P%(>%@d1ob5ReNrdQr@R$dt}i2HFOT@u=~~m2e!T%b zHoT1qR_tHkVN1X9_A_U$i2Qudn}wjyRV??{m*Fk;@(4Q}(MfBBE?Usz&??3AueNKQ zB@}LZ=`TfpUX;fPTztbD-YxO7T=@(vYQghI=&9?aL(?+&&A!jRDCYi7q8d?>g*K*k zzq<};!AHC_;Shwn+To+|C&U##mYcfh(aW`}PyYCs@FsaBm-XAvtDSIuR)X)hzxmhc zp*A&`Ot;~85=WqbPx>uM161l+L<9WN03S^75i#_QYoBE>@suPi*ciS)KO8*m0=RDo z9(DoT2ZHAsfSozWx7A8~ z5L}kCR-@9W$7kbuqta+)WyuBjNvqXe$ugI6E=q_J2~Tk1Xbl+dH!y#Xq!dZcfeRr7 z2hJs;RKja8;CBG69m3~7B#CVs2qwUv(W{BiSTB?-0eyuQvv+_kRTJLH!KsG_TFeu( z1zTV#0|s?3X<{Q11GP35;x<1-=Q&YP=206(Yo7VX+*p2uw4-b1D{ zZ){VA4;HaWV8tWBlQL~Pr2r;aKq<3d-U;SvUPrJf&-!8rg6b%()lzwSZ*O67alxj9 zQrmVa3#<)gA0>^yw!*$=brGmXAV?QYG4=p^K!m@8fV*Mx%GF7M4DM(X)4STgeDvCZ zXamwCf-rs)N6}C2EU#0uc^62ceb+qyqetnw$OkqlBTVqCxrz`h4X!;t;-{STd0b^i zHf0S$b*@ZSSJVN9u-?m8ZOrN)U?vE|2m1&0M<&Owu8tiA7!GA{e%SNCeG+E9Qh3w6 zU?NJ!33;~|wMCyhzZG}$acu8!Vci6;)hP_N0EX^? zr3d%CbPruz4qLm*z(5ks^O-#`1oBys`y%-q>{(kf4!7#Y~P zY8Q~GLbX@)FK%Mo0m$<$&wm~z%dK(<%Sj`H4nhE%7yCu8+JUgVbNAglww3}AWc9Qh zx|QkGyLZZ=7Mj-)mUr%k%hkBdFe(E;Tt)Qyn%xFcn7GDR;NjrSEQ!c~&Q(9odeH?w`53H*=;>98W%w1{eM7Mz39K?Jt9afg=%k6Lk<#MM! z|NHTBG6aFxdUeEAeE-z5y)+mOubqjAn#3_pfUFXDp2Y7SmdO?_-|}K40;;;v(&i{l zM*YEXKr}!?-dVfQAN2>6A{Z!$EJIR*aUkB{ykJWh<6IDnp54|ytWrNnAxQ(Dt^yL> zPU3=NjM##6c7j-UkyO9dAy}PO2GP#@*WmXJ{&jzCMx9oeIIs0b{`(yNXLPoS`_`5Q zfIU5+#iF1PNkmH_t|*Z;HBlsVv6Mm@$ADAy^U<~;Kf1+XQm-fPjN^As7vDsYtH*;s z^ZVxhoBJBUSn|l>2L15qU1M5*W3`O>mf~k-?%gxA#{tiu!)mo!@<^Nb8q!%o%U>0~ zxK%HRoT%=Mca9saEP*#gg4t#g^!HB?4AmfHBuN_SxjTMo%FfYkJT#jIoDF-Bf=Q2E z`p#W_O-jj*Y=8+V=d0`5xyutQmW~^gZpB=p%@mavi(M4Pmq}J0oDF!<+tsxxR3o*| z+1^2KSHHN7C$B|k?wzi)&-{LeDLy|NTU5s8fa|kRuZlL^<@r1kKC_ihw_DzCc9FHW z!?hj#G&^x$r+gi?H9PM2&V1Gne){}>Y_k-1N^kqGvG*o$psZ&(&As!$unNU9>Zt46 zPg9ByF#mk1#+~+5vE_}2O7hzUJT)Vs=`5QAXLe&n!|>;wi)e~D#VFYZRObNfL&oLj zQB~%+NsKxuStQ7O`UE7Ksfolfw!yclib)lV)86vsxkZhOW)Q$e70H&wYb`iPp12;= zK7(&sC2PsLMpp;M|JVMzKgKp5`{U1U&FQG!65wdN#4xiR_h&t&p57g1N7;;Xr8u9; z?uS3Em(_H3JoKc*tHg2q`KJy!$mDdSDJ!lxC`p8W9(}KjJ!foL9m)jr25<*c`6X(t zFn|-Q#!grhBz01QL;Ac$pyZ}rcXn4`tOT&z2u?X6IupPybL_ziTb<2TFD-=8H?$V) zd(RU?iJ`~5?b*&z387R-Ie)EfM6=Fpm8}4*Tv}LPA5DK*N~!Y!lp}*ZS&*SY4%E>w zZiBGBe%yXn_=HWnuq6+WG0ZlJ1`buiQ2UYuQ+L0dB?-AJ(l#`%Tns$@=RI@OD0(9S(uvi$k;yWNu~Z)Zu|C<4Kbh`UBnR$-nSw}U$_elRcK zRn=N8X*N!2TglUu7=(kcTuvD6+;6uV1l2pQzkX#!)B|@BF?^%4=*R23aw2s2vMHBY zW+3vL$YGNvBz01uM9YUekU0uQVQDQl{s)Z^GLT+_7w*KaMRg2J=&g^l zP68|(+d&)Cn;)dEfllb>1kF)BA@IRN{Q={&n=I_-M+M}SVULBho2?I9o+KEb?Le5M zJt~>PazhLn@6#7sOOfy9t4x&q9-L^Tcf^~By05sRl3ky)LwU=ua{kqIw}sCjsE{K zTHo$YxCWpA6riQ$xiMG^oxpFW=QxC;ach`e?Ws_yerp1Rd&4diq$T7%I?~#}PExZW z9;uIzlE%OPeR%n;$Rs}|-~b*#rJ8df<5(UbP)$DY!2BEOB68>|wg?P}8pdN;fkhET zx|1G*6@Nl{SsRM)#bAy{TLpP&7sl%zYJfA1-qf>JfaR&tUjpl9M5>;vWdIi51_Vt|~{ z?3n~~syc8op(djYaXcha>O-~o0QRk>wQlXUs*tWOI^pL^`nsS2%BMFsfto{~Fr%J2 z)^@=fWSh~MGsAMzv7`dwnGv`73?NP0?b4Zh{#3uWU47}ynWdq+sT$w^*2NocSS0JG zRMK|CPk5te&Xkr6md>>OR!7!xY=_b5XsCrFqm|J)1PR3mC<{QeBZA$^`EbZf<#KhC z^SiiK+|rvjtL1WOWomGduv{_8OPrR4v<9`%`&9Zq^tjOq;7N&zeh!MFuC%FIYGit{ zyi&eK3+E#j6NHixS6@%bUC)E;;e`30MpBA%y51DzoGZh-+W*7((ZEg2%pQmj%|9p$ zTpjvWrWUVXziDy0;LTJr1t(}PCORTpDs;=%JJ~VCSmz{Qz;vfVPG~`C#PCmAOf|U0 zG6skxpxGQsXiYO@{ri#BUmPqFtcl`&+!CZ*K%C0=;+mOC#V~#P^goJ|aRu#BGU!CY zR8sWkq+TNOaI)=t(OohHLvVn$Iz+q}=Y{=85(N$T6ygFb8f#-R$iK(8a>`{4V8Tg^ z$98;Qg@;WXd#q*!em}D0WmU6UrJ2wH0?^>`P2M+u$lkG<*o=jq-Kb%4XH}MEnv*AwA3wewxGq5D%z132vSLR>Mw&2z)7AqGSl;Wfu_AB~pd|05$@&fO-&AXSU-BXNUVy z&*Bs1Ex&X(H>{4eVr|$RVr4JB0!v`{Pk?UOE;XI*1*8A;*QM`Zb{T93S`;3fW7ns7 z{V(lOh4bIXsv^Gs_dU@2K)G@{f#-if(kBtU6RFyQ45-WzK8a8Zze9|>x2|=hGktM; zHhSPj0KI8FixlzjR@R7TtB|4}K({edbvS-%MLi-=vFL`IrU1aU=&|GJ+sTFiN$Jf&h6o#y;Tzb(DQQ zM=;78Kh_GL5RPr1$Wj>iLO&2!21%5CgYa0HUN61jvLkG;g=V^tQiG7iderh1<$AE%}@ho-o{a5QHuF4+5ne@OpW40_%sw0H}QUbZ*!m&9cO&myvKq<7ky zSHbjWrp`|!5a~OS=|@QJ6Izv=fu9t`npR`%UeEJ>_Ur?2FAZ)td3gEAn_+fuv|t{5 zgQ<4gwtv2Wq}-kNUA}y=V%&viUH+c*TQZcByy~p@!|*fP_K>a(!K2cMou5fefNw==KM^p$FAjzD(5AMK5%pT$DsgN8`bWeza-P#u)lv*2AaSV!p0I}-CUD9WKib! zj|i{LTkYLmNRu11av*sKELw5uWF3HmUZ7xeweb6{e@UKGQnGUso9}6-H~*2#QY(rV zb~#B(&1MO{>gYXgPtGwZ^f`IScYE`{zpSLhtjnclvy_bOEaU@~y0z5iE@|R``htWq zA~L#BPC+@y;&-szuq3hj{=CHU7w0;gKTkMpbvl&_CXd;w6ixwzQKOXVmkm2;?6I-S<0HvmV#h2U@iQrV7FT*OhYSFd?)mo5ae z*>%3}dbN757sY~q_YPiZ{A0lS1y+n2nVvkP_BHx_tUrhbE#!`dAdqQ1u|Mt)?qu7b z52wQ6N~K)Zd=w~GRuV>}Wdrjgq9S<+I>l24&DGTjiIK7K+G;ZiB2Ey@+5-tz?!B+J zDV11ofz8_TthRN0RBugKVH4G}XVPL6=UpbmsJNOkF6VZ)3Dp@dyi6!FFqa|-K<*bD zP9|P_>;6U8&F=bF*&U-ScLChDzG3;X+x=I_UP0!f@Uxs)%!W(*5%bjiD6HSQ|AMyJ zn5K67@69qs`uYvNrR{B9nmG;Q84&rqmcf%Mv`$!YMx|TBFcV64 zik|^}=M|&yW39L~9O(!~t%-58a7>Ijx_7~`*!7keTP&9rd*yPkS1vCyw&b~SU&z3p zANk=hq(g8w4`p!vUdOq&)9DBpdUWFFi-=CA^AVsw@B{QOG&ZdDR)AfNBdf4)AK9|O zF&a_4^jP0Ng2nhUEsJ{|mUqy^Q$xTGLk%UUhUrUfb~Iz`HaP=J5Bj?3HF7+mNUDI7 zdxwXIgpc@nV?6}Z`Vr};Bu()5_X8i;)h!!N#9olI;AdaWW$R(zr2pNwxw*ZZfPy$=IXZ3zPc%;)?2G>DSvErb#?W+iF=Y{WtWPN z-s#^QZ7J=)1n0I~cphqmj#EDHcNQ;v}j>ATJL z&{JppL~9l8W^tAe+A-cEBV17rB3Z+d;AK^pR}Exqz&SLb?k@1DmZXqXjqKWPk4?x$ zEdUS|iBYNcMBzv@^LDjr+}K>JM^-&9qoKg55>q>$PK38r#-wKm-=RiZa6IB1hkOJ8 z+ZEwZh7God7?-dK!e&QV;q^!(y!7Nm+7~sM+WD4BYxe6W2w7c_`~>$Cy$#<%_896i zhVgsfIO-OrQUqAf(Y7eW-vE4gD!lml*OVG*AS*q?wLAO*@k&yAVBeX#vy6?oiV90nUxuQrTL% zZXCO=)b{6%l-e+2_6@^MQ~JPQ_aoUbgqeK@Gh@4sAXzQ1#=Ep_^my)@GPah=USkJQ z@(ofNgfKCNW!N!HU7E{fMk;0;oH65Ig2r!?`I)Maw?a}cX5jN_9YJF>5eW}9p8GAo z?M#YdDEia%tlvsF7lN1P z;cL)WFkTDT1%124sEvETo z%DBtkygYd8yH;KK4?he;nqb({WYRmxWfgWGrP(-eR*C&}}~VXD?$PQQ>YcWbw!x8IJ}t)>oX*=mcauE*gst7|9cr^yqE++bcm9)wJ90J|I`s8 zNETxW%a}5`*`iVr_@%v>@3!=#$^DXy8ZX(uJP6?e0!{G+2_OanxDdxLV5$Kvgz%sc z9K?VFEH&HYMkP^UiVnm~%lIb5pxvj%^FhPnt#r|jGc&n;nqQ!SPSHP{D_;(ScUqd13Nzt4QR_Gr+{BNp|7YgcPkLYqD$ zFO4@E(|T>yRAv>kTB}pM%^3)-)>f|BnR?W@L_@#Z@55og51ak|rESY?)M)cGP`k0d zw&jAN%BUz>eILCjMzF;RTWjl${V3vE+{1pP-|zSP?J1CHtzui(Tm4&c*#+z4Jf0?1 zh`9-v>Q`880u-7FR#M71V{jYW_Ymb3@4tAJ9L)}dZwmXEhozG3BrdyL0cd))@725} zKw9d-3zodYg53fBVKwoqRXTZA9)u{v1xgd3T*QrfR!X#?QV~}R+)|v;`soCrZ+7~9VQ#DN*0E_a>Yzm{7tE5z zYRlrubTlryQ+THQc0@Iaow2vD5F-#nmRfm`%x`BN7*9fdSwd}!ZoQe=mom5OSgTHw^?*nu6AuWAk=fZ`Z6S{>gClTogHl*2m{#p13 zcm@_qT|+aE!ybvsM?@u#lnJHkpPT2}k`d(IuzzefZftl9E;ForyxhKHQLcFOYy=hu zI>}{!ntDC2dA8ZUyQ^Igw(Fb0^!qmG8BDfthgj^vLqh z%Ww%E4hOW zF-V7HH);Sg%#6ml57OgWwrFA0kjotm<8^g@O0q7@AH8qhPu7Jcc|!%o$3GQBNPxlGXd@CvXm#|s-9*JTTn z)zzlqU=G$hcI^6be^c0-f(w2;@&q6?#E(bhDee##j|aUKCb-zNMelWwKk)GON`iX} z_rjwpO$zhD9AH?lLGXahtAtQFb@U~F=y8EeUUbI|7$CA%>mCuuL7m}Wpg6*sH-Qv$Mt9YqBxDjxY=`giT{|9xe`hc-?~ zb@glPdXyw+cmgE}Ix$2^g09E->~-Is`O1W^N`jh@075i(UhHip^sQLdk>8{^3q#_0*CHeRTunT5ADHxmAFws}u;W zbsFo<1UFR+t-{`kWk`TzKnwycN?|%NAB$HosKvc9QgF__G(aBFK1YNg*%Dy3f>L|u zZ4TL!x8H{GZMQ!;{SrI}TDNQ6nhw-z*47$+hWTEScs{FVS^a0bwRZE_;&!&G0$tmL zYQonB8m(3X4x!aPezenSw_7_$$J;G95RP$ez8u8WNDId`j<`9p9mkF^em|#{c_rZ( z*AY6Z#=){+JTcnN8OAt#AKYayYO3lI22(VhSgTJg5k|_T4rvG=ZDRBNv*IT0wEO)H ziHY_Eum>pSj1VFV0qi!86Rk05?Je(|II+8aOlgPNUqtO|yNMG;NsHeRLpZ7(7+>>OWm=#rkw{UtC1$; z+MajU!T9w3R#h;NzGr68334u;=sZAD_Y*cTJPf(E-LgX_+r0V|k(emsk#m6Gv-TBo2`28u~VyR(|YTgcyVSR=&=`!VSeosRXyu1 z`e%ao2cgoN>flGd_tJptg}E60mnxJI;PQT@VfDEm4^9X6*|f{i8+;ghofFlv!F+6D zaYWis_D6EuO$VyuFM;A%WEO7B(DmvO= zYJAry0YZmC5F|QQ@?8Kozzl#H2eS^{hf>KYqd=st*7ast3vTywnOarrv~_C=OMvHH(=ukI z=ntR+Ejx>*NpPO`N6(afAGGq9+s0@+%WQ4jcH1@DZk+^(Grg7S#I=K!1v3c z>+)Vx+pekCjce;H)3$3G#}OFo>LRnf0PUe5K)rG`#nnlCi*?baQLb}VWBz}tjW=#) z4k>aGqQjpZC%U~-g8Z(u_v{EcP6>kiA~|DGl32gK|Gqma=u+%=-aslGW2;6Ml90uK zjDK|B%0=znp5pJY+?bvJqt=+jw2Q{*9zwPd*aI-pb^>()xYcl*2BH1lqPgX zDHgDT(ZG$4dAD#My|u17nNH}^y8h}q-3wXzK&roeH9OEp+uP^+y~Vm0xK2JCZeyW< zq4U@9w|Ej`f|3N$f325ogoPb={-m=21UR!pLeXG&(?dckz5oJoZu?Y0qV+?eaeqL> znC(&zjGH8`0fiV>K0*BeDXpu`7z-207LLwuZ=)z|yN(wumP*Jlq;OX2IL0iA6YaBx zR4cESL~CVJW2gP9l)D{U2nZLK$~O&$=z0NS?L>KD!IlcV<>m5X7I+$})y48;LrV!} zd#5~%I){}f6D6R`&#DUX0PO5*A>) zRm#;$?7DHKTK?Krj?;0BL}6t zFQM$RK)fVUFvLX5zT4)Ax=iWR2ri84dPZSNPmjkjd>G0Vn^NL2zmDep%l6+Ic-~97 zFYCQPh@h}JL$a)^4mC9q8Dd04xF<0soM>BnaTNLyfnIO6ySb{U*3Qr;!k9~DrfsC- zAdQT(k~M9A$n1x?d|hXNCAK#iCtG=6ba)R$1p~LDOkH<<+jxq-?EBLbR1W}Ci^4i zWQH>*bHunXB6w)O6>B-K=1ptIy573ky5D+rN*qpeDgNR9Of~f9t~U;Hoegmj$GZ-A zVbjQ>{S#D-VkcT1XkSdR;{@wd5jYM$J)6yDdlOit#E(zHwB%5$eJ}TXTW|KX?e}_{ zz25%RZF;_~dz;$!dmV!hkXd<`T8Dl}ebu&2(m_%lL0do6*tL4`*V`>%r@Kw617{dWT@X*=c0l)hZ5eR=QtC)&yD=3#7Rm4NC zCi(nxKYN(DgA?fBx$7rtsA4)dTflDlbX|jgb-%!R=lB-n8zT~&uh}BrAN1pV3;EiS z?QHbfDYjrV*nYLpg;PH!(?!D=W0>?7ZxChRxuufp0cH4bgAX(M2r`N<>zt_(@#TdRo zK1au=?0kk`PoB=EpX_r0z219DqO5IbqNzxbz?Z@#!HFpi0>jYo*B z%_oRwv`sKy`=#h(#*Jg!V=Mf=UfB&AWqjiIjCYQ>4iW#<=!xWFmH3k#T04l`eZ>>eTRVU(p1*ZrkF zk&^T=#+>5iWlTB7SP}AfwNJ$+X4!JLx^>FB*Lt<}gVra;i4c`8Xsi~rZeY^ei$|-AoFT=}-<&fvHiMcDfFF zbW(Klbzm#|tH0DuI{faz6)I7Y^aqXrj?N}o7+$DKDJ#1S@595p## zzQ_FM8xcs%x!Tv)4qic|r1k!AB0#RJtu5qI`tzU1WQ$_T7;YfO!B$W@#p5CCBf0HAD#Tr{HCF$i;nZo2c1lP9!Vyh;Gku8-)^pHGH>RQZp@2TY$Wa#_#D59a?s+Y~6p)eglN1p&gCcB+_C zpl!Nd)V%KCBZPpy%yXykz6_7{1TaYey#3_UF%6>4x~9x|OwhcRsz zgT7ELu=F3}5=pLuN3jl{P^WUsRU2i)`=Ubovs+}Y4dDowM;DJ-u zd!D+fZz5*IW_*4Mai$QsL12tw#(0sD@aq7e8AlR<6L{;M)C7Th&mHi1e;G5H{ifX@ zx_Ra;<(obu%yEpVhp}T2!uKEFL1^dr{TLzR#9`ey#$n`jLAB}t*Z__a_1~wIVuIw| z08X_UNC1K<iFq1#}(rw=(xA^`z4diGnJjB*1IctsEh)NrNlHq zWdzxB8%O6(o~)*|_3F~4o4-_Ducg(KC(rGj-YI2+-rmoxmJtL}27t<|RR+g(3r|`R z+1R;n-iB2R92s`lnbZkge?FM_`fY!NQJ#zc>i}o;!QK8-y*BUuTZk0&m85uinGl6B z=2`nu%)fI*t#Ut&LD@|YSTFNXs3>KRGtPK7ibo?=3;Cq2Lwm(6-^8&| z|367+M}4R9qQfl~wOfNgvXD(eAA5Sy9vNDh&jo2b!-;X%QQ9ctGR9*2-926dbR(YE z8+_Ov)O7Qy16%ujwmk`x1=$9xcJON-Z2s{?w)Xo+iyUv9fQCziqcaE2* zL|crlw~b(`VSr{Swxs~MPB!IOr#3I@9lC3g{bTj|TV(eyfqQZZAI1_sDoH1v{DG;a zvAlxv7m=~DQaU1R1uvhY4rpEmz=Q=IxKbWCf1nl_f~9UP4bw)Md!iGrzP)|^nc*x? zZH5%`Leefhf|5ZCTt?g5=i$Yygo+@@5Ip4mb9RID510Uu`c>K48k;81Qy*J5TKne; zL`j`D*LWkhvvB{q^VE5(&Yb??P%d{~X=-G^Zab@)SNhsU+uP?aUk!<2Z~c4Em3w;k z>rGwhl*=F^HV_=!Q(OC2&TnrcdzSL0(fwWfeLG8LF6;NLGP?t*wwK;7GA($@2jj`K zLoQC?V6>1R81MlS`49b|Q`r7={g2Om4MOm>37n}de$$lB45O$2XbqJrKe2nd$<_4< z;y>`m^-rIpXG-Qbg)?yiW6%E~fU;7-#y*rD4~iW1|skE6YY;1cUbpXuN$9c zfKKp}-Y?<5`)CX_oe4|{G{o=^kXcP@#H6rMjJc*Oegmhe6Fg7VSI_f$8D=)mp94w? zQ$HU2cU5T03qV$hHSxfg3wygf0VVT4$;K(B+FIy%!A-ItHpA>Bo&Sjk)Cs_**qMa` zu*ft}LJEV(m}Zh$7iPO3OpxhL%+JL!ie(Dl#{odfLpibx&67b)b%G@9WRZ+}?+)A= zc8Gn_TF2|al*?$gi_>HqhbIrcC;Az&koATE1tGn+$5jQl5{fmKJK|hrVFVC`nc}>>st#yOHFm8RTaS(N zG6OpbW|=a~&P#W<>Cb!`1E7rBK`*JLF@<|Cw{rorM*3;BTWuTcr&OfVkO_>&jEPrN z(73oV!cu{~tH)zAH=4zZev)#mnLqy$7sm#RW!ptVzd9~}ivS^Rmlu2M{ncwZeyQ#L zaX8}mrR7f82LO&AEOp3(Y3^xlYyT4*%UL`}pWBH|Bn9ocTx-S4+xqJ2{VrjUMhk)g znC~-7^X{j$l!(3F@Q)()`Wj5U&X2ynia^`mp091h+K`_54_F_uezcubbO_)&^89Y? zqC4Fp{ZW5BY`_;y(cjW;(a$#q?Jd$BZ%nu7Xrt+3kZ<9V@q?81>J1%mU|bPOiQ==( zag`yUAa-91!-QbglD`fi%m2fH0%Fxz@z3@G9`M(M(p)fv5Mn|oB?QyJLik}{Oekd- zGfD{t05rt?6i~j#8d~&J8M7JhHgtSWfARi|^%uYV81=p_|A#wxH+1JLdvD!?|7#!o zGIsA7_5AkSdY|=q>*uWB@v)2b@lYXn!*JT|(RoBCY4IdC#`zZR-XxFJA8ib_3XA@; zE?b_9i{ALcv=-c7q?YaC#(!ABX%qY((}_mq=09u3>jJ0THH~_yq(lEV?~I>=P_bNm z(=61fMa{YJJ3m~_`W@J%#`S$6NlM+jd?6`uO%TzS_f|@=R5$p~#gWt(nw5$aj#EKg z93UqMu0MGy$Q-MwAB3w%eJLq*-Qa>jCF**?yn?vhi>&&P_MBc&1hy*-NVGhs;eTB5 z8O)i6NHdPz<>h0?mY3c2lWMExLU6rdMoDHc-J1*M+!gj0~)wOq4`s)p-f0VAfkGB<^(7A2t zpRY~`9G7(DxISJT5;e}X7wiw1OTajzY_4{st@0j-e`P%tPR~px+-Mn!9%RGXd_DtA zi&-ruk5+{GFy^2P&|gan2WuMgP!pPM;f#>wkUpGnDH{XPSrr1J<+I)Bylf!{=_Ag_m4w^Bb%{j_!Twz!>lI<ibSZSd{sKlkZ#yhbY*c<2cW zD#oYMoBab&`5L zIcJRTpYwg^k+*!YTK(EvW;e!E6ZbFo#uxiv^)h%xipcy<6KMVy%U={CMRaKM1uO|+tAwmNAb~7!dq`0VA7?y8 z;J!v}eEj1Z>NPH)l(YFCSzhz)zh9{}0-Wlc&;cVDrwFCQ3@FJpM0<)C@PAU62Y)mJkpVNAeKyUtB56E7fnOAx5 zanTk1c|r7w@HM6O2%UceuAyXa{{Lw;*|9uY&5Ob@BTuav6@i~^l92p@o_jj7vlf1+Xeoi9bAAd;Mw)(lJ z)N*2NEs3iu&8f!8+S-Y4MxT^S?5VY&bv>m%?*!?jtL~xUbh3li*)nH5!a|AdxXZ|H z{$cX*)vp*SJx4xWtu9`1Eka1~3MHWb!E6E^{r5eck;Cks_^||`)=}4!#NcuVe>KNe z)heu22}9!Rt@|yLTriQR1ikp%{Ipw)k|uCM%N8_QTc_P3N%O)*{2)U{$nR~F|F&(Q@H)sc4Q!aADrCKA6? zoCW-DSE+VBo$LZUnl2a?@!2cCr)TQtep}B}$g5x3C*-5h4)+g5^$z%AMINDNUnu>o zn(5#BpK7M>sEXnJ@TqAgWdAEw9{NgMeJ4LXlpS*qmaPmF_RDH!3M^uJJ})krb|$;1 zfXmaTHa%}GNRnVrUTNCrne%a~x6{i8j)}DTmlt*GYw zp91qSr3-Pu9KMB*JM6sOydvLh<#xQXwMY+cx3dA6PT+n}*#%x_0pp=+Kkw&G{dtCi z{PJyP#Y zc2tU!9QK+TE|<>>ZSQxquTJjV5EebZ%|G`rKI-{D~x-} zy-VY_cW#PY56pP}fhR7?jhl|($bWDdM1S8)Pgf4#JoEDm+ipNMgXE$;_y`!iCrhIP zijD5jWvid-mQsaNMz37C>^FHZ025I~vgYjIeq!EU=TyDX>o+qv+*hZ|QSKyd3uoVB zS(N2%#kh^`189N4lO>h8*T4y?QIw7_lFcOsl9uc%ymSi@YZRe8z&@E6yyhKQa!ihT-qH0PZ*1$Bvx=^4-p{W9@Ia0PgPqt|ePQxy(yj z#>E-PgwZZ8TV3mzb=JC%2NzDq0*{TZOdj`oBghdqDz346RKW0Vt_BdZaC>Yi4gL5bdoG}nt)*Jhnytl&yi)4+ zj;KbCsj$;#ouE6yg@H|e72~`^p7#M4i=A`EX_3dHe4NLlyiGvUxSWvi0k+s8m}d^; zT>{}2zS^_R_Rn*Gzr5|X&;8~8K6pZ(x!f7PLliY1KYVo~e3i>J$mFV!pGV&ND-Gw* z?*l)-zrVll3)2QTCi))xybC2y`=ysh#sM}8j^G(EXkd?>vTnEXh*-$Cq3enELmvRT zD2YNQ5rZTq0&LhW(7u+Zk1C%svmJO71;QGmjUC9Npk%zoc*|0hA-^+d-7gcx&Ber7 z4DPe3R9;PH(VYQvOz?U_@s8ikod#aTV9EiK&yYc-$ZVW5|4n7XS@CeMQ7kT2WVzfr z(}*L0ynOoBde=1}RkL^!7>9J4O~GWCPg)1czU+?HO9c3tRP3T8L4@>ZC#_K`)oagv z*&10dwB8Hr4HAjZOi<)MpSsgY3i3@SDezr30sN+uA$SEZ1oAFn4U&n)XL12fEL(jm zN7ekBxIL6OHu{6;e}5lC~jC?XzS2f*FAe^E1CmrD>om1jb17_8=cOE zQz~&umdT%m1H&|pLAU_eUYYLtTx<*rzoX4BoM@zJl3|Lnq$oZ@ zh#_=!`ka-fkuA|TKa1C(cc7m|evdH4jMd)B9ARZDXNCuNYK{!F{WdyYx z!~hE*p~ppVt>>;NMPwH|?b57$^x4!b>=vwCzBGW?8!lf(XGeXxgtGDS?vgNrBEE2v zHfxj4u%C)u+_nWJu;2*L-NlwTQk2+0TY!WyWOOwm_lH}W?&d_aW;kgf0evq^AbAUEPACNJ_2eDE!hWAqIth-AHxX# zJPZ*=YgeauM-QGiY7wE5^39gyog7v5?ym}_X^+RY zY2e~TPKdLgD~`+ZeTPc=#lj9>zPI9?;8N+r?62-~G&Q*S`YZ^2KB{kluAh>%Ayn7F zVn|oWY@igRX<7pTlGDkAEaIl1_vh_5YK&Y8f0G!e^v<&E3j)TFxok`h)2tkepP>MB zXJ==J16Qzvu4x@25{FZdQ2q=nb6D+QUf0^bDf?9}o#u?q8#1{GYzd@h3i{j~(_A6b z`o!8cS)HMilR`WL{phOaA1j|hj5lLgSUAaYR#S`CR|aEP$5BHFpS2k;NuxkJLygdV z`IEBi!C6JumD?2MPv)mcK=JG^G9|`zNb5)7gatDuN7n=6+2fJ~!li~j6BYWt^+c3(9(fWM|K`P&6h=t?{S(s0r<6UUdG=3TxX7olnYAN|^h2c9f z090%BisiaiIW$e_@b+Cr#1r!Q#)pYTcic?!5r0jxbIjHpAa`yZ7TkTnwmXol`Y%~W z-Y5_F$1@||A2eR^9#Fm1A4H_ua)~ivv1*bi9vh^Q2ZHT*+C;(G`ZKLkiGUUGVvtI4 z9-d;^H*3JSTAS9YSor)tRr&F?JqTKIinxMMQksRoga0D{xTAZ(1^kb*|FgFRui3^J zrijHq%M_A^Sqn9Zdoud%E+d??MJzdou&WzY384^3Q+~s6_U9D~Kg{|v#f?TBGh8R} zLZSR=y=`i5nEius!}y}|c0i7^e_rq2FdX7{7fZeE?JO&5n%QV9yjf<}=-#M(*$6r= z`&{_%^ai>P-7j$)g^a00>4?xG(9!dg7x-Px{|UzJW+A9lf`Vx&3Z=3;-xa&_u1qN~ z#j4{=cwZ87^v`oF+qPv%5@f@W1xb=^+qS;g?|80}-)Ph%FPuxrL2vin609Ct{+BDd zY^XiINzFdOU%^Med1^p~ImvdUs$8kaRmriXIe`W$R4Uz8MRZkJ(F-m388^i;U$%nR zO((b9@GB+HRe%xdNQcg{bL^U3!Zlr&O6>YvQe<4ItiWC%XW9WobQE1c zccOdILkL0QMQMUdmA$B+7NK1`L_0>I#pt9JL$mnO4#_S;R*#fhz z@x3rK<5bKp19#6Cz@G$c*IJ1F@@GpJLLP>*X&Ay@81^?|K3HbFp$2DcdL;vowtzge zd#|!M1SiNCW>G6aJ5os?7V$?7cd!fctp%?QG1|ABEL*xJ?MU1RU=kQy+L1KfS^|+N zyfD#{DBKbEQ517_5IDB&1Q433zG=%5FL*3M9PI7^(L0iJ6^Q~rk|hN!h?JgEl6bUQ z|8}(1x2f0$-#@KYkp>U%iBMB@Wha__+0b0V~`-=hcR%DU(KcC~8zzPDZ}<9=^j=UmY>m2>@Y zzmH)Tf(H9wmktg30wGjYDIr3CaOmE{hjAqS>?nvLK79CIgb+h9s9v2S&095%yU?TP zNe``fsBi!q?14Yb%)qB4M&__W5Kquh)I-fy$&m2yCJ#Y()avWQIozXgW9Xer?ZcQ6 z5gJJ);Fy=opv#G7GPo++(wQO%3ezq78%0Wu<_#w4%cpDJ&@?D5&VTccVlj|qz@RG{ z5wWJ|fI*gn8@JbDTc5;IVa1B7`YWBGAYo1U8(>7Rd-C~Fz!)FMQjrhMq;ks5^MtAZR)Yhk)3^^FAMi{ttiyg+O2L)E^n{Y{Gxf#Dr z5Q9X&08<$AO+#>A)k`G?2G=!@bM9lVdxBy5`@U%ip3bq)IrlW38-eKKstLg`eXzuS zFa^Ub%=QYV0i64`a{d~q3Ul33!EqET2rP@pEU--CbI!R>Oe- zu2SrCzE#9!g5)V9c&0}ukr!};Z^|GInyRYND2qAS#x#Zh8DCxDh9*md3zJoWX<5JW zFMiQSWsTb+TUCX^g_>m|%Y#pGt0IQQ?Qu2?VoD5Q zO~^pVjv|`!v*ET-)Pl;5p#hDz(i~j~&Y$%zsj-ZIgL6#fi}O|qsB*3dAhjs%ADS+-!rZswn?h76{c1T~$S9Dsq`((=0wy3GLC&?a2$MlOnqs(YgB$*bw%He6r#s{Rn7p)M>^8^k%>5e&{%rd4y6io0y`dcY6+rh_m%msm1lqh4WQ|#CyVBjr3CZpx6z5I$B{!W2T9Z6&f-PvIVSF zx)?J&e1oM92@PNcdaK~fij3i9f0tB?hRKl*ACB3k0j5#J_L+h)TW)>wBom4oswl{N zOqmo+f~?E>%)X{tp=qoRy-s$$av(dqHY+vxF+-BKFLBM>e+*qWUj17R>O*e z2(N)tntj*8 zILs4P@S9!=3k3T)Pn#%0L(7;5!t9S054T|!jQzP*7R=u(0~Qp1pabat_4dG@rr*y# zeYVwl_St8@?Ezm^ln1C`&Pg;_!+-7cE5r7~`OW2UP z`E{=%+*aGy+NQi0nfv0{%&W_}!GKlQC|V%B4z31n?~8^0pvwiP3o*4an?qj#6>~&< zegKsv1TXYr+@n{)_j<#1;C7u6^|3{jF?I27U879D9UQl@qpIzpr$8wQD~$c}6y8!Y z$nsE9@IAwd#pdY^qU!8Kg)&AfhZxgUvT?duEHYbwF`!_FDmZR+nHZ(FAXFL0P1r&i z;bfNO1f3uqA$jYR(n~E78eH+c6fe&>#tbx2qdc3La0xe9htrJASXD?ECBpAzfwi8* zbc@~<+{&;70=rq&o>88Sbb~-Z&kdYSFF=y59&8m6hxiTp;f<~#B$p~Gkc5U(@w&LZ z)vL$l2844&4`ZOFRH+vWSY;rim%`l~2?;C5Bpv!T5US3XWJ39;SGaL~c+l@d+y2`L zI(LuMc+GRU4*vHvRmF2f8t&fW9l_b~UgWMqN2!qQn{Uz#KB@BSQrQ@0Klf}Ox)$vu3Ix7PK`NJ8S+Bcf6PH~8{GiD-nVyf7(I>@65}`5#@0dLh zsC)HB9UF0#u*qzjjhL+1Q(KYQsH~~hK3S(H-M3V2ayp=p>h&fjEjigVC$sP0>oGOe z`qoM5wh;p4U!B4~!4$RLk|?W?@Kb~<|2l+bG6nc6knQvQ?GR}8FMkBS%Ri-QIyH$P zXiq(*2?8;xu4zxLAP)O)yLX==f~ZzBO{XVMQeD$3swj|C@BaH|YH}X&y7>?E03Aa& zqleIw6m{ohlOoIFO$jpQ7E{@T6C%$e!R6H{#pkZ)s%rbk!f@Y-tthCCIO7l{z+Rjz zugL9;T+^iA^)dFp>zfq2+l~zovtd%UjQuwMAl1#9rsIAXy>`DAxAqp%6Qg*pnqxq7 z&>WCQDHir@f>g)}+f4b*hOJRKvDM0{b$?$=)gSs*!FEcIjR`1)T=r?Ry%i$b?k6)Jim{<_pab3U+#W7{s{&5@jV%^qr@Av&4MPfze9dI$Pgsoa!5S`wZj>>Hgm zvWNvm(n@4pr%X!xlqjt-3KFFIcdxai&3FhgMKoj)vWmJS5~HxV1Eivma|dUaSGa#) z?^@{rcX8ag*WDl1*j^x}>VW!dMnw?VWUw)_Fo@u-VPE1q7}w#J)o3@ZA!E= zl@fqu3$5UIF{i=&XGtrO-wzClBJqCrI(`H1f4KlwD9tWKG?<}`{}$!){NZ4~<*C1h zFrgnOPKfwckiXyZ9i>B{!2Ig^u4V~TEAi}taGU16j7fQPrCS4S@9H;mv|+a%+ClfD z*P{=kU#4?RD3^U1iRdXWx#?&0MyOfDt1Hto-uL<*sB9|k5849}Lg~-}l7f_^fi2>| zA(<1${glH75Rn@0fDDMfdt&i@Qm-##D2fxTTQ9(K|F8DhaA|RgF|LFp3?zwwWIH8M zbfKV^iZK9RT)gqdIA*d?kQdG~0W)Tqx{d)9-7vKK{kdpyX>loBkPCvWwBs9ZTwLTT zF0kr*HN)Tx1WmV0reF}{XGB>u49-B%HOo{OR*f)hw~IwdlBCv3vPdKmpRE=PA{K0G zc!)ZpfJw==MR-f#Yl3ErR*Q*+Vm03Q zMMJX+1+JUcnBaEX045X4Ifti=gL4N04O8}%)5SPGPQ zu9gpuJ6jJ$i2TeDptkj+bGu6|y7<>2qD}NF^hWd*^m_<_m&6elYJn~xR0NU}q9#ee z32Hrv{XnV-!JGoJFET)z5(6A&gpmyn@OxqLP;y2ANbcyiY)OKSfNc{Nbr{cN99a|1@1uRZTISsEJYPv zc~3_WuD{ex^}FyrxBCvA2F7{beh}%LH3-5jDy@y>oU#61{l&lEQrZWh$2VS~owKp4 z^0QCXv8CLj$CwRs+)qR&&<*Hrgdm90BgpZh|Q%2<=56S?8?JP_8lKaCrGO zAHV*66A(tZ0Y*FMgQJr{;|*^N9&>B=Tr{kGZo~dh1EbN%+(Y?Sa%O6hTUM-TzDtA5Gg*FTxuMpVyXlfJj$8D)dp4JQ=JttNn@&{bhb$>`U6tEw%l-oCCMSy-?e z9mBdzyUA#>3W}EgWD+tS1Q3B04R{fpO>xZ_K(Pzw&6Ep(QDTQOVXVIhGLsmP7|uw#djy zpbjq~0xYe2-m}NsGikIo%V-`QLbvo*blF0s;%#E=8x&ge3Lw|gPVc?&mizIj7 z1hHpFP#Vp>U@4V((}`S+qIo%bKBOpe@bD`*i+@p|69WL`dikM^7_T!AMpQY|sAgHv zCPWv(9fdh&==5eLeRu0d{XQ+ z{D~otL^XCLBgc)T4vxWO;s?^yc8}0_;Wn@32P@}V_shjgv%!z7H+i&#ez(5Y-~a@L zg(|hsr`DjSHG#s>q@bPMBU4Hvw#BF-w3eYZRG8I%uxO8;Vvb zKO=sM&go~9!US(_UI^#24YEw)sP!B8!fl7Q1h{cH!~ua;KRp~eegwWDO(|f(w=L{2 z3PjdTM-*);T7saN>--Tl*v|o5Apn%=+_H(_-(|R4CLtWqgqdM4{)cRB?G~Am!Qj;9 zVnCsVD*X70!CbTsnT@f{G1cldRS`jwBtaG=kO4%~mH{NeSnKcB!GdwTi7T^JT_k(WCD~*KS-OSCe50Zn9wMNvqR5?!^l!f><9Mn;;=YJ zw6kT>GD~GL9qFsTX=5F9DpnoeVMPVMsZwlLpMO=;L88G=zeWXLAps|K=e=D;m;NMj@<+m&-i!YC1wPoR zFFP)M{3-JCBRuObN zgQs4KCz9*n6AGlLvq;iWj4aYw=F{pv50zQjN7nfSi+U^LZhYNPGdVcaAiGNm;0{qT zt#Jk*E!D)_o3o3BXd>#-+$eiHr+U z#y|pNkSp|W+D9nkVeC@u076<=m3Ap(z`o~tYpEXqSIWOHyD$2w<@A78(y(k6Le$e6A=KCKq$c zbp&V1)|no~<9tjaW-=bddE*4E|5YE2^Y~YZ>ky3pH@1T&Q%XzhuSl=xpn`@u|M6(S z2jLM+5yXJb8*0{RNJ6pIULPXvaYSH)DoWlaUZV_=&#p)Di?75g{)`?L94U9`V1NJM zu*Jda&trpj`1`5@HD;MekmPjGa#Qa)M91Er>KZ-1tJZXc29y~}-;rt#A$~w&QdWzd zP>FhC#t!?r#TS7_{$Kj;j<30-FiJ>>xnXuHFgl zmW8b9^WQ`ZG&R&KJdiGB=rTm0u_y|dbOmq=N>q5UQd8L!rGT~st>k%KaX<0>|M^)5 z$V$9)HVS=NTud2`@4UuUPI`}kByq=MH%UO;lRC;fd1s7i>0%-MFgjb}B}zQkxPl4c z2fiUlJHF>QfVA{Ir4iVU7dRSW*AG1@xNwc0*gSad5KGr4)-h6VKS=3n_du@EDcacU^-(aNn~XO0@C=*8y7i(1;aE;0e1B%wDbOp+1v)gW)&2l7M zN!JhsR*|%Z!LHU8B^Ow@ZbhpCLA}0)fK(VEI7X!VXgA~Oj1huEM4-X}`22qoh%g~6 z!-yls5Jz~e|GMuZazeKA{#M7fqeeY1r&gR2_Ly?ckqvkDm*66DB)9ui!F_M|IsbFzG z?yEx^=I!{t1_)Q2J!bF1#$aQxVQ1j!ZqbdCkz&IuipoUrjzj@IZ$4<}yx7RG_aO*Q zsXstXiFQGC|M8+b-H7jmPr0x_&e9$4iD`!brWlnJnWh({N(f_&aEHc>Qd)6RYL_W- zxo)CS7NbQ57zc9t(uNWY)J7N~5nvpMK^fzB1dW=SyM&f)nmScNBrzTO*NNu=WQ0-_ z4`>r#=TfN~BaAS{8G!G`lu}ly_`nwGj3Uk@MWtU=S41qCUH4Hm5mttoI`wph=%)!jet{R4&!nj?7nJFkK;LPdM zr%ykM@g7gT_yWL%*h~4|{QGyU`P6l=^Y&EiA+#sbw>#KzslRp?r3gDNjj-d=s6kx^ zN7Qw2q_E>sn^K#)4z>Yo%d#;3$U=Z4>;GB*4$P<3b-qPe`7VsOTOw6F4PMJrQM!c_ zOC2*6)Jsn%DW6Oyd<$(uG)hMOu2us71iv;%1DR$~c>Dp}gpi$*vQa2Y~;!>2NR|ZJ`Z%1n5UF7Ds7f%;P<`$Yi=PolG~T z{jmzsbhQt+$W@Em6m)80rl#*J)t6MMog0CQ>v?VkFaj!<78bK0s9?s0=JaE*KLAkf zMzQMwxLz2!R`0kj0ED;_>TI@(ae`11k)9~Nb#Qw$^}BY!WHmdhgyQI4mm}PbLeB;8 z+&FTDT0KobxfVRJC?7RjYA-#tD*^zO{Yn*}ssZ$wiLU{CU5K8r0Rci70NhGB%gPlO z5XA^=u@IJOX}ewYdqgQgjl!&1E+=qWXvHa!dhMWb+aOC zHCqw1LY6F3ZY$uk4zBI=<6TJEt?x?DdEQTuenfd`J}Yq=^~p~>ld4U`PDvP6K7%d~ER$4v>iE2GUEgj5T zF%{#{6-h%Kx^G1JXGF*ytajx1f=Y?%>KPZaV17s{2=>p9BAR&Ee9uVv34}5FgbNE7 zLH0K7IQnfi{RL9HI%+#diCySoC1QBhjM~Gi&7?HWYXJaW>li8TdI<;MFL~z!SKet^ z`7cjx1>|eZ{qL|YSx;H-xBip$H`c#^gi<8$ko7JklK*3F?80cly6JTFJM0IU9E~~f z8WjkBm&7bI;U_CKkBTq#P=IX-N+{1w!FNSe+z~?quu+5vwWHD zA~sIDnu*U$YkYe9S++kVWUL_FH{;u^7sg)#A$r#OTjoTaP&d?p%8$nuy zsI&}=g$H?5o|q?+U?9$9=q1OtQo0HL7_JCfX|)y!A!HSxKL2DLU=e^99w0$bN7sRNln;4sZ&$Lc5Vi8qHU1jA#m2Zj-hwV?>6G#I7LNPz(2r)K$gMUU z`-hJop91*q^})s<*K6}?x;f)%H}7ttc+ek2gMJ)AuDQjD!0wcAgNEEDSV!+Z{sK1I z!f_J!OEe5NxWsfa>JP@FEz~c%McV$osLk7Pn@9Oexl}*z7TrO6i@@u(_EdD>&r#m| zpKKp0a=NwyAVkuOc;9ZhY+QyBP{)9$G4HI)dVZFTC!GcG)1p?D#v7(_*AblgDvpiU zp5!b^qe}mBv{$Q2XP_Z#_PceRmN3~bUHNwc!i*Egwe_k~f~jEQC8#mZ+{SCza!Xf%VEN&Zw@XHxqbBArp| zTg-S;N+vaDWdAgOR08hH`w&%>HbL}*NqtRq3zoMJKFAmVbjPV_w}=M!kGn-*FCMQb z7|RFH(-N+rT?f6RNfAp{E?OuR4S6_pUU<$W(Ke%>g{?RDJ+Ip7R0G|g-_WZMf+|LC zGm-)TN#PPqsNjyN6lH08yy{a{EFOvpqgd%wdv27xXa|e|Ksm;%`95Q$lryPaqq2WF zjuR(QU77^5ojA$*<%1~KXvy*trOXRztq4I}XdV)Iu1>YWl<_&;2M{poS1ZQhf|8=P zn7)1D3W)%OjG8V&SeGxAJdK5+xZ_NIrc~yaS3O80E^CDV+0sRrI$%2lk6R$2~$;qIIwYOB&uH20= zPtqa`S?2O2rA0i6_=n?*(@u|pA3#^{>rH;>hfb}m0+gV0-=w!#hSil*KQzDa`}Ze) z;^ti5_sXAgMjJPbmxeb#A@kwV_=b&<^HZ<9zXy!**;HS()nH?6Sz&BO2L8AKdLDMy zv*HRteJWd19h%o}zPgyGTl)nq%ClOZmB0RD?j5fvnd<-8-+SM({r^!lrB^uetdwV^ z^PgQMYXd@KOvSBh1-D^A85nGSTC6k*nkLf;vjCd7ZqclL=fMp$eDseBaH1SXlm5|7 z|DgLL_kxu2i#uK`;rJA_>_pU!hsfSXOB2`Se)!Awd{Za|*@^wtZ&~<Jv0-?8em5 zNo0Icc9(mr-}SRHJh&v=!J&8e;1sfN?Rh?yD&Kd`I|!8aJZG~~UMhv5-FG>!OzxKS z;H;jlDIP!K2+8*EW)4RBjP{VrFQZl$T43AJ3mVNvT(mhHR;dQ(?y>qJsRP=^>P38U zsQ;f}6Hde!bA=tHsJ(=cHloSYN>?n$o7HqeeTn_Tr7kdK6XTjbxBDsO+!l{Jpl*%9 zcHAFbE?3|ibr0`q`*_ zgC~yZEuwTY8{JS6gj+l}P37C!$L9 z4dv)(o`lB7vw^p$PVTQv600NhL*E((lBRg_#W!J}PBlW{SftfQ_gsx25| zajBCDIr&pG?lOn6%NC3Ct5Dhr$ey>X1k!wqOeGDdCiQIrm8oRxE7+KAO^Ps6auL9D zvC5)U3?tesCb34d1cb+%{MQK(;U)^(Hdcu%FiM12nsgQ*B8&loYPHv^RsjKHI4YKz z{l^}6#k${$rRYE5MYq5cN)>Y0>g#Q74~9bjNf)=#{w0*1ouyP>DE&A6#E>M zihJ;Fr<;^}%3ze%2RgmoCMuOet;13YxuG@Pib%i}4QRPLlSyF@#3(Xskv5V4Ngd^{MfKMX;Yi7I`ri%IYpva)ue>W)vasJ+U#wOk^x#uo5)1(w{a*IZp}ULSXEP?J zc~5sjl}Eg+v_l77h7?xJSbWmqbLhS>m12`Dhc4cf?7D|XaHly?Wxkh$l=rNMcogfG zh9AZm))vB(cfB&aC;g>x@v`QP!)szY+%jj)KN1q~`}CdzXkLc)C+=r#N1p3L_;;K? zDFY;_vtHpWr#TDD75SsNs44gzk~6wFYIzX-coE!f1hEHMT8`VyMZ*=1up{_kxPS`> zBfT=jntaI~!;Il3qNf=kGQu`c!gdMSWt4!8M+5;oO;Pg*$#OEFj)U+wW;-+x59G!l zqjW|=0cB)}=_y7qZXwjdn6Oh64+&dg*37bm<;h7u99znoTHDsHb**(emNEC>qji+? zbTl2k%>*O0-#gNQOIk{%cw02*) z>6A*&<||#T*?8_B-Lk*lp3-zOiQ}agSF4kcFU4^jFU3z(tCM%M+mrL>&!4|rYES8l zAwYOhYgfy=@0Qxt`rZg2y7$X#KA-2CD=V9M{+@RGo|P2~&eW(_OV%+Cvj7W?)?qoF zrRsH92JiNaggyil5J?{bOiMsU)ijMO5QXR7NCQ?V8na7Yz(lDhYT%tihF*FNUuR@sd*KHQy^x?)$yhbV@Io6VMr@ zr%mv5MdZ!X^ZyS)P<=Wurzy>z5Tb@ut)@_Is%%+u2C4(w+cZ*Z+1eZJDK2`y^&;y* z>t)ufEGr#(7PMdIZ9vO!`F2<^=3`9{-h2zWjks`28A%Y<4VTZ;A{|YOoTq$LO!IV_ zU;wAd(}IuoVH$kZPZ}wzi z;qu1D#zv`DJ6o$AUsyO^t6i$quI07D+S9op7`2WBBa~CfiR-sSGRej$oLySIPh^n$ zhTEWwR*N^=pY6{=e*X$KMuU2DK5y0s2-~zo3P47}@}jSmH2^F0 z0+Vz#FOPFw^_Z#-Y`RWn5*4KFe*=!DlWDpP@irRMcpHzGVXzEPE+a%Kr3jTrJ1IsK zGXR7Lmz=SzTSu5+0)Q|f4B;YM4FDxQDUFd**<{u^dFNh&AXv=`j2Iyhu3-e>dN-q7 z2rg~G^I@1GobHT%Xn+4q|IG8&+e)Rky{%Mw8~m6*>SG+pU+_3Wlq-rUrkj*h9J#TO zkzbC$a7}L0APK|)=k0cs1k}#20eqJmB#aOYAe{=KqE9g;L&cOFPf8h8l`>+zbu9XoULI%b~cp>mDJ3i(EK6w&@ ze=N3Z!kJuu{q@&R963G%NW7uX*K4estUIk&U=L-Xa=?<+1e0;Z#n2Lf&`Ss@g>{D_ zI!_(I{TTsECs5UtzdF;@RFZply&e~yG@Lj$-|P@>9)5Uw$@n~E%NnTt5r)oqFf$Ug zTt1||$5Ssf{n?pX+&E-yZQ6iiknL`dV#OL2b$o2qB|-mfj{;9|K239YLU#RfQTW6) z?Ej6I($sT#vayH#H~kBbJ@)wH@Gg1u%Y%WxTMr$d|G5{^Fc?w_J?I>tKk~^>_TlxL zH1_J7)(fp%q3}TtFvP(E0qabXVgz~@kQHjx3)9$@$MAJ7%&@WFUUYsNJcK^3yZ2GI-~Vo?JT7JMp=G@w5a34_Q{ns&t~~pz>DqyRrT0EKSWAEP z+2_E|Jl8NN5Hj-4*>_zp^ZOFSN9ajDulh`Tue=!eLlUlh>1XaHWED`|s`~q5NDwEh zHz-WscnZ%6p)r!}C<3&&c*1csBVxQ~jGyCXW7TrF&3#Cd_WZd^M3^KdxoN5-u@hB1 z&*0V7pu0JsH4j_u#ofh4gpkr=13~VuHX6wFZ<|ige6O9PmzsK~T;XcW#*Q6R{XVS} zEKV9_sK&N+#(Ft+FvTXwDeb(?$6`B_hf`2siZ}(K-sjXUeZ_QF4DAYJ?h*vkF2QYp z@`AY7*3~eeWiHlw-NPZpMBHVN5d8FME|@AkM0jq0>2Gdf)6|Dc=APJ;OPNnGk$0L0 zupYSo_Z9%ME4sQzQ6?Q?S?DP^u0odk4?n~Nq>RtI`@@H!$dP%NR;y6a$W+sO4tfE*eLa&QXSH#1UZV#$;P+aVFL zCBhy$jvLMV2fbN;)NTu?g)}PcYw$+0E6yL|DE>-}c-rpw+v!!KD#hm&3$2XU2N2i2 zT4E}-!uCDPhQ{8FKr8>Hkxy^;_0Y~Y);tf|?a?G`22NHM9-j5Ou@}xRNBT#N%G;E- z09q++AyNM)JMazLv`$%fS}(RXHQh4l-r!0EW@Wda#ljr;wWb#A`sfa z_;fNIOD@$Ij;51wQf}`&?yf_dM@<-xiu9BtjC`*<;!S`pj>q**AVM{2YDg~0=IFp_ zG8&AHxCJYW-or%;C?&_ay=LH&|A(u4i*&|CiG8lC4|cNOG?PCLfrEONhsN) zl1REoD3NeY#_V?~1P@Rl>DucVLiD1MRZ59Ee3#8KDg}K2kUCU^(yBBUDMIY^-w%=5 zU@#-vy${%iL7}V3?9CCO^uvNbE(AyA$GP~hjUvQ_&~KMq0O#`UN_|Ukc}0jTk_!u4 z*12DOElwEqFeWNY!m~a{(-6iU98X)5ly?Byzkbv2!f&MMPYVF?4I#cE0K`v!`57UE z_)H0e5O75Z0j19fA;dHA^IGpyDd{CbE>S7z{^35Ae~gvx)$EJNHN;H_j)Du)c8q$IStzY&>W zGw~_itjvq)<%ip?#urvr7MieXtA>m-9fuQMYIuj2i)GgWt#^i?5)+`L)J_v<&R!@R zRrR%D7@jB-|1%i4{`8vhQkXsSi85s5PzU3`K;x`+r}eO2gQ9ZPw~gbXf-H~;#0Glx zppHtw((&nJ)G^9!yaUsyRb1E~^tXT)QEZaYa4>O37)6F4Ro@N*`x^>@T3ivB=*yY6 z-q_MN>HkE<)zHbH^>EoS&}^=^XY0Ol+~7KafR!^PC23Mu5=vC9Uau)iNJB=P zF&@dr{9iaAd2QeUAeS|XlqxI1F#sCxB|to)l)T=M`PZaU#3PXG#S{=R*AMp+gp$1k zp#=Jq8}{Z0zugz~+v-uWKdPZb?A7J~;HXt5m8gY`V0AWh9#>Mmj##R*(W`$`{j+_c zBwLdX065`Ug$WC5Z>KatC|O)k2(vXEo@EHD1+B{ur!>@S??JH+ZQLk2n1`=WgPEP? zPcp0(J@-9p;fX9RTXpM<^)l-R`@#rsEKso>4Sb#-n-ttm416Qw9Y_C0j$Y)|D4hg$rjRrOcHRptg*pbFt%fZ{`4_!VwybiG-%xHZJ&y8p z^#clL4$Ey2oeDTkxSUuaa#sCCE_jX!yFi0dAt-I62XE1pK&GhJ+ShC$FB$B*+3M6H=967;YH%63}DRhUaOU$tvvQEcB~_HldtK}IfNy`qWCS*emkt^Q%wiz z297RLj)J6c@4~@%36QLldYUrN3irc3MzywW*`z{HbKJiYRVoB+O54_t?<+C2hmD9bQ_XNoGS|iw6T3t|{@cq*8D6wzJ7~#*K`fJL&Qhg^~2;Yhu zs{8?ObxWDOLY1Vk4c(>Az!S!leEj@h_BWMauLu{?L8?ubLmIx7TO0IV zukRu2N$XwKr>q~gzHEKX`c>=qtUrZZvPmNBMP&Ah>D*j8!ypEGr?1vV8Lz`=2UH{M z;8b>9&Ob)*@{3soiC6&-fJ`4M;KbQ7hGT>?wja-wae@dz^Q&`nCsuhv!WrED=L4>N zd|z4F?T-}BdzQb-&TeH7r)e%XdmHfQo}tG9t=W78zbstW&6l0Ca9x3Me^zEZ`3Fpxl2M0v<7vWjr4tBN7<%JZ5%lny>Xn1|pv#xM9iyQctD&k(JNnZ7mqy?=rjLoB8= zWmJjrA5W7e5L95(wRi&oGNBF;sSwJHr~MES4)+C$VxBu6w>k=kvM=s= zV~NK+PAUz*)+F$;TTwCQa|@I%Rftb9za2IgSlm~PqOypi8nHP?iEG#L3ulKWy0Eu~ z-f?3&zq8^vjF*?`=c-NeBD?(wMA83#2V+Ywc1De(*9Z5!J46Brsqd|No|HBps`igs zue2Vs4y?Clf9=?$3B%-~AhTwnQ-X>`L-lj@ru|$ZRD$S%;oZ>Zb;FSHqG3R_5B5G|eC`?J`%aj8T!mqI zF(pPumO^h1utreo`21>HwPLijN~&jV+p>7q`PgE}bvy{7+c!6<;2I<1`(IT^Lo$|= zpeqC9ZC;xZEyd-|IMRWlao1$)#MFk^c8CfNh$yI}+Ql3vXlQ~7#_t$l;**o99bKX5jf^M%G5 z`YG5M0sPS0lyGRg1W|cBv~t&Wzu5+yc0exbL$fis=nZ>)_cm?6&X$rNze};^OEc!v ze;G`rG`3^C9$LN)z1Fc`z=gE$I4y0JXL)#`n7%53-#0RhRG(PWeq*56SN2}&wFeTB zL|&a8L(>S6@rR-t0x_67Yh^bKhnJgL5s#R?qvR>N747h3xwRY{2lW3qr%t_Lgwb`U z*F7QvS=L{kF0;0(B6f$L_v?oX;A{kriG%q5E!Dxs)r(xOpJmXdt|MO!x~duX0;(Nd zY*PL;mNZoOH5Ur$GdLv2TL&DvQGf4buiD+MQ~(W!H;N)mx##gTEX0k&A%aR}clT8~ zQQvR&y#WVMtF5nZ-L<*~?R5vhS#QJI>Rnsw>$O@oQ8<*KCR_y4;Z@b!`TN480UKFU z6sHA;jo`{ z-$Sf@>m}enj$KEGP$ovFv1r1Xbl`evEi({uEV6NRJRZe@b#Q+`1DG8gz(It&GcVjAn`taf{Gan4;e7T>#*z`Ixo7>D^)uGj zwI?Agq@degh&I7!7ukMOSXtfUp^QWOR1|mANOK-xrytJZPPIYzM^77OsYjK^9iKY8 zTkJr02f-z%gh2Xbolu7;#v>hLJRS*9tct3nI4jHc`on+TmO8L)^pkem36yk%%>&wf z2)BiBoG@l|CEC?Zhp1r!7-DxW3$6=soC^mN6J58o=cAv?r@V-rpU6&561AgCZi z&QxCPql`Ay_d0<*nqoKe2+Lmf#hqH+#?&}K+|lS1SmEzd^~Pv;0*bUA1M?Han{@$7 zdP(Q`wF-+=2ruvg+2BWlfp9NF03;Xp|gbG4W^asRU$n zhT0SE^-I_-#xn>`U%GTU0Hv$b3m2wUt&UD_jLw`HZJd7If>Nn{<|QvV)22Nlr?2)o zJLmv(0$Uo#ts+!)9V2_-O@`MCy-v02=K#51t#4M%)f zrCI<`23v?7fa?a^;v>ja_TD($g)0#;Y%1VGe#V~Pb`y-7ob&MR%HYAVXPq!=wwlyV zOO1e^-YJ%>wAJ^6e!GNTTuO7#=0Af|4WngMhp891_`tg_i6UowVKC!pdx`71V&QhX zcnUO`1))0$T?(ykmO902o>i?dzqW|0NBmx9Jga9N%VcVbQ&MnqK?qV1)f^N(g|~mv z4~<}pij96kkdjgN(KAObdwgZ;o?reVQZ^lYvyk^bL;S}0zW+RX(O;G~#qXa7;a%&0m zpkU@)WG)~f9S8B_s!ZW>8QI&UbKhbULN8nhNT%ww*Yc#tan2V!;V8#K>Q*>O zc?M5FcS_w+vD?2t+D58AFrs8Mu`l8>}{2FOC}-95Xn#%nkPXSLD_x(Xl3VTjpJVneaDUjW#GEND35KY zPf!M*NhAyJYFD3!;XYarf>junOrOv@V-m&p=$NRw9Ps+ak)H)+K7kdfm(iS9svFhvRfgKNbNOuf}`%n#_-Wo*H?ov#j2^+$j0NdW@icx;JX}U=D7pTRcL+?+Z5ZD z6+r7!ma%BbA9>k&)OxG+F6*#8#T$Yx{0$HWcpX!KjDU51wVSiVB&8%f;Inf3<3=60JSzv6N#!y)!!uvi<%2 zz($p-YVA0Ka=Eis@kpthoBDag;`nRBh~-!{E4MbS>oDo3M%{u>1w1K_ru4Kq4cdU$ zHogwi6y+%xrFlcxc0>2QbsNTvvpDq7R6bZx?DO1%cInz~xxU!U+}rQv3>w0`9^U?9 z*ZJUyW3Bb;0)5iI-IjbiT~zv0QaJ5~@3VjUiuSjBt>JPb8eV5ZoGe`T|6rjr5kiPb zX8|@T|N5nUek&wtyBgbr??CWnN5~&8=L?HLWE{93J5GQX!TGi6HRGH#108*{qgtNq z;M^PiD?&5R);=2qLG|$>%-m+v&BCtkJOy*_0u~+A@eH?sw6$k7fZD8m)h5mBa?jks zQOmRNxm9~Ks__PF!@5no350ppirqk8`iv>LE zd%`&92)G`n^Z%_}qn4DuOu@|=fiPpp9Wgd?5o5?~Ft%Y3oBzoC9T<9?Azo#yN}`ab zUO4=cV}$4L6CS%j=g(@jq>QUxM0w2x^8NY$+3n^ujfTG*!k|S!)8j)coj<=s;qxM9H|rA6r2?5QdNzXL~=RI;6AH zzir?F{=f%U*Dxv87+V&kapWyi@HbfG(j^F-_}bbljNE||a{g!6 zX#Iecw~VM!zX1?(;Kt2nv-H_8JRerd(dSA+aN-QMI&c0{*GTz**59mt50x$JC;45G z{vO`w(g0H@K+tLV{#E;V-``c};)=7Nimvyj!AAPxZZUXU+8?xk9zGb3`n(+%iPeDmN+sBg2w9Zn`iKG4sJBxCKs2UEM%-B0PRM>NbwxWXl^of12mJga z1FchlRO?`j^Mr{g8`Y}kgV*+Js#GRQl|@@Op6AbWWCa2Ebg$%qS53{fxj5!xu|+R z$^*tG3xZOci*Bh5wjE1^HD#|xZH|MW{x&u$krO8-357%pe-5fv7r~qVkI-bw4^5_n z{mCJ41dYbJQ!Z!K`5O{iA%JqJTZ$r^X^rLwkS6U$5;NiWv)IIT)?WNM0eGdduvo3a zg7W*J(Lp~nQ|4z*_59kdAGC5HC;|s#l=1gZvqMfN8(Mrq?GByG*BEaQ%aaT2ulzR$ z)IWAyd)Dl?DixDZs&h% z!oII=tn85v0Y)A0AH8LrvtDO?(E6Fo#OT4OCP!)Fifco%vW;=8XiieQH%lGI?6=IQ-k%nT@^7_F@#Oc4dpnw^!-pJ%&8lx~0r2!);> z_N2RHe3ZB@*k{q*^?}bX{KMNnF!v9?hA>8|gaMFG5XNzgF(N(!z$Nvv#m9~C>+XGh zaOi1@R#p(T-N@GDjD%rj>?66(vSxSR=ZcyH*G)qAKBlxcKeE=BaeK#iV;m8Tv6o=v zH_Hxp!2|=Q<;<@H=Gnnv-6$RVjOCZ*8w$bJ9C;?e3|~@f>$#7D`x( z&Y&R0+1w%s$inZ@1+P@&24`6FR?R-)r!`)6Ov|-X-l{tPl-hCh7W#W{QuC?SZfm!- zHKL8khvCO9?9U|J{>FFU0DhU$S#X;BIZ1U$pFQ2ij7ZC)&n*Je?OCWlIO`%XHxCAA zx;woA`|-J3w>BN{b`7?-Huv^6H;p@Uu(P$f*EcY$OT;9zo}t?EFO%y*3|Tp( zG`rk661i|FJcpu2bdb?&%}iR_fAN3v6120tq%qf-zGzr4E$^V@7yq{bC0f1>x;12H zc}eR8&95H(l6#sma`x%EhRURag$;v)sXe*4Qym&a!khz4j752H@|% zg9VpgUawZs*0FQbwe{Ld38AWS6K#TZ=YJr0gY{ZuYt;82eDkJ;B9y$ew!St!cWet) ztLtBuT;T5@Yy*7L7skPU%QO3jq@+DhzV245@{Z)MWJAU1Xxw^bM&yIR2w@;aFqS5x z@JuaRkNji@$a?_gfMW@VTAc>{anlkvxR{dnnv@jN++M6OGajv#(uDn^oVhRrA4JR$U^sA``t$5*Rw8Jv;K{HK#>l}?dd%&1A zluOJLB&smXauz&KwvCcBfn_jck8XNtl}08TkSd%r6BU2w-nqXfjys{uf&k`nMu1b{SMSXJs!V4_cLP&s+?;RrYvj?;2|2!d+hvQnwl zOjFT_4z$*u=N&^0&x3Ng(<+si8%RQPRkBm5u>a24o4Hc(kQSUBh`%zHFHmOItUIlz zYJc3=Met>G>Hy8Jd=kaV;FOcJe!{v--ynn@_GYAm=n&ismX~fgbK%078 zepEN?DKA+zjV!dfdL1+Me^UDISK*K#yN(gyh+<62Wr8u#7y}|T8T?8T(5E1H2JwjWCR$vMm(9u^e;Rt8OzX)Kq zIj_%$!@SC2dx+h{UHIlp}kKqkV$t%HoqR zO#&p^T;2)J zkFt*PMmsTYm`C`2i`p~D&`i-*lD2aEtSKE$#v4V-xd22&s5ORGR zyF>HSX-a;$un>ly&#y+}XVUXz<|kg?^P5SgOrX@Z8!yG~aT?Z_mzL`vc3d}^UrJm9 z&WE8-82pf1N2nf#J_m97j?;IZEr}#ku6vE=U5iIXuG{}nH%eR=jGIL6kK#i4P8LmM zCemk(wtQ+u96-iN?CZ)XZiUO7fvBp+XATOm;RW*p*z)~Xtw*~_v>Xio)F>=zJJ%0qBfoI}COzrvv z&1%^P!*Kq%;cWr&Yvx`uyIXXpP2i}v)Sq&47t(3FC)480eN@@+m*BGUJhQMk8Z9mu z&r=E`rxT?|X|!MvR`(pQlrpRP5`7IIhHu^(3IN^DiS83h6}aS=-uYVwVO41~Dt_$0 z3QtvCwXmSvs`5PZ)p56!O5K;}E92T{#^W=#YgGRsM#`Ob(8kYO%37m^<5@P4@+Sd5 zk{fiBC6I););>Ye9dE-J9FR*fB8G$YyoD>36=iGwI-~|cQt$Qqe%4%ETwPgOY-YZH z^>9AoGdXfn=~k@GwY3IASp8^L@A>UL>n`Ngs&QRYt>z2e+4DiELOL=wW^iPk^$W6k zVx;W|+Sf7I^2Sd!+2JlLgtzKVK4_)Ugq}55e084iKiCmlO((@6gTkNREyqP+dx8R~ zx;pLV#u~=;_5VM~+x#anT0?%jxe);(d}(v5+p5*33?aNZX+%+TyonKF(^{?7-P&Ac z1VJ=zw0*ROutbg!wc21%s|n#Cc`c-+QVNikN+}#usG-ByF)WGOia)&4!AwO_l0=bW zxV!vL-9U<~cAPNd#33A^_0zo;@f#Z$G17%&JN>?gIRaS=DwUun0deg0`#Z;=OBli% z4WG1nr`Hk4g>JW60jN~F-31Br_TP8$`-7y`vjHzJER>ZE5BRnYHDfx|w*UQJEeUK@ zURYShVE6lr>((@K8o*`}=UXXBHFi6RUo6+KenK=Y-K8s+i_6j+(z@c!+hTkqgS6WU z-tPQl01rdR8i)cLo1SDF>Micr*b5h&;IQINbzm7!-u`}7ofd_+Plf|Ad6CjrJ@z`e zv)DtA1qoKEFC<_!G$wgS4?`J5>Pf0KILVKV)HT3cq9bJ)wTVu68y|+Xq8^^B0*=Pl zvwOk*3wOkGe_!hp)*|5i!#2PAudt8K(_#&o=9jq~K+J7s|l^tpg)KEPlPIwMe; z)u#!8?F1srjU?sWV&ve8CsS_3Bzfb-!3!vlku(4ClrrO%OC`874;7((w0mli7nJfRJTP~GS%FKUA+xa4! zEia+e|MD*NZf-I2|@HkoRaDWjTBs$nP@dkb3I z(ORkL{QIu0+3tqcN*!PGv#gkQJCNf@%97tBd&1Tw)km#2T^VGO*7$KEWs)e5Bx)0k zdhU1K8iIc8{mWp52_Zb;M5|j&?Z4?wsafKX=+InlpKIFNDg{2l`49#$nw1hiDE(>k zP~X)|l7L)h-ZN%}qZVmaqGZn)k0>_l z5fLb<_6Z7nvtq_`_^w~lhV*V=La+tKZty}Sd``Le2F4bc5bOp|RIdNLb%&$4a^-jb zs>{+`OEa_0e7Hj)dBGCyU_9oA1X=Rrv?|NBBC8n#jBKgbU8i|E&iOVf@_5@K0o#i_ zr6ShI>!@zEELj~nUvO*avkIW_zRJD-JbciY0(Z!T0 zU;EL3KDG}ba|;f^yA$IO9sIHog&5!I!9mv8XG5}>=(C^UAyYh;DCP6B#=3Qj^)hP{ zIbR>DU1|_ZHH$`rVcH${2SO*4+`nC0FIkO1aQ?1oO$^u@amJ+#4ktCpM~V%&y`EM9 zUAt!Zv9tyV%!~fO_`caeubl1LzK`%PGd}-avZoI62YsZK_hw2N88w@LR#vZ%WXbm; z(`i>JV^TL}e{BwqFZRo&U%_CXEx51yf0CzHP#? z_;zhG+*Y?K1v1+ZYKy1V@0Dz zBcd`XN46iD7=mc{Kqk1F`X1}oVu(%X+d=yAu0K&?<7iG{;r_lJDWP^`LaJ1IJy)!T z^M8Vzs6Bbv7VaIuW`ep3jY%*xrisO;teYtBqdY*EK5Qf8kTY+ea-T0qO8}6H=0Fd` zgB@F48RqD|>XfI`V#L#F@kvvD>Z!81HP*V*T=ehk%R-&gryW~U%BZv0TwCa9*VPN1 zwdSHj8Kv5GPM;o;&R)jFW=HGz)|J{l&Q1UOthrEypHBGN8c#m9ys}(9=MqXC$JVq` znfk}exziH9pt;{HehBQf<|K3OvIz&pD`D`zD|^+V^m2>hIyGOs_XxV1yoCdCU^-GP=F(8R}=}96iy%rk(~IL z83NRfZ9snA1#n|6C z%UEp=L_q;#UHC5@?gF93(CN;5s-8=Di2M@B4iM-p%>KLdmCCDW|Bl8C_Z) zjq)l2DV@YGB{wfp(l@3ze;zFHo`bI%(`?X-|zi1WW}(1?UV%{wtBIy#s)Z|Mrm&yPKZSLq^>qZZ0>3vTWAf>B7B)rijUtz zAYIwW;vp-;hYkoIWcrqyFvj>2A%s0}meAdb2zs0TlPlRiRI#Wv4hT>T(WXC|A1%m;R| zndTv|Xzk&+Bz_4X`N9q&rDNA>p-~c3AqnB!PW}m6Hnc$q!wg|c#D}zPdo%r@YI9DA z6ckJ8hP9gg^+TAR(L}YH4N>Zo2WM#8p3y$bBZ#7Ax(TsqKU?^k1-LD+(JiHdwv|?MT!TL0-#5 zYY9hIcPQn54Q8;mcFT|YjstG1Q?EM%L1|FV$zrADM@mwL=8yJYJ z2HrgH19zM}Sv$^@^aGW|QTxrMaw#YQG`kETr?8oHvM@xanLB~CfULCza6M85*NoBe z#@JMCVH9UiyN5Yb8Mpp17iVt*A#C_HN64Fr>yn*1aa~&b(njhv>bms3V*$<(WD6mM zgYdldvJLjGxB%wWGG(3inaeJKssA(!%e7)_oPq8tWg8=P(^huQpp7bi}f^}KAly4rkZ{*7z)q5ioU%w{Do{Y>i@L{+@8Bgn3) z!#;fajoa-upf^6@G@EPL!Kq^h2M1Z{$&h9ToL9r_!2B1~cM5PaWb$;W)9O!8<0y`> zUScJ3!KnLA2MpW*?9Z=mQ&jY}$*|jXGCykIYT$!^YZkExsY?-S+t)e!oiN(&6$t8$ z!5gC#0>4@g&K0%!rbGWCRf&BE%DZa8-O!Jz2!h2!H13YuNNo5HlqdxD$3)(uBOc~n z?!(>`+u7lKg21sIuo>H6IjuLT<3V#Lfe3P_KR<$f;OQFP;r)#1p>Twfy6-DcLWF|9 z`8V>WL^esYvDfviJ$|kI@7=}R80aF9pYP)LSspz{by5!Ok-XiAH=Z=nFs8Ms7}VH> zWHcRkm4a!To+Dqw|MT-ty6B1#5_qN3(~iy0^pfBVC)imVkJlVb_?aNNdIfdQaNE(n zN`-@vM!3A22{c)k(u`MDngV0{sSoL=Y};m0#BAGsO7E>FPuUoY=E}+#aATejC618sSwn*?Gv02q%`APAZ(xTT3K<6aat&jvd(VHy3V@IdV4FQ88LMpJ8lG# zKG8kGz$Xmzkbe|tyV^}|3Y#r;bQjuN$&7yVm=B^4xxGVz*=}8<2_42rLzT;!$jW8a z#EjXIV$4R;lI5=4{U2Eh1SLpPa*cIeskGabif&-WY)T~}R0P}LQlp*Y#xhlMuksTv zrCK{LZUkW%G+yjzO{x2deOfz!{d^re6`oB{e7d~_5#F-OcI&{;D;@X3cZ}F2hxetNUYLJ&8aRc=S)cb&h^ExKc# zPP?s1d{FrO1SUH>vL008kP&ysaGqy1bXg5xo8Ee<_6n|{eyKiG#h6F)7dAIBr<`k_ zY6r91#;6S~F@o|pef?r}Z(c*?qQ*Jpcysf@e8e$UU*Aw?H{MY_88#Rq`+SZK5rXQj zi%GduC7vRWIbm`ZI$a!7z^|K>P9Mgo^M;)ssQbR%^K>X z!|3{WK(x+<9K0|L*3VRFd=sn)rz|FGis{N~CQ0rk3965$!$9gy7G&nIFQ9dR-3}Pz zpKPjXaORMz-j6_QapL66F_v%mr%cz|Me|~=HEzsYI-CZ*dgJ4trq6ECdgRO52>o6 zY^p>cI;VUd{cpx7p_;donRV^(C6^}Y`ffwVDr90c5K$llju%ug9LDBb!vH3Ox!0Eq zUjEtJH1~Ix&-%hkIvQZM@pEe!PAEUqf2IGs!yY_wHmI(~x@j3_jwy;vZBLaw|MxWaJF=w{%B>PWLs8Xo zQX<3BPO8kdH9IW8q_rTukS2?R9c z#>$|-2{)MRpdtihsTIIYjx3ZYAXt(u%flekqF+LV>?J5%m=86!84f844F3^;wXTn` z|8{^+0DP(Lx^VgkPn3Ixn1so84HN~E@}egZqEjy`zz49G(-Z-L6}Luz@V}l0zjeY@LkCDz8bwr9!7#v)WLa`La_y)53MRUyn;0y^ zv~}4whH#N^Ea;4h;RNyK>j0`|Vr&`Q)VLsVmB|u@*_C_m=x_{NlnlVY#T;?`^Ain@ zB|}qJjzoxTHw3xYlLb*lR%!H0gp{QIyoQk~wgCiDpt2t@23RtpQ=0?=fB<;$US7at z06{XiFyOVBck4oa{4CVZVw|P$B$93x(T+qKcZ$=kPib6vR4WS5&J0>tsCpgRsibKt zl{SqSDk-7gmDeL}Y$4d~@W+~_(c(g_@CMbb_S4m!Qw@t!(H6DyM3%aB-->C@CKmTO zw=AE?L87p}b!VWtEZ2;pB#PRpPYT$=G==acXqBNAj3j+CD>8BJH3ayuwXni{&p&IB zhNvD5@^-@gqz_l*g7r?RnpjL@JHVu}qG=kP{o)p5XBDz&H?<1fb?OrKwkE%4XHR&} zE`BpYSmYcH1GsK^7#!@AuHKY!ZC=lXSJdQ4G6L&V#DJ}66*ed%HEe-Zh)%1;#Jy>-- z_g=aebWiN%kG}uBJ^$J`9r3EUi@gJ&emw-W>kKW~RL<5}pw3z6IpA}sX-4md5kUQX z^!EXu#cQZ2^I$y5N1y+LR_Bcw#`vQ=U9lI1^?u1uN=|eE*KWM|lx5GJ`6=5^>!KhE zx`yih#-%nc#f7k?6!O{6xsg{s^Gs2>UQ=rzI1!Ku|RSSVA`@3qk?IoL_b&rkH?Cg-K4yp93fgf+U5nYqkd$L85+dcem$L38qKa zz8?zON~4d;4j;nUjZSt;#-Sv7cwUE$Quk!!2>3(T_wiMPMM8C*5)p$mb&>>Q0IG=+ zFIr8bjn%s)R)I;By?7G>Dz(k`Yq~kbpDlXXlF5Pt&&A2G4!ZE93*ci z1-VCtndctIuCt7e)k+V&L=^?wTBSGU6pH>!*u_m39H&}wAJ^o=AB2Aq1S*qU3UdDE zm=la;!`zzh;F~Muif6V0A7)4QmEfzY-Z89;>?MA&;Otq-bT?ZN)t3x4x;uL^%K7(s zJZv2P(El?z!$7vBVCcWzoXAuVaA$rCZl+ZBOgm~W&JGT1<^kn*O#MJxjJ9>WEFvNrWux#6KbOC!l>GuWCr;FD>21LC-Gufz_ik0^JkoR09UNaOZ_HG4IoKLK{?RMI2A|4JZ%(p7Ea5hn5Kyz@D@G{U@ z4sC!rm{A%e(Hu{mBv1(Y?`7~MoA*jLFD=zDRC~*}MpYFH#w|-rRiW1FUzokUc=upX z$MrbA7P^5ZO4c<|tB&g{$-S=Cg%?GwFBcaU210Kz=vfubH08y9uO|$amP)gsK3H8H z2>n*8pa;5bk)YY^3xnY>Va6iDpN>PcgcG-FL%9ZfbUbtSFc8XmytLmh7C2;e?f_45 z_g(!yV@uHny>WsXaRA`^ZM=-FD$dRAZ3-wlbZ9Wh5n!YD6jA`9F&G>=6vY(NXcTQX zYRP7<+IAHAd6yn@fr<(UwY12iPIJnjt)^v-;k9JQP4O04d^6IITX$V9@8fNKFhUL*Jp#{+;_bWU4F8?L z_G_wUi41*CSD>zu6=_q*+G+ES;4>yQs&&jsj<%a_2Kz9YuXV~Xjy6vTeMzX6LPNIY zPvkY}!|l5h?gi@>z73B682wsgVR%=1PaSfr#tusJeZQDQ2nad0K4TE7r-a|#sXyos zvY5tMOwBhsHydsP%(Gz6M%-l28#P^0!1O*s^jCc!VULyF=*-gUvDKxs!7+`pa~7tW zlS(<**mjyQwja6W#~8iWd}D3*QCuKSboQRNc;@&qjbiH@qhoP|LV1)LUxzMeSk3@I zkMf};TrWZjAqQ=H$u$PF`AgXjuIBE*>gS zpd;g5p00XJlNo^H_InpD^!ly?z+`Rq)QtxMn^DBJ60*8#W#BT5E0bzA6`GJ&Q^tw& zO*&w*CJ1_~)ofziY_?juAZRkBE?NJ-w=YGL7S&WWT34LVeN&H08aLAPnzrC<@GTDf zol!!YK@BMkSsS!zZg|byXjMN9d>lMdXc)4LwmUw+5?S1M+$AI#J*0Usj6~Em)%peN z6NmIP)6zP|V1AFKIf%nqbh>WQ98K4L!!9p|TqCgWDl8fvR8rkj0BgF=7C^j zjXEQR`9|rX`Y$h0pZC6-2O&W`l#K9xqU)V{m|}khW$YQMaWGe`qJW*KA6?kD6S@wC zeKFw;Rv`{AY;Y>NsL%~L$IF)Y$3p*|4DOY4V-UkX2ElAn48ua90HZ<}7ME4kb}=Zr zKP~paAZXkNP3LpIRCE_K&bgwjyZ8Tt(6p@YS%rdSer{r?*?|eq7sIePn}i!(8;pYB zq@tJuBHk4=jq4itToV9G(wr{=6=R8TRbBH6vo}C!T7`o3J4%YL}Dvh z2B_8C@gM5|`X66^41K|*<=}{BLkexdQkUwM_E(zn+>*2eJDMLEB{@$-v@>m6X;Yn$ zmk7#4{i4LUqNYu&O-oM?A~2#1)p81Re>EaN;fZ-*=9z|m!{h{(sllDu(k7rc0*XWD zS}KpKcC?v8u#(87#y0_yv5LeUEJbari$FYL4_r_m1qmV^!($G(4Z{q6+0`Wu+5#|1 z)2T|0p)qQI%MwLiZ*?P?OU#~Ky)%05Z^ku+t=n#Ybko*#g_$cG75F-{S*cnrEgq>9 zbY|m@CkDg- zg3rp=dxdFiBMF4b&tZ*r+>H7iOxMX;#3>V$!cI^Dd){YkU6V8bdS^Q>nu-E+xp(I5 z9e>~Vd!ZyfAW*{Kb;ft0TXtP2w?Cs5UIaxk7iqLz8bFgY-M-`Oncgy;JtRq(F=w8= zg%N@u2$Fak4x8)GaLCI&PeVQQYJ?!Ve=s;cm2lD@*DCPPi8bu6>yxm$fk0|1CNJgs zyT?4KT}j-wG7i70n`FW6gp6v1j&%A|1%l*UPvpY^YQ}cl@k@tIj``H^NNsk&6f&= zLZ8v02;xMUvi|gRzu)f{ewo}U6#9DF^Z8YYIS98cMF>)Sz-S{vF>s3pc*9?awRmzm z9?t@LKLE~*_1Osa^A0Y>^2Z3naAYajCz&0DVZKOww$QE-mrxwBp1mYd8gXct(T33a zSQY|@6OdMdM0)Ts+tDvP5t|VwGxsepj=jFErV5Qf{3qO{3QWT?VuJMu*^#N>Yk&Q2Ys@QrwxP)DeI=+vjjo4kSR!@4LPpg-VX zIdF^tF*U5~cxvnv<^q@(qv8iQ*jd zHbgOEsYgW7=|sn2n$No6Xl*EL5?j{@o;x~*iFZGV=Zvz5d`SK?nJ&-j4XG7PV?AUT zmp_qDLA;+yvt_p|(L88GaZEpqzB~HMMjDC*XZ28Ax|qF;GIg_uxZ*G;pV8j6xg_TB&#|whj%O=)=iV&7+@$KdQr1@_s~33dpB%PXYU3Z3nuFjx3U-3a&#KnDIX|G z?o=vP`(+O_Yiuc%L1Tb9%pHUbN+QYSC9NZL!-0P|mLWgx(8*p+ZyBFVQRsZ=vuS?Z z1)hAL%BtayoRUMHg$XJDdtv z8myNfo_?_qmW9jlcjk7eB2 zz-+uU4|5#<_BQ>?v2JI!V|T-_%kd6^rUO^ea!04ul>J>D_jI%qr{1dFX*T6U*h^Es znoexQUKMtWqR+Nprgs<2+t?XU1_vlaV%Ddkz^9$ zQHxX2L->s?vm=e{T%J1((XOY zaDKH-gV5_owvgQb2x3Zy`GH@@nG1(F>14*TBx|97i<5-6#q<)r&jxXjQ z@=9E<>Ctsf)iQN-EL}wcgDnb!*(I0WxJO*Pywx%-_KAHp1m~$yl}9Pay@ zp-*LCF)25Kdgv(z!2hrDaLtWoTnQH@T$`QOn044M} z6-5yzzpUA>XzD#4K*1}O9Yt}(Sd)Y$Fsb4uvwXFi7I*_A2}34bh$z)Eh9 zOLqfGtc!(fPeJ$SS&ypfO+L%!uSreYS0B|5Z!BY^iz4%wX7UH>esNNps|4T+5vq5o z$p2kNMC!zGE9=f8ZRuBuqXC7wJ>#IC4*G*Ep|Nw#H0xg*vzWRvbY}Liiw}_@9qn3F ziR*}r4Ui9*;Ky}reQZGobgrCZ+uomZZj z{o_0FC*yGUVH9i1ACL1Fq~Oc3;lup5yKwsT;|c!46nyEy`?0ir)m4g_?tp_gMUO!y%A~3y@rS1xz#mwq(JBu}PU^Iw!tzhl|FI+<{_~N2jC_<>Foh{NlTaJxDQUYyiKc1c#DU4u(%60` z3&0PWbH3%pDFyLT0%}YYe#E@B{%7*{XMDui6sH0Haj1-t=4lX08a}edO+=F>QNjC6 zH^|u#Gc-S1lkR1^A8qi7fG+p|j7WN)0Kwi5F>rEYjkgD)CFq+5Ys? zFhMPEJCS7=gQP`xBx?TfSLimI-Z0adg=DDui5LHWSOPzQ+*K)KX3=qVPQ!(6nsygL z%5~Q%nv7A^{iC_n)e|RH)2JPClZ!#Ob@=cdcN{)Ew-|`rN3$DUnKL9O$30yoBMTi4m6zAnmedX!I<)KzyM4=O`$|-c zUxD6%ug1+7GI{jq;=y^Uo2?OVm)Y|3tax;txs^42 zJb}v{+bPL^4vvvD^424v%vL67)aPfWcm~k|+Ny2!=TDksy1SKOFpYw!=pGa(GtO~a zGjuvvGvuXcMsHHZxVQ}PF1GkI?106OXaM;)jTn`6Jd&#+fNo>j(AbQ!pPh8vve`=p zuFT7+sG!KI(Fq4--FJYF4)n?%1K9$b*Jfm;fmh~Hdx>Sc*Opy0%+}mi)NJC-UH(5uN9wosW*jy^ZqxVM=~mNsz>p)O`|JXC*e!$n$k7Q~4yv^<>xHh9&#- zpQYabS(YVxee^5va4Bcf+5+)v(AX`H838OcCbBaP%97&WAfV9WXr3+4y9u-MY#-%pk;cHD`lwI3H0Xd7=rEwO=8Fl4zPs~hZ1kX;7Z0T|+k96RJRLCzjlqI0N z9?Bz7F%~LfP+0|)VW?_?>M+!}pwM_eyB+T7h88>gX(HU~fcp{< ziopFLc%Td(*ai=_LAVaWN%%`MJQRSXM_^eTmam5uEzs(LhZFEfAN=(Qv>kxQ+Tigt zJn4g{64360m0j>m4|KTT*)I6URETuJbKUTK7p#uKnkcMILg#vTF$nASz=kH+*aVwm z5RJl1LHJi4yj%}48*H{<^AUK(3GqgFH4d*Gfi5R>^}rh*=pGAO`rz#Zyc>h}UC@(( z52KLq!bfq~+5{i>!6)tTX%e<2V21@e+aXmCpEbg6V9)umHv;=2u)hO(`{DB=@I^O# zc>)f^;ZP6`dm-(HbQHew!`J1Wp|+=X_OO1xiOU2jPhfspcxHyqQY&c zXce-qMOHs5j-!$oDovrXg{a(xsv1zWA5~{itp_=K(C{iWvH^_>p)p-(tR0ODpmF_Z z{5CYP6-{=dDQPs#hh~%^R|A?EM6;HnGkZ{763tGcU-Tn)9Xi{G&S^*W7CP6D&g(+w z7ohX|(1ml*MQ+rPK)+svE>0ki2VD|DmqyTKPoT@)=!z)vb)u^s=o&k^wg6o>7F|CQ z-H<_Z-DqADn!gJ9-$6Ip(M>+o=tnn?M7PwVTN_c+6X>=ST3|;DI#F{wx;=>QaG=0? zbmts&*Lt+bjTZHy#R;?|iGo3NcQd-D0sXNPwWQI#@1Xm9C{%|+t>}I?y1x%SunIl6 z5QRbErRdKg^pG7bwWDPnXt{+}I8kdOdUzFjqzARNphqL(J>7*?wxMSh zqK+!`YzqB7iXsjaIe`Azg`SI`=ObuU6s_(+Yg}k;2(9fyogwtXNc2K0dLe^e45RfU z(fUrbp%HDgqm6UW#uVDrf;RP|=pOV^8olg5FDFpUi#9vZ<}~{ELiEa16mLYYM$l^$ z(Q8rkx*xqBLtSq4A1``i8+x-Fb=RTp6KG2uy=6ylrO?}9^iCCeXAgSUhu-s|_Zv{p zSoDD%eUL^UCeVKmphOydbOimc8EqYlwuaHxew1uQA1_3ow4qNj=+hSTX&P;th_=Pi zc01a>5N*$(9e%W95862q?F^w*6-u?E&+5@6H| zYhiNJn0zOuAb=@2f*Bgd6oxTH1(>1~#tLJsZJ6Q^reqbS^Z=&33{&2RsR&{!Qkcp; zn5qP(+JUL|V5(a&)d@_E8&k6$Q*#7UI}uaciE%b!hPg1qS~0`l!Hh^^Mn*8Be3;Qr z%;*$mOdDow5;Gpm_)g3OH)g^S%%qW+$qO-4nlMuvFw-VtroDriJ_j@X1ZKukjLV5} z#V|8%n3>mN&GMASBb+LoXfN1}Kf#otBkVyN9Q)IJ-v{{eNFi8?Ms zonoj{5$YU6or_SH9VjP(x&=|UQ>c3`>M<7eC`LVVQLjSOJB9j&QNK9qzXJ^np@CP> zpaL{Fj0P`6gG+nP~V9l!&62=AscnG$J3pT#QD3j7F_PqoZg{5REB8 z<1*3sTr}YZnwW$63yv>=0?!GEHpok z7KG4(BD64t7UiPFLA3Y^TJkPh8b!-N%k$BSxoG7Ov}z<;lZMt5q1=h+m38RVnP}Za zv_2iJ-+?ytK^sG8<2tnY2ec&*ZB0j8ub{jn`bQ)5+DeokK-p1=&Mucn{-r?hrZo}z8i_YKZbs2 zgno>npR&=eqbbi;23@| zf*-tsA4=kf`{0L9;YYIYBPsmoLR=<-%N)aH^KiK+E|_D@ZTrm^e|5U64%*;>-NBPf54BQ!Wnt^iIuos0zbJ4XI8+OS8)9_ zT)z}I1UDLs8+XKw3viPH+;lFE*1%5%@lzk;r!U~F7=CsvelCWe?}1}M9LvYe2H@sF z+#-NmT)-_8xK&Hsssy)A;x>i&g)+G9So~rZjt6jjC2sc+Zg&BIu~~viM!v2yQgr^9Na5{dq;7fY}~ga?pFo(-;W2}zynv}!67_&9UjsW z4~^qt8F+XZoXEk6AMi_4@rVF^c`hEAg-1p4=n_2U7#>@M$1TOqdc`5u_4F59?=g06i@V5Q0;gp>LB^|5$+7;i7YJF4Ivd3a|&-j$1Y z@5cpW@t%k9o9k|zw;1&w+4PM z6Tf#1zdsj$kcK}f#YY49XcB)E#2<(8Ct3K@bo^Nq9}D5*VSM}`KG6f84B%4%e0m5z zlY`IZ;^GWkT#P?2#OIgd3)%Q$9A9dQFTIN|m*T56@U@TejS#*W#J7U@_E`Mi82%!G z|2GwX6~|w1!X+vET?PF85d6b_{9^(BIe<&E@h_S9*KEv(|3{Ev7%eSEUxYzfj9Cw3 zcfvT0Fm4#*9mV*2F~Ogh@FpgDh>5#ll5UtZD~9r7=pZI*gvrxlidLB74W=B2sd{1R zZkQ&7X&z$QUom|m%n-ti6){s7GcUs|6*0>P%(e`(SHtXgF~>>Fxeap-!rYrMZwT{V z#C-o^{zX_|5*A#Bh00=~hgf6~7R`#qe#PQHV2NH>avYYLhouK$nTuHVE|w2r`6#Tg z3@diSN;$D|S*+3wt5(CRAMmri_<1k_2Oau zoY){OHi%%uFgALKO+(nM8@32xi&fZa7PcOSt^dU~tFT=nY~Kt!%)*Xuu=8H*x(K@+ z#h$IO=fBuH7WTc0{X;l#8xEO=L#yJ@2oAf5!w=$!emHU&jyj5?BRD1s#}31B&2W4z zoKO-c7R5=^aB?S{8p5f2aoRzg9>JL(8#W|aB?w>fn8ZHzUZo);=aPdK0 zG76U+#1#c` z5uRCxXQS|Z2roRui#PFdMZB^JuTH~j1@U@HynYgIM&Ye(czYJ!Es6K4;eGLb7#|$O zhs*GBMSRi^pH{Ub3dVwR(9vlGR1Yd9fa5&Bg4qfOf!uKdl3CWQ;j;2*K_9|)FM}%s!AKAhY zUe$I`!I=-%KlGnXe`FsANvr*jv0DyKbURQRdHVGu4%N=}k3`rJ`TUU_+MTg%biI!V zY3J)lwonn-_{a_x`SX#TxRHU6?4!te{@-%j&yL9F*=4P=I$0d5brV_@m|$Zqkx^dC z_$t&^CH%Cq#;mP+*6laGRb5#fnsu$}RewfnHDlJibY;C;>sGIlwD2vS*O|~WzG`Tw zcH>n|bv0Ghb*f}#Z*#S)YgYActy^8XY;6!W$71J%DATUC-ORnGxW3Tdf)5RD>>CP^_NignB&%{)vIUT z4%|!44_Qdn5bu?=H_$*eF7ZHre)ZN-LlxE3p(xpEesfEz`EkUv-@lY)ES7+fW@5yc zIG*ZbNqkrC-VD~CyW??!`e&W3+)JwG9cjKm29Kr3YF3nxqV!d!c*e#S7{4IxF9QGo DT5c?X literal 0 HcmV?d00001 diff --git a/latest-tag/deps/font-awesome-6.4.2/webfonts/fa-v4compatibility.ttf b/latest-tag/deps/font-awesome-6.4.2/webfonts/fa-v4compatibility.ttf new file mode 100644 index 0000000000000000000000000000000000000000..ba6cb258e0f33ffddffd24b18d7162a32cb9a6da GIT binary patch literal 10172 zcmbtadu&_Rc|YIfQkaca>t^fxV+pu9+H*{T){v!;<7HmZ!2Q8XE zHfq1`+{=gLXFJ-w_dDM?=R4o|-sez)gbr_KC$(@!W7deFS5_f$z*B5Kc{51pG662NzdrmyZka z0p`Ah_8ZIP`QjJzulxt*=g~g7QoLLdF+gw+l5_}|zDPN@Sg zl;e!q@kvWeTKv9HC7%0f$0#SgPwT=3^kZ}$y${>(Ux{AvN1!QOjy6RCyY0_?r$6(! zXl&j0(G`ma@RmoX^5h^ z7JbI^xV%W?tJ>c*<0M|#^Xq6gI`nL`1MdjzgxQJ+r_|X|@a`1*x9;0|cNC*80}hw!X3T&8_#gezf(It)FfE{JQUY>iW^^k6eH3`l;(rUoT#tzkd1pSFeBk zQSV1rwzq|l^GjP-Z#iF+^N-u}3z&bgGv8r`uqAn>HnzzQ2&AcSNiw$cl0mo_vrq>H+JY48~ZIjN8vsHMN{-5eVcxv zyr}%U<1xp(j-NPJoqyxH$JKD3Ql07<^-t9gJ(Hfg=X;)CdhhqXsm*FX_Er3Q{g3)D z```5cIPhrTdx4MjI$PLwgV=&2VvpDh`LgdNlBzozio|2t#AI$}Iy99YPN|`EPEBVG zB{ZOI zku=DSb+ggbG?qgn8X2G%mYoI)T%nPjJVrC%F$EMDLt}bsjE2D=CS_-GGh;+?EfCQ1 zy6TOZ1qz3Qd9ODg42P*;M!l+@w|YIEoA=Upev{F=X|ZG4dy0yK*i|+bkAm|t;_vV= zl2UY}hSSsFb%6LAGe}C9&aB52(sh6ObSPx%`suXK?GC=X8>zY6L24+m^`P7BOP|(t zGZZ?V_Uk$|#cr(6&>`gAJaw(vVdUnV(syiDc-1y1%~#rB7pL@>HZQzto0I0R+h7-~ zmhKUD8zL!0csSKj{SA7SO8tsQ(>#lsTQv?HGE}#=xVuf?^>`{iBR~xb7{1Dm20R^N z5XWF^d0ua*+h}gEG-+1!@eT?*8sFT#_U<;;V&C4xdK;o&jEVcjDY1loPR1u<(NHX& zn2g7w5!Ibau)l?-ax>XD_w6G_Cey=5h;cbNrrqR8cR9?2)008#R(NPulJOWJDAcXC z^{fNYZrjn~COx#l(d1o&=cAiq@g$uDz(K~I#jpH^HP?Qb=q*?neib$?*nU|yX~}0m zJOuTyJmB!w-rEwGqiL4IWIUZ}Ma4O?Wp|LI>|h-^46wvWS6=Y>Uf_77)vX2V8n-^k zTdmgXKz~0`fBylyO{ZS)1%sG~!AI?F9R&?dLt|T5bMfRUBzQ9LXhY8(-|Cb5kk*R; z&hZ)es!zP*^>ToD`v6BT zELm@Eu_C1k^0rEuEu+~mh>VKGXaz2SsCVFPp6m6#^9hcGb@Z7$vDs*mXbtD7lyB+R z-cy%u!gtV3Sn|zz>L9^ZSz~ipDHabw7%J;hD)EDxtL9ouCl>ShyH!o|?;#M{shjPc zx*-uqem#H~vj;w(OEDX{>@*l+xg%jjv(fA{#r4nmefvi!7TudmnDpsSpWArq-iU6{ z35UZpk396$<|c~Kc3huBwwf0r7mp!y$2kebS=+f8Xa2|UvX_#A9GD=|1m zNls&`YdDQ3r?Ua%FIWR|FIzd&;(qA{2n@5{K<{k7LIx|x-|JD-J)St}PCarkWF%F^ zWw=5VQ=nHzW#tq-|LBB4Nt_W2E7iKQ+4|MzF;&G4n>?aGLk=bD5JT2zQAP2 z>CyFvW@iVJ$-&v#hdK~!$cZnU1GTAB^oap64Ik&6JtVzAO3m^DcH#s@6Vr)XbT#Vr zW+kErhyr?qHd;`#K$m7LP2_$|$hXEzpt)(9`5Vz-m4vyyNt=k*dBkaG$*L~m zYI>MW1w{!|G)B3&Dk~JI8H^j0h8eIgqacwARns}HPX5l$k0U2ejBsYQQy@1vr{BJp zHh!bwyNF>gvaVE2Y4@WdD;^Ui?1_WYfWlEp;q<4#2@G<$qZ3?X!G-Ng3a;TLY$NWv z$#_MUWV?tc|2^z-4eN@_rRW{#2fCipBVmKC+CcLU={rk%mi9DL9?zku!{c#853A~7 z3pzxFn_|DZn=CKT+%UpnBL%r$HNp`rql5M#rk&hPmWghQn_(d*6or>5CK)6-e@QJ3`EXeu|Ho)}JNqpqngFXp`6^5W?n0tBoh7N0|O z;L24ROTNFShp4BgpBgQwna@jXJ}>cge%cu1U!$*Y|Ng!{1D_W37#YqUMd{hIho4TR zC)lH)rpL5x(IQF`YBnUzhNR!%zI?+Xzl)?KJ|1+AD?LoJM|qH6Lb%)UuNQLqq&SUw z<^@E-TgI-TDab`;ZJ7#@*&Ox(gFVEcxPYr*Cr7G75 zc8^)_CTVu}unD$h+b3pVeF^JqU{yqjCew*l4n^&Q0#X)^)~N!i6?p|#8BLg!=d=&z z3tTR=kBALxHE`M0i?M|~*BAvZ5(+v_LwSqEZDGCJz21Vsz2H;kKol1Z_)A*c15Twz z6YLc-*IU&r-Js2IB9sUTmJCS)WT+{vknGj0HS1s!?$}aMvkg(bS??Aa+87Czz*7)8 z9wpCCo^b4H)gSD;q4147r9#>s&GJ35PYeAN)97?w(}wo+AfFlweC{9ffYu zI9HYdf7I{9NNi##79(Zkz>#s5a-%gv0$_%7F5iDV1Kx(v;2NoO41|rNhT`fN;e!Jw zk}Og%J?+w7QZ-F|Npq>j8SLA3-IFszgLKAFNjH4!9?!Zw30fw@?nZ+{GdLLTT5||T zZ`4{?80527=?zzthcByyjlz)bxaPCEe_d7AInmqB)>%@=KH0xF;Wrzw(J`hkd9)I3>0lF1w;^zS8|EA~=V%|WQ1!H7j@9rfz6#=-3>FxUE4fyA zKgZSXL4^YqyL3MIt|k0Kx|*_}K`oIOeMNI99uI7369FTBvNs&@zHW8D?u~?dgL&3f z-NN7*>$AM^p8fmP1)j#!H}qJP0)d#-6$=Capvp|+ylr3~Nu0Ky7H4s%)mAM6CJx``tlwQ@r_5%= zqp+^EyOLQvi4w><4!7M1r_bLScPF{+5SzI}Y=s?S+t?wt%{$7CuR*LSc;23*84jVgXUZ#8 z%wd<7r*J-+r9nboVsG5eMKzsLx-lMbv%B~LZo6h1AGdZJ3?vY9WM})WdK3kRU%z)E zO1-^9L%qF3!BD?>8;3dPIlhRBX9Pj0^e~fZr{Qng@kbAP~1K%S^98qdK_!!v9IHX+Lf@#wv*;2ajLRu zAG2uh%*p(l1a99@$%i;A9qrD%oX!<5|DYnvh-?DqcskL^)?MuBw;|4T3hF@E@d@Ne>z>xi zAZ*3aE)a4-oq`9~pM0@r-G3cHPG=Km>zwWa`EIiS30;DPI2~?x;N=dKpBfx=lIGO| zUXOcb=2JW_-WqqH13|0uhBlRC0z4qSuLFLKlF4ZgNFZ=+NQT~a!`d|1o`)Xa6Ffa zBcsO;Qam?=%fiFK70pMNpL(kQ@yDEcd zP`Li%kYxoe4qI$EZZ(n`j~;d+u1T|sWz+4`pPhpFF5Es3+L&ydOSrb87)#>#;n`%8 zUBM^OXcW4|EvSF**x2ms*w|jaLTC7sQ$u~a z>QsnOm@A>6(X-V(qZ>B67lA}#2s-@B41fB>n>I{15B#kSE6`HIh8;Ln?XzKL*BBS< zv}WVo(C5!>Si>2$_4~h1;NsNA2SG(fIy^&?KF&!_4y{wlv#<+>Nj$3jxeY5qSH5n; z4si}Ei*GZH9Gm(#gcicbT(ZzSMWC#W~E%= zK67clylPfUHM6*S&a9Qo%NLevqo+!1)g|C_xyjM-iNn*`iOFemWanbmkXcg_Kkz`g z^8DJ;!eY%FnLl85kF=TSM%}QD7N1tl`4$vJVq8Rp^VO#SMU={ z83k)im~fy=aG)yMD|nmYIWdLj1F~HaMT}X(Az(=?<69HY|37A$;t8yyC|Y{4(}T9WzfGM zxgEvNg(a+1MavqW6FIClibMQ_IE=zQi}xhnrWm>H&bq10VswY|15#EMNM;SIEMOnZ z&j>g?fcLHByi*dl8g*xF?3QVZ(oU_iPH(Nz%r3p~4r-8hRm>v5j*0V9b~VVj+fqiQ z4p!jod@kqD_D}J5JvaNL5hN6Hkds{GCY6vxNh2TmDL^`YGYe9PdMHd0ic*Z?w1;}B zkNRmZ?W6rPfTAZsNlMWm4bd>A{rpE!HTYZRT)EbBv4Yp7)y#6~d@UngDhZSXGVsx^ z&hu;KmCSr;wN_d)J5zW}dqS;j4^VBgT8FHEezCY#%gmRTFRrZiSPMv!XV;4J7fQ8A zn~KC%pS@UID+R4_plgrP&KKuPXUpXane%!H^cFMt3$C@L`C>J2wzyW!RMyH1Yo%({ zgTM4zT3NbM3SVLlSBeWu^Ofy|U*V0gVb%9l}jJH>+f}z$n0fxZbg{A6ZW_f9?C|U5<){1D)7grq%r4>Jd N-3nCBrc#A%{vU!|PjCPL literal 0 HcmV?d00001 diff --git a/latest-tag/deps/font-awesome-6.4.2/webfonts/fa-v4compatibility.woff2 b/latest-tag/deps/font-awesome-6.4.2/webfonts/fa-v4compatibility.woff2 new file mode 100644 index 0000000000000000000000000000000000000000..23b1c47ba29581512bbffdf82a9d88fb898faee6 GIT binary patch literal 4568 zcmV;}5hw0IqfgAvW9Ll-?%TNW000$t2K@t&FwI2eYk+N|9en0)D|D7`lGYo+l z5(3jU_clVywAU&$&^k}d^Ta%Beb2XVO}k>ws(k5J);wMRBL4s9)b5jIeShgovIADI z{juXHB?ATu3?Vmh!Sci^aJrh#ZdQd^0l^A@wmGvxT($rIoUb3Cn=l+Tqk!au+?k(H z_r4TfK_nezv;jF|8)7+hee>%eD|fscrv>00NZC z2K@0CH@@i#x&9xhkpBa~G=skn2QR%*PWqSwfC*LX&~`hRk8%Lg2LK=^LQ{TFo!eBy zBfu!W6*YHV#-HO37yx)GUIzdt{NB=kU>sfwK)@&g9Tr{qI!9e_&S{VjkNW3BcqCyC z8D2X9z!iKK0JH$G|J@#e%9vJ`b(n$jwnzrBW9!fyp5G;XNYl8ddHcdZGSDOgw(X@c zD`R1h`fMI45|;p=4kgt7)YYMkCIDVP)WM*6(iD-ri`*c9nN{Yn3e(+Fv=nwrAhkphStqV zLXza{vy+67$)?r~O9*Rh%B$79skb<8FQ;p3hijQhlCux5t)9ckq*1DAE;mplkUJ-iY)?|K?h)Z;;7Fy zm|CNV{|7l(AI68Q@1Nm-0PaEq=HO^JA8rKj7S%%FR|C~vRDoY-G;BvY($%FjiPfT7 z2=-&0(XgYV4O~i-IQ6sU#G(rPss&ntbCkFv2Xu-%Ix0ZUMPDx9FLyuHP;j29mKMfc z&PDN2iCKn$>fo7z^PF?hQj!VIvt20Ur?17i=;ca!g7e;>2HBwuLK+!1;dlU4fnS$6 zFw5|Iu3`7pMTe5nvxfAme-HynnxrvFjqss|E0u>WAuMaS%kEp25Y{h|HRN1mvyBE~ zqcNLjjORaGaox(pmSu^}s(PEkvMh^pF@pAnf^%#%W)Wr^4SHx`ebI@rfTZ#=>erRH zqH12nv81DeXi&C5Sv63BY9}T6!w#eSk+Sw+lH$cQsed5Y^End--p{$^IM(}SUC(2o zs2FxsfNXGPYz1aZi(DegI|h2fcVLd5=gz**avY2E_a6|9`+dQA3j@_DaxiR@6_JP% zGdV6ff7-Eg3bPzi_>XsV>Do|C*C=0VkA^s(M+Qs$?6yM`q;ISnm(+Fym<1FSd z1~8q5-K8{1<8FucXO;P(ANz3^mAJ^ROwM57?@QY)JnO7$b}w763yEixO0Cw$@s|w- zRt%A&N4*9o!PNk%3Yr)wKTv`4>ncc-EljpBNySV4gV>qFNJm=&mrE1*ub<3eC!~xr zBhpNzd7GGY+sYT#u0#cjGP=J1b#$axxih}nASfkrZjYOUjhPBXS=0|!Wrq<%Qmdkb z$xI=oE3A@XoY3w1_!=CaunnW^(&~Ov%wxN=zH!Vk8|$4mV!N}xam+Cr>zy`^T*az< z|4hv^YuaX%@bOB+F~mNzF3Q5Jmzm2;Mwyi>cE$DUUbSAvnVD>B`LJbFD$ZiHYzlGw z>grUdGqt*U{PC-+Q=QJ#>gw?TPzC@XfPE;#1T>)wfMGZGrzu;Y%n#H`xIrQv={EZ9 zZo7DQdB0!WQ5O!PBkH)D+9X8}cSeA4FWPFgdgzFHmJf-A6oXbPdxO!e-x>{YfV+?Z zcp)y{EmTaa3O-Qwg^((ysu05@i8@{7e57rZLQJQLj`VlbWwx0!&X}=e$8H=eLepIKOQu=! zR~TE_PJ9`AB~f1u`els_BPw>K=gCkAd{laM5eiYf1eb3dyK$_uSxM7e_D8Vg0erS+ zgk;zUAND~A2O);j;A#MmifdY(jWN8i8>m3(ZoH1cWWHiQOe4Jz>DV`xp6&Vdop;kf z`5|S2TEirbbU0x)mo;iN#9FO^c}ggLo*Wm0o%q-oa?bOAfA$l=nw59pN7@|zl1=<1;6-SRxC)d;E8$gwp-Y5-IQ zK49Z;5RQWj;6`{DAeCPOrkC`BtVMO0oF?6pld+jJ(UHW?LTAC*h+e86V2kNOq9viE z1el`&DBE~!sWVh+v_*}g)r@(5B}1f*=FEETGgI?%F0cn;pt+E9?^YSWxhSYvpiz6l zuQ2Zw>C_sctDdIgT=sp6B*B@423bZX)?TT#?r>g&FoTpEJ?39LMSj)ATb0`4Q|ay9T@`(UTuA$}#sP zL>dkOcwBY2^*vg&(UDssuU&0dVPadRevQHn#Mpx1ykF#)VZWLsH9qzW^Oa(43V3zW z9p!jO?YMf_3a?36jjOI}&?h7`KS63rojDksVi8D3IXMw?nDn*$k*1Oug`iNt7`On_ zL`QR^R8>I}8EvADLOh>I$&P)!G|JZP6U4Tir=D_bn@rqpl@0m&V}q{Y+;_f-^&1U} zA9oO)=M)jby6sJZb9T~6jC1j(+bkhU!E?oPxOhvYR6UQ|4=k4e0Y-dt&C>^M4IxMq zb%icJxY(-t_oTw^4hE_-=6M#i9osG&wASey+L)aoE;%erJ(?lTcT|9m?c3$DL2cWa z4gw@Ivm5i`hb15+ob&Oun4=u8z#OIlFi}9PD+oqDSjb@a177jPNs5;n)|1S#*prsQ z6e=h+zw$5Bjb>@Vu^_h*4^aA zf$H}<+$roG#&&EW4(uAg+X3dd3qH&NV4xNjjU-8vv|UmSj=Bdyq8;<9AWqUnQ9FNj zvkc`^K4I;nDV?IGNsn$b?i1Xt3mPE7KRhLo_?)$mrlv`!C_TE(h|h;T4lu)AI1#{G zN*9u~l9SaZP;uHv8D%OQO^vPL#TMtZ-1%tRvX5`>j2Ub^WD21Nl+fK) zw3$pZTJ0oElXD9y+*{L%O?M-Pr8tNYz+rEeq7$7-5&zf`$xU*iHVv9mS~gs;-C=M8 zoajtt)IPT-Me#)3ZqFj6SYkPsy{uc7zMB(qLzueaF(-9X7@UajzQVaR)y$c-HYdMi z8RvSaQC}Pms;Uq|Ft(d9A%sxXV0f|K*wow96xH=1Lzp@z;@y-Ke_hb| zBN6fQ#!9X^doZrk&iSy8BtKgmQi(LEL>9_OIn9d#UK!~qWrhmfI)4#saS)ROoS2&# zwLESyv*=bV?s;V$d&2Hn< z=@E;x3BUwT1>B@9VkUiNhC&xY0Z(i#uUv^ zn0`ZJ_UZ_VcD2%S(v;zDPOB>dzs4dJy-Xr->njxtkzBDv3$?Ml1BDiL-VFVDNa|k+ zqyjpGyV8R7WSs!$JYCHidlrfu$5An3y%OAI@U@9Q8*p`*63fbUHAfxVL;yL8gKBkv zdeT2dcVkl=m+z!?J}=X3YEIIet&*=;D(0e2v{P?l%#59@<1V5?@(JWi#oUm^`6*Kv zYCzM>I}V90&UbgKq;t#UA+1>4IAn3|)TlITD{yW_X|E_A3uu~Y3PW`}?}^?;u~;Ew z@(^*|3T@2g6wa-OS@mIz8Cz#j-Z^3Ci`(ELiv_kpuvp4PXKG1T+$WiLkV>q}`lpT= z2^QqQbIHuN^QO~HoXK<6t{-oRjeD*eGh`FlHj$l>(vuOQh_#e)L669)T1JyLZPO1N zW9(_VMGqqY27&OOL0mjeDekbG>&zjil*qX(7|`M|H_)?)ii4D$Nm2I5uH8?4yZUl} z_St8jefH+n%WL}R+3%TQuDS})pUbSA2`syVZ(*D-b!!S(0dD^5W%*u>-u%u z2A~45)hkV53AUjOB#M9bhG&n)oaub5DV}cbS2aU0SS&?zt_Nu0fog8HoRcqQip+{5 z=Jpf_tNRCHF``&(pnrAxDW-yv5DO^80zrLlPrNbR=VK}FN|#t4)ZURw4Gg4G9qn{` z8Y}XNHjAkW5z}=g61E$DXOd#8ktOiwz*T+MlhLVUXy!{sy+z#QzXtkz?K$l^+hh8W5i9dq#&W5B^8CX7zRXrvFL07&Hk z^2&`pLs%O~FbXWiWdhhqpI27*Ob}yAAw|P-YR~A{t$fTFNYZ^w7z0!}uMt>^yV9%r zOHP&R-tqFu{4(!&ZP>4jRE|~rGxN5V$4?v^o?PiweR-Q+p3KX${)F$>>I13NVXx{B zgPQf^sCT@bQv%O4ub9jyvl-9G&hliv;#Fm-luf2H{e@g6Tadl5uo^cY+uqHrWnS&f zWMzE9m%XF2WUpWJ+b7EMRC&a$%i~_%mo=|;;@I${tc-e9Sugu?xH=|%&pURs;wP~^ zDy)QuDts)#DU?x%hvO*YB=T66eh+FGhK~wHP{A=&;NuMDVJpfQ#|a$6FyKU!XDZKw zD$l!~fDa!v)G>e*QaHTP4sV4dMscg4zM@I2uhl#hkwhLzWXF?#C(4*a9hLrG?-C`H zkVO({WYCWSa>yWy0wj8IQa~Da0J2Xy3)lQLoWUe27^lOgljy}LWs.tolerance[a.direction],e(a),l=t,i=!1}function h(){i||(i=!0,n=requestAnimationFrame(c))}var u=!!o&&{passive:!0,capture:!1};return t.addEventListener("scroll",h,u),c(),{destroy:function(){cancelAnimationFrame(n),t.removeEventListener("scroll",h,u)}}}function o(t,n){n=n||{},Object.assign(this,o.options,n),this.classes=Object.assign({},o.options.classes,n.classes),this.elem=t,this.tolerance=function(t){return t===Object(t)?t:{down:t,up:t}}(this.tolerance),this.initialised=!1,this.frozen=!1}return o.prototype={constructor:o,init:function(){return o.cutsTheMustard&&!this.initialised&&(this.addClass("initial"),this.initialised=!0,setTimeout(function(t){t.scrollTracker=n(t.scroller,{offset:t.offset,tolerance:t.tolerance},t.update.bind(t))},100,this)),this},destroy:function(){this.initialised=!1,Object.keys(this.classes).forEach(this.removeClass,this),this.scrollTracker.destroy()},unpin:function(){!this.hasClass("pinned")&&this.hasClass("unpinned")||(this.addClass("unpinned"),this.removeClass("pinned"),this.onUnpin&&this.onUnpin.call(this))},pin:function(){this.hasClass("unpinned")&&(this.addClass("pinned"),this.removeClass("unpinned"),this.onPin&&this.onPin.call(this))},freeze:function(){this.frozen=!0,this.addClass("frozen")},unfreeze:function(){this.frozen=!1,this.removeClass("frozen")},top:function(){this.hasClass("top")||(this.addClass("top"),this.removeClass("notTop"),this.onTop&&this.onTop.call(this))},notTop:function(){this.hasClass("notTop")||(this.addClass("notTop"),this.removeClass("top"),this.onNotTop&&this.onNotTop.call(this))},bottom:function(){this.hasClass("bottom")||(this.addClass("bottom"),this.removeClass("notBottom"),this.onBottom&&this.onBottom.call(this))},notBottom:function(){this.hasClass("notBottom")||(this.addClass("notBottom"),this.removeClass("bottom"),this.onNotBottom&&this.onNotBottom.call(this))},shouldUnpin:function(t){return"down"===t.direction&&!t.top&&t.toleranceExceeded},shouldPin:function(t){return"up"===t.direction&&t.toleranceExceeded||t.top},addClass:function(t){this.elem.classList.add.apply(this.elem.classList,this.classes[t].split(" "))},removeClass:function(t){this.elem.classList.remove.apply(this.elem.classList,this.classes[t].split(" "))},hasClass:function(t){return this.classes[t].split(" ").every(function(t){return this.classList.contains(t)},this.elem)},update:function(t){t.isOutOfBounds||!0!==this.frozen&&(t.top?this.top():this.notTop(),t.bottom?this.bottom():this.notBottom(),this.shouldUnpin(t)?this.unpin():this.shouldPin(t)&&this.pin())}},o.options={tolerance:{up:0,down:0},offset:0,scroller:t()?window:null,classes:{frozen:"headroom--frozen",pinned:"headroom--pinned",unpinned:"headroom--unpinned",top:"headroom--top",notTop:"headroom--not-top",bottom:"headroom--bottom",notBottom:"headroom--not-bottom",initial:"headroom"}},o.cutsTheMustard=!!(t()&&function(){}.bind&&"classList"in document.documentElement&&Object.assign&&Object.keys&&requestAnimationFrame),o}); \ No newline at end of file diff --git a/latest-tag/deps/headroom-0.11.0/jQuery.headroom.min.js b/latest-tag/deps/headroom-0.11.0/jQuery.headroom.min.js new file mode 100644 index 00000000..17f70c9e --- /dev/null +++ b/latest-tag/deps/headroom-0.11.0/jQuery.headroom.min.js @@ -0,0 +1,7 @@ +/*! + * headroom.js v0.9.4 - Give your page some headroom. Hide your header until you need it + * Copyright (c) 2017 Nick Williams - http://wicky.nillia.ms/headroom.js + * License: MIT + */ + +!function(a){a&&(a.fn.headroom=function(b){return this.each(function(){var c=a(this),d=c.data("headroom"),e="object"==typeof b&&b;e=a.extend(!0,{},Headroom.options,e),d||(d=new Headroom(this,e),d.init(),c.data("headroom",d)),"string"==typeof b&&(d[b](),"destroy"===b&&c.removeData("headroom"))})},a("[data-headroom]").each(function(){var b=a(this);b.headroom(b.data())}))}(window.Zepto||window.jQuery); \ No newline at end of file diff --git a/latest-tag/deps/jquery-3.6.0/jquery-3.6.0.js b/latest-tag/deps/jquery-3.6.0/jquery-3.6.0.js new file mode 100644 index 00000000..fc6c299b --- /dev/null +++ b/latest-tag/deps/jquery-3.6.0/jquery-3.6.0.js @@ -0,0 +1,10881 @@ +/*! + * jQuery JavaScript Library v3.6.0 + * https://jquery.com/ + * + * Includes Sizzle.js + * https://sizzlejs.com/ + * + * Copyright OpenJS Foundation and other contributors + * Released under the MIT license + * https://jquery.org/license + * + * Date: 2021-03-02T17:08Z + */ +( function( global, factory ) { + + "use strict"; + + if ( typeof module === "object" && typeof module.exports === "object" ) { + + // For CommonJS and CommonJS-like environments where a proper `window` + // is present, execute the factory and get jQuery. + // For environments that do not have a `window` with a `document` + // (such as Node.js), expose a factory as module.exports. + // This accentuates the need for the creation of a real `window`. + // e.g. var jQuery = require("jquery")(window); + // See ticket #14549 for more info. + module.exports = global.document ? + factory( global, true ) : + function( w ) { + if ( !w.document ) { + throw new Error( "jQuery requires a window with a document" ); + } + return factory( w ); + }; + } else { + factory( global ); + } + +// Pass this if window is not defined yet +} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) { + +// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1 +// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode +// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common +// enough that all such attempts are guarded in a try block. +"use strict"; + +var arr = []; + +var getProto = Object.getPrototypeOf; + +var slice = arr.slice; + +var flat = arr.flat ? function( array ) { + return arr.flat.call( array ); +} : function( array ) { + return arr.concat.apply( [], array ); +}; + + +var push = arr.push; + +var indexOf = arr.indexOf; + +var class2type = {}; + +var toString = class2type.toString; + +var hasOwn = class2type.hasOwnProperty; + +var fnToString = hasOwn.toString; + +var ObjectFunctionString = fnToString.call( Object ); + +var support = {}; + +var isFunction = function isFunction( obj ) { + + // Support: Chrome <=57, Firefox <=52 + // In some browsers, typeof returns "function" for HTML elements + // (i.e., `typeof document.createElement( "object" ) === "function"`). + // We don't want to classify *any* DOM node as a function. + // Support: QtWeb <=3.8.5, WebKit <=534.34, wkhtmltopdf tool <=0.12.5 + // Plus for old WebKit, typeof returns "function" for HTML collections + // (e.g., `typeof document.getElementsByTagName("div") === "function"`). (gh-4756) + return typeof obj === "function" && typeof obj.nodeType !== "number" && + typeof obj.item !== "function"; + }; + + +var isWindow = function isWindow( obj ) { + return obj != null && obj === obj.window; + }; + + +var document = window.document; + + + + var preservedScriptAttributes = { + type: true, + src: true, + nonce: true, + noModule: true + }; + + function DOMEval( code, node, doc ) { + doc = doc || document; + + var i, val, + script = doc.createElement( "script" ); + + script.text = code; + if ( node ) { + for ( i in preservedScriptAttributes ) { + + // Support: Firefox 64+, Edge 18+ + // Some browsers don't support the "nonce" property on scripts. + // On the other hand, just using `getAttribute` is not enough as + // the `nonce` attribute is reset to an empty string whenever it + // becomes browsing-context connected. + // See https://github.com/whatwg/html/issues/2369 + // See https://html.spec.whatwg.org/#nonce-attributes + // The `node.getAttribute` check was added for the sake of + // `jQuery.globalEval` so that it can fake a nonce-containing node + // via an object. + val = node[ i ] || node.getAttribute && node.getAttribute( i ); + if ( val ) { + script.setAttribute( i, val ); + } + } + } + doc.head.appendChild( script ).parentNode.removeChild( script ); + } + + +function toType( obj ) { + if ( obj == null ) { + return obj + ""; + } + + // Support: Android <=2.3 only (functionish RegExp) + return typeof obj === "object" || typeof obj === "function" ? + class2type[ toString.call( obj ) ] || "object" : + typeof obj; +} +/* global Symbol */ +// Defining this global in .eslintrc.json would create a danger of using the global +// unguarded in another place, it seems safer to define global only for this module + + + +var + version = "3.6.0", + + // Define a local copy of jQuery + jQuery = function( selector, context ) { + + // The jQuery object is actually just the init constructor 'enhanced' + // Need init if jQuery is called (just allow error to be thrown if not included) + return new jQuery.fn.init( selector, context ); + }; + +jQuery.fn = jQuery.prototype = { + + // The current version of jQuery being used + jquery: version, + + constructor: jQuery, + + // The default length of a jQuery object is 0 + length: 0, + + toArray: function() { + return slice.call( this ); + }, + + // Get the Nth element in the matched element set OR + // Get the whole matched element set as a clean array + get: function( num ) { + + // Return all the elements in a clean array + if ( num == null ) { + return slice.call( this ); + } + + // Return just the one element from the set + return num < 0 ? this[ num + this.length ] : this[ num ]; + }, + + // Take an array of elements and push it onto the stack + // (returning the new matched element set) + pushStack: function( elems ) { + + // Build a new jQuery matched element set + var ret = jQuery.merge( this.constructor(), elems ); + + // Add the old object onto the stack (as a reference) + ret.prevObject = this; + + // Return the newly-formed element set + return ret; + }, + + // Execute a callback for every element in the matched set. + each: function( callback ) { + return jQuery.each( this, callback ); + }, + + map: function( callback ) { + return this.pushStack( jQuery.map( this, function( elem, i ) { + return callback.call( elem, i, elem ); + } ) ); + }, + + slice: function() { + return this.pushStack( slice.apply( this, arguments ) ); + }, + + first: function() { + return this.eq( 0 ); + }, + + last: function() { + return this.eq( -1 ); + }, + + even: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return ( i + 1 ) % 2; + } ) ); + }, + + odd: function() { + return this.pushStack( jQuery.grep( this, function( _elem, i ) { + return i % 2; + } ) ); + }, + + eq: function( i ) { + var len = this.length, + j = +i + ( i < 0 ? len : 0 ); + return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] ); + }, + + end: function() { + return this.prevObject || this.constructor(); + }, + + // For internal use only. + // Behaves like an Array's method, not like a jQuery method. + push: push, + sort: arr.sort, + splice: arr.splice +}; + +jQuery.extend = jQuery.fn.extend = function() { + var options, name, src, copy, copyIsArray, clone, + target = arguments[ 0 ] || {}, + i = 1, + length = arguments.length, + deep = false; + + // Handle a deep copy situation + if ( typeof target === "boolean" ) { + deep = target; + + // Skip the boolean and the target + target = arguments[ i ] || {}; + i++; + } + + // Handle case when target is a string or something (possible in deep copy) + if ( typeof target !== "object" && !isFunction( target ) ) { + target = {}; + } + + // Extend jQuery itself if only one argument is passed + if ( i === length ) { + target = this; + i--; + } + + for ( ; i < length; i++ ) { + + // Only deal with non-null/undefined values + if ( ( options = arguments[ i ] ) != null ) { + + // Extend the base object + for ( name in options ) { + copy = options[ name ]; + + // Prevent Object.prototype pollution + // Prevent never-ending loop + if ( name === "__proto__" || target === copy ) { + continue; + } + + // Recurse if we're merging plain objects or arrays + if ( deep && copy && ( jQuery.isPlainObject( copy ) || + ( copyIsArray = Array.isArray( copy ) ) ) ) { + src = target[ name ]; + + // Ensure proper type for the source value + if ( copyIsArray && !Array.isArray( src ) ) { + clone = []; + } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) { + clone = {}; + } else { + clone = src; + } + copyIsArray = false; + + // Never move original objects, clone them + target[ name ] = jQuery.extend( deep, clone, copy ); + + // Don't bring in undefined values + } else if ( copy !== undefined ) { + target[ name ] = copy; + } + } + } + } + + // Return the modified object + return target; +}; + +jQuery.extend( { + + // Unique for each copy of jQuery on the page + expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ), + + // Assume jQuery is ready without the ready module + isReady: true, + + error: function( msg ) { + throw new Error( msg ); + }, + + noop: function() {}, + + isPlainObject: function( obj ) { + var proto, Ctor; + + // Detect obvious negatives + // Use toString instead of jQuery.type to catch host objects + if ( !obj || toString.call( obj ) !== "[object Object]" ) { + return false; + } + + proto = getProto( obj ); + + // Objects with no prototype (e.g., `Object.create( null )`) are plain + if ( !proto ) { + return true; + } + + // Objects with prototype are plain iff they were constructed by a global Object function + Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor; + return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString; + }, + + isEmptyObject: function( obj ) { + var name; + + for ( name in obj ) { + return false; + } + return true; + }, + + // Evaluates a script in a provided context; falls back to the global one + // if not specified. + globalEval: function( code, options, doc ) { + DOMEval( code, { nonce: options && options.nonce }, doc ); + }, + + each: function( obj, callback ) { + var length, i = 0; + + if ( isArrayLike( obj ) ) { + length = obj.length; + for ( ; i < length; i++ ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } else { + for ( i in obj ) { + if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) { + break; + } + } + } + + return obj; + }, + + // results is for internal usage only + makeArray: function( arr, results ) { + var ret = results || []; + + if ( arr != null ) { + if ( isArrayLike( Object( arr ) ) ) { + jQuery.merge( ret, + typeof arr === "string" ? + [ arr ] : arr + ); + } else { + push.call( ret, arr ); + } + } + + return ret; + }, + + inArray: function( elem, arr, i ) { + return arr == null ? -1 : indexOf.call( arr, elem, i ); + }, + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + merge: function( first, second ) { + var len = +second.length, + j = 0, + i = first.length; + + for ( ; j < len; j++ ) { + first[ i++ ] = second[ j ]; + } + + first.length = i; + + return first; + }, + + grep: function( elems, callback, invert ) { + var callbackInverse, + matches = [], + i = 0, + length = elems.length, + callbackExpect = !invert; + + // Go through the array, only saving the items + // that pass the validator function + for ( ; i < length; i++ ) { + callbackInverse = !callback( elems[ i ], i ); + if ( callbackInverse !== callbackExpect ) { + matches.push( elems[ i ] ); + } + } + + return matches; + }, + + // arg is for internal usage only + map: function( elems, callback, arg ) { + var length, value, + i = 0, + ret = []; + + // Go through the array, translating each of the items to their new values + if ( isArrayLike( elems ) ) { + length = elems.length; + for ( ; i < length; i++ ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + + // Go through every key on the object, + } else { + for ( i in elems ) { + value = callback( elems[ i ], i, arg ); + + if ( value != null ) { + ret.push( value ); + } + } + } + + // Flatten any nested arrays + return flat( ret ); + }, + + // A global GUID counter for objects + guid: 1, + + // jQuery.support is not used in Core but other projects attach their + // properties to it so it needs to exist. + support: support +} ); + +if ( typeof Symbol === "function" ) { + jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ]; +} + +// Populate the class2type map +jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ), + function( _i, name ) { + class2type[ "[object " + name + "]" ] = name.toLowerCase(); + } ); + +function isArrayLike( obj ) { + + // Support: real iOS 8.2 only (not reproducible in simulator) + // `in` check used to prevent JIT error (gh-2145) + // hasOwn isn't used here due to false negatives + // regarding Nodelist length in IE + var length = !!obj && "length" in obj && obj.length, + type = toType( obj ); + + if ( isFunction( obj ) || isWindow( obj ) ) { + return false; + } + + return type === "array" || length === 0 || + typeof length === "number" && length > 0 && ( length - 1 ) in obj; +} +var Sizzle = +/*! + * Sizzle CSS Selector Engine v2.3.6 + * https://sizzlejs.com/ + * + * Copyright JS Foundation and other contributors + * Released under the MIT license + * https://js.foundation/ + * + * Date: 2021-02-16 + */ +( function( window ) { +var i, + support, + Expr, + getText, + isXML, + tokenize, + compile, + select, + outermostContext, + sortInput, + hasDuplicate, + + // Local document vars + setDocument, + document, + docElem, + documentIsHTML, + rbuggyQSA, + rbuggyMatches, + matches, + contains, + + // Instance-specific data + expando = "sizzle" + 1 * new Date(), + preferredDoc = window.document, + dirruns = 0, + done = 0, + classCache = createCache(), + tokenCache = createCache(), + compilerCache = createCache(), + nonnativeSelectorCache = createCache(), + sortOrder = function( a, b ) { + if ( a === b ) { + hasDuplicate = true; + } + return 0; + }, + + // Instance methods + hasOwn = ( {} ).hasOwnProperty, + arr = [], + pop = arr.pop, + pushNative = arr.push, + push = arr.push, + slice = arr.slice, + + // Use a stripped-down indexOf as it's faster than native + // https://jsperf.com/thor-indexof-vs-for/5 + indexOf = function( list, elem ) { + var i = 0, + len = list.length; + for ( ; i < len; i++ ) { + if ( list[ i ] === elem ) { + return i; + } + } + return -1; + }, + + booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" + + "ismap|loop|multiple|open|readonly|required|scoped", + + // Regular expressions + + // http://www.w3.org/TR/css3-selectors/#whitespace + whitespace = "[\\x20\\t\\r\\n\\f]", + + // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram + identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace + + "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+", + + // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors + attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace + + + // Operator (capture 2) + "*([*^$|!~]?=)" + whitespace + + + // "Attribute values must be CSS identifiers [capture 5] + // or strings [capture 3 or capture 4]" + "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" + + whitespace + "*\\]", + + pseudos = ":(" + identifier + ")(?:\\((" + + + // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments: + // 1. quoted (capture 3; capture 4 or capture 5) + "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" + + + // 2. simple (capture 6) + "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" + + + // 3. anything else (capture 2) + ".*" + + ")\\)|)", + + // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter + rwhitespace = new RegExp( whitespace + "+", "g" ), + rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" + + whitespace + "+$", "g" ), + + rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ), + rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace + + "*" ), + rdescend = new RegExp( whitespace + "|>" ), + + rpseudo = new RegExp( pseudos ), + ridentifier = new RegExp( "^" + identifier + "$" ), + + matchExpr = { + "ID": new RegExp( "^#(" + identifier + ")" ), + "CLASS": new RegExp( "^\\.(" + identifier + ")" ), + "TAG": new RegExp( "^(" + identifier + "|[*])" ), + "ATTR": new RegExp( "^" + attributes ), + "PSEUDO": new RegExp( "^" + pseudos ), + "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" + + whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" + + whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ), + "bool": new RegExp( "^(?:" + booleans + ")$", "i" ), + + // For use in libraries implementing .is() + // We use this for POS matching in `select` + "needsContext": new RegExp( "^" + whitespace + + "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace + + "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" ) + }, + + rhtml = /HTML$/i, + rinputs = /^(?:input|select|textarea|button)$/i, + rheader = /^h\d$/i, + + rnative = /^[^{]+\{\s*\[native \w/, + + // Easily-parseable/retrievable ID or TAG or CLASS selectors + rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/, + + rsibling = /[+~]/, + + // CSS escapes + // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters + runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ), + funescape = function( escape, nonHex ) { + var high = "0x" + escape.slice( 1 ) - 0x10000; + + return nonHex ? + + // Strip the backslash prefix from a non-hex escape sequence + nonHex : + + // Replace a hexadecimal escape sequence with the encoded Unicode code point + // Support: IE <=11+ + // For values outside the Basic Multilingual Plane (BMP), manually construct a + // surrogate pair + high < 0 ? + String.fromCharCode( high + 0x10000 ) : + String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 ); + }, + + // CSS string/identifier serialization + // https://drafts.csswg.org/cssom/#common-serializing-idioms + rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g, + fcssescape = function( ch, asCodePoint ) { + if ( asCodePoint ) { + + // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER + if ( ch === "\0" ) { + return "\uFFFD"; + } + + // Control characters and (dependent upon position) numbers get escaped as code points + return ch.slice( 0, -1 ) + "\\" + + ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " "; + } + + // Other potentially-special ASCII characters get backslash-escaped + return "\\" + ch; + }, + + // Used for iframes + // See setDocument() + // Removing the function wrapper causes a "Permission Denied" + // error in IE + unloadHandler = function() { + setDocument(); + }, + + inDisabledFieldset = addCombinator( + function( elem ) { + return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset"; + }, + { dir: "parentNode", next: "legend" } + ); + +// Optimize for push.apply( _, NodeList ) +try { + push.apply( + ( arr = slice.call( preferredDoc.childNodes ) ), + preferredDoc.childNodes + ); + + // Support: Android<4.0 + // Detect silently failing push.apply + // eslint-disable-next-line no-unused-expressions + arr[ preferredDoc.childNodes.length ].nodeType; +} catch ( e ) { + push = { apply: arr.length ? + + // Leverage slice if possible + function( target, els ) { + pushNative.apply( target, slice.call( els ) ); + } : + + // Support: IE<9 + // Otherwise append directly + function( target, els ) { + var j = target.length, + i = 0; + + // Can't trust NodeList.length + while ( ( target[ j++ ] = els[ i++ ] ) ) {} + target.length = j - 1; + } + }; +} + +function Sizzle( selector, context, results, seed ) { + var m, i, elem, nid, match, groups, newSelector, + newContext = context && context.ownerDocument, + + // nodeType defaults to 9, since context defaults to document + nodeType = context ? context.nodeType : 9; + + results = results || []; + + // Return early from calls with invalid selector or context + if ( typeof selector !== "string" || !selector || + nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) { + + return results; + } + + // Try to shortcut find operations (as opposed to filters) in HTML documents + if ( !seed ) { + setDocument( context ); + context = context || document; + + if ( documentIsHTML ) { + + // If the selector is sufficiently simple, try using a "get*By*" DOM method + // (excepting DocumentFragment context, where the methods don't exist) + if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) { + + // ID selector + if ( ( m = match[ 1 ] ) ) { + + // Document context + if ( nodeType === 9 ) { + if ( ( elem = context.getElementById( m ) ) ) { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( elem.id === m ) { + results.push( elem ); + return results; + } + } else { + return results; + } + + // Element context + } else { + + // Support: IE, Opera, Webkit + // TODO: identify versions + // getElementById can match elements by name instead of ID + if ( newContext && ( elem = newContext.getElementById( m ) ) && + contains( context, elem ) && + elem.id === m ) { + + results.push( elem ); + return results; + } + } + + // Type selector + } else if ( match[ 2 ] ) { + push.apply( results, context.getElementsByTagName( selector ) ); + return results; + + // Class selector + } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName && + context.getElementsByClassName ) { + + push.apply( results, context.getElementsByClassName( m ) ); + return results; + } + } + + // Take advantage of querySelectorAll + if ( support.qsa && + !nonnativeSelectorCache[ selector + " " ] && + ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) && + + // Support: IE 8 only + // Exclude object elements + ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) { + + newSelector = selector; + newContext = context; + + // qSA considers elements outside a scoping root when evaluating child or + // descendant combinators, which is not what we want. + // In such cases, we work around the behavior by prefixing every selector in the + // list with an ID selector referencing the scope context. + // The technique has to be used as well when a leading combinator is used + // as such selectors are not recognized by querySelectorAll. + // Thanks to Andrew Dupont for this technique. + if ( nodeType === 1 && + ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) { + + // Expand context for sibling selectors + newContext = rsibling.test( selector ) && testContext( context.parentNode ) || + context; + + // We can use :scope instead of the ID hack if the browser + // supports it & if we're not changing the context. + if ( newContext !== context || !support.scope ) { + + // Capture the context ID, setting it first if necessary + if ( ( nid = context.getAttribute( "id" ) ) ) { + nid = nid.replace( rcssescape, fcssescape ); + } else { + context.setAttribute( "id", ( nid = expando ) ); + } + } + + // Prefix every selector in the list + groups = tokenize( selector ); + i = groups.length; + while ( i-- ) { + groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " + + toSelector( groups[ i ] ); + } + newSelector = groups.join( "," ); + } + + try { + push.apply( results, + newContext.querySelectorAll( newSelector ) + ); + return results; + } catch ( qsaError ) { + nonnativeSelectorCache( selector, true ); + } finally { + if ( nid === expando ) { + context.removeAttribute( "id" ); + } + } + } + } + } + + // All others + return select( selector.replace( rtrim, "$1" ), context, results, seed ); +} + +/** + * Create key-value caches of limited size + * @returns {function(string, object)} Returns the Object data after storing it on itself with + * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength) + * deleting the oldest entry + */ +function createCache() { + var keys = []; + + function cache( key, value ) { + + // Use (key + " ") to avoid collision with native prototype properties (see Issue #157) + if ( keys.push( key + " " ) > Expr.cacheLength ) { + + // Only keep the most recent entries + delete cache[ keys.shift() ]; + } + return ( cache[ key + " " ] = value ); + } + return cache; +} + +/** + * Mark a function for special use by Sizzle + * @param {Function} fn The function to mark + */ +function markFunction( fn ) { + fn[ expando ] = true; + return fn; +} + +/** + * Support testing using an element + * @param {Function} fn Passed the created element and returns a boolean result + */ +function assert( fn ) { + var el = document.createElement( "fieldset" ); + + try { + return !!fn( el ); + } catch ( e ) { + return false; + } finally { + + // Remove from its parent by default + if ( el.parentNode ) { + el.parentNode.removeChild( el ); + } + + // release memory in IE + el = null; + } +} + +/** + * Adds the same handler for all of the specified attrs + * @param {String} attrs Pipe-separated list of attributes + * @param {Function} handler The method that will be applied + */ +function addHandle( attrs, handler ) { + var arr = attrs.split( "|" ), + i = arr.length; + + while ( i-- ) { + Expr.attrHandle[ arr[ i ] ] = handler; + } +} + +/** + * Checks document order of two siblings + * @param {Element} a + * @param {Element} b + * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b + */ +function siblingCheck( a, b ) { + var cur = b && a, + diff = cur && a.nodeType === 1 && b.nodeType === 1 && + a.sourceIndex - b.sourceIndex; + + // Use IE sourceIndex if available on both nodes + if ( diff ) { + return diff; + } + + // Check if b follows a + if ( cur ) { + while ( ( cur = cur.nextSibling ) ) { + if ( cur === b ) { + return -1; + } + } + } + + return a ? 1 : -1; +} + +/** + * Returns a function to use in pseudos for input types + * @param {String} type + */ +function createInputPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for buttons + * @param {String} type + */ +function createButtonPseudo( type ) { + return function( elem ) { + var name = elem.nodeName.toLowerCase(); + return ( name === "input" || name === "button" ) && elem.type === type; + }; +} + +/** + * Returns a function to use in pseudos for :enabled/:disabled + * @param {Boolean} disabled true for :disabled; false for :enabled + */ +function createDisabledPseudo( disabled ) { + + // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable + return function( elem ) { + + // Only certain elements can match :enabled or :disabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled + // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled + if ( "form" in elem ) { + + // Check for inherited disabledness on relevant non-disabled elements: + // * listed form-associated elements in a disabled fieldset + // https://html.spec.whatwg.org/multipage/forms.html#category-listed + // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled + // * option elements in a disabled optgroup + // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled + // All such elements have a "form" property. + if ( elem.parentNode && elem.disabled === false ) { + + // Option elements defer to a parent optgroup if present + if ( "label" in elem ) { + if ( "label" in elem.parentNode ) { + return elem.parentNode.disabled === disabled; + } else { + return elem.disabled === disabled; + } + } + + // Support: IE 6 - 11 + // Use the isDisabled shortcut property to check for disabled fieldset ancestors + return elem.isDisabled === disabled || + + // Where there is no isDisabled, check manually + /* jshint -W018 */ + elem.isDisabled !== !disabled && + inDisabledFieldset( elem ) === disabled; + } + + return elem.disabled === disabled; + + // Try to winnow out elements that can't be disabled before trusting the disabled property. + // Some victims get caught in our net (label, legend, menu, track), but it shouldn't + // even exist on them, let alone have a boolean value. + } else if ( "label" in elem ) { + return elem.disabled === disabled; + } + + // Remaining elements are neither :enabled nor :disabled + return false; + }; +} + +/** + * Returns a function to use in pseudos for positionals + * @param {Function} fn + */ +function createPositionalPseudo( fn ) { + return markFunction( function( argument ) { + argument = +argument; + return markFunction( function( seed, matches ) { + var j, + matchIndexes = fn( [], seed.length, argument ), + i = matchIndexes.length; + + // Match elements found at the specified indexes + while ( i-- ) { + if ( seed[ ( j = matchIndexes[ i ] ) ] ) { + seed[ j ] = !( matches[ j ] = seed[ j ] ); + } + } + } ); + } ); +} + +/** + * Checks a node for validity as a Sizzle context + * @param {Element|Object=} context + * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value + */ +function testContext( context ) { + return context && typeof context.getElementsByTagName !== "undefined" && context; +} + +// Expose support vars for convenience +support = Sizzle.support = {}; + +/** + * Detects XML nodes + * @param {Element|Object} elem An element or a document + * @returns {Boolean} True iff elem is a non-HTML XML node + */ +isXML = Sizzle.isXML = function( elem ) { + var namespace = elem && elem.namespaceURI, + docElem = elem && ( elem.ownerDocument || elem ).documentElement; + + // Support: IE <=8 + // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes + // https://bugs.jquery.com/ticket/4833 + return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" ); +}; + +/** + * Sets document-related variables once based on the current document + * @param {Element|Object} [doc] An element or document object to use to set the document + * @returns {Object} Returns the current document + */ +setDocument = Sizzle.setDocument = function( node ) { + var hasCompare, subWindow, + doc = node ? node.ownerDocument || node : preferredDoc; + + // Return early if doc is invalid or already selected + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) { + return document; + } + + // Update global variables + document = doc; + docElem = document.documentElement; + documentIsHTML = !isXML( document ); + + // Support: IE 9 - 11+, Edge 12 - 18+ + // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936) + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( preferredDoc != document && + ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) { + + // Support: IE 11, Edge + if ( subWindow.addEventListener ) { + subWindow.addEventListener( "unload", unloadHandler, false ); + + // Support: IE 9 - 10 only + } else if ( subWindow.attachEvent ) { + subWindow.attachEvent( "onunload", unloadHandler ); + } + } + + // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only, + // Safari 4 - 5 only, Opera <=11.6 - 12.x only + // IE/Edge & older browsers don't support the :scope pseudo-class. + // Support: Safari 6.0 only + // Safari 6.0 supports :scope but it's an alias of :root there. + support.scope = assert( function( el ) { + docElem.appendChild( el ).appendChild( document.createElement( "div" ) ); + return typeof el.querySelectorAll !== "undefined" && + !el.querySelectorAll( ":scope fieldset div" ).length; + } ); + + /* Attributes + ---------------------------------------------------------------------- */ + + // Support: IE<8 + // Verify that getAttribute really returns attributes and not properties + // (excepting IE8 booleans) + support.attributes = assert( function( el ) { + el.className = "i"; + return !el.getAttribute( "className" ); + } ); + + /* getElement(s)By* + ---------------------------------------------------------------------- */ + + // Check if getElementsByTagName("*") returns only elements + support.getElementsByTagName = assert( function( el ) { + el.appendChild( document.createComment( "" ) ); + return !el.getElementsByTagName( "*" ).length; + } ); + + // Support: IE<9 + support.getElementsByClassName = rnative.test( document.getElementsByClassName ); + + // Support: IE<10 + // Check if getElementById returns elements by name + // The broken getElementById methods don't pick up programmatically-set names, + // so use a roundabout getElementsByName test + support.getById = assert( function( el ) { + docElem.appendChild( el ).id = expando; + return !document.getElementsByName || !document.getElementsByName( expando ).length; + } ); + + // ID filter and find + if ( support.getById ) { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + return elem.getAttribute( "id" ) === attrId; + }; + }; + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var elem = context.getElementById( id ); + return elem ? [ elem ] : []; + } + }; + } else { + Expr.filter[ "ID" ] = function( id ) { + var attrId = id.replace( runescape, funescape ); + return function( elem ) { + var node = typeof elem.getAttributeNode !== "undefined" && + elem.getAttributeNode( "id" ); + return node && node.value === attrId; + }; + }; + + // Support: IE 6 - 7 only + // getElementById is not reliable as a find shortcut + Expr.find[ "ID" ] = function( id, context ) { + if ( typeof context.getElementById !== "undefined" && documentIsHTML ) { + var node, i, elems, + elem = context.getElementById( id ); + + if ( elem ) { + + // Verify the id attribute + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + + // Fall back on getElementsByName + elems = context.getElementsByName( id ); + i = 0; + while ( ( elem = elems[ i++ ] ) ) { + node = elem.getAttributeNode( "id" ); + if ( node && node.value === id ) { + return [ elem ]; + } + } + } + + return []; + } + }; + } + + // Tag + Expr.find[ "TAG" ] = support.getElementsByTagName ? + function( tag, context ) { + if ( typeof context.getElementsByTagName !== "undefined" ) { + return context.getElementsByTagName( tag ); + + // DocumentFragment nodes don't have gEBTN + } else if ( support.qsa ) { + return context.querySelectorAll( tag ); + } + } : + + function( tag, context ) { + var elem, + tmp = [], + i = 0, + + // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too + results = context.getElementsByTagName( tag ); + + // Filter out possible comments + if ( tag === "*" ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem.nodeType === 1 ) { + tmp.push( elem ); + } + } + + return tmp; + } + return results; + }; + + // Class + Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) { + if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) { + return context.getElementsByClassName( className ); + } + }; + + /* QSA/matchesSelector + ---------------------------------------------------------------------- */ + + // QSA and matchesSelector support + + // matchesSelector(:active) reports false when true (IE9/Opera 11.5) + rbuggyMatches = []; + + // qSa(:focus) reports false when true (Chrome 21) + // We allow this because of a bug in IE8/9 that throws an error + // whenever `document.activeElement` is accessed on an iframe + // So, we allow :focus to pass through QSA all the time to avoid the IE error + // See https://bugs.jquery.com/ticket/13378 + rbuggyQSA = []; + + if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) { + + // Build QSA regex + // Regex strategy adopted from Diego Perini + assert( function( el ) { + + var input; + + // Select is set to empty string on purpose + // This is to test IE's treatment of not explicitly + // setting a boolean content attribute, + // since its presence should be enough + // https://bugs.jquery.com/ticket/12359 + docElem.appendChild( el ).innerHTML = "" + + ""; + + // Support: IE8, Opera 11-12.16 + // Nothing should be selected when empty strings follow ^= or $= or *= + // The test attribute must be unknown in Opera but "safe" for WinRT + // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section + if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) { + rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" ); + } + + // Support: IE8 + // Boolean attributes and "value" are not treated correctly + if ( !el.querySelectorAll( "[selected]" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" ); + } + + // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+ + if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) { + rbuggyQSA.push( "~=" ); + } + + // Support: IE 11+, Edge 15 - 18+ + // IE 11/Edge don't find elements on a `[name='']` query in some cases. + // Adding a temporary attribute to the document before the selection works + // around the issue. + // Interestingly, IE 10 & older don't seem to have the issue. + input = document.createElement( "input" ); + input.setAttribute( "name", "" ); + el.appendChild( input ); + if ( !el.querySelectorAll( "[name='']" ).length ) { + rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" + + whitespace + "*(?:''|\"\")" ); + } + + // Webkit/Opera - :checked should return selected option elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + // IE8 throws error here and will not see later tests + if ( !el.querySelectorAll( ":checked" ).length ) { + rbuggyQSA.push( ":checked" ); + } + + // Support: Safari 8+, iOS 8+ + // https://bugs.webkit.org/show_bug.cgi?id=136851 + // In-page `selector#id sibling-combinator selector` fails + if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) { + rbuggyQSA.push( ".#.+[+~]" ); + } + + // Support: Firefox <=3.6 - 5 only + // Old Firefox doesn't throw on a badly-escaped identifier. + el.querySelectorAll( "\\\f" ); + rbuggyQSA.push( "[\\r\\n\\f]" ); + } ); + + assert( function( el ) { + el.innerHTML = "" + + ""; + + // Support: Windows 8 Native Apps + // The type and name attributes are restricted during .innerHTML assignment + var input = document.createElement( "input" ); + input.setAttribute( "type", "hidden" ); + el.appendChild( input ).setAttribute( "name", "D" ); + + // Support: IE8 + // Enforce case-sensitivity of name attribute + if ( el.querySelectorAll( "[name=d]" ).length ) { + rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" ); + } + + // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled) + // IE8 throws error here and will not see later tests + if ( el.querySelectorAll( ":enabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: IE9-11+ + // IE's :disabled selector does not pick up the children of disabled fieldsets + docElem.appendChild( el ).disabled = true; + if ( el.querySelectorAll( ":disabled" ).length !== 2 ) { + rbuggyQSA.push( ":enabled", ":disabled" ); + } + + // Support: Opera 10 - 11 only + // Opera 10-11 does not throw on post-comma invalid pseudos + el.querySelectorAll( "*,:x" ); + rbuggyQSA.push( ",.*:" ); + } ); + } + + if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches || + docElem.webkitMatchesSelector || + docElem.mozMatchesSelector || + docElem.oMatchesSelector || + docElem.msMatchesSelector ) ) ) ) { + + assert( function( el ) { + + // Check to see if it's possible to do matchesSelector + // on a disconnected node (IE 9) + support.disconnectedMatch = matches.call( el, "*" ); + + // This should fail with an exception + // Gecko does not error, returns false instead + matches.call( el, "[s!='']:x" ); + rbuggyMatches.push( "!=", pseudos ); + } ); + } + + rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) ); + rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) ); + + /* Contains + ---------------------------------------------------------------------- */ + hasCompare = rnative.test( docElem.compareDocumentPosition ); + + // Element contains another + // Purposefully self-exclusive + // As in, an element does not contain itself + contains = hasCompare || rnative.test( docElem.contains ) ? + function( a, b ) { + var adown = a.nodeType === 9 ? a.documentElement : a, + bup = b && b.parentNode; + return a === bup || !!( bup && bup.nodeType === 1 && ( + adown.contains ? + adown.contains( bup ) : + a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16 + ) ); + } : + function( a, b ) { + if ( b ) { + while ( ( b = b.parentNode ) ) { + if ( b === a ) { + return true; + } + } + } + return false; + }; + + /* Sorting + ---------------------------------------------------------------------- */ + + // Document order sorting + sortOrder = hasCompare ? + function( a, b ) { + + // Flag for duplicate removal + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + // Sort on method existence if only one input has compareDocumentPosition + var compare = !a.compareDocumentPosition - !b.compareDocumentPosition; + if ( compare ) { + return compare; + } + + // Calculate position if both inputs belong to the same document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ? + a.compareDocumentPosition( b ) : + + // Otherwise we know they are disconnected + 1; + + // Disconnected nodes + if ( compare & 1 || + ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) { + + // Choose the first element that is related to our preferred document + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( a == document || a.ownerDocument == preferredDoc && + contains( preferredDoc, a ) ) { + return -1; + } + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( b == document || b.ownerDocument == preferredDoc && + contains( preferredDoc, b ) ) { + return 1; + } + + // Maintain original order + return sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + } + + return compare & 4 ? -1 : 1; + } : + function( a, b ) { + + // Exit early if the nodes are identical + if ( a === b ) { + hasDuplicate = true; + return 0; + } + + var cur, + i = 0, + aup = a.parentNode, + bup = b.parentNode, + ap = [ a ], + bp = [ b ]; + + // Parentless nodes are either documents or disconnected + if ( !aup || !bup ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + return a == document ? -1 : + b == document ? 1 : + /* eslint-enable eqeqeq */ + aup ? -1 : + bup ? 1 : + sortInput ? + ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) : + 0; + + // If the nodes are siblings, we can do a quick check + } else if ( aup === bup ) { + return siblingCheck( a, b ); + } + + // Otherwise we need full lists of their ancestors for comparison + cur = a; + while ( ( cur = cur.parentNode ) ) { + ap.unshift( cur ); + } + cur = b; + while ( ( cur = cur.parentNode ) ) { + bp.unshift( cur ); + } + + // Walk down the tree looking for a discrepancy + while ( ap[ i ] === bp[ i ] ) { + i++; + } + + return i ? + + // Do a sibling check if the nodes have a common ancestor + siblingCheck( ap[ i ], bp[ i ] ) : + + // Otherwise nodes in our document sort first + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + /* eslint-disable eqeqeq */ + ap[ i ] == preferredDoc ? -1 : + bp[ i ] == preferredDoc ? 1 : + /* eslint-enable eqeqeq */ + 0; + }; + + return document; +}; + +Sizzle.matches = function( expr, elements ) { + return Sizzle( expr, null, null, elements ); +}; + +Sizzle.matchesSelector = function( elem, expr ) { + setDocument( elem ); + + if ( support.matchesSelector && documentIsHTML && + !nonnativeSelectorCache[ expr + " " ] && + ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) && + ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) { + + try { + var ret = matches.call( elem, expr ); + + // IE 9's matchesSelector returns false on disconnected nodes + if ( ret || support.disconnectedMatch || + + // As well, disconnected nodes are said to be in a document + // fragment in IE 9 + elem.document && elem.document.nodeType !== 11 ) { + return ret; + } + } catch ( e ) { + nonnativeSelectorCache( expr, true ); + } + } + + return Sizzle( expr, document, null, [ elem ] ).length > 0; +}; + +Sizzle.contains = function( context, elem ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( context.ownerDocument || context ) != document ) { + setDocument( context ); + } + return contains( context, elem ); +}; + +Sizzle.attr = function( elem, name ) { + + // Set document vars if needed + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( ( elem.ownerDocument || elem ) != document ) { + setDocument( elem ); + } + + var fn = Expr.attrHandle[ name.toLowerCase() ], + + // Don't get fooled by Object.prototype properties (jQuery #13807) + val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ? + fn( elem, name, !documentIsHTML ) : + undefined; + + return val !== undefined ? + val : + support.attributes || !documentIsHTML ? + elem.getAttribute( name ) : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; +}; + +Sizzle.escape = function( sel ) { + return ( sel + "" ).replace( rcssescape, fcssescape ); +}; + +Sizzle.error = function( msg ) { + throw new Error( "Syntax error, unrecognized expression: " + msg ); +}; + +/** + * Document sorting and removing duplicates + * @param {ArrayLike} results + */ +Sizzle.uniqueSort = function( results ) { + var elem, + duplicates = [], + j = 0, + i = 0; + + // Unless we *know* we can detect duplicates, assume their presence + hasDuplicate = !support.detectDuplicates; + sortInput = !support.sortStable && results.slice( 0 ); + results.sort( sortOrder ); + + if ( hasDuplicate ) { + while ( ( elem = results[ i++ ] ) ) { + if ( elem === results[ i ] ) { + j = duplicates.push( i ); + } + } + while ( j-- ) { + results.splice( duplicates[ j ], 1 ); + } + } + + // Clear input after sorting to release objects + // See https://github.com/jquery/sizzle/pull/225 + sortInput = null; + + return results; +}; + +/** + * Utility function for retrieving the text value of an array of DOM nodes + * @param {Array|Element} elem + */ +getText = Sizzle.getText = function( elem ) { + var node, + ret = "", + i = 0, + nodeType = elem.nodeType; + + if ( !nodeType ) { + + // If no nodeType, this is expected to be an array + while ( ( node = elem[ i++ ] ) ) { + + // Do not traverse comment nodes + ret += getText( node ); + } + } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) { + + // Use textContent for elements + // innerText usage removed for consistency of new lines (jQuery #11153) + if ( typeof elem.textContent === "string" ) { + return elem.textContent; + } else { + + // Traverse its children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + ret += getText( elem ); + } + } + } else if ( nodeType === 3 || nodeType === 4 ) { + return elem.nodeValue; + } + + // Do not include comment or processing instruction nodes + + return ret; +}; + +Expr = Sizzle.selectors = { + + // Can be adjusted by the user + cacheLength: 50, + + createPseudo: markFunction, + + match: matchExpr, + + attrHandle: {}, + + find: {}, + + relative: { + ">": { dir: "parentNode", first: true }, + " ": { dir: "parentNode" }, + "+": { dir: "previousSibling", first: true }, + "~": { dir: "previousSibling" } + }, + + preFilter: { + "ATTR": function( match ) { + match[ 1 ] = match[ 1 ].replace( runescape, funescape ); + + // Move the given value to match[3] whether quoted or unquoted + match[ 3 ] = ( match[ 3 ] || match[ 4 ] || + match[ 5 ] || "" ).replace( runescape, funescape ); + + if ( match[ 2 ] === "~=" ) { + match[ 3 ] = " " + match[ 3 ] + " "; + } + + return match.slice( 0, 4 ); + }, + + "CHILD": function( match ) { + + /* matches from matchExpr["CHILD"] + 1 type (only|nth|...) + 2 what (child|of-type) + 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...) + 4 xn-component of xn+y argument ([+-]?\d*n|) + 5 sign of xn-component + 6 x of xn-component + 7 sign of y-component + 8 y of y-component + */ + match[ 1 ] = match[ 1 ].toLowerCase(); + + if ( match[ 1 ].slice( 0, 3 ) === "nth" ) { + + // nth-* requires argument + if ( !match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + // numeric x and y parameters for Expr.filter.CHILD + // remember that false/true cast respectively to 0/1 + match[ 4 ] = +( match[ 4 ] ? + match[ 5 ] + ( match[ 6 ] || 1 ) : + 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) ); + match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" ); + + // other types prohibit arguments + } else if ( match[ 3 ] ) { + Sizzle.error( match[ 0 ] ); + } + + return match; + }, + + "PSEUDO": function( match ) { + var excess, + unquoted = !match[ 6 ] && match[ 2 ]; + + if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) { + return null; + } + + // Accept quoted arguments as-is + if ( match[ 3 ] ) { + match[ 2 ] = match[ 4 ] || match[ 5 ] || ""; + + // Strip excess characters from unquoted arguments + } else if ( unquoted && rpseudo.test( unquoted ) && + + // Get excess from tokenize (recursively) + ( excess = tokenize( unquoted, true ) ) && + + // advance to the next closing parenthesis + ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) { + + // excess is a negative index + match[ 0 ] = match[ 0 ].slice( 0, excess ); + match[ 2 ] = unquoted.slice( 0, excess ); + } + + // Return only captures needed by the pseudo filter method (type and argument) + return match.slice( 0, 3 ); + } + }, + + filter: { + + "TAG": function( nodeNameSelector ) { + var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase(); + return nodeNameSelector === "*" ? + function() { + return true; + } : + function( elem ) { + return elem.nodeName && elem.nodeName.toLowerCase() === nodeName; + }; + }, + + "CLASS": function( className ) { + var pattern = classCache[ className + " " ]; + + return pattern || + ( pattern = new RegExp( "(^|" + whitespace + + ")" + className + "(" + whitespace + "|$)" ) ) && classCache( + className, function( elem ) { + return pattern.test( + typeof elem.className === "string" && elem.className || + typeof elem.getAttribute !== "undefined" && + elem.getAttribute( "class" ) || + "" + ); + } ); + }, + + "ATTR": function( name, operator, check ) { + return function( elem ) { + var result = Sizzle.attr( elem, name ); + + if ( result == null ) { + return operator === "!="; + } + if ( !operator ) { + return true; + } + + result += ""; + + /* eslint-disable max-len */ + + return operator === "=" ? result === check : + operator === "!=" ? result !== check : + operator === "^=" ? check && result.indexOf( check ) === 0 : + operator === "*=" ? check && result.indexOf( check ) > -1 : + operator === "$=" ? check && result.slice( -check.length ) === check : + operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 : + operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" : + false; + /* eslint-enable max-len */ + + }; + }, + + "CHILD": function( type, what, _argument, first, last ) { + var simple = type.slice( 0, 3 ) !== "nth", + forward = type.slice( -4 ) !== "last", + ofType = what === "of-type"; + + return first === 1 && last === 0 ? + + // Shortcut for :nth-*(n) + function( elem ) { + return !!elem.parentNode; + } : + + function( elem, _context, xml ) { + var cache, uniqueCache, outerCache, node, nodeIndex, start, + dir = simple !== forward ? "nextSibling" : "previousSibling", + parent = elem.parentNode, + name = ofType && elem.nodeName.toLowerCase(), + useCache = !xml && !ofType, + diff = false; + + if ( parent ) { + + // :(first|last|only)-(child|of-type) + if ( simple ) { + while ( dir ) { + node = elem; + while ( ( node = node[ dir ] ) ) { + if ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) { + + return false; + } + } + + // Reverse direction for :only-* (if we haven't yet done so) + start = dir = type === "only" && !start && "nextSibling"; + } + return true; + } + + start = [ forward ? parent.firstChild : parent.lastChild ]; + + // non-xml :nth-child(...) stores cache data on `parent` + if ( forward && useCache ) { + + // Seek `elem` from a previously-cached index + + // ...in a gzip-friendly way + node = parent; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex && cache[ 2 ]; + node = nodeIndex && parent.childNodes[ nodeIndex ]; + + while ( ( node = ++nodeIndex && node && node[ dir ] || + + // Fallback to seeking `elem` from the start + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + // When found, cache indexes on `parent` and break + if ( node.nodeType === 1 && ++diff && node === elem ) { + uniqueCache[ type ] = [ dirruns, nodeIndex, diff ]; + break; + } + } + + } else { + + // Use previously-cached element index if available + if ( useCache ) { + + // ...in a gzip-friendly way + node = elem; + outerCache = node[ expando ] || ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + cache = uniqueCache[ type ] || []; + nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ]; + diff = nodeIndex; + } + + // xml :nth-child(...) + // or :nth-last-child(...) or :nth(-last)?-of-type(...) + if ( diff === false ) { + + // Use the same loop as above to seek `elem` from the start + while ( ( node = ++nodeIndex && node && node[ dir ] || + ( diff = nodeIndex = 0 ) || start.pop() ) ) { + + if ( ( ofType ? + node.nodeName.toLowerCase() === name : + node.nodeType === 1 ) && + ++diff ) { + + // Cache the index of each encountered element + if ( useCache ) { + outerCache = node[ expando ] || + ( node[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ node.uniqueID ] || + ( outerCache[ node.uniqueID ] = {} ); + + uniqueCache[ type ] = [ dirruns, diff ]; + } + + if ( node === elem ) { + break; + } + } + } + } + } + + // Incorporate the offset, then check against cycle size + diff -= last; + return diff === first || ( diff % first === 0 && diff / first >= 0 ); + } + }; + }, + + "PSEUDO": function( pseudo, argument ) { + + // pseudo-class names are case-insensitive + // http://www.w3.org/TR/selectors/#pseudo-classes + // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters + // Remember that setFilters inherits from pseudos + var args, + fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] || + Sizzle.error( "unsupported pseudo: " + pseudo ); + + // The user may use createPseudo to indicate that + // arguments are needed to create the filter function + // just as Sizzle does + if ( fn[ expando ] ) { + return fn( argument ); + } + + // But maintain support for old signatures + if ( fn.length > 1 ) { + args = [ pseudo, pseudo, "", argument ]; + return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ? + markFunction( function( seed, matches ) { + var idx, + matched = fn( seed, argument ), + i = matched.length; + while ( i-- ) { + idx = indexOf( seed, matched[ i ] ); + seed[ idx ] = !( matches[ idx ] = matched[ i ] ); + } + } ) : + function( elem ) { + return fn( elem, 0, args ); + }; + } + + return fn; + } + }, + + pseudos: { + + // Potentially complex pseudos + "not": markFunction( function( selector ) { + + // Trim the selector passed to compile + // to avoid treating leading and trailing + // spaces as combinators + var input = [], + results = [], + matcher = compile( selector.replace( rtrim, "$1" ) ); + + return matcher[ expando ] ? + markFunction( function( seed, matches, _context, xml ) { + var elem, + unmatched = matcher( seed, null, xml, [] ), + i = seed.length; + + // Match elements unmatched by `matcher` + while ( i-- ) { + if ( ( elem = unmatched[ i ] ) ) { + seed[ i ] = !( matches[ i ] = elem ); + } + } + } ) : + function( elem, _context, xml ) { + input[ 0 ] = elem; + matcher( input, null, xml, results ); + + // Don't keep the element (issue #299) + input[ 0 ] = null; + return !results.pop(); + }; + } ), + + "has": markFunction( function( selector ) { + return function( elem ) { + return Sizzle( selector, elem ).length > 0; + }; + } ), + + "contains": markFunction( function( text ) { + text = text.replace( runescape, funescape ); + return function( elem ) { + return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1; + }; + } ), + + // "Whether an element is represented by a :lang() selector + // is based solely on the element's language value + // being equal to the identifier C, + // or beginning with the identifier C immediately followed by "-". + // The matching of C against the element's language value is performed case-insensitively. + // The identifier C does not have to be a valid language name." + // http://www.w3.org/TR/selectors/#lang-pseudo + "lang": markFunction( function( lang ) { + + // lang value must be a valid identifier + if ( !ridentifier.test( lang || "" ) ) { + Sizzle.error( "unsupported lang: " + lang ); + } + lang = lang.replace( runescape, funescape ).toLowerCase(); + return function( elem ) { + var elemLang; + do { + if ( ( elemLang = documentIsHTML ? + elem.lang : + elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) { + + elemLang = elemLang.toLowerCase(); + return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0; + } + } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 ); + return false; + }; + } ), + + // Miscellaneous + "target": function( elem ) { + var hash = window.location && window.location.hash; + return hash && hash.slice( 1 ) === elem.id; + }, + + "root": function( elem ) { + return elem === docElem; + }, + + "focus": function( elem ) { + return elem === document.activeElement && + ( !document.hasFocus || document.hasFocus() ) && + !!( elem.type || elem.href || ~elem.tabIndex ); + }, + + // Boolean properties + "enabled": createDisabledPseudo( false ), + "disabled": createDisabledPseudo( true ), + + "checked": function( elem ) { + + // In CSS3, :checked should return both checked and selected elements + // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked + var nodeName = elem.nodeName.toLowerCase(); + return ( nodeName === "input" && !!elem.checked ) || + ( nodeName === "option" && !!elem.selected ); + }, + + "selected": function( elem ) { + + // Accessing this property makes selected-by-default + // options in Safari work properly + if ( elem.parentNode ) { + // eslint-disable-next-line no-unused-expressions + elem.parentNode.selectedIndex; + } + + return elem.selected === true; + }, + + // Contents + "empty": function( elem ) { + + // http://www.w3.org/TR/selectors/#empty-pseudo + // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5), + // but not by others (comment: 8; processing instruction: 7; etc.) + // nodeType < 6 works because attributes (2) do not appear as children + for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) { + if ( elem.nodeType < 6 ) { + return false; + } + } + return true; + }, + + "parent": function( elem ) { + return !Expr.pseudos[ "empty" ]( elem ); + }, + + // Element/input types + "header": function( elem ) { + return rheader.test( elem.nodeName ); + }, + + "input": function( elem ) { + return rinputs.test( elem.nodeName ); + }, + + "button": function( elem ) { + var name = elem.nodeName.toLowerCase(); + return name === "input" && elem.type === "button" || name === "button"; + }, + + "text": function( elem ) { + var attr; + return elem.nodeName.toLowerCase() === "input" && + elem.type === "text" && + + // Support: IE<8 + // New HTML5 attribute values (e.g., "search") appear with elem.type === "text" + ( ( attr = elem.getAttribute( "type" ) ) == null || + attr.toLowerCase() === "text" ); + }, + + // Position-in-collection + "first": createPositionalPseudo( function() { + return [ 0 ]; + } ), + + "last": createPositionalPseudo( function( _matchIndexes, length ) { + return [ length - 1 ]; + } ), + + "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) { + return [ argument < 0 ? argument + length : argument ]; + } ), + + "even": createPositionalPseudo( function( matchIndexes, length ) { + var i = 0; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "odd": createPositionalPseudo( function( matchIndexes, length ) { + var i = 1; + for ( ; i < length; i += 2 ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "lt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? + argument + length : + argument > length ? + length : + argument; + for ( ; --i >= 0; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ), + + "gt": createPositionalPseudo( function( matchIndexes, length, argument ) { + var i = argument < 0 ? argument + length : argument; + for ( ; ++i < length; ) { + matchIndexes.push( i ); + } + return matchIndexes; + } ) + } +}; + +Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ]; + +// Add button/input type pseudos +for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) { + Expr.pseudos[ i ] = createInputPseudo( i ); +} +for ( i in { submit: true, reset: true } ) { + Expr.pseudos[ i ] = createButtonPseudo( i ); +} + +// Easy API for creating new setFilters +function setFilters() {} +setFilters.prototype = Expr.filters = Expr.pseudos; +Expr.setFilters = new setFilters(); + +tokenize = Sizzle.tokenize = function( selector, parseOnly ) { + var matched, match, tokens, type, + soFar, groups, preFilters, + cached = tokenCache[ selector + " " ]; + + if ( cached ) { + return parseOnly ? 0 : cached.slice( 0 ); + } + + soFar = selector; + groups = []; + preFilters = Expr.preFilter; + + while ( soFar ) { + + // Comma and first run + if ( !matched || ( match = rcomma.exec( soFar ) ) ) { + if ( match ) { + + // Don't consume trailing commas as valid + soFar = soFar.slice( match[ 0 ].length ) || soFar; + } + groups.push( ( tokens = [] ) ); + } + + matched = false; + + // Combinators + if ( ( match = rcombinators.exec( soFar ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + + // Cast descendant combinators to space + type: match[ 0 ].replace( rtrim, " " ) + } ); + soFar = soFar.slice( matched.length ); + } + + // Filters + for ( type in Expr.filter ) { + if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] || + ( match = preFilters[ type ]( match ) ) ) ) { + matched = match.shift(); + tokens.push( { + value: matched, + type: type, + matches: match + } ); + soFar = soFar.slice( matched.length ); + } + } + + if ( !matched ) { + break; + } + } + + // Return the length of the invalid excess + // if we're just parsing + // Otherwise, throw an error or return tokens + return parseOnly ? + soFar.length : + soFar ? + Sizzle.error( selector ) : + + // Cache the tokens + tokenCache( selector, groups ).slice( 0 ); +}; + +function toSelector( tokens ) { + var i = 0, + len = tokens.length, + selector = ""; + for ( ; i < len; i++ ) { + selector += tokens[ i ].value; + } + return selector; +} + +function addCombinator( matcher, combinator, base ) { + var dir = combinator.dir, + skip = combinator.next, + key = skip || dir, + checkNonElements = base && key === "parentNode", + doneName = done++; + + return combinator.first ? + + // Check against closest ancestor/preceding element + function( elem, context, xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + return matcher( elem, context, xml ); + } + } + return false; + } : + + // Check against all ancestor/preceding elements + function( elem, context, xml ) { + var oldCache, uniqueCache, outerCache, + newCache = [ dirruns, doneName ]; + + // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching + if ( xml ) { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + if ( matcher( elem, context, xml ) ) { + return true; + } + } + } + } else { + while ( ( elem = elem[ dir ] ) ) { + if ( elem.nodeType === 1 || checkNonElements ) { + outerCache = elem[ expando ] || ( elem[ expando ] = {} ); + + // Support: IE <9 only + // Defend against cloned attroperties (jQuery gh-1709) + uniqueCache = outerCache[ elem.uniqueID ] || + ( outerCache[ elem.uniqueID ] = {} ); + + if ( skip && skip === elem.nodeName.toLowerCase() ) { + elem = elem[ dir ] || elem; + } else if ( ( oldCache = uniqueCache[ key ] ) && + oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) { + + // Assign to newCache so results back-propagate to previous elements + return ( newCache[ 2 ] = oldCache[ 2 ] ); + } else { + + // Reuse newcache so results back-propagate to previous elements + uniqueCache[ key ] = newCache; + + // A match means we're done; a fail means we have to keep checking + if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) { + return true; + } + } + } + } + } + return false; + }; +} + +function elementMatcher( matchers ) { + return matchers.length > 1 ? + function( elem, context, xml ) { + var i = matchers.length; + while ( i-- ) { + if ( !matchers[ i ]( elem, context, xml ) ) { + return false; + } + } + return true; + } : + matchers[ 0 ]; +} + +function multipleContexts( selector, contexts, results ) { + var i = 0, + len = contexts.length; + for ( ; i < len; i++ ) { + Sizzle( selector, contexts[ i ], results ); + } + return results; +} + +function condense( unmatched, map, filter, context, xml ) { + var elem, + newUnmatched = [], + i = 0, + len = unmatched.length, + mapped = map != null; + + for ( ; i < len; i++ ) { + if ( ( elem = unmatched[ i ] ) ) { + if ( !filter || filter( elem, context, xml ) ) { + newUnmatched.push( elem ); + if ( mapped ) { + map.push( i ); + } + } + } + } + + return newUnmatched; +} + +function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) { + if ( postFilter && !postFilter[ expando ] ) { + postFilter = setMatcher( postFilter ); + } + if ( postFinder && !postFinder[ expando ] ) { + postFinder = setMatcher( postFinder, postSelector ); + } + return markFunction( function( seed, results, context, xml ) { + var temp, i, elem, + preMap = [], + postMap = [], + preexisting = results.length, + + // Get initial elements from seed or context + elems = seed || multipleContexts( + selector || "*", + context.nodeType ? [ context ] : context, + [] + ), + + // Prefilter to get matcher input, preserving a map for seed-results synchronization + matcherIn = preFilter && ( seed || !selector ) ? + condense( elems, preMap, preFilter, context, xml ) : + elems, + + matcherOut = matcher ? + + // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results, + postFinder || ( seed ? preFilter : preexisting || postFilter ) ? + + // ...intermediate processing is necessary + [] : + + // ...otherwise use results directly + results : + matcherIn; + + // Find primary matches + if ( matcher ) { + matcher( matcherIn, matcherOut, context, xml ); + } + + // Apply postFilter + if ( postFilter ) { + temp = condense( matcherOut, postMap ); + postFilter( temp, [], context, xml ); + + // Un-match failing elements by moving them back to matcherIn + i = temp.length; + while ( i-- ) { + if ( ( elem = temp[ i ] ) ) { + matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem ); + } + } + } + + if ( seed ) { + if ( postFinder || preFilter ) { + if ( postFinder ) { + + // Get the final matcherOut by condensing this intermediate into postFinder contexts + temp = []; + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) ) { + + // Restore matcherIn since elem is not yet a final match + temp.push( ( matcherIn[ i ] = elem ) ); + } + } + postFinder( null, ( matcherOut = [] ), temp, xml ); + } + + // Move matched elements from seed to results to keep them synchronized + i = matcherOut.length; + while ( i-- ) { + if ( ( elem = matcherOut[ i ] ) && + ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) { + + seed[ temp ] = !( results[ temp ] = elem ); + } + } + } + + // Add elements to results, through postFinder if defined + } else { + matcherOut = condense( + matcherOut === results ? + matcherOut.splice( preexisting, matcherOut.length ) : + matcherOut + ); + if ( postFinder ) { + postFinder( null, results, matcherOut, xml ); + } else { + push.apply( results, matcherOut ); + } + } + } ); +} + +function matcherFromTokens( tokens ) { + var checkContext, matcher, j, + len = tokens.length, + leadingRelative = Expr.relative[ tokens[ 0 ].type ], + implicitRelative = leadingRelative || Expr.relative[ " " ], + i = leadingRelative ? 1 : 0, + + // The foundational matcher ensures that elements are reachable from top-level context(s) + matchContext = addCombinator( function( elem ) { + return elem === checkContext; + }, implicitRelative, true ), + matchAnyContext = addCombinator( function( elem ) { + return indexOf( checkContext, elem ) > -1; + }, implicitRelative, true ), + matchers = [ function( elem, context, xml ) { + var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || ( + ( checkContext = context ).nodeType ? + matchContext( elem, context, xml ) : + matchAnyContext( elem, context, xml ) ); + + // Avoid hanging onto element (issue #299) + checkContext = null; + return ret; + } ]; + + for ( ; i < len; i++ ) { + if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) { + matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ]; + } else { + matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches ); + + // Return special upon seeing a positional matcher + if ( matcher[ expando ] ) { + + // Find the next relative operator (if any) for proper handling + j = ++i; + for ( ; j < len; j++ ) { + if ( Expr.relative[ tokens[ j ].type ] ) { + break; + } + } + return setMatcher( + i > 1 && elementMatcher( matchers ), + i > 1 && toSelector( + + // If the preceding token was a descendant combinator, insert an implicit any-element `*` + tokens + .slice( 0, i - 1 ) + .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } ) + ).replace( rtrim, "$1" ), + matcher, + i < j && matcherFromTokens( tokens.slice( i, j ) ), + j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ), + j < len && toSelector( tokens ) + ); + } + matchers.push( matcher ); + } + } + + return elementMatcher( matchers ); +} + +function matcherFromGroupMatchers( elementMatchers, setMatchers ) { + var bySet = setMatchers.length > 0, + byElement = elementMatchers.length > 0, + superMatcher = function( seed, context, xml, results, outermost ) { + var elem, j, matcher, + matchedCount = 0, + i = "0", + unmatched = seed && [], + setMatched = [], + contextBackup = outermostContext, + + // We must always have either seed elements or outermost context + elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ), + + // Use integer dirruns iff this is the outermost matcher + dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ), + len = elems.length; + + if ( outermost ) { + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + outermostContext = context == document || context || outermost; + } + + // Add elements passing elementMatchers directly to results + // Support: IE<9, Safari + // Tolerate NodeList properties (IE: "length"; Safari: ) matching elements by id + for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) { + if ( byElement && elem ) { + j = 0; + + // Support: IE 11+, Edge 17 - 18+ + // IE/Edge sometimes throw a "Permission denied" error when strict-comparing + // two documents; shallow comparisons work. + // eslint-disable-next-line eqeqeq + if ( !context && elem.ownerDocument != document ) { + setDocument( elem ); + xml = !documentIsHTML; + } + while ( ( matcher = elementMatchers[ j++ ] ) ) { + if ( matcher( elem, context || document, xml ) ) { + results.push( elem ); + break; + } + } + if ( outermost ) { + dirruns = dirrunsUnique; + } + } + + // Track unmatched elements for set filters + if ( bySet ) { + + // They will have gone through all possible matchers + if ( ( elem = !matcher && elem ) ) { + matchedCount--; + } + + // Lengthen the array for every element, matched or not + if ( seed ) { + unmatched.push( elem ); + } + } + } + + // `i` is now the count of elements visited above, and adding it to `matchedCount` + // makes the latter nonnegative. + matchedCount += i; + + // Apply set filters to unmatched elements + // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount` + // equals `i`), unless we didn't visit _any_ elements in the above loop because we have + // no element matchers and no seed. + // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that + // case, which will result in a "00" `matchedCount` that differs from `i` but is also + // numerically zero. + if ( bySet && i !== matchedCount ) { + j = 0; + while ( ( matcher = setMatchers[ j++ ] ) ) { + matcher( unmatched, setMatched, context, xml ); + } + + if ( seed ) { + + // Reintegrate element matches to eliminate the need for sorting + if ( matchedCount > 0 ) { + while ( i-- ) { + if ( !( unmatched[ i ] || setMatched[ i ] ) ) { + setMatched[ i ] = pop.call( results ); + } + } + } + + // Discard index placeholder values to get only actual matches + setMatched = condense( setMatched ); + } + + // Add matches to results + push.apply( results, setMatched ); + + // Seedless set matches succeeding multiple successful matchers stipulate sorting + if ( outermost && !seed && setMatched.length > 0 && + ( matchedCount + setMatchers.length ) > 1 ) { + + Sizzle.uniqueSort( results ); + } + } + + // Override manipulation of globals by nested matchers + if ( outermost ) { + dirruns = dirrunsUnique; + outermostContext = contextBackup; + } + + return unmatched; + }; + + return bySet ? + markFunction( superMatcher ) : + superMatcher; +} + +compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) { + var i, + setMatchers = [], + elementMatchers = [], + cached = compilerCache[ selector + " " ]; + + if ( !cached ) { + + // Generate a function of recursive functions that can be used to check each element + if ( !match ) { + match = tokenize( selector ); + } + i = match.length; + while ( i-- ) { + cached = matcherFromTokens( match[ i ] ); + if ( cached[ expando ] ) { + setMatchers.push( cached ); + } else { + elementMatchers.push( cached ); + } + } + + // Cache the compiled function + cached = compilerCache( + selector, + matcherFromGroupMatchers( elementMatchers, setMatchers ) + ); + + // Save selector and tokenization + cached.selector = selector; + } + return cached; +}; + +/** + * A low-level selection function that works with Sizzle's compiled + * selector functions + * @param {String|Function} selector A selector or a pre-compiled + * selector function built with Sizzle.compile + * @param {Element} context + * @param {Array} [results] + * @param {Array} [seed] A set of elements to match against + */ +select = Sizzle.select = function( selector, context, results, seed ) { + var i, tokens, token, type, find, + compiled = typeof selector === "function" && selector, + match = !seed && tokenize( ( selector = compiled.selector || selector ) ); + + results = results || []; + + // Try to minimize operations if there is only one selector in the list and no seed + // (the latter of which guarantees us context) + if ( match.length === 1 ) { + + // Reduce context if the leading compound selector is an ID + tokens = match[ 0 ] = match[ 0 ].slice( 0 ); + if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" && + context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) { + + context = ( Expr.find[ "ID" ]( token.matches[ 0 ] + .replace( runescape, funescape ), context ) || [] )[ 0 ]; + if ( !context ) { + return results; + + // Precompiled matchers will still verify ancestry, so step up a level + } else if ( compiled ) { + context = context.parentNode; + } + + selector = selector.slice( tokens.shift().value.length ); + } + + // Fetch a seed set for right-to-left matching + i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length; + while ( i-- ) { + token = tokens[ i ]; + + // Abort if we hit a combinator + if ( Expr.relative[ ( type = token.type ) ] ) { + break; + } + if ( ( find = Expr.find[ type ] ) ) { + + // Search, expanding context for leading sibling combinators + if ( ( seed = find( + token.matches[ 0 ].replace( runescape, funescape ), + rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) || + context + ) ) ) { + + // If seed is empty or no tokens remain, we can return early + tokens.splice( i, 1 ); + selector = seed.length && toSelector( tokens ); + if ( !selector ) { + push.apply( results, seed ); + return results; + } + + break; + } + } + } + } + + // Compile and execute a filtering function if one is not provided + // Provide `match` to avoid retokenization if we modified the selector above + ( compiled || compile( selector, match ) )( + seed, + context, + !documentIsHTML, + results, + !context || rsibling.test( selector ) && testContext( context.parentNode ) || context + ); + return results; +}; + +// One-time assignments + +// Sort stability +support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando; + +// Support: Chrome 14-35+ +// Always assume duplicates if they aren't passed to the comparison function +support.detectDuplicates = !!hasDuplicate; + +// Initialize against the default document +setDocument(); + +// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27) +// Detached nodes confoundingly follow *each other* +support.sortDetached = assert( function( el ) { + + // Should return 1, but returns 4 (following) + return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1; +} ); + +// Support: IE<8 +// Prevent attribute/property "interpolation" +// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx +if ( !assert( function( el ) { + el.innerHTML = ""; + return el.firstChild.getAttribute( "href" ) === "#"; +} ) ) { + addHandle( "type|href|height|width", function( elem, name, isXML ) { + if ( !isXML ) { + return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 ); + } + } ); +} + +// Support: IE<9 +// Use defaultValue in place of getAttribute("value") +if ( !support.attributes || !assert( function( el ) { + el.innerHTML = ""; + el.firstChild.setAttribute( "value", "" ); + return el.firstChild.getAttribute( "value" ) === ""; +} ) ) { + addHandle( "value", function( elem, _name, isXML ) { + if ( !isXML && elem.nodeName.toLowerCase() === "input" ) { + return elem.defaultValue; + } + } ); +} + +// Support: IE<9 +// Use getAttributeNode to fetch booleans when getAttribute lies +if ( !assert( function( el ) { + return el.getAttribute( "disabled" ) == null; +} ) ) { + addHandle( booleans, function( elem, name, isXML ) { + var val; + if ( !isXML ) { + return elem[ name ] === true ? name.toLowerCase() : + ( val = elem.getAttributeNode( name ) ) && val.specified ? + val.value : + null; + } + } ); +} + +return Sizzle; + +} )( window ); + + + +jQuery.find = Sizzle; +jQuery.expr = Sizzle.selectors; + +// Deprecated +jQuery.expr[ ":" ] = jQuery.expr.pseudos; +jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort; +jQuery.text = Sizzle.getText; +jQuery.isXMLDoc = Sizzle.isXML; +jQuery.contains = Sizzle.contains; +jQuery.escapeSelector = Sizzle.escape; + + + + +var dir = function( elem, dir, until ) { + var matched = [], + truncate = until !== undefined; + + while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) { + if ( elem.nodeType === 1 ) { + if ( truncate && jQuery( elem ).is( until ) ) { + break; + } + matched.push( elem ); + } + } + return matched; +}; + + +var siblings = function( n, elem ) { + var matched = []; + + for ( ; n; n = n.nextSibling ) { + if ( n.nodeType === 1 && n !== elem ) { + matched.push( n ); + } + } + + return matched; +}; + + +var rneedsContext = jQuery.expr.match.needsContext; + + + +function nodeName( elem, name ) { + + return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase(); + +} +var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i ); + + + +// Implement the identical functionality for filter and not +function winnow( elements, qualifier, not ) { + if ( isFunction( qualifier ) ) { + return jQuery.grep( elements, function( elem, i ) { + return !!qualifier.call( elem, i, elem ) !== not; + } ); + } + + // Single element + if ( qualifier.nodeType ) { + return jQuery.grep( elements, function( elem ) { + return ( elem === qualifier ) !== not; + } ); + } + + // Arraylike of elements (jQuery, arguments, Array) + if ( typeof qualifier !== "string" ) { + return jQuery.grep( elements, function( elem ) { + return ( indexOf.call( qualifier, elem ) > -1 ) !== not; + } ); + } + + // Filtered directly for both simple and complex selectors + return jQuery.filter( qualifier, elements, not ); +} + +jQuery.filter = function( expr, elems, not ) { + var elem = elems[ 0 ]; + + if ( not ) { + expr = ":not(" + expr + ")"; + } + + if ( elems.length === 1 && elem.nodeType === 1 ) { + return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : []; + } + + return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) { + return elem.nodeType === 1; + } ) ); +}; + +jQuery.fn.extend( { + find: function( selector ) { + var i, ret, + len = this.length, + self = this; + + if ( typeof selector !== "string" ) { + return this.pushStack( jQuery( selector ).filter( function() { + for ( i = 0; i < len; i++ ) { + if ( jQuery.contains( self[ i ], this ) ) { + return true; + } + } + } ) ); + } + + ret = this.pushStack( [] ); + + for ( i = 0; i < len; i++ ) { + jQuery.find( selector, self[ i ], ret ); + } + + return len > 1 ? jQuery.uniqueSort( ret ) : ret; + }, + filter: function( selector ) { + return this.pushStack( winnow( this, selector || [], false ) ); + }, + not: function( selector ) { + return this.pushStack( winnow( this, selector || [], true ) ); + }, + is: function( selector ) { + return !!winnow( + this, + + // If this is a positional/relative selector, check membership in the returned set + // so $("p:first").is("p:last") won't return true for a doc with two "p". + typeof selector === "string" && rneedsContext.test( selector ) ? + jQuery( selector ) : + selector || [], + false + ).length; + } +} ); + + +// Initialize a jQuery object + + +// A central reference to the root jQuery(document) +var rootjQuery, + + // A simple way to check for HTML strings + // Prioritize #id over to avoid XSS via location.hash (#9521) + // Strict HTML recognition (#11290: must start with <) + // Shortcut simple #id case for speed + rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/, + + init = jQuery.fn.init = function( selector, context, root ) { + var match, elem; + + // HANDLE: $(""), $(null), $(undefined), $(false) + if ( !selector ) { + return this; + } + + // Method init() accepts an alternate rootjQuery + // so migrate can support jQuery.sub (gh-2101) + root = root || rootjQuery; + + // Handle HTML strings + if ( typeof selector === "string" ) { + if ( selector[ 0 ] === "<" && + selector[ selector.length - 1 ] === ">" && + selector.length >= 3 ) { + + // Assume that strings that start and end with <> are HTML and skip the regex check + match = [ null, selector, null ]; + + } else { + match = rquickExpr.exec( selector ); + } + + // Match html or make sure no context is specified for #id + if ( match && ( match[ 1 ] || !context ) ) { + + // HANDLE: $(html) -> $(array) + if ( match[ 1 ] ) { + context = context instanceof jQuery ? context[ 0 ] : context; + + // Option to run scripts is true for back-compat + // Intentionally let the error be thrown if parseHTML is not present + jQuery.merge( this, jQuery.parseHTML( + match[ 1 ], + context && context.nodeType ? context.ownerDocument || context : document, + true + ) ); + + // HANDLE: $(html, props) + if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) { + for ( match in context ) { + + // Properties of context are called as methods if possible + if ( isFunction( this[ match ] ) ) { + this[ match ]( context[ match ] ); + + // ...and otherwise set as attributes + } else { + this.attr( match, context[ match ] ); + } + } + } + + return this; + + // HANDLE: $(#id) + } else { + elem = document.getElementById( match[ 2 ] ); + + if ( elem ) { + + // Inject the element directly into the jQuery object + this[ 0 ] = elem; + this.length = 1; + } + return this; + } + + // HANDLE: $(expr, $(...)) + } else if ( !context || context.jquery ) { + return ( context || root ).find( selector ); + + // HANDLE: $(expr, context) + // (which is just equivalent to: $(context).find(expr) + } else { + return this.constructor( context ).find( selector ); + } + + // HANDLE: $(DOMElement) + } else if ( selector.nodeType ) { + this[ 0 ] = selector; + this.length = 1; + return this; + + // HANDLE: $(function) + // Shortcut for document ready + } else if ( isFunction( selector ) ) { + return root.ready !== undefined ? + root.ready( selector ) : + + // Execute immediately if ready is not present + selector( jQuery ); + } + + return jQuery.makeArray( selector, this ); + }; + +// Give the init function the jQuery prototype for later instantiation +init.prototype = jQuery.fn; + +// Initialize central reference +rootjQuery = jQuery( document ); + + +var rparentsprev = /^(?:parents|prev(?:Until|All))/, + + // Methods guaranteed to produce a unique set when starting from a unique set + guaranteedUnique = { + children: true, + contents: true, + next: true, + prev: true + }; + +jQuery.fn.extend( { + has: function( target ) { + var targets = jQuery( target, this ), + l = targets.length; + + return this.filter( function() { + var i = 0; + for ( ; i < l; i++ ) { + if ( jQuery.contains( this, targets[ i ] ) ) { + return true; + } + } + } ); + }, + + closest: function( selectors, context ) { + var cur, + i = 0, + l = this.length, + matched = [], + targets = typeof selectors !== "string" && jQuery( selectors ); + + // Positional selectors never match, since there's no _selection_ context + if ( !rneedsContext.test( selectors ) ) { + for ( ; i < l; i++ ) { + for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) { + + // Always skip document fragments + if ( cur.nodeType < 11 && ( targets ? + targets.index( cur ) > -1 : + + // Don't pass non-elements to Sizzle + cur.nodeType === 1 && + jQuery.find.matchesSelector( cur, selectors ) ) ) { + + matched.push( cur ); + break; + } + } + } + } + + return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched ); + }, + + // Determine the position of an element within the set + index: function( elem ) { + + // No argument, return index in parent + if ( !elem ) { + return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1; + } + + // Index in selector + if ( typeof elem === "string" ) { + return indexOf.call( jQuery( elem ), this[ 0 ] ); + } + + // Locate the position of the desired element + return indexOf.call( this, + + // If it receives a jQuery object, the first element is used + elem.jquery ? elem[ 0 ] : elem + ); + }, + + add: function( selector, context ) { + return this.pushStack( + jQuery.uniqueSort( + jQuery.merge( this.get(), jQuery( selector, context ) ) + ) + ); + }, + + addBack: function( selector ) { + return this.add( selector == null ? + this.prevObject : this.prevObject.filter( selector ) + ); + } +} ); + +function sibling( cur, dir ) { + while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {} + return cur; +} + +jQuery.each( { + parent: function( elem ) { + var parent = elem.parentNode; + return parent && parent.nodeType !== 11 ? parent : null; + }, + parents: function( elem ) { + return dir( elem, "parentNode" ); + }, + parentsUntil: function( elem, _i, until ) { + return dir( elem, "parentNode", until ); + }, + next: function( elem ) { + return sibling( elem, "nextSibling" ); + }, + prev: function( elem ) { + return sibling( elem, "previousSibling" ); + }, + nextAll: function( elem ) { + return dir( elem, "nextSibling" ); + }, + prevAll: function( elem ) { + return dir( elem, "previousSibling" ); + }, + nextUntil: function( elem, _i, until ) { + return dir( elem, "nextSibling", until ); + }, + prevUntil: function( elem, _i, until ) { + return dir( elem, "previousSibling", until ); + }, + siblings: function( elem ) { + return siblings( ( elem.parentNode || {} ).firstChild, elem ); + }, + children: function( elem ) { + return siblings( elem.firstChild ); + }, + contents: function( elem ) { + if ( elem.contentDocument != null && + + // Support: IE 11+ + // elements with no `data` attribute has an object + // `contentDocument` with a `null` prototype. + getProto( elem.contentDocument ) ) { + + return elem.contentDocument; + } + + // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only + // Treat the template element as a regular one in browsers that + // don't support it. + if ( nodeName( elem, "template" ) ) { + elem = elem.content || elem; + } + + return jQuery.merge( [], elem.childNodes ); + } +}, function( name, fn ) { + jQuery.fn[ name ] = function( until, selector ) { + var matched = jQuery.map( this, fn, until ); + + if ( name.slice( -5 ) !== "Until" ) { + selector = until; + } + + if ( selector && typeof selector === "string" ) { + matched = jQuery.filter( selector, matched ); + } + + if ( this.length > 1 ) { + + // Remove duplicates + if ( !guaranteedUnique[ name ] ) { + jQuery.uniqueSort( matched ); + } + + // Reverse order for parents* and prev-derivatives + if ( rparentsprev.test( name ) ) { + matched.reverse(); + } + } + + return this.pushStack( matched ); + }; +} ); +var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g ); + + + +// Convert String-formatted options into Object-formatted ones +function createOptions( options ) { + var object = {}; + jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) { + object[ flag ] = true; + } ); + return object; +} + +/* + * Create a callback list using the following parameters: + * + * options: an optional list of space-separated options that will change how + * the callback list behaves or a more traditional option object + * + * By default a callback list will act like an event callback list and can be + * "fired" multiple times. + * + * Possible options: + * + * once: will ensure the callback list can only be fired once (like a Deferred) + * + * memory: will keep track of previous values and will call any callback added + * after the list has been fired right away with the latest "memorized" + * values (like a Deferred) + * + * unique: will ensure a callback can only be added once (no duplicate in the list) + * + * stopOnFalse: interrupt callings when a callback returns false + * + */ +jQuery.Callbacks = function( options ) { + + // Convert options from String-formatted to Object-formatted if needed + // (we check in cache first) + options = typeof options === "string" ? + createOptions( options ) : + jQuery.extend( {}, options ); + + var // Flag to know if list is currently firing + firing, + + // Last fire value for non-forgettable lists + memory, + + // Flag to know if list was already fired + fired, + + // Flag to prevent firing + locked, + + // Actual callback list + list = [], + + // Queue of execution data for repeatable lists + queue = [], + + // Index of currently firing callback (modified by add/remove as needed) + firingIndex = -1, + + // Fire callbacks + fire = function() { + + // Enforce single-firing + locked = locked || options.once; + + // Execute callbacks for all pending executions, + // respecting firingIndex overrides and runtime changes + fired = firing = true; + for ( ; queue.length; firingIndex = -1 ) { + memory = queue.shift(); + while ( ++firingIndex < list.length ) { + + // Run callback and check for early termination + if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false && + options.stopOnFalse ) { + + // Jump to end and forget the data so .add doesn't re-fire + firingIndex = list.length; + memory = false; + } + } + } + + // Forget the data if we're done with it + if ( !options.memory ) { + memory = false; + } + + firing = false; + + // Clean up if we're done firing for good + if ( locked ) { + + // Keep an empty list if we have data for future add calls + if ( memory ) { + list = []; + + // Otherwise, this object is spent + } else { + list = ""; + } + } + }, + + // Actual Callbacks object + self = { + + // Add a callback or a collection of callbacks to the list + add: function() { + if ( list ) { + + // If we have memory from a past run, we should fire after adding + if ( memory && !firing ) { + firingIndex = list.length - 1; + queue.push( memory ); + } + + ( function add( args ) { + jQuery.each( args, function( _, arg ) { + if ( isFunction( arg ) ) { + if ( !options.unique || !self.has( arg ) ) { + list.push( arg ); + } + } else if ( arg && arg.length && toType( arg ) !== "string" ) { + + // Inspect recursively + add( arg ); + } + } ); + } )( arguments ); + + if ( memory && !firing ) { + fire(); + } + } + return this; + }, + + // Remove a callback from the list + remove: function() { + jQuery.each( arguments, function( _, arg ) { + var index; + while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) { + list.splice( index, 1 ); + + // Handle firing indexes + if ( index <= firingIndex ) { + firingIndex--; + } + } + } ); + return this; + }, + + // Check if a given callback is in the list. + // If no argument is given, return whether or not list has callbacks attached. + has: function( fn ) { + return fn ? + jQuery.inArray( fn, list ) > -1 : + list.length > 0; + }, + + // Remove all callbacks from the list + empty: function() { + if ( list ) { + list = []; + } + return this; + }, + + // Disable .fire and .add + // Abort any current/pending executions + // Clear all callbacks and values + disable: function() { + locked = queue = []; + list = memory = ""; + return this; + }, + disabled: function() { + return !list; + }, + + // Disable .fire + // Also disable .add unless we have memory (since it would have no effect) + // Abort any pending executions + lock: function() { + locked = queue = []; + if ( !memory && !firing ) { + list = memory = ""; + } + return this; + }, + locked: function() { + return !!locked; + }, + + // Call all callbacks with the given context and arguments + fireWith: function( context, args ) { + if ( !locked ) { + args = args || []; + args = [ context, args.slice ? args.slice() : args ]; + queue.push( args ); + if ( !firing ) { + fire(); + } + } + return this; + }, + + // Call all the callbacks with the given arguments + fire: function() { + self.fireWith( this, arguments ); + return this; + }, + + // To know if the callbacks have already been called at least once + fired: function() { + return !!fired; + } + }; + + return self; +}; + + +function Identity( v ) { + return v; +} +function Thrower( ex ) { + throw ex; +} + +function adoptValue( value, resolve, reject, noValue ) { + var method; + + try { + + // Check for promise aspect first to privilege synchronous behavior + if ( value && isFunction( ( method = value.promise ) ) ) { + method.call( value ).done( resolve ).fail( reject ); + + // Other thenables + } else if ( value && isFunction( ( method = value.then ) ) ) { + method.call( value, resolve, reject ); + + // Other non-thenables + } else { + + // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer: + // * false: [ value ].slice( 0 ) => resolve( value ) + // * true: [ value ].slice( 1 ) => resolve() + resolve.apply( undefined, [ value ].slice( noValue ) ); + } + + // For Promises/A+, convert exceptions into rejections + // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in + // Deferred#then to conditionally suppress rejection. + } catch ( value ) { + + // Support: Android 4.0 only + // Strict mode functions invoked without .call/.apply get global-object context + reject.apply( undefined, [ value ] ); + } +} + +jQuery.extend( { + + Deferred: function( func ) { + var tuples = [ + + // action, add listener, callbacks, + // ... .then handlers, argument index, [final state] + [ "notify", "progress", jQuery.Callbacks( "memory" ), + jQuery.Callbacks( "memory" ), 2 ], + [ "resolve", "done", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 0, "resolved" ], + [ "reject", "fail", jQuery.Callbacks( "once memory" ), + jQuery.Callbacks( "once memory" ), 1, "rejected" ] + ], + state = "pending", + promise = { + state: function() { + return state; + }, + always: function() { + deferred.done( arguments ).fail( arguments ); + return this; + }, + "catch": function( fn ) { + return promise.then( null, fn ); + }, + + // Keep pipe for back-compat + pipe: function( /* fnDone, fnFail, fnProgress */ ) { + var fns = arguments; + + return jQuery.Deferred( function( newDefer ) { + jQuery.each( tuples, function( _i, tuple ) { + + // Map tuples (progress, done, fail) to arguments (done, fail, progress) + var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ]; + + // deferred.progress(function() { bind to newDefer or newDefer.notify }) + // deferred.done(function() { bind to newDefer or newDefer.resolve }) + // deferred.fail(function() { bind to newDefer or newDefer.reject }) + deferred[ tuple[ 1 ] ]( function() { + var returned = fn && fn.apply( this, arguments ); + if ( returned && isFunction( returned.promise ) ) { + returned.promise() + .progress( newDefer.notify ) + .done( newDefer.resolve ) + .fail( newDefer.reject ); + } else { + newDefer[ tuple[ 0 ] + "With" ]( + this, + fn ? [ returned ] : arguments + ); + } + } ); + } ); + fns = null; + } ).promise(); + }, + then: function( onFulfilled, onRejected, onProgress ) { + var maxDepth = 0; + function resolve( depth, deferred, handler, special ) { + return function() { + var that = this, + args = arguments, + mightThrow = function() { + var returned, then; + + // Support: Promises/A+ section 2.3.3.3.3 + // https://promisesaplus.com/#point-59 + // Ignore double-resolution attempts + if ( depth < maxDepth ) { + return; + } + + returned = handler.apply( that, args ); + + // Support: Promises/A+ section 2.3.1 + // https://promisesaplus.com/#point-48 + if ( returned === deferred.promise() ) { + throw new TypeError( "Thenable self-resolution" ); + } + + // Support: Promises/A+ sections 2.3.3.1, 3.5 + // https://promisesaplus.com/#point-54 + // https://promisesaplus.com/#point-75 + // Retrieve `then` only once + then = returned && + + // Support: Promises/A+ section 2.3.4 + // https://promisesaplus.com/#point-64 + // Only check objects and functions for thenability + ( typeof returned === "object" || + typeof returned === "function" ) && + returned.then; + + // Handle a returned thenable + if ( isFunction( then ) ) { + + // Special processors (notify) just wait for resolution + if ( special ) { + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ) + ); + + // Normal processors (resolve) also hook into progress + } else { + + // ...and disregard older resolution values + maxDepth++; + + then.call( + returned, + resolve( maxDepth, deferred, Identity, special ), + resolve( maxDepth, deferred, Thrower, special ), + resolve( maxDepth, deferred, Identity, + deferred.notifyWith ) + ); + } + + // Handle all other returned values + } else { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Identity ) { + that = undefined; + args = [ returned ]; + } + + // Process the value(s) + // Default process is resolve + ( special || deferred.resolveWith )( that, args ); + } + }, + + // Only normal processors (resolve) catch and reject exceptions + process = special ? + mightThrow : + function() { + try { + mightThrow(); + } catch ( e ) { + + if ( jQuery.Deferred.exceptionHook ) { + jQuery.Deferred.exceptionHook( e, + process.stackTrace ); + } + + // Support: Promises/A+ section 2.3.3.3.4.1 + // https://promisesaplus.com/#point-61 + // Ignore post-resolution exceptions + if ( depth + 1 >= maxDepth ) { + + // Only substitute handlers pass on context + // and multiple values (non-spec behavior) + if ( handler !== Thrower ) { + that = undefined; + args = [ e ]; + } + + deferred.rejectWith( that, args ); + } + } + }; + + // Support: Promises/A+ section 2.3.3.3.1 + // https://promisesaplus.com/#point-57 + // Re-resolve promises immediately to dodge false rejection from + // subsequent errors + if ( depth ) { + process(); + } else { + + // Call an optional hook to record the stack, in case of exception + // since it's otherwise lost when execution goes async + if ( jQuery.Deferred.getStackHook ) { + process.stackTrace = jQuery.Deferred.getStackHook(); + } + window.setTimeout( process ); + } + }; + } + + return jQuery.Deferred( function( newDefer ) { + + // progress_handlers.add( ... ) + tuples[ 0 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onProgress ) ? + onProgress : + Identity, + newDefer.notifyWith + ) + ); + + // fulfilled_handlers.add( ... ) + tuples[ 1 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onFulfilled ) ? + onFulfilled : + Identity + ) + ); + + // rejected_handlers.add( ... ) + tuples[ 2 ][ 3 ].add( + resolve( + 0, + newDefer, + isFunction( onRejected ) ? + onRejected : + Thrower + ) + ); + } ).promise(); + }, + + // Get a promise for this deferred + // If obj is provided, the promise aspect is added to the object + promise: function( obj ) { + return obj != null ? jQuery.extend( obj, promise ) : promise; + } + }, + deferred = {}; + + // Add list-specific methods + jQuery.each( tuples, function( i, tuple ) { + var list = tuple[ 2 ], + stateString = tuple[ 5 ]; + + // promise.progress = list.add + // promise.done = list.add + // promise.fail = list.add + promise[ tuple[ 1 ] ] = list.add; + + // Handle state + if ( stateString ) { + list.add( + function() { + + // state = "resolved" (i.e., fulfilled) + // state = "rejected" + state = stateString; + }, + + // rejected_callbacks.disable + // fulfilled_callbacks.disable + tuples[ 3 - i ][ 2 ].disable, + + // rejected_handlers.disable + // fulfilled_handlers.disable + tuples[ 3 - i ][ 3 ].disable, + + // progress_callbacks.lock + tuples[ 0 ][ 2 ].lock, + + // progress_handlers.lock + tuples[ 0 ][ 3 ].lock + ); + } + + // progress_handlers.fire + // fulfilled_handlers.fire + // rejected_handlers.fire + list.add( tuple[ 3 ].fire ); + + // deferred.notify = function() { deferred.notifyWith(...) } + // deferred.resolve = function() { deferred.resolveWith(...) } + // deferred.reject = function() { deferred.rejectWith(...) } + deferred[ tuple[ 0 ] ] = function() { + deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments ); + return this; + }; + + // deferred.notifyWith = list.fireWith + // deferred.resolveWith = list.fireWith + // deferred.rejectWith = list.fireWith + deferred[ tuple[ 0 ] + "With" ] = list.fireWith; + } ); + + // Make the deferred a promise + promise.promise( deferred ); + + // Call given func if any + if ( func ) { + func.call( deferred, deferred ); + } + + // All done! + return deferred; + }, + + // Deferred helper + when: function( singleValue ) { + var + + // count of uncompleted subordinates + remaining = arguments.length, + + // count of unprocessed arguments + i = remaining, + + // subordinate fulfillment data + resolveContexts = Array( i ), + resolveValues = slice.call( arguments ), + + // the primary Deferred + primary = jQuery.Deferred(), + + // subordinate callback factory + updateFunc = function( i ) { + return function( value ) { + resolveContexts[ i ] = this; + resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value; + if ( !( --remaining ) ) { + primary.resolveWith( resolveContexts, resolveValues ); + } + }; + }; + + // Single- and empty arguments are adopted like Promise.resolve + if ( remaining <= 1 ) { + adoptValue( singleValue, primary.done( updateFunc( i ) ).resolve, primary.reject, + !remaining ); + + // Use .then() to unwrap secondary thenables (cf. gh-3000) + if ( primary.state() === "pending" || + isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) { + + return primary.then(); + } + } + + // Multiple arguments are aggregated like Promise.all array elements + while ( i-- ) { + adoptValue( resolveValues[ i ], updateFunc( i ), primary.reject ); + } + + return primary.promise(); + } +} ); + + +// These usually indicate a programmer mistake during development, +// warn about them ASAP rather than swallowing them by default. +var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/; + +jQuery.Deferred.exceptionHook = function( error, stack ) { + + // Support: IE 8 - 9 only + // Console exists when dev tools are open, which can happen at any time + if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) { + window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack ); + } +}; + + + + +jQuery.readyException = function( error ) { + window.setTimeout( function() { + throw error; + } ); +}; + + + + +// The deferred used on DOM ready +var readyList = jQuery.Deferred(); + +jQuery.fn.ready = function( fn ) { + + readyList + .then( fn ) + + // Wrap jQuery.readyException in a function so that the lookup + // happens at the time of error handling instead of callback + // registration. + .catch( function( error ) { + jQuery.readyException( error ); + } ); + + return this; +}; + +jQuery.extend( { + + // Is the DOM ready to be used? Set to true once it occurs. + isReady: false, + + // A counter to track how many items to wait for before + // the ready event fires. See #6781 + readyWait: 1, + + // Handle when the DOM is ready + ready: function( wait ) { + + // Abort if there are pending holds or we're already ready + if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) { + return; + } + + // Remember that the DOM is ready + jQuery.isReady = true; + + // If a normal DOM Ready event fired, decrement, and wait if need be + if ( wait !== true && --jQuery.readyWait > 0 ) { + return; + } + + // If there are functions bound, to execute + readyList.resolveWith( document, [ jQuery ] ); + } +} ); + +jQuery.ready.then = readyList.then; + +// The ready event handler and self cleanup method +function completed() { + document.removeEventListener( "DOMContentLoaded", completed ); + window.removeEventListener( "load", completed ); + jQuery.ready(); +} + +// Catch cases where $(document).ready() is called +// after the browser event has already occurred. +// Support: IE <=9 - 10 only +// Older IE sometimes signals "interactive" too soon +if ( document.readyState === "complete" || + ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) { + + // Handle it asynchronously to allow scripts the opportunity to delay ready + window.setTimeout( jQuery.ready ); + +} else { + + // Use the handy event callback + document.addEventListener( "DOMContentLoaded", completed ); + + // A fallback to window.onload, that will always work + window.addEventListener( "load", completed ); +} + + + + +// Multifunctional method to get and set values of a collection +// The value/s can optionally be executed if it's a function +var access = function( elems, fn, key, value, chainable, emptyGet, raw ) { + var i = 0, + len = elems.length, + bulk = key == null; + + // Sets many values + if ( toType( key ) === "object" ) { + chainable = true; + for ( i in key ) { + access( elems, fn, i, key[ i ], true, emptyGet, raw ); + } + + // Sets one value + } else if ( value !== undefined ) { + chainable = true; + + if ( !isFunction( value ) ) { + raw = true; + } + + if ( bulk ) { + + // Bulk operations run against the entire set + if ( raw ) { + fn.call( elems, value ); + fn = null; + + // ...except when executing function values + } else { + bulk = fn; + fn = function( elem, _key, value ) { + return bulk.call( jQuery( elem ), value ); + }; + } + } + + if ( fn ) { + for ( ; i < len; i++ ) { + fn( + elems[ i ], key, raw ? + value : + value.call( elems[ i ], i, fn( elems[ i ], key ) ) + ); + } + } + } + + if ( chainable ) { + return elems; + } + + // Gets + if ( bulk ) { + return fn.call( elems ); + } + + return len ? fn( elems[ 0 ], key ) : emptyGet; +}; + + +// Matches dashed string for camelizing +var rmsPrefix = /^-ms-/, + rdashAlpha = /-([a-z])/g; + +// Used by camelCase as callback to replace() +function fcamelCase( _all, letter ) { + return letter.toUpperCase(); +} + +// Convert dashed to camelCase; used by the css and data modules +// Support: IE <=9 - 11, Edge 12 - 15 +// Microsoft forgot to hump their vendor prefix (#9572) +function camelCase( string ) { + return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase ); +} +var acceptData = function( owner ) { + + // Accepts only: + // - Node + // - Node.ELEMENT_NODE + // - Node.DOCUMENT_NODE + // - Object + // - Any + return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType ); +}; + + + + +function Data() { + this.expando = jQuery.expando + Data.uid++; +} + +Data.uid = 1; + +Data.prototype = { + + cache: function( owner ) { + + // Check if the owner object already has a cache + var value = owner[ this.expando ]; + + // If not, create one + if ( !value ) { + value = {}; + + // We can accept data for non-element nodes in modern browsers, + // but we should not, see #8335. + // Always return an empty object. + if ( acceptData( owner ) ) { + + // If it is a node unlikely to be stringify-ed or looped over + // use plain assignment + if ( owner.nodeType ) { + owner[ this.expando ] = value; + + // Otherwise secure it in a non-enumerable property + // configurable must be true to allow the property to be + // deleted when data is removed + } else { + Object.defineProperty( owner, this.expando, { + value: value, + configurable: true + } ); + } + } + } + + return value; + }, + set: function( owner, data, value ) { + var prop, + cache = this.cache( owner ); + + // Handle: [ owner, key, value ] args + // Always use camelCase key (gh-2257) + if ( typeof data === "string" ) { + cache[ camelCase( data ) ] = value; + + // Handle: [ owner, { properties } ] args + } else { + + // Copy the properties one-by-one to the cache object + for ( prop in data ) { + cache[ camelCase( prop ) ] = data[ prop ]; + } + } + return cache; + }, + get: function( owner, key ) { + return key === undefined ? + this.cache( owner ) : + + // Always use camelCase key (gh-2257) + owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ]; + }, + access: function( owner, key, value ) { + + // In cases where either: + // + // 1. No key was specified + // 2. A string key was specified, but no value provided + // + // Take the "read" path and allow the get method to determine + // which value to return, respectively either: + // + // 1. The entire cache object + // 2. The data stored at the key + // + if ( key === undefined || + ( ( key && typeof key === "string" ) && value === undefined ) ) { + + return this.get( owner, key ); + } + + // When the key is not a string, or both a key and value + // are specified, set or extend (existing objects) with either: + // + // 1. An object of properties + // 2. A key and value + // + this.set( owner, key, value ); + + // Since the "set" path can have two possible entry points + // return the expected data based on which path was taken[*] + return value !== undefined ? value : key; + }, + remove: function( owner, key ) { + var i, + cache = owner[ this.expando ]; + + if ( cache === undefined ) { + return; + } + + if ( key !== undefined ) { + + // Support array or space separated string of keys + if ( Array.isArray( key ) ) { + + // If key is an array of keys... + // We always set camelCase keys, so remove that. + key = key.map( camelCase ); + } else { + key = camelCase( key ); + + // If a key with the spaces exists, use it. + // Otherwise, create an array by matching non-whitespace + key = key in cache ? + [ key ] : + ( key.match( rnothtmlwhite ) || [] ); + } + + i = key.length; + + while ( i-- ) { + delete cache[ key[ i ] ]; + } + } + + // Remove the expando if there's no more data + if ( key === undefined || jQuery.isEmptyObject( cache ) ) { + + // Support: Chrome <=35 - 45 + // Webkit & Blink performance suffers when deleting properties + // from DOM nodes, so set to undefined instead + // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted) + if ( owner.nodeType ) { + owner[ this.expando ] = undefined; + } else { + delete owner[ this.expando ]; + } + } + }, + hasData: function( owner ) { + var cache = owner[ this.expando ]; + return cache !== undefined && !jQuery.isEmptyObject( cache ); + } +}; +var dataPriv = new Data(); + +var dataUser = new Data(); + + + +// Implementation Summary +// +// 1. Enforce API surface and semantic compatibility with 1.9.x branch +// 2. Improve the module's maintainability by reducing the storage +// paths to a single mechanism. +// 3. Use the same single mechanism to support "private" and "user" data. +// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData) +// 5. Avoid exposing implementation details on user objects (eg. expando properties) +// 6. Provide a clear path for implementation upgrade to WeakMap in 2014 + +var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/, + rmultiDash = /[A-Z]/g; + +function getData( data ) { + if ( data === "true" ) { + return true; + } + + if ( data === "false" ) { + return false; + } + + if ( data === "null" ) { + return null; + } + + // Only convert to a number if it doesn't change the string + if ( data === +data + "" ) { + return +data; + } + + if ( rbrace.test( data ) ) { + return JSON.parse( data ); + } + + return data; +} + +function dataAttr( elem, key, data ) { + var name; + + // If nothing was found internally, try to fetch any + // data from the HTML5 data-* attribute + if ( data === undefined && elem.nodeType === 1 ) { + name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase(); + data = elem.getAttribute( name ); + + if ( typeof data === "string" ) { + try { + data = getData( data ); + } catch ( e ) {} + + // Make sure we set the data so it isn't changed later + dataUser.set( elem, key, data ); + } else { + data = undefined; + } + } + return data; +} + +jQuery.extend( { + hasData: function( elem ) { + return dataUser.hasData( elem ) || dataPriv.hasData( elem ); + }, + + data: function( elem, name, data ) { + return dataUser.access( elem, name, data ); + }, + + removeData: function( elem, name ) { + dataUser.remove( elem, name ); + }, + + // TODO: Now that all calls to _data and _removeData have been replaced + // with direct calls to dataPriv methods, these can be deprecated. + _data: function( elem, name, data ) { + return dataPriv.access( elem, name, data ); + }, + + _removeData: function( elem, name ) { + dataPriv.remove( elem, name ); + } +} ); + +jQuery.fn.extend( { + data: function( key, value ) { + var i, name, data, + elem = this[ 0 ], + attrs = elem && elem.attributes; + + // Gets all values + if ( key === undefined ) { + if ( this.length ) { + data = dataUser.get( elem ); + + if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) { + i = attrs.length; + while ( i-- ) { + + // Support: IE 11 only + // The attrs elements can be null (#14894) + if ( attrs[ i ] ) { + name = attrs[ i ].name; + if ( name.indexOf( "data-" ) === 0 ) { + name = camelCase( name.slice( 5 ) ); + dataAttr( elem, name, data[ name ] ); + } + } + } + dataPriv.set( elem, "hasDataAttrs", true ); + } + } + + return data; + } + + // Sets multiple values + if ( typeof key === "object" ) { + return this.each( function() { + dataUser.set( this, key ); + } ); + } + + return access( this, function( value ) { + var data; + + // The calling jQuery object (element matches) is not empty + // (and therefore has an element appears at this[ 0 ]) and the + // `value` parameter was not undefined. An empty jQuery object + // will result in `undefined` for elem = this[ 0 ] which will + // throw an exception if an attempt to read a data cache is made. + if ( elem && value === undefined ) { + + // Attempt to get data from the cache + // The key will always be camelCased in Data + data = dataUser.get( elem, key ); + if ( data !== undefined ) { + return data; + } + + // Attempt to "discover" the data in + // HTML5 custom data-* attrs + data = dataAttr( elem, key ); + if ( data !== undefined ) { + return data; + } + + // We tried really hard, but the data doesn't exist. + return; + } + + // Set the data... + this.each( function() { + + // We always store the camelCased key + dataUser.set( this, key, value ); + } ); + }, null, value, arguments.length > 1, null, true ); + }, + + removeData: function( key ) { + return this.each( function() { + dataUser.remove( this, key ); + } ); + } +} ); + + +jQuery.extend( { + queue: function( elem, type, data ) { + var queue; + + if ( elem ) { + type = ( type || "fx" ) + "queue"; + queue = dataPriv.get( elem, type ); + + // Speed up dequeue by getting out quickly if this is just a lookup + if ( data ) { + if ( !queue || Array.isArray( data ) ) { + queue = dataPriv.access( elem, type, jQuery.makeArray( data ) ); + } else { + queue.push( data ); + } + } + return queue || []; + } + }, + + dequeue: function( elem, type ) { + type = type || "fx"; + + var queue = jQuery.queue( elem, type ), + startLength = queue.length, + fn = queue.shift(), + hooks = jQuery._queueHooks( elem, type ), + next = function() { + jQuery.dequeue( elem, type ); + }; + + // If the fx queue is dequeued, always remove the progress sentinel + if ( fn === "inprogress" ) { + fn = queue.shift(); + startLength--; + } + + if ( fn ) { + + // Add a progress sentinel to prevent the fx queue from being + // automatically dequeued + if ( type === "fx" ) { + queue.unshift( "inprogress" ); + } + + // Clear up the last queue stop function + delete hooks.stop; + fn.call( elem, next, hooks ); + } + + if ( !startLength && hooks ) { + hooks.empty.fire(); + } + }, + + // Not public - generate a queueHooks object, or return the current one + _queueHooks: function( elem, type ) { + var key = type + "queueHooks"; + return dataPriv.get( elem, key ) || dataPriv.access( elem, key, { + empty: jQuery.Callbacks( "once memory" ).add( function() { + dataPriv.remove( elem, [ type + "queue", key ] ); + } ) + } ); + } +} ); + +jQuery.fn.extend( { + queue: function( type, data ) { + var setter = 2; + + if ( typeof type !== "string" ) { + data = type; + type = "fx"; + setter--; + } + + if ( arguments.length < setter ) { + return jQuery.queue( this[ 0 ], type ); + } + + return data === undefined ? + this : + this.each( function() { + var queue = jQuery.queue( this, type, data ); + + // Ensure a hooks for this queue + jQuery._queueHooks( this, type ); + + if ( type === "fx" && queue[ 0 ] !== "inprogress" ) { + jQuery.dequeue( this, type ); + } + } ); + }, + dequeue: function( type ) { + return this.each( function() { + jQuery.dequeue( this, type ); + } ); + }, + clearQueue: function( type ) { + return this.queue( type || "fx", [] ); + }, + + // Get a promise resolved when queues of a certain type + // are emptied (fx is the type by default) + promise: function( type, obj ) { + var tmp, + count = 1, + defer = jQuery.Deferred(), + elements = this, + i = this.length, + resolve = function() { + if ( !( --count ) ) { + defer.resolveWith( elements, [ elements ] ); + } + }; + + if ( typeof type !== "string" ) { + obj = type; + type = undefined; + } + type = type || "fx"; + + while ( i-- ) { + tmp = dataPriv.get( elements[ i ], type + "queueHooks" ); + if ( tmp && tmp.empty ) { + count++; + tmp.empty.add( resolve ); + } + } + resolve(); + return defer.promise( obj ); + } +} ); +var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source; + +var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" ); + + +var cssExpand = [ "Top", "Right", "Bottom", "Left" ]; + +var documentElement = document.documentElement; + + + + var isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ); + }, + composed = { composed: true }; + + // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only + // Check attachment across shadow DOM boundaries when possible (gh-3504) + // Support: iOS 10.0-10.2 only + // Early iOS 10 versions support `attachShadow` but not `getRootNode`, + // leading to errors. We need to check for `getRootNode`. + if ( documentElement.getRootNode ) { + isAttached = function( elem ) { + return jQuery.contains( elem.ownerDocument, elem ) || + elem.getRootNode( composed ) === elem.ownerDocument; + }; + } +var isHiddenWithinTree = function( elem, el ) { + + // isHiddenWithinTree might be called from jQuery#filter function; + // in that case, element will be second argument + elem = el || elem; + + // Inline style trumps all + return elem.style.display === "none" || + elem.style.display === "" && + + // Otherwise, check computed style + // Support: Firefox <=43 - 45 + // Disconnected elements can have computed display: none, so first confirm that elem is + // in the document. + isAttached( elem ) && + + jQuery.css( elem, "display" ) === "none"; + }; + + + +function adjustCSS( elem, prop, valueParts, tween ) { + var adjusted, scale, + maxIterations = 20, + currentValue = tween ? + function() { + return tween.cur(); + } : + function() { + return jQuery.css( elem, prop, "" ); + }, + initial = currentValue(), + unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ), + + // Starting value computation is required for potential unit mismatches + initialInUnit = elem.nodeType && + ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) && + rcssNum.exec( jQuery.css( elem, prop ) ); + + if ( initialInUnit && initialInUnit[ 3 ] !== unit ) { + + // Support: Firefox <=54 + // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144) + initial = initial / 2; + + // Trust units reported by jQuery.css + unit = unit || initialInUnit[ 3 ]; + + // Iteratively approximate from a nonzero starting point + initialInUnit = +initial || 1; + + while ( maxIterations-- ) { + + // Evaluate and update our best guess (doubling guesses that zero out). + // Finish if the scale equals or crosses 1 (making the old*new product non-positive). + jQuery.style( elem, prop, initialInUnit + unit ); + if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) { + maxIterations = 0; + } + initialInUnit = initialInUnit / scale; + + } + + initialInUnit = initialInUnit * 2; + jQuery.style( elem, prop, initialInUnit + unit ); + + // Make sure we update the tween properties later on + valueParts = valueParts || []; + } + + if ( valueParts ) { + initialInUnit = +initialInUnit || +initial || 0; + + // Apply relative offset (+=/-=) if specified + adjusted = valueParts[ 1 ] ? + initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] : + +valueParts[ 2 ]; + if ( tween ) { + tween.unit = unit; + tween.start = initialInUnit; + tween.end = adjusted; + } + } + return adjusted; +} + + +var defaultDisplayMap = {}; + +function getDefaultDisplay( elem ) { + var temp, + doc = elem.ownerDocument, + nodeName = elem.nodeName, + display = defaultDisplayMap[ nodeName ]; + + if ( display ) { + return display; + } + + temp = doc.body.appendChild( doc.createElement( nodeName ) ); + display = jQuery.css( temp, "display" ); + + temp.parentNode.removeChild( temp ); + + if ( display === "none" ) { + display = "block"; + } + defaultDisplayMap[ nodeName ] = display; + + return display; +} + +function showHide( elements, show ) { + var display, elem, + values = [], + index = 0, + length = elements.length; + + // Determine new display value for elements that need to change + for ( ; index < length; index++ ) { + elem = elements[ index ]; + if ( !elem.style ) { + continue; + } + + display = elem.style.display; + if ( show ) { + + // Since we force visibility upon cascade-hidden elements, an immediate (and slow) + // check is required in this first loop unless we have a nonempty display value (either + // inline or about-to-be-restored) + if ( display === "none" ) { + values[ index ] = dataPriv.get( elem, "display" ) || null; + if ( !values[ index ] ) { + elem.style.display = ""; + } + } + if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) { + values[ index ] = getDefaultDisplay( elem ); + } + } else { + if ( display !== "none" ) { + values[ index ] = "none"; + + // Remember what we're overwriting + dataPriv.set( elem, "display", display ); + } + } + } + + // Set the display of the elements in a second loop to avoid constant reflow + for ( index = 0; index < length; index++ ) { + if ( values[ index ] != null ) { + elements[ index ].style.display = values[ index ]; + } + } + + return elements; +} + +jQuery.fn.extend( { + show: function() { + return showHide( this, true ); + }, + hide: function() { + return showHide( this ); + }, + toggle: function( state ) { + if ( typeof state === "boolean" ) { + return state ? this.show() : this.hide(); + } + + return this.each( function() { + if ( isHiddenWithinTree( this ) ) { + jQuery( this ).show(); + } else { + jQuery( this ).hide(); + } + } ); + } +} ); +var rcheckableType = ( /^(?:checkbox|radio)$/i ); + +var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i ); + +var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i ); + + + +( function() { + var fragment = document.createDocumentFragment(), + div = fragment.appendChild( document.createElement( "div" ) ), + input = document.createElement( "input" ); + + // Support: Android 4.0 - 4.3 only + // Check state lost if the name is set (#11217) + // Support: Windows Web Apps (WWA) + // `name` and `type` must use .setAttribute for WWA (#14901) + input.setAttribute( "type", "radio" ); + input.setAttribute( "checked", "checked" ); + input.setAttribute( "name", "t" ); + + div.appendChild( input ); + + // Support: Android <=4.1 only + // Older WebKit doesn't clone checked state correctly in fragments + support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked; + + // Support: IE <=11 only + // Make sure textarea (and checkbox) defaultValue is properly cloned + div.innerHTML = ""; + support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue; + + // Support: IE <=9 only + // IE <=9 replaces "; + support.option = !!div.lastChild; +} )(); + + +// We have to close these tags to support XHTML (#13200) +var wrapMap = { + + // XHTML parsers do not magically insert elements in the + // same way that tag soup parsers do. So we cannot shorten + // this by omitting or other required elements. + thead: [ 1, "", "
    " ], + col: [ 2, "", "
    " ], + tr: [ 2, "", "
    " ], + td: [ 3, "", "
    " ], + + _default: [ 0, "", "" ] +}; + +wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead; +wrapMap.th = wrapMap.td; + +// Support: IE <=9 only +if ( !support.option ) { + wrapMap.optgroup = wrapMap.option = [ 1, "" ]; +} + + +function getAll( context, tag ) { + + // Support: IE <=9 - 11 only + // Use typeof to avoid zero-argument method invocation on host objects (#15151) + var ret; + + if ( typeof context.getElementsByTagName !== "undefined" ) { + ret = context.getElementsByTagName( tag || "*" ); + + } else if ( typeof context.querySelectorAll !== "undefined" ) { + ret = context.querySelectorAll( tag || "*" ); + + } else { + ret = []; + } + + if ( tag === undefined || tag && nodeName( context, tag ) ) { + return jQuery.merge( [ context ], ret ); + } + + return ret; +} + + +// Mark scripts as having already been evaluated +function setGlobalEval( elems, refElements ) { + var i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + dataPriv.set( + elems[ i ], + "globalEval", + !refElements || dataPriv.get( refElements[ i ], "globalEval" ) + ); + } +} + + +var rhtml = /<|&#?\w+;/; + +function buildFragment( elems, context, scripts, selection, ignored ) { + var elem, tmp, tag, wrap, attached, j, + fragment = context.createDocumentFragment(), + nodes = [], + i = 0, + l = elems.length; + + for ( ; i < l; i++ ) { + elem = elems[ i ]; + + if ( elem || elem === 0 ) { + + // Add nodes directly + if ( toType( elem ) === "object" ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem ); + + // Convert non-html into a text node + } else if ( !rhtml.test( elem ) ) { + nodes.push( context.createTextNode( elem ) ); + + // Convert html into DOM nodes + } else { + tmp = tmp || fragment.appendChild( context.createElement( "div" ) ); + + // Deserialize a standard representation + tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase(); + wrap = wrapMap[ tag ] || wrapMap._default; + tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ]; + + // Descend through wrappers to the right content + j = wrap[ 0 ]; + while ( j-- ) { + tmp = tmp.lastChild; + } + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( nodes, tmp.childNodes ); + + // Remember the top-level container + tmp = fragment.firstChild; + + // Ensure the created nodes are orphaned (#12392) + tmp.textContent = ""; + } + } + } + + // Remove wrapper from fragment + fragment.textContent = ""; + + i = 0; + while ( ( elem = nodes[ i++ ] ) ) { + + // Skip elements already in the context collection (trac-4087) + if ( selection && jQuery.inArray( elem, selection ) > -1 ) { + if ( ignored ) { + ignored.push( elem ); + } + continue; + } + + attached = isAttached( elem ); + + // Append to fragment + tmp = getAll( fragment.appendChild( elem ), "script" ); + + // Preserve script evaluation history + if ( attached ) { + setGlobalEval( tmp ); + } + + // Capture executables + if ( scripts ) { + j = 0; + while ( ( elem = tmp[ j++ ] ) ) { + if ( rscriptType.test( elem.type || "" ) ) { + scripts.push( elem ); + } + } + } + } + + return fragment; +} + + +var rtypenamespace = /^([^.]*)(?:\.(.+)|)/; + +function returnTrue() { + return true; +} + +function returnFalse() { + return false; +} + +// Support: IE <=9 - 11+ +// focus() and blur() are asynchronous, except when they are no-op. +// So expect focus to be synchronous when the element is already active, +// and blur to be synchronous when the element is not already active. +// (focus and blur are always synchronous in other supported browsers, +// this just defines when we can count on it). +function expectSync( elem, type ) { + return ( elem === safeActiveElement() ) === ( type === "focus" ); +} + +// Support: IE <=9 only +// Accessing document.activeElement can throw unexpectedly +// https://bugs.jquery.com/ticket/13393 +function safeActiveElement() { + try { + return document.activeElement; + } catch ( err ) { } +} + +function on( elem, types, selector, data, fn, one ) { + var origFn, type; + + // Types can be a map of types/handlers + if ( typeof types === "object" ) { + + // ( types-Object, selector, data ) + if ( typeof selector !== "string" ) { + + // ( types-Object, data ) + data = data || selector; + selector = undefined; + } + for ( type in types ) { + on( elem, type, selector, data, types[ type ], one ); + } + return elem; + } + + if ( data == null && fn == null ) { + + // ( types, fn ) + fn = selector; + data = selector = undefined; + } else if ( fn == null ) { + if ( typeof selector === "string" ) { + + // ( types, selector, fn ) + fn = data; + data = undefined; + } else { + + // ( types, data, fn ) + fn = data; + data = selector; + selector = undefined; + } + } + if ( fn === false ) { + fn = returnFalse; + } else if ( !fn ) { + return elem; + } + + if ( one === 1 ) { + origFn = fn; + fn = function( event ) { + + // Can use an empty set, since event contains the info + jQuery().off( event ); + return origFn.apply( this, arguments ); + }; + + // Use same guid so caller can remove using origFn + fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ ); + } + return elem.each( function() { + jQuery.event.add( this, types, fn, data, selector ); + } ); +} + +/* + * Helper functions for managing events -- not part of the public interface. + * Props to Dean Edwards' addEvent library for many of the ideas. + */ +jQuery.event = { + + global: {}, + + add: function( elem, types, handler, data, selector ) { + + var handleObjIn, eventHandle, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.get( elem ); + + // Only attach events to objects that accept data + if ( !acceptData( elem ) ) { + return; + } + + // Caller can pass in an object of custom data in lieu of the handler + if ( handler.handler ) { + handleObjIn = handler; + handler = handleObjIn.handler; + selector = handleObjIn.selector; + } + + // Ensure that invalid selectors throw exceptions at attach time + // Evaluate against documentElement in case elem is a non-element node (e.g., document) + if ( selector ) { + jQuery.find.matchesSelector( documentElement, selector ); + } + + // Make sure that the handler has a unique ID, used to find/remove it later + if ( !handler.guid ) { + handler.guid = jQuery.guid++; + } + + // Init the element's event structure and main handler, if this is the first + if ( !( events = elemData.events ) ) { + events = elemData.events = Object.create( null ); + } + if ( !( eventHandle = elemData.handle ) ) { + eventHandle = elemData.handle = function( e ) { + + // Discard the second event of a jQuery.event.trigger() and + // when an event is called after a page has unloaded + return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ? + jQuery.event.dispatch.apply( elem, arguments ) : undefined; + }; + } + + // Handle multiple events separated by a space + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // There *must* be a type, no attaching namespace-only handlers + if ( !type ) { + continue; + } + + // If event changes its type, use the special event handlers for the changed type + special = jQuery.event.special[ type ] || {}; + + // If selector defined, determine special event api type, otherwise given type + type = ( selector ? special.delegateType : special.bindType ) || type; + + // Update special based on newly reset type + special = jQuery.event.special[ type ] || {}; + + // handleObj is passed to all event handlers + handleObj = jQuery.extend( { + type: type, + origType: origType, + data: data, + handler: handler, + guid: handler.guid, + selector: selector, + needsContext: selector && jQuery.expr.match.needsContext.test( selector ), + namespace: namespaces.join( "." ) + }, handleObjIn ); + + // Init the event handler queue if we're the first + if ( !( handlers = events[ type ] ) ) { + handlers = events[ type ] = []; + handlers.delegateCount = 0; + + // Only use addEventListener if the special events handler returns false + if ( !special.setup || + special.setup.call( elem, data, namespaces, eventHandle ) === false ) { + + if ( elem.addEventListener ) { + elem.addEventListener( type, eventHandle ); + } + } + } + + if ( special.add ) { + special.add.call( elem, handleObj ); + + if ( !handleObj.handler.guid ) { + handleObj.handler.guid = handler.guid; + } + } + + // Add to the element's handler list, delegates in front + if ( selector ) { + handlers.splice( handlers.delegateCount++, 0, handleObj ); + } else { + handlers.push( handleObj ); + } + + // Keep track of which events have ever been used, for event optimization + jQuery.event.global[ type ] = true; + } + + }, + + // Detach an event or set of events from an element + remove: function( elem, types, handler, selector, mappedTypes ) { + + var j, origCount, tmp, + events, t, handleObj, + special, handlers, type, namespaces, origType, + elemData = dataPriv.hasData( elem ) && dataPriv.get( elem ); + + if ( !elemData || !( events = elemData.events ) ) { + return; + } + + // Once for each type.namespace in types; type may be omitted + types = ( types || "" ).match( rnothtmlwhite ) || [ "" ]; + t = types.length; + while ( t-- ) { + tmp = rtypenamespace.exec( types[ t ] ) || []; + type = origType = tmp[ 1 ]; + namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort(); + + // Unbind all events (on this namespace, if provided) for the element + if ( !type ) { + for ( type in events ) { + jQuery.event.remove( elem, type + types[ t ], handler, selector, true ); + } + continue; + } + + special = jQuery.event.special[ type ] || {}; + type = ( selector ? special.delegateType : special.bindType ) || type; + handlers = events[ type ] || []; + tmp = tmp[ 2 ] && + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ); + + // Remove matching events + origCount = j = handlers.length; + while ( j-- ) { + handleObj = handlers[ j ]; + + if ( ( mappedTypes || origType === handleObj.origType ) && + ( !handler || handler.guid === handleObj.guid ) && + ( !tmp || tmp.test( handleObj.namespace ) ) && + ( !selector || selector === handleObj.selector || + selector === "**" && handleObj.selector ) ) { + handlers.splice( j, 1 ); + + if ( handleObj.selector ) { + handlers.delegateCount--; + } + if ( special.remove ) { + special.remove.call( elem, handleObj ); + } + } + } + + // Remove generic event handler if we removed something and no more handlers exist + // (avoids potential for endless recursion during removal of special event handlers) + if ( origCount && !handlers.length ) { + if ( !special.teardown || + special.teardown.call( elem, namespaces, elemData.handle ) === false ) { + + jQuery.removeEvent( elem, type, elemData.handle ); + } + + delete events[ type ]; + } + } + + // Remove data and the expando if it's no longer used + if ( jQuery.isEmptyObject( events ) ) { + dataPriv.remove( elem, "handle events" ); + } + }, + + dispatch: function( nativeEvent ) { + + var i, j, ret, matched, handleObj, handlerQueue, + args = new Array( arguments.length ), + + // Make a writable jQuery.Event from the native event object + event = jQuery.event.fix( nativeEvent ), + + handlers = ( + dataPriv.get( this, "events" ) || Object.create( null ) + )[ event.type ] || [], + special = jQuery.event.special[ event.type ] || {}; + + // Use the fix-ed jQuery.Event rather than the (read-only) native event + args[ 0 ] = event; + + for ( i = 1; i < arguments.length; i++ ) { + args[ i ] = arguments[ i ]; + } + + event.delegateTarget = this; + + // Call the preDispatch hook for the mapped type, and let it bail if desired + if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) { + return; + } + + // Determine handlers + handlerQueue = jQuery.event.handlers.call( this, event, handlers ); + + // Run delegates first; they may want to stop propagation beneath us + i = 0; + while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) { + event.currentTarget = matched.elem; + + j = 0; + while ( ( handleObj = matched.handlers[ j++ ] ) && + !event.isImmediatePropagationStopped() ) { + + // If the event is namespaced, then each handler is only invoked if it is + // specially universal or its namespaces are a superset of the event's. + if ( !event.rnamespace || handleObj.namespace === false || + event.rnamespace.test( handleObj.namespace ) ) { + + event.handleObj = handleObj; + event.data = handleObj.data; + + ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle || + handleObj.handler ).apply( matched.elem, args ); + + if ( ret !== undefined ) { + if ( ( event.result = ret ) === false ) { + event.preventDefault(); + event.stopPropagation(); + } + } + } + } + } + + // Call the postDispatch hook for the mapped type + if ( special.postDispatch ) { + special.postDispatch.call( this, event ); + } + + return event.result; + }, + + handlers: function( event, handlers ) { + var i, handleObj, sel, matchedHandlers, matchedSelectors, + handlerQueue = [], + delegateCount = handlers.delegateCount, + cur = event.target; + + // Find delegate handlers + if ( delegateCount && + + // Support: IE <=9 + // Black-hole SVG instance trees (trac-13180) + cur.nodeType && + + // Support: Firefox <=42 + // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861) + // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click + // Support: IE 11 only + // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343) + !( event.type === "click" && event.button >= 1 ) ) { + + for ( ; cur !== this; cur = cur.parentNode || this ) { + + // Don't check non-elements (#13208) + // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764) + if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) { + matchedHandlers = []; + matchedSelectors = {}; + for ( i = 0; i < delegateCount; i++ ) { + handleObj = handlers[ i ]; + + // Don't conflict with Object.prototype properties (#13203) + sel = handleObj.selector + " "; + + if ( matchedSelectors[ sel ] === undefined ) { + matchedSelectors[ sel ] = handleObj.needsContext ? + jQuery( sel, this ).index( cur ) > -1 : + jQuery.find( sel, this, null, [ cur ] ).length; + } + if ( matchedSelectors[ sel ] ) { + matchedHandlers.push( handleObj ); + } + } + if ( matchedHandlers.length ) { + handlerQueue.push( { elem: cur, handlers: matchedHandlers } ); + } + } + } + } + + // Add the remaining (directly-bound) handlers + cur = this; + if ( delegateCount < handlers.length ) { + handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } ); + } + + return handlerQueue; + }, + + addProp: function( name, hook ) { + Object.defineProperty( jQuery.Event.prototype, name, { + enumerable: true, + configurable: true, + + get: isFunction( hook ) ? + function() { + if ( this.originalEvent ) { + return hook( this.originalEvent ); + } + } : + function() { + if ( this.originalEvent ) { + return this.originalEvent[ name ]; + } + }, + + set: function( value ) { + Object.defineProperty( this, name, { + enumerable: true, + configurable: true, + writable: true, + value: value + } ); + } + } ); + }, + + fix: function( originalEvent ) { + return originalEvent[ jQuery.expando ] ? + originalEvent : + new jQuery.Event( originalEvent ); + }, + + special: { + load: { + + // Prevent triggered image.load events from bubbling to window.load + noBubble: true + }, + click: { + + // Utilize native event to ensure correct state for checkable inputs + setup: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Claim the first handler + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + // dataPriv.set( el, "click", ... ) + leverageNative( el, "click", returnTrue ); + } + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function( data ) { + + // For mutual compressibility with _default, replace `this` access with a local var. + // `|| data` is dead code meant only to preserve the variable through minification. + var el = this || data; + + // Force setup before triggering a click + if ( rcheckableType.test( el.type ) && + el.click && nodeName( el, "input" ) ) { + + leverageNative( el, "click" ); + } + + // Return non-false to allow normal event-path propagation + return true; + }, + + // For cross-browser consistency, suppress native .click() on links + // Also prevent it if we're currently inside a leveraged native-event stack + _default: function( event ) { + var target = event.target; + return rcheckableType.test( target.type ) && + target.click && nodeName( target, "input" ) && + dataPriv.get( target, "click" ) || + nodeName( target, "a" ); + } + }, + + beforeunload: { + postDispatch: function( event ) { + + // Support: Firefox 20+ + // Firefox doesn't alert if the returnValue field is not set. + if ( event.result !== undefined && event.originalEvent ) { + event.originalEvent.returnValue = event.result; + } + } + } + } +}; + +// Ensure the presence of an event listener that handles manually-triggered +// synthetic events by interrupting progress until reinvoked in response to +// *native* events that it fires directly, ensuring that state changes have +// already occurred before other listeners are invoked. +function leverageNative( el, type, expectSync ) { + + // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add + if ( !expectSync ) { + if ( dataPriv.get( el, type ) === undefined ) { + jQuery.event.add( el, type, returnTrue ); + } + return; + } + + // Register the controller as a special universal handler for all event namespaces + dataPriv.set( el, type, false ); + jQuery.event.add( el, type, { + namespace: false, + handler: function( event ) { + var notAsync, result, + saved = dataPriv.get( this, type ); + + if ( ( event.isTrigger & 1 ) && this[ type ] ) { + + // Interrupt processing of the outer synthetic .trigger()ed event + // Saved data should be false in such cases, but might be a leftover capture object + // from an async native handler (gh-4350) + if ( !saved.length ) { + + // Store arguments for use when handling the inner native event + // There will always be at least one argument (an event object), so this array + // will not be confused with a leftover capture object. + saved = slice.call( arguments ); + dataPriv.set( this, type, saved ); + + // Trigger the native event and capture its result + // Support: IE <=9 - 11+ + // focus() and blur() are asynchronous + notAsync = expectSync( this, type ); + this[ type ](); + result = dataPriv.get( this, type ); + if ( saved !== result || notAsync ) { + dataPriv.set( this, type, false ); + } else { + result = {}; + } + if ( saved !== result ) { + + // Cancel the outer synthetic event + event.stopImmediatePropagation(); + event.preventDefault(); + + // Support: Chrome 86+ + // In Chrome, if an element having a focusout handler is blurred by + // clicking outside of it, it invokes the handler synchronously. If + // that handler calls `.remove()` on the element, the data is cleared, + // leaving `result` undefined. We need to guard against this. + return result && result.value; + } + + // If this is an inner synthetic event for an event with a bubbling surrogate + // (focus or blur), assume that the surrogate already propagated from triggering the + // native event and prevent that from happening again here. + // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the + // bubbling surrogate propagates *after* the non-bubbling base), but that seems + // less bad than duplication. + } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) { + event.stopPropagation(); + } + + // If this is a native event triggered above, everything is now in order + // Fire an inner synthetic event with the original arguments + } else if ( saved.length ) { + + // ...and capture the result + dataPriv.set( this, type, { + value: jQuery.event.trigger( + + // Support: IE <=9 - 11+ + // Extend with the prototype to reset the above stopImmediatePropagation() + jQuery.extend( saved[ 0 ], jQuery.Event.prototype ), + saved.slice( 1 ), + this + ) + } ); + + // Abort handling of the native event + event.stopImmediatePropagation(); + } + } + } ); +} + +jQuery.removeEvent = function( elem, type, handle ) { + + // This "if" is needed for plain objects + if ( elem.removeEventListener ) { + elem.removeEventListener( type, handle ); + } +}; + +jQuery.Event = function( src, props ) { + + // Allow instantiation without the 'new' keyword + if ( !( this instanceof jQuery.Event ) ) { + return new jQuery.Event( src, props ); + } + + // Event object + if ( src && src.type ) { + this.originalEvent = src; + this.type = src.type; + + // Events bubbling up the document may have been marked as prevented + // by a handler lower down the tree; reflect the correct value. + this.isDefaultPrevented = src.defaultPrevented || + src.defaultPrevented === undefined && + + // Support: Android <=2.3 only + src.returnValue === false ? + returnTrue : + returnFalse; + + // Create target properties + // Support: Safari <=6 - 7 only + // Target should not be a text node (#504, #13143) + this.target = ( src.target && src.target.nodeType === 3 ) ? + src.target.parentNode : + src.target; + + this.currentTarget = src.currentTarget; + this.relatedTarget = src.relatedTarget; + + // Event type + } else { + this.type = src; + } + + // Put explicitly provided properties onto the event object + if ( props ) { + jQuery.extend( this, props ); + } + + // Create a timestamp if incoming event doesn't have one + this.timeStamp = src && src.timeStamp || Date.now(); + + // Mark it as fixed + this[ jQuery.expando ] = true; +}; + +// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding +// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html +jQuery.Event.prototype = { + constructor: jQuery.Event, + isDefaultPrevented: returnFalse, + isPropagationStopped: returnFalse, + isImmediatePropagationStopped: returnFalse, + isSimulated: false, + + preventDefault: function() { + var e = this.originalEvent; + + this.isDefaultPrevented = returnTrue; + + if ( e && !this.isSimulated ) { + e.preventDefault(); + } + }, + stopPropagation: function() { + var e = this.originalEvent; + + this.isPropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopPropagation(); + } + }, + stopImmediatePropagation: function() { + var e = this.originalEvent; + + this.isImmediatePropagationStopped = returnTrue; + + if ( e && !this.isSimulated ) { + e.stopImmediatePropagation(); + } + + this.stopPropagation(); + } +}; + +// Includes all common event props including KeyEvent and MouseEvent specific props +jQuery.each( { + altKey: true, + bubbles: true, + cancelable: true, + changedTouches: true, + ctrlKey: true, + detail: true, + eventPhase: true, + metaKey: true, + pageX: true, + pageY: true, + shiftKey: true, + view: true, + "char": true, + code: true, + charCode: true, + key: true, + keyCode: true, + button: true, + buttons: true, + clientX: true, + clientY: true, + offsetX: true, + offsetY: true, + pointerId: true, + pointerType: true, + screenX: true, + screenY: true, + targetTouches: true, + toElement: true, + touches: true, + which: true +}, jQuery.event.addProp ); + +jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) { + jQuery.event.special[ type ] = { + + // Utilize native event if possible so blur/focus sequence is correct + setup: function() { + + // Claim the first handler + // dataPriv.set( this, "focus", ... ) + // dataPriv.set( this, "blur", ... ) + leverageNative( this, type, expectSync ); + + // Return false to allow normal processing in the caller + return false; + }, + trigger: function() { + + // Force setup before trigger + leverageNative( this, type ); + + // Return non-false to allow normal event-path propagation + return true; + }, + + // Suppress native focus or blur as it's already being fired + // in leverageNative. + _default: function() { + return true; + }, + + delegateType: delegateType + }; +} ); + +// Create mouseenter/leave events using mouseover/out and event-time checks +// so that event delegation works in jQuery. +// Do the same for pointerenter/pointerleave and pointerover/pointerout +// +// Support: Safari 7 only +// Safari sends mouseenter too often; see: +// https://bugs.chromium.org/p/chromium/issues/detail?id=470258 +// for the description of the bug (it existed in older Chrome versions as well). +jQuery.each( { + mouseenter: "mouseover", + mouseleave: "mouseout", + pointerenter: "pointerover", + pointerleave: "pointerout" +}, function( orig, fix ) { + jQuery.event.special[ orig ] = { + delegateType: fix, + bindType: fix, + + handle: function( event ) { + var ret, + target = this, + related = event.relatedTarget, + handleObj = event.handleObj; + + // For mouseenter/leave call the handler if related is outside the target. + // NB: No relatedTarget if the mouse left/entered the browser window + if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) { + event.type = handleObj.origType; + ret = handleObj.handler.apply( this, arguments ); + event.type = fix; + } + return ret; + } + }; +} ); + +jQuery.fn.extend( { + + on: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn ); + }, + one: function( types, selector, data, fn ) { + return on( this, types, selector, data, fn, 1 ); + }, + off: function( types, selector, fn ) { + var handleObj, type; + if ( types && types.preventDefault && types.handleObj ) { + + // ( event ) dispatched jQuery.Event + handleObj = types.handleObj; + jQuery( types.delegateTarget ).off( + handleObj.namespace ? + handleObj.origType + "." + handleObj.namespace : + handleObj.origType, + handleObj.selector, + handleObj.handler + ); + return this; + } + if ( typeof types === "object" ) { + + // ( types-object [, selector] ) + for ( type in types ) { + this.off( type, selector, types[ type ] ); + } + return this; + } + if ( selector === false || typeof selector === "function" ) { + + // ( types [, fn] ) + fn = selector; + selector = undefined; + } + if ( fn === false ) { + fn = returnFalse; + } + return this.each( function() { + jQuery.event.remove( this, types, fn, selector ); + } ); + } +} ); + + +var + + // Support: IE <=10 - 11, Edge 12 - 13 only + // In IE/Edge using regex groups here causes severe slowdowns. + // See https://connect.microsoft.com/IE/feedback/details/1736512/ + rnoInnerhtml = /\s*$/g; + +// Prefer a tbody over its parent table for containing new rows +function manipulationTarget( elem, content ) { + if ( nodeName( elem, "table" ) && + nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) { + + return jQuery( elem ).children( "tbody" )[ 0 ] || elem; + } + + return elem; +} + +// Replace/restore the type attribute of script elements for safe DOM manipulation +function disableScript( elem ) { + elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type; + return elem; +} +function restoreScript( elem ) { + if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) { + elem.type = elem.type.slice( 5 ); + } else { + elem.removeAttribute( "type" ); + } + + return elem; +} + +function cloneCopyEvent( src, dest ) { + var i, l, type, pdataOld, udataOld, udataCur, events; + + if ( dest.nodeType !== 1 ) { + return; + } + + // 1. Copy private data: events, handlers, etc. + if ( dataPriv.hasData( src ) ) { + pdataOld = dataPriv.get( src ); + events = pdataOld.events; + + if ( events ) { + dataPriv.remove( dest, "handle events" ); + + for ( type in events ) { + for ( i = 0, l = events[ type ].length; i < l; i++ ) { + jQuery.event.add( dest, type, events[ type ][ i ] ); + } + } + } + } + + // 2. Copy user data + if ( dataUser.hasData( src ) ) { + udataOld = dataUser.access( src ); + udataCur = jQuery.extend( {}, udataOld ); + + dataUser.set( dest, udataCur ); + } +} + +// Fix IE bugs, see support tests +function fixInput( src, dest ) { + var nodeName = dest.nodeName.toLowerCase(); + + // Fails to persist the checked state of a cloned checkbox or radio button. + if ( nodeName === "input" && rcheckableType.test( src.type ) ) { + dest.checked = src.checked; + + // Fails to return the selected option to the default selected state when cloning options + } else if ( nodeName === "input" || nodeName === "textarea" ) { + dest.defaultValue = src.defaultValue; + } +} + +function domManip( collection, args, callback, ignored ) { + + // Flatten any nested arrays + args = flat( args ); + + var fragment, first, scripts, hasScripts, node, doc, + i = 0, + l = collection.length, + iNoClone = l - 1, + value = args[ 0 ], + valueIsFunction = isFunction( value ); + + // We can't cloneNode fragments that contain checked, in WebKit + if ( valueIsFunction || + ( l > 1 && typeof value === "string" && + !support.checkClone && rchecked.test( value ) ) ) { + return collection.each( function( index ) { + var self = collection.eq( index ); + if ( valueIsFunction ) { + args[ 0 ] = value.call( this, index, self.html() ); + } + domManip( self, args, callback, ignored ); + } ); + } + + if ( l ) { + fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored ); + first = fragment.firstChild; + + if ( fragment.childNodes.length === 1 ) { + fragment = first; + } + + // Require either new content or an interest in ignored elements to invoke the callback + if ( first || ignored ) { + scripts = jQuery.map( getAll( fragment, "script" ), disableScript ); + hasScripts = scripts.length; + + // Use the original fragment for the last item + // instead of the first because it can end up + // being emptied incorrectly in certain situations (#8070). + for ( ; i < l; i++ ) { + node = fragment; + + if ( i !== iNoClone ) { + node = jQuery.clone( node, true, true ); + + // Keep references to cloned scripts for later restoration + if ( hasScripts ) { + + // Support: Android <=4.0 only, PhantomJS 1 only + // push.apply(_, arraylike) throws on ancient WebKit + jQuery.merge( scripts, getAll( node, "script" ) ); + } + } + + callback.call( collection[ i ], node, i ); + } + + if ( hasScripts ) { + doc = scripts[ scripts.length - 1 ].ownerDocument; + + // Reenable scripts + jQuery.map( scripts, restoreScript ); + + // Evaluate executable scripts on first document insertion + for ( i = 0; i < hasScripts; i++ ) { + node = scripts[ i ]; + if ( rscriptType.test( node.type || "" ) && + !dataPriv.access( node, "globalEval" ) && + jQuery.contains( doc, node ) ) { + + if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) { + + // Optional AJAX dependency, but won't run scripts if not present + if ( jQuery._evalUrl && !node.noModule ) { + jQuery._evalUrl( node.src, { + nonce: node.nonce || node.getAttribute( "nonce" ) + }, doc ); + } + } else { + DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc ); + } + } + } + } + } + } + + return collection; +} + +function remove( elem, selector, keepData ) { + var node, + nodes = selector ? jQuery.filter( selector, elem ) : elem, + i = 0; + + for ( ; ( node = nodes[ i ] ) != null; i++ ) { + if ( !keepData && node.nodeType === 1 ) { + jQuery.cleanData( getAll( node ) ); + } + + if ( node.parentNode ) { + if ( keepData && isAttached( node ) ) { + setGlobalEval( getAll( node, "script" ) ); + } + node.parentNode.removeChild( node ); + } + } + + return elem; +} + +jQuery.extend( { + htmlPrefilter: function( html ) { + return html; + }, + + clone: function( elem, dataAndEvents, deepDataAndEvents ) { + var i, l, srcElements, destElements, + clone = elem.cloneNode( true ), + inPage = isAttached( elem ); + + // Fix IE cloning issues + if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) && + !jQuery.isXMLDoc( elem ) ) { + + // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2 + destElements = getAll( clone ); + srcElements = getAll( elem ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + fixInput( srcElements[ i ], destElements[ i ] ); + } + } + + // Copy the events from the original to the clone + if ( dataAndEvents ) { + if ( deepDataAndEvents ) { + srcElements = srcElements || getAll( elem ); + destElements = destElements || getAll( clone ); + + for ( i = 0, l = srcElements.length; i < l; i++ ) { + cloneCopyEvent( srcElements[ i ], destElements[ i ] ); + } + } else { + cloneCopyEvent( elem, clone ); + } + } + + // Preserve script evaluation history + destElements = getAll( clone, "script" ); + if ( destElements.length > 0 ) { + setGlobalEval( destElements, !inPage && getAll( elem, "script" ) ); + } + + // Return the cloned set + return clone; + }, + + cleanData: function( elems ) { + var data, elem, type, + special = jQuery.event.special, + i = 0; + + for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) { + if ( acceptData( elem ) ) { + if ( ( data = elem[ dataPriv.expando ] ) ) { + if ( data.events ) { + for ( type in data.events ) { + if ( special[ type ] ) { + jQuery.event.remove( elem, type ); + + // This is a shortcut to avoid jQuery.event.remove's overhead + } else { + jQuery.removeEvent( elem, type, data.handle ); + } + } + } + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataPriv.expando ] = undefined; + } + if ( elem[ dataUser.expando ] ) { + + // Support: Chrome <=35 - 45+ + // Assign undefined instead of using delete, see Data#remove + elem[ dataUser.expando ] = undefined; + } + } + } + } +} ); + +jQuery.fn.extend( { + detach: function( selector ) { + return remove( this, selector, true ); + }, + + remove: function( selector ) { + return remove( this, selector ); + }, + + text: function( value ) { + return access( this, function( value ) { + return value === undefined ? + jQuery.text( this ) : + this.empty().each( function() { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + this.textContent = value; + } + } ); + }, null, value, arguments.length ); + }, + + append: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.appendChild( elem ); + } + } ); + }, + + prepend: function() { + return domManip( this, arguments, function( elem ) { + if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) { + var target = manipulationTarget( this, elem ); + target.insertBefore( elem, target.firstChild ); + } + } ); + }, + + before: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this ); + } + } ); + }, + + after: function() { + return domManip( this, arguments, function( elem ) { + if ( this.parentNode ) { + this.parentNode.insertBefore( elem, this.nextSibling ); + } + } ); + }, + + empty: function() { + var elem, + i = 0; + + for ( ; ( elem = this[ i ] ) != null; i++ ) { + if ( elem.nodeType === 1 ) { + + // Prevent memory leaks + jQuery.cleanData( getAll( elem, false ) ); + + // Remove any remaining nodes + elem.textContent = ""; + } + } + + return this; + }, + + clone: function( dataAndEvents, deepDataAndEvents ) { + dataAndEvents = dataAndEvents == null ? false : dataAndEvents; + deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents; + + return this.map( function() { + return jQuery.clone( this, dataAndEvents, deepDataAndEvents ); + } ); + }, + + html: function( value ) { + return access( this, function( value ) { + var elem = this[ 0 ] || {}, + i = 0, + l = this.length; + + if ( value === undefined && elem.nodeType === 1 ) { + return elem.innerHTML; + } + + // See if we can take a shortcut and just use innerHTML + if ( typeof value === "string" && !rnoInnerhtml.test( value ) && + !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) { + + value = jQuery.htmlPrefilter( value ); + + try { + for ( ; i < l; i++ ) { + elem = this[ i ] || {}; + + // Remove element nodes and prevent memory leaks + if ( elem.nodeType === 1 ) { + jQuery.cleanData( getAll( elem, false ) ); + elem.innerHTML = value; + } + } + + elem = 0; + + // If using innerHTML throws an exception, use the fallback method + } catch ( e ) {} + } + + if ( elem ) { + this.empty().append( value ); + } + }, null, value, arguments.length ); + }, + + replaceWith: function() { + var ignored = []; + + // Make the changes, replacing each non-ignored context element with the new content + return domManip( this, arguments, function( elem ) { + var parent = this.parentNode; + + if ( jQuery.inArray( this, ignored ) < 0 ) { + jQuery.cleanData( getAll( this ) ); + if ( parent ) { + parent.replaceChild( elem, this ); + } + } + + // Force callback invocation + }, ignored ); + } +} ); + +jQuery.each( { + appendTo: "append", + prependTo: "prepend", + insertBefore: "before", + insertAfter: "after", + replaceAll: "replaceWith" +}, function( name, original ) { + jQuery.fn[ name ] = function( selector ) { + var elems, + ret = [], + insert = jQuery( selector ), + last = insert.length - 1, + i = 0; + + for ( ; i <= last; i++ ) { + elems = i === last ? this : this.clone( true ); + jQuery( insert[ i ] )[ original ]( elems ); + + // Support: Android <=4.0 only, PhantomJS 1 only + // .get() because push.apply(_, arraylike) throws on ancient WebKit + push.apply( ret, elems.get() ); + } + + return this.pushStack( ret ); + }; +} ); +var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" ); + +var getStyles = function( elem ) { + + // Support: IE <=11 only, Firefox <=30 (#15098, #14150) + // IE throws on elements created in popups + // FF meanwhile throws on frame elements through "defaultView.getComputedStyle" + var view = elem.ownerDocument.defaultView; + + if ( !view || !view.opener ) { + view = window; + } + + return view.getComputedStyle( elem ); + }; + +var swap = function( elem, options, callback ) { + var ret, name, + old = {}; + + // Remember the old values, and insert the new ones + for ( name in options ) { + old[ name ] = elem.style[ name ]; + elem.style[ name ] = options[ name ]; + } + + ret = callback.call( elem ); + + // Revert the old values + for ( name in options ) { + elem.style[ name ] = old[ name ]; + } + + return ret; +}; + + +var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" ); + + + +( function() { + + // Executing both pixelPosition & boxSizingReliable tests require only one layout + // so they're executed at the same time to save the second computation. + function computeStyleTests() { + + // This is a singleton, we need to execute it only once + if ( !div ) { + return; + } + + container.style.cssText = "position:absolute;left:-11111px;width:60px;" + + "margin-top:1px;padding:0;border:0"; + div.style.cssText = + "position:relative;display:block;box-sizing:border-box;overflow:scroll;" + + "margin:auto;border:1px;padding:1px;" + + "width:60%;top:1%"; + documentElement.appendChild( container ).appendChild( div ); + + var divStyle = window.getComputedStyle( div ); + pixelPositionVal = divStyle.top !== "1%"; + + // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44 + reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12; + + // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3 + // Some styles come back with percentage values, even though they shouldn't + div.style.right = "60%"; + pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36; + + // Support: IE 9 - 11 only + // Detect misreporting of content dimensions for box-sizing:border-box elements + boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36; + + // Support: IE 9 only + // Detect overflow:scroll screwiness (gh-3699) + // Support: Chrome <=64 + // Don't get tricked when zoom affects offsetWidth (gh-4029) + div.style.position = "absolute"; + scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12; + + documentElement.removeChild( container ); + + // Nullify the div so it wouldn't be stored in the memory and + // it will also be a sign that checks already performed + div = null; + } + + function roundPixelMeasures( measure ) { + return Math.round( parseFloat( measure ) ); + } + + var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal, + reliableTrDimensionsVal, reliableMarginLeftVal, + container = document.createElement( "div" ), + div = document.createElement( "div" ); + + // Finish early in limited (non-browser) environments + if ( !div.style ) { + return; + } + + // Support: IE <=9 - 11 only + // Style of cloned element affects source element cloned (#8908) + div.style.backgroundClip = "content-box"; + div.cloneNode( true ).style.backgroundClip = ""; + support.clearCloneStyle = div.style.backgroundClip === "content-box"; + + jQuery.extend( support, { + boxSizingReliable: function() { + computeStyleTests(); + return boxSizingReliableVal; + }, + pixelBoxStyles: function() { + computeStyleTests(); + return pixelBoxStylesVal; + }, + pixelPosition: function() { + computeStyleTests(); + return pixelPositionVal; + }, + reliableMarginLeft: function() { + computeStyleTests(); + return reliableMarginLeftVal; + }, + scrollboxSize: function() { + computeStyleTests(); + return scrollboxSizeVal; + }, + + // Support: IE 9 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Behavior in IE 9 is more subtle than in newer versions & it passes + // some versions of this test; make sure not to make it pass there! + // + // Support: Firefox 70+ + // Only Firefox includes border widths + // in computed dimensions. (gh-4529) + reliableTrDimensions: function() { + var table, tr, trChild, trStyle; + if ( reliableTrDimensionsVal == null ) { + table = document.createElement( "table" ); + tr = document.createElement( "tr" ); + trChild = document.createElement( "div" ); + + table.style.cssText = "position:absolute;left:-11111px;border-collapse:separate"; + tr.style.cssText = "border:1px solid"; + + // Support: Chrome 86+ + // Height set through cssText does not get applied. + // Computed height then comes back as 0. + tr.style.height = "1px"; + trChild.style.height = "9px"; + + // Support: Android 8 Chrome 86+ + // In our bodyBackground.html iframe, + // display for all div elements is set to "inline", + // which causes a problem only in Android 8 Chrome 86. + // Ensuring the div is display: block + // gets around this issue. + trChild.style.display = "block"; + + documentElement + .appendChild( table ) + .appendChild( tr ) + .appendChild( trChild ); + + trStyle = window.getComputedStyle( tr ); + reliableTrDimensionsVal = ( parseInt( trStyle.height, 10 ) + + parseInt( trStyle.borderTopWidth, 10 ) + + parseInt( trStyle.borderBottomWidth, 10 ) ) === tr.offsetHeight; + + documentElement.removeChild( table ); + } + return reliableTrDimensionsVal; + } + } ); +} )(); + + +function curCSS( elem, name, computed ) { + var width, minWidth, maxWidth, ret, + + // Support: Firefox 51+ + // Retrieving style before computed somehow + // fixes an issue with getting wrong values + // on detached elements + style = elem.style; + + computed = computed || getStyles( elem ); + + // getPropertyValue is needed for: + // .css('filter') (IE 9 only, #12537) + // .css('--customProperty) (#3144) + if ( computed ) { + ret = computed.getPropertyValue( name ) || computed[ name ]; + + if ( ret === "" && !isAttached( elem ) ) { + ret = jQuery.style( elem, name ); + } + + // A tribute to the "awesome hack by Dean Edwards" + // Android Browser returns percentage for some values, + // but width seems to be reliably pixels. + // This is against the CSSOM draft spec: + // https://drafts.csswg.org/cssom/#resolved-values + if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) { + + // Remember the original values + width = style.width; + minWidth = style.minWidth; + maxWidth = style.maxWidth; + + // Put in the new values to get a computed value out + style.minWidth = style.maxWidth = style.width = ret; + ret = computed.width; + + // Revert the changed values + style.width = width; + style.minWidth = minWidth; + style.maxWidth = maxWidth; + } + } + + return ret !== undefined ? + + // Support: IE <=9 - 11 only + // IE returns zIndex value as an integer. + ret + "" : + ret; +} + + +function addGetHookIf( conditionFn, hookFn ) { + + // Define the hook, we'll check on the first run if it's really needed. + return { + get: function() { + if ( conditionFn() ) { + + // Hook not needed (or it's not possible to use it due + // to missing dependency), remove it. + delete this.get; + return; + } + + // Hook needed; redefine it so that the support test is not executed again. + return ( this.get = hookFn ).apply( this, arguments ); + } + }; +} + + +var cssPrefixes = [ "Webkit", "Moz", "ms" ], + emptyStyle = document.createElement( "div" ).style, + vendorProps = {}; + +// Return a vendor-prefixed property or undefined +function vendorPropName( name ) { + + // Check for vendor prefixed names + var capName = name[ 0 ].toUpperCase() + name.slice( 1 ), + i = cssPrefixes.length; + + while ( i-- ) { + name = cssPrefixes[ i ] + capName; + if ( name in emptyStyle ) { + return name; + } + } +} + +// Return a potentially-mapped jQuery.cssProps or vendor prefixed property +function finalPropName( name ) { + var final = jQuery.cssProps[ name ] || vendorProps[ name ]; + + if ( final ) { + return final; + } + if ( name in emptyStyle ) { + return name; + } + return vendorProps[ name ] = vendorPropName( name ) || name; +} + + +var + + // Swappable if display is none or starts with table + // except "table", "table-cell", or "table-caption" + // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display + rdisplayswap = /^(none|table(?!-c[ea]).+)/, + rcustomProp = /^--/, + cssShow = { position: "absolute", visibility: "hidden", display: "block" }, + cssNormalTransform = { + letterSpacing: "0", + fontWeight: "400" + }; + +function setPositiveNumber( _elem, value, subtract ) { + + // Any relative (+/-) values have already been + // normalized at this point + var matches = rcssNum.exec( value ); + return matches ? + + // Guard against undefined "subtract", e.g., when used as in cssHooks + Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) : + value; +} + +function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) { + var i = dimension === "width" ? 1 : 0, + extra = 0, + delta = 0; + + // Adjustment may not be necessary + if ( box === ( isBorderBox ? "border" : "content" ) ) { + return 0; + } + + for ( ; i < 4; i += 2 ) { + + // Both box models exclude margin + if ( box === "margin" ) { + delta += jQuery.css( elem, box + cssExpand[ i ], true, styles ); + } + + // If we get here with a content-box, we're seeking "padding" or "border" or "margin" + if ( !isBorderBox ) { + + // Add padding + delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + + // For "border" or "margin", add border + if ( box !== "padding" ) { + delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + + // But still keep track of it otherwise + } else { + extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + + // If we get here with a border-box (content + padding + border), we're seeking "content" or + // "padding" or "margin" + } else { + + // For "content", subtract padding + if ( box === "content" ) { + delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles ); + } + + // For "content" or "padding", subtract border + if ( box !== "margin" ) { + delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles ); + } + } + } + + // Account for positive content-box scroll gutter when requested by providing computedVal + if ( !isBorderBox && computedVal >= 0 ) { + + // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border + // Assuming integer scroll gutter, subtract the rest and round down + delta += Math.max( 0, Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + computedVal - + delta - + extra - + 0.5 + + // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter + // Use an explicit zero to avoid NaN (gh-3964) + ) ) || 0; + } + + return delta; +} + +function getWidthOrHeight( elem, dimension, extra ) { + + // Start with computed style + var styles = getStyles( elem ), + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322). + // Fake content-box until we know it's needed to know the true value. + boxSizingNeeded = !support.boxSizingReliable() || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + valueIsBorderBox = isBorderBox, + + val = curCSS( elem, dimension, styles ), + offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ); + + // Support: Firefox <=54 + // Return a confounding non-pixel value or feign ignorance, as appropriate. + if ( rnumnonpx.test( val ) ) { + if ( !extra ) { + return val; + } + val = "auto"; + } + + + // Support: IE 9 - 11 only + // Use offsetWidth/offsetHeight for when box sizing is unreliable. + // In those cases, the computed value can be trusted to be border-box. + if ( ( !support.boxSizingReliable() && isBorderBox || + + // Support: IE 10 - 11+, Edge 15 - 18+ + // IE/Edge misreport `getComputedStyle` of table rows with width/height + // set in CSS while `offset*` properties report correct values. + // Interestingly, in some cases IE 9 doesn't suffer from this issue. + !support.reliableTrDimensions() && nodeName( elem, "tr" ) || + + // Fall back to offsetWidth/offsetHeight when value is "auto" + // This happens for inline elements with no explicit setting (gh-3571) + val === "auto" || + + // Support: Android <=4.1 - 4.3 only + // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602) + !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) && + + // Make sure the element is visible & connected + elem.getClientRects().length ) { + + isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box"; + + // Where available, offsetWidth/offsetHeight approximate border box dimensions. + // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the + // retrieved value as a content box dimension. + valueIsBorderBox = offsetProp in elem; + if ( valueIsBorderBox ) { + val = elem[ offsetProp ]; + } + } + + // Normalize "" and auto + val = parseFloat( val ) || 0; + + // Adjust for the element's box model + return ( val + + boxModelAdjustment( + elem, + dimension, + extra || ( isBorderBox ? "border" : "content" ), + valueIsBorderBox, + styles, + + // Provide the current computed size to request scroll gutter calculation (gh-3589) + val + ) + ) + "px"; +} + +jQuery.extend( { + + // Add in style property hooks for overriding the default + // behavior of getting and setting a style property + cssHooks: { + opacity: { + get: function( elem, computed ) { + if ( computed ) { + + // We should always get a number back from opacity + var ret = curCSS( elem, "opacity" ); + return ret === "" ? "1" : ret; + } + } + } + }, + + // Don't automatically add "px" to these possibly-unitless properties + cssNumber: { + "animationIterationCount": true, + "columnCount": true, + "fillOpacity": true, + "flexGrow": true, + "flexShrink": true, + "fontWeight": true, + "gridArea": true, + "gridColumn": true, + "gridColumnEnd": true, + "gridColumnStart": true, + "gridRow": true, + "gridRowEnd": true, + "gridRowStart": true, + "lineHeight": true, + "opacity": true, + "order": true, + "orphans": true, + "widows": true, + "zIndex": true, + "zoom": true + }, + + // Add in properties whose names you wish to fix before + // setting or getting the value + cssProps: {}, + + // Get and set the style property on a DOM Node + style: function( elem, name, value, extra ) { + + // Don't set styles on text and comment nodes + if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) { + return; + } + + // Make sure that we're working with the right name + var ret, type, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ), + style = elem.style; + + // Make sure that we're working with the right name. We don't + // want to query the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Gets hook for the prefixed version, then unprefixed version + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // Check if we're setting a value + if ( value !== undefined ) { + type = typeof value; + + // Convert "+=" or "-=" to relative numbers (#7345) + if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) { + value = adjustCSS( elem, name, ret ); + + // Fixes bug #9237 + type = "number"; + } + + // Make sure that null and NaN values aren't set (#7116) + if ( value == null || value !== value ) { + return; + } + + // If a number was passed in, add the unit (except for certain CSS properties) + // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append + // "px" to a few hardcoded values. + if ( type === "number" && !isCustomProp ) { + value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" ); + } + + // background-* props affect original clone's values + if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) { + style[ name ] = "inherit"; + } + + // If a hook was provided, use that value, otherwise just set the specified value + if ( !hooks || !( "set" in hooks ) || + ( value = hooks.set( elem, value, extra ) ) !== undefined ) { + + if ( isCustomProp ) { + style.setProperty( name, value ); + } else { + style[ name ] = value; + } + } + + } else { + + // If a hook was provided get the non-computed value from there + if ( hooks && "get" in hooks && + ( ret = hooks.get( elem, false, extra ) ) !== undefined ) { + + return ret; + } + + // Otherwise just get the value from the style object + return style[ name ]; + } + }, + + css: function( elem, name, extra, styles ) { + var val, num, hooks, + origName = camelCase( name ), + isCustomProp = rcustomProp.test( name ); + + // Make sure that we're working with the right name. We don't + // want to modify the value if it is a CSS custom property + // since they are user-defined. + if ( !isCustomProp ) { + name = finalPropName( origName ); + } + + // Try prefixed name followed by the unprefixed name + hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ]; + + // If a hook was provided get the computed value from there + if ( hooks && "get" in hooks ) { + val = hooks.get( elem, true, extra ); + } + + // Otherwise, if a way to get the computed value exists, use that + if ( val === undefined ) { + val = curCSS( elem, name, styles ); + } + + // Convert "normal" to computed value + if ( val === "normal" && name in cssNormalTransform ) { + val = cssNormalTransform[ name ]; + } + + // Make numeric if forced or a qualifier was provided and val looks numeric + if ( extra === "" || extra ) { + num = parseFloat( val ); + return extra === true || isFinite( num ) ? num || 0 : val; + } + + return val; + } +} ); + +jQuery.each( [ "height", "width" ], function( _i, dimension ) { + jQuery.cssHooks[ dimension ] = { + get: function( elem, computed, extra ) { + if ( computed ) { + + // Certain elements can have dimension info if we invisibly show them + // but it must have a current display style that would benefit + return rdisplayswap.test( jQuery.css( elem, "display" ) ) && + + // Support: Safari 8+ + // Table columns in Safari have non-zero offsetWidth & zero + // getBoundingClientRect().width unless display is changed. + // Support: IE <=11 only + // Running getBoundingClientRect on a disconnected node + // in IE throws an error. + ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ? + swap( elem, cssShow, function() { + return getWidthOrHeight( elem, dimension, extra ); + } ) : + getWidthOrHeight( elem, dimension, extra ); + } + }, + + set: function( elem, value, extra ) { + var matches, + styles = getStyles( elem ), + + // Only read styles.position if the test has a chance to fail + // to avoid forcing a reflow. + scrollboxSizeBuggy = !support.scrollboxSize() && + styles.position === "absolute", + + // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991) + boxSizingNeeded = scrollboxSizeBuggy || extra, + isBorderBox = boxSizingNeeded && + jQuery.css( elem, "boxSizing", false, styles ) === "border-box", + subtract = extra ? + boxModelAdjustment( + elem, + dimension, + extra, + isBorderBox, + styles + ) : + 0; + + // Account for unreliable border-box dimensions by comparing offset* to computed and + // faking a content-box to get border and padding (gh-3699) + if ( isBorderBox && scrollboxSizeBuggy ) { + subtract -= Math.ceil( + elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] - + parseFloat( styles[ dimension ] ) - + boxModelAdjustment( elem, dimension, "border", false, styles ) - + 0.5 + ); + } + + // Convert to pixels if value adjustment is needed + if ( subtract && ( matches = rcssNum.exec( value ) ) && + ( matches[ 3 ] || "px" ) !== "px" ) { + + elem.style[ dimension ] = value; + value = jQuery.css( elem, dimension ); + } + + return setPositiveNumber( elem, value, subtract ); + } + }; +} ); + +jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft, + function( elem, computed ) { + if ( computed ) { + return ( parseFloat( curCSS( elem, "marginLeft" ) ) || + elem.getBoundingClientRect().left - + swap( elem, { marginLeft: 0 }, function() { + return elem.getBoundingClientRect().left; + } ) + ) + "px"; + } + } +); + +// These hooks are used by animate to expand properties +jQuery.each( { + margin: "", + padding: "", + border: "Width" +}, function( prefix, suffix ) { + jQuery.cssHooks[ prefix + suffix ] = { + expand: function( value ) { + var i = 0, + expanded = {}, + + // Assumes a single number if not a string + parts = typeof value === "string" ? value.split( " " ) : [ value ]; + + for ( ; i < 4; i++ ) { + expanded[ prefix + cssExpand[ i ] + suffix ] = + parts[ i ] || parts[ i - 2 ] || parts[ 0 ]; + } + + return expanded; + } + }; + + if ( prefix !== "margin" ) { + jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber; + } +} ); + +jQuery.fn.extend( { + css: function( name, value ) { + return access( this, function( elem, name, value ) { + var styles, len, + map = {}, + i = 0; + + if ( Array.isArray( name ) ) { + styles = getStyles( elem ); + len = name.length; + + for ( ; i < len; i++ ) { + map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles ); + } + + return map; + } + + return value !== undefined ? + jQuery.style( elem, name, value ) : + jQuery.css( elem, name ); + }, name, value, arguments.length > 1 ); + } +} ); + + +function Tween( elem, options, prop, end, easing ) { + return new Tween.prototype.init( elem, options, prop, end, easing ); +} +jQuery.Tween = Tween; + +Tween.prototype = { + constructor: Tween, + init: function( elem, options, prop, end, easing, unit ) { + this.elem = elem; + this.prop = prop; + this.easing = easing || jQuery.easing._default; + this.options = options; + this.start = this.now = this.cur(); + this.end = end; + this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" ); + }, + cur: function() { + var hooks = Tween.propHooks[ this.prop ]; + + return hooks && hooks.get ? + hooks.get( this ) : + Tween.propHooks._default.get( this ); + }, + run: function( percent ) { + var eased, + hooks = Tween.propHooks[ this.prop ]; + + if ( this.options.duration ) { + this.pos = eased = jQuery.easing[ this.easing ]( + percent, this.options.duration * percent, 0, 1, this.options.duration + ); + } else { + this.pos = eased = percent; + } + this.now = ( this.end - this.start ) * eased + this.start; + + if ( this.options.step ) { + this.options.step.call( this.elem, this.now, this ); + } + + if ( hooks && hooks.set ) { + hooks.set( this ); + } else { + Tween.propHooks._default.set( this ); + } + return this; + } +}; + +Tween.prototype.init.prototype = Tween.prototype; + +Tween.propHooks = { + _default: { + get: function( tween ) { + var result; + + // Use a property on the element directly when it is not a DOM element, + // or when there is no matching style property that exists. + if ( tween.elem.nodeType !== 1 || + tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) { + return tween.elem[ tween.prop ]; + } + + // Passing an empty string as a 3rd parameter to .css will automatically + // attempt a parseFloat and fallback to a string if the parse fails. + // Simple values such as "10px" are parsed to Float; + // complex values such as "rotate(1rad)" are returned as-is. + result = jQuery.css( tween.elem, tween.prop, "" ); + + // Empty strings, null, undefined and "auto" are converted to 0. + return !result || result === "auto" ? 0 : result; + }, + set: function( tween ) { + + // Use step hook for back compat. + // Use cssHook if its there. + // Use .style if available and use plain properties where available. + if ( jQuery.fx.step[ tween.prop ] ) { + jQuery.fx.step[ tween.prop ]( tween ); + } else if ( tween.elem.nodeType === 1 && ( + jQuery.cssHooks[ tween.prop ] || + tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) { + jQuery.style( tween.elem, tween.prop, tween.now + tween.unit ); + } else { + tween.elem[ tween.prop ] = tween.now; + } + } + } +}; + +// Support: IE <=9 only +// Panic based approach to setting things on disconnected nodes +Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = { + set: function( tween ) { + if ( tween.elem.nodeType && tween.elem.parentNode ) { + tween.elem[ tween.prop ] = tween.now; + } + } +}; + +jQuery.easing = { + linear: function( p ) { + return p; + }, + swing: function( p ) { + return 0.5 - Math.cos( p * Math.PI ) / 2; + }, + _default: "swing" +}; + +jQuery.fx = Tween.prototype.init; + +// Back compat <1.8 extension point +jQuery.fx.step = {}; + + + + +var + fxNow, inProgress, + rfxtypes = /^(?:toggle|show|hide)$/, + rrun = /queueHooks$/; + +function schedule() { + if ( inProgress ) { + if ( document.hidden === false && window.requestAnimationFrame ) { + window.requestAnimationFrame( schedule ); + } else { + window.setTimeout( schedule, jQuery.fx.interval ); + } + + jQuery.fx.tick(); + } +} + +// Animations created synchronously will run synchronously +function createFxNow() { + window.setTimeout( function() { + fxNow = undefined; + } ); + return ( fxNow = Date.now() ); +} + +// Generate parameters to create a standard animation +function genFx( type, includeWidth ) { + var which, + i = 0, + attrs = { height: type }; + + // If we include width, step value is 1 to do all cssExpand values, + // otherwise step value is 2 to skip over Left and Right + includeWidth = includeWidth ? 1 : 0; + for ( ; i < 4; i += 2 - includeWidth ) { + which = cssExpand[ i ]; + attrs[ "margin" + which ] = attrs[ "padding" + which ] = type; + } + + if ( includeWidth ) { + attrs.opacity = attrs.width = type; + } + + return attrs; +} + +function createTween( value, prop, animation ) { + var tween, + collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ), + index = 0, + length = collection.length; + for ( ; index < length; index++ ) { + if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) { + + // We're done with this property + return tween; + } + } +} + +function defaultPrefilter( elem, props, opts ) { + var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display, + isBox = "width" in props || "height" in props, + anim = this, + orig = {}, + style = elem.style, + hidden = elem.nodeType && isHiddenWithinTree( elem ), + dataShow = dataPriv.get( elem, "fxshow" ); + + // Queue-skipping animations hijack the fx hooks + if ( !opts.queue ) { + hooks = jQuery._queueHooks( elem, "fx" ); + if ( hooks.unqueued == null ) { + hooks.unqueued = 0; + oldfire = hooks.empty.fire; + hooks.empty.fire = function() { + if ( !hooks.unqueued ) { + oldfire(); + } + }; + } + hooks.unqueued++; + + anim.always( function() { + + // Ensure the complete handler is called before this completes + anim.always( function() { + hooks.unqueued--; + if ( !jQuery.queue( elem, "fx" ).length ) { + hooks.empty.fire(); + } + } ); + } ); + } + + // Detect show/hide animations + for ( prop in props ) { + value = props[ prop ]; + if ( rfxtypes.test( value ) ) { + delete props[ prop ]; + toggle = toggle || value === "toggle"; + if ( value === ( hidden ? "hide" : "show" ) ) { + + // Pretend to be hidden if this is a "show" and + // there is still data from a stopped show/hide + if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) { + hidden = true; + + // Ignore all other no-op show/hide data + } else { + continue; + } + } + orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop ); + } + } + + // Bail out if this is a no-op like .hide().hide() + propTween = !jQuery.isEmptyObject( props ); + if ( !propTween && jQuery.isEmptyObject( orig ) ) { + return; + } + + // Restrict "overflow" and "display" styles during box animations + if ( isBox && elem.nodeType === 1 ) { + + // Support: IE <=9 - 11, Edge 12 - 15 + // Record all 3 overflow attributes because IE does not infer the shorthand + // from identically-valued overflowX and overflowY and Edge just mirrors + // the overflowX value there. + opts.overflow = [ style.overflow, style.overflowX, style.overflowY ]; + + // Identify a display type, preferring old show/hide data over the CSS cascade + restoreDisplay = dataShow && dataShow.display; + if ( restoreDisplay == null ) { + restoreDisplay = dataPriv.get( elem, "display" ); + } + display = jQuery.css( elem, "display" ); + if ( display === "none" ) { + if ( restoreDisplay ) { + display = restoreDisplay; + } else { + + // Get nonempty value(s) by temporarily forcing visibility + showHide( [ elem ], true ); + restoreDisplay = elem.style.display || restoreDisplay; + display = jQuery.css( elem, "display" ); + showHide( [ elem ] ); + } + } + + // Animate inline elements as inline-block + if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) { + if ( jQuery.css( elem, "float" ) === "none" ) { + + // Restore the original display value at the end of pure show/hide animations + if ( !propTween ) { + anim.done( function() { + style.display = restoreDisplay; + } ); + if ( restoreDisplay == null ) { + display = style.display; + restoreDisplay = display === "none" ? "" : display; + } + } + style.display = "inline-block"; + } + } + } + + if ( opts.overflow ) { + style.overflow = "hidden"; + anim.always( function() { + style.overflow = opts.overflow[ 0 ]; + style.overflowX = opts.overflow[ 1 ]; + style.overflowY = opts.overflow[ 2 ]; + } ); + } + + // Implement show/hide animations + propTween = false; + for ( prop in orig ) { + + // General show/hide setup for this element animation + if ( !propTween ) { + if ( dataShow ) { + if ( "hidden" in dataShow ) { + hidden = dataShow.hidden; + } + } else { + dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } ); + } + + // Store hidden/visible for toggle so `.stop().toggle()` "reverses" + if ( toggle ) { + dataShow.hidden = !hidden; + } + + // Show elements before animating them + if ( hidden ) { + showHide( [ elem ], true ); + } + + /* eslint-disable no-loop-func */ + + anim.done( function() { + + /* eslint-enable no-loop-func */ + + // The final step of a "hide" animation is actually hiding the element + if ( !hidden ) { + showHide( [ elem ] ); + } + dataPriv.remove( elem, "fxshow" ); + for ( prop in orig ) { + jQuery.style( elem, prop, orig[ prop ] ); + } + } ); + } + + // Per-property setup + propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim ); + if ( !( prop in dataShow ) ) { + dataShow[ prop ] = propTween.start; + if ( hidden ) { + propTween.end = propTween.start; + propTween.start = 0; + } + } + } +} + +function propFilter( props, specialEasing ) { + var index, name, easing, value, hooks; + + // camelCase, specialEasing and expand cssHook pass + for ( index in props ) { + name = camelCase( index ); + easing = specialEasing[ name ]; + value = props[ index ]; + if ( Array.isArray( value ) ) { + easing = value[ 1 ]; + value = props[ index ] = value[ 0 ]; + } + + if ( index !== name ) { + props[ name ] = value; + delete props[ index ]; + } + + hooks = jQuery.cssHooks[ name ]; + if ( hooks && "expand" in hooks ) { + value = hooks.expand( value ); + delete props[ name ]; + + // Not quite $.extend, this won't overwrite existing keys. + // Reusing 'index' because we have the correct "name" + for ( index in value ) { + if ( !( index in props ) ) { + props[ index ] = value[ index ]; + specialEasing[ index ] = easing; + } + } + } else { + specialEasing[ name ] = easing; + } + } +} + +function Animation( elem, properties, options ) { + var result, + stopped, + index = 0, + length = Animation.prefilters.length, + deferred = jQuery.Deferred().always( function() { + + // Don't match elem in the :animated selector + delete tick.elem; + } ), + tick = function() { + if ( stopped ) { + return false; + } + var currentTime = fxNow || createFxNow(), + remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ), + + // Support: Android 2.3 only + // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497) + temp = remaining / animation.duration || 0, + percent = 1 - temp, + index = 0, + length = animation.tweens.length; + + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( percent ); + } + + deferred.notifyWith( elem, [ animation, percent, remaining ] ); + + // If there's more to do, yield + if ( percent < 1 && length ) { + return remaining; + } + + // If this was an empty animation, synthesize a final progress notification + if ( !length ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + } + + // Resolve the animation and report its conclusion + deferred.resolveWith( elem, [ animation ] ); + return false; + }, + animation = deferred.promise( { + elem: elem, + props: jQuery.extend( {}, properties ), + opts: jQuery.extend( true, { + specialEasing: {}, + easing: jQuery.easing._default + }, options ), + originalProperties: properties, + originalOptions: options, + startTime: fxNow || createFxNow(), + duration: options.duration, + tweens: [], + createTween: function( prop, end ) { + var tween = jQuery.Tween( elem, animation.opts, prop, end, + animation.opts.specialEasing[ prop ] || animation.opts.easing ); + animation.tweens.push( tween ); + return tween; + }, + stop: function( gotoEnd ) { + var index = 0, + + // If we are going to the end, we want to run all the tweens + // otherwise we skip this part + length = gotoEnd ? animation.tweens.length : 0; + if ( stopped ) { + return this; + } + stopped = true; + for ( ; index < length; index++ ) { + animation.tweens[ index ].run( 1 ); + } + + // Resolve when we played the last frame; otherwise, reject + if ( gotoEnd ) { + deferred.notifyWith( elem, [ animation, 1, 0 ] ); + deferred.resolveWith( elem, [ animation, gotoEnd ] ); + } else { + deferred.rejectWith( elem, [ animation, gotoEnd ] ); + } + return this; + } + } ), + props = animation.props; + + propFilter( props, animation.opts.specialEasing ); + + for ( ; index < length; index++ ) { + result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts ); + if ( result ) { + if ( isFunction( result.stop ) ) { + jQuery._queueHooks( animation.elem, animation.opts.queue ).stop = + result.stop.bind( result ); + } + return result; + } + } + + jQuery.map( props, createTween, animation ); + + if ( isFunction( animation.opts.start ) ) { + animation.opts.start.call( elem, animation ); + } + + // Attach callbacks from options + animation + .progress( animation.opts.progress ) + .done( animation.opts.done, animation.opts.complete ) + .fail( animation.opts.fail ) + .always( animation.opts.always ); + + jQuery.fx.timer( + jQuery.extend( tick, { + elem: elem, + anim: animation, + queue: animation.opts.queue + } ) + ); + + return animation; +} + +jQuery.Animation = jQuery.extend( Animation, { + + tweeners: { + "*": [ function( prop, value ) { + var tween = this.createTween( prop, value ); + adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween ); + return tween; + } ] + }, + + tweener: function( props, callback ) { + if ( isFunction( props ) ) { + callback = props; + props = [ "*" ]; + } else { + props = props.match( rnothtmlwhite ); + } + + var prop, + index = 0, + length = props.length; + + for ( ; index < length; index++ ) { + prop = props[ index ]; + Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || []; + Animation.tweeners[ prop ].unshift( callback ); + } + }, + + prefilters: [ defaultPrefilter ], + + prefilter: function( callback, prepend ) { + if ( prepend ) { + Animation.prefilters.unshift( callback ); + } else { + Animation.prefilters.push( callback ); + } + } +} ); + +jQuery.speed = function( speed, easing, fn ) { + var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : { + complete: fn || !fn && easing || + isFunction( speed ) && speed, + duration: speed, + easing: fn && easing || easing && !isFunction( easing ) && easing + }; + + // Go to the end state if fx are off + if ( jQuery.fx.off ) { + opt.duration = 0; + + } else { + if ( typeof opt.duration !== "number" ) { + if ( opt.duration in jQuery.fx.speeds ) { + opt.duration = jQuery.fx.speeds[ opt.duration ]; + + } else { + opt.duration = jQuery.fx.speeds._default; + } + } + } + + // Normalize opt.queue - true/undefined/null -> "fx" + if ( opt.queue == null || opt.queue === true ) { + opt.queue = "fx"; + } + + // Queueing + opt.old = opt.complete; + + opt.complete = function() { + if ( isFunction( opt.old ) ) { + opt.old.call( this ); + } + + if ( opt.queue ) { + jQuery.dequeue( this, opt.queue ); + } + }; + + return opt; +}; + +jQuery.fn.extend( { + fadeTo: function( speed, to, easing, callback ) { + + // Show any hidden elements after setting opacity to 0 + return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show() + + // Animate to the value specified + .end().animate( { opacity: to }, speed, easing, callback ); + }, + animate: function( prop, speed, easing, callback ) { + var empty = jQuery.isEmptyObject( prop ), + optall = jQuery.speed( speed, easing, callback ), + doAnimation = function() { + + // Operate on a copy of prop so per-property easing won't be lost + var anim = Animation( this, jQuery.extend( {}, prop ), optall ); + + // Empty animations, or finishing resolves immediately + if ( empty || dataPriv.get( this, "finish" ) ) { + anim.stop( true ); + } + }; + + doAnimation.finish = doAnimation; + + return empty || optall.queue === false ? + this.each( doAnimation ) : + this.queue( optall.queue, doAnimation ); + }, + stop: function( type, clearQueue, gotoEnd ) { + var stopQueue = function( hooks ) { + var stop = hooks.stop; + delete hooks.stop; + stop( gotoEnd ); + }; + + if ( typeof type !== "string" ) { + gotoEnd = clearQueue; + clearQueue = type; + type = undefined; + } + if ( clearQueue ) { + this.queue( type || "fx", [] ); + } + + return this.each( function() { + var dequeue = true, + index = type != null && type + "queueHooks", + timers = jQuery.timers, + data = dataPriv.get( this ); + + if ( index ) { + if ( data[ index ] && data[ index ].stop ) { + stopQueue( data[ index ] ); + } + } else { + for ( index in data ) { + if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) { + stopQueue( data[ index ] ); + } + } + } + + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && + ( type == null || timers[ index ].queue === type ) ) { + + timers[ index ].anim.stop( gotoEnd ); + dequeue = false; + timers.splice( index, 1 ); + } + } + + // Start the next in the queue if the last step wasn't forced. + // Timers currently will call their complete callbacks, which + // will dequeue but only if they were gotoEnd. + if ( dequeue || !gotoEnd ) { + jQuery.dequeue( this, type ); + } + } ); + }, + finish: function( type ) { + if ( type !== false ) { + type = type || "fx"; + } + return this.each( function() { + var index, + data = dataPriv.get( this ), + queue = data[ type + "queue" ], + hooks = data[ type + "queueHooks" ], + timers = jQuery.timers, + length = queue ? queue.length : 0; + + // Enable finishing flag on private data + data.finish = true; + + // Empty the queue first + jQuery.queue( this, type, [] ); + + if ( hooks && hooks.stop ) { + hooks.stop.call( this, true ); + } + + // Look for any active animations, and finish them + for ( index = timers.length; index--; ) { + if ( timers[ index ].elem === this && timers[ index ].queue === type ) { + timers[ index ].anim.stop( true ); + timers.splice( index, 1 ); + } + } + + // Look for any animations in the old queue and finish them + for ( index = 0; index < length; index++ ) { + if ( queue[ index ] && queue[ index ].finish ) { + queue[ index ].finish.call( this ); + } + } + + // Turn off finishing flag + delete data.finish; + } ); + } +} ); + +jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) { + var cssFn = jQuery.fn[ name ]; + jQuery.fn[ name ] = function( speed, easing, callback ) { + return speed == null || typeof speed === "boolean" ? + cssFn.apply( this, arguments ) : + this.animate( genFx( name, true ), speed, easing, callback ); + }; +} ); + +// Generate shortcuts for custom animations +jQuery.each( { + slideDown: genFx( "show" ), + slideUp: genFx( "hide" ), + slideToggle: genFx( "toggle" ), + fadeIn: { opacity: "show" }, + fadeOut: { opacity: "hide" }, + fadeToggle: { opacity: "toggle" } +}, function( name, props ) { + jQuery.fn[ name ] = function( speed, easing, callback ) { + return this.animate( props, speed, easing, callback ); + }; +} ); + +jQuery.timers = []; +jQuery.fx.tick = function() { + var timer, + i = 0, + timers = jQuery.timers; + + fxNow = Date.now(); + + for ( ; i < timers.length; i++ ) { + timer = timers[ i ]; + + // Run the timer and safely remove it when done (allowing for external removal) + if ( !timer() && timers[ i ] === timer ) { + timers.splice( i--, 1 ); + } + } + + if ( !timers.length ) { + jQuery.fx.stop(); + } + fxNow = undefined; +}; + +jQuery.fx.timer = function( timer ) { + jQuery.timers.push( timer ); + jQuery.fx.start(); +}; + +jQuery.fx.interval = 13; +jQuery.fx.start = function() { + if ( inProgress ) { + return; + } + + inProgress = true; + schedule(); +}; + +jQuery.fx.stop = function() { + inProgress = null; +}; + +jQuery.fx.speeds = { + slow: 600, + fast: 200, + + // Default speed + _default: 400 +}; + + +// Based off of the plugin by Clint Helfers, with permission. +// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/ +jQuery.fn.delay = function( time, type ) { + time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time; + type = type || "fx"; + + return this.queue( type, function( next, hooks ) { + var timeout = window.setTimeout( next, time ); + hooks.stop = function() { + window.clearTimeout( timeout ); + }; + } ); +}; + + +( function() { + var input = document.createElement( "input" ), + select = document.createElement( "select" ), + opt = select.appendChild( document.createElement( "option" ) ); + + input.type = "checkbox"; + + // Support: Android <=4.3 only + // Default value for a checkbox should be "on" + support.checkOn = input.value !== ""; + + // Support: IE <=11 only + // Must access selectedIndex to make default options select + support.optSelected = opt.selected; + + // Support: IE <=11 only + // An input loses its value after becoming a radio + input = document.createElement( "input" ); + input.value = "t"; + input.type = "radio"; + support.radioValue = input.value === "t"; +} )(); + + +var boolHook, + attrHandle = jQuery.expr.attrHandle; + +jQuery.fn.extend( { + attr: function( name, value ) { + return access( this, jQuery.attr, name, value, arguments.length > 1 ); + }, + + removeAttr: function( name ) { + return this.each( function() { + jQuery.removeAttr( this, name ); + } ); + } +} ); + +jQuery.extend( { + attr: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set attributes on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + // Fallback to prop when attributes are not supported + if ( typeof elem.getAttribute === "undefined" ) { + return jQuery.prop( elem, name, value ); + } + + // Attribute hooks are determined by the lowercase version + // Grab necessary hook if one is defined + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + hooks = jQuery.attrHooks[ name.toLowerCase() ] || + ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined ); + } + + if ( value !== undefined ) { + if ( value === null ) { + jQuery.removeAttr( elem, name ); + return; + } + + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + elem.setAttribute( name, value + "" ); + return value; + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + ret = jQuery.find.attr( elem, name ); + + // Non-existent attributes return null, we normalize to undefined + return ret == null ? undefined : ret; + }, + + attrHooks: { + type: { + set: function( elem, value ) { + if ( !support.radioValue && value === "radio" && + nodeName( elem, "input" ) ) { + var val = elem.value; + elem.setAttribute( "type", value ); + if ( val ) { + elem.value = val; + } + return value; + } + } + } + }, + + removeAttr: function( elem, value ) { + var name, + i = 0, + + // Attribute names can contain non-HTML whitespace characters + // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2 + attrNames = value && value.match( rnothtmlwhite ); + + if ( attrNames && elem.nodeType === 1 ) { + while ( ( name = attrNames[ i++ ] ) ) { + elem.removeAttribute( name ); + } + } + } +} ); + +// Hooks for boolean attributes +boolHook = { + set: function( elem, value, name ) { + if ( value === false ) { + + // Remove boolean attributes when set to false + jQuery.removeAttr( elem, name ); + } else { + elem.setAttribute( name, name ); + } + return name; + } +}; + +jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) { + var getter = attrHandle[ name ] || jQuery.find.attr; + + attrHandle[ name ] = function( elem, name, isXML ) { + var ret, handle, + lowercaseName = name.toLowerCase(); + + if ( !isXML ) { + + // Avoid an infinite loop by temporarily removing this function from the getter + handle = attrHandle[ lowercaseName ]; + attrHandle[ lowercaseName ] = ret; + ret = getter( elem, name, isXML ) != null ? + lowercaseName : + null; + attrHandle[ lowercaseName ] = handle; + } + return ret; + }; +} ); + + + + +var rfocusable = /^(?:input|select|textarea|button)$/i, + rclickable = /^(?:a|area)$/i; + +jQuery.fn.extend( { + prop: function( name, value ) { + return access( this, jQuery.prop, name, value, arguments.length > 1 ); + }, + + removeProp: function( name ) { + return this.each( function() { + delete this[ jQuery.propFix[ name ] || name ]; + } ); + } +} ); + +jQuery.extend( { + prop: function( elem, name, value ) { + var ret, hooks, + nType = elem.nodeType; + + // Don't get/set properties on text, comment and attribute nodes + if ( nType === 3 || nType === 8 || nType === 2 ) { + return; + } + + if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) { + + // Fix name and attach hooks + name = jQuery.propFix[ name ] || name; + hooks = jQuery.propHooks[ name ]; + } + + if ( value !== undefined ) { + if ( hooks && "set" in hooks && + ( ret = hooks.set( elem, value, name ) ) !== undefined ) { + return ret; + } + + return ( elem[ name ] = value ); + } + + if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) { + return ret; + } + + return elem[ name ]; + }, + + propHooks: { + tabIndex: { + get: function( elem ) { + + // Support: IE <=9 - 11 only + // elem.tabIndex doesn't always return the + // correct value when it hasn't been explicitly set + // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/ + // Use proper attribute retrieval(#12072) + var tabindex = jQuery.find.attr( elem, "tabindex" ); + + if ( tabindex ) { + return parseInt( tabindex, 10 ); + } + + if ( + rfocusable.test( elem.nodeName ) || + rclickable.test( elem.nodeName ) && + elem.href + ) { + return 0; + } + + return -1; + } + } + }, + + propFix: { + "for": "htmlFor", + "class": "className" + } +} ); + +// Support: IE <=11 only +// Accessing the selectedIndex property +// forces the browser to respect setting selected +// on the option +// The getter ensures a default option is selected +// when in an optgroup +// eslint rule "no-unused-expressions" is disabled for this code +// since it considers such accessions noop +if ( !support.optSelected ) { + jQuery.propHooks.selected = { + get: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent && parent.parentNode ) { + parent.parentNode.selectedIndex; + } + return null; + }, + set: function( elem ) { + + /* eslint no-unused-expressions: "off" */ + + var parent = elem.parentNode; + if ( parent ) { + parent.selectedIndex; + + if ( parent.parentNode ) { + parent.parentNode.selectedIndex; + } + } + } + }; +} + +jQuery.each( [ + "tabIndex", + "readOnly", + "maxLength", + "cellSpacing", + "cellPadding", + "rowSpan", + "colSpan", + "useMap", + "frameBorder", + "contentEditable" +], function() { + jQuery.propFix[ this.toLowerCase() ] = this; +} ); + + + + + // Strip and collapse whitespace according to HTML spec + // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace + function stripAndCollapse( value ) { + var tokens = value.match( rnothtmlwhite ) || []; + return tokens.join( " " ); + } + + +function getClass( elem ) { + return elem.getAttribute && elem.getAttribute( "class" ) || ""; +} + +function classesToArray( value ) { + if ( Array.isArray( value ) ) { + return value; + } + if ( typeof value === "string" ) { + return value.match( rnothtmlwhite ) || []; + } + return []; +} + +jQuery.fn.extend( { + addClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).addClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + if ( cur.indexOf( " " + clazz + " " ) < 0 ) { + cur += clazz + " "; + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + removeClass: function( value ) { + var classes, elem, cur, curValue, clazz, j, finalValue, + i = 0; + + if ( isFunction( value ) ) { + return this.each( function( j ) { + jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) ); + } ); + } + + if ( !arguments.length ) { + return this.attr( "class", "" ); + } + + classes = classesToArray( value ); + + if ( classes.length ) { + while ( ( elem = this[ i++ ] ) ) { + curValue = getClass( elem ); + + // This expression is here for better compressibility (see addClass) + cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " ); + + if ( cur ) { + j = 0; + while ( ( clazz = classes[ j++ ] ) ) { + + // Remove *all* instances + while ( cur.indexOf( " " + clazz + " " ) > -1 ) { + cur = cur.replace( " " + clazz + " ", " " ); + } + } + + // Only assign if different to avoid unneeded rendering. + finalValue = stripAndCollapse( cur ); + if ( curValue !== finalValue ) { + elem.setAttribute( "class", finalValue ); + } + } + } + } + + return this; + }, + + toggleClass: function( value, stateVal ) { + var type = typeof value, + isValidValue = type === "string" || Array.isArray( value ); + + if ( typeof stateVal === "boolean" && isValidValue ) { + return stateVal ? this.addClass( value ) : this.removeClass( value ); + } + + if ( isFunction( value ) ) { + return this.each( function( i ) { + jQuery( this ).toggleClass( + value.call( this, i, getClass( this ), stateVal ), + stateVal + ); + } ); + } + + return this.each( function() { + var className, i, self, classNames; + + if ( isValidValue ) { + + // Toggle individual class names + i = 0; + self = jQuery( this ); + classNames = classesToArray( value ); + + while ( ( className = classNames[ i++ ] ) ) { + + // Check each className given, space separated list + if ( self.hasClass( className ) ) { + self.removeClass( className ); + } else { + self.addClass( className ); + } + } + + // Toggle whole class name + } else if ( value === undefined || type === "boolean" ) { + className = getClass( this ); + if ( className ) { + + // Store className if set + dataPriv.set( this, "__className__", className ); + } + + // If the element has a class name or if we're passed `false`, + // then remove the whole classname (if there was one, the above saved it). + // Otherwise bring back whatever was previously saved (if anything), + // falling back to the empty string if nothing was stored. + if ( this.setAttribute ) { + this.setAttribute( "class", + className || value === false ? + "" : + dataPriv.get( this, "__className__" ) || "" + ); + } + } + } ); + }, + + hasClass: function( selector ) { + var className, elem, + i = 0; + + className = " " + selector + " "; + while ( ( elem = this[ i++ ] ) ) { + if ( elem.nodeType === 1 && + ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) { + return true; + } + } + + return false; + } +} ); + + + + +var rreturn = /\r/g; + +jQuery.fn.extend( { + val: function( value ) { + var hooks, ret, valueIsFunction, + elem = this[ 0 ]; + + if ( !arguments.length ) { + if ( elem ) { + hooks = jQuery.valHooks[ elem.type ] || + jQuery.valHooks[ elem.nodeName.toLowerCase() ]; + + if ( hooks && + "get" in hooks && + ( ret = hooks.get( elem, "value" ) ) !== undefined + ) { + return ret; + } + + ret = elem.value; + + // Handle most common string cases + if ( typeof ret === "string" ) { + return ret.replace( rreturn, "" ); + } + + // Handle cases where value is null/undef or number + return ret == null ? "" : ret; + } + + return; + } + + valueIsFunction = isFunction( value ); + + return this.each( function( i ) { + var val; + + if ( this.nodeType !== 1 ) { + return; + } + + if ( valueIsFunction ) { + val = value.call( this, i, jQuery( this ).val() ); + } else { + val = value; + } + + // Treat null/undefined as ""; convert numbers to string + if ( val == null ) { + val = ""; + + } else if ( typeof val === "number" ) { + val += ""; + + } else if ( Array.isArray( val ) ) { + val = jQuery.map( val, function( value ) { + return value == null ? "" : value + ""; + } ); + } + + hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ]; + + // If set returns undefined, fall back to normal setting + if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) { + this.value = val; + } + } ); + } +} ); + +jQuery.extend( { + valHooks: { + option: { + get: function( elem ) { + + var val = jQuery.find.attr( elem, "value" ); + return val != null ? + val : + + // Support: IE <=10 - 11 only + // option.text throws exceptions (#14686, #14858) + // Strip and collapse whitespace + // https://html.spec.whatwg.org/#strip-and-collapse-whitespace + stripAndCollapse( jQuery.text( elem ) ); + } + }, + select: { + get: function( elem ) { + var value, option, i, + options = elem.options, + index = elem.selectedIndex, + one = elem.type === "select-one", + values = one ? null : [], + max = one ? index + 1 : options.length; + + if ( index < 0 ) { + i = max; + + } else { + i = one ? index : 0; + } + + // Loop through all the selected options + for ( ; i < max; i++ ) { + option = options[ i ]; + + // Support: IE <=9 only + // IE8-9 doesn't update selected after form reset (#2551) + if ( ( option.selected || i === index ) && + + // Don't return options that are disabled or in a disabled optgroup + !option.disabled && + ( !option.parentNode.disabled || + !nodeName( option.parentNode, "optgroup" ) ) ) { + + // Get the specific value for the option + value = jQuery( option ).val(); + + // We don't need an array for one selects + if ( one ) { + return value; + } + + // Multi-Selects return an array + values.push( value ); + } + } + + return values; + }, + + set: function( elem, value ) { + var optionSet, option, + options = elem.options, + values = jQuery.makeArray( value ), + i = options.length; + + while ( i-- ) { + option = options[ i ]; + + /* eslint-disable no-cond-assign */ + + if ( option.selected = + jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1 + ) { + optionSet = true; + } + + /* eslint-enable no-cond-assign */ + } + + // Force browsers to behave consistently when non-matching value is set + if ( !optionSet ) { + elem.selectedIndex = -1; + } + return values; + } + } + } +} ); + +// Radios and checkboxes getter/setter +jQuery.each( [ "radio", "checkbox" ], function() { + jQuery.valHooks[ this ] = { + set: function( elem, value ) { + if ( Array.isArray( value ) ) { + return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 ); + } + } + }; + if ( !support.checkOn ) { + jQuery.valHooks[ this ].get = function( elem ) { + return elem.getAttribute( "value" ) === null ? "on" : elem.value; + }; + } +} ); + + + + +// Return jQuery for attributes-only inclusion + + +support.focusin = "onfocusin" in window; + + +var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/, + stopPropagationCallback = function( e ) { + e.stopPropagation(); + }; + +jQuery.extend( jQuery.event, { + + trigger: function( event, data, elem, onlyHandlers ) { + + var i, cur, tmp, bubbleType, ontype, handle, special, lastElement, + eventPath = [ elem || document ], + type = hasOwn.call( event, "type" ) ? event.type : event, + namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : []; + + cur = lastElement = tmp = elem = elem || document; + + // Don't do events on text and comment nodes + if ( elem.nodeType === 3 || elem.nodeType === 8 ) { + return; + } + + // focus/blur morphs to focusin/out; ensure we're not firing them right now + if ( rfocusMorph.test( type + jQuery.event.triggered ) ) { + return; + } + + if ( type.indexOf( "." ) > -1 ) { + + // Namespaced trigger; create a regexp to match event type in handle() + namespaces = type.split( "." ); + type = namespaces.shift(); + namespaces.sort(); + } + ontype = type.indexOf( ":" ) < 0 && "on" + type; + + // Caller can pass in a jQuery.Event object, Object, or just an event type string + event = event[ jQuery.expando ] ? + event : + new jQuery.Event( type, typeof event === "object" && event ); + + // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true) + event.isTrigger = onlyHandlers ? 2 : 3; + event.namespace = namespaces.join( "." ); + event.rnamespace = event.namespace ? + new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) : + null; + + // Clean up the event in case it is being reused + event.result = undefined; + if ( !event.target ) { + event.target = elem; + } + + // Clone any incoming data and prepend the event, creating the handler arg list + data = data == null ? + [ event ] : + jQuery.makeArray( data, [ event ] ); + + // Allow special events to draw outside the lines + special = jQuery.event.special[ type ] || {}; + if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) { + return; + } + + // Determine event propagation path in advance, per W3C events spec (#9951) + // Bubble up to document, then to window; watch for a global ownerDocument var (#9724) + if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) { + + bubbleType = special.delegateType || type; + if ( !rfocusMorph.test( bubbleType + type ) ) { + cur = cur.parentNode; + } + for ( ; cur; cur = cur.parentNode ) { + eventPath.push( cur ); + tmp = cur; + } + + // Only add window if we got to document (e.g., not plain obj or detached DOM) + if ( tmp === ( elem.ownerDocument || document ) ) { + eventPath.push( tmp.defaultView || tmp.parentWindow || window ); + } + } + + // Fire handlers on the event path + i = 0; + while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) { + lastElement = cur; + event.type = i > 1 ? + bubbleType : + special.bindType || type; + + // jQuery handler + handle = ( dataPriv.get( cur, "events" ) || Object.create( null ) )[ event.type ] && + dataPriv.get( cur, "handle" ); + if ( handle ) { + handle.apply( cur, data ); + } + + // Native handler + handle = ontype && cur[ ontype ]; + if ( handle && handle.apply && acceptData( cur ) ) { + event.result = handle.apply( cur, data ); + if ( event.result === false ) { + event.preventDefault(); + } + } + } + event.type = type; + + // If nobody prevented the default action, do it now + if ( !onlyHandlers && !event.isDefaultPrevented() ) { + + if ( ( !special._default || + special._default.apply( eventPath.pop(), data ) === false ) && + acceptData( elem ) ) { + + // Call a native DOM method on the target with the same name as the event. + // Don't do default actions on window, that's where global variables be (#6170) + if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) { + + // Don't re-trigger an onFOO event when we call its FOO() method + tmp = elem[ ontype ]; + + if ( tmp ) { + elem[ ontype ] = null; + } + + // Prevent re-triggering of the same event, since we already bubbled it above + jQuery.event.triggered = type; + + if ( event.isPropagationStopped() ) { + lastElement.addEventListener( type, stopPropagationCallback ); + } + + elem[ type ](); + + if ( event.isPropagationStopped() ) { + lastElement.removeEventListener( type, stopPropagationCallback ); + } + + jQuery.event.triggered = undefined; + + if ( tmp ) { + elem[ ontype ] = tmp; + } + } + } + } + + return event.result; + }, + + // Piggyback on a donor event to simulate a different one + // Used only for `focus(in | out)` events + simulate: function( type, elem, event ) { + var e = jQuery.extend( + new jQuery.Event(), + event, + { + type: type, + isSimulated: true + } + ); + + jQuery.event.trigger( e, null, elem ); + } + +} ); + +jQuery.fn.extend( { + + trigger: function( type, data ) { + return this.each( function() { + jQuery.event.trigger( type, data, this ); + } ); + }, + triggerHandler: function( type, data ) { + var elem = this[ 0 ]; + if ( elem ) { + return jQuery.event.trigger( type, data, elem, true ); + } + } +} ); + + +// Support: Firefox <=44 +// Firefox doesn't have focus(in | out) events +// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787 +// +// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1 +// focus(in | out) events fire after focus & blur events, +// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order +// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857 +if ( !support.focusin ) { + jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) { + + // Attach a single capturing handler on the document while someone wants focusin/focusout + var handler = function( event ) { + jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) ); + }; + + jQuery.event.special[ fix ] = { + setup: function() { + + // Handle: regular nodes (via `this.ownerDocument`), window + // (via `this.document`) & document (via `this`). + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ); + + if ( !attaches ) { + doc.addEventListener( orig, handler, true ); + } + dataPriv.access( doc, fix, ( attaches || 0 ) + 1 ); + }, + teardown: function() { + var doc = this.ownerDocument || this.document || this, + attaches = dataPriv.access( doc, fix ) - 1; + + if ( !attaches ) { + doc.removeEventListener( orig, handler, true ); + dataPriv.remove( doc, fix ); + + } else { + dataPriv.access( doc, fix, attaches ); + } + } + }; + } ); +} +var location = window.location; + +var nonce = { guid: Date.now() }; + +var rquery = ( /\?/ ); + + + +// Cross-browser xml parsing +jQuery.parseXML = function( data ) { + var xml, parserErrorElem; + if ( !data || typeof data !== "string" ) { + return null; + } + + // Support: IE 9 - 11 only + // IE throws on parseFromString with invalid input. + try { + xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" ); + } catch ( e ) {} + + parserErrorElem = xml && xml.getElementsByTagName( "parsererror" )[ 0 ]; + if ( !xml || parserErrorElem ) { + jQuery.error( "Invalid XML: " + ( + parserErrorElem ? + jQuery.map( parserErrorElem.childNodes, function( el ) { + return el.textContent; + } ).join( "\n" ) : + data + ) ); + } + return xml; +}; + + +var + rbracket = /\[\]$/, + rCRLF = /\r?\n/g, + rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i, + rsubmittable = /^(?:input|select|textarea|keygen)/i; + +function buildParams( prefix, obj, traditional, add ) { + var name; + + if ( Array.isArray( obj ) ) { + + // Serialize array item. + jQuery.each( obj, function( i, v ) { + if ( traditional || rbracket.test( prefix ) ) { + + // Treat each array item as a scalar. + add( prefix, v ); + + } else { + + // Item is non-scalar (array or object), encode its numeric index. + buildParams( + prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]", + v, + traditional, + add + ); + } + } ); + + } else if ( !traditional && toType( obj ) === "object" ) { + + // Serialize object item. + for ( name in obj ) { + buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add ); + } + + } else { + + // Serialize scalar item. + add( prefix, obj ); + } +} + +// Serialize an array of form elements or a set of +// key/values into a query string +jQuery.param = function( a, traditional ) { + var prefix, + s = [], + add = function( key, valueOrFunction ) { + + // If value is a function, invoke it and use its return value + var value = isFunction( valueOrFunction ) ? + valueOrFunction() : + valueOrFunction; + + s[ s.length ] = encodeURIComponent( key ) + "=" + + encodeURIComponent( value == null ? "" : value ); + }; + + if ( a == null ) { + return ""; + } + + // If an array was passed in, assume that it is an array of form elements. + if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) { + + // Serialize the form elements + jQuery.each( a, function() { + add( this.name, this.value ); + } ); + + } else { + + // If traditional, encode the "old" way (the way 1.3.2 or older + // did it), otherwise encode params recursively. + for ( prefix in a ) { + buildParams( prefix, a[ prefix ], traditional, add ); + } + } + + // Return the resulting serialization + return s.join( "&" ); +}; + +jQuery.fn.extend( { + serialize: function() { + return jQuery.param( this.serializeArray() ); + }, + serializeArray: function() { + return this.map( function() { + + // Can add propHook for "elements" to filter or add form elements + var elements = jQuery.prop( this, "elements" ); + return elements ? jQuery.makeArray( elements ) : this; + } ).filter( function() { + var type = this.type; + + // Use .is( ":disabled" ) so that fieldset[disabled] works + return this.name && !jQuery( this ).is( ":disabled" ) && + rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) && + ( this.checked || !rcheckableType.test( type ) ); + } ).map( function( _i, elem ) { + var val = jQuery( this ).val(); + + if ( val == null ) { + return null; + } + + if ( Array.isArray( val ) ) { + return jQuery.map( val, function( val ) { + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ); + } + + return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) }; + } ).get(); + } +} ); + + +var + r20 = /%20/g, + rhash = /#.*$/, + rantiCache = /([?&])_=[^&]*/, + rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg, + + // #7653, #8125, #8152: local protocol detection + rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/, + rnoContent = /^(?:GET|HEAD)$/, + rprotocol = /^\/\//, + + /* Prefilters + * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example) + * 2) These are called: + * - BEFORE asking for a transport + * - AFTER param serialization (s.data is a string if s.processData is true) + * 3) key is the dataType + * 4) the catchall symbol "*" can be used + * 5) execution will start with transport dataType and THEN continue down to "*" if needed + */ + prefilters = {}, + + /* Transports bindings + * 1) key is the dataType + * 2) the catchall symbol "*" can be used + * 3) selection will start with transport dataType and THEN go to "*" if needed + */ + transports = {}, + + // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression + allTypes = "*/".concat( "*" ), + + // Anchor tag for parsing the document origin + originAnchor = document.createElement( "a" ); + +originAnchor.href = location.href; + +// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport +function addToPrefiltersOrTransports( structure ) { + + // dataTypeExpression is optional and defaults to "*" + return function( dataTypeExpression, func ) { + + if ( typeof dataTypeExpression !== "string" ) { + func = dataTypeExpression; + dataTypeExpression = "*"; + } + + var dataType, + i = 0, + dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || []; + + if ( isFunction( func ) ) { + + // For each dataType in the dataTypeExpression + while ( ( dataType = dataTypes[ i++ ] ) ) { + + // Prepend if requested + if ( dataType[ 0 ] === "+" ) { + dataType = dataType.slice( 1 ) || "*"; + ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func ); + + // Otherwise append + } else { + ( structure[ dataType ] = structure[ dataType ] || [] ).push( func ); + } + } + } + }; +} + +// Base inspection function for prefilters and transports +function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) { + + var inspected = {}, + seekingTransport = ( structure === transports ); + + function inspect( dataType ) { + var selected; + inspected[ dataType ] = true; + jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) { + var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR ); + if ( typeof dataTypeOrTransport === "string" && + !seekingTransport && !inspected[ dataTypeOrTransport ] ) { + + options.dataTypes.unshift( dataTypeOrTransport ); + inspect( dataTypeOrTransport ); + return false; + } else if ( seekingTransport ) { + return !( selected = dataTypeOrTransport ); + } + } ); + return selected; + } + + return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" ); +} + +// A special extend for ajax options +// that takes "flat" options (not to be deep extended) +// Fixes #9887 +function ajaxExtend( target, src ) { + var key, deep, + flatOptions = jQuery.ajaxSettings.flatOptions || {}; + + for ( key in src ) { + if ( src[ key ] !== undefined ) { + ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ]; + } + } + if ( deep ) { + jQuery.extend( true, target, deep ); + } + + return target; +} + +/* Handles responses to an ajax request: + * - finds the right dataType (mediates between content-type and expected dataType) + * - returns the corresponding response + */ +function ajaxHandleResponses( s, jqXHR, responses ) { + + var ct, type, finalDataType, firstDataType, + contents = s.contents, + dataTypes = s.dataTypes; + + // Remove auto dataType and get content-type in the process + while ( dataTypes[ 0 ] === "*" ) { + dataTypes.shift(); + if ( ct === undefined ) { + ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" ); + } + } + + // Check if we're dealing with a known content-type + if ( ct ) { + for ( type in contents ) { + if ( contents[ type ] && contents[ type ].test( ct ) ) { + dataTypes.unshift( type ); + break; + } + } + } + + // Check to see if we have a response for the expected dataType + if ( dataTypes[ 0 ] in responses ) { + finalDataType = dataTypes[ 0 ]; + } else { + + // Try convertible dataTypes + for ( type in responses ) { + if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) { + finalDataType = type; + break; + } + if ( !firstDataType ) { + firstDataType = type; + } + } + + // Or just use first one + finalDataType = finalDataType || firstDataType; + } + + // If we found a dataType + // We add the dataType to the list if needed + // and return the corresponding response + if ( finalDataType ) { + if ( finalDataType !== dataTypes[ 0 ] ) { + dataTypes.unshift( finalDataType ); + } + return responses[ finalDataType ]; + } +} + +/* Chain conversions given the request and the original response + * Also sets the responseXXX fields on the jqXHR instance + */ +function ajaxConvert( s, response, jqXHR, isSuccess ) { + var conv2, current, conv, tmp, prev, + converters = {}, + + // Work with a copy of dataTypes in case we need to modify it for conversion + dataTypes = s.dataTypes.slice(); + + // Create converters map with lowercased keys + if ( dataTypes[ 1 ] ) { + for ( conv in s.converters ) { + converters[ conv.toLowerCase() ] = s.converters[ conv ]; + } + } + + current = dataTypes.shift(); + + // Convert to each sequential dataType + while ( current ) { + + if ( s.responseFields[ current ] ) { + jqXHR[ s.responseFields[ current ] ] = response; + } + + // Apply the dataFilter if provided + if ( !prev && isSuccess && s.dataFilter ) { + response = s.dataFilter( response, s.dataType ); + } + + prev = current; + current = dataTypes.shift(); + + if ( current ) { + + // There's only work to do if current dataType is non-auto + if ( current === "*" ) { + + current = prev; + + // Convert response if prev dataType is non-auto and differs from current + } else if ( prev !== "*" && prev !== current ) { + + // Seek a direct converter + conv = converters[ prev + " " + current ] || converters[ "* " + current ]; + + // If none found, seek a pair + if ( !conv ) { + for ( conv2 in converters ) { + + // If conv2 outputs current + tmp = conv2.split( " " ); + if ( tmp[ 1 ] === current ) { + + // If prev can be converted to accepted input + conv = converters[ prev + " " + tmp[ 0 ] ] || + converters[ "* " + tmp[ 0 ] ]; + if ( conv ) { + + // Condense equivalence converters + if ( conv === true ) { + conv = converters[ conv2 ]; + + // Otherwise, insert the intermediate dataType + } else if ( converters[ conv2 ] !== true ) { + current = tmp[ 0 ]; + dataTypes.unshift( tmp[ 1 ] ); + } + break; + } + } + } + } + + // Apply converter (if not an equivalence) + if ( conv !== true ) { + + // Unless errors are allowed to bubble, catch and return them + if ( conv && s.throws ) { + response = conv( response ); + } else { + try { + response = conv( response ); + } catch ( e ) { + return { + state: "parsererror", + error: conv ? e : "No conversion from " + prev + " to " + current + }; + } + } + } + } + } + } + + return { state: "success", data: response }; +} + +jQuery.extend( { + + // Counter for holding the number of active queries + active: 0, + + // Last-Modified header cache for next request + lastModified: {}, + etag: {}, + + ajaxSettings: { + url: location.href, + type: "GET", + isLocal: rlocalProtocol.test( location.protocol ), + global: true, + processData: true, + async: true, + contentType: "application/x-www-form-urlencoded; charset=UTF-8", + + /* + timeout: 0, + data: null, + dataType: null, + username: null, + password: null, + cache: null, + throws: false, + traditional: false, + headers: {}, + */ + + accepts: { + "*": allTypes, + text: "text/plain", + html: "text/html", + xml: "application/xml, text/xml", + json: "application/json, text/javascript" + }, + + contents: { + xml: /\bxml\b/, + html: /\bhtml/, + json: /\bjson\b/ + }, + + responseFields: { + xml: "responseXML", + text: "responseText", + json: "responseJSON" + }, + + // Data converters + // Keys separate source (or catchall "*") and destination types with a single space + converters: { + + // Convert anything to text + "* text": String, + + // Text to html (true = no transformation) + "text html": true, + + // Evaluate text as a json expression + "text json": JSON.parse, + + // Parse text as xml + "text xml": jQuery.parseXML + }, + + // For options that shouldn't be deep extended: + // you can add your own custom options here if + // and when you create one that shouldn't be + // deep extended (see ajaxExtend) + flatOptions: { + url: true, + context: true + } + }, + + // Creates a full fledged settings object into target + // with both ajaxSettings and settings fields. + // If target is omitted, writes into ajaxSettings. + ajaxSetup: function( target, settings ) { + return settings ? + + // Building a settings object + ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) : + + // Extending ajaxSettings + ajaxExtend( jQuery.ajaxSettings, target ); + }, + + ajaxPrefilter: addToPrefiltersOrTransports( prefilters ), + ajaxTransport: addToPrefiltersOrTransports( transports ), + + // Main method + ajax: function( url, options ) { + + // If url is an object, simulate pre-1.5 signature + if ( typeof url === "object" ) { + options = url; + url = undefined; + } + + // Force options to be an object + options = options || {}; + + var transport, + + // URL without anti-cache param + cacheURL, + + // Response headers + responseHeadersString, + responseHeaders, + + // timeout handle + timeoutTimer, + + // Url cleanup var + urlAnchor, + + // Request state (becomes false upon send and true upon completion) + completed, + + // To know if global events are to be dispatched + fireGlobals, + + // Loop variable + i, + + // uncached part of the url + uncached, + + // Create the final options object + s = jQuery.ajaxSetup( {}, options ), + + // Callbacks context + callbackContext = s.context || s, + + // Context for global events is callbackContext if it is a DOM node or jQuery collection + globalEventContext = s.context && + ( callbackContext.nodeType || callbackContext.jquery ) ? + jQuery( callbackContext ) : + jQuery.event, + + // Deferreds + deferred = jQuery.Deferred(), + completeDeferred = jQuery.Callbacks( "once memory" ), + + // Status-dependent callbacks + statusCode = s.statusCode || {}, + + // Headers (they are sent all at once) + requestHeaders = {}, + requestHeadersNames = {}, + + // Default abort message + strAbort = "canceled", + + // Fake xhr + jqXHR = { + readyState: 0, + + // Builds headers hashtable if needed + getResponseHeader: function( key ) { + var match; + if ( completed ) { + if ( !responseHeaders ) { + responseHeaders = {}; + while ( ( match = rheaders.exec( responseHeadersString ) ) ) { + responseHeaders[ match[ 1 ].toLowerCase() + " " ] = + ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] ) + .concat( match[ 2 ] ); + } + } + match = responseHeaders[ key.toLowerCase() + " " ]; + } + return match == null ? null : match.join( ", " ); + }, + + // Raw string + getAllResponseHeaders: function() { + return completed ? responseHeadersString : null; + }, + + // Caches the header + setRequestHeader: function( name, value ) { + if ( completed == null ) { + name = requestHeadersNames[ name.toLowerCase() ] = + requestHeadersNames[ name.toLowerCase() ] || name; + requestHeaders[ name ] = value; + } + return this; + }, + + // Overrides response content-type header + overrideMimeType: function( type ) { + if ( completed == null ) { + s.mimeType = type; + } + return this; + }, + + // Status-dependent callbacks + statusCode: function( map ) { + var code; + if ( map ) { + if ( completed ) { + + // Execute the appropriate callbacks + jqXHR.always( map[ jqXHR.status ] ); + } else { + + // Lazy-add the new callbacks in a way that preserves old ones + for ( code in map ) { + statusCode[ code ] = [ statusCode[ code ], map[ code ] ]; + } + } + } + return this; + }, + + // Cancel the request + abort: function( statusText ) { + var finalText = statusText || strAbort; + if ( transport ) { + transport.abort( finalText ); + } + done( 0, finalText ); + return this; + } + }; + + // Attach deferreds + deferred.promise( jqXHR ); + + // Add protocol if not provided (prefilters might expect it) + // Handle falsy url in the settings object (#10093: consistency with old signature) + // We also use the url parameter if available + s.url = ( ( url || s.url || location.href ) + "" ) + .replace( rprotocol, location.protocol + "//" ); + + // Alias method option to type as per ticket #12004 + s.type = options.method || options.type || s.method || s.type; + + // Extract dataTypes list + s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ]; + + // A cross-domain request is in order when the origin doesn't match the current origin. + if ( s.crossDomain == null ) { + urlAnchor = document.createElement( "a" ); + + // Support: IE <=8 - 11, Edge 12 - 15 + // IE throws exception on accessing the href property if url is malformed, + // e.g. http://example.com:80x/ + try { + urlAnchor.href = s.url; + + // Support: IE <=8 - 11 only + // Anchor's host property isn't correctly set when s.url is relative + urlAnchor.href = urlAnchor.href; + s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !== + urlAnchor.protocol + "//" + urlAnchor.host; + } catch ( e ) { + + // If there is an error parsing the URL, assume it is crossDomain, + // it can be rejected by the transport if it is invalid + s.crossDomain = true; + } + } + + // Convert data if not already a string + if ( s.data && s.processData && typeof s.data !== "string" ) { + s.data = jQuery.param( s.data, s.traditional ); + } + + // Apply prefilters + inspectPrefiltersOrTransports( prefilters, s, options, jqXHR ); + + // If request was aborted inside a prefilter, stop there + if ( completed ) { + return jqXHR; + } + + // We can fire global events as of now if asked to + // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118) + fireGlobals = jQuery.event && s.global; + + // Watch for a new set of requests + if ( fireGlobals && jQuery.active++ === 0 ) { + jQuery.event.trigger( "ajaxStart" ); + } + + // Uppercase the type + s.type = s.type.toUpperCase(); + + // Determine if request has content + s.hasContent = !rnoContent.test( s.type ); + + // Save the URL in case we're toying with the If-Modified-Since + // and/or If-None-Match header later on + // Remove hash to simplify url manipulation + cacheURL = s.url.replace( rhash, "" ); + + // More options handling for requests with no content + if ( !s.hasContent ) { + + // Remember the hash so we can put it back + uncached = s.url.slice( cacheURL.length ); + + // If data is available and should be processed, append data to url + if ( s.data && ( s.processData || typeof s.data === "string" ) ) { + cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data; + + // #9682: remove data so that it's not used in an eventual retry + delete s.data; + } + + // Add or update anti-cache param if needed + if ( s.cache === false ) { + cacheURL = cacheURL.replace( rantiCache, "$1" ); + uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) + + uncached; + } + + // Put hash and anti-cache on the URL that will be requested (gh-1732) + s.url = cacheURL + uncached; + + // Change '%20' to '+' if this is encoded form body content (gh-2658) + } else if ( s.data && s.processData && + ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) { + s.data = s.data.replace( r20, "+" ); + } + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + if ( jQuery.lastModified[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] ); + } + if ( jQuery.etag[ cacheURL ] ) { + jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] ); + } + } + + // Set the correct header, if data is being sent + if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) { + jqXHR.setRequestHeader( "Content-Type", s.contentType ); + } + + // Set the Accepts header for the server, depending on the dataType + jqXHR.setRequestHeader( + "Accept", + s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ? + s.accepts[ s.dataTypes[ 0 ] ] + + ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) : + s.accepts[ "*" ] + ); + + // Check for headers option + for ( i in s.headers ) { + jqXHR.setRequestHeader( i, s.headers[ i ] ); + } + + // Allow custom headers/mimetypes and early abort + if ( s.beforeSend && + ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) { + + // Abort if not done already and return + return jqXHR.abort(); + } + + // Aborting is no longer a cancellation + strAbort = "abort"; + + // Install callbacks on deferreds + completeDeferred.add( s.complete ); + jqXHR.done( s.success ); + jqXHR.fail( s.error ); + + // Get transport + transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR ); + + // If no transport, we auto-abort + if ( !transport ) { + done( -1, "No Transport" ); + } else { + jqXHR.readyState = 1; + + // Send global event + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] ); + } + + // If request was aborted inside ajaxSend, stop there + if ( completed ) { + return jqXHR; + } + + // Timeout + if ( s.async && s.timeout > 0 ) { + timeoutTimer = window.setTimeout( function() { + jqXHR.abort( "timeout" ); + }, s.timeout ); + } + + try { + completed = false; + transport.send( requestHeaders, done ); + } catch ( e ) { + + // Rethrow post-completion exceptions + if ( completed ) { + throw e; + } + + // Propagate others as results + done( -1, e ); + } + } + + // Callback for when everything is done + function done( status, nativeStatusText, responses, headers ) { + var isSuccess, success, error, response, modified, + statusText = nativeStatusText; + + // Ignore repeat invocations + if ( completed ) { + return; + } + + completed = true; + + // Clear timeout if it exists + if ( timeoutTimer ) { + window.clearTimeout( timeoutTimer ); + } + + // Dereference transport for early garbage collection + // (no matter how long the jqXHR object will be used) + transport = undefined; + + // Cache response headers + responseHeadersString = headers || ""; + + // Set readyState + jqXHR.readyState = status > 0 ? 4 : 0; + + // Determine if successful + isSuccess = status >= 200 && status < 300 || status === 304; + + // Get response data + if ( responses ) { + response = ajaxHandleResponses( s, jqXHR, responses ); + } + + // Use a noop converter for missing script but not if jsonp + if ( !isSuccess && + jQuery.inArray( "script", s.dataTypes ) > -1 && + jQuery.inArray( "json", s.dataTypes ) < 0 ) { + s.converters[ "text script" ] = function() {}; + } + + // Convert no matter what (that way responseXXX fields are always set) + response = ajaxConvert( s, response, jqXHR, isSuccess ); + + // If successful, handle type chaining + if ( isSuccess ) { + + // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode. + if ( s.ifModified ) { + modified = jqXHR.getResponseHeader( "Last-Modified" ); + if ( modified ) { + jQuery.lastModified[ cacheURL ] = modified; + } + modified = jqXHR.getResponseHeader( "etag" ); + if ( modified ) { + jQuery.etag[ cacheURL ] = modified; + } + } + + // if no content + if ( status === 204 || s.type === "HEAD" ) { + statusText = "nocontent"; + + // if not modified + } else if ( status === 304 ) { + statusText = "notmodified"; + + // If we have data, let's convert it + } else { + statusText = response.state; + success = response.data; + error = response.error; + isSuccess = !error; + } + } else { + + // Extract error from statusText and normalize for non-aborts + error = statusText; + if ( status || !statusText ) { + statusText = "error"; + if ( status < 0 ) { + status = 0; + } + } + } + + // Set data for the fake xhr object + jqXHR.status = status; + jqXHR.statusText = ( nativeStatusText || statusText ) + ""; + + // Success/Error + if ( isSuccess ) { + deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] ); + } else { + deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] ); + } + + // Status-dependent callbacks + jqXHR.statusCode( statusCode ); + statusCode = undefined; + + if ( fireGlobals ) { + globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError", + [ jqXHR, s, isSuccess ? success : error ] ); + } + + // Complete + completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] ); + + if ( fireGlobals ) { + globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] ); + + // Handle the global AJAX counter + if ( !( --jQuery.active ) ) { + jQuery.event.trigger( "ajaxStop" ); + } + } + } + + return jqXHR; + }, + + getJSON: function( url, data, callback ) { + return jQuery.get( url, data, callback, "json" ); + }, + + getScript: function( url, callback ) { + return jQuery.get( url, undefined, callback, "script" ); + } +} ); + +jQuery.each( [ "get", "post" ], function( _i, method ) { + jQuery[ method ] = function( url, data, callback, type ) { + + // Shift arguments if data argument was omitted + if ( isFunction( data ) ) { + type = type || callback; + callback = data; + data = undefined; + } + + // The url can be an options object (which then must have .url) + return jQuery.ajax( jQuery.extend( { + url: url, + type: method, + dataType: type, + data: data, + success: callback + }, jQuery.isPlainObject( url ) && url ) ); + }; +} ); + +jQuery.ajaxPrefilter( function( s ) { + var i; + for ( i in s.headers ) { + if ( i.toLowerCase() === "content-type" ) { + s.contentType = s.headers[ i ] || ""; + } + } +} ); + + +jQuery._evalUrl = function( url, options, doc ) { + return jQuery.ajax( { + url: url, + + // Make this explicit, since user can override this through ajaxSetup (#11264) + type: "GET", + dataType: "script", + cache: true, + async: false, + global: false, + + // Only evaluate the response if it is successful (gh-4126) + // dataFilter is not invoked for failure responses, so using it instead + // of the default converter is kludgy but it works. + converters: { + "text script": function() {} + }, + dataFilter: function( response ) { + jQuery.globalEval( response, options, doc ); + } + } ); +}; + + +jQuery.fn.extend( { + wrapAll: function( html ) { + var wrap; + + if ( this[ 0 ] ) { + if ( isFunction( html ) ) { + html = html.call( this[ 0 ] ); + } + + // The elements to wrap the target around + wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true ); + + if ( this[ 0 ].parentNode ) { + wrap.insertBefore( this[ 0 ] ); + } + + wrap.map( function() { + var elem = this; + + while ( elem.firstElementChild ) { + elem = elem.firstElementChild; + } + + return elem; + } ).append( this ); + } + + return this; + }, + + wrapInner: function( html ) { + if ( isFunction( html ) ) { + return this.each( function( i ) { + jQuery( this ).wrapInner( html.call( this, i ) ); + } ); + } + + return this.each( function() { + var self = jQuery( this ), + contents = self.contents(); + + if ( contents.length ) { + contents.wrapAll( html ); + + } else { + self.append( html ); + } + } ); + }, + + wrap: function( html ) { + var htmlIsFunction = isFunction( html ); + + return this.each( function( i ) { + jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html ); + } ); + }, + + unwrap: function( selector ) { + this.parent( selector ).not( "body" ).each( function() { + jQuery( this ).replaceWith( this.childNodes ); + } ); + return this; + } +} ); + + +jQuery.expr.pseudos.hidden = function( elem ) { + return !jQuery.expr.pseudos.visible( elem ); +}; +jQuery.expr.pseudos.visible = function( elem ) { + return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length ); +}; + + + + +jQuery.ajaxSettings.xhr = function() { + try { + return new window.XMLHttpRequest(); + } catch ( e ) {} +}; + +var xhrSuccessStatus = { + + // File protocol always yields status code 0, assume 200 + 0: 200, + + // Support: IE <=9 only + // #1450: sometimes IE returns 1223 when it should be 204 + 1223: 204 + }, + xhrSupported = jQuery.ajaxSettings.xhr(); + +support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported ); +support.ajax = xhrSupported = !!xhrSupported; + +jQuery.ajaxTransport( function( options ) { + var callback, errorCallback; + + // Cross domain only allowed if supported through XMLHttpRequest + if ( support.cors || xhrSupported && !options.crossDomain ) { + return { + send: function( headers, complete ) { + var i, + xhr = options.xhr(); + + xhr.open( + options.type, + options.url, + options.async, + options.username, + options.password + ); + + // Apply custom fields if provided + if ( options.xhrFields ) { + for ( i in options.xhrFields ) { + xhr[ i ] = options.xhrFields[ i ]; + } + } + + // Override mime type if needed + if ( options.mimeType && xhr.overrideMimeType ) { + xhr.overrideMimeType( options.mimeType ); + } + + // X-Requested-With header + // For cross-domain requests, seeing as conditions for a preflight are + // akin to a jigsaw puzzle, we simply never set it to be sure. + // (it can always be set on a per-request basis or even using ajaxSetup) + // For same-domain requests, won't change header if already provided. + if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) { + headers[ "X-Requested-With" ] = "XMLHttpRequest"; + } + + // Set headers + for ( i in headers ) { + xhr.setRequestHeader( i, headers[ i ] ); + } + + // Callback + callback = function( type ) { + return function() { + if ( callback ) { + callback = errorCallback = xhr.onload = + xhr.onerror = xhr.onabort = xhr.ontimeout = + xhr.onreadystatechange = null; + + if ( type === "abort" ) { + xhr.abort(); + } else if ( type === "error" ) { + + // Support: IE <=9 only + // On a manual native abort, IE9 throws + // errors on any property access that is not readyState + if ( typeof xhr.status !== "number" ) { + complete( 0, "error" ); + } else { + complete( + + // File: protocol always yields status 0; see #8605, #14207 + xhr.status, + xhr.statusText + ); + } + } else { + complete( + xhrSuccessStatus[ xhr.status ] || xhr.status, + xhr.statusText, + + // Support: IE <=9 only + // IE9 has no XHR2 but throws on binary (trac-11426) + // For XHR2 non-text, let the caller handle it (gh-2498) + ( xhr.responseType || "text" ) !== "text" || + typeof xhr.responseText !== "string" ? + { binary: xhr.response } : + { text: xhr.responseText }, + xhr.getAllResponseHeaders() + ); + } + } + }; + }; + + // Listen to events + xhr.onload = callback(); + errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" ); + + // Support: IE 9 only + // Use onreadystatechange to replace onabort + // to handle uncaught aborts + if ( xhr.onabort !== undefined ) { + xhr.onabort = errorCallback; + } else { + xhr.onreadystatechange = function() { + + // Check readyState before timeout as it changes + if ( xhr.readyState === 4 ) { + + // Allow onerror to be called first, + // but that will not handle a native abort + // Also, save errorCallback to a variable + // as xhr.onerror cannot be accessed + window.setTimeout( function() { + if ( callback ) { + errorCallback(); + } + } ); + } + }; + } + + // Create the abort callback + callback = callback( "abort" ); + + try { + + // Do send the request (this may raise an exception) + xhr.send( options.hasContent && options.data || null ); + } catch ( e ) { + + // #14683: Only rethrow if this hasn't been notified as an error yet + if ( callback ) { + throw e; + } + } + }, + + abort: function() { + if ( callback ) { + callback(); + } + } + }; + } +} ); + + + + +// Prevent auto-execution of scripts when no explicit dataType was provided (See gh-2432) +jQuery.ajaxPrefilter( function( s ) { + if ( s.crossDomain ) { + s.contents.script = false; + } +} ); + +// Install script dataType +jQuery.ajaxSetup( { + accepts: { + script: "text/javascript, application/javascript, " + + "application/ecmascript, application/x-ecmascript" + }, + contents: { + script: /\b(?:java|ecma)script\b/ + }, + converters: { + "text script": function( text ) { + jQuery.globalEval( text ); + return text; + } + } +} ); + +// Handle cache's special case and crossDomain +jQuery.ajaxPrefilter( "script", function( s ) { + if ( s.cache === undefined ) { + s.cache = false; + } + if ( s.crossDomain ) { + s.type = "GET"; + } +} ); + +// Bind script tag hack transport +jQuery.ajaxTransport( "script", function( s ) { + + // This transport only deals with cross domain or forced-by-attrs requests + if ( s.crossDomain || s.scriptAttrs ) { + var script, callback; + return { + send: function( _, complete ) { + script = jQuery( " + + + + + + + + + Skip to contents + + +
    +
    +
    +
    +

    Reference Based Multiple Imputation (rbmi) +

    +
    +

    Overview +

    +

    rbmi is a R package for imputation of missing data in clinical trials with continuous multivariate normal longitudinal outcomes. It supports imputation under a missing at random (MAR) assumption, reference-based imputation methods, and delta adjustments (as required for sensitivity analysis such as tipping point analyses). The package implements both Bayesian and approximate Bayesian multiple imputation combined with Rubin’s rules for inference, and frequentist conditional mean imputation combined with (jackknife or bootstrap) resampling.

    +
    +
    +

    Installation +

    +

    The package can be installed directly from CRAN via:

    +
    install.packages("rbmi")
    +
    +
    +

    Usage +

    +

    The package is designed around its 4 core functions:

    +
      +
    • +draws() - Fits multiple imputation models
      • +
    • +impute() - Imputes multiple datasets
      • +
    • +analyse() - Analyses multiple datasets
      • +
    • +pool() - Pools multiple results into a single statistic
      • +
    +

    The basic usage of these core functions is described in the quickstart vignette:

    +
    vignette(topic = "quickstart", package = "rbmi")
    +
    +
    +

    Support +

    +

    For any help with regards to using the package or if you find a bug please create a GitHub issue

    +
    +
    + +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    +
    + + + + + + + diff --git a/latest-tag/katex-auto.js b/latest-tag/katex-auto.js new file mode 100644 index 00000000..20651d9f --- /dev/null +++ b/latest-tag/katex-auto.js @@ -0,0 +1,14 @@ +// https://github.com/jgm/pandoc/blob/29fa97ab96b8e2d62d48326e1b949a71dc41f47a/src/Text/Pandoc/Writers/HTML.hs#L332-L345 +document.addEventListener("DOMContentLoaded", function () { + var mathElements = document.getElementsByClassName("math"); + var macros = []; + for (var i = 0; i < mathElements.length; i++) { + var texText = mathElements[i].firstChild; + if (mathElements[i].tagName == "SPAN") { + katex.render(texText.data, mathElements[i], { + displayMode: mathElements[i].classList.contains("display"), + throwOnError: false, + macros: macros, + fleqn: false + }); + }}}); diff --git a/latest-tag/lightswitch.js b/latest-tag/lightswitch.js new file mode 100644 index 00000000..9467125a --- /dev/null +++ b/latest-tag/lightswitch.js @@ -0,0 +1,85 @@ + +/*! + * Color mode toggler for Bootstrap's docs (https://getbootstrap.com/) + * Copyright 2011-2023 The Bootstrap Authors + * Licensed under the Creative Commons Attribution 3.0 Unported License. + * Updates for {pkgdown} by the {bslib} authors, also licensed under CC-BY-3.0. + */ + +const getStoredTheme = () => localStorage.getItem('theme') +const setStoredTheme = theme => localStorage.setItem('theme', theme) + +const getPreferredTheme = () => { + const storedTheme = getStoredTheme() + if (storedTheme) { + return storedTheme + } + + return window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light' +} + +const setTheme = theme => { + if (theme === 'auto') { + document.documentElement.setAttribute('data-bs-theme', (window.matchMedia('(prefers-color-scheme: dark)').matches ? 'dark' : 'light')) + } else { + document.documentElement.setAttribute('data-bs-theme', theme) + } +} + +function bsSetupThemeToggle () { + 'use strict' + + const showActiveTheme = (theme, focus = false) => { + var activeLabel, activeIcon; + + document.querySelectorAll('[data-bs-theme-value]').forEach(element => { + const buttonTheme = element.getAttribute('data-bs-theme-value') + const isActive = buttonTheme == theme + + element.classList.toggle('active', isActive) + element.setAttribute('aria-pressed', isActive) + + if (isActive) { + activeLabel = element.textContent; + activeIcon = element.querySelector('span').classList.value; + } + }) + + const themeSwitcher = document.querySelector('#dropdown-lightswitch') + if (!themeSwitcher) { + return + } + + themeSwitcher.setAttribute('aria-label', activeLabel) + themeSwitcher.querySelector('span').classList.value = activeIcon; + + if (focus) { + themeSwitcher.focus() + } + } + + window.matchMedia('(prefers-color-scheme: dark)').addEventListener('change', () => { + const storedTheme = getStoredTheme() + if (storedTheme !== 'light' && storedTheme !== 'dark') { + setTheme(getPreferredTheme()) + } + }) + + window.addEventListener('DOMContentLoaded', () => { + showActiveTheme(getPreferredTheme()) + + document + .querySelectorAll('[data-bs-theme-value]') + .forEach(toggle => { + toggle.addEventListener('click', () => { + const theme = toggle.getAttribute('data-bs-theme-value') + setTheme(theme) + setStoredTheme(theme) + showActiveTheme(theme, true) + }) + }) + }) +} + +setTheme(getPreferredTheme()); +bsSetupThemeToggle(); diff --git a/latest-tag/link.svg b/latest-tag/link.svg new file mode 100644 index 00000000..88ad8276 --- /dev/null +++ b/latest-tag/link.svg @@ -0,0 +1,12 @@ + + + + + + diff --git a/latest-tag/news/index.html b/latest-tag/news/index.html new file mode 100644 index 00000000..7a35370c --- /dev/null +++ b/latest-tag/news/index.html @@ -0,0 +1,106 @@ + +Changelog • rbmi + Skip to contents + + +
    +
    +
    + +

    Changelog

    + Source: NEWS.md +
    + +
    +

    rbmi 1.2.6

    CRAN release: 2023-11-24

    +
    • Updated unit tests to fix false-positive error on CRAN’s testing servers
      • +
    +
    +

    rbmi 1.2.5

    CRAN release: 2023-09-20

    +
    • Updated internal Stan code to ensure future compatibility (@andrjohns, #390)
      • +
    • Updated package description to include relevant references (#393)
      • +
    • Fixed documentation typos (#393)
      • +
    +
    +

    rbmi 1.2.3

    CRAN release: 2022-11-14

    +
    • Minor internal tweaks to ensure compatibility with the packages rbmi depends on
      • +
    +
    +

    rbmi 1.2.1

    CRAN release: 2022-10-25

    +
    • Removed native pipes |> in testing code so package is backwards compatible with older servers
      • +
    • Replaced our glmmTMB dependency with the mmrm package. This has resulted in the package being more stable (less model fitting convergence issues) as well as speeding up run times 3-fold.
      • +
    +
    +

    rbmi 1.1.4

    CRAN release: 2022-05-18

    +
    • Updated urls for references in vignettes
      • +
    • Fixed a bug where visit factor levels were re-constructed incorrectly in delta_template() +
      • +
    • Fixed a bug where the wrong visit was displayed in the error message for when a specific visit doesn’t have any data in draws() +
      • +
    • Fixed a bug where the wrong input parameter was displayed in an error message in simulate_data() +
      • +
    +
    +

    rbmi 1.1.1 & 1.1.3

    CRAN release: 2022-03-08

    +
    • No change in functionality from 1.1.0
      • +
    • Various minor tweaks to address CRAN checks messages
      • +
    +
    +

    rbmi 1.1.0

    CRAN release: 2022-03-02

    +
    • Initial public release
      • +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/pkgdown.js b/latest-tag/pkgdown.js new file mode 100644 index 00000000..9757bf9e --- /dev/null +++ b/latest-tag/pkgdown.js @@ -0,0 +1,154 @@ +/* http://gregfranko.com/blog/jquery-best-practices/ */ +(function($) { + $(function() { + + $('nav.navbar').headroom(); + + Toc.init({ + $nav: $("#toc"), + $scope: $("main h2, main h3, main h4, main h5, main h6") + }); + + if ($('#toc').length) { + $('body').scrollspy({ + target: '#toc', + offset: $("nav.navbar").outerHeight() + 1 + }); + } + + // Activate popovers + $('[data-bs-toggle="popover"]').popover({ + container: 'body', + html: true, + trigger: 'focus', + placement: "top", + sanitize: false, + }); + + $('[data-bs-toggle="tooltip"]').tooltip(); + + /* Clipboard --------------------------*/ + + function changeTooltipMessage(element, msg) { + var tooltipOriginalTitle=element.getAttribute('data-bs-original-title'); + element.setAttribute('data-bs-original-title', msg); + $(element).tooltip('show'); + element.setAttribute('data-bs-original-title', tooltipOriginalTitle); + } + + if(ClipboardJS.isSupported()) { + $(document).ready(function() { + var copyButton = ""; + + $("div.sourceCode").addClass("hasCopyButton"); + + // Insert copy buttons: + $(copyButton).prependTo(".hasCopyButton"); + + // Initialize tooltips: + $('.btn-copy-ex').tooltip({container: 'body'}); + + // Initialize clipboard: + var clipboard = new ClipboardJS('[data-clipboard-copy]', { + text: function(trigger) { + return trigger.parentNode.textContent.replace(/\n#>[^\n]*/g, ""); + } + }); + + clipboard.on('success', function(e) { + changeTooltipMessage(e.trigger, 'Copied!'); + e.clearSelection(); + }); + + clipboard.on('error', function(e) { + changeTooltipMessage(e.trigger,'Press Ctrl+C or Command+C to copy'); + }); + + }); + } + + /* Search marking --------------------------*/ + var url = new URL(window.location.href); + var toMark = url.searchParams.get("q"); + var mark = new Mark("main#main"); + if (toMark) { + mark.mark(toMark, { + accuracy: { + value: "complementary", + limiters: [",", ".", ":", "/"], + } + }); + } + + /* Search --------------------------*/ + /* Adapted from https://github.com/rstudio/bookdown/blob/2d692ba4b61f1e466c92e78fd712b0ab08c11d31/inst/resources/bs4_book/bs4_book.js#L25 */ + // Initialise search index on focus + var fuse; + $("#search-input").focus(async function(e) { + if (fuse) { + return; + } + + $(e.target).addClass("loading"); + var response = await fetch($("#search-input").data("search-index")); + var data = await response.json(); + + var options = { + keys: ["what", "text", "code"], + ignoreLocation: true, + threshold: 0.1, + includeMatches: true, + includeScore: true, + }; + fuse = new Fuse(data, options); + + $(e.target).removeClass("loading"); + }); + + // Use algolia autocomplete + var options = { + autoselect: true, + debug: true, + hint: false, + minLength: 2, + }; + var q; +async function searchFuse(query, callback) { + await fuse; + + var items; + if (!fuse) { + items = []; + } else { + q = query; + var results = fuse.search(query, { limit: 20 }); + items = results + .filter((x) => x.score <= 0.75) + .map((x) => x.item); + if (items.length === 0) { + items = [{dir:"Sorry 😿",previous_headings:"",title:"No results found.",what:"No results found.",path:window.location.href}]; + } + } + callback(items); +} + $("#search-input").autocomplete(options, [ + { + name: "content", + source: searchFuse, + templates: { + suggestion: (s) => { + if (s.title == s.what) { + return `${s.dir} >
    ${s.title}
    `; + } else if (s.previous_headings == "") { + return `${s.dir} >
    ${s.title}
    > ${s.what}`; + } else { + return `${s.dir} >
    ${s.title}
    > ${s.previous_headings} > ${s.what}`; + } + }, + }, + }, + ]).on('autocomplete:selected', function(event, s) { + window.location.href = s.path + "?q=" + q + "#" + s.id; + }); + }); +})(window.jQuery || window.$) diff --git a/latest-tag/pkgdown.yml b/latest-tag/pkgdown.yml new file mode 100644 index 00000000..62b16bc2 --- /dev/null +++ b/latest-tag/pkgdown.yml @@ -0,0 +1,8 @@ +pandoc: '3.3' +pkgdown: 2.1.0 +pkgdown_sha: ~ +articles: + advanced: advanced.html + quickstart: quickstart.html + stat_specs: stat_specs.html +last_built: 2024-09-24T15:03Z diff --git a/latest-tag/reference/QR_decomp.html b/latest-tag/reference/QR_decomp.html new file mode 100644 index 00000000..cbc5d0f3 --- /dev/null +++ b/latest-tag/reference/QR_decomp.html @@ -0,0 +1,90 @@ + +QR decomposition — QR_decomp • rbmi + Skip to contents + + +
    +
    +
    + +

    QR decomposition

    + Source: R/mcmc.R +
    QR_decomp.Rd
    +
    + +
    +

    QR decomposition as defined in the +Stan user's guide (section 1.2).

    +
    + +
    +

    Usage

    + 
    QR_decomp(mat)
    +
    + +
    +

    Arguments

    + + +
    mat
    +

    A matrix to perform the QR decomposition on.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/Rplot001.png b/latest-tag/reference/Rplot001.png new file mode 100644 index 0000000000000000000000000000000000000000..17a358060aed2a86950757bbd25c6f92c08c458f GIT binary patch literal 1011 zcmeAS@N?(olHy`uVBq!ia0y~yV0-|=9Be?5+AI5}0x7m6Z+90U4Fo@(ch>_c&H|6f zVg?3oArNM~bhqvg0|WD9PZ!6KiaBo&GBN^{G%5UFpXcEKVvd5*5Eu=C0SJK)8A6*F U7`aXvEC5;V>FVdQ&MBb@00SN#Z2$lO literal 0 HcmV?d00001 diff --git a/latest-tag/reference/Stack.html b/latest-tag/reference/Stack.html new file mode 100644 index 00000000..40ca4c6a --- /dev/null +++ b/latest-tag/reference/Stack.html @@ -0,0 +1,144 @@ + +R6 Class for a FIFO stack — Stack • rbmi + Skip to contents + + +
    +
    +
    + +

    R6 Class for a FIFO stack

    + Source: R/stack.R +
    Stack.Rd
    +
    + +
    +

    This is a simple stack object offering add / pop functionality

    +
    + + +
    +

    Public fields

    +

    stack
    +

    A list containing the current stack

    + + +

    +
    +
    +

    Methods

    + +
    +

    Public methods

    + +

    +

    Method add()

    +

    Adds content to the end of the stack (must be a list)

    +

    Usage

    +

    Stack$add(x)

    +
    + +
    +

    Arguments

    +

    x
    +

    content to add to the stack

    + + +

    +
    + +

    +

    Method pop()

    +

    Retrieve content from the stack

    +

    Usage

    +

    Stack$pop(i)

    +
    + +
    +

    Arguments

    +

    i
    +

    the number of items to retrieve from the stack. If there are less than i +items left on the stack it will just return everything that is left.

    + + +

    +
    + +

    +

    Method clone()

    +

    The objects of this class are cloneable with this method.

    +

    Usage

    +

    Stack$clone(deep = FALSE)

    +
    + +
    +

    Arguments

    +

    deep
    +

    Whether to make a deep clone.

    + + +

    +
    + +
    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/add_class.html b/latest-tag/reference/add_class.html new file mode 100644 index 00000000..38b948ea --- /dev/null +++ b/latest-tag/reference/add_class.html @@ -0,0 +1,94 @@ + +Add a class — add_class • rbmi + Skip to contents + + +
    +
    +
    + +

    Add a class

    + Source: R/utilities.R +
    add_class.Rd
    +
    + +
    +

    Utility function to add a class to an object. Adds the new class +after any existing classes.

    +
    + +
    +

    Usage

    + 
    add_class(x, cls)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    object to add a class to.

    + + +
    cls
    +

    the class to be added.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/adjust_trajectories.html b/latest-tag/reference/adjust_trajectories.html new file mode 100644 index 00000000..01827594 --- /dev/null +++ b/latest-tag/reference/adjust_trajectories.html @@ -0,0 +1,132 @@ + +Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories • rbmi + Skip to contents + + +
    +
    +
    + +

    Adjust trajectories due to the intercurrent event (ICE)

    + Source: R/simulate_data.R +
    adjust_trajectories.Rd
    +
    + +
    +

    Adjust trajectories due to the intercurrent event (ICE)

    +
    + +
    +

    Usage

    + 
    adjust_trajectories(
+  distr_pars_group,
+  outcome,
+  ids,
+  ind_ice,
+  strategy_fun,
+  distr_pars_ref = NULL
+)
    +
    + +
    +

    Arguments

    + + +
    distr_pars_group
    +

    Named list containing the simulation parameters of the multivariate +normal distribution assumed for the given treatment group. It contains the following elements:

    • mu: Numeric vector indicating the mean outcome trajectory. It should include the outcome +at baseline.

      • +

    • sigma Covariance matrix of the outcome trajectory.

      • +
    + + +
    outcome
    +

    Numeric variable that specifies the longitudinal outcome.

    + + +
    ids
    +

    Factor variable that specifies the id of each subject.

    + + +
    ind_ice
    +

    A binary variable that takes value 1 if the corresponding outcome is affected +by the ICE and 0 otherwise.

    + + +
    strategy_fun
    +

    Function implementing trajectories after the intercurrent event (ICE). Must +be one of getStrategies(). See getStrategies() for details.

    + + +
    distr_pars_ref
    +

    Optional. Named list containing the simulation parameters of the +reference arm. It contains the following elements:

    • mu: Numeric vector indicating the mean outcome trajectory assuming no ICEs. It should +include the outcome at baseline.

      • +

    • sigma Covariance matrix of the outcome trajectory assuming no ICEs.

      • +
    + +
    +
    +

    Value

    +

    A numeric vector containing the adjusted trajectories.

    +
    +
    +

    See also

    +

    adjust_trajectories_single().

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/adjust_trajectories_single.html b/latest-tag/reference/adjust_trajectories_single.html new file mode 100644 index 00000000..d3dff856 --- /dev/null +++ b/latest-tag/reference/adjust_trajectories_single.html @@ -0,0 +1,123 @@ + +Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single • rbmi + Skip to contents + + +
    +
    +
    + +

    Adjust trajectory of a subject's outcome due to the intercurrent event (ICE)

    + Source: R/simulate_data.R +
    adjust_trajectories_single.Rd
    +
    + +
    +

    Adjust trajectory of a subject's outcome due to the intercurrent event (ICE)

    +
    + +
    +

    Usage

    + 
    adjust_trajectories_single(
+  distr_pars_group,
+  outcome,
+  strategy_fun,
+  distr_pars_ref = NULL
+)
    +
    + +
    +

    Arguments

    + + +
    distr_pars_group
    +

    Named list containing the simulation parameters of the multivariate +normal distribution assumed for the given treatment group. It contains the following elements:

    • mu: Numeric vector indicating the mean outcome trajectory. It should include the +outcome at baseline.

      • +

    • sigma Covariance matrix of the outcome trajectory.

      • +
    + + +
    outcome
    +

    Numeric variable that specifies the longitudinal outcome.

    + + +
    strategy_fun
    +

    Function implementing trajectories after the intercurrent event (ICE). +Must be one of getStrategies(). See getStrategies() for details.

    + + +
    distr_pars_ref
    +

    Optional. Named list containing the simulation parameters of the +reference arm. It contains the following elements:

    • mu: Numeric vector indicating the mean outcome trajectory assuming no ICEs. It should +include the outcome at baseline.

      • +

    • sigma Covariance matrix of the outcome trajectory assuming no ICEs.

      • +
    + +
    +
    +

    Value

    +

    A numeric vector containing the adjusted trajectory for a single subject.

    +
    +
    +

    Details

    +

    outcome should be specified such that all-and-only the post-ICE observations +(i.e. the +observations to be adjusted) are set to NA.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/analyse.html b/latest-tag/reference/analyse.html new file mode 100644 index 00000000..4881cb19 --- /dev/null +++ b/latest-tag/reference/analyse.html @@ -0,0 +1,204 @@ + +Analyse Multiple Imputed Datasets — analyse • rbmi + Skip to contents + + +
    +
    +
    + +

    Analyse Multiple Imputed Datasets

    + Source: R/analyse.R +
    analyse.Rd
    +
    + +
    +

    This function takes multiple imputed datasets (as generated by +the impute() function) and runs an analysis function on +each of them.

    +
    + +
    +

    Usage

    + 
    analyse(imputations, fun = ancova, delta = NULL, ...)
    +
    + +
    +

    Arguments

    + + +
    imputations
    +

    An imputations object as created by impute().

    + + +
    fun
    +

    An analysis function to be applied to each imputed dataset. See details.

    + + +
    delta
    +

    A data.frame containing the delta transformation to be applied to the imputed +datasets prior to running fun. See details.

    + + +
    ...
    +

    Additional arguments passed onto fun.

    + +
    +
    +

    Details

    +

    This function works by performing the following steps:

    1. Extract a dataset from the imputations object.

      2. +

    2. Apply any delta adjustments as specified by the delta argument.

      3. +

    3. Run the analysis function fun on the dataset.

      4. +

    4. Repeat steps 1-3 across all of the datasets inside the imputations +object.

      5. +

    5. Collect and return all of the analysis results.

      6. +

    The analysis function fun must take a data.frame as its first +argument. All other options to analyse() are passed onto fun +via .... +fun must return a named list with each element itself being a +list containing a single +numeric element called est (or additionally se and df if +you had originally specified method_bayes() or method_approxbayes()) +i.e.:

    +

    myfun <- function(dat, ...) {
+    mod_1 <- lm(data = dat, outcome ~ group)
+    mod_2 <- lm(data = dat, outcome ~ group + covar)
+    x <- list(
+        trt_1 = list(
+            est = coef(mod_1)[[group]],
+            se = sqrt(vcov(mod_1)[group, group]),
+            df = df.residual(mod_1)
+        ),
+        trt_2 = list(
+            est = coef(mod_2)[[group]],
+            se = sqrt(vcov(mod_2)[group, group]),
+            df = df.residual(mod_2)
+        )
+     )
+     return(x)
+ }

    +

    Please note that the vars$subjid column (as defined in the original call to +draws()) will be scrambled in the data.frames that are provided to fun. +This is to say they will not contain the original subject values and as such +any hard coding of subject ids is strictly to be avoided.

    +

    By default fun is the ancova() function. +Please note that this function +requires that a vars object, as created by set_vars(), is provided via +the vars argument e.g. analyse(imputeObj, vars = set_vars(...)). Please +see the documentation for ancova() for full details. +Please also note that the theoretical justification for the conditional mean imputation +method (method = method_condmean() in draws()) relies on the fact that ANCOVA is +a linear transformation of the outcomes. +Thus care is required when applying alternative analysis functions in this setting.

    +

    The delta argument can be used to specify offsets to be applied +to the outcome variable in the imputed datasets prior to the analysis. +This is typically used for sensitivity or tipping point analyses. The +delta dataset must contain columns vars$subjid, vars$visit (as specified +in the original call to draws()) and delta. Essentially this data.frame +is merged onto the imputed dataset by vars$subjid and vars$visit and then +the outcome variable is modified by:

    +

    imputed_data[[vars$outcome]] <- imputed_data[[vars$outcome]] + imputed_data[["delta"]]

    +

    Please note that in order to provide maximum flexibility, the delta argument +can be used to modify any/all outcome values including those that were not +imputed. Care must be taken when defining offsets. It is recommend that you +use the helper function delta_template() to define the delta datasets as +this provides utility variables such as is_missing which can be used to identify +exactly which visits have been imputed.

    +
    +
    +

    See also

    +

    extract_imputed_dfs() for manually extracting imputed +datasets.

    +

    delta_template() for creating delta data.frames.

    +

    ancova() for the default analysis function.

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+vars <- set_vars(
+    subjid = "subjid",
+    visit = "visit",
+    outcome = "outcome",
+    group = "group",
+    covariates = c("sex", "age", "sex*age")
+)
+
+analyse(
+    imputations = imputeObj,
+    vars = vars
+)
+
+deltadf <- data.frame(
+    subjid = c("Pt1", "Pt1", "Pt2"),
+    visit = c("Visit_1", "Visit_2", "Visit_2"),
+    delta = c( 5, 9, -10)
+)
+
+analyse(
+    imputations = imputeObj,
+    delta = deltadf,
+    vars = vars
+)
+} # }
+
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/ancova.html b/latest-tag/reference/ancova.html new file mode 100644 index 00000000..324f76e8 --- /dev/null +++ b/latest-tag/reference/ancova.html @@ -0,0 +1,161 @@ + +Analysis of Covariance — ancova • rbmi + Skip to contents + + +
    +
    +
    + +

    Analysis of Covariance

    + Source: R/ancova.R +
    ancova.Rd
    +
    + +
    +

    Performs an analysis of covariance between two groups returning the estimated +"treatment effect" (i.e. the contrast between the two treatment groups) and +the least square means estimates in each group.

    +
    + +
    +

    Usage

    + 
    ancova(data, vars, visits = NULL, weights = c("proportional", "equal"))
    +
    + +
    +

    Arguments

    + + +
    data
    +

    A data.frame containing the data to be used in the model.

    + + +
    vars
    +

    A vars object as generated by set_vars(). Only the group, +visit, outcome and covariates elements are required. See details.

    + + +
    visits
    +

    An optional character vector specifying which visits to +fit the ancova model at. If NULL, a separate ancova model will be fit to the +outcomes for each visit (as determined by unique(data[[vars$visit]])). +See details.

    + + +
    weights
    +

    Character, either "proportional" (default) or "equal". Specifies the +weighting strategy to be used for categorical covariates when calculating the lsmeans. +See details.

    + +
    +
    +

    Details

    +

    The function works as follows:

    1. Select the first value from visits.

      2. +

    2. Subset the data to only the observations that occurred on this visit.

      3. +

    3. Fit a linear model as vars$outcome ~ vars$group + vars$covariates.

      4. +

    4. Extract the "treatment effect" & least square means for each treatment group.

      5. +

    5. Repeat points 2-3 for all other values in visits.

      6. +

    If no value for visits is provided then it will be set to +unique(data[[vars$visit]]).

    +

    In order to meet the formatting standards set by analyse() the results will be collapsed +into a single list suffixed by the visit name, e.g.:

    +

    list(
+   trt_visit_1 = list(est = ...),
+   lsm_ref_visit_1 = list(est = ...),
+   lsm_alt_visit_1 = list(est = ...),
+   trt_visit_2 = list(est = ...),
+   lsm_ref_visit_2 = list(est = ...),
+   lsm_alt_visit_2 = list(est = ...),
+   ...
+)

    +

    Please note that "ref" refers to the first factor level of vars$group which does not necessarily +coincide with the control arm. Analogously, "alt" refers to the second factor level of vars$group. +"trt" refers to the model contrast translating the mean difference between the second level and first level.

    +

    If you want to include interaction terms in your model this can be done +by providing them to the covariates argument of set_vars() +e.g. set_vars(covariates = c("sex*age")).

    +

    Weighting

    + + +

    "proportional" is the default scheme that is used. This is equivalent to standardization, +i.e. the lsmeans in +each group are equal to the predicted mean outcome from the ancova model for +that group based on baseline characteristics of all subjects regardless of +their assigned group. The alternative weighting scheme, "equal", creates hypothetical +patients by expanding out all combinations of the models categorical covariates. The +lsmeans are then calculated as the average of +the predicted mean outcome for these hypothetical patients assuming they come from each +group in turn.

    +

    In short:

    • "proportional" weights categorical covariates based upon their frequency of occurrence +in the data.

      • +

    • "equal" weights categorical covariates equally across all theoretical combinations.

      • +
    + +
    +
    +

    See also

    +

    analyse()

    +

    stats::lm()

    +

    set_vars()

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/ancova_single.html b/latest-tag/reference/ancova_single.html new file mode 100644 index 00000000..64073f44 --- /dev/null +++ b/latest-tag/reference/ancova_single.html @@ -0,0 +1,130 @@ + +Implements an Analysis of Covariance (ANCOVA) — ancova_single • rbmi + Skip to contents + + +
    +
    +
    + +

    Implements an Analysis of Covariance (ANCOVA)

    + Source: R/ancova.R +
    ancova_single.Rd
    +
    + +
    +

    Performance analysis of covariance. See ancova() for full details.

    +
    + +
    +

    Usage

    + 
    ancova_single(
+  data,
+  outcome,
+  group,
+  covariates,
+  weights = c("proportional", "equal")
+)
    +
    + +
    +

    Arguments

    + + +
    data
    +

    The data.frame containing all of the data required for the model.

    + + +
    outcome
    +

    Character, the name of the outcome variable in data.

    + + +
    group
    +

    Character, the name of the group variable in data.

    + + +
    covariates
    +

    Character vector containing the name of any additional covariates +to be included in the model as well as any interaction terms.

    + + +
    weights
    +

    Character, specifies whether to use "proportional" or "equal" weighting for each +categorical covariate combination when calculating the lsmeans.

    + +
    +
    +

    Details

    + +

    • group must be a factor variable with only 2 levels.

      • +

    • outcome must be a continuous numeric variable.

      • +
    +
    +

    See also

    +

    ancova()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+iris2 <- iris[ iris$Species %in% c("versicolor", "virginica"), ]
+iris2$Species <- factor(iris2$Species)
+ancova_single(iris2, "Sepal.Length", "Species", c("Petal.Length * Petal.Width"))
+} # }
+
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/antidepressant_data.html b/latest-tag/reference/antidepressant_data.html new file mode 100644 index 00000000..e75a78ab --- /dev/null +++ b/latest-tag/reference/antidepressant_data.html @@ -0,0 +1,136 @@ + +Antidepressant trial data — antidepressant_data • rbmi + Skip to contents + + +
    +
    +
    + +

    Antidepressant trial data

    + Source: R/data.R +
    antidepressant_data.Rd
    +
    + +
    +

    A dataset containing data from a publicly available example data set from an antidepressant +clinical trial. +The dataset is available on the website of the +Drug Information Association Scientific Working Group on Estimands and Missing Data. +As per that website, the original data are from an antidepressant clinical trial with four +treatments; two doses of an experimental medication, +a positive control, and placebo and was published in Goldstein et al (2004). To mask the real +data, week 8 observations were removed and two arms were created: +the original placebo arm and a "drug arm" created by randomly selecting patients from the +three non-placebo arms.

    +
    + +
    +

    Usage

    + 
    antidepressant_data
    +
    + +
    +

    Format

    +

    A data.frame with 608 rows and 11 variables:

    • PATIENT: patients IDs.

      • +

    • HAMATOTL: total score Hamilton Anxiety Rating Scale.

      • +

    • PGIIMP: patient's Global Impression of Improvement Rating Scale.

      • +

    • RELDAYS: number of days between visit and baseline.

      • +

    • VISIT: post-baseline visit. Has levels 4,5,6,7.

      • +

    • THERAPY: the treatment group variable. It is equal to PLACEBO for observations from +the placebo arm, or DRUG for observations from the active arm.

      • +

    • GENDER: patient's gender.

      • +

    • POOLINV: pooled investigator.

      • +

    • BASVAL: baseline outcome value.

      • +

    • HAMDTL17: Hamilton 17-item rating scale value.

      • +

    • CHANGE: change from baseline in the Hamilton 17-item rating scale.

      • +
    +
    +

    Details

    +

    The relevant endpoint is the Hamilton 17-item rating scale for depression (HAMD17) for +which baseline and weeks 1, 2, 4, and 6 assessments are included. +Study drug discontinuation occurred in 24% subjects from the active drug and 26% from +placebo. +All data after study drug discontinuation are missing and there is a single additional +intermittent missing observation.

    +
    +
    +

    References

    +

    Goldstein, Lu, Detke, Wiltse, Mallinckrodt, Demitrack. Duloxetine in the treatment of +depression: a double-blind placebo-controlled comparison with paroxetine. +J Clin Psychopharmacol 2004;24: 389-399.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/apply_delta.html b/latest-tag/reference/apply_delta.html new file mode 100644 index 00000000..aa63ddfe --- /dev/null +++ b/latest-tag/reference/apply_delta.html @@ -0,0 +1,103 @@ + +Applies delta adjustment — apply_delta • rbmi + Skip to contents + + +
    +
    +
    + +

    Applies delta adjustment

    + Source: R/delta.R +
    apply_delta.Rd
    +
    + +
    +

    Takes a delta dataset and adjusts the outcome variable by adding the +corresponding delta.

    +
    + +
    +

    Usage

    + 
    apply_delta(data, delta = NULL, group = NULL, outcome = NULL)
    +
    + +
    +

    Arguments

    + + +
    data
    +

    data.frame which will have its outcome column adjusted.

    + + +
    delta
    +

    data.frame (must contain a column called delta).

    + + +
    group
    +

    character vector of variables in both data and delta that will be used +to merge the 2 data.frames together by.

    + + +
    outcome
    +

    character, name of the outcome variable in data.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/as_analysis.html b/latest-tag/reference/as_analysis.html new file mode 100644 index 00000000..552567d6 --- /dev/null +++ b/latest-tag/reference/as_analysis.html @@ -0,0 +1,109 @@ + +Construct an analysis object — as_analysis • rbmi + Skip to contents + + +
    +
    +
    + +

    Construct an analysis object

    + Source: R/analyse.R +
    as_analysis.Rd
    +
    + +
    +

    Creates an analysis object ensuring that all components are +correctly defined.

    +
    + +
    +

    Usage

    + 
    as_analysis(results, method, delta = NULL, fun = NULL, fun_name = NULL)
    +
    + +
    +

    Arguments

    + + +
    results
    +

    A list of lists contain the analysis results for each imputation +See analyse() for details on what this object should look like.

    + + +
    method
    +

    The method object as specified in draws().

    + + +
    delta
    +

    The delta dataset used. See analyse() for details on how this +should be specified.

    + + +
    fun
    +

    The analysis function that was used.

    + + +
    fun_name
    +

    The character name of the analysis function (used for printing) +purposes.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/as_ascii_table.html b/latest-tag/reference/as_ascii_table.html new file mode 100644 index 00000000..a83ee31b --- /dev/null +++ b/latest-tag/reference/as_ascii_table.html @@ -0,0 +1,104 @@ + +as_ascii_table — as_ascii_table • rbmi + Skip to contents + + +
    +
    +
    + +

    as_ascii_table

    + Source: R/as_ascii_table.R +
    as_ascii_table.Rd
    +
    + +
    +

    This function takes a data.frame and attempts to convert it into +a simple ascii format suitable for printing to the screen +It is assumed all variable values have a as.character() method +in order to cast them to character.

    +
    + +
    +

    Usage

    + 
    as_ascii_table(dat, line_prefix = "  ", pcol = NULL)
    +
    + +
    +

    Arguments

    + + +
    dat
    +

    Input dataset to convert into a ascii table

    + + +
    line_prefix
    +

    Symbols to prefix infront of every line of the table

    + + +
    pcol
    +

    name of column to be handled as a p-value. Sets the value to <0.001 if the value is 0 after rounding

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/as_class.html b/latest-tag/reference/as_class.html new file mode 100644 index 00000000..ab1d85e4 --- /dev/null +++ b/latest-tag/reference/as_class.html @@ -0,0 +1,91 @@ + +Set Class — as_class • rbmi + Skip to contents + + +
    +
    +
    + +

    Set Class

    + Source: R/utilities.R +
    as_class.Rd
    +
    + +
    +

    Utility function to set an objects class.

    +
    + +
    +

    Usage

    + 
    as_class(x, cls)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    object to set the class of.

    + + +
    cls
    +

    the class to be set.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/as_cropped_char.html b/latest-tag/reference/as_cropped_char.html new file mode 100644 index 00000000..0d1cacc5 --- /dev/null +++ b/latest-tag/reference/as_cropped_char.html @@ -0,0 +1,98 @@ + +as_cropped_char — as_cropped_char • rbmi + Skip to contents + + +
    +
    +
    + +

    as_cropped_char

    + Source: R/as_ascii_table.R +
    as_cropped_char.Rd
    +
    + +
    +

    Makes any character string above x chars +Reduce down to a x char string with ...

    +
    + +
    +

    Usage

    + 
    as_cropped_char(inval, crop_at = 30, ndp = 3)
    +
    + +
    +

    Arguments

    + + +
    inval
    +

    a single element value

    + + +
    crop_at
    +

    character limit

    + + +
    ndp
    +

    Number of decimal places to display

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/as_dataframe.html b/latest-tag/reference/as_dataframe.html new file mode 100644 index 00000000..0e7de733 --- /dev/null +++ b/latest-tag/reference/as_dataframe.html @@ -0,0 +1,89 @@ + +Convert object to dataframe — as_dataframe • rbmi + Skip to contents + + +
    +
    +
    + +

    Convert object to dataframe

    + Source: R/utilities.R +
    as_dataframe.Rd
    +
    + +
    +

    Convert object to dataframe

    +
    + +
    +

    Usage

    + 
    as_dataframe(x)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    a data.frame like object

    +

    Utility function to convert a "data.frame-like" object to an actual data.frame +to avoid issues with inconsistency on methods (such as [() and dplyr's grouped dataframes)

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/as_draws.html b/latest-tag/reference/as_draws.html new file mode 100644 index 00000000..f3a16b4d --- /dev/null +++ b/latest-tag/reference/as_draws.html @@ -0,0 +1,126 @@ + +Creates a draws object — as_draws • rbmi + Skip to contents + + +
    +
    +
    + +

    Creates a draws object

    + Source: R/draws.R +
    as_draws.Rd
    +
    + +
    +

    Creates a draws object which is the final output of a call to draws().

    +
    + +
    +

    Usage

    + 
    as_draws(method, samples, data, formula, n_failures = NULL, fit = NULL)
    +
    + +
    +

    Arguments

    + + +
    method
    +

    A method object as generated by either method_bayes(), +method_approxbayes(), method_condmean() or method_bmlmi().

    + + +
    samples
    +

    A list of sample_single objects. See sample_single().

    + + +
    data
    +

    R6 longdata object containing all relevant input data information.

    + + +
    formula
    +

    Fixed effects formula object used for the model specification.

    + + +
    n_failures
    +

    Absolute number of failures of the model fit.

    + + +
    fit
    +

    If method_bayes() is chosen, returns the MCMC Stan fit object. Otherwise NULL.

    + +
    +
    +

    Value

    +

    A draws object which is a named list containing the following:

    • data: R6 longdata object containing all relevant input data information.

      • +

    • method: A method object as generated by either method_bayes(), +method_approxbayes() or method_condmean().

      • +

    • samples: list containing the estimated parameters of interest. +Each element of samples is a named list containing the following:

      • ids: vector of characters containing the ids of the subjects included in the original dataset.

        • +

      • beta: numeric vector of estimated regression coefficients.

        • +

      • sigma: list of estimated covariance matrices (one for each level of vars$group).

        • +

      • theta: numeric vector of transformed covariances.

        • +

      • failed: Logical. TRUE if the model fit failed.

        • +

      • ids_samp: vector of characters containing the ids of the subjects included in the given sample.

        • +
      • +

    • fit: if method_bayes() is chosen, returns the MCMC Stan fit object. Otherwise NULL.

      • +

    • n_failures: absolute number of failures of the model fit. +Relevant only for method_condmean(type = "bootstrap"), method_approxbayes() and method_bmlmi().

      • +

    • formula: fixed effects formula object used for the model specification.

      • +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/as_imputation.html b/latest-tag/reference/as_imputation.html new file mode 100644 index 00000000..0e484c12 --- /dev/null +++ b/latest-tag/reference/as_imputation.html @@ -0,0 +1,107 @@ + +Create an imputation object — as_imputation • rbmi + Skip to contents + + +
    +
    +
    + +

    Create an imputation object

    + Source: R/impute.R +
    as_imputation.Rd
    +
    + +
    +

    This function creates the object that is returned from impute(). Essentially +it is a glorified wrapper around list() ensuring that the required elements have been +set and that the class is added as expected.

    +
    + +
    +

    Usage

    + 
    as_imputation(imputations, data, method, references)
    +
    + +
    +

    Arguments

    + + +
    imputations
    +

    A list of imputations_list's as created by imputation_df()

    + + +
    data
    +

    A longdata object as created by longDataConstructor()

    + + +
    method
    +

    A method object as created by method_condmean(), method_bayes() or +method_approxbayes()

    + + +
    references
    +

    A named vector. Identifies the references to be used when generating the +imputed values. Should be of the form c("Group" = "Reference", "Group" = "Reference").

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/as_indices.html b/latest-tag/reference/as_indices.html new file mode 100644 index 00000000..d5b28c1d --- /dev/null +++ b/latest-tag/reference/as_indices.html @@ -0,0 +1,96 @@ + +Convert indicator to index — as_indices • rbmi + Skip to contents + + +
    +
    +
    + +

    Convert indicator to index

    + Source: R/mcmc.R +
    as_indices.Rd
    +
    + +
    +

    Converts a string of 0's and 1's into index positions of the 1's +padding the results by 0's so they are all the same length

    +
    + +
    +

    Usage

    + 
    as_indices(x)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    a character vector whose values are all either "0" or "1". All elements of +the vector must be the same length

    + +
    +
    +

    Details

    +

    i.e.

    +

    patmap(c("1101", "0001"))  ->   list(c(1,2,4,999), c(4,999, 999, 999))

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/as_mmrm_df.html b/latest-tag/reference/as_mmrm_df.html new file mode 100644 index 00000000..8ac24e3b --- /dev/null +++ b/latest-tag/reference/as_mmrm_df.html @@ -0,0 +1,127 @@ + +Creates a "MMRM" ready dataset — as_mmrm_df • rbmi + Skip to contents + + +
    +
    +
    + +

    Creates a "MMRM" ready dataset

    + Source: R/mmrm.R +
    as_mmrm_df.Rd
    +
    + +
    +

    Converts a design matrix + key variables into a common format +In particular this function does the following:

    • Renames all covariates as V1, V2, etc to avoid issues of special characters in variable names

      • +

    • Ensures all key variables are of the right type

      • +

    • Inserts the outcome, visit and subjid variables into the data.frame +naming them as outcome, visit and subjid

      • +

    • If provided will also insert the group variable into the data.frame named as group

      • +
    + +
    +

    Usage

    + 
    as_mmrm_df(designmat, outcome, visit, subjid, group = NULL)
    +
    + +
    +

    Arguments

    + + +
    designmat
    +

    a data.frame or matrix containing the covariates to use in the MMRM model. +Dummy variables must already be expanded out, i.e. via stats::model.matrix(). Cannot contain +any missing values

    + + +
    outcome
    +

    a numeric vector. The outcome value to be regressed on in the MMRM model.

    + + +
    visit
    +

    a character / factor vector. Indicates which visit the outcome value occurred on.

    + + +
    subjid
    +

    a character / factor vector. The subject identifier used to link separate visits +that belong to the same subject.

    + + +
    group
    +

    a character / factor vector. Indicates which treatment group the patient belongs to.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/as_mmrm_formula.html b/latest-tag/reference/as_mmrm_formula.html new file mode 100644 index 00000000..240d0ec1 --- /dev/null +++ b/latest-tag/reference/as_mmrm_formula.html @@ -0,0 +1,99 @@ + +Create MMRM formula — as_mmrm_formula • rbmi + Skip to contents + + +
    +
    +
    + +

    Create MMRM formula

    + Source: R/mmrm.R +
    as_mmrm_formula.Rd
    +
    + +
    +

    Derives the MMRM model formula from the structure of mmrm_df. +returns a formula object of the form:

    +
    + +
    +

    Usage

    + 
    as_mmrm_formula(mmrm_df, cov_struct)
    +
    + +
    +

    Arguments

    + + +
    mmrm_df
    +

    an mmrm data.frame as created by as_mmrm_df()

    + + +
    cov_struct
    +

    Character - The covariance structure to be used, must be one of "us", +"toep", "cs", "ar1"

    + +
    +
    +

    Details

    + 

    outcome ~ 0 + V1 + V2 + V4 + ... + us(visit | group / subjid)

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/as_model_df.html b/latest-tag/reference/as_model_df.html new file mode 100644 index 00000000..dc58c7d0 --- /dev/null +++ b/latest-tag/reference/as_model_df.html @@ -0,0 +1,102 @@ + +Expand data.frame into a design matrix — as_model_df • rbmi + Skip to contents + + +
    +
    +
    + +

    Expand data.frame into a design matrix

    + Source: R/utilities.R +
    as_model_df.Rd
    +
    + +
    +

    Expands out a data.frame using a formula to create a design matrix. +Key details are that it will always place the outcome variable into +the first column of the return object.

    +
    + +
    +

    Usage

    + 
    as_model_df(dat, frm)
    +
    + +
    +

    Arguments

    + + +
    dat
    +

    a data.frame

    + + +
    frm
    +

    a formula

    + +
    +
    +

    Details

    +

    The outcome column may contain NA's but none of the other variables +listed in the formula should contain missing values

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/as_simple_formula.html b/latest-tag/reference/as_simple_formula.html new file mode 100644 index 00000000..85446771 --- /dev/null +++ b/latest-tag/reference/as_simple_formula.html @@ -0,0 +1,95 @@ + +Creates a simple formula object from a string — as_simple_formula • rbmi + Skip to contents + + +
    +
    +
    + +

    Creates a simple formula object from a string

    + Source: R/utilities.R +
    as_simple_formula.Rd
    +
    + +
    +

    Converts a string list of variables into a formula object

    +
    + +
    +

    Usage

    + 
    as_simple_formula(outcome, covars)
    +
    + +
    +

    Arguments

    + + +
    outcome
    +

    character (length 1 vector). Name of the outcome variable

    + + +
    covars
    +

    character (vector). Name of covariates

    + +
    +
    +

    Value

    +

    A formula

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/as_stan_array.html b/latest-tag/reference/as_stan_array.html new file mode 100644 index 00000000..21f95be4 --- /dev/null +++ b/latest-tag/reference/as_stan_array.html @@ -0,0 +1,93 @@ + +As array — as_stan_array • rbmi + Skip to contents + + +
    +
    +
    + +

    As array

    + Source: R/mcmc.R +
    as_stan_array.Rd
    +
    + +
    +

    Converts a numeric value of length 1 into a 1 dimension array. +This is to avoid type errors that are thrown by stan when length 1 numeric vectors +are provided by R for stan::vector inputs

    +
    + +
    +

    Usage

    + 
    as_stan_array(x)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    a numeric vector

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/as_strata.html b/latest-tag/reference/as_strata.html new file mode 100644 index 00000000..7803b671 --- /dev/null +++ b/latest-tag/reference/as_strata.html @@ -0,0 +1,110 @@ + +Create vector of Stratas — as_strata • rbmi + Skip to contents + + +
    +
    +
    + +

    Create vector of Stratas

    + Source: R/bootstrap.R +
    as_strata.Rd
    +
    + +
    +

    Collapse multiple categorical variables into distinct unique categories. +e.g.

    +

    as_strata(c(1,1,2,2,2,1), c(5,6,5,5,6,5))

    +

    would return

    +

    c(1,2,3,3,4,1)

    +
    + +
    +

    Usage

    + 
    as_strata(...)
    +
    + +
    +

    Arguments

    + + +
    ...
    +

    numeric/character/factor vectors of the same length

    + +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+as_strata(c(1,1,2,2,2,1), c(5,6,5,5,6,5))
+} # }
+
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/assert_variables_exist.html b/latest-tag/reference/assert_variables_exist.html new file mode 100644 index 00000000..eba66688 --- /dev/null +++ b/latest-tag/reference/assert_variables_exist.html @@ -0,0 +1,91 @@ + +Assert that all variables exist within a dataset — assert_variables_exist • rbmi + Skip to contents + + +
    +
    +
    + +

    Assert that all variables exist within a dataset

    + Source: R/expand.R +
    assert_variables_exist.Rd
    +
    + +
    +

    Performs an assertion check to ensure that a vector of variable exists within a data.frame as expected.

    +
    + +
    +

    Usage

    + 
    assert_variables_exist(data, vars)
    +
    + +
    +

    Arguments

    + + +
    data
    +

    a data.frame

    + + +
    vars
    +

    a character vector of variable names

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/char2fct.html b/latest-tag/reference/char2fct.html new file mode 100644 index 00000000..f31d125e --- /dev/null +++ b/latest-tag/reference/char2fct.html @@ -0,0 +1,97 @@ + +Convert character variables to factor — char2fct • rbmi + Skip to contents + + +
    +
    +
    + +

    Convert character variables to factor

    + Source: R/utilities.R +
    char2fct.Rd
    +
    + +
    +

    Provided a vector of variable names this function converts any +character variables into factors. Has no affect on numeric or existing +factor variables

    +
    + +
    +

    Usage

    + 
    char2fct(data, vars = NULL)
    +
    + +
    +

    Arguments

    + + +
    data
    +

    A data.frame

    + + +
    vars
    +

    a character vector of variables in data

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/check_ESS.html b/latest-tag/reference/check_ESS.html new file mode 100644 index 00000000..48d93510 --- /dev/null +++ b/latest-tag/reference/check_ESS.html @@ -0,0 +1,111 @@ + +Diagnostics of the MCMC based on ESS — check_ESS • rbmi + Skip to contents + + +
    +
    +
    + +

    Diagnostics of the MCMC based on ESS

    + Source: R/mcmc.R +
    check_ESS.Rd
    +
    + +
    +

    Check the quality of the MCMC draws from the posterior distribution +by checking whether the relative ESS is sufficiently large.

    +
    + +
    +

    Usage

    + 
    check_ESS(stan_fit, n_draws, threshold_lowESS = 0.4)
    +
    + +
    +

    Arguments

    + + +
    stan_fit
    +

    A stanfit object.

    + + +
    n_draws
    +

    Number of MCMC draws.

    + + +
    threshold_lowESS
    +

    A number in [0,1] indicating the minimum acceptable +value of the relative ESS. See details.

    + +
    +
    +

    Value

    +

    A warning message in case of detected problems.

    +
    +
    +

    Details

    +

    check_ESS() works as follows:

    1. Extract the ESS from stan_fit for each parameter of the model.

      2. +

    2. Compute the relative ESS (i.e. the ESS divided by the number of draws).

      3. +

    3. Check whether for any of the parameter the ESS is lower than threshold. +If for at least one parameter the relative ESS is below the threshold, +a warning is thrown.

      4. +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/check_hmc_diagn.html b/latest-tag/reference/check_hmc_diagn.html new file mode 100644 index 00000000..2fd9751a --- /dev/null +++ b/latest-tag/reference/check_hmc_diagn.html @@ -0,0 +1,106 @@ + +Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn • rbmi + Skip to contents + + +
    +
    +
    + +

    Diagnostics of the MCMC based on HMC-related measures.

    + Source: R/mcmc.R +
    check_hmc_diagn.Rd
    +
    + +
    +

    Check that:

    1. There are no divergent iterations.

      2. +

    2. The Bayesian Fraction of Missing Information (BFMI) is sufficiently low.

      3. +

    3. The number of iterations that saturated the max treedepth is zero.

      4. +

    Please see rstan::check_hmc_diagnostics() for details.

    +
    + +
    +

    Usage

    + 
    check_hmc_diagn(stan_fit)
    +
    + +
    +

    Arguments

    + + +
    stan_fit
    +

    A stanfit object.

    + +
    +
    +

    Value

    +

    A warning message in case of detected problems.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/check_mcmc.html b/latest-tag/reference/check_mcmc.html new file mode 100644 index 00000000..062ddaea --- /dev/null +++ b/latest-tag/reference/check_mcmc.html @@ -0,0 +1,105 @@ + +Diagnostics of the MCMC — check_mcmc • rbmi + Skip to contents + + +
    +
    +
    + +

    Diagnostics of the MCMC

    + Source: R/mcmc.R +
    check_mcmc.Rd
    +
    + +
    +

    Diagnostics of the MCMC

    +
    + +
    +

    Usage

    + 
    check_mcmc(stan_fit, n_draws, threshold_lowESS = 0.4)
    +
    + +
    +

    Arguments

    + + +
    stan_fit
    +

    A stanfit object.

    + + +
    n_draws
    +

    Number of MCMC draws.

    + + +
    threshold_lowESS
    +

    A number in [0,1] indicating the minimum acceptable +value of the relative ESS. See details.

    + +
    +
    +

    Value

    +

    A warning message in case of detected problems.

    +
    +
    +

    Details

    +

    Performs checks of the quality of the MCMC. See check_ESS() and check_hmc_diagn() +for details.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/compute_sigma.html b/latest-tag/reference/compute_sigma.html new file mode 100644 index 00000000..e1f4f848 --- /dev/null +++ b/latest-tag/reference/compute_sigma.html @@ -0,0 +1,115 @@ + +Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma • rbmi + Skip to contents + + +
    +
    +
    + +

    Compute covariance matrix for some reference-based methods (JR, CIR)

    + Source: R/strategies.R +
    compute_sigma.Rd
    +
    + +
    +

    Adapt covariance matrix in reference-based methods. Used for Copy Increments in +Reference (CIR) and Jump To Reference (JTR) methods, to adapt the covariance matrix +to different pre-deviation and post deviation covariance structures. See Carpenter +et al. (2013)

    +
    + +
    +

    Usage

    + 
    compute_sigma(sigma_group, sigma_ref, index_mar)
    +
    + +
    +

    Arguments

    + + +
    sigma_group
    +

    the covariance matrix with dimensions equal to index_mar for +the subjects original group

    + + +
    sigma_ref
    +

    the covariance matrix with dimensions equal to index_mar for +the subjects reference group

    + + +
    index_mar
    +

    A logical vector indicating which visits meet the MAR assumption +for the subject. I.e. this identifies the observations that after a non-MAR +intercurrent event (ICE).

    + +
    +
    +

    References

    +

    Carpenter, James R., James H. Roger, and Michael G. Kenward. "Analysis of longitudinal +trials with protocol deviation: a framework for relevant, accessible assumptions, and +inference via multiple imputation." Journal of Biopharmaceutical statistics 23.6 (2013): +1352-1371.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/convert_to_imputation_list_df.html b/latest-tag/reference/convert_to_imputation_list_df.html new file mode 100644 index 00000000..a80c720a --- /dev/null +++ b/latest-tag/reference/convert_to_imputation_list_df.html @@ -0,0 +1,148 @@ + +Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's) — convert_to_imputation_list_df • rbmi + Skip to contents + + +
    +
    +
    + +

    Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's)

    + Source: R/impute.R +
    convert_to_imputation_list_df.Rd
    +
    + +
    +

    Convert list of imputation_list_single() objects to an imputation_list_df() object +(i.e. a list of imputation_df() objects's)

    +
    + +
    +

    Usage

    + 
    convert_to_imputation_list_df(imputes, sample_ids)
    +
    + +
    +

    Arguments

    + + +
    imputes
    +

    a list of imputation_list_single() objects

    + + +
    sample_ids
    +

    A list with 1 element per required imputation_df. Each element +must contain a vector of "ID"'s which correspond to the imputation_single() ID's +that are required for that dataset. The total number of ID's must by equal to the +total number of rows within all of imputes$imputations

    +

    To accommodate for method_bmlmi() the impute_data_individual() function returns +a list of imputation_list_single() objects with 1 object per each subject.

    +

    imputation_list_single() stores the subjects imputations as a matrix where the columns +of the matrix correspond to the D of method_bmlmi(). Note that all other methods +(i.e. methods_*()) are a special case of this with D = 1. The number of rows in the +matrix varies for each subject and is equal to the number of times the patient was selected +for imputation (for non-conditional mean methods this should be 1 per subject per imputed +dataset).

    +

    This function is best illustrated by an example:

    +

    imputes = list(
+    imputation_list_single(
+        id = "Tom",
+        imputations = matrix(
+             imputation_single_t_1_1,  imputation_single_t_1_2,
+             imputation_single_t_2_1,  imputation_single_t_2_2,
+             imputation_single_t_3_1,  imputation_single_t_3_2
+        )
+    ),
+    imputation_list_single(
+        id = "Tom",
+        imputations = matrix(
+             imputation_single_h_1_1,  imputation_single_h_1_2,
+        )
+    )
+)
+
+sample_ids <- list(
+    c("Tom", "Harry", "Tom"),
+    c("Tom")
+)

    +

    Then convert_to_imputation_df(imputes, sample_ids) would result in:

    +

    imputation_list_df(
+    imputation_df(
+        imputation_single_t_1_1,
+        imputation_single_h_1_1,
+        imputation_single_t_2_1
+    ),
+    imputation_df(
+        imputation_single_t_1_2,
+        imputation_single_h_1_2,
+        imputation_single_t_2_2
+    ),
+    imputation_df(
+        imputation_single_t_3_1
+    ),
+    imputation_df(
+        imputation_single_t_3_2
+    )
+)

    +

    Note that the different repetitions (i.e. the value set for D) are grouped together +sequentially.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/d_lagscale.html b/latest-tag/reference/d_lagscale.html new file mode 100644 index 00000000..30cc9e0d --- /dev/null +++ b/latest-tag/reference/d_lagscale.html @@ -0,0 +1,106 @@ + +Calculate delta from a lagged scale coefficient — d_lagscale • rbmi + Skip to contents + + +
    +
    +
    + +

    Calculate delta from a lagged scale coefficient

    + Source: R/delta.R +
    d_lagscale.Rd
    +
    + +
    +

    Calculates a delta value based upon a baseline delta value and a +post ICE scaling coefficient.

    +
    + +
    +

    Usage

    + 
    d_lagscale(delta, dlag, is_post_ice)
    +
    + +
    +

    Arguments

    + + +
    delta
    +

    a numeric vector. Determines the baseline amount of delta +to be applied to each visit.

    + + +
    dlag
    +

    a numeric vector. Determines the scaling to be applied +to delta based upon with visit the ICE occurred on. Must be the same +length as delta.

    + + +
    is_post_ice
    +

    logical vector. Indicates whether a visit is "post-ICE" or +not.

    + +
    +
    +

    Details

    +

    See delta_template() for full details on how this calculation is performed.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/delta_template.html b/latest-tag/reference/delta_template.html new file mode 100644 index 00000000..517e09d2 --- /dev/null +++ b/latest-tag/reference/delta_template.html @@ -0,0 +1,203 @@ + +Create a delta data.frame template — delta_template • rbmi + Skip to contents + + +
    +
    +
    + +

    Create a delta data.frame template

    + Source: R/delta.R +
    delta_template.Rd
    +
    + +
    +

    Creates a data.frame in the format required by analyse() for the use +of applying a delta adjustment.

    +
    + +
    +

    Usage

    + 
    delta_template(imputations, delta = NULL, dlag = NULL, missing_only = TRUE)
    +
    + +
    +

    Arguments

    + + +
    imputations
    +

    an imputation object as created by impute().

    + + +
    delta
    +

    NULL or a numeric vector. Determines the baseline amount of delta +to be applied to each visit. See details. If a numeric vector it must have +the same length as the number of unique visits in the original dataset.

    + + +
    dlag
    +

    NULL or a numeric vector. Determines the scaling to be applied +to delta based upon which visit the ICE occurred on. See details. If a +numeric vector it must have the same length as the number of unique visits in +the original dataset.

    + + +
    missing_only
    +

    Logical, if TRUE then non-missing post-ICE data will have a delta value +of 0 assigned. Note that the calculation (as described in the details section) is performed +first and then overwritten with 0's at the end (i.e. the delta values for missing +post-ICE visits will stay the same regardless of this option).

    + +
    +
    +

    Details

    +

    To apply a delta adjustment the analyse() function expects +a delta data.frame with 3 variables: vars$subjid, vars$visit and delta +(where vars is the object supplied in the original call to draws() +as created by the set_vars() function).

    +

    This function will return a data.frame with the aforementioned variables with one +row per subject per visit. If the delta argument to this function is NULL +then the delta column in the returned data.frame will be 0 for all observations. +If the delta argument is not NULL then delta will be calculated separately +for each subject as the accumulative sum of delta multiplied by the scaling +coefficient dlag based upon how many visits after the subject's intercurrent +event (ICE) the visit in question is. +This is best illustrated with an example:

    +

    Let delta = c(5,6,7,8) and dlag=c(1,2,3,4) (i.e. assuming there are 4 visits) +and lets say that the subject had an ICE on visit 2. The calculation would then be +as follows:

    +

    v1  v2  v3  v4
+--------------
+ 5   6   7   8  # delta assigned to each visit
+ 0   1   2   3  # lagged scaling starting from the first visit after the subjects ICE
+--------------
+ 0   6  14  24  # delta * lagged scaling
+--------------
+ 0   6  20  44  # accumulative sum of delta to be applied to each visit

    +

    That is to say the subject would have a delta offset of 0 applied for visit-1, 6 +for visit-2, 20 for visit-3 and 44 for visit-4. As a comparison, lets say that the +subject instead had their ICE on visit 3, the calculation would then be as follows:

    +

    v1  v2  v3  v4
+--------------
+ 5   6   7   8  # delta assigned to each visit
+ 0   0   1   2  # lagged scaling starting from the first visit after the subjects ICE
+--------------
+ 0   0   7  16  # delta * lagged scaling
+--------------
+ 0   0   7  23  # accumulative sum of delta to be applied to each visit

    +

    In terms of practical usage, lets say that you wanted a delta of 5 to be used for all post ICE visits +regardless of their proximity to the ICE visit. This can be achieved by setting +delta = c(5,5,5,5) and dlag = c(1,0,0,0). For example lets say a subject had their +ICE on visit-1, then the calculation would be as follows:

    +

    v1  v2  v3  v4
+--------------
+ 5   5   5   5  # delta assigned to each visit
+ 1   0   0   0  # lagged scaling starting from the first visit after the subjects ICE
+--------------
+ 5   0   0  0  # delta * lagged scaling
+--------------
+ 5   5   5  5  # accumulative sum of delta to be applied to each visit

    +

    Another way of using these arguments +is to set delta to be the difference in time between visits and dlag to be the +amount of delta per unit of time. For example lets say that we have a visit on weeks +1, 5, 6 & 9 and that we want a delta of 3 to be applied for each week after an ICE. This +can be achieved by setting delta = c(0,4,1,3) (the difference in weeks between each visit) +and dlag = c(3, 3, 3, 3). For example lets say we have a subject who had their ICE on week-5 +(i.e. visit-2) then the calculation would be:

    +

    v1  v2  v3  v4
+--------------
+ 0   4   1   3  # delta assigned to each visit
+ 0   0   3   3  # lagged scaling starting from the first visit after the subjects ICE
+--------------
+ 0   0   3   9  # delta * lagged scaling
+--------------
+ 0   0   3  12  # accumulative sum of delta to be applied to each visit

    +

    i.e. on week-6 (1 week after the ICE) they have a delta of 3 and on week-9 (4 weeks after the ICE) +they have a delta of 12.

    +

    Please note that this function also returns several utility variables so that +the user can create their own custom logic for defining what delta +should be set to. These additional variables include:

    • is_mar - If the observation was missing would it be regarded as MAR? This variable +is set to FALSE for observations that occurred after a non-MAR ICE, otherwise it is set to TRUE.

      • +

    • is_missing - Is the outcome variable for this observation missing.

      • +

    • is_post_ice - Does the observation occur after the patient's ICE as defined by the +data_ice dataset supplied to draws().

      • +

    • strategy - What imputation strategy was assigned to for this subject.

      • +

    The design and implementation of this function is largely based upon the same functionality +as implemented in the so called "five marcos" by James Roger. See Roger (2021).

    +
    +
    +

    References

    +

    Roger, James. Reference-based mi via multivariate normal rm (the “five macros” and miwithd), 2021. URL +https://www.lshtm.ac.uk/research/centres-projects-groups/missing-data#dia-missing-data.

    +
    +
    +

    See also

    +

    analyse()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+delta_template(imputeObj)
+delta_template(imputeObj, delta = c(5,6,7,8), dlag = c(1,2,3,4))
+} # }
+
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/do_not_run.html b/latest-tag/reference/do_not_run.html new file mode 100644 index 00000000..b1791c1f --- /dev/null +++ b/latest-tag/reference/do_not_run.html @@ -0,0 +1,89 @@ + +Do not run this function — do_not_run • rbmi + Skip to contents + + +
    +
    +
    + +

    Do not run this function

    + Source: R/utilities.R +
    do_not_run.Rd
    +
    + +
    +

    This function only exists to suppress the false positive +from R CMD Check about unused libraries

    +
    + +
    +

    Usage

    + 
    do_not_run()
    +
    + +
    +

    Details

    +

    Both rstantools and RcppParallel are required but are only used at +installation time. In the case of RcppParallel it is used in the +src/Makevars file which is created on the fly during installation +by rstantools. rstantools is used in the configure file.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/draws.html b/latest-tag/reference/draws.html new file mode 100644 index 00000000..39fe2875 --- /dev/null +++ b/latest-tag/reference/draws.html @@ -0,0 +1,306 @@ + +Fit the base imputation model and get parameter estimates — draws • rbmi + Skip to contents + + +
    +
    +
    + +

    Fit the base imputation model and get parameter estimates

    + Source: R/draws.R +
    draws.Rd
    +
    + +
    +

    draws fits the base imputation model to the observed outcome data +according to the given multiple imputation methodology. +According to the user's method specification, it returns either draws from the posterior distribution of the +model parameters as required for Bayesian multiple imputation or frequentist parameter estimates from the +original data and bootstrapped or leave-one-out datasets as required for conditional mean imputation. +The purpose of the imputation model is to estimate model parameters +in the absence of intercurrent events (ICEs) handled using reference-based imputation methods. +For this reason, any observed outcome data after ICEs, for which reference-based imputation methods are +specified, are removed and considered as missing for the purpose of estimating the imputation model, and for +this purpose only. The imputation model is a mixed model for repeated measures (MMRM) that is valid +under a missing-at-random (MAR) assumption. +It can be fit using maximum likelihood (ML) or restricted ML (REML) estimation, +a Bayesian approach, or an approximate Bayesian approach according to the user's method specification. +The ML/REML approaches and the approximate Bayesian approach support several possible covariance structures, +while the Bayesian approach based on MCMC sampling supports only an unstructured covariance structure. +In any case the covariance matrix can be assumed to be the same or different across each group.

    +
    + +
    +

    Usage

    + 
    draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE)
+
+# S3 method for class 'approxbayes'
+draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE)
+
+# S3 method for class 'condmean'
+draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE)
+
+# S3 method for class 'bmlmi'
+draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE)
+
+# S3 method for class 'bayes'
+draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE)
    +
    + +
    +

    Arguments

    + + +
    data
    +

    A data.frame containing the data to be used in the model. See details.

    + + +
    data_ice
    +

    A data.frame that specifies the information related +to the ICEs and the imputation strategies. See details.

    + + +
    vars
    +

    A vars object as generated by set_vars(). See details.

    + + +
    method
    +

    A method object as generated by either method_bayes(), +method_approxbayes(), method_condmean() or method_bmlmi(). +It specifies the multiple imputation methodology to be used. See details.

    + + +
    ncores
    +

    A single numeric specifying the number of cores to use in creating the draws object. +Note that this parameter is ignored for method_bayes() (Default = 1).

    + + +
    quiet
    +

    Logical, if TRUE will suppress printing of progress information that is printed to +the console.

    + +
    +
    +

    Value

    +

    A draws object which is a named list containing the following:

    • data: R6 longdata object containing all relevant input data information.

      • +

    • method: A method object as generated by either method_bayes(), +method_approxbayes() or method_condmean().

      • +

    • samples: list containing the estimated parameters of interest. +Each element of samples is a named list containing the following:

      • ids: vector of characters containing the ids of the subjects included in the original dataset.

        • +

      • beta: numeric vector of estimated regression coefficients.

        • +

      • sigma: list of estimated covariance matrices (one for each level of vars$group).

        • +

      • theta: numeric vector of transformed covariances.

        • +

      • failed: Logical. TRUE if the model fit failed.

        • +

      • ids_samp: vector of characters containing the ids of the subjects included in the given sample.

        • +
      • +

    • fit: if method_bayes() is chosen, returns the MCMC Stan fit object. Otherwise NULL.

      • +

    • n_failures: absolute number of failures of the model fit. +Relevant only for method_condmean(type = "bootstrap"), method_approxbayes() and method_bmlmi().

      • +

    • formula: fixed effects formula object used for the model specification.

      • +
    +
    +

    Details

    +

    draws performs the first step of the multiple imputation (MI) procedure: fitting the +base imputation model. The goal is to estimate the parameters of interest needed +for the imputation phase (i.e. the regression coefficients and the covariance matrices +from a MMRM model).

    +

    The function distinguishes between the following methods:

    • Bayesian MI based on MCMC sampling: draws returns the draws +from the posterior distribution of the parameters using a Bayesian approach based on +MCMC sampling. This method can be specified by using method = method_bayes().

      • +

    • Approximate Bayesian MI based on bootstrapping: draws returns +the draws from the posterior distribution of the parameters using an approximate Bayesian approach, +where the sampling from the posterior distribution is simulated by fitting the MMRM model +on bootstrap samples of the original dataset. This method can be specified by using +method = method_approxbayes()].

      • +

    • Conditional mean imputation with bootstrap re-sampling: draws returns the +MMRM parameter estimates from the original dataset and from n_samples bootstrap samples. +This method can be specified by using method = method_condmean() with +argument type = "bootstrap".

      • +

    • Conditional mean imputation with jackknife re-sampling: draws returns the +MMRM parameter estimates from the original dataset and from each leave-one-subject-out sample. +This method can be specified by using method = method_condmean() with +argument type = "jackknife".

      • +

    • Bootstrapped Maximum Likelihood MI: draws returns the MMRM parameter estimates from +a given number of bootstrap samples needed to perform random imputations of the bootstrapped samples. +This method can be specified by using method = method_bmlmi().

      • +

    Bayesian MI based on MCMC sampling has been proposed in Carpenter, Roger, and Kenward (2013) who first introduced +reference-based imputation methods. Approximate Bayesian MI is discussed in Little and Rubin (2002). +Conditional mean imputation methods are discussed in Wolbers et al (2022). +Bootstrapped Maximum Likelihood MI is described in Von Hippel & Bartlett (2021).

    +

    The argument data contains the longitudinal data. It must have at least the following variables:

    • subjid: a factor vector containing the subject ids.

      • +

    • visit: a factor vector containing the visit the outcome was observed on.

      • +

    • group: a factor vector containing the group that the subject belongs to.

      • +

    • outcome: a numeric vector containing the outcome variable. It might contain missing values. +Additional baseline or time-varying covariates must be included in data.

      • +

    data must have one row per visit per subject. This means that incomplete +outcome data must be set as NA instead of having the related row missing. Missing values +in the covariates are not allowed. +If data is incomplete +then the expand_locf() helper function can be used to insert any missing rows using +Last Observation Carried Forward (LOCF) imputation to impute the covariates values. +Note that LOCF is generally not a principled imputation method and should only be used when appropriate +for the specific covariate.

    +

    Please note that there is no special provisioning for the baseline outcome values. If you do not want baseline +observations to be included in the model as part of the response variable then these should be removed in advance +from the outcome variable in data. At the same time if you want to include the baseline outcome as covariate in +the model, then this should be included as a separate column of data (as any other covariate).

    +

    Character covariates will be explicitly +cast to factors. If you use a custom analysis function that requires specific reference +levels for the character covariates (for example in the computation of the least square means +computation) then you are advised +to manually cast your character covariates to factor in advance of running draws().

    +

    The argument data_ice contains information about the occurrence of ICEs. It is a +data.frame with 3 columns:

    • Subject ID: a character vector containing the ids of the subjects that experienced +the ICE. This column must be named as specified in vars$subjid.

      • +

    • Visit: a character vector containing the first visit after the occurrence of the ICE +(i.e. the first visit affected by the ICE). +The visits must be equal to one of the levels of data[[vars$visit]]. +If multiple ICEs happen for the same subject, then only the first non-MAR visit should be used. +This column must be named as specified in vars$visit.

      • +

    • Strategy: a character vector specifying the imputation strategy to address the ICE for this subject. +This column must be named as specified in vars$strategy. +Possible imputation strategies are:

      • "MAR": Missing At Random.

        • +

      • "CIR": Copy Increments in Reference.

        • +

      • "CR": Copy Reference.

        • +

      • "JR": Jump to Reference.

        • +

      • "LMCF": Last Mean Carried Forward. +For explanations of these imputation strategies, see Carpenter, Roger, and Kenward (2013), Cro et al (2021), +and Wolbers et al (2022). +Please note that user-defined imputation strategies can also be set.

        • +
      • +

    The data_ice argument is necessary at this stage since (as explained in Wolbers et al (2022)), the model is fitted +after removing the observations which are incompatible with the imputation model, i.e. +any observed data on or after data_ice[[vars$visit]] that are addressed with an imputation +strategy different from MAR are excluded for the model fit. However such observations +will not be discarded from the data in the imputation phase +(performed with the function (impute()). To summarize, at this stage only pre-ICE data +and post-ICE data that is after ICEs for which MAR imputation is specified are used.

    +

    If the data_ice argument is omitted, or if a subject doesn't have a record within data_ice, then it is +assumed that all of the relevant subject's data is pre-ICE and as such all missing +visits will be imputed under the MAR assumption and all observed data will be used to fit the base imputation model. +Please note that the ICE visit cannot be updated via the update_strategy argument +in impute(); this means that subjects who didn't have a record in data_ice will always have their +missing data imputed under the MAR assumption even if their strategy is updated.

    +

    The vars argument is a named list that specifies the names of key variables within +data and data_ice. This list is created by set_vars() and contains the following named elements:

    • subjid: name of the column in data and data_ice which contains the subject ids variable.

      • +

    • visit: name of the column in data and data_ice which contains the visit variable.

      • +

    • group: name of the column in data which contains the group variable.

      • +

    • outcome: name of the column in data which contains the outcome variable.

      • +

    • covariates: vector of characters which contains the covariates to be included +in the model (including interactions which are specified as "covariateName1*covariateName2"``). If no covariates are provided the default model specification of outcome ~ 1 + visit + groupwill be used. Please note that thegroup*visit` interaction +is not included in the model by default.

      • +

    • strata: covariates used as stratification variables in the bootstrap sampling. +By default only the vars$group is set as stratification variable. +Needed only for method_condmean(type = "bootstrap") and method_approxbayes().

      • +

    • strategy: name of the column in data_ice which contains the subject-specific imputation strategy.

      • +
    +
    +

    References

    +

    James R Carpenter, James H Roger, and Michael G Kenward. Analysis of longitudinal trials with protocol deviation: a +framework for relevant, accessible assumptions, and inference via multiple imputation. Journal of Biopharmaceutical +Statistics, 23(6):1352–1371, 2013.

    +

    Suzie Cro, Tim P Morris, Michael G Kenward, and James R Carpenter. Sensitivity analysis for clinical trials with +missing continuous outcome data using controlled multiple imputation: a practical guide. Statistics in +Medicine, 39(21):2815–2842, 2020.

    +

    Roderick J. A. Little and Donald B. Rubin. Statistical Analysis with Missing Data, Second Edition. John Wiley & Sons, +Hoboken, New Jersey, 2002. [Section 10.2.3]

    +

    Marcel Wolbers, Alessandro Noci, Paul Delmar, Craig Gower-Page, Sean Yiu, Jonathan W. Bartlett. Standard and reference-based +conditional mean imputation. https://arxiv.org/abs/2109.11162, 2022.

    +

    Von Hippel, Paul T and Bartlett, Jonathan W. +Maximum likelihood multiple imputation: Faster imputations and consistent standard errors without posterior draws. 2021.

    +
    +
    +

    See also

    +

    method_bayes(), method_approxbayes(), method_condmean(), method_bmlmi() for setting method.

    +

    set_vars() for setting vars.

    +

    expand_locf() for expanding data in case of missing rows.

    +

    For more details see the quickstart vignette: +vignette("quickstart", package = "rbmi").

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/encap_get_mmrm_sample.html b/latest-tag/reference/encap_get_mmrm_sample.html new file mode 100644 index 00000000..7f2ea581 --- /dev/null +++ b/latest-tag/reference/encap_get_mmrm_sample.html @@ -0,0 +1,111 @@ + +Encapsulate get_mmrm_sample — encap_get_mmrm_sample • rbmi + Skip to contents + + +
    +
    +
    + +

    Encapsulate get_mmrm_sample

    + Source: R/parallel.R +
    encap_get_mmrm_sample.Rd
    +
    + +
    +

    Function creates a new wrapper function around get_mmrm_sample() +so that the arguments of get_mmrm_sample() are enclosed within +the new function. This makes running parallel and single process +calls to the function smoother. In particular this function takes care +of exporting the arguments if required to parallel process in a cluster

    +
    + +
    +

    Usage

    + 
    encap_get_mmrm_sample(cl, longdata, method)
    +
    + +
    +

    Arguments

    + + +
    cl
    +

    Either a cluster from get_cluster() or NULL

    + + +
    longdata
    +

    A longdata object from longDataConstructor$new()

    + + +
    method
    +

    A method object

    + +
    +
    +

    See also

    +

    get_cluster() for more documentation on the function inputs

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/eval_mmrm.html b/latest-tag/reference/eval_mmrm.html new file mode 100644 index 00000000..686ed79b --- /dev/null +++ b/latest-tag/reference/eval_mmrm.html @@ -0,0 +1,122 @@ + +Evaluate a call to mmrm — eval_mmrm • rbmi + Skip to contents + + +
    +
    +
    + +

    Evaluate a call to mmrm

    + Source: R/mmrm.R +
    eval_mmrm.Rd
    +
    + +
    +

    This is a utility function that attempts to evaluate a call to mmrm +managing any warnings or errors that are thrown. In particular +this function attempts to catch any warnings or errors and instead +of surfacing them it will simply add an additional element failed +with a value of TRUE. This allows for multiple calls to be made +without the program exiting.

    +
    + +
    +

    Usage

    + 
    eval_mmrm(expr)
    +
    + +
    +

    Arguments

    + + +
    expr
    +

    An expression to be evaluated. Should be a call to mmrm::mmrm().

    + +
    +
    +

    Details

    +

    This function was originally developed for use with glmmTMB which needed +more hand-holding and dropping of false-positive warnings. It is not +as important now but is kept around encase we need to catch +false-positive warnings again in the future.

    +
    +
    +

    See also

    +

    record()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+eval_mmrm({
+    mmrm::mmrm(formula, data)
+})
+} # }
+
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/expand.html b/latest-tag/reference/expand.html new file mode 100644 index 00000000..b433b881 --- /dev/null +++ b/latest-tag/reference/expand.html @@ -0,0 +1,185 @@ + +Expand and fill in missing data.frame rows — expand • rbmi + Skip to contents + + +
    +
    +
    + +

    Expand and fill in missing data.frame rows

    + Source: R/expand.R +
    expand.Rd
    +
    + +
    +

    These functions are essentially wrappers around base::expand.grid() to ensure that missing +combinations of data are inserted into a data.frame with imputation/fill methods for updating +covariate values of newly created rows.

    +
    + +
    +

    Usage

    + 
    expand(data, ...)
+
+fill_locf(data, vars, group = NULL, order = NULL)
+
+expand_locf(data, ..., vars, group, order)
    +
    + +
    +

    Arguments

    + + +
    data
    +

    dataset to expand or fill in.

    + + +
    ...
    +

    variables and the levels that should be expanded out (note that duplicate entries of +levels will result in multiple rows for that level).

    + + +
    vars
    +

    character vector containing the names of variables that need to be filled in.

    + + +
    group
    +

    character vector containing the names of variables to group +by when performing LOCF imputation of var.

    + + +
    order
    +

    character vector containing the names of additional variables to sort the data.frame +by before performing LOCF.

    + +
    +
    +

    Details

    +

    The draws() function makes the assumption that all subjects and visits are present +in the data.frame and that all covariate values are non missing; expand(), +fill_locf() and expand_locf() are utility functions to support users in ensuring +that their data.frame's conform to these assumptions.

    +

    expand() takes vectors for expected levels in a data.frame and expands out all +combinations inserting any missing rows into the data.frame. Note that all "expanded" +variables are cast as factors.

    +

    fill_locf() applies LOCF imputation to named covariates to fill in any NAs created +by the insertion of new rows by expand() (though do note that no distinction is +made between existing NAs and newly created NAs). Note that the data.frame is sorted +by c(group, order) before performing the LOCF imputation; the data.frame +will be returned in the original sort order however.

    +

    expand_locf() a simple composition function of fill_locf() and expand() i.e. +fill_locf(expand(...)).

    +

    Missing First Values

    + + +

    The fill_locf() function performs last observation carried forward imputation. +A natural consequence of this is that it is unable to impute missing observations if the +observation is the first value for a given subject / grouping. +These values are deliberately not imputed as doing so risks silent errors in the case of time +varying covariates. +One solution is to first use expand_locf() on just +the visit variable and time varying covariates and then merge on the baseline covariates +afterwards i.e.

    +

    library(dplyr)
+
+dat_expanded <- expand(
+    data = dat,
+    subject = c("pt1", "pt2", "pt3", "pt4"),
+    visit = c("vis1", "vis2", "vis3")
+)
+
+dat_filled <- dat_expanded %>%
+    left_join(baseline_covariates, by = "subject")

    +
    + +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+dat_expanded <- expand(
+    data = dat,
+    subject = c("pt1", "pt2", "pt3", "pt4"),
+    visit = c("vis1", "vis2", "vis3")
+)
+
+dat_filled <- fill_loc(
+    data = dat_expanded,
+    vars = c("Sex", "Age"),
+    group = "subject",
+    order = "visit"
+)
+
+## Or
+
+dat_filled <- expand_locf(
+    data = dat,
+    subject = c("pt1", "pt2", "pt3", "pt4"),
+    visit = c("vis1", "vis2", "vis3"),
+    vars = c("Sex", "Age"),
+    group = "subject",
+    order = "visit"
+)
+} # }
+
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/extract_covariates.html b/latest-tag/reference/extract_covariates.html new file mode 100644 index 00000000..139ba4d3 --- /dev/null +++ b/latest-tag/reference/extract_covariates.html @@ -0,0 +1,94 @@ + +Extract Variables from string vector — extract_covariates • rbmi + Skip to contents + + +
    +
    +
    + +

    Extract Variables from string vector

    + Source: R/utilities.R +
    extract_covariates.Rd
    +
    + +
    +

    Takes a string including potentially model terms like * and : and +extracts out the individual variables

    +
    + +
    +

    Usage

    + 
    extract_covariates(x)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    string of variable names potentially including interaction terms

    + +
    +
    +

    Details

    +

    i.e. c("v1", "v2", "v2*v3", "v1:v2") becomes c("v1", "v2", "v3")

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/extract_data_nmar_as_na.html b/latest-tag/reference/extract_data_nmar_as_na.html new file mode 100644 index 00000000..790820eb --- /dev/null +++ b/latest-tag/reference/extract_data_nmar_as_na.html @@ -0,0 +1,95 @@ + +Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na • rbmi + Skip to contents + + +
    +
    +
    + +

    Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy)

    + Source: R/draws.R +
    extract_data_nmar_as_na.Rd
    +
    + +
    +

    Set to NA outcome values that would be MNAR if they were missing +(i.e. which occur after an ICE handled using a reference-based imputation strategy)

    +
    + +
    +

    Usage

    + 
    extract_data_nmar_as_na(longdata)
    +
    + +
    +

    Arguments

    + + +
    longdata
    +

    R6 longdata object containing all relevant input data information.

    + +
    +
    +

    Value

    +

    A data.frame containing longdata$get_data(longdata$ids), but MNAR outcome +values are set to NA.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/extract_draws.html b/latest-tag/reference/extract_draws.html new file mode 100644 index 00000000..5096dba0 --- /dev/null +++ b/latest-tag/reference/extract_draws.html @@ -0,0 +1,105 @@ + +Extract draws from a stanfit object — extract_draws • rbmi + Skip to contents + + +
    +
    +
    + +

    Extract draws from a stanfit object

    + Source: R/mcmc.R +
    extract_draws.Rd
    +
    + +
    +

    Extract draws from a stanfit object and convert them into lists.

    +

    The function rstan::extract() returns the draws for a given parameter as an array. This function +calls rstan::extract() to extract the draws from a stanfit object +and then convert the arrays into lists.

    +
    + +
    +

    Usage

    + 
    extract_draws(stan_fit)
    +
    + +
    +

    Arguments

    + + +
    stan_fit
    +

    A stanfit object.

    + +
    +
    +

    Value

    +

    A named list of length 2 containing:

    • beta: a list of length equal to the number of draws containing +the draws from the posterior distribution of the regression coefficients.

      • +

    • sigma: a list of length equal to the number of draws containing +the draws from the posterior distribution of the covariance matrices. Each element +of the list is a list with length equal to 1 if same_cov = TRUE or equal to the +number of groups if same_cov = FALSE.

      • +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/extract_imputed_df.html b/latest-tag/reference/extract_imputed_df.html new file mode 100644 index 00000000..0dcf1747 --- /dev/null +++ b/latest-tag/reference/extract_imputed_df.html @@ -0,0 +1,123 @@ + +Extract imputed dataset — extract_imputed_df • rbmi + Skip to contents + + +
    +
    +
    + +

    Extract imputed dataset

    + Source: R/analyse.R +
    extract_imputed_df.Rd
    +
    + +
    +

    Takes an imputation object as generated by imputation_df() and uses +this to extract a completed dataset from a longdata object as created +by longDataConstructor(). Also applies a delta transformation +if a data.frame is provided to the delta argument. See analyse() for +details on the structure of this data.frame.

    +

    Subject IDs in the returned data.frame are scrambled i.e. are not the original +values.

    +
    + +
    +

    Usage

    + 
    extract_imputed_df(imputation, ld, delta = NULL, idmap = FALSE)
    +
    + +
    +

    Arguments

    + + +
    imputation
    +

    An imputation object as generated by imputation_df().

    + + +
    ld
    +

    A longdata object as generated by longDataConstructor().

    + + +
    delta
    +

    Either NULL or a data.frame. Is used to offset outcome values in the imputed dataset.

    + + +
    idmap
    +

    Logical. If TRUE an attribute called "idmap" is attached to +the return object which contains a list that maps the old subject ids +the new subject ids.

    + +
    +
    +

    Value

    +

    A data.frame.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/extract_imputed_dfs.html b/latest-tag/reference/extract_imputed_dfs.html new file mode 100644 index 00000000..de0ca0f2 --- /dev/null +++ b/latest-tag/reference/extract_imputed_dfs.html @@ -0,0 +1,130 @@ + +Extract imputed datasets — extract_imputed_dfs • rbmi + Skip to contents + + +
    +
    +
    + +

    Extract imputed datasets

    + Source: R/analyse.R +
    extract_imputed_dfs.Rd
    +
    + +
    +

    Extracts the imputed datasets contained within an imputations object generated +by impute().

    +
    + +
    +

    Usage

    + 
    extract_imputed_dfs(
+  imputations,
+  index = seq_along(imputations$imputations),
+  delta = NULL,
+  idmap = FALSE
+)
    +
    + +
    +

    Arguments

    + + +
    imputations
    +

    An imputations object as created by impute().

    + + +
    index
    +

    The indexes of the imputed datasets to return. By default, +all datasets within the imputations object will be returned.

    + + +
    delta
    +

    A data.frame containing the delta transformation to be +applied to the imputed dataset. See analyse() for details on the +format and specification of this data.frame.

    + + +
    idmap
    +

    Logical. The subject IDs in the imputed data.frame's are +replaced with new IDs to ensure they are unique. Setting this argument to +TRUE attaches an attribute, called idmap, to the returned data.frame's +that will provide a map from the new subject IDs to the old subject IDs.

    + +
    +
    +

    Value

    +

    A list of data.frames equal in length to the index argument.

    +
    +
    +

    See also

    +

    delta_template() for creating delta data.frames.

    +

    analyse().

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+extract_imputed_dfs(imputeObj)
+extract_imputed_dfs(imputeObj, c(1:3))
+} # }
+
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/extract_params.html b/latest-tag/reference/extract_params.html new file mode 100644 index 00000000..295316d9 --- /dev/null +++ b/latest-tag/reference/extract_params.html @@ -0,0 +1,90 @@ + +Extract parameters from a MMRM model — extract_params • rbmi + Skip to contents + + +
    +
    +
    + +

    Extract parameters from a MMRM model

    + Source: R/mmrm.R +
    extract_params.Rd
    +
    + +
    +

    Extracts the beta and sigma coefficients from an MMRM model created +by mmrm::mmrm().

    +
    + +
    +

    Usage

    + 
    extract_params(fit)
    +
    + +
    +

    Arguments

    + + +
    fit
    +

    an object created by mmrm::mmrm()

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/fit_mcmc.html b/latest-tag/reference/fit_mcmc.html new file mode 100644 index 00000000..e16b03af --- /dev/null +++ b/latest-tag/reference/fit_mcmc.html @@ -0,0 +1,151 @@ + +Fit the base imputation model using a Bayesian approach — fit_mcmc • rbmi + Skip to contents + + +
    +
    +
    + +

    Fit the base imputation model using a Bayesian approach

    + Source: R/mcmc.R +
    fit_mcmc.Rd
    +
    + +
    +

    fit_mcmc() fits the base imputation model using a Bayesian approach. +This is done through a MCMC method that is implemented in stan +and is run by using the function rstan::sampling(). +The function returns the draws from the posterior distribution of the model parameters +and the stanfit object. Additionally it performs multiple diagnostics checks of the chain +and returns warnings in case of any detected issues.

    +
    + +
    +

    Usage

    + 
    fit_mcmc(designmat, outcome, group, subjid, visit, method, quiet = FALSE)
    +
    + +
    +

    Arguments

    + + +
    designmat
    +

    The design matrix of the fixed effects.

    + + +
    outcome
    +

    The response variable. Must be numeric.

    + + +
    group
    +

    Character vector containing the group variable.

    + + +
    subjid
    +

    Character vector containing the subjects IDs.

    + + +
    visit
    +

    Character vector containing the visit variable.

    + + +
    method
    +

    A method object as generated by method_bayes().

    + + +
    quiet
    +

    Specify whether the stan sampling log should be printed to the console.

    + +
    +
    +

    Value

    +

    A named list composed by the following:

    • samples: a named list containing the draws for each parameter. It corresponds to the output of extract_draws().

      • +

    • fit: a stanfit object.

      • +
    +
    +

    Details

    +

    The Bayesian model assumes a multivariate normal likelihood function and weakly-informative +priors for the model parameters: in particular, uniform priors are assumed for the regression +coefficients and inverse-Wishart priors for the covariance matrices. +The chain is initialized using the REML parameter estimates from MMRM as starting values.

    +

    The function performs the following steps:

    1. Fit MMRM using a REML approach.

      2. +

    2. Prepare the input data for the MCMC fit as described in the data{} +block of the Stan file. See prepare_stan_data() for details.

      3. +

    3. Run the MCMC according the input arguments and using as starting values the REML parameter estimates +estimated at point 1.

      4. +

    4. Performs diagnostics checks of the MCMC. See check_mcmc() for details.

      5. +

    5. Extract the draws from the model fit.

      6. +

    The chains perform method$n_samples draws by keeping one every method$burn_between iterations. Additionally +the first method$burn_in iterations are discarded. The total number of iterations will +then be method$burn_in + method$burn_between*method$n_samples. +The purpose of method$burn_in is to ensure that the samples are drawn from the stationary +distribution of the Markov Chain. +The method$burn_between aims to keep the draws uncorrelated each from other.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/fit_mmrm.html b/latest-tag/reference/fit_mmrm.html new file mode 100644 index 00000000..e8b0b9d8 --- /dev/null +++ b/latest-tag/reference/fit_mmrm.html @@ -0,0 +1,138 @@ + +Fit a MMRM model — fit_mmrm • rbmi + Skip to contents + + +
    +
    +
    + +

    Fit a MMRM model

    + Source: R/mmrm.R +
    fit_mmrm.Rd
    +
    + +
    +

    Fits a MMRM model allowing for different covariance structures using mmrm::mmrm(). +Returns a list of key model parameters beta, sigma and an additional element failed +indicating whether or not the fit failed to converge. If the fit did fail to converge +beta and sigma will not be present.

    +
    + +
    +

    Usage

    + 
    fit_mmrm(
+  designmat,
+  outcome,
+  subjid,
+  visit,
+  group,
+  cov_struct = c("us", "toep", "cs", "ar1"),
+  REML = TRUE,
+  same_cov = TRUE
+)
    +
    + +
    +

    Arguments

    + + +
    designmat
    +

    a data.frame or matrix containing the covariates to use in the MMRM model. +Dummy variables must already be expanded out, i.e. via stats::model.matrix(). Cannot contain +any missing values

    + + +
    outcome
    +

    a numeric vector. The outcome value to be regressed on in the MMRM model.

    + + +
    subjid
    +

    a character / factor vector. The subject identifier used to link separate visits +that belong to the same subject.

    + + +
    visit
    +

    a character / factor vector. Indicates which visit the outcome value occurred on.

    + + +
    group
    +

    a character / factor vector. Indicates which treatment group the patient belongs to.

    + + +
    cov_struct
    +

    a character value. Specifies which covariance structure to use. Must be one of +"us", "toep", "cs" or "ar1"

    + + +
    REML
    +

    logical. Specifies whether restricted maximum likelihood should be used

    + + +
    same_cov
    +

    logical. Used to specify if a shared or individual covariance matrix should be +used per group

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/generate_data_single.html b/latest-tag/reference/generate_data_single.html new file mode 100644 index 00000000..b551b294 --- /dev/null +++ b/latest-tag/reference/generate_data_single.html @@ -0,0 +1,125 @@ + +Generate data for a single group — generate_data_single • rbmi + Skip to contents + + +
    +
    +
    + +

    Generate data for a single group

    + Source: R/simulate_data.R +
    generate_data_single.Rd
    +
    + +
    +

    Generate data for a single group

    +
    + +
    +

    Usage

    + 
    generate_data_single(pars_group, strategy_fun = NULL, distr_pars_ref = NULL)
    +
    + +
    +

    Arguments

    + + +
    pars_group
    +

    A simul_pars object as generated by set_simul_pars(). It specifies +the simulation parameters of the given group.

    + + +
    strategy_fun
    +

    Function implementing trajectories after the intercurrent event (ICE). +Must be one of getStrategies(). See getStrategies() for details. If NULL then post-ICE +outcomes are untouched.

    + + +
    distr_pars_ref
    +

    Optional. Named list containing the simulation parameters of the +reference arm. It contains the following elements:

    • mu: Numeric vector indicating the mean outcome trajectory assuming no ICEs. It should +include the outcome at baseline.

      • +

    • sigma Covariance matrix of the outcome trajectory assuming no ICEs. +If NULL, then these parameters are inherited from pars_group.

      • +
    + +
    +
    +

    Value

    +

    A data.frame containing the simulated data. It includes the following variables:

    • id: Factor variable that specifies the id of each subject.

      • +

    • visit: Factor variable that specifies the visit of each assessment. Visit 0 denotes +the baseline visit.

      • +

    • group: Factor variable that specifies which treatment group each subject belongs to.

      • +

    • outcome_bl: Numeric variable that specifies the baseline outcome.

      • +

    • outcome_noICE: Numeric variable that specifies the longitudinal outcome assuming +no ICEs.

      • +

    • ind_ice1: Binary variable that takes value 1 if the corresponding visit is +affected by ICE1 and 0 otherwise.

      • +

    • dropout_ice1: Binary variable that takes value 1 if the corresponding visit is +affected by the drop-out following ICE1 and 0 otherwise.

      • +

    • ind_ice2: Binary variable that takes value 1 if the corresponding visit is affected +by ICE2.

      • +

    • outcome: Numeric variable that specifies the longitudinal outcome including ICE1, ICE2 +and the intermittent missing values.

      • +
    +
    +

    See also

    +

    simulate_data().

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/getStrategies.html b/latest-tag/reference/getStrategies.html new file mode 100644 index 00000000..ed445259 --- /dev/null +++ b/latest-tag/reference/getStrategies.html @@ -0,0 +1,122 @@ + +Get imputation strategies — getStrategies • rbmi + Skip to contents + + +
    +
    +
    + +

    Get imputation strategies

    + Source: R/strategies.R +
    getStrategies.Rd
    +
    + +
    +

    Returns a list defining the imputation strategies to be used to create the +multivariate normal distribution parameters by merging those of the source +group and reference group per patient.

    +
    + +
    +

    Usage

    + 
    getStrategies(...)
    +
    + +
    +

    Arguments

    + + +
    ...
    +

    User defined methods to be added to the return list. Input must +be a function.

    + +
    +
    +

    Details

    +

    By default Jump to Reference (JR), Copy Reference (CR), Copy Increments in +Reference (CIR), Last Mean Carried Forward (LMCF) and Missing at Random (MAR) +are defined.

    +

    The user can define their own strategy functions (or overwrite the pre-defined ones) +by specifying a named input to the function i.e. NEW = function(...) .... +Only exception is MAR which cannot be overwritten.

    +

    All user defined functions must take 3 inputs: pars_group, pars_ref and +index_mar. pars_group and pars_ref are both lists with elements mu +and sigma representing the multivariate normal distribution parameters for +the subject's current group and reference group respectively. index_mar will be +a logical vector specifying which visits the subject met the MAR assumption +at. The function must return a list with elements mu and sigma. See the implementation +of strategy_JR() for an example.

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+getStrategies()
+getStrategies(
+    NEW = function(pars_group, pars_ref, index_mar) code ,
+    JR = function(pars_group, pars_ref, index_mar)  more_code
+)
+} # }
+
+
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/get_ESS.html b/latest-tag/reference/get_ESS.html new file mode 100644 index 00000000..230bf3b4 --- /dev/null +++ b/latest-tag/reference/get_ESS.html @@ -0,0 +1,91 @@ + +Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS • rbmi + Skip to contents + + +
    +
    +
    + +

    Extract the Effective Sample Size (ESS) from a stanfit object

    + Source: R/mcmc.R +
    get_ESS.Rd
    +
    + +
    +

    Extract the Effective Sample Size (ESS) from a stanfit object

    +
    + +
    +

    Usage

    + 
    get_ESS(stan_fit)
    +
    + +
    +

    Arguments

    + + +
    stan_fit
    +

    A stanfit object.

    + +
    +
    +

    Value

    +

    A named vector containing the ESS for each parameter of the model.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/get_bootstrap_stack.html b/latest-tag/reference/get_bootstrap_stack.html new file mode 100644 index 00000000..9c20c125 --- /dev/null +++ b/latest-tag/reference/get_bootstrap_stack.html @@ -0,0 +1,98 @@ + +Creates a stack object populated with bootstrapped samples — get_bootstrap_stack • rbmi + Skip to contents + + +
    +
    +
    + +

    Creates a stack object populated with bootstrapped samples

    + Source: R/stack.R +
    get_bootstrap_stack.Rd
    +
    + +
    +

    Function creates a Stack() object and populated the stack with bootstrap +samples based upon method$n_samples

    +
    + +
    +

    Usage

    + 
    get_bootstrap_stack(longdata, method, stack = Stack$new())
    +
    + +
    +

    Arguments

    + + +
    longdata
    +

    A longDataConstructor() object

    + + +
    method
    +

    A method object

    + + +
    stack
    +

    A Stack() object (this is only exposed for unit testing purposes)

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/get_cluster.html b/latest-tag/reference/get_cluster.html new file mode 100644 index 00000000..2b1eb92b --- /dev/null +++ b/latest-tag/reference/get_cluster.html @@ -0,0 +1,91 @@ + +Create cluster — get_cluster • rbmi + Skip to contents + + +
    +
    +
    + +

    Create cluster

    + Source: R/parallel.R +
    get_cluster.Rd
    +
    + +
    +

    Create cluster

    +
    + +
    +

    Usage

    + 
    get_cluster(ncores = 1)
    +
    + +
    +

    Arguments

    + + +
    ncores
    +

    Number of parallel processes to use

    +

    If ncores is 1 this function will return NULL +This function spawns a PSOCK cluster. +Ensures that rbmi and assert_that have been loaded +on the sub-processes

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/get_conditional_parameters.html b/latest-tag/reference/get_conditional_parameters.html new file mode 100644 index 00000000..c86b17ec --- /dev/null +++ b/latest-tag/reference/get_conditional_parameters.html @@ -0,0 +1,101 @@ + +Derive conditional multivariate normal parameters — get_conditional_parameters • rbmi + Skip to contents + + +
    +
    +
    + +

    Derive conditional multivariate normal parameters

    + Source: R/impute.R +
    get_conditional_parameters.Rd
    +
    + +
    +

    Takes parameters for a multivariate normal distribution and observed values +to calculate the conditional distribution for the unobserved values.

    +
    + +
    +

    Usage

    + 
    get_conditional_parameters(pars, values)
    +
    + +
    +

    Arguments

    + + +
    pars
    +

    a list with elements mu and sigma defining the mean vector and +covariance matrix respectively.

    + + +
    values
    +

    a vector of observed values to condition on, must be same length as pars$mu. +Missing values must be represented by an NA.

    + +
    +
    +

    Value

    +

    A list with the conditional distribution parameters:

    • mu - The conditional mean vector.

      • +

    • sigma - The conditional covariance matrix.

      • +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/get_delta_template.html b/latest-tag/reference/get_delta_template.html new file mode 100644 index 00000000..373a77fa --- /dev/null +++ b/latest-tag/reference/get_delta_template.html @@ -0,0 +1,93 @@ + +Get delta utility variables — get_delta_template • rbmi + Skip to contents + + +
    +
    +
    + +

    Get delta utility variables

    + Source: R/delta.R +
    get_delta_template.Rd
    +
    + +
    +

    This function creates the default delta template (1 row per subject per visit) +and extracts all the utility information that users need to define their own logic +for defining delta. See delta_template() for full details.

    +
    + +
    +

    Usage

    + 
    get_delta_template(imputations)
    +
    + +
    +

    Arguments

    + + +
    imputations
    +

    an imputations object created by impute().

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/get_draws_mle.html b/latest-tag/reference/get_draws_mle.html new file mode 100644 index 00000000..97768bc7 --- /dev/null +++ b/latest-tag/reference/get_draws_mle.html @@ -0,0 +1,168 @@ + +Fit the base imputation model on bootstrap samples — get_draws_mle • rbmi + Skip to contents + + +
    +
    +
    + +

    Fit the base imputation model on bootstrap samples

    + Source: R/draws.R +
    get_draws_mle.Rd
    +
    + +
    +

    Fit the base imputation model using a ML/REML approach on a given number of bootstrap samples as +specified by method$n_samples. Returns the parameter estimates from the model fit.

    +
    + +
    +

    Usage

    + 
    get_draws_mle(
+  longdata,
+  method,
+  sample_stack,
+  n_target_samples,
+  first_sample_orig,
+  use_samp_ids,
+  failure_limit = 0,
+  ncores = 1,
+  quiet = FALSE
+)
    +
    + +
    +

    Arguments

    + + +
    longdata
    +

    R6 longdata object containing all relevant input data information.

    + + +
    method
    +

    A method object as generated by either +method_approxbayes() or method_condmean() with argument type = "bootstrap".

    + + +
    sample_stack
    +

    A stack object containing the subject ids to be used on each mmrm iteration.

    + + +
    n_target_samples
    +

    Number of samples needed to be created

    + + +
    first_sample_orig
    +

    Logical. If TRUE the function returns method$n_samples + 1 samples where +the first sample contains the parameter estimates from the original dataset and method$n_samples +samples contain the parameter estimates from bootstrap samples. +If FALSE the function returns method$n_samples samples containing the parameter estimates from +bootstrap samples.

    + + +
    use_samp_ids
    +

    Logical. If TRUE, the sampled subject ids are returned. Otherwise +the subject ids from the original dataset are returned. These values are used to tell impute() +what subjects should be used to derive the imputed dataset.

    + + +
    failure_limit
    +

    Number of failed samples that are allowed before throwing an error

    + + +
    ncores
    +

    Number of processes to parallelise the job over

    + + +
    quiet
    +

    Logical, If TRUE will suppress printing of progress information that is printed to +the console.

    + +
    +
    +

    Value

    +

    A draws object which is a named list containing the following:

    • data: R6 longdata object containing all relevant input data information.

      • +

    • method: A method object as generated by either method_bayes(), +method_approxbayes() or method_condmean().

      • +

    • samples: list containing the estimated parameters of interest. +Each element of samples is a named list containing the following:

      • ids: vector of characters containing the ids of the subjects included in the original dataset.

        • +

      • beta: numeric vector of estimated regression coefficients.

        • +

      • sigma: list of estimated covariance matrices (one for each level of vars$group).

        • +

      • theta: numeric vector of transformed covariances.

        • +

      • failed: Logical. TRUE if the model fit failed.

        • +

      • ids_samp: vector of characters containing the ids of the subjects included in the given sample.

        • +
      • +

    • fit: if method_bayes() is chosen, returns the MCMC Stan fit object. Otherwise NULL.

      • +

    • n_failures: absolute number of failures of the model fit. +Relevant only for method_condmean(type = "bootstrap"), method_approxbayes() and method_bmlmi().

      • +

    • formula: fixed effects formula object used for the model specification.

      • +
    +
    +

    Details

    +

    This function takes a Stack object which contains multiple lists of patient ids. The function +takes this Stack and pulls a set ids and then constructs a dataset just consisting of these +patients (i.e. potentially a bootstrap or a jackknife sample).

    +

    The function then fits a MMRM model to this dataset to create a sample object. The function +repeats this process until n_target_samples have been reached. If more than failure_limit +samples fail to converge then the function throws an error.

    +

    After reaching the desired number of samples the function generates and returns a draws object.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/get_ests_bmlmi.html b/latest-tag/reference/get_ests_bmlmi.html new file mode 100644 index 00000000..c42917a0 --- /dev/null +++ b/latest-tag/reference/get_ests_bmlmi.html @@ -0,0 +1,112 @@ + +Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi • rbmi + Skip to contents + + +
    +
    +
    + +

    Von Hippel and Bartlett pooling of BMLMI method

    + Source: R/pool.R +
    get_ests_bmlmi.Rd
    +
    + +
    +

    Compute pooled point estimates, standard error and degrees of freedom +according to the Von Hippel and Bartlett formula for Bootstrapped Maximum Likelihood +Multiple Imputation (BMLMI).

    +
    + +
    +

    Usage

    + 
    get_ests_bmlmi(ests, D)
    +
    + +
    +

    Arguments

    + + +
    ests
    +

    numeric vector containing estimates from the analysis of the imputed datasets.

    + + +
    D
    +

    numeric representing the number of imputations between each bootstrap sample in the BMLMI method.

    + +
    +
    +

    Value

    +

    a list containing point estimate, standard error and degrees of freedom.

    +
    +
    +

    Details

    +

    ests must be provided in the following order: the firsts D elements are related to analyses from +random imputation of one bootstrap sample. The second set of D elements (i.e. from D+1 to 2*D) +are related to the second bootstrap sample and so on.

    +
    +
    +

    References

    +

    Von Hippel, Paul T and Bartlett, Jonathan W8. +Maximum likelihood multiple imputation: Faster imputations and consistent standard errors without posterior draws. 2021

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/get_example_data.html b/latest-tag/reference/get_example_data.html new file mode 100644 index 00000000..f8c348e6 --- /dev/null +++ b/latest-tag/reference/get_example_data.html @@ -0,0 +1,114 @@ + +Simulate a realistic example dataset — get_example_data • rbmi + Skip to contents + + +
    +
    +
    + +

    Simulate a realistic example dataset

    + Source: R/simulate_data.R +
    get_example_data.Rd
    +
    + +
    +

    Simulate a realistic example dataset using simulate_data() with hard-coded +values of all the input arguments.

    +
    + +
    +

    Usage

    + 
    get_example_data()
    +
    + +
    +

    Details

    +

    get_example_data() simulates a 1:1 randomized trial of +an active drug (intervention) versus placebo (control) with 100 subjects per +group and 6 post-baseline assessments (bi-monthly visits until 12 months). +One intercurrent event corresponding to treatment discontinuation is also simulated. +Specifically, data are simulated under the following assumptions:

    • The mean outcome trajectory in the placebo group increases linearly from +50 at baseline (visit 0) to 60 at visit 6, i.e. the slope is 10 points/year.

      • +

    • The mean outcome trajectory in the intervention group is identical to the +placebo group up to visit 2. From visit 2 onward, the slope decreases by 50% to 5 points/year.

      • +

    • The covariance structure of the baseline and follow-up values in both groups +is implied by a random intercept and slope model with a standard deviation of 5 +for both the intercept and the slope, and a correlation of 0.25. +In addition, an independent residual error with standard deviation 2.5 is added +to each assessment.

      • +

    • The probability of study drug discontinuation after each visit is calculated +according to a logistic model which depends on the observed outcome at that visit. +Specifically, a visit-wise discontinuation probability of 2% and 3% in the control +and intervention group, respectively, is specified in case the observed outcome is +equal to 50 (the mean value at baseline). The odds of a discontinuation is simulated +to increase by +10% for each +1 point increase of the observed outcome.

      • +

    • Study drug discontinuation is simulated to have no effect on the mean trajectory in +the placebo group. In the intervention group, subjects who discontinue follow +the slope of the mean trajectory from the placebo group from that time point onward. +This is compatible with a copy increments in reference (CIR) assumption.

      • +

    • Study drop-out at the study drug discontinuation visit occurs with a probability +of 50% leading to missing outcome data from that time point onward.

      • +
    +
    +

    See also

    +

    simulate_data(), set_simul_pars()

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/get_jackknife_stack.html b/latest-tag/reference/get_jackknife_stack.html new file mode 100644 index 00000000..3d693b5e --- /dev/null +++ b/latest-tag/reference/get_jackknife_stack.html @@ -0,0 +1,98 @@ + +Creates a stack object populated with jackknife samples — get_jackknife_stack • rbmi + Skip to contents + + +
    +
    +
    + +

    Creates a stack object populated with jackknife samples

    + Source: R/stack.R +
    get_jackknife_stack.Rd
    +
    + +
    +

    Function creates a Stack() object and populated the stack with jackknife +samples based upon

    +
    + +
    +

    Usage

    + 
    get_jackknife_stack(longdata, method, stack = Stack$new())
    +
    + +
    +

    Arguments

    + + +
    longdata
    +

    A longDataConstructor() object

    + + +
    method
    +

    A method object

    + + +
    stack
    +

    A Stack() object (this is only exposed for unit testing purposes)

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/get_mmrm_sample.html b/latest-tag/reference/get_mmrm_sample.html new file mode 100644 index 00000000..e27c0069 --- /dev/null +++ b/latest-tag/reference/get_mmrm_sample.html @@ -0,0 +1,108 @@ + +Fit MMRM and returns parameter estimates — get_mmrm_sample • rbmi + Skip to contents + + +
    +
    +
    + +

    Fit MMRM and returns parameter estimates

    + Source: R/draws.R +
    get_mmrm_sample.Rd
    +
    + +
    +

    get_mmrm_sample fits the base imputation model using a ML/REML approach. +Returns the parameter estimates from the fit.

    +
    + +
    +

    Usage

    + 
    get_mmrm_sample(ids, longdata, method)
    +
    + +
    +

    Arguments

    + + +
    ids
    +

    vector of characters containing the ids of the subjects.

    + + +
    longdata
    +

    R6 longdata object containing all relevant input data information.

    + + +
    method
    +

    A method object as generated by either +method_approxbayes() or method_condmean().

    + +
    +
    +

    Value

    +

    A named list of class sample_single. It contains the following:

    • ids vector of characters containing the ids of the subjects included in the original dataset.

      • +

    • beta numeric vector of estimated regression coefficients.

      • +

    • sigma list of estimated covariance matrices (one for each level of vars$group).

      • +

    • theta numeric vector of transformed covariances.

      • +

    • failed logical. TRUE if the model fit failed.

      • +

    • ids_samp vector of characters containing the ids of the subjects included in the given sample.

      • +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/get_pattern_groups.html b/latest-tag/reference/get_pattern_groups.html new file mode 100644 index 00000000..b09df765 --- /dev/null +++ b/latest-tag/reference/get_pattern_groups.html @@ -0,0 +1,98 @@ + +Determine patients missingness group — get_pattern_groups • rbmi + Skip to contents + + +
    +
    +
    + +

    Determine patients missingness group

    + Source: R/mcmc.R +
    get_pattern_groups.Rd
    +
    + +
    +

    Takes a design matrix with multiple rows per subject and returns a dataset +with 1 row per subject with a new column pgroup indicating which group +the patient belongs to (based upon their missingness pattern and treatment group)

    +
    + +
    +

    Usage

    + 
    get_pattern_groups(ddat)
    +
    + +
    +

    Arguments

    + + +
    ddat
    +

    a data.frame with columns subjid, visit, group, is_avail

    + +
    +
    +

    Details

    + +

    • The column is_avail must be a character or numeric 0 or 1

      • +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/get_pattern_groups_unique.html b/latest-tag/reference/get_pattern_groups_unique.html new file mode 100644 index 00000000..97c8029f --- /dev/null +++ b/latest-tag/reference/get_pattern_groups_unique.html @@ -0,0 +1,99 @@ + +Get Pattern Summary — get_pattern_groups_unique • rbmi + Skip to contents + + +
    +
    +
    + +

    Get Pattern Summary

    + Source: R/mcmc.R +
    get_pattern_groups_unique.Rd
    +
    + +
    +

    Takes a dataset of pattern information and creates a summary dataset of it +with just 1 row per pattern

    +
    + +
    +

    Usage

    + 
    get_pattern_groups_unique(patterns)
    +
    + +
    +

    Arguments

    + + +
    patterns
    +

    A data.frame with the columns pgroup, pattern and group

    + +
    +
    +

    Details

    + +

    • The column pgroup must be a numeric vector indicating which pattern group the patient belongs to

      • +

    • The column pattern must be a character string of 0's or 1's. It must be identical for all +rows within the same pgroup

      • +

    • The column group must be a character / numeric vector indicating which covariance group the observation +belongs to. It must be identical within the same pgroup

      • +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/get_pool_components.html b/latest-tag/reference/get_pool_components.html new file mode 100644 index 00000000..28c8371f --- /dev/null +++ b/latest-tag/reference/get_pool_components.html @@ -0,0 +1,91 @@ + +Expected Pool Components — get_pool_components • rbmi + Skip to contents + + +
    +
    +
    + +

    Expected Pool Components

    + Source: R/pool.R +
    get_pool_components.Rd
    +
    + +
    +

    Returns the elements expected to be contained in the analyse object +depending on what analysis method was specified.

    +
    + +
    +

    Usage

    + 
    get_pool_components(x)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    Character name of the analysis method, must one of +either "rubin", "jackknife", "bootstrap" or "bmlmi".

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/get_visit_distribution_parameters.html b/latest-tag/reference/get_visit_distribution_parameters.html new file mode 100644 index 00000000..a842d70d --- /dev/null +++ b/latest-tag/reference/get_visit_distribution_parameters.html @@ -0,0 +1,111 @@ + +Derive visit distribution parameters — get_visit_distribution_parameters • rbmi + Skip to contents + + +
    +
    +
    + +

    Derive visit distribution parameters

    + Source: R/impute.R +
    get_visit_distribution_parameters.Rd
    +
    + +
    +

    Takes patient level data and beta coefficients and expands them +to get a patient specific estimate for the visit distribution parameters +mu and sigma. Returns the values in a specific format +which is expected by downstream functions in the imputation process +(namely list(list(mu = ..., sigma = ...), list(mu = ..., sigma = ...))).

    +
    + +
    +

    Usage

    + 
    get_visit_distribution_parameters(dat, beta, sigma)
    +
    + +
    +

    Arguments

    + + +
    dat
    +

    Patient level dataset, must be 1 row per visit. Column order must +be in the same order as beta. The number of columns must match the length of beta

    + + +
    beta
    +

    List of model beta coefficients. There should be 1 element for each sample +e.g. if there were 3 samples and the models each had 4 beta coefficients then this argument +should be of the form list( c(1,2,3,4) , c(5,6,7,8), c(9,10,11,12)). +All elements of beta must be the same length and must be the same length and order as dat.

    + + +
    sigma
    +

    List of sigma. Must have the same number of entries as beta.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/has_class.html b/latest-tag/reference/has_class.html new file mode 100644 index 00000000..c771957c --- /dev/null +++ b/latest-tag/reference/has_class.html @@ -0,0 +1,102 @@ + +Does object have a class ? — has_class • rbmi + Skip to contents + + +
    +
    +
    + +

    Does object have a class ?

    + Source: R/utilities.R +
    has_class.Rd
    +
    + +
    +

    Utility function to see if an object has a particular class. +Useful when we don't know how many other classes the object may +have.

    +
    + +
    +

    Usage

    + 
    has_class(x, cls)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    the object we want to check the class of.

    + + +
    cls
    +

    the class we want to know if it has or not.

    + +
    +
    +

    Value

    +

    TRUE if the object has the class. +FALSE if the object does not have the class.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/ife.html b/latest-tag/reference/ife.html new file mode 100644 index 00000000..ea2409e7 --- /dev/null +++ b/latest-tag/reference/ife.html @@ -0,0 +1,104 @@ + +if else — ife • rbmi + Skip to contents + + +
    +
    +
    + +

    if else

    + Source: R/utilities.R +
    ife.Rd
    +
    + +
    +

    A wrapper around if() else() to prevent unexpected +interactions between ifelse() and factor variables

    +
    + +
    +

    Usage

    + 
    ife(x, a, b)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    True / False

    + + +
    a
    +

    value to return if True

    + + +
    b
    +

    value to return if False

    + +
    +
    +

    Details

    +

    By default ifelse() will convert factor variables to their +numeric values which is often undesirable. This connivance +function avoids that problem

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/imputation_df.html b/latest-tag/reference/imputation_df.html new file mode 100644 index 00000000..e9976606 --- /dev/null +++ b/latest-tag/reference/imputation_df.html @@ -0,0 +1,87 @@ + +Create a valid imputation_df object — imputation_df • rbmi + Skip to contents + + +
    +
    +
    + +

    Create a valid imputation_df object

    + Source: R/dataclasses.R +
    imputation_df.Rd
    +
    + +
    +

    Create a valid imputation_df object

    +
    + +
    +

    Usage

    + 
    imputation_df(...)
    +
    + +
    +

    Arguments

    + + +
    ...
    +

    a list of imputation_single.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/imputation_list_df.html b/latest-tag/reference/imputation_list_df.html new file mode 100644 index 00000000..3d15eb19 --- /dev/null +++ b/latest-tag/reference/imputation_list_df.html @@ -0,0 +1,87 @@ + +List of imputations_df — imputation_list_df • rbmi + Skip to contents + + +
    +
    +
    + +

    List of imputations_df

    + Source: R/dataclasses.R +
    imputation_list_df.Rd
    +
    + +
    +

    A container for multiple imputation_df's

    +
    + +
    +

    Usage

    + 
    imputation_list_df(...)
    +
    + +
    +

    Arguments

    + + +
    ...
    +

    objects of class imputation_df

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/imputation_list_single.html b/latest-tag/reference/imputation_list_single.html new file mode 100644 index 00000000..ffd84fb4 --- /dev/null +++ b/latest-tag/reference/imputation_list_single.html @@ -0,0 +1,98 @@ + +A collection of imputation_singles() grouped by a single subjid ID — imputation_list_single • rbmi + Skip to contents + + +
    +
    +
    + +

    A collection of imputation_singles() grouped by a single subjid ID

    + Source: R/dataclasses.R +
    imputation_list_single.Rd
    +
    + +
    +

    A collection of imputation_singles() grouped by a single subjid ID

    +
    + +
    +

    Usage

    + 
    imputation_list_single(imputations, D = 1)
    +
    + +
    +

    Arguments

    + + +
    imputations
    +

    a list of imputation_single() objects ordered so that repetitions +are grouped sequentially

    + + +
    D
    +

    the number of repetitions that were performed which determines how many columns +the imputation matrix should have

    +

    This is a constructor function to create a imputation_list_single object +which contains a matrix of imputation_single() objects grouped by a single id. The matrix +is split so that it has D columns (i.e. for non-bmlmi methods this will always be 1)

    +

    The id attribute is determined by extracting the id attribute from the contributing +imputation_single() objects. An error is throw if multiple id are detected

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/imputation_single.html b/latest-tag/reference/imputation_single.html new file mode 100644 index 00000000..67088147 --- /dev/null +++ b/latest-tag/reference/imputation_single.html @@ -0,0 +1,91 @@ + +Create a valid imputation_single object — imputation_single • rbmi + Skip to contents + + +
    +
    +
    + +

    Create a valid imputation_single object

    + Source: R/dataclasses.R +
    imputation_single.Rd
    +
    + +
    +

    Create a valid imputation_single object

    +
    + +
    +

    Usage

    + 
    imputation_single(id, values)
    +
    + +
    +

    Arguments

    + + +
    id
    +

    a character string specifying the subject id.

    + + +
    values
    +

    a numeric vector indicating the imputed values.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/impute.html b/latest-tag/reference/impute.html new file mode 100644 index 00000000..81f8b31e --- /dev/null +++ b/latest-tag/reference/impute.html @@ -0,0 +1,208 @@ + +Create imputed datasets — impute • rbmi + Skip to contents + + +
    +
    +
    + +

    Create imputed datasets

    + Source: R/impute.R +
    impute.Rd
    +
    + +
    +

    impute() creates imputed datasets based upon the data and options specified in +the call to draws(). One imputed dataset is created per each "sample" created by +draws().

    +
    + +
    +

    Usage

    + 
    impute(
+  draws,
+  references = NULL,
+  update_strategy = NULL,
+  strategies = getStrategies()
+)
+
+# S3 method for class 'random'
+impute(
+  draws,
+  references = NULL,
+  update_strategy = NULL,
+  strategies = getStrategies()
+)
+
+# S3 method for class 'condmean'
+impute(
+  draws,
+  references = NULL,
+  update_strategy = NULL,
+  strategies = getStrategies()
+)
    +
    + +
    +

    Arguments

    + + +
    draws
    +

    A draws object created by draws().

    + + +
    references
    +

    A named vector. Identifies the references to be used for reference-based +imputation methods. Should be of the form c("Group1" = "Reference1", "Group2" = "Reference2"). +If NULL (default), the references are assumed to be of the form +c("Group1" = "Group1", "Group2" = "Group2"). This argument cannot be NULL if +an imputation strategy (as defined by data_ice[[vars$strategy]] in the call to draws) other than MAR is set.

    + + +
    update_strategy
    +

    An optional data.frame. Updates the imputation method that was +originally set via the data_ice option in draws(). See the details section for more +information.

    + + +
    strategies
    +

    A named list of functions. Defines the imputation functions to be used. +The names of the list should mirror the values specified in strategy column of data_ice. +Default = getStrategies(). See getStrategies() for more details.

    + +
    +
    +

    Details

    +

    impute() uses the imputation model parameter estimates, as generated by draws(), to +first calculate the marginal (multivariate normal) distribution of a subject's longitudinal +outcome variable +depending on their covariate values. +For subjects with intercurrent events (ICEs) handled using non-MAR methods, this marginal distribution +is then updated depending on the time of the first visit affected by the ICE, +the chosen imputation strategy and the chosen reference group as described in Carpenter, Roger, and Kenward (2013) . +The subject's imputation distribution used for imputing missing values is then defined as +their marginal distribution conditional on their observed outcome values. +One dataset is being generated per set of parameter estimates provided by draws().

    +

    The exact manner in how missing values are imputed from this conditional imputation distribution depends +on the method object that was provided to draws(), in particular:

    • Bayes & Approximate Bayes: each imputed dataset contains 1 row per subject & visit +from the original dataset with missing values imputed by taking a single random sample +from the conditional imputation distribution.

      • +

    • Conditional Mean: each imputed dataset contains 1 row per subject & visit from the +bootstrapped or jackknife dataset that was used to generate the corresponding parameter +estimates in draws(). Missing values are imputed by using the mean of the conditional +imputation distribution. Please note that the first imputed dataset refers to the conditional +mean imputation on the original dataset whereas all subsequent imputed datasets refer to +conditional mean imputations for bootstrap or jackknife samples, respectively, of the original data.

      • +

    • Bootstrapped Maximum Likelihood MI (BMLMI): it performs D random imputations of each bootstrapped +dataset that was used to generate the corresponding parameter estimates in draws(). A total number of +B*D imputed datasets is provided, where B is the number of bootstrapped datasets. Missing values +are imputed by taking a random sample from the conditional imputation distribution.

      • +

    The update_strategy argument can be used to update the imputation strategy that was +originally set via the data_ice option in draws(). This avoids having to re-run the draws() +function when changing the imputation strategy in certain circumstances (as detailed below). +The data.frame provided to update_strategy argument must contain two columns, +one for the subject ID and another for the imputation strategy, whose names are the same as +those defined in the vars argument as specified in the call to draws(). Please note that this +argument only allows you to update the imputation strategy and not other arguments such as the +time of the first visit affected by the ICE. +A key limitation of this functionality is +that one can only switch between a MAR and a non-MAR strategy (or vice versa) for subjects without +observed post-ICE data. The reason for this is that such a change would affect whether the post-ICE data is included +in the base imputation model or not (as explained in the help to draws()). +As an example, if a subject had their ICE on "Visit 2" +but had observed/known values for "Visit 3" then the function will throw an error +if one tries to switch the strategy from MAR to a non-MAR strategy. In contrast, switching from +a non-MAR to a MAR strategy, whilst valid, will raise a warning as not all usable data +will have been utilised in the imputation model.

    +
    +
    +

    References

    +

    James R Carpenter, James H Roger, and Michael G Kenward. Analysis of longitudinal trials with protocol deviation: +a framework for relevant, +accessible assumptions, and inference via multiple imputation. Journal of Biopharmaceutical Statistics, +23(6):1352–1371, 2013. [Section 4.2 and 4.3]

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+
+impute(
+    draws = drawobj,
+    references = c("Trt" = "Placebo", "Placebo" = "Placebo")
+)
+
+new_strategy <- data.frame(
+  subjid = c("Pt1", "Pt2"),
+  strategy = c("MAR", "JR")
+)
+
+impute(
+    draws = drawobj,
+    references = c("Trt" = "Placebo", "Placebo" = "Placebo"),
+    update_strategy = new_strategy
+)
+} # }
+
+
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/impute_data_individual.html b/latest-tag/reference/impute_data_individual.html new file mode 100644 index 00000000..b9bb5b95 --- /dev/null +++ b/latest-tag/reference/impute_data_individual.html @@ -0,0 +1,149 @@ + +Impute data for a single subject — impute_data_individual • rbmi + Skip to contents + + +
    +
    +
    + +

    Impute data for a single subject

    + Source: R/impute.R +
    impute_data_individual.Rd
    +
    + +
    +

    This function performs the imputation for a single subject at a time implementing the +process as detailed in impute().

    +
    + +
    +

    Usage

    + 
    impute_data_individual(
+  id,
+  index,
+  beta,
+  sigma,
+  data,
+  references,
+  strategies,
+  condmean,
+  n_imputations = 1
+)
    +
    + +
    +

    Arguments

    + + +
    id
    +

    Character string identifying the subject.

    + + +
    index
    +

    The sample indexes which the subject belongs to e.g c(1,1,1,2,2,4).

    + + +
    beta
    +

    A list of beta coefficients for each sample, i.e. beta[[1]] is the set of +beta coefficients for the first sample.

    + + +
    sigma
    +

    A list of the sigma coefficients for each sample split by group i.e. +sigma[[1]][["A"]] would give the sigma coefficients for group A for the first sample.

    + + +
    data
    +

    A longdata object created by longDataConstructor()

    + + +
    references
    +

    A named vector. Identifies the references to be used when generating the +imputed values. Should be of the form c("Group" = "Reference", "Group" = "Reference").

    + + +
    strategies
    +

    A named list of functions. Defines the imputation functions to be used. +The names of the list should mirror the values specified in method column of data_ice. +Default = getStrategies(). See getStrategies() for more details.

    + + +
    condmean
    +

    Logical. If TRUE will impute using the conditional mean values, if FALSE +will impute by taking a random draw from the multivariate normal distribution.

    + + +
    n_imputations
    +

    When condmean = FALSE numeric representing the number of random imputations to be performed for each sample. +Default is 1 (one random imputation per sample).

    + +
    +
    +

    Details

    +

    Note that this function performs all of the required imputations for a subject at the +same time. I.e. if a subject is included in samples 1,3,5,9 then all imputations (using +sample-dependent imputation model parameters) are performed in one step in order to avoid +having to look up a subjects's covariates and expanding them to a design matrix multiple times +(which would be more computationally expensive). +The function also supports subject belonging to the same sample multiple times, +i.e. 1,1,2,3,5,5, as will typically occur for bootstrapped datasets.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/impute_internal.html b/latest-tag/reference/impute_internal.html new file mode 100644 index 00000000..b986198e --- /dev/null +++ b/latest-tag/reference/impute_internal.html @@ -0,0 +1,121 @@ + +Create imputed datasets — impute_internal • rbmi + Skip to contents + + +
    +
    +
    + +

    Create imputed datasets

    + Source: R/impute.R +
    impute_internal.Rd
    +
    + +
    +

    This is the work horse function that implements most of the functionality of impute. +See the user level function impute() for further details.

    +
    + +
    +

    Usage

    + 
    impute_internal(
+  draws,
+  references = NULL,
+  update_strategy,
+  strategies,
+  condmean
+)
    +
    + +
    +

    Arguments

    + + +
    draws
    +

    A draws object created by draws().

    + + +
    references
    +

    A named vector. Identifies the references to be used for reference-based +imputation methods. Should be of the form c("Group1" = "Reference1", "Group2" = "Reference2"). +If NULL (default), the references are assumed to be of the form +c("Group1" = "Group1", "Group2" = "Group2"). This argument cannot be NULL if +an imputation strategy (as defined by data_ice[[vars$strategy]] in the call to draws) other than MAR is set.

    + + +
    update_strategy
    +

    An optional data.frame. Updates the imputation method that was +originally set via the data_ice option in draws(). See the details section for more +information.

    + + +
    strategies
    +

    A named list of functions. Defines the imputation functions to be used. +The names of the list should mirror the values specified in strategy column of data_ice. +Default = getStrategies(). See getStrategies() for more details.

    + + +
    condmean
    +

    logical. If TRUE will impute using the conditional mean values, if values +will impute by taking a random draw from the multivariate normal distribution.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/impute_outcome.html b/latest-tag/reference/impute_outcome.html new file mode 100644 index 00000000..a3e1cfa8 --- /dev/null +++ b/latest-tag/reference/impute_outcome.html @@ -0,0 +1,98 @@ + +Sample outcome value — impute_outcome • rbmi + Skip to contents + + +
    +
    +
    + +

    Sample outcome value

    + Source: R/impute.R +
    impute_outcome.Rd
    +
    + +
    +

    Draws a random sample from a multivariate normal distribution.

    +
    + +
    +

    Usage

    + 
    impute_outcome(conditional_parameters, n_imputations = 1, condmean = FALSE)
    +
    + +
    +

    Arguments

    + + +
    conditional_parameters
    +

    a list with elements mu and sigma which +contain the mean vector and covariance matrix to sample from.

    + + +
    n_imputations
    +

    numeric representing the number of random samples from the multivariate +normal distribution to be performed. Default is 1.

    + + +
    condmean
    +

    should conditional mean imputation be performed (as opposed to random +sampling)

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/index.html b/latest-tag/reference/index.html new file mode 100644 index 00000000..13b62008 --- /dev/null +++ b/latest-tag/reference/index.html @@ -0,0 +1,882 @@ + +Package index • rbmi + Skip to contents + + +
    +
    +
    + +

    Package index

    +
    + +
    +

    All functions

    + + + + +
    + + + + +
    + + QR_decomp() + +
    +
    QR decomposition
    +
    + + Stack + +
    +
    R6 Class for a FIFO stack
    +
    + + add_class() + +
    +
    Add a class
    +
    + + adjust_trajectories() + +
    +
    Adjust trajectories due to the intercurrent event (ICE)
    +
    + + adjust_trajectories_single() + +
    +
    Adjust trajectory of a subject's outcome due to the intercurrent event (ICE)
    +
    + + analyse() + +
    +
    Analyse Multiple Imputed Datasets
    +
    + + ancova() + +
    +
    Analysis of Covariance
    +
    + + ancova_single() + +
    +
    Implements an Analysis of Covariance (ANCOVA)
    +
    + + antidepressant_data + +
    +
    Antidepressant trial data
    +
    + + apply_delta() + +
    +
    Applies delta adjustment
    +
    + + as_analysis() + +
    +
    Construct an analysis object
    +
    + + as_ascii_table() + +
    +
    as_ascii_table
    +
    + + as_class() + +
    +
    Set Class
    +
    + + as_cropped_char() + +
    +
    as_cropped_char
    +
    + + as_dataframe() + +
    +
    Convert object to dataframe
    +
    + + as_draws() + +
    +
    Creates a draws object
    +
    + + as_imputation() + +
    +
    Create an imputation object
    +
    + + as_indices() + +
    +
    Convert indicator to index
    +
    + + as_mmrm_df() + +
    +
    Creates a "MMRM" ready dataset
    +
    + + as_mmrm_formula() + +
    +
    Create MMRM formula
    +
    + + as_model_df() + +
    +
    Expand data.frame into a design matrix
    +
    + + as_simple_formula() + +
    +
    Creates a simple formula object from a string
    +
    + + as_stan_array() + +
    +
    As array
    +
    + + as_strata() + +
    +
    Create vector of Stratas
    +
    + + assert_variables_exist() + +
    +
    Assert that all variables exist within a dataset
    +
    + + char2fct() + +
    +
    Convert character variables to factor
    +
    + + check_ESS() + +
    +
    Diagnostics of the MCMC based on ESS
    +
    + + check_hmc_diagn() + +
    +
    Diagnostics of the MCMC based on HMC-related measures.
    +
    + + check_mcmc() + +
    +
    Diagnostics of the MCMC
    +
    + + compute_sigma() + +
    +
    Compute covariance matrix for some reference-based methods (JR, CIR)
    +
    + + convert_to_imputation_list_df() + +
    +
    Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's)
    +
    + + d_lagscale() + +
    +
    Calculate delta from a lagged scale coefficient
    +
    + + delta_template() + +
    +
    Create a delta data.frame template
    +
    + + do_not_run() + +
    +
    Do not run this function
    +
    + + draws() + +
    +
    Fit the base imputation model and get parameter estimates
    +
    + + encap_get_mmrm_sample() + +
    +
    Encapsulate get_mmrm_sample
    +
    + + eval_mmrm() + +
    +
    Evaluate a call to mmrm
    +
    + + expand() fill_locf() expand_locf() + +
    +
    Expand and fill in missing data.frame rows
    +
    + + extract_covariates() + +
    +
    Extract Variables from string vector
    +
    + + extract_data_nmar_as_na() + +
    +
    Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy)
    +
    + + extract_draws() + +
    +
    Extract draws from a stanfit object
    +
    + + extract_imputed_df() + +
    +
    Extract imputed dataset
    +
    + + extract_imputed_dfs() + +
    +
    Extract imputed datasets
    +
    + + extract_params() + +
    +
    Extract parameters from a MMRM model
    +
    + + fit_mcmc() + +
    +
    Fit the base imputation model using a Bayesian approach
    +
    + + fit_mmrm() + +
    +
    Fit a MMRM model
    +
    + + generate_data_single() + +
    +
    Generate data for a single group
    +
    + + getStrategies() + +
    +
    Get imputation strategies
    +
    + + get_ESS() + +
    +
    Extract the Effective Sample Size (ESS) from a stanfit object
    +
    + + get_bootstrap_stack() + +
    +
    Creates a stack object populated with bootstrapped samples
    +
    + + get_cluster() + +
    +
    Create cluster
    +
    + + get_conditional_parameters() + +
    +
    Derive conditional multivariate normal parameters
    +
    + + get_delta_template() + +
    +
    Get delta utility variables
    +
    + + get_draws_mle() + +
    +
    Fit the base imputation model on bootstrap samples
    +
    + + get_ests_bmlmi() + +
    +
    Von Hippel and Bartlett pooling of BMLMI method
    +
    + + get_example_data() + +
    +
    Simulate a realistic example dataset
    +
    + + get_jackknife_stack() + +
    +
    Creates a stack object populated with jackknife samples
    +
    + + get_mmrm_sample() + +
    +
    Fit MMRM and returns parameter estimates
    +
    + + get_pattern_groups() + +
    +
    Determine patients missingness group
    +
    + + get_pattern_groups_unique() + +
    +
    Get Pattern Summary
    +
    + + get_pool_components() + +
    +
    Expected Pool Components
    +
    + + get_visit_distribution_parameters() + +
    +
    Derive visit distribution parameters
    +
    + + has_class() + +
    +
    Does object have a class ?
    +
    + + ife() + +
    +
    if else
    +
    + + imputation_df() + +
    +
    Create a valid imputation_df object
    +
    + + imputation_list_df() + +
    +
    List of imputations_df
    +
    + + imputation_list_single() + +
    +
    A collection of imputation_singles() grouped by a single subjid ID
    +
    + + imputation_single() + +
    +
    Create a valid imputation_single object
    +
    + + impute() + +
    +
    Create imputed datasets
    +
    + + impute_data_individual() + +
    +
    Impute data for a single subject
    +
    + + impute_internal() + +
    +
    Create imputed datasets
    +
    + + impute_outcome() + +
    +
    Sample outcome value
    +
    + + invert() + +
    +
    invert
    +
    + + invert_indexes() + +
    +
    Invert and derive indexes
    +
    + + is_absent() + +
    +
    Is value absent
    +
    + + is_char_fact() + +
    +
    Is character or factor
    +
    + + is_char_one() + +
    +
    Is single character
    +
    + + is_in_rbmi_development() + +
    +
    Is package in development mode?
    +
    + + is_num_char_fact() + +
    +
    Is character, factor or numeric
    +
    + + locf() + +
    +
    Last Observation Carried Forward
    +
    + + longDataConstructor + +
    +
    R6 Class for Storing / Accessing & Sampling Longitudinal Data
    +
    + + ls_design_equal() ls_design_proportional() + +
    +
    Calculate design vector for the lsmeans
    +
    + + lsmeans() + +
    +
    Least Square Means
    +
    + + method_bayes() method_approxbayes() method_condmean() method_bmlmi() + +
    +
    Set the multiple imputation methodology
    +
    + + parametric_ci() + +
    +
    Calculate parametric confidence intervals
    +
    + + pool() as.data.frame(<pool>) print(<pool>) + +
    +
    Pool analysis results obtained from the imputed datasets
    +
    + + pool_bootstrap_normal() + +
    +
    Bootstrap Pooling via normal approximation
    +
    + + pool_bootstrap_percentile() + +
    +
    Bootstrap Pooling via Percentiles
    +
    + + pool_internal() + +
    +
    Internal Pool Methods
    +
    + + prepare_stan_data() + +
    +
    Prepare input data to run the Stan model
    +
    + + print(<analysis>) + +
    +
    Print analysis object
    +
    + + print(<draws>) + +
    +
    Print draws object
    +
    + + print(<imputation>) + +
    +
    Print imputation object
    +
    + + progressLogger + +
    +
    R6 Class for printing current sampling progress
    +
    + + pval_percentile() + +
    +
    P-value of percentile bootstrap
    +
    + + random_effects_expr() + +
    +
    Construct random effects formula
    +
    + + record() + +
    +
    Capture all Output
    +
    + + recursive_reduce() + +
    +
    recursive_reduce
    +
    + + remove_if_all_missing() + +
    +
    Remove subjects from dataset if they have no observed values
    +
    + + rubin_df() + +
    +
    Barnard and Rubin degrees of freedom adjustment
    +
    + + rubin_rules() + +
    +
    Combine estimates using Rubin's rules
    +
    + + sample_ids() + +
    +
    Sample Patient Ids
    +
    + + sample_list() + +
    +
    Create and validate a sample_list object
    +
    + + sample_mvnorm() + +
    +
    Sample random values from the multivariate normal distribution
    +
    + + sample_single() + +
    +
    Create object of sample_single class
    +
    + + scalerConstructor + +
    +
    R6 Class for scaling (and un-scaling) design matrices
    +
    + + set_simul_pars() + +
    +
    Set simulation parameters of a study group.
    +
    + + set_vars() + +
    +
    Set key variables
    +
    + + simulate_data() + +
    +
    Generate data
    +
    + + simulate_dropout() + +
    +
    Simulate drop-out
    +
    + + simulate_ice() + +
    +
    Simulate intercurrent event
    +
    + + simulate_test_data() as_vcov() + +
    +
    Create simulated datasets
    +
    + + sort_by() + +
    +
    Sort data.frame
    +
    + + split_dim() + +
    +
    Transform array into list of arrays
    +
    + + split_imputations() + +
    +
    Split a flat list of imputation_single() into multiple imputation_df()'s by ID
    +
    + + str_contains() + +
    +
    Does a string contain a substring
    +
    + + strategy_MAR() strategy_JR() strategy_CR() strategy_CIR() strategy_LMCF() + +
    +
    Strategies
    +
    + + string_pad() + +
    +
    string_pad
    +
    + + transpose_imputations() + +
    +
    Transpose imputations
    +
    + + transpose_results() + +
    +
    Transpose results object
    +
    + + transpose_samples() + +
    +
    Transpose samples
    +
    + + validate() + +
    +
    Generic validation method
    +
    + + validate(<analysis>) + +
    +
    Validate analysis objects
    +
    + + validate(<draws>) + +
    +
    Validate draws object
    +
    + + validate(<is_mar>) + +
    +
    Validate is_mar for a given subject
    +
    + + validate(<ivars>) + +
    +
    Validate inputs for vars
    +
    + + validate(<references>) + +
    +
    Validate user supplied references
    +
    + + validate(<sample_list>) + +
    +
    Validate sample_list object
    +
    + + validate(<sample_single>) + +
    +
    Validate sample_single object
    +
    + + validate(<simul_pars>) + +
    +
    Validate a simul_pars object
    +
    + + validate(<stan_data>) + +
    +
    Validate a stan_data object
    +
    + + validate_analyse_pars() + +
    +
    Validate analysis results
    +
    + + validate_datalong() validate_datalong_varExists() validate_datalong_types() validate_datalong_notMissing() validate_datalong_complete() validate_datalong_unifromStrata() validate_dataice() + +
    +
    Validate a longdata object
    +
    + + validate_strategies() + +
    +
    Validate user specified strategies
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/invert.html b/latest-tag/reference/invert.html new file mode 100644 index 00000000..f1680ea4 --- /dev/null +++ b/latest-tag/reference/invert.html @@ -0,0 +1,90 @@ + +invert — invert • rbmi + Skip to contents + + +
    +
    +
    + +

    invert

    + Source: R/as_ascii_table.R +
    invert.Rd
    +
    + +
    +

    Utility function used to replicated purrr::transpose. Turns a list inside +out.

    +
    + +
    +

    Usage

    + 
    invert(x)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    list

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/invert_indexes.html b/latest-tag/reference/invert_indexes.html new file mode 100644 index 00000000..d802106c --- /dev/null +++ b/latest-tag/reference/invert_indexes.html @@ -0,0 +1,101 @@ + +Invert and derive indexes — invert_indexes • rbmi + Skip to contents + + +
    +
    +
    + +

    Invert and derive indexes

    + Source: R/impute.R +
    invert_indexes.Rd
    +
    + +
    +

    Takes a list of elements and creates a new list +containing 1 entry per unique element value containing +the indexes of which original elements it occurred in.

    +
    + +
    +

    Usage

    + 
    invert_indexes(x)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    list of elements to invert and calculate index from (see details).

    + +
    +
    +

    Details

    +

    This functions purpose is best illustrated by an example:

    +

    input:

    +

    list( c("A", "B", "C"), c("A", "A", "B"))}

    +

    becomes:

    +

    list( "A" = c(1,2,2), "B" = c(1,2), "C" = 1 )

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/is_absent.html b/latest-tag/reference/is_absent.html new file mode 100644 index 00000000..6ea45975 --- /dev/null +++ b/latest-tag/reference/is_absent.html @@ -0,0 +1,101 @@ + +Is value absent — is_absent • rbmi + Skip to contents + + +
    +
    +
    + +

    Is value absent

    + Source: R/utilities.R +
    is_absent.Rd
    +
    + +
    +

    Returns true if a value is either NULL, NA or "". +In the case of a vector all values must be NULL/NA/"" +for x to be regarded as absent.

    +
    + +
    +

    Usage

    + 
    is_absent(x, na = TRUE, blank = TRUE)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    a value to check if it is absent or not

    + + +
    na
    +

    do NAs count as absent

    + + +
    blank
    +

    do blanks i.e. "" count as absent

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/is_char_fact.html b/latest-tag/reference/is_char_fact.html new file mode 100644 index 00000000..d989683d --- /dev/null +++ b/latest-tag/reference/is_char_fact.html @@ -0,0 +1,87 @@ + +Is character or factor — is_char_fact • rbmi + Skip to contents + + +
    +
    +
    + +

    Is character or factor

    + Source: R/utilities.R +
    is_char_fact.Rd
    +
    + +
    +

    returns true if x is character or factor vector

    +
    + +
    +

    Usage

    + 
    is_char_fact(x)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    a character or factor vector

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/is_char_one.html b/latest-tag/reference/is_char_one.html new file mode 100644 index 00000000..0e78ca91 --- /dev/null +++ b/latest-tag/reference/is_char_one.html @@ -0,0 +1,87 @@ + +Is single character — is_char_one • rbmi + Skip to contents + + +
    +
    +
    + +

    Is single character

    + Source: R/utilities.R +
    is_char_one.Rd
    +
    + +
    +

    returns true if x is a length 1 character vector

    +
    + +
    +

    Usage

    + 
    is_char_one(x)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    a character vector

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/is_in_rbmi_development.html b/latest-tag/reference/is_in_rbmi_development.html new file mode 100644 index 00000000..f2ffa392 --- /dev/null +++ b/latest-tag/reference/is_in_rbmi_development.html @@ -0,0 +1,91 @@ + +Is package in development mode? — is_in_rbmi_development • rbmi + Skip to contents + + +
    +
    +
    + +

    Is package in development mode?

    + Source: R/parallel.R +
    is_in_rbmi_development.Rd
    +
    + +
    +

    Returns TRUE if the package is being developed on i.e. you have a local copy of the +source code which you are actively editing +Returns FALSE otherwise

    +
    + +
    +

    Usage

    + 
    is_in_rbmi_development()
    +
    + +
    +

    Details

    +

    Main use of this function is in parallel processing to indicate whether the sub-processes +need to load the current development version of the code or whether they should load +the main installed package on the system

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/is_num_char_fact.html b/latest-tag/reference/is_num_char_fact.html new file mode 100644 index 00000000..f63bceea --- /dev/null +++ b/latest-tag/reference/is_num_char_fact.html @@ -0,0 +1,87 @@ + +Is character, factor or numeric — is_num_char_fact • rbmi + Skip to contents + + +
    +
    +
    + +

    Is character, factor or numeric

    + Source: R/utilities.R +
    is_num_char_fact.Rd
    +
    + +
    +

    returns true if x is a character, numeric or factor vector

    +
    + +
    +

    Usage

    + 
    is_num_char_fact(x)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    a character, numeric or factor vector

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/locf.html b/latest-tag/reference/locf.html new file mode 100644 index 00000000..57afb9b6 --- /dev/null +++ b/latest-tag/reference/locf.html @@ -0,0 +1,94 @@ + +Last Observation Carried Forward — locf • rbmi + Skip to contents + + +
    +
    +
    + +

    Last Observation Carried Forward

    + Source: R/expand.R +
    locf.Rd
    +
    + +
    +

    Returns a vector after applied last observation carried forward imputation.

    +
    + +
    +

    Usage

    + 
    locf(x)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    a vector.

    + +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+locf(c(NA, 1, 2, 3, NA, 4)) # Returns c(NA, 1, 2, 3, 3, 4)
+} # }
+
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/longDataConstructor.html b/latest-tag/reference/longDataConstructor.html new file mode 100644 index 00000000..2d743767 --- /dev/null +++ b/latest-tag/reference/longDataConstructor.html @@ -0,0 +1,459 @@ + +R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor • rbmi + Skip to contents + + +
    +
    +
    + +

    R6 Class for Storing / Accessing & Sampling Longitudinal Data

    + Source: R/longData.R +
    longDataConstructor.Rd
    +
    + +
    +

    A longdata object allows for efficient storage and recall of longitudinal datasets for use in +bootstrap sampling. The object works by de-constructing the data into lists based upon subject id +thus enabling efficient lookup.

    +
    + + +
    +

    Details

    +

    The object also handles multiple other operations specific to rbmi such as defining whether an +outcome value is MAR / Missing or not as well as tracking which imputation strategy is assigned +to each subject.

    +

    It is recognised that this objects functionality is fairly overloaded and is hoped that this can +be split out into more area specific objects / functions in the future. Further additions of functionality +to this object should be avoided if possible.

    +
    +
    +

    Public fields

    +

    data
    +

    The original dataset passed to the constructor (sorted by id and visit)

    + + +
    vars
    +

    The vars object (list of key variables) passed to the constructor

    + + +
    visits
    +

    A character vector containing the distinct visit levels

    + + +
    ids
    +

    A character vector containing the unique ids of each subject in self$data

    + + +
    formula
    +

    A formula expressing how the design matrix for the data should be constructed

    + + +
    strata
    +

    A numeric vector indicating which strata each corresponding value of +self$ids belongs to. +If no stratification variable is defined this will default to 1 for all subjects +(i.e. same group). +This field is only used as part of the self$sample_ids() function to enable +stratified bootstrap +sampling

    + + +
    ice_visit_index
    +

    A list indexed by subject storing the index number of the first visit +affected by the ICE. If there is no ICE then it is set equal to the number of visits plus 1.

    + + +
    values
    +

    A list indexed by subject storing a numeric vector of the +original (unimputed) outcome values

    + + +
    group
    +

    A list indexed by subject storing a single character +indicating which imputation group the subject belongs to as defined +by self$data[id, self$ivars$group] +It is used +to determine what reference group should be used when imputing the subjects data.

    + + +
    is_mar
    +

    A list indexed by subject storing logical values indicating +if the subjects outcome values are MAR or not. +This list is defaulted to TRUE for all subjects & outcomes and is then +modified by calls to self$set_strategies(). +Note that this does not indicate which values are missing, this variable +is True for outcome values that either occurred before the ICE visit +or are post the ICE visit and have an imputation strategy of MAR

    + + +
    strategies
    +

    A list indexed by subject storing a single character +value indicating the imputation +strategy assigned to that subject. This list is defaulted to "MAR" +for all subjects and is then +modified by calls to either self$set_strategies() or self$update_strategies()

    + + +
    strategy_lock
    +

    A list indexed by subject storing a single +logical value indicating whether a +patients imputation strategy is locked or not. If a strategy is +locked it means that it can't change +from MAR to non-MAR. Strategies can be changed from non-MAR to MAR though +this will trigger a warning. +Strategies are locked if the patient is assigned a MAR strategy and +has non-missing after their ICE date. This list is populated by a call to +self$set_strategies().

    + + +
    indexes
    +

    A list indexed by subject storing a numeric vector of +indexes which specify which rows in the +original dataset belong to this subject i.e. to recover the full data +for subject "pt3" you can use +self$data[self$indexes[["pt3"]],]. This may seem redundant over filtering +the data directly +however it enables efficient bootstrap sampling of the data i.e.

    +

    indexes <- unlist(self$indexes[c("pt3", "pt3")])
+self$data[indexes,]

    +

    This list is populated during the object initialisation.

    + + +
    is_missing
    +

    A list indexed by subject storing a logical vector +indicating whether the corresponding +outcome of a subject is missing. This list is populated during the +object initialisation.

    + + +
    is_post_ice
    +

    A list indexed by subject storing a logical vector +indicating whether the corresponding +outcome of a subject is post the date of their ICE. If no ICE data has +been provided this defaults to False +for all observations. This list is populated by a call to self$set_strategies().

    + + +

    +
    +
    +

    Methods

    + +
    +

    Public methods

    + +

    +

    Method get_data()

    +

    Returns a data.frame based upon required subject IDs. Replaces missing +values with new ones if provided.

    +

    Usage

    +

    longDataConstructor$get_data(
+  obj = NULL,
+  nmar.rm = FALSE,
+  na.rm = FALSE,
+  idmap = FALSE
+)

    +
    + +
    +

    Arguments

    +

    obj
    +

    Either NULL, a character vector of subjects IDs or a +imputation list object. See details.

    + + +
    nmar.rm
    +

    Logical value. If TRUE will remove observations that are +not regarded as MAR (as determined from self$is_mar).

    + + +
    na.rm
    +

    Logical value. If TRUE will remove outcome values that are +missing (as determined from self$is_missing).

    + + +
    idmap
    +

    Logical value. If TRUE will add an attribute idmap which +contains a mapping from the new subject ids to the old subject ids. See details.

    + + +

    +
    +
    +

    Details

    +

    If obj is NULL then the full original dataset is returned.

    +

    If obj is a character vector then a new dataset consisting of just those subjects is +returned; if the character vector contains duplicate entries then that subject will be +returned multiple times.

    +

    If obj is an imputation_df object (as created by imputation_df()) then the +subject ids specified in the object will be returned and missing values will be filled +in by those specified in the imputation list object. i.e.

    +

    obj <- imputation_df(
+  imputation_single( id = "pt1", values = c(1,2,3)),
+  imputation_single( id = "pt1", values = c(4,5,6)),
+  imputation_single( id = "pt3", values = c(7,8))
+)
+longdata$get_data(obj)

    +

    Will return a data.frame consisting of all observations for pt1 twice and all of the +observations for pt3 once. The first set of observations for pt1 will have missing +values filled in with c(1,2,3) and the second set will be filled in by c(4,5,6). The +length of the values must be equal to sum(self$is_missing[[id]]).

    +

    If obj is not NULL then all subject IDs will be scrambled in order to ensure that they +are unique +i.e. If the pt2 is requested twice then this process guarantees that each set of observations +be have a unique subject ID number. The idmap attribute (if requested) can be used +to map from the new ids back to the old ids.

    +
    + +
    +

    Returns

    +

    A data.frame.

    +
    + +

    +

    Method add_subject()

    +

    This function decomposes a patient data from self$data and populates +all the corresponding lists i.e. self$is_missing, self$values, self$group, etc. +This function is only called upon the objects initialization.

    +

    Usage

    +

    longDataConstructor$add_subject(id)

    +
    + +
    +

    Arguments

    +

    id
    +

    Character subject id that exists within self$data.

    + + +

    +
    + +

    +

    Method validate_ids()

    +

    Throws an error if any element of ids is not within the source data self$data.

    +

    Usage

    +

    longDataConstructor$validate_ids(ids)

    +
    + +
    +

    Arguments

    +

    ids
    +

    A character vector of ids.

    + + +

    +
    +
    +

    Returns

    +

    TRUE

    +
    + +

    +

    Method sample_ids()

    +

    Performs random stratified sampling of patient ids (with replacement) +Each patient has an equal weight of being picked within their strata (i.e is not dependent on +how many non-missing visits they had).

    +

    Usage

    +

    longDataConstructor$sample_ids()

    +
    + +
    +

    Returns

    +

    Character vector of ids.

    +
    + +

    +

    Method extract_by_id()

    +

    Returns a list of key information for a given subject. Is a convenience wrapper +to save having to manually grab each element.

    +

    Usage

    +

    longDataConstructor$extract_by_id(id)

    +
    + +
    +

    Arguments

    +

    id
    +

    Character subject id that exists within self$data.

    + + +

    +
    + +

    +

    Method update_strategies()

    +

    Convenience function to run self$set_strategies(dat_ice, update=TRUE) +kept for legacy reasons.

    +

    Usage

    +

    longDataConstructor$update_strategies(dat_ice)

    +
    + +
    +

    Arguments

    +

    dat_ice
    +

    A data.frame containing ICE information see impute() for the format of this dataframe.

    + + +

    +
    + +

    +

    Method set_strategies()

    +

    Updates the self$strategies, self$is_mar, self$is_post_ice variables based upon the provided ICE +information.

    +

    Usage

    +

    longDataConstructor$set_strategies(dat_ice = NULL, update = FALSE)

    +
    + +
    +

    Arguments

    +

    dat_ice
    +

    a data.frame containing ICE information. See details.

    + + +
    update
    +

    Logical, indicates that the ICE data should be used as an update. See details.

    + + +

    +
    +
    +

    Details

    +

    See draws() for the specification of dat_ice if update=FALSE. +See impute() for the format of dat_ice if update=TRUE. +If update=TRUE this function ensures that MAR strategies cannot be changed to non-MAR in the presence +of post-ICE observations.

    +
    + + +

    +

    Method check_has_data_at_each_visit()

    +

    Ensures that all visits have at least 1 observed "MAR" observation. Throws +an error if this criteria is not met. This is to ensure that the initial +MMRM can be resolved.

    +

    Usage

    +

    longDataConstructor$check_has_data_at_each_visit()

    +
    + + +

    +

    Method set_strata()

    +

    Populates the self$strata variable. If the user has specified stratification variables +The first visit is used to determine the value of those variables. If no stratification variables +have been specified then everyone is defined as being in strata 1.

    +

    Usage

    +

    longDataConstructor$set_strata()

    +
    + + +

    +

    Method new()

    +

    Constructor function.

    +

    Usage

    +

    longDataConstructor$new(data, vars)

    +
    + +
    +

    Arguments

    +

    data
    +

    longitudinal dataset.

    + + +
    vars
    +

    an ivars object created by set_vars().

    + + +

    +
    + +

    +

    Method clone()

    +

    The objects of this class are cloneable with this method.

    +

    Usage

    +

    longDataConstructor$clone(deep = FALSE)

    +
    + +
    +

    Arguments

    +

    deep
    +

    Whether to make a deep clone.

    + + +

    +
    + +
    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/ls_design.html b/latest-tag/reference/ls_design.html new file mode 100644 index 00000000..487ce781 --- /dev/null +++ b/latest-tag/reference/ls_design.html @@ -0,0 +1,117 @@ + +Calculate design vector for the lsmeans — ls_design • rbmi + Skip to contents + + +
    +
    +
    + +

    Calculate design vector for the lsmeans

    + Source: R/lsmeans.R +
    ls_design.Rd
    +
    + +
    +

    Calculates the design vector as required to generate the lsmean +and standard error. ls_design_equal calculates it by +applying an equal weight per covariate combination whilst +ls_design_proportional applies weighting proportional +to the frequency in which the covariate combination occurred +in the actual dataset.

    +
    + +
    +

    Usage

    + 
    ls_design_equal(data, frm, covars, fix)
+
+ls_design_proportional(data, frm, covars, fix)
    +
    + +
    +

    Arguments

    + + +
    data
    +

    A data.frame

    + + +
    frm
    +

    Formula used to fit the original model

    + + +
    covars
    +

    a character vector of variables names that exist in +data which should be extracted (ls_design_equal only)

    + + +
    fix
    +

    A named list of variables with fixed values

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/lsmeans.html b/latest-tag/reference/lsmeans.html new file mode 100644 index 00000000..5abc7489 --- /dev/null +++ b/latest-tag/reference/lsmeans.html @@ -0,0 +1,138 @@ + +Least Square Means — lsmeans • rbmi + Skip to contents + + +
    +
    +
    + +

    Least Square Means

    + Source: R/lsmeans.R +
    lsmeans.Rd
    +
    + +
    +

    Estimates the least square means from a linear model. This is done by +generating a prediction from the model using an hypothetical observation +that is constructed by averaging the data. See details for more information.

    +
    + +
    +

    Usage

    + 
    lsmeans(model, ..., .weights = c("proportional", "equal"))
    +
    + +
    +

    Arguments

    + + +
    model
    +

    A model created by lm.

    + + +
    ...
    +

    Fixes specific variables to specific values i.e. +trt = 1 or age = 50. The name of the argument must be the name +of the variable within the dataset.

    + + +
    .weights
    +

    Character, specifies whether to use "proportional" or "equal" weighting for each +categorical covariate combination when calculating the lsmeans.

    + +
    +
    +

    Details

    +

    The lsmeans are obtained by calculating hypothetical patients +and predicting their expected values. These hypothetical patients +are constructed by expanding out all possible combinations of each +categorical covariate and by setting any numerical covariates equal +to the mean.

    +

    A final lsmean value is calculated by averaging these hypothetical +patients. If .weights equals "proportional" then the values are weighted +by the frequency in which they occur in the full dataset. If .weights +equals "equal" then each hypothetical patient is given an equal weight +regardless of what actually occurs in the dataset.

    +

    Use the ... argument to fix specific variables to specific values.

    +

    See the references for identical implementations as done in SAS and +in R via the emmeans package. This function attempts to re-implement the +emmeans derivation for standard linear models but without having to include +all of it's dependencies.

    +
    +
    +

    References

    +

    https://CRAN.R-project.org/package=emmeans

    +

    https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.3/statug/statug_glm_details41.htm

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+mod <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
+lsmeans(mod)
+lsmeans(mod, Species = "virginica")
+lsmeans(mod, Species = "versicolor")
+lsmeans(mod, Species = "versicolor", Petal.Length = 1)
+} # }
+
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/method.html b/latest-tag/reference/method.html new file mode 100644 index 00000000..0528a501 --- /dev/null +++ b/latest-tag/reference/method.html @@ -0,0 +1,214 @@ + +Set the multiple imputation methodology — method • rbmi + Skip to contents + + +
    +
    +
    + +

    Set the multiple imputation methodology

    + Source: R/methods.R +
    method.Rd
    +
    + +
    +

    These functions determine what methods rbmi should use when creating +the imputation models, generating imputed values and pooling the results.

    +
    + +
    +

    Usage

    + 
    method_bayes(
+  burn_in = 200,
+  burn_between = 50,
+  same_cov = TRUE,
+  n_samples = 20,
+  seed = sample.int(.Machine$integer.max, 1)
+)
+
+method_approxbayes(
+  covariance = c("us", "toep", "cs", "ar1"),
+  threshold = 0.01,
+  same_cov = TRUE,
+  REML = TRUE,
+  n_samples = 20
+)
+
+method_condmean(
+  covariance = c("us", "toep", "cs", "ar1"),
+  threshold = 0.01,
+  same_cov = TRUE,
+  REML = TRUE,
+  n_samples = NULL,
+  type = c("bootstrap", "jackknife")
+)
+
+method_bmlmi(
+  covariance = c("us", "toep", "cs", "ar1"),
+  threshold = 0.01,
+  same_cov = TRUE,
+  REML = TRUE,
+  B = 20,
+  D = 2
+)
    +
    + +
    +

    Arguments

    + + +
    burn_in
    +

    a numeric that specifies how many observations should be discarded +prior to extracting actual samples. Note that the sampler +is initialized at the maximum likelihood estimates and a weakly informative +prior is used thus in theory this value should not need to be that high.

    + + +
    burn_between
    +

    a numeric that specifies the "thinning" rate i.e. how many +observations should be discarded between each sample. This is used to prevent +issues associated with autocorrelation between the samples.

    + + +
    same_cov
    +

    a logical, if TRUE the imputation model will be fitted using a single +shared covariance matrix for all observations. If FALSE a separate covariance +matrix will be fit for each group as determined by the group argument of +set_vars().

    + + +
    n_samples
    +

    a numeric that determines how many imputed datasets are generated. +In the case of method_condmean(type = "jackknife") this argument +must be set to NULL. See details.

    + + +
    seed
    +

    a numeric that specifies the seed to be used in the call to Stan. This +argument is passed onto the seed argument of rstan::sampling(). Note that +this is only required for method_bayes(), for all other methods you can achieve +reproducible results by setting the seed via set.seed(). See details.

    + + +
    covariance
    +

    a character string that specifies the structure of the covariance +matrix to be used in the imputation model. Must be one of "us" (default), "toep", +"cs" or "ar1". See details.

    + + +
    threshold
    +

    a numeric between 0 and 1, specifies the proportion of bootstrap +datasets that can fail to produce valid samples before an error is thrown. +See details.

    + + +
    REML
    +

    a logical indicating whether to use REML estimation rather than maximum +likelihood.

    + + +
    type
    +

    a character string that specifies the resampling method used to perform inference +when a conditional mean imputation approach (set via method_condmean()) is used. Must be one of "bootstrap" or "jackknife".

    + + +
    B
    +

    a numeric that determines the number of bootstrap samples for method_bmlmi.

    + + +
    D
    +

    a numeric that determines the number of random imputations for each bootstrap sample. +Needed for method_bmlmi().

    + +
    +
    +

    Details

    +

    In the case of method_condmean(type = "bootstrap") there will be n_samples + 1 +imputation models and datasets generated as the first sample will be based on +the original dataset whilst the other n_samples samples will be +bootstrapped datasets. Likewise, for method_condmean(type = "jackknife") there will +be length(unique(data$subjid)) + 1 imputation models and datasets generated. In both cases this is +represented by n + 1 being displayed in the print message.

    +

    The user is able to specify different covariance structures using the the covariance +argument. Currently supported structures include:

    • Unstructured ("us")

      • +

    • Toeplitz ("toep")

      • +

    • Compound Symmetry ("cs")

      • +

    • Autoregression-1 ("ar1")

      • +

    Note that at present Bayesian methods only support unstructured.

    +

    In the case of method_condmean(type = "bootstrap"), method_approxbayes() and method_bmlmi() repeated +bootstrap samples of the original dataset are taken with an MMRM fitted to each sample. +Due to the randomness of these sampled datasets, as well as limitations in the optimisers +used to fit the models, it is not uncommon that estimates for a particular dataset can't +be generated. In these instances rbmi is designed to throw out that bootstrapped dataset +and try again with another. However to ensure that these errors are due to chance and +not due to some underlying misspecification in the data and/or model a tolerance limit +is set on how many samples can be discarded. Once the tolerance limit has been reached +an error will be thrown and the process aborted. The tolerance limit is defined as +ceiling(threshold * n_samples). Note that for the jackknife method estimates need to be +generated for all leave-one-out datasets and as such an error will be thrown if +any of them fail to fit.

    +

    Please note that at the time of writing (September 2021) Stan is unable to produce +reproducible samples across different operating systems even when the same seed is used. +As such care must be taken when using Stan across different machines. For more information +on this limitation please consult the Stan documentation +https://mc-stan.org/docs/2_27/reference-manual/reproducibility-chapter.html

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/parametric_ci.html b/latest-tag/reference/parametric_ci.html new file mode 100644 index 00000000..01f4bf47 --- /dev/null +++ b/latest-tag/reference/parametric_ci.html @@ -0,0 +1,116 @@ + +Calculate parametric confidence intervals — parametric_ci • rbmi + Skip to contents + + +
    +
    +
    + +

    Calculate parametric confidence intervals

    + Source: R/pool.R +
    parametric_ci.Rd
    +
    + +
    +

    Calculates confidence intervals based upon a parametric +distribution.

    +
    + +
    +

    Usage

    + 
    parametric_ci(point, se, alpha, alternative, qfun, pfun, ...)
    +
    + +
    +

    Arguments

    + + +
    point
    +

    The point estimate.

    + + +
    se
    +

    The standard error of the point estimate. If using a non-"normal" +distribution this should be set to 1.

    + + +
    alpha
    +

    The type 1 error rate, should be a value between 0 and 1.

    + + +
    alternative
    +

    a character string specifying the alternative hypothesis, +must be one of "two.sided" (default), "greater" or "less".

    + + +
    qfun
    +

    The quantile function for the assumed distribution i.e. qnorm.

    + + +
    pfun
    +

    The CDF function for the assumed distribution i.e. pnorm.

    + + +
    ...
    +

    additional arguments passed on qfun and pfun i.e. df = 102.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/pool.html b/latest-tag/reference/pool.html new file mode 100644 index 00000000..237e2ce9 --- /dev/null +++ b/latest-tag/reference/pool.html @@ -0,0 +1,148 @@ + +Pool analysis results obtained from the imputed datasets — pool • rbmi + Skip to contents + + +
    +
    +
    + +

    Pool analysis results obtained from the imputed datasets

    + Source: R/pool.R +
    pool.Rd
    +
    + +
    +

    Pool analysis results obtained from the imputed datasets

    +
    + +
    +

    Usage

    + 
    pool(
+  results,
+  conf.level = 0.95,
+  alternative = c("two.sided", "less", "greater"),
+  type = c("percentile", "normal")
+)
+
+# S3 method for class 'pool'
+as.data.frame(x, ...)
+
+# S3 method for class 'pool'
+print(x, ...)
    +
    + +
    +

    Arguments

    + + +
    results
    +

    an analysis object created by analyse().

    + + +
    conf.level
    +

    confidence level of the returned confidence interval. +Must be a single number between 0 and 1. Default is 0.95.

    + + +
    alternative
    +

    a character string specifying the alternative hypothesis, +must be one of "two.sided" (default), "greater" or "less".

    + + +
    type
    +

    a character string of either "percentile" (default) or +"normal". Determines what method should be used to calculate the bootstrap confidence +intervals. See details. +Only used if method_condmean(type = "bootstrap") was specified +in the original call to draws().

    + + +
    x
    +

    a pool object generated by pool().

    + + +
    ...
    +

    not used.

    + +
    +
    +

    Details

    +

    The calculation used to generate the point estimate, standard errors and +confidence interval depends upon the method specified in the original +call to draws(); In particular:

    • method_approxbayes() & method_bayes() both use Rubin's rules to pool estimates +and variances across multiple imputed datasets, and the Barnard-Rubin rule to pool +degree's of freedom; see Little & Rubin (2002).

      • +

    • method_condmean(type = "bootstrap") uses percentile or normal approximation; +see Efron & Tibshirani (1994). Note that for the percentile bootstrap, no standard error is +calculated, i.e. the standard errors will be NA in the object / data.frame.

      • +

    • method_condmean(type = "jackknife") uses the standard jackknife variance formula; +see Efron & Tibshirani (1994).

      • +

    • method_bmlmi uses pooling procedure for Bootstrapped Maximum Likelihood MI (BMLMI). +See Von Hippel & Bartlett (2021).

      • +
    +
    +

    References

    +

    Bradley Efron and Robert J Tibshirani. An introduction to the bootstrap. CRC +press, 1994. [Section 11]

    +

    Roderick J. A. Little and Donald B. Rubin. Statistical Analysis with Missing +Data, Second Edition. John Wiley & Sons, Hoboken, New Jersey, 2002. [Section 5.4]

    +

    Von Hippel, Paul T and Bartlett, Jonathan W. +Maximum likelihood multiple imputation: Faster imputations and consistent standard errors without posterior draws. 2021.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/pool_bootstrap_normal.html b/latest-tag/reference/pool_bootstrap_normal.html new file mode 100644 index 00000000..c2bf0bc5 --- /dev/null +++ b/latest-tag/reference/pool_bootstrap_normal.html @@ -0,0 +1,105 @@ + +Bootstrap Pooling via normal approximation — pool_bootstrap_normal • rbmi + Skip to contents + + +
    +
    +
    + +

    Bootstrap Pooling via normal approximation

    + Source: R/pool.R +
    pool_bootstrap_normal.Rd
    +
    + +
    +

    Get point estimate, confidence interval and p-value using +the normal approximation.

    +
    + +
    +

    Usage

    + 
    pool_bootstrap_normal(est, conf.level, alternative)
    +
    + +
    +

    Arguments

    + + +
    est
    +

    a numeric vector of point estimates from each bootstrap sample.

    + + +
    conf.level
    +

    confidence level of the returned confidence interval. +Must be a single number between 0 and 1. Default is 0.95.

    + + +
    alternative
    +

    a character string specifying the alternative hypothesis, +must be one of "two.sided" (default), "greater" or "less".

    + +
    +
    +

    Details

    +

    The point estimate is taken to be the first element of est. The remaining +n-1 values of est are then used to generate the confidence intervals.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/pool_bootstrap_percentile.html b/latest-tag/reference/pool_bootstrap_percentile.html new file mode 100644 index 00000000..31256cb6 --- /dev/null +++ b/latest-tag/reference/pool_bootstrap_percentile.html @@ -0,0 +1,108 @@ + +Bootstrap Pooling via Percentiles — pool_bootstrap_percentile • rbmi + Skip to contents + + +
    +
    +
    + +

    Bootstrap Pooling via Percentiles

    + Source: R/pool.R +
    pool_bootstrap_percentile.Rd
    +
    + +
    +

    Get point estimate, confidence interval and p-value using +percentiles. Note that quantile "type=6" is used, +see stats::quantile() for details.

    +
    + +
    +

    Usage

    + 
    pool_bootstrap_percentile(est, conf.level, alternative)
    +
    + +
    +

    Arguments

    + + +
    est
    +

    a numeric vector of point estimates from each bootstrap sample.

    + + +
    conf.level
    +

    confidence level of the returned confidence interval. +Must be a single number between 0 and 1. Default is 0.95.

    + + +
    alternative
    +

    a character string specifying the alternative hypothesis, +must be one of "two.sided" (default), "greater" or "less".

    + +
    +
    +

    Details

    +

    The point estimate is taken to be the first element of est. The remaining +n-1 values of est are then used to generate the confidence intervals.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/pool_internal.html b/latest-tag/reference/pool_internal.html new file mode 100644 index 00000000..65f530b7 --- /dev/null +++ b/latest-tag/reference/pool_internal.html @@ -0,0 +1,131 @@ + +Internal Pool Methods — pool_internal • rbmi + Skip to contents + + +
    +
    +
    + +

    Internal Pool Methods

    + Source: R/pool.R +
    pool_internal.Rd
    +
    + +
    +

    Dispatches pool methods based upon results object class. See +pool() for details.

    +
    + +
    +

    Usage

    + 
    pool_internal(results, conf.level, alternative, type, D)
+
+# S3 method for class 'jackknife'
+pool_internal(results, conf.level, alternative, type, D)
+
+# S3 method for class 'bootstrap'
+pool_internal(
+  results,
+  conf.level,
+  alternative,
+  type = c("percentile", "normal"),
+  D
+)
+
+# S3 method for class 'bmlmi'
+pool_internal(results, conf.level, alternative, type, D)
+
+# S3 method for class 'rubin'
+pool_internal(results, conf.level, alternative, type, D)
    +
    + +
    +

    Arguments

    + + +
    results
    +

    a list of results i.e. the x$results element of an +analyse object created by analyse()).

    + + +
    conf.level
    +

    confidence level of the returned confidence interval. +Must be a single number between 0 and 1. Default is 0.95.

    + + +
    alternative
    +

    a character string specifying the alternative hypothesis, +must be one of "two.sided" (default), "greater" or "less".

    + + +
    type
    +

    a character string of either "percentile" (default) or +"normal". Determines what method should be used to calculate the bootstrap confidence +intervals. See details. +Only used if method_condmean(type = "bootstrap") was specified +in the original call to draws().

    + + +
    D
    +

    numeric representing the number of imputations between each bootstrap sample in the BMLMI method.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/prepare_stan_data.html b/latest-tag/reference/prepare_stan_data.html new file mode 100644 index 00000000..6c643204 --- /dev/null +++ b/latest-tag/reference/prepare_stan_data.html @@ -0,0 +1,127 @@ + +Prepare input data to run the Stan model — prepare_stan_data • rbmi + Skip to contents + + +
    +
    +
    + +

    Prepare input data to run the Stan model

    + Source: R/mcmc.R +
    prepare_stan_data.Rd
    +
    + +
    +

    Prepare input data to run the Stan model. +Creates / calculates all the required inputs as required by the data{} block of the MMRM Stan program.

    +
    + +
    +

    Usage

    + 
    prepare_stan_data(ddat, subjid, visit, outcome, group)
    +
    + +
    +

    Arguments

    + + +
    ddat
    +

    A design matrix

    + + +
    subjid
    +

    Character vector containing the subjects IDs.

    + + +
    visit
    +

    Vector containing the visits.

    + + +
    outcome
    +

    Numeric vector containing the outcome variable.

    + + +
    group
    +

    Vector containing the group variable.

    + +
    +
    +

    Value

    +

    A stan_data object. A named list as per data{} block of the related Stan file. In particular it returns:

    • N - The number of rows in the design matrix

      • +

    • P - The number of columns in the design matrix

      • +

    • G - The number of distinct covariance matrix groups (i.e. length(unique(group)))

      • +

    • n_visit - The number of unique outcome visits

      • +

    • n_pat - The total number of pattern groups (as defined by missingness patterns & covariance group)

      • +

    • pat_G - Index for which Sigma each pattern group should use

      • +

    • pat_n_pt - number of patients within each pattern group

      • +

    • pat_n_visit - number of non-missing visits in each pattern group

      • +

    • pat_sigma_index - rows/cols from Sigma to subset on for the pattern group (padded by 0's)

      • +

    • y - The outcome variable

      • +

    • Q - design matrix (after QR decomposition)

      • +

    • R - R matrix from the QR decomposition of the design matrix

      • +
    +
    +

    Details

    + +

    • The group argument determines which covariance matrix group the subject belongs to. If you +want all subjects to use a shared covariance matrix then set group to "1" for everyone.

      • +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/print.analysis.html b/latest-tag/reference/print.analysis.html new file mode 100644 index 00000000..a57b555d --- /dev/null +++ b/latest-tag/reference/print.analysis.html @@ -0,0 +1,92 @@ + +Print analysis object — print.analysis • rbmi + Skip to contents + + +
    +
    +
    + +

    Print analysis object

    + Source: R/analyse.R +
    print.analysis.Rd
    +
    + +
    +

    Print analysis object

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'analysis'
+print(x, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    An analysis object generated by analyse().

    + + +
    ...
    +

    Not used.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/print.draws.html b/latest-tag/reference/print.draws.html new file mode 100644 index 00000000..d8291cab --- /dev/null +++ b/latest-tag/reference/print.draws.html @@ -0,0 +1,92 @@ + +Print draws object — print.draws • rbmi + Skip to contents + + +
    +
    +
    + +

    Print draws object

    + Source: R/draws.R +
    print.draws.Rd
    +
    + +
    +

    Print draws object

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'draws'
+print(x, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    A draws object generated by draws().

    + + +
    ...
    +

    not used.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/print.imputation.html b/latest-tag/reference/print.imputation.html new file mode 100644 index 00000000..b8c93d4d --- /dev/null +++ b/latest-tag/reference/print.imputation.html @@ -0,0 +1,92 @@ + +Print imputation object — print.imputation • rbmi + Skip to contents + + +
    +
    +
    + +

    Print imputation object

    + Source: R/impute.R +
    print.imputation.Rd
    +
    + +
    +

    Print imputation object

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'imputation'
+print(x, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    An imputation object generated by impute().

    + + +
    ...
    +

    Not used.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/progressLogger.html b/latest-tag/reference/progressLogger.html new file mode 100644 index 00000000..db3871d5 --- /dev/null +++ b/latest-tag/reference/progressLogger.html @@ -0,0 +1,194 @@ + +R6 Class for printing current sampling progress — progressLogger • rbmi + Skip to contents + + +
    +
    +
    + +

    R6 Class for printing current sampling progress

    + Source: R/draws.R +
    progressLogger.Rd
    +
    + +
    +

    Object is initalised with total number of iterations that are expected to occur. +User can then update the object with the add method to indicate how many more iterations +have just occurred. +Every time step * 100 % of iterations have occurred a message is printed to the console. +Use the quiet argument to prevent the object from printing anything at all

    +
    + + +
    +

    Public fields

    +

    step
    +

    real, percentage of iterations to allow before printing the +progress to the console

    + + +
    step_current
    +

    integer, the total number of iterations completed since +progress was last printed to the console

    + + +
    n
    +

    integer, the current number of completed iterations

    + + +
    n_max
    +

    integer, total number of expected iterations to be completed +acts as the denominator for calculating progress percentages

    + + +
    quiet
    +

    logical holds whether or not to print anything

    + + +

    +
    +
    +

    Methods

    + +
    +

    Public methods

    + +

    +

    Method new()

    +

    Create progressLogger object

    +

    Usage

    +

    progressLogger$new(n_max, quiet = FALSE, step = 0.1)

    +
    + +
    +

    Arguments

    +

    n_max
    +

    integer, sets field n_max

    + + +
    quiet
    +

    logical, sets field quiet

    + + +
    step
    +

    real, sets field step

    + + +

    +
    + +

    +

    Method add()

    +

    Records that n more iterations have been completed +this will add that number to the current step count (step_current) and will +print a progress message to the log if the step limit (step) has been reached. +This function will do nothing if quiet has been set to TRUE

    +

    Usage

    +

    progressLogger$add(n)

    +
    + +
    +

    Arguments

    +

    n
    +

    the number of successfully complete iterations since add() was last called

    + + +

    +
    + +

    +

    Method print_progress()

    +

    method to print the current state of progress

    +

    Usage

    +

    progressLogger$print_progress()

    +
    + + +

    +

    Method clone()

    +

    The objects of this class are cloneable with this method.

    +

    Usage

    +

    progressLogger$clone(deep = FALSE)

    +
    + +
    +

    Arguments

    +

    deep
    +

    Whether to make a deep clone.

    + + +

    +
    + +
    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/pval_percentile.html b/latest-tag/reference/pval_percentile.html new file mode 100644 index 00000000..fb1954c5 --- /dev/null +++ b/latest-tag/reference/pval_percentile.html @@ -0,0 +1,102 @@ + +P-value of percentile bootstrap — pval_percentile • rbmi + Skip to contents + + +
    +
    +
    + +

    P-value of percentile bootstrap

    + Source: R/pool.R +
    pval_percentile.Rd
    +
    + +
    +

    Determines the (not necessarily unique) quantile (type=6) of "est" which gives a value of 0 +From this, derive the p-value corresponding to the percentile bootstrap via inversion.

    +
    + +
    +

    Usage

    + 
    pval_percentile(est)
    +
    + +
    +

    Arguments

    + + +
    est
    +

    a numeric vector of point estimates from each bootstrap sample.

    + +
    +
    +

    Value

    +

    A named numeric vector of length 2 containing the p-value for H_0: theta=0 vs H_A: theta>0 +("pval_greater") and the p-value for H_0: theta=0 vs H_A: theta<0 ("pval_less").

    +
    +
    +

    Details

    +

    The p-value for H_0: theta=0 vs H_A: theta>0 is the value alpha for which q_alpha = 0. +If there is at least one estimate equal to zero it returns the largest alpha such that q_alpha = 0. +If all bootstrap estimates are > 0 it returns 0; if all bootstrap estimates are < 0 it returns 1. Analogous +reasoning is applied for the p-value for H_0: theta=0 vs H_A: theta<0.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/random_effects_expr.html b/latest-tag/reference/random_effects_expr.html new file mode 100644 index 00000000..aadde329 --- /dev/null +++ b/latest-tag/reference/random_effects_expr.html @@ -0,0 +1,107 @@ + +Construct random effects formula — random_effects_expr • rbmi + Skip to contents + + +
    +
    +
    + +

    Construct random effects formula

    + Source: R/mmrm.R +
    random_effects_expr.Rd
    +
    + +
    +

    Constructs a character representation of the random effects formula +for fitting a MMRM for subject by visit in the format required for mmrm::mmrm().

    +
    + +
    +

    Usage

    + 
    random_effects_expr(
+  cov_struct = c("us", "toep", "cs", "ar1"),
+  cov_by_group = FALSE
+)
    +
    + +
    +

    Arguments

    + + +
    cov_struct
    +

    Character - The covariance structure to be used, must be one of "us", +"toep", "cs", "ar1"

    + + +
    cov_by_group
    +

    Boolean - Whenever or not to use separate covariances per each group level

    + +
    +
    +

    Details

    +

    For example assuming the user specified a covariance structure of "us" and that no groups +were provided this will return

    +

    us(visit | subjid)

    +

    If cov_by_group is set to FALSE then this indicates that separate covariance matrices +are required per group and as such the following will be returned:

    +

    us( visit | group / subjid )

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/rbmi-package.html b/latest-tag/reference/rbmi-package.html new file mode 100644 index 00000000..f5a29193 --- /dev/null +++ b/latest-tag/reference/rbmi-package.html @@ -0,0 +1,119 @@ + +rbmi: Reference Based Multiple Imputation — rbmi-package • rbmi + Skip to contents + + +
    +
    +
    + +

    rbmi: Reference Based Multiple Imputation

    + Source: R/rbmi.R +
    rbmi-package.Rd
    +
    + +
    +

    The rbmi package is used to perform reference based multiple imputation. The package +provides implementations for common, patient-specific imputation strategies whilst allowing the user to +select between various standard Bayesian and frequentist approaches.

    +

    The package is designed around 4 core functions:

    • draws() - Fits multiple imputation models

      • +

    • impute() - Imputes multiple datasets

      • +

    • analyse() - Analyses multiple datasets

      • +

    • pool() - Pools multiple results into a single statistic

      • +

    To learn more about rbmi, please see the quickstart vignette:

    +

    vignette(topic= "quickstart", package = "rbmi")

    +
    + + +
    +

    See also

    +

    Useful links:

    +
    +
    +

    Author

    +

    Maintainer: Craig Gower-Page craig.gower-page@roche.com

    +

    Authors:

    Other contributors:

    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/record.html b/latest-tag/reference/record.html new file mode 100644 index 00000000..af879568 --- /dev/null +++ b/latest-tag/reference/record.html @@ -0,0 +1,113 @@ + +Capture all Output — record • rbmi + Skip to contents + + +
    +
    +
    + +

    Capture all Output

    + Source: R/utilities.R +
    record.Rd
    +
    + +
    +

    This function silences all warnings, errors & messages and instead returns a list +containing the results (if it didn't error) + the warning and error messages as +character vectors.

    +
    + +
    +

    Usage

    + 
    record(expr)
    +
    + +
    +

    Arguments

    + + +
    expr
    +

    An expression to be executed

    + +
    +
    +

    Value

    +

    A list containing

    • results - The object returned by expr or list() if an error was thrown

      • +

    • warnings - NULL or a character vector if warnings were thrown

      • +

    • errors - NULL or a string if an error was thrown

      • +

    • messages - NULL or a character vector if messages were produced

      • +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+record({
+  x <- 1
+  y <- 2
+  warning("something went wrong")
+  message("O nearly done")
+  x + y
+})
+} # }
+
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/recursive_reduce.html b/latest-tag/reference/recursive_reduce.html new file mode 100644 index 00000000..1b89a690 --- /dev/null +++ b/latest-tag/reference/recursive_reduce.html @@ -0,0 +1,95 @@ + +recursive_reduce — recursive_reduce • rbmi + Skip to contents + + +
    +
    +
    + +

    recursive_reduce

    + Source: R/as_ascii_table.R +
    recursive_reduce.Rd
    +
    + +
    +

    Utility function used to replicated purrr::reduce. Recursively applies a +function to a list of elements until only 1 element remains

    +
    + +
    +

    Usage

    + 
    recursive_reduce(.l, .f)
    +
    + +
    +

    Arguments

    + + +
    .l
    +

    list of values to apply a function to

    + + +
    .f
    +

    function to apply to each each element of the list in turn +i.e. .l[[1]] <- .f( .l[[1]] , .l[[2]]) ; .l[[1]] <- .f( .l[[1]] , .l[[3]])

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/remove_if_all_missing.html b/latest-tag/reference/remove_if_all_missing.html new file mode 100644 index 00000000..845e2ef6 --- /dev/null +++ b/latest-tag/reference/remove_if_all_missing.html @@ -0,0 +1,93 @@ + +Remove subjects from dataset if they have no observed values — remove_if_all_missing • rbmi + Skip to contents + + +
    +
    +
    + +

    Remove subjects from dataset if they have no observed values

    + Source: R/mcmc.R +
    remove_if_all_missing.Rd
    +
    + +
    +

    This function takes a data.frame with variables visit, outcome & subjid. +It then removes all rows for a given subjid if they don't have any non-missing +values for outcome.

    +
    + +
    +

    Usage

    + 
    remove_if_all_missing(dat)
    +
    + +
    +

    Arguments

    + + +
    dat
    +

    a data.frame

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/rubin_df.html b/latest-tag/reference/rubin_df.html new file mode 100644 index 00000000..7c815a24 --- /dev/null +++ b/latest-tag/reference/rubin_df.html @@ -0,0 +1,114 @@ + +Barnard and Rubin degrees of freedom adjustment — rubin_df • rbmi + Skip to contents + + +
    +
    +
    + +

    Barnard and Rubin degrees of freedom adjustment

    + Source: R/pool.R +
    rubin_df.Rd
    +
    + +
    +

    Compute degrees of freedom according to the Barnard-Rubin formula.

    +
    + +
    +

    Usage

    + 
    rubin_df(v_com, var_b, var_t, M)
    +
    + +
    +

    Arguments

    + + +
    v_com
    +

    Positive number representing the degrees of freedom in the complete-data analysis.

    + + +
    var_b
    +

    Between-variance of point estimate across multiply imputed datasets.

    + + +
    var_t
    +

    Total-variance of point estimate according to Rubin's rules.

    + + +
    M
    +

    Number of imputations.

    + +
    +
    +

    Value

    +

    Degrees of freedom according to Barnard-Rubin formula. See Barnard-Rubin (1999).

    +
    +
    +

    Details

    +

    The computation takes into account limit cases where there is no missing data +(i.e. the between-variance var_b is zero) or where the complete-data degrees of freedom is +set to Inf. Moreover, if v_com is given as NA, the function returns Inf.

    +
    +
    +

    References

    +

    Barnard, J. and Rubin, D.B. (1999). +Small sample degrees of freedom with multiple imputation. Biometrika, 86, 948-955.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/rubin_rules.html b/latest-tag/reference/rubin_rules.html new file mode 100644 index 00000000..1d9411ea --- /dev/null +++ b/latest-tag/reference/rubin_rules.html @@ -0,0 +1,122 @@ + +Combine estimates using Rubin's rules — rubin_rules • rbmi + Skip to contents + + +
    +
    +
    + +

    Combine estimates using Rubin's rules

    + Source: R/pool.R +
    rubin_rules.Rd
    +
    + +
    +

    Pool together the results from M complete-data analyses according to Rubin's rules. See details.

    +
    + +
    +

    Usage

    + 
    rubin_rules(ests, ses, v_com)
    +
    + +
    +

    Arguments

    + + +
    ests
    +

    Numeric vector containing the point estimates from the complete-data analyses.

    + + +
    ses
    +

    Numeric vector containing the standard errors from the complete-data analyses.

    + + +
    v_com
    +

    Positive number representing the degrees of freedom in the complete-data analysis.

    + +
    +
    +

    Value

    +

    A list containing:

    • est_point: the pooled point estimate according to Little-Rubin (2002).

      • +

    • var_t: total variance according to Little-Rubin (2002).

      • +

    • df: degrees of freedom according to Barnard-Rubin (1999).

      • +
    +
    +

    Details

    +

    rubin_rules applies Rubin's rules (Rubin, 1987) for pooling together +the results from a multiple imputation procedure. The pooled point estimate est_point is +is the average across the point estimates from the complete-data analyses (given by the input argument ests). +The total variance var_t is the sum of two terms representing the within-variance +and the between-variance (see Little-Rubin (2002)). The function +also returns df, the estimated pooled degrees of freedom according to Barnard-Rubin (1999) +that can be used for inference based on the t-distribution.

    +
    +
    +

    References

    +

    Barnard, J. and Rubin, D.B. (1999). +Small sample degrees of freedom with multiple imputation. Biometrika, 86, 948-955

    +

    Roderick J. A. Little and Donald B. Rubin. Statistical Analysis with Missing +Data, Second Edition. John Wiley & Sons, Hoboken, New Jersey, 2002. [Section 5.4]

    +
    +
    +

    See also

    +

    rubin_df() for the degrees of freedom estimation.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/sample_ids.html b/latest-tag/reference/sample_ids.html new file mode 100644 index 00000000..b5ce5917 --- /dev/null +++ b/latest-tag/reference/sample_ids.html @@ -0,0 +1,102 @@ + +Sample Patient Ids — sample_ids • rbmi + Skip to contents + + +
    +
    +
    + +

    Sample Patient Ids

    + Source: R/bootstrap.R +
    sample_ids.Rd
    +
    + +
    +

    Performs a stratified bootstrap sample of IDS +ensuring the return vector is the same length as the input vector

    +
    + +
    +

    Usage

    + 
    sample_ids(ids, strata = rep(1, length(ids)))
    +
    + +
    +

    Arguments

    + + +
    ids
    +

    vector to sample from

    + + +
    strata
    +

    strata indicator, ids are sampled within each strata +ensuring the that the numbers of each strata are maintained

    + +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+sample_ids( c("a", "b", "c", "d"), strata = c(1,1,2,2))
+} # }
+
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/sample_list.html b/latest-tag/reference/sample_list.html new file mode 100644 index 00000000..08b564d5 --- /dev/null +++ b/latest-tag/reference/sample_list.html @@ -0,0 +1,90 @@ + +Create and validate a sample_list object — sample_list • rbmi + Skip to contents + + +
    +
    +
    + +

    Create and validate a sample_list object

    + Source: R/dataclasses.R +
    sample_list.Rd
    +
    + +
    +

    Given a list of sample_single objects generate by sample_single(), +creates a sample_list objects and validate it.

    +
    + +
    +

    Usage

    + 
    sample_list(...)
    +
    + +
    +

    Arguments

    + + +
    ...
    +

    A list of sample_single objects.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/sample_mvnorm.html b/latest-tag/reference/sample_mvnorm.html new file mode 100644 index 00000000..a93c7808 --- /dev/null +++ b/latest-tag/reference/sample_mvnorm.html @@ -0,0 +1,95 @@ + +Sample random values from the multivariate normal distribution — sample_mvnorm • rbmi + Skip to contents + + +
    +
    +
    + +

    Sample random values from the multivariate normal distribution

    + Source: R/utilities.R +
    sample_mvnorm.Rd
    +
    + +
    +

    Sample random values from the multivariate normal distribution

    +
    + +
    +

    Usage

    + 
    sample_mvnorm(mu, sigma)
    +
    + +
    +

    Arguments

    + + +
    mu
    +

    mean vector

    + + +
    sigma
    +

    covariance matrix

    +

    Samples multivariate normal variables by multiplying +univariate random normal variables by the cholesky +decomposition of the covariance matrix.

    +

    If mu is length 1 then just uses rnorm instead.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/sample_single.html b/latest-tag/reference/sample_single.html new file mode 100644 index 00000000..6ac8d09d --- /dev/null +++ b/latest-tag/reference/sample_single.html @@ -0,0 +1,126 @@ + +Create object of sample_single class — sample_single • rbmi + Skip to contents + + +
    +
    +
    + +

    Create object of sample_single class

    + Source: R/dataclasses.R +
    sample_single.Rd
    +
    + +
    +

    Creates an object of class sample_single which is a named list +containing the input parameters and validate them.

    +
    + +
    +

    Usage

    + 
    sample_single(
+  ids,
+  beta = NA,
+  sigma = NA,
+  theta = NA,
+  failed = any(is.na(beta)),
+  ids_samp = ids
+)
    +
    + +
    +

    Arguments

    + + +
    ids
    +

    Vector of characters containing the ids of the subjects included in the original dataset.

    + + +
    beta
    +

    Numeric vector of estimated regression coefficients.

    + + +
    sigma
    +

    List of estimated covariance matrices (one for each level of vars$group).

    + + +
    theta
    +

    Numeric vector of transformed covariances.

    + + +
    failed
    +

    Logical. TRUE if the model fit failed.

    + + +
    ids_samp
    +

    Vector of characters containing the ids of the subjects included in the given sample.

    + +
    +
    +

    Value

    +

    A named list of class sample_single. It contains the following:

    • ids vector of characters containing the ids of the subjects included in the original dataset.

      • +

    • beta numeric vector of estimated regression coefficients.

      • +

    • sigma list of estimated covariance matrices (one for each level of vars$group).

      • +

    • theta numeric vector of transformed covariances.

      • +

    • failed logical. TRUE if the model fit failed.

      • +

    • ids_samp vector of characters containing the ids of the subjects included in the given sample.

      • +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/scalerConstructor.html b/latest-tag/reference/scalerConstructor.html new file mode 100644 index 00000000..146ee08c --- /dev/null +++ b/latest-tag/reference/scalerConstructor.html @@ -0,0 +1,224 @@ + +R6 Class for scaling (and un-scaling) design matrices — scalerConstructor • rbmi + Skip to contents + + +
    +
    +
    + +

    R6 Class for scaling (and un-scaling) design matrices

    + Source: R/scaling.R +
    scalerConstructor.Rd
    +
    + +
    +

    Scales a design matrix so that all non-categorical columns have a mean +of 0 and an standard deviation of 1.

    +
    + + +
    +

    Details

    +

    The object initialisation +is used to determine the relevant mean and SD's to scale by and then +the scaling (and un-scaling) itself is performed by the relevant object +methods.

    +

    Un-scaling is done on linear model Beta and Sigma coefficients. For this purpose +the first column on the dataset to be scaled is assumed to be the outcome variable +with all other variables assumed to be post-transformation predictor variables (i.e. +all dummy variables have already been expanded).

    +
    +
    +

    Public fields

    +

    centre
    +

    Vector of column means. The first value is the outcome +variable, all other variables are the predictors.

    + + +
    scales
    +

    Vector of column standard deviations. The first value is the outcome +variable, all other variables are the predictors.

    + + +

    +
    +
    +

    Methods

    + +
    +

    Public methods

    + +

    +

    Method new()

    +

    Uses dat to determine the relevant column means and standard deviations to use +when scaling and un-scaling future datasets. Implicitly assumes that new datasets +have the same column order as dat

    +

    Usage

    +

    scalerConstructor$new(dat)

    +
    + +
    +

    Arguments

    +

    dat
    +

    A data.frame or matrix. All columns must be numeric (i.e dummy variables, +must have already been expanded out).

    + + +

    +
    +
    +

    Details

    +

    Categorical columns (as determined by those who's values are entirely 1 or 0) +will not be scaled. This is achieved by setting the corresponding values of centre +to 0 and scale to 1.

    +
    + + +

    +

    Method scale()

    +

    Scales a dataset so that all continuous variables have a mean of 0 and a +standard deviation of 1.

    +

    Usage

    +

    scalerConstructor$scale(dat)

    +
    + +
    +

    Arguments

    +

    dat
    +

    A data.frame or matrix whose columns are all numeric (i.e. dummy +variables have all been expanded out) and whose columns are in the same +order as the dataset used in the initialization function.

    + + +

    +
    + +

    +

    Method unscale_sigma()

    +

    Unscales a sigma value (or matrix) as estimated by a linear model +using a design matrix scaled by this object. This function only +works if the first column of the initialisation data.frame was the outcome +variable.

    +

    Usage

    +

    scalerConstructor$unscale_sigma(sigma)

    +
    + +
    +

    Arguments

    +

    sigma
    +

    A numeric value or matrix.

    + + +

    +
    +
    +

    Returns

    +

    A numeric value or matrix

    +
    + +

    +

    Method unscale_beta()

    +

    Unscales a beta value (or vector) as estimated by a linear model +using a design matrix scaled by this object. This function only +works if the first column of the initialization data.frame was the outcome +variable.

    +

    Usage

    +

    scalerConstructor$unscale_beta(beta)

    +
    + +
    +

    Arguments

    +

    beta
    +

    A numeric vector of beta coefficients as estimated from a linear model.

    + + +

    +
    +
    +

    Returns

    +

    A numeric vector.

    +
    + +

    +

    Method clone()

    +

    The objects of this class are cloneable with this method.

    +

    Usage

    +

    scalerConstructor$clone(deep = FALSE)

    +
    + +
    +

    Arguments

    +

    deep
    +

    Whether to make a deep clone.

    + + +

    +
    + +
    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/set_simul_pars.html b/latest-tag/reference/set_simul_pars.html new file mode 100644 index 00000000..418bc464 --- /dev/null +++ b/latest-tag/reference/set_simul_pars.html @@ -0,0 +1,171 @@ + +Set simulation parameters of a study group. — set_simul_pars • rbmi + Skip to contents + + +
    +
    +
    + +

    Set simulation parameters of a study group.

    + Source: R/simulate_data.R +
    set_simul_pars.Rd
    +
    + +
    +

    This function provides input arguments for each study group needed to +simulate data with simulate_data(). simulate_data() generates data for a two-arms +clinical trial with longitudinal continuous outcomes and two intercurrent events (ICEs). +ICE1 may be thought of as a discontinuation from study treatment due to study drug or +condition related (SDCR) reasons. ICE2 may be thought of as discontinuation from study +treatment due to uninformative study drop-out, i.e. due to not study drug or +condition related (NSDRC) reasons and outcome data after ICE2 is always missing.

    +
    + +
    +

    Usage

    + 
    set_simul_pars(
+  mu,
+  sigma,
+  n,
+  prob_ice1 = 0,
+  or_outcome_ice1 = 1,
+  prob_post_ice1_dropout = 0,
+  prob_ice2 = 0,
+  prob_miss = 0
+)
    +
    + +
    +

    Arguments

    + + +
    mu
    +

    Numeric vector describing the mean outcome trajectory at each visit (including +baseline) assuming no ICEs.

    + + +
    sigma
    +

    Covariance matrix of the outcome trajectory assuming no ICEs.

    + + +
    n
    +

    Number of subjects belonging to the group.

    + + +
    prob_ice1
    +

    Numeric vector that specifies the probability of experiencing ICE1 +(discontinuation from study treatment due to SDCR reasons) after each visit for a subject +with observed outcome at that visit equal to the mean at baseline (mu[1]). +If a single numeric is provided, then the same probability is applied to each visit.

    + + +
    or_outcome_ice1
    +

    Numeric value that specifies the odds ratio of experiencing ICE1 after +each visit corresponding to a +1 higher value of the observed outcome at that visit.

    + + +
    prob_post_ice1_dropout
    +

    Numeric value that specifies the probability of study +drop-out following ICE1. If a subject is simulated to drop-out after ICE1, all outcomes after +ICE1 are set to missing.

    + + +
    prob_ice2
    +

    Numeric that specifies an additional probability that a post-baseline +visit is affected by study drop-out. Outcome data at the subject's first simulated visit +affected by study drop-out and all subsequent visits are set to missing. This generates +a second intercurrent event ICE2, which may be thought as treatment discontinuation due to +NSDRC reasons with subsequent drop-out. +If for a subject, both ICE1 and ICE2 are simulated to occur, +then it is assumed that only the earlier of them counts. +In case both ICEs are simulated to occur at the same time, it is assumed that ICE1 counts. +This means that a single subject can experience either ICE1 or ICE2, but not both of them.

    + + +
    prob_miss
    +

    Numeric value that specifies an additional probability for a given +post-baseline observation to be missing. This can be used to produce +"intermittent" missing values which are not associated with any ICE.

    + +
    +
    +

    Value

    +

    A simul_pars object which is a named list containing the simulation parameters.

    +
    +
    +

    Details

    +

    For the details, please see simulate_data().

    +
    +
    +

    See also

    +

    simulate_data()

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/set_vars.html b/latest-tag/reference/set_vars.html new file mode 100644 index 00000000..eff72f22 --- /dev/null +++ b/latest-tag/reference/set_vars.html @@ -0,0 +1,161 @@ + +Set key variables — set_vars • rbmi + Skip to contents + + +
    +
    +
    + +

    Set key variables

    + Source: R/utilities.R +
    set_vars.Rd
    +
    + +
    +

    This function is used to define the names of key variables within the data.frame's +that are provided as input arguments to draws() and ancova().

    +
    + +
    +

    Usage

    + 
    set_vars(
+  subjid = "subjid",
+  visit = "visit",
+  outcome = "outcome",
+  group = "group",
+  covariates = character(0),
+  strata = group,
+  strategy = "strategy"
+)
    +
    + +
    +

    Arguments

    + + +
    subjid
    +

    The name of the "Subject ID" variable. A length 1 character vector.

    + + +
    visit
    +

    The name of the "Visit" variable. A length 1 character vector.

    + + +
    outcome
    +

    The name of the "Outcome" variable. A length 1 character vector.

    + + +
    group
    +

    The name of the "Group" variable. A length 1 character vector.

    + + +
    covariates
    +

    The name of any covariates to be used in the context of modeling. +See details.

    + + +
    strata
    +

    The name of the any stratification variable to be used in the context of bootstrap +sampling. See details.

    + + +
    strategy
    +

    The name of the "strategy" variable. A length 1 character vector.

    + +
    +
    +

    Details

    +

    In both draws() and ancova() the covariates argument can be specified to indicate +which variables should be included in the imputation and analysis models respectively. If you wish +to include interaction terms these need to be manually specified i.e. +covariates = c("group*visit", "age*sex"). Please note that the use of the I() function to +inhibit the interpretation/conversion of objects is not supported.

    +

    Currently strata is only used by draws() in combination with method_condmean(type = "bootstrap") +and method_approxbayes() in order to allow for the specification of stratified bootstrap sampling. +By default strata is set equal to the value of group as it is assumed most users will want to +preserve the group size between samples. See draws() for more details.

    +

    Likewise, currently the strategy argument is only used by draws() to specify the name of the +strategy variable within the data_ice data.frame. See draws() for more details.

    +
    +
    +

    See also

    +

    draws()

    +

    ancova()

    +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+
+# Using CDISC variable names as an example
+set_vars(
+    subjid = "usubjid",
+    visit = "avisit",
+    outcome = "aval",
+    group = "arm",
+    covariates = c("bwt", "bht", "arm * avisit"),
+    strategy = "strat"
+)
+
+} # }
+
+
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/simulate_data.html b/latest-tag/reference/simulate_data.html new file mode 100644 index 00000000..a1327b8b --- /dev/null +++ b/latest-tag/reference/simulate_data.html @@ -0,0 +1,187 @@ + +Generate data — simulate_data • rbmi + Skip to contents + + +
    +
    +
    + +

    Generate data

    + Source: R/simulate_data.R +
    simulate_data.Rd
    +
    + +
    +

    Generate data for a two-arms clinical trial with longitudinal continuous +outcome and two intercurrent events (ICEs). +ICE1 may be thought of as a discontinuation from study treatment due to study drug or +condition related (SDCR) reasons. +ICE2 may be thought of as discontinuation from study treatment due to uninformative +study drop-out, i.e. due to not study drug or +condition related (NSDRC) reasons and outcome data after ICE2 is always missing.

    +
    + +
    +

    Usage

    + 
    simulate_data(pars_c, pars_t, post_ice1_traj, strategies = getStrategies())
    +
    + +
    +

    Arguments

    + + +
    pars_c
    +

    A simul_pars object as generated by set_simul_pars(). It specifies +the simulation parameters of the control arm.

    + + +
    pars_t
    +

    A simul_pars object as generated by set_simul_pars(). It specifies +the simulation parameters of the treatment arm.

    + + +
    post_ice1_traj
    +

    A string which specifies how observed outcomes occurring after +ICE1 are simulated. +Must target a function included in strategies. Possible choices are: Missing At +Random "MAR", Jump to Reference "JR", +Copy Reference "CR", Copy Increments in Reference "CIR", Last Mean Carried +Forward "LMCF". User-defined strategies +could also be added. See getStrategies() for details.

    + + +
    strategies
    +

    A named list of functions. Default equal to getStrategies(). +See getStrategies() for details.

    + +
    +
    +

    Value

    +

    A data.frame containing the simulated data. It includes the following variables:

    • id: Factor variable that specifies the id of each subject.

      • +

    • visit: Factor variable that specifies the visit of each assessment. Visit 0 denotes +the baseline visit.

      • +

    • group: Factor variable that specifies which treatment group each subject belongs to.

      • +

    • outcome_bl: Numeric variable that specifies the baseline outcome.

      • +

    • outcome_noICE: Numeric variable that specifies the longitudinal outcome assuming +no ICEs.

      • +

    • ind_ice1: Binary variable that takes value 1 if the corresponding visit is +affected by ICE1 and 0 otherwise.

      • +

    • dropout_ice1: Binary variable that takes value 1 if the corresponding visit is +affected by the drop-out following ICE1 and 0 otherwise.

      • +

    • ind_ice2: Binary variable that takes value 1 if the corresponding visit is affected +by ICE2.

      • +

    • outcome: Numeric variable that specifies the longitudinal outcome including ICE1, ICE2 +and the intermittent missing values.

      • +
    +
    +

    Details

    +

    The data generation works as follows:

    • Generate outcome data for all visits (including baseline) from a multivariate +normal distribution with parameters pars_c$mu and pars_c$sigma +for the control arm and parameters pars_t$mu and pars_t$sigma for the treatment +arm, respectively. +Note that for a randomized trial, outcomes have the same distribution at baseline +in both treatment groups, i.e. one should set +pars_c$mu[1]=pars_t$mu[1] and pars_c$sigma[1,1]=pars_t$sigma[1,1].

      • +

    • Simulate whether ICE1 (study treatment discontinuation due to SDCR reasons) occurs +after each visit according to parameters pars_c$prob_ice1 and pars_c$or_outcome_ice1 +for the control arm and pars_t$prob_ice1 and pars_t$or_outcome_ice1 for the +treatment arm, respectively.

      • +

    • Simulate drop-out following ICE1 according to pars_c$prob_post_ice1_dropout and +pars_t$prob_post_ice1_dropout.

      • +

    • Simulate an additional uninformative study drop-out with probabilities pars_c$prob_ice2 +and pars_t$prob_ice2 at each visit. This generates a second intercurrent event ICE2, which +may be thought as treatment discontinuation due to NSDRC reasons with subsequent drop-out. +The simulated time of drop-out is the subject's first visit which is affected by +drop-out and data from this visit and all subsequent visits are consequently set to missing. +If for a subject, both ICE1 and ICE2 are simulated to occur, +then it is assumed that only the earlier of them counts. +In case both ICEs are simulated to occur at the same time, it is assumed that ICE1 counts. +This means that a single subject can experience either ICE1 or ICE2, but not both of them.

      • +

    • Adjust trajectories after ICE1 according to the given assumption expressed with +the post_ice1_traj argument. Note that only post-ICE1 outcomes in the intervention arm can be +adjusted. Post-ICE1 outcomes from the control arm are not adjusted.

      • +

    • Simulate additional intermittent missing outcome data as per arguments pars_c$prob_miss +and pars_t$prob_miss.

      • +

    The probability of the ICE after each visit is modeled according to the following +logistic regression model: +~ 1 + I(visit == 0) + ... + I(visit == n_visits-1) + I((x-alpha)) where:

    • n_visits is the number of visits (including baseline).

      • +

    • alpha is the baseline outcome mean. +The term I((x-alpha)) specifies the dependency of the probability of the ICE on +the current outcome value. +The corresponding regression coefficients of the logistic model are defined as follows: +The intercept is set to 0, the coefficients corresponding to discontinuation after +each visit for a subject with outcome equal to +the mean at baseline are set according to parameters pars_c$prob_ice1 (pars_t$prob_ice1), +and the regression coefficient associated with the covariate I((x-alpha)) is set +to log(pars_c$or_outcome_ice1) (log(pars_t$or_outcome_ice1)).

      • +

    Please note that the baseline outcome cannot be missing nor be affected by any ICEs.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/simulate_dropout.html b/latest-tag/reference/simulate_dropout.html new file mode 100644 index 00000000..15887b17 --- /dev/null +++ b/latest-tag/reference/simulate_dropout.html @@ -0,0 +1,114 @@ + +Simulate drop-out — simulate_dropout • rbmi + Skip to contents + + +
    +
    +
    + +

    Simulate drop-out

    + Source: R/simulate_data.R +
    simulate_dropout.Rd
    +
    + +
    +

    Simulate drop-out

    +
    + +
    +

    Usage

    + 
    simulate_dropout(prob_dropout, ids, subset = rep(1, length(ids)))
    +
    + +
    +

    Arguments

    + + +
    prob_dropout
    +

    Numeric that specifies the probability that a post-baseline visit is +affected by study drop-out.

    + + +
    ids
    +

    Factor variable that specifies the id of each subject.

    + + +
    subset
    +

    Binary variable that specifies the subset that could be affected by drop-out. +I.e. subset is a binary vector +of length equal to the length of ids that takes value 1 if the corresponding visit could +be affected by drop-out and 0 otherwise.

    + +
    +
    +

    Value

    +

    A binary vector of length equal to the length of ids that takes value 1 if the +corresponding outcome is +affected by study drop-out.

    +
    +
    +

    Details

    +

    subset can be used to specify outcome values that cannot be affected by the +drop-out. By default +subset will be set to 1 for all the values except the values corresponding to the +baseline outcome, since baseline is supposed to not be affected by drop-out. +Even if subset is specified by the user, the values corresponding to the baseline +outcome are still hard-coded to be 0.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/simulate_ice.html b/latest-tag/reference/simulate_ice.html new file mode 100644 index 00000000..e8e69c16 --- /dev/null +++ b/latest-tag/reference/simulate_ice.html @@ -0,0 +1,130 @@ + +Simulate intercurrent event — simulate_ice • rbmi + Skip to contents + + +
    +
    +
    + +

    Simulate intercurrent event

    + Source: R/simulate_data.R +
    simulate_ice.Rd
    +
    + +
    +

    Simulate intercurrent event

    +
    + +
    +

    Usage

    + 
    simulate_ice(outcome, visits, ids, prob_ice, or_outcome_ice, baseline_mean)
    +
    + +
    +

    Arguments

    + + +
    outcome
    +

    Numeric variable that specifies the longitudinal outcome for a single group.

    + + +
    visits
    +

    Factor variable that specifies the visit of each assessment.

    + + +
    ids
    +

    Factor variable that specifies the id of each subject.

    + + +
    prob_ice
    +

    Numeric vector that specifies for each visit the probability of experiencing +the ICE after the current visit for a subject with outcome equal to the mean at baseline. +If a single numeric is provided, then the same probability is applied to each visit.

    + + +
    or_outcome_ice
    +

    Numeric value that specifies the odds ratio of the ICE corresponding to +a +1 higher value of the outcome at the visit.

    + + +
    baseline_mean
    +

    Mean outcome value at baseline.

    + +
    +
    +

    Value

    +

    A binary variable that takes value 1 if the corresponding outcome is affected +by the ICE and 0 otherwise.

    +
    +
    +

    Details

    +

    The probability of the ICE after each visit is modeled according to the following +logistic regression model: +~ 1 + I(visit == 0) + ... + I(visit == n_visits-1) + I((x-alpha)) where:

    • n_visits is the number of visits (including baseline).

      • +

    • alpha is the baseline outcome mean set via argument baseline_mean. +The term I((x-alpha)) specifies the dependency of the probability of the ICE on the current +outcome value. +The corresponding regression coefficients of the logistic model are defined as follows: +The intercept is set to 0, the coefficients corresponding to discontinuation after each visit +for a subject with outcome equal to +the mean at baseline are set according to parameter or_outcome_ice, +and the regression coefficient associated with the covariate I((x-alpha)) is set to +log(or_outcome_ice).

      • +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/simulate_test_data.html b/latest-tag/reference/simulate_test_data.html new file mode 100644 index 00000000..8c0c29b8 --- /dev/null +++ b/latest-tag/reference/simulate_test_data.html @@ -0,0 +1,139 @@ + +Create simulated datasets — simulate_test_data • rbmi + Skip to contents + + +
    +
    +
    + +

    Create simulated datasets

    + Source: R/simulate.R +
    simulate_test_data.Rd
    +
    + +
    +

    Creates a longitudinal dataset in the format that rbmi was +designed to analyse.

    +
    + +
    +

    Usage

    + 
    simulate_test_data(
+  n = 200,
+  sd = c(3, 5, 7),
+  cor = c(0.1, 0.7, 0.4),
+  mu = list(int = 10, age = 3, sex = 2, trt = c(0, 4, 8), visit = c(0, 1, 2))
+)
+
+as_vcov(sd, cor)
    +
    + +
    +

    Arguments

    + + +
    n
    +

    the number of subjects to sample. Total number of observations returned +is thus n * length(sd)

    + + +
    sd
    +

    the standard deviations for the outcome at each visit. +i.e. the square root of the diagonal of the covariance matrix for the outcome

    + + +
    cor
    +

    the correlation coefficients between the outcome values at each visit. +See details.

    + + +
    mu
    +

    the coefficients to use to construct the mean outcome value at each visit. Must +be a named list with elements int, age, sex, trt & visit. See details.

    + +
    +
    +

    Details

    +

    The number of visits is determined by the size of the variance covariance matrix. +i.e. if 3 standard deviation values are provided then 3 visits per patient will be +created.

    +

    The covariates in the simulated dataset are produced as follows:

    • Patients age is sampled at random from a N(0,1) distribution

      • +

    • Patients sex is sampled at random with a 50/50 split

      • +

    • Patients group is sampled at random but fixed so that each group has n/2 patients

      • +

    • The outcome variable is sampled from a multivariate normal distribution, see below +for details

      • +

    The mean for the outcome variable is derived as:

    +

    outcome = Intercept + age + sex + visit + treatment

    +

    The coefficients for the intercept, age and sex are taken from mu$int, +mu$age and mu$sex respectively, all of which must be a length 1 numeric.

    +

    Treatment and visit coefficients are taken from mu$trt and mu$visit respectively +and must either be of length 1 (i.e. a constant affect across all visits) or equal to the +number of visits (as determined by the length of sd). I.e. if you wanted a treatment +slope of 5 and a visit slope of 1 you could specify:

    +

    mu = list(..., "trt" = c(0,5,10), "visit" = c(0,1,2))

    +

    The correlation matrix is constructed from cor as follows. +Let cor = c(a, b, c, d, e, f) then the correlation matrix would be:

    +

    1  a  b  d
+a  1  c  e
+b  c  1  f
+d  e  f  1

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/sort_by.html b/latest-tag/reference/sort_by.html new file mode 100644 index 00000000..fa22559e --- /dev/null +++ b/latest-tag/reference/sort_by.html @@ -0,0 +1,104 @@ + +Sort data.frame — sort_by • rbmi + Skip to contents + + +
    +
    +
    + +

    Sort data.frame

    + Source: R/utilities.R +
    sort_by.Rd
    +
    + +
    +

    Sorts a data.frame (ascending by default) based upon variables within the dataset

    +
    + +
    +

    Usage

    + 
    sort_by(df, vars = NULL, decreasing = FALSE)
    +
    + +
    +

    Arguments

    + + +
    df
    +

    data.frame

    + + +
    vars
    +

    character vector of variables

    + + +
    decreasing
    +

    logical whether sort order should be in descending or ascending (default) order. +Can be either a single logical value (in which case it is applied to +all variables) or a vector which is the same length as vars

    + +
    + +
    +

    Examples

    + 
    if (FALSE) { # \dontrun{
+sort_by(iris, c("Sepal.Length", "Sepal.Width"), decreasing = c(TRUE, FALSE))
+} # }
+
    +
    +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/split_dim.html b/latest-tag/reference/split_dim.html new file mode 100644 index 00000000..ea06f98a --- /dev/null +++ b/latest-tag/reference/split_dim.html @@ -0,0 +1,124 @@ + +Transform array into list of arrays — split_dim • rbmi + Skip to contents + + +
    +
    +
    + +

    Transform array into list of arrays

    + Source: R/mcmc.R +
    split_dim.Rd
    +
    + +
    +

    Transform an array into list of arrays where the listing +is performed on a given dimension.

    +
    + +
    +

    Usage

    + 
    split_dim(a, n)
    +
    + +
    +

    Arguments

    + + +
    a
    +

    Array with number of dimensions at least 2.

    + + +
    n
    +

    Positive integer. Dimension of a to be listed.

    + +
    +
    +

    Value

    +

    A list of length n of arrays with number of dimensions equal to the +number of dimensions of a minus 1.

    +
    +
    +

    Details

    +

    For example, if a is a 3 dimensional array and n = 1, +split_dim(a,n) returns a list of 2 dimensional arrays (i.e. +a list of matrices) where each element of the list is a[i, , ], where +i takes values from 1 to the length of the first dimension of the array.

    +

    Example:

    +

    inputs: +a <- array( c(1,2,3,4,5,6,7,8,9,10,11,12), dim = c(3,2,2)), +which means that:

    +

    a[1,,]     a[2,,]     a[3,,]
+
+[,1] [,2]  [,1] [,2]  [,1] [,2]
+---------  ---------  ---------
+ 1    7     2    8     3    9
+ 4    10    5    11    6    12

    +

    n <- 1

    +

    output of res <- split_dim(a,n) is a list of 3 elements:

    +

    res[[1]]   res[[2]]   res[[3]]
+
+[,1] [,2]  [,1] [,2]  [,1] [,2]
+---------  ---------  ---------
+ 1    7     2    8     3    9
+ 4    10    5    11    6    12

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/split_imputations.html b/latest-tag/reference/split_imputations.html new file mode 100644 index 00000000..abb29fe4 --- /dev/null +++ b/latest-tag/reference/split_imputations.html @@ -0,0 +1,127 @@ + +Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations • rbmi + Skip to contents + + +
    +
    +
    + +

    Split a flat list of imputation_single() into multiple imputation_df()'s by ID

    + Source: R/impute.R +
    split_imputations.Rd
    +
    + +
    +

    Split a flat list of imputation_single() into multiple imputation_df()'s by ID

    +
    + +
    +

    Usage

    + 
    split_imputations(list_of_singles, split_ids)
    +
    + +
    +

    Arguments

    + + +
    list_of_singles
    +

    A list of imputation_single()'s

    + + +
    split_ids
    +

    A list with 1 element per required split. Each element +must contain a vector of "ID"'s which correspond to the imputation_single() ID's +that are required within that sample. The total number of ID's must by equal to the +length of list_of_singles

    + +
    +
    +

    Details

    +

    This function converts a list of imputations from being structured per patient +to being structured per sample i.e. it converts

    +

    obj <- list(
+    imputation_single("Ben", numeric(0)),
+    imputation_single("Ben", numeric(0)),
+    imputation_single("Ben", numeric(0)),
+    imputation_single("Harry", c(1, 2)),
+    imputation_single("Phil", c(3, 4)),
+    imputation_single("Phil", c(5, 6)),
+    imputation_single("Tom", c(7, 8, 9))
+)
+
+index <- list(
+    c("Ben", "Harry", "Phil", "Tom"),
+    c("Ben", "Ben", "Phil")
+)

    +

    Into:

    +

    output <- list(
+    imputation_df(
+        imputation_single(id = "Ben", values = numeric(0)),
+        imputation_single(id = "Harry", values = c(1, 2)),
+        imputation_single(id = "Phil", values = c(3, 4)),
+        imputation_single(id = "Tom", values = c(7, 8, 9))
+    ),
+    imputation_df(
+        imputation_single(id = "Ben", values = numeric(0)),
+        imputation_single(id = "Ben", values = numeric(0)),
+        imputation_single(id = "Phil", values = c(5, 6))
+    )
+)

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/str_contains.html b/latest-tag/reference/str_contains.html new file mode 100644 index 00000000..d03eac51 --- /dev/null +++ b/latest-tag/reference/str_contains.html @@ -0,0 +1,105 @@ + +Does a string contain a substring — str_contains • rbmi + Skip to contents + + +
    +
    +
    + +

    Does a string contain a substring

    + Source: R/utilities.R +
    str_contains.Rd
    +
    + +
    +

    Returns a vector of TRUE/FALSE for each element of x +if it contains any element in subs

    +

    i.e.

    +

    str_contains( c("ben", "tom", "harry"), c("e", "y"))
+[1] TRUE FALSE TRUE

    +
    + +
    +

    Usage

    + 
    str_contains(x, subs)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    character vector

    + + +
    subs
    +

    a character vector of substrings to look for

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/strategies.html b/latest-tag/reference/strategies.html new file mode 100644 index 00000000..5c3f54d4 --- /dev/null +++ b/latest-tag/reference/strategies.html @@ -0,0 +1,131 @@ + +Strategies — strategies • rbmi + Skip to contents + + +
    +
    +
    + +

    Strategies

    + Source: R/strategies.R +
    strategies.Rd
    +
    + +
    +

    These functions are used to implement various reference based imputation +strategies by combining a subjects own distribution with that of +a reference distribution based upon which of their visits failed to meet +the Missing-at-Random (MAR) assumption.

    +
    + +
    +

    Usage

    + 
    strategy_MAR(pars_group, pars_ref, index_mar)
+
+strategy_JR(pars_group, pars_ref, index_mar)
+
+strategy_CR(pars_group, pars_ref, index_mar)
+
+strategy_CIR(pars_group, pars_ref, index_mar)
+
+strategy_LMCF(pars_group, pars_ref, index_mar)
    +
    + +
    +

    Arguments

    + + +
    pars_group
    +

    A list of parameters for the subject's group. See details.

    + + +
    pars_ref
    +

    A list of parameters for the subject's reference group. See details.

    + + +
    index_mar
    +

    A logical vector indicating which visits meet the MAR assumption +for the subject. I.e. this identifies the observations after a non-MAR +intercurrent event (ICE).

    + +
    +
    +

    Details

    +

    pars_group and pars_ref both must be a list containing elements mu and sigma. +mu must be a numeric vector and sigma must be a square matrix symmetric covariance +matrix with dimensions equal to the length of mu and index_mar. e.g.

    +

    list(
+    mu = c(1,2,3),
+    sigma = matrix(c(4,3,2,3,5,4,2,4,6), nrow = 3, ncol = 3)
+)

    +

    Users can define their own strategy functions and include them via the strategies +argument to impute() using getStrategies(). That being said the following +strategies are available "out the box":

    • Missing at Random (MAR)

      • +

    • Jump to Reference (JR)

      • +

    • Copy Reference (CR)

      • +

    • Copy Increments in Reference (CIR)

      • +

    • Last Mean Carried Forward (LMCF)

      • +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/string_pad.html b/latest-tag/reference/string_pad.html new file mode 100644 index 00000000..73e11efd --- /dev/null +++ b/latest-tag/reference/string_pad.html @@ -0,0 +1,94 @@ + +string_pad — string_pad • rbmi + Skip to contents + + +
    +
    +
    + +

    string_pad

    + Source: R/as_ascii_table.R +
    string_pad.Rd
    +
    + +
    +

    Utility function used to replicate str_pad. Adds white space to either end +of a string to get it to equal the desired length

    +
    + +
    +

    Usage

    + 
    string_pad(x, width)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    string

    + + +
    width
    +

    desired length

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/transpose_imputations.html b/latest-tag/reference/transpose_imputations.html new file mode 100644 index 00000000..cb4fb627 --- /dev/null +++ b/latest-tag/reference/transpose_imputations.html @@ -0,0 +1,112 @@ + +Transpose imputations — transpose_imputations • rbmi + Skip to contents + + +
    +
    +
    + +

    Transpose imputations

    + Source: R/longData.R +
    transpose_imputations.Rd
    +
    + +
    +

    Takes an imputation_df object and transposes it e.g.

    +

    list(
+    list(id = "a", values = c(1,2,3)),
+    list(id = "b", values = c(4,5,6)
+    )
+)

    +
    + +
    +

    Usage

    + 
    transpose_imputations(imputations)
    +
    + +
    +

    Arguments

    + + +
    imputations
    +

    An imputation_df object created by imputation_df()

    + +
    +
    +

    Details

    +

    becomes

    +

    list(
+    ids = c("a", "b"),
+    values = c(1,2,3,4,5,6)
+)

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/transpose_results.html b/latest-tag/reference/transpose_results.html new file mode 100644 index 00000000..078a807d --- /dev/null +++ b/latest-tag/reference/transpose_results.html @@ -0,0 +1,132 @@ + +Transpose results object — transpose_results • rbmi + Skip to contents + + +
    +
    +
    + +

    Transpose results object

    + Source: R/pool.R +
    transpose_results.Rd
    +
    + +
    +

    Transposes a Results object (as created by analyse()) in order to group +the same estimates together into vectors.

    +
    + +
    +

    Usage

    + 
    transpose_results(results, components)
    +
    + +
    +

    Arguments

    + + +
    results
    +

    A list of results.

    + + +
    components
    +

    a character vector of components to extract +(i.e. "est", "se").

    + +
    +
    +

    Details

    +

    Essentially this function takes an object of the format:

    +

    x <- list(
+    list(
+        "trt1" = list(
+            est = 1,
+            se  = 2
+        ),
+        "trt2" = list(
+            est = 3,
+            se  = 4
+        )
+    ),
+    list(
+        "trt1" = list(
+            est = 5,
+            se  = 6
+        ),
+        "trt2" = list(
+            est = 7,
+            se  = 8
+        )
+    )
+)

    +

    and produces:

    +

    list(
+    trt1 = list(
+        est = c(1,5),
+        se = c(2,6)
+    ),
+    trt2 = list(
+        est = c(3,7),
+        se = c(4,8)
+    )
+)

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/transpose_samples.html b/latest-tag/reference/transpose_samples.html new file mode 100644 index 00000000..27a783c3 --- /dev/null +++ b/latest-tag/reference/transpose_samples.html @@ -0,0 +1,90 @@ + +Transpose samples — transpose_samples • rbmi + Skip to contents + + +
    +
    +
    + +

    Transpose samples

    + Source: R/impute.R +
    transpose_samples.Rd
    +
    + +
    +

    Transposes samples generated by draws() so that they are grouped +by subjid instead of by sample number.

    +
    + +
    +

    Usage

    + 
    transpose_samples(samples)
    +
    + +
    +

    Arguments

    + + +
    samples
    +

    A list of samples generated by draws().

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/validate.analysis.html b/latest-tag/reference/validate.analysis.html new file mode 100644 index 00000000..2dac1005 --- /dev/null +++ b/latest-tag/reference/validate.analysis.html @@ -0,0 +1,92 @@ + +Validate analysis objects — validate.analysis • rbmi + Skip to contents + + +
    +
    +
    + +

    Validate analysis objects

    + Source: R/analyse.R +
    validate.analysis.Rd
    +
    + +
    +

    Validates the return object of the analyse() function.

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'analysis'
+validate(x, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    An analysis results object (of class "jackknife", "bootstrap", "rubin").

    + + +
    ...
    +

    Not used.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/validate.draws.html b/latest-tag/reference/validate.draws.html new file mode 100644 index 00000000..9fec8702 --- /dev/null +++ b/latest-tag/reference/validate.draws.html @@ -0,0 +1,92 @@ + +Validate draws object — validate.draws • rbmi + Skip to contents + + +
    +
    +
    + +

    Validate draws object

    + Source: R/draws.R +
    validate.draws.Rd
    +
    + +
    +

    Validate draws object

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'draws'
+validate(x, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    A draws object generated by as_draws().

    + + +
    ...
    +

    Not used.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/validate.html b/latest-tag/reference/validate.html new file mode 100644 index 00000000..5f1fb82b --- /dev/null +++ b/latest-tag/reference/validate.html @@ -0,0 +1,97 @@ + +Generic validation method — validate • rbmi + Skip to contents + + +
    +
    +
    + +

    Generic validation method

    + Source: R/validate.R +
    validate.Rd
    +
    + +
    +

    This function is used to perform assertions that an object +conforms to its expected structure and no basic assumptions +have been violated. Will throw an error if checks do not pass.

    +
    + +
    +

    Usage

    + 
    validate(x, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    object to be validated.

    + + +
    ...
    +

    additional arguments to pass to the specific validation method.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/validate.is_mar.html b/latest-tag/reference/validate.is_mar.html new file mode 100644 index 00000000..6ee7f6c5 --- /dev/null +++ b/latest-tag/reference/validate.is_mar.html @@ -0,0 +1,102 @@ + +Validate is_mar for a given subject — validate.is_mar • rbmi + Skip to contents + + +
    +
    +
    + +

    Validate is_mar for a given subject

    + Source: R/longData.R +
    validate.is_mar.Rd
    +
    + +
    +

    Checks that the longitudinal data for a patient is divided in MAR +followed by non-MAR data; a non-MAR observation followed by a MAR +observation is not allowed.

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'is_mar'
+validate(x, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    Object of class is_mar. Logical vector indicating whether observations are MAR.

    + + +
    ...
    +

    Not used.

    + +
    +
    +

    Value

    +

    Will error if there is an issue otherwise will return TRUE.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/validate.ivars.html b/latest-tag/reference/validate.ivars.html new file mode 100644 index 00000000..c393c350 --- /dev/null +++ b/latest-tag/reference/validate.ivars.html @@ -0,0 +1,95 @@ + +Validate inputs for vars — validate.ivars • rbmi + Skip to contents + + +
    +
    +
    + +

    Validate inputs for vars

    + Source: R/utilities.R +
    validate.ivars.Rd
    +
    + +
    +

    Checks that the required variable names are defined within vars and +are of appropriate datatypes

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'ivars'
+validate(x, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    named list indicating the names of key variables in the source dataset

    + + +
    ...
    +

    not used

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/validate.references.html b/latest-tag/reference/validate.references.html new file mode 100644 index 00000000..6711a5d1 --- /dev/null +++ b/latest-tag/reference/validate.references.html @@ -0,0 +1,103 @@ + +Validate user supplied references — validate.references • rbmi + Skip to contents + + +
    +
    +
    + +

    Validate user supplied references

    + Source: R/impute.R +
    validate.references.Rd
    +
    + +
    +

    Checks to ensure that the user specified references are +expect values (i.e. those found within the source data).

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'references'
+validate(x, control, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    named character vector.

    + + +
    control
    +

    factor variable (should be the group variable from the source dataset).

    + + +
    ...
    +

    Not used.

    + +
    +
    +

    Value

    +

    Will error if there is an issue otherwise will return TRUE.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/validate.sample_list.html b/latest-tag/reference/validate.sample_list.html new file mode 100644 index 00000000..67d86ec6 --- /dev/null +++ b/latest-tag/reference/validate.sample_list.html @@ -0,0 +1,92 @@ + +Validate sample_list object — validate.sample_list • rbmi + Skip to contents + + +
    +
    +
    + +

    Validate sample_list object

    + Source: R/dataclasses.R +
    validate.sample_list.Rd
    +
    + +
    +

    Validate sample_list object

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'sample_list'
+validate(x, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    A sample_list object generated by sample_list().

    + + +
    ...
    +

    Not used.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/validate.sample_single.html b/latest-tag/reference/validate.sample_single.html new file mode 100644 index 00000000..082e2360 --- /dev/null +++ b/latest-tag/reference/validate.sample_single.html @@ -0,0 +1,92 @@ + +Validate sample_single object — validate.sample_single • rbmi + Skip to contents + + +
    +
    +
    + +

    Validate sample_single object

    + Source: R/dataclasses.R +
    validate.sample_single.Rd
    +
    + +
    +

    Validate sample_single object

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'sample_single'
+validate(x, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    A sample_single object generated by sample_single().

    + + +
    ...
    +

    Not used.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/validate.simul_pars.html b/latest-tag/reference/validate.simul_pars.html new file mode 100644 index 00000000..3cb41060 --- /dev/null +++ b/latest-tag/reference/validate.simul_pars.html @@ -0,0 +1,92 @@ + +Validate a simul_pars object — validate.simul_pars • rbmi + Skip to contents + + +
    +
    +
    + +

    Validate a simul_pars object

    + Source: R/simulate_data.R +
    validate.simul_pars.Rd
    +
    + +
    +

    Validate a simul_pars object

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'simul_pars'
+validate(x, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    An simul_pars object as generated by set_simul_pars().

    + + +
    ...
    +

    Not used.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/validate.stan_data.html b/latest-tag/reference/validate.stan_data.html new file mode 100644 index 00000000..389af110 --- /dev/null +++ b/latest-tag/reference/validate.stan_data.html @@ -0,0 +1,92 @@ + +Validate a stan_data object — validate.stan_data • rbmi + Skip to contents + + +
    +
    +
    + +

    Validate a stan_data object

    + Source: R/mcmc.R +
    validate.stan_data.Rd
    +
    + +
    +

    Validate a stan_data object

    +
    + +
    +

    Usage

    + 
    # S3 method for class 'stan_data'
+validate(x, ...)
    +
    + +
    +

    Arguments

    + + +
    x
    +

    A stan_data object.

    + + +
    ...
    +

    Not used.

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/validate_analyse_pars.html b/latest-tag/reference/validate_analyse_pars.html new file mode 100644 index 00000000..32b270b6 --- /dev/null +++ b/latest-tag/reference/validate_analyse_pars.html @@ -0,0 +1,93 @@ + +Validate analysis results — validate_analyse_pars • rbmi + Skip to contents + + +
    +
    +
    + +

    Validate analysis results

    + Source: R/analyse.R +
    validate_analyse_pars.Rd
    +
    + +
    +

    Validates analysis results generated by analyse().

    +
    + +
    +

    Usage

    + 
    validate_analyse_pars(results, pars)
    +
    + +
    +

    Arguments

    + + +
    results
    +

    A list of results generated by the analysis fun +used in analyse().

    + + +
    pars
    +

    A list of expected parameters in each of the analysis. +lists i.e. c("est", "se", "df").

    + +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/validate_datalong.html b/latest-tag/reference/validate_datalong.html new file mode 100644 index 00000000..53e40662 --- /dev/null +++ b/latest-tag/reference/validate_datalong.html @@ -0,0 +1,127 @@ + +Validate a longdata object — validate_datalong • rbmi + Skip to contents + + +
    +
    +
    + +

    Validate a longdata object

    + Source: R/validate_datalong.R +
    validate_datalong.Rd
    +
    + +
    +

    Validate a longdata object

    +
    + +
    +

    Usage

    + 
    validate_datalong(data, vars)
+
+validate_datalong_varExists(data, vars)
+
+validate_datalong_types(data, vars)
+
+validate_datalong_notMissing(data, vars)
+
+validate_datalong_complete(data, vars)
+
+validate_datalong_unifromStrata(data, vars)
+
+validate_dataice(data, data_ice, vars, update = FALSE)
    +
    + +
    +

    Arguments

    + + +
    data
    +

    a data.frame containing the longitudinal outcome data + covariates +for multiple subjects

    + + +
    vars
    +

    a vars object as created by set_vars()

    + + +
    data_ice
    +

    a data.frame containing the subjects ICE data. See draws() for details.

    + + +
    update
    +

    logical, indicates if the ICE data is being set for the first time or if an update +is being applied

    + +
    +
    +

    Details

    +

    These functions are used to validate various different parts of the longdata object +to be used in draws(), impute(), analyse() and pool(). In particular:

    • validate_datalong_varExists - Checks that each variable listed in vars actually exists +in the data

      • +

    • validate_datalong_types - Checks that the types of each key variable is as expected +i.e. that visit is a factor variable

      • +

    • validate_datalong_notMissing - Checks that none of the key variables (except the outcome variable) +contain any missing values

      • +

    • validate_datalong_complete - Checks that data is complete i.e. there is 1 row for each subject * +visit combination. e.g. that nrow(data) == length(unique(subjects)) * length(unique(visits))

      • +

    • validate_datalong_unifromStrata - Checks to make sure that any variables listed as stratification +variables do not vary over time. e.g. that subjects don't switch between stratification groups.

      • +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/reference/validate_strategies.html b/latest-tag/reference/validate_strategies.html new file mode 100644 index 00000000..5e2d6939 --- /dev/null +++ b/latest-tag/reference/validate_strategies.html @@ -0,0 +1,101 @@ + +Validate user specified strategies — validate_strategies • rbmi + Skip to contents + + +
    +
    +
    + +

    Validate user specified strategies

    + Source: R/impute.R +
    validate_strategies.Rd
    +
    + +
    +

    Compares the user provided strategies to those that are +required (the reference). Will throw an error if not all values +of reference have been defined.

    +
    + +
    +

    Usage

    + 
    validate_strategies(strategies, reference)
    +
    + +
    +

    Arguments

    + + +
    strategies
    +

    named list of strategies.

    + + +
    reference
    +

    list or character vector of strategies that need to be defined.

    + +
    +
    +

    Value

    +

    Will throw an error if there is an issue otherwise will return TRUE.

    +
    + +
    + + +
    +

    Developed by Craig Gower-Page, Alessandro Noci, Roche.

    +
    + +
    +

    Site built with pkgdown 2.1.0.

    +
    + +
    + + + + + + + diff --git a/latest-tag/search.json b/latest-tag/search.json new file mode 100644 index 00000000..c495408f --- /dev/null +++ b/latest-tag/search.json @@ -0,0 +1 @@ +[{"path":"/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to rbmi","title":"Contributing to rbmi","text":"file outlines propose make changes rbmi well providing details obscure aspects package’s development process.","code":""},{"path":"/CONTRIBUTING.html","id":"setup","dir":"","previous_headings":"","what":"Setup","title":"Contributing to rbmi","text":"order develop contribute rbmi need access C/C++ compiler. Windows install rtools macOS install Xcode. Likewise, also need install package’s development dependencies. can done launching R within project root executing:","code":"devtools::install_dev_deps()"},{"path":"/CONTRIBUTING.html","id":"code-changes","dir":"","previous_headings":"","what":"Code changes","title":"Contributing to rbmi","text":"want make code contribution, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed).","code":""},{"path":"/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Code changes","what":"Pull request process","title":"Contributing to rbmi","text":"project uses simple GitHub flow model development. , code changes done feature branch based main branch merged back main branch complete. Pull Requests accepted unless CI/CD checks passed. (See CI/CD section information). Pull Requests relating package’s core R code must accompanied corresponding unit test. pull requests containing changes core R code contain unit test demonstrate working intended accepted. (See Unit Testing section information). Pull Requests add lines changed NEWS.md file.","code":""},{"path":"/CONTRIBUTING.html","id":"coding-considerations","dir":"","previous_headings":"Code changes","what":"Coding Considerations","title":"Contributing to rbmi","text":"use roxygen2, Markdown syntax, documentation. Please ensure code conforms lintr. can check running lintr::lint(\"FILE NAME\") files modified ensuring findings kept possible. hard requirements following lintr’s conventions encourage developers follow guidance closely possible. project uses 4 space indents, contributions following accepted. project makes use S3 R6 OOP. Usage S4 OOP systems avoided unless absolutely necessary ensure consistency. said recommended stick S3 unless modification place R6 specific features required. current desire package keep dependency tree small possible. end discouraged adding additional packages “Depends” / “Imports” section unless absolutely essential. importing package just use single function consider just copying source code function instead, though please check licence include proper attribution/notices. expectations “Suggests” free use package vignettes / unit tests, though please mindful unnecessarily excessive .","code":""},{"path":"/CONTRIBUTING.html","id":"unit-testing--cicd","dir":"","previous_headings":"","what":"Unit Testing & CI/CD","title":"Contributing to rbmi","text":"project uses testthat perform unit testing combination GitHub Actions CI/CD.","code":""},{"path":"/CONTRIBUTING.html","id":"scheduled-testing","dir":"","previous_headings":"Unit Testing & CI/CD","what":"Scheduled Testing","title":"Contributing to rbmi","text":"Due stochastic nature package unit tests take considerable amount time execute. avoid issues usability, unit tests take couple seconds run deferred scheduled testing. tests run occasionally periodic basis (currently twice month) every pull request / push event. defer test scheduled build simply include skip_if_not(is_full_test()) top test_that() block .e. scheduled tests can also manually activated going “https://github.com/insightsengineering/rbmi” -> “Actions” -> “Bi-Weekly” -> “Run Workflow”. advisable releasing CRAN.","code":"test_that(\"some unit test\", { skip_if_not(is_full_test()) expect_equal(1,1) })"},{"path":"/CONTRIBUTING.html","id":"cran-releases","dir":"","previous_headings":"Unit Testing & CI/CD","what":"CRAN Releases","title":"Contributing to rbmi","text":"order release package CRAN needs tested across multiple different OS’s versions R. implemented project via GitHub Action Workflow titled “Check CRAN” needs manually activated. go “https://github.com/insightsengineering/rbmi” -> “Actions” -> “Check CRAN” -> “Run Workflow”. tests pass package can safely released CRAN (updating relevant cran-comments.md file)","code":""},{"path":"/CONTRIBUTING.html","id":"docker-images","dir":"","previous_headings":"Unit Testing & CI/CD","what":"Docker Images","title":"Contributing to rbmi","text":"support CI/CD terms reducing installation time, several Docker images pre-built contain packages system dependencies project needs. current relevant images can found : ghcr.io/insightsengineering/rbmi:r404 ghcr.io/insightsengineering/rbmi:r410 ghcr.io/insightsengineering/rbmi:latest latest image automatically re-built month contain latest version R packages. versions built older versions R (indicated tag number) contain package versions version R released. important ensure package works older versions R many companies typically run due delays validation processes. code create images can found misc/docker. legacy images (.e. everything excluding “latest” image) built manual request running corresponding GitHub Actions Workflow.","code":""},{"path":"/CONTRIBUTING.html","id":"reproducibility-print-tests--snaps","dir":"","previous_headings":"Unit Testing & CI/CD","what":"Reproducibility, Print Tests & Snaps","title":"Contributing to rbmi","text":"particular issue testing package reproducibility. part handled well via set.seed() however stan/rstan guarantee reproducibility even seed run different hardware. issue surfaces testing print messages pool object displays treatment estimates thus identical run different machines. address issue pre-made pool objects generated stored R/sysdata.rda (generated data-raw/create_print_test_data.R). generated print messages compared expected values stored tests/testthat/_snaps/ (automatically created testthat::expect_snapshot())","code":""},{"path":"/CONTRIBUTING.html","id":"fitting-mmrms","dir":"","previous_headings":"","what":"Fitting MMRM’s","title":"Contributing to rbmi","text":"package currently uses mmrm package fit MMRM models. package still fairly new far proven stable, fast reliable. spot issues MMRM package please raise corresponding GitHub Repository - link mmrm package uses TMB uncommon see warnings either inconsistent versions TMB Matrix package compiled . order resolve may wish re-compile packages source using: Note need rtools installed Windows machine Xcode running macOS (somehow else access C/C++ compiler).","code":"install.packages(c(\"TMB\", \"mmrm\"), type = \"source\")"},{"path":"/CONTRIBUTING.html","id":"rstan","dir":"","previous_headings":"","what":"rstan","title":"Contributing to rbmi","text":"Bayesian models fitted package implemented via stan/rstan. code can found inst/stan/MMRM.stan. Note package automatically take care compiling code install run devtools::load_all(). Please note package won’t recompile code unless changed source code delete src directory.","code":""},{"path":"/CONTRIBUTING.html","id":"vignettes","dir":"","previous_headings":"","what":"Vignettes","title":"Contributing to rbmi","text":"CRAN imposes 10-minute run limit building, compiling testing package. keep limit vignettes pre-built; say simply changing source code automatically update vignettes, need manually re-build . need run: re-built need commit updated *.html files git repository. reference static vignette process works using “asis” vignette engine provided R.rsp. works getting R recognise vignettes files ending *.html.asis; builds simply copying corresponding files ending *.html relevent docs/ folder built package.","code":"Rscript vignettes/build.R"},{"path":"/CONTRIBUTING.html","id":"misc--local-folders","dir":"","previous_headings":"","what":"Misc & Local Folders","title":"Contributing to rbmi","text":"misc/ folder project used hold useful scripts, analyses, simulations & infrastructure code wish keep isn’t essential build deployment package. Feel free store additional stuff feel worth keeping. Likewise, local/ added .gitignore file meaning anything stored folder won’t committed repository. example, may find useful storing personal scripts testing generally exploring package development.","code":""},{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"Apache License","title":"Apache License","text":"Version 2.0, January 2004 ","code":""},{"path":[]},{"path":"/LICENSE.html","id":"id_1-definitions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"1. Definitions","title":"Apache License","text":"“License” shall mean terms conditions use, reproduction, distribution defined Sections 1 9 document. “Licensor” shall mean copyright owner entity authorized copyright owner granting License. “Legal Entity” shall mean union acting entity entities control, controlled , common control entity. purposes definition, “control” means () power, direct indirect, cause direction management entity, whether contract otherwise, (ii) ownership fifty percent (50%) outstanding shares, (iii) beneficial ownership entity. “” (“”) shall mean individual Legal Entity exercising permissions granted License. “Source” form shall mean preferred form making modifications, including limited software source code, documentation source, configuration files. “Object” form shall mean form resulting mechanical transformation translation Source form, including limited compiled object code, generated documentation, conversions media types. “Work” shall mean work authorship, whether Source Object form, made available License, indicated copyright notice included attached work (example provided Appendix ). “Derivative Works” shall mean work, whether Source Object form, based (derived ) Work editorial revisions, annotations, elaborations, modifications represent, whole, original work authorship. purposes License, Derivative Works shall include works remain separable , merely link (bind name) interfaces , Work Derivative Works thereof. “Contribution” shall mean work authorship, including original version Work modifications additions Work Derivative Works thereof, intentionally submitted Licensor inclusion Work copyright owner individual Legal Entity authorized submit behalf copyright owner. purposes definition, “submitted” means form electronic, verbal, written communication sent Licensor representatives, including limited communication electronic mailing lists, source code control systems, issue tracking systems managed , behalf , Licensor purpose discussing improving Work, excluding communication conspicuously marked otherwise designated writing copyright owner “Contribution.” “Contributor” shall mean Licensor individual Legal Entity behalf Contribution received Licensor subsequently incorporated within Work.","code":""},{"path":"/LICENSE.html","id":"id_2-grant-of-copyright-license","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"2. Grant of Copyright License","title":"Apache License","text":"Subject terms conditions License, Contributor hereby grants perpetual, worldwide, non-exclusive, -charge, royalty-free, irrevocable copyright license reproduce, prepare Derivative Works , publicly display, publicly perform, sublicense, distribute Work Derivative Works Source Object form.","code":""},{"path":"/LICENSE.html","id":"id_3-grant-of-patent-license","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"3. Grant of Patent License","title":"Apache License","text":"Subject terms conditions License, Contributor hereby grants perpetual, worldwide, non-exclusive, -charge, royalty-free, irrevocable (except stated section) patent license make, made, use, offer sell, sell, import, otherwise transfer Work, license applies patent claims licensable Contributor necessarily infringed Contribution(s) alone combination Contribution(s) Work Contribution(s) submitted. institute patent litigation entity (including cross-claim counterclaim lawsuit) alleging Work Contribution incorporated within Work constitutes direct contributory patent infringement, patent licenses granted License Work shall terminate date litigation filed.","code":""},{"path":"/LICENSE.html","id":"id_4-redistribution","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"4. Redistribution","title":"Apache License","text":"may reproduce distribute copies Work Derivative Works thereof medium, without modifications, Source Object form, provided meet following conditions: () must give recipients Work Derivative Works copy License; (b) must cause modified files carry prominent notices stating changed files; (c) must retain, Source form Derivative Works distribute, copyright, patent, trademark, attribution notices Source form Work, excluding notices pertain part Derivative Works; (d) Work includes “NOTICE” text file part distribution, Derivative Works distribute must include readable copy attribution notices contained within NOTICE file, excluding notices pertain part Derivative Works, least one following places: within NOTICE text file distributed part Derivative Works; within Source form documentation, provided along Derivative Works; , within display generated Derivative Works, wherever third-party notices normally appear. contents NOTICE file informational purposes modify License. may add attribution notices within Derivative Works distribute, alongside addendum NOTICE text Work, provided additional attribution notices construed modifying License. may add copyright statement modifications may provide additional different license terms conditions use, reproduction, distribution modifications, Derivative Works whole, provided use, reproduction, distribution Work otherwise complies conditions stated License.","code":""},{"path":"/LICENSE.html","id":"id_5-submission-of-contributions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"5. Submission of Contributions","title":"Apache License","text":"Unless explicitly state otherwise, Contribution intentionally submitted inclusion Work Licensor shall terms conditions License, without additional terms conditions. Notwithstanding , nothing herein shall supersede modify terms separate license agreement may executed Licensor regarding Contributions.","code":""},{"path":"/LICENSE.html","id":"id_6-trademarks","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"6. Trademarks","title":"Apache License","text":"License grant permission use trade names, trademarks, service marks, product names Licensor, except required reasonable customary use describing origin Work reproducing content NOTICE file.","code":""},{"path":"/LICENSE.html","id":"id_7-disclaimer-of-warranty","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"7. Disclaimer of Warranty","title":"Apache License","text":"Unless required applicable law agreed writing, Licensor provides Work (Contributor provides Contributions) “” BASIS, WITHOUT WARRANTIES CONDITIONS KIND, either express implied, including, without limitation, warranties conditions TITLE, NON-INFRINGEMENT, MERCHANTABILITY, FITNESS PARTICULAR PURPOSE. solely responsible determining appropriateness using redistributing Work assume risks associated exercise permissions License.","code":""},{"path":"/LICENSE.html","id":"id_8-limitation-of-liability","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"8. Limitation of Liability","title":"Apache License","text":"event legal theory, whether tort (including negligence), contract, otherwise, unless required applicable law (deliberate grossly negligent acts) agreed writing, shall Contributor liable damages, including direct, indirect, special, incidental, consequential damages character arising result License use inability use Work (including limited damages loss goodwill, work stoppage, computer failure malfunction, commercial damages losses), even Contributor advised possibility damages.","code":""},{"path":"/LICENSE.html","id":"id_9-accepting-warranty-or-additional-liability","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"9. Accepting Warranty or Additional Liability","title":"Apache License","text":"redistributing Work Derivative Works thereof, may choose offer, charge fee , acceptance support, warranty, indemnity, liability obligations /rights consistent License. However, accepting obligations, may act behalf sole responsibility, behalf Contributor, agree indemnify, defend, hold Contributor harmless liability incurred , claims asserted , Contributor reason accepting warranty additional liability. END TERMS CONDITIONS","code":""},{"path":"/LICENSE.html","id":"appendix-how-to-apply-the-apache-license-to-your-work","dir":"","previous_headings":"","what":"APPENDIX: How to apply the Apache License to your work","title":"Apache License","text":"apply Apache License work, attach following boilerplate notice, fields enclosed brackets [] replaced identifying information. (Don’t include brackets!) text enclosed appropriate comment syntax file format. also recommend file class name description purpose included “printed page” copyright notice easier identification within third-party archives.","code":"Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."},{"path":"/articles/advanced.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"rbmi: Advanced Functionality","text":"purpose vignette provide overview advanced features rbmi package. sections vignette relatively self-contained, .e. readers able jump directly section covers functionality interested .","code":""},{"path":"/articles/advanced.html","id":"sec:dataSimul","dir":"Articles","previous_headings":"","what":"Data simulation using function simulate_data()","title":"rbmi: Advanced Functionality","text":"order demonstrate advanced functions first create simulated dataset rbmi function simulate_data(). simulate_data() function generates data randomized clinical trial longitudinal continuous outcomes two different types intercurrent events (ICEs). One intercurrent event (ICE1) may thought discontinuation study treatment due study drug condition related (SDCR) reasons. event (ICE2) may thought discontinuation study treatment due study drug condition related (NSDCR) reasons. purpose vignette, simulate data similarly simulation study reported Wolbers et al. (2022) (though change simulation parameters) include one ICE type (ICE1). Specifically, simulate 1:1 randomized trial active drug (intervention) versus placebo (control) 100 subjects per group 6 post-baseline assessments (bi-monthly visits 12 months) following assumptions: mean outcome trajectory placebo group increases linearly 50 baseline (visit 0) 60 visit 6, .e. slope 10 points/year. mean outcome trajectory intervention group identical placebo group visit 2. visit 2 onward, slope decreases 50% 5 points/year. covariance structure baseline follow-values groups implied random intercept slope model standard deviation 5 intercept slope, correlation 0.25. addition, independent residual error standard deviation 2.5 added assessment. probability study drug discontinuation visit calculated according logistic model depends observed outcome visit. Specifically, visit-wise discontinuation probability 2% 3% control intervention group, respectively, specified case observed outcome equal 50 (mean value baseline). odds discontinuation simulated increase +10% +1 point increase observed outcome. Study drug discontinuation simulated effect mean trajectory placebo group. intervention group, subjects discontinue follow slope mean trajectory placebo group time point onward. compatible copy increments reference (CIR) assumption. Study drop-study drug discontinuation visit occurs probability 50% leading missing outcome data time point onward. function simulate_data() requires 3 arguments (see function documentation help(simulate_data) details): pars_c: simulation parameters control group pars_t: simulation parameters intervention group post_ice1_traj: Specifies observed outcomes ICE1 simulated , report data according specifications can simulated function simulate_data():","code":"library(rbmi) library(dplyr) library(ggplot2) library(purrr) set.seed(122) n <- 100 time <- c(0, 2, 4, 6, 8, 10, 12) # Mean trajectory control muC <- c(50.0, 51.66667, 53.33333, 55.0, 56.66667, 58.33333, 60.0) # Mean trajectory intervention muT <- c(50.0, 51.66667, 53.33333, 54.16667, 55.0, 55.83333, 56.66667) # Create Sigma sd_error <- 2.5 covRE <- rbind( c(25.0, 6.25), c(6.25, 25.0) ) Sigma <- cbind(1, time / 12) %*% covRE %*% rbind(1, time / 12) + diag(sd_error^2, nrow = length(time)) # Set probability of discontinuation probDisc_C <- 0.02 probDisc_T <- 0.03 or_outcome <- 1.10 # +1 point increase => +10% odds of discontinuation # Set drop-out rate following discontinuation prob_dropout <- 0.5 # Set simulation parameters of the control group parsC <- set_simul_pars( mu = muC, sigma = Sigma, n = n, prob_ice1 = probDisc_C, or_outcome_ice1 = or_outcome, prob_post_ice1_dropout = prob_dropout ) # Set simulation parameters of the intervention group parsT <- parsC parsT$mu <- muT parsT$prob_ice1 <- probDisc_T # Set assumption about post-ice trajectory post_ice_traj <- \"CIR\" # Simulate data data <- simulate_data( pars_c = parsC, pars_t = parsT, post_ice1_traj = post_ice_traj ) head(data) #> id visit group outcome_bl outcome_noICE ind_ice1 ind_ice2 dropout_ice1 #> 1 id_1 0 Control 57.32704 57.32704 0 0 0 #> 2 id_1 1 Control 57.32704 54.69751 1 0 1 #> 3 id_1 2 Control 57.32704 58.60702 1 0 1 #> 4 id_1 3 Control 57.32704 61.50119 1 0 1 #> 5 id_1 4 Control 57.32704 56.68363 1 0 1 #> 6 id_1 5 Control 57.32704 66.14799 1 0 1 #> outcome #> 1 57.32704 #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA # As a simple descriptive of the simulated data, summarize the number of subjects with ICEs and missing data data %>% group_by(id) %>% summarise( group = group[1], any_ICE = (any(ind_ice1 == 1)), any_NA = any(is.na(outcome))) %>% group_by(group) %>% summarise( subjects_with_ICE = sum(any_ICE), subjects_with_missings = sum(any_NA) ) #> # A tibble: 2 × 3 #> group subjects_with_ICE subjects_with_missings #> #> 1 Control 18 8 #> 2 Intervention 25 14"},{"path":"/articles/advanced.html","id":"sec:postICEobs","dir":"Articles","previous_headings":"","what":"Handling of observed post-ICE data in rbmi under reference-based imputation","title":"rbmi: Advanced Functionality","text":"rbmi always uses non-missing outcome data input data set, .e. data never overwritten imputation step removed analysis step. implies data considered irrelevant treatment effect estimation (e.g. data ICE estimand specified hypothetical strategy), data need removed input data set user prior calling rbmi functions. imputation missing random (MAR) strategy, observed outcome data also included fitting base imputation model. However, ICEs handled using reference-based imputation methods (CIR, CR, JR), rbmi excludes observed post-ICE data base imputation model. data excluded, base imputation model mistakenly estimate mean trajectories based mixture observed pre- post-ICE data relevant reference-based imputations. However, observed post-ICE data added back data set fitting base imputation model included subsequent imputation analysis steps. Post-ICE data control reference group also excluded base imputation model user specifies reference-based imputation strategy ICEs. ensures ICE impact data included base imputation model regardless whether ICE occurred control intervention group. hand, imputation reference group based MAR assumption even reference-based imputation methods may preferable settings include post-ICE data control group base imputation model. can implemented specifying MAR strategy ICE control group reference-based strategy ICE intervention group. use latter approach example . simulated trial data section 2 assumed outcomes intervention group observed ICE “treatment discontinuation” follow increments observed control group. Thus imputation missing data intervention group treatment discontinuation might performed reference-based copy increments reference (CIR) assumption. Specifically, implement estimator following assumptions: endpoint interest change outcome baseline visit. imputation model includes treatment group, (categorical) visit, treatment--visit interactions, baseline outcome, baseline outcome--visit interactions covariates. imputation model assumes common unstructured covariance matrix treatment groups control group, missing data imputed MAR whereas intervention group, missing post-ICE data imputed CIR assumption analysis model endpoint imputed datasets separate ANCOVA model visit treatment group primary covariate adjustment baseline outcome value. illustration purposes, chose MI based approximate Bayesian posterior draws 20 random imputations demanding computational perspective. practical applications, number random imputations may need increased. Moreover, imputations also supported rbmi. guidance regarding choice imputation approach, refer user comparison implemented approaches Section 3.9 “Statistical Specifications” vignette (vignette(\"stat_specs\", package = \"rbmi\")). first report code set variables imputation analysis models. yet familiar syntax, recommend first check “quickstart” vignette (vignette(\"quickstart\", package = \"rbmi\")). chosen imputation method can set function method_approxbayes() follows: can now sequentially call 4 key functions rbmi perform multiple imputation. Please note management observed post-ICE data performed without additional complexity user. draws() automatically excludes post-ICE data handled reference-based method (keeps post-ICE data handled using MAR) using information provided argument data_ice. impute() impute truly missing data data[[vars$outcome]]. last output gives estimated difference -4.537 (95% CI -6.420 -2.655) two groups last visit associated p-value lower 0.001.","code":"# Create data_ice including the subject's first visit affected by the ICE and the imputation strategy # Imputation strategy for post-ICE data is CIR in the intervention group and MAR for the control group # (note that ICEs which are handled using MAR are optional and do not impact the analysis # because imputation of missing data under MAR is the default) data_ice_CIR <- data %>% group_by(id) %>% filter(ind_ice1 == 1) %>% # select visits with ICEs mutate(strategy = ifelse(group == \"Intervention\", \"CIR\", \"MAR\")) %>% summarise( visit = visit[1], # Select first visit affected by the ICE strategy = strategy[1] ) # Compute endpoint of interest: change from baseline and # remove rows corresponding to baseline visits data <- data %>% filter(visit != 0) %>% mutate( change = outcome - outcome_bl, visit = factor(visit, levels = unique(visit)) ) # Define key variables for the imputation and analysis models vars <- set_vars( subjid = \"id\", visit = \"visit\", outcome = \"change\", group = \"group\", covariates = c(\"visit*outcome_bl\", \"visit*group\"), strategy = \"strategy\" ) vars_an <- vars vars_an$covariates <- \"outcome_bl\" method <- method_approxbayes(n_sample = 20) draw_obj <- draws( data = data, data_ice = data_ice_CIR, vars = vars, method = method, quiet = TRUE, ncores = 2 ) impute_obj_CIR <- impute( draw_obj, references = c(\"Control\" = \"Control\", \"Intervention\" = \"Control\") ) ana_obj_CIR <- analyse( impute_obj_CIR, vars = vars_an ) pool_obj_CIR <- pool(ana_obj_CIR) pool_obj_CIR #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_1 -0.486 0.512 -1.496 0.524 0.343 #> lsm_ref_1 2.62 0.362 1.907 3.333 <0.001 #> lsm_alt_1 2.133 0.362 1.42 2.847 <0.001 #> trt_2 -0.066 0.542 -1.135 1.004 0.904 #> lsm_ref_2 3.707 0.384 2.95 4.464 <0.001 #> lsm_alt_2 3.641 0.383 2.885 4.397 <0.001 #> trt_3 -1.782 0.607 -2.979 -0.585 0.004 #> lsm_ref_3 5.841 0.428 4.997 6.685 <0.001 #> lsm_alt_3 4.059 0.428 3.214 4.904 <0.001 #> trt_4 -2.518 0.692 -3.884 -1.152 <0.001 #> lsm_ref_4 7.656 0.492 6.685 8.627 <0.001 #> lsm_alt_4 5.138 0.488 4.176 6.1 <0.001 #> trt_5 -3.658 0.856 -5.346 -1.97 <0.001 #> lsm_ref_5 9.558 0.598 8.379 10.737 <0.001 #> lsm_alt_5 5.9 0.608 4.699 7.101 <0.001 #> trt_6 -4.537 0.954 -6.42 -2.655 <0.001 #> lsm_ref_6 11.048 0.666 9.735 12.362 <0.001 #> lsm_alt_6 6.511 0.674 5.181 7.841 <0.001 #> --------------------------------------------------"},{"path":"/articles/advanced.html","id":"efficiently-changing-reference-based-imputation-strategies","dir":"Articles","previous_headings":"","what":"Efficiently changing reference-based imputation strategies","title":"rbmi: Advanced Functionality","text":"draws() function far computationally intensive function rbmi. settings, may important explore impact change reference-based imputation strategy results. change affect imputation model affect subsequent imputation step. order allow changes imputation strategy without re-run draws() function, function impute() additional argument update_strategies. However, please note functionality comes important limitations: described beginning Section 3, post-ICE outcomes included input dataset base imputation model imputation method MAR excluded reference-based imputation methods (CIR, CR, JR). Therefore, updata_strategies applied imputation strategy changed MAR non-MAR strategy presence observed post-ICE outcomes. Similarly, change non-MAR strategy MAR triggers warning presence observed post-ICE outcomes base imputation model fitted relevant data MAR. Finally, update_strategies applied timing ICEs changed (argument data_ice) addition imputation strategy. example, described analysis copy increments reference (CIR) assumption previous section. Let’s assume want change strategy jump reference imputation strategy sensitivity analysis. can efficiently implemented using update_strategies follows: imputations jump reference assumption, get estimated difference -4.360 (95% CI -6.238 -2.482) two groups last visit associated p-value <0.001.","code":"# Change ICE strategy from CIR to JR data_ice_JR <- data_ice_CIR %>% mutate(strategy = ifelse(strategy == \"CIR\", \"JR\", strategy)) impute_obj_JR <- impute( draw_obj, references = c(\"Control\" = \"Control\", \"Intervention\" = \"Control\"), update_strategy = data_ice_JR ) ana_obj_JR <- analyse( impute_obj_JR, vars = vars_an ) pool_obj_JR <- pool(ana_obj_JR) pool_obj_JR #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_1 -0.485 0.513 -1.496 0.526 0.346 #> lsm_ref_1 2.609 0.363 1.892 3.325 <0.001 #> lsm_alt_1 2.124 0.361 1.412 2.836 <0.001 #> trt_2 -0.06 0.535 -1.115 0.995 0.911 #> lsm_ref_2 3.694 0.378 2.948 4.441 <0.001 #> lsm_alt_2 3.634 0.381 2.882 4.387 <0.001 #> trt_3 -1.767 0.598 -2.948 -0.587 0.004 #> lsm_ref_3 5.845 0.422 5.012 6.677 <0.001 #> lsm_alt_3 4.077 0.432 3.225 4.93 <0.001 #> trt_4 -2.529 0.686 -3.883 -1.175 <0.001 #> lsm_ref_4 7.637 0.495 6.659 8.614 <0.001 #> lsm_alt_4 5.108 0.492 4.138 6.078 <0.001 #> trt_5 -3.523 0.856 -5.212 -1.833 <0.001 #> lsm_ref_5 9.554 0.61 8.351 10.758 <0.001 #> lsm_alt_5 6.032 0.611 4.827 7.237 <0.001 #> trt_6 -4.36 0.952 -6.238 -2.482 <0.001 #> lsm_ref_6 11.003 0.676 9.669 12.337 <0.001 #> lsm_alt_6 6.643 0.687 5.287 8 <0.001 #> --------------------------------------------------"},{"path":"/articles/advanced.html","id":"imputation-under-mar-with-time-varying-covariates","dir":"Articles","previous_headings":"","what":"Imputation under MAR with time-varying covariates","title":"rbmi: Advanced Functionality","text":"Guizzaro et al. (2021) suggested implement treatment policy strategy via imputation MAR assumption conditioning subject’s ICE status, .e. impute missing post-ICE data based observed post-ICE data. One possible implementation proposal add time-varying covariates imputation model. case study implements proposal compares reference-based imputation methods estimators early Parkinson’s disease can found Noci et al. (2021). settings, may carried including binary time-varying indicator subject’s ICE status visit (defined 0 pre-ICE visits 1 post-ICE visits) imputation model. However, simulated data introduced section 2, may plausible assume treatment discontinuation leads change “slope” mean outcome trajectory. can implemented including time-varying covariate equal 0 visits prior treatment discontinuation equal time treatment discontinuation subsequent visits. regression coefficient corresponding change post-ICE “slope” allowed depend assigned treatment group, .e. imputation model include interaction time-varying covariate treatment group. Let’s first define time-varying covariate: can include time-varying covariate imputation model, crossed group variable: now sequentially call 4 key rbmi functions:","code":"data <- data %>% group_by(id) %>% mutate(time_from_ice1 = cumsum(ind_ice1)*2/12 ) # multiplication by 2/12 because visits are bi-monthly vars_tv <- set_vars( subjid = \"id\", visit = \"visit\", outcome = \"change\", group = \"group\", covariates = c(\"visit*outcome_bl\", \"visit*group\", \"time_from_ice1*group\"), strategy = \"strategy\" ) draw_obj <- draws( data = data, data_ice = NULL, # if NULL, MAR is assumed for all missing data vars = vars_tv, method = method, quiet = TRUE ) impute_obj_tv <- impute( draw_obj, references = c(\"Control\" = \"Control\", \"Intervention\" = \"Intervention\") ) ana_obj_tv <- analyse( impute_obj_tv, vars = vars_an ) pool(ana_obj_tv) #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_1 -0.492 0.515 -1.507 0.524 0.341 #> lsm_ref_1 2.623 0.362 1.908 3.338 <0.001 #> lsm_alt_1 2.131 0.366 1.409 2.854 <0.001 #> trt_2 0.018 0.55 -1.067 1.103 0.974 #> lsm_ref_2 3.697 0.382 2.943 4.45 <0.001 #> lsm_alt_2 3.715 0.394 2.936 4.493 <0.001 #> trt_3 -1.802 0.614 -3.015 -0.59 0.004 #> lsm_ref_3 5.815 0.429 4.97 6.661 <0.001 #> lsm_alt_3 4.013 0.441 3.142 4.884 <0.001 #> trt_4 -2.543 0.704 -3.932 -1.154 <0.001 #> lsm_ref_4 7.609 0.486 6.65 8.568 <0.001 #> lsm_alt_4 5.066 0.516 4.046 6.086 <0.001 #> trt_5 -3.739 0.879 -5.475 -2.004 <0.001 #> lsm_ref_5 9.499 0.606 8.302 10.695 <0.001 #> lsm_alt_5 5.759 0.636 4.502 7.017 <0.001 #> trt_6 -4.685 0.98 -6.622 -2.748 <0.001 #> lsm_ref_6 10.988 0.667 9.67 12.305 <0.001 #> lsm_alt_6 6.302 0.712 4.894 7.711 <0.001 #> --------------------------------------------------"},{"path":"/articles/advanced.html","id":"custom-imputation-strategies","dir":"Articles","previous_headings":"","what":"Custom imputation strategies","title":"rbmi: Advanced Functionality","text":"following imputation strategies implemented rbmi: Missing Random (MAR) Jump Reference (JR) Copy Reference (CR) Copy Increments Reference (CIR) Last Mean Carried Forward (LMCF) addition, rbmi allows user implement imputation strategy. , user needs three things: Define function implementing new imputation strategy. Specify patients use strategy data_ice dataset provided draws(). Provide imputation strategy function impute(). imputation strategy function must take 3 arguments (pars_group, pars_ref, index_mar) calculates mean covariance matrix subject’s marginal imputation distribution applied subjects strategy applies. , pars_group contains predicted mean trajectory (pars_group$mu, numeric vector) covariance matrix (pars_group$sigma) subject conditional assigned treatment group covariates. pars_ref contains corresponding mean trajectory covariance matrix conditional reference group subject’s covariates. index_mar logical vector specifies visit whether visit unaffected ICE handled using non-MAR method . example, user can check CIR strategy implemented looking function strategy_CIR(). illustrate simple example, assume new strategy implemented follows: - marginal mean imputation distribution equal marginal mean trajectory subject according assigned group covariates ICE. - ICE marginal mean imputation distribution equal average visit-wise marginal means based subjects covariates assigned group reference group, respectively. - covariance matrix marginal imputation distribution, covariance matrix assigned group taken. , first need define imputation function example coded follows: example showing use: incorporate rbmi, data_ice needs updated strategy AVG specified visits affected ICE. Additionally, function needs provided impute() via getStrategies() function shown : , analysis proceed calling analyse() pool() .","code":"strategy_CIR #> function (pars_group, pars_ref, index_mar) #> { #> if (all(index_mar)) { #> return(pars_group) #> } #> else if (all(!index_mar)) { #> return(pars_ref) #> } #> mu <- pars_group$mu #> last_mar <- which(!index_mar)[1] - 1 #> increments_from_last_mar_ref <- pars_ref$mu[!index_mar] - #> pars_ref$mu[last_mar] #> mu[!index_mar] <- mu[last_mar] + increments_from_last_mar_ref #> sigma <- compute_sigma(sigma_group = pars_group$sigma, sigma_ref = pars_ref$sigma, #> index_mar = index_mar) #> pars <- list(mu = mu, sigma = sigma) #> return(pars) #> } #> #> strategy_AVG <- function(pars_group, pars_ref, index_mar) { mu_mean <- (pars_group$mu + pars_ref$mu) / 2 x <- pars_group x$mu[!index_mar] <- mu_mean[!index_mar] return(x) } pars_group <- list( mu = c(1, 2, 3), sigma = as_vcov(c(1, 3, 2), c(0.4, 0.5, 0.45)) ) pars_ref <- list( mu = c(5, 6, 7), sigma = as_vcov(c(2, 1, 1), c(0.7, 0.8, 0.5)) ) index_mar <- c(TRUE, TRUE, FALSE) strategy_AVG(pars_group, pars_ref, index_mar) #> $mu #> [1] 1 2 5 #> #> $sigma #> [,1] [,2] [,3] #> [1,] 1.0 1.2 1.0 #> [2,] 1.2 9.0 2.7 #> [3,] 1.0 2.7 4.0 data_ice_AVG <- data_ice_CIR %>% mutate(strategy = ifelse(strategy == \"CIR\", \"AVG\", strategy)) draw_obj <- draws( data = data, data_ice = data_ice_AVG, vars = vars, method = method, quiet = TRUE ) impute_obj <- impute( draw_obj, references = c(\"Control\" = \"Control\", \"Intervention\" = \"Control\"), strategies = getStrategies(AVG = strategy_AVG) )"},{"path":"/articles/advanced.html","id":"custom-analysis-functions","dir":"Articles","previous_headings":"","what":"Custom analysis functions","title":"rbmi: Advanced Functionality","text":"default rbmi analyse data using ancova() function. analysis function fits ANCOVA model outcomes visit separately, returns “treatment effect” estimate well corresponding least square means group. user wants perform different analysis, return different statistics analysis, can done using custom analysis function. Beware validity conditional mean imputation method formally established analysis functions corresponding linear models (ANCOVA) caution required applying alternative analysis functions method. custom analysis function must take data.frame first argument return named list element list containing minimum point estimate, called est. method method_bayes() method_approxbayes(), list must additionally contain standard error (element se) , available, degrees freedom complete-data analysis model (element df). simple example, replicate ANCOVA analysis last visit CIR-based imputations user-defined analysis function : second example, assume supplementary analysis user wants compare proportion subjects change baseline >10 points last visit treatment groups baseline outcome additional covariate. lead following basic analysis function: Note user wants rbmi use normal approximation pooled test statistics, degrees freedom need set df = NA (per example). degrees freedom complete data test statistics known degrees freedom set df = Inf, rbmi pools degrees freedom across imputed datasets according rule Barnard Rubin (see “Statistical Specifications” vignette (vignette(\"stat_specs\", package = \"rbmi\") details). According rule, infinite degrees freedom complete data analysis imply pooled degrees freedom also infinite. Rather, case pooled degrees freedom (M-1)/lambda^2, M number imputations lambda fraction missing information (see Barnard Rubin (1999) details).","code":"compare_change_lastvisit <- function(data, ...) { fit <- lm(change ~ group + outcome_bl, data = data, subset = (visit == 6) ) res <- list( trt = list( est = coef(fit)[\"groupIntervention\"], se = sqrt(vcov(fit)[\"groupIntervention\", \"groupIntervention\"]), df = df.residual(fit) ) ) return(res) } ana_obj_CIR6 <- analyse( impute_obj_CIR, fun = compare_change_lastvisit, vars = vars_an ) pool(ana_obj_CIR6) #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================= #> parameter est se lci uci pval #> ------------------------------------------------- #> trt -4.537 0.954 -6.42 -2.655 <0.001 #> ------------------------------------------------- compare_prop_lastvisit <- function(data, ...) { fit <- glm( I(change > 10) ~ group + outcome_bl, family = binomial(), data = data, subset = (visit == 6) ) res <- list( trt = list( est = coef(fit)[\"groupIntervention\"], se = sqrt(vcov(fit)[\"groupIntervention\", \"groupIntervention\"]), df = NA ) ) return(res) } ana_obj_prop <- analyse( impute_obj_CIR, fun = compare_prop_lastvisit, vars = vars_an ) pool_obj_prop <- pool(ana_obj_prop) pool_obj_prop #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================= #> parameter est se lci uci pval #> ------------------------------------------------- #> trt -1.052 0.314 -1.667 -0.438 0.001 #> ------------------------------------------------- tmp <- as.data.frame(pool_obj_prop) %>% mutate( OR = exp(est), OR.lci = exp(lci), OR.uci = exp(uci) ) %>% select(parameter, OR, OR.lci, OR.uci) tmp #> parameter OR OR.lci OR.uci #> 1 trt 0.3491078 0.188807 0.6455073"},{"path":"/articles/advanced.html","id":"sensitivity-analyses-delta-adjustments-and-tipping-point-analyses","dir":"Articles","previous_headings":"","what":"Sensitivity analyses: Delta adjustments and tipping point analyses","title":"rbmi: Advanced Functionality","text":"Delta-adjustments used impute missing data missing random (NMAR) assumption. reflects belief unobserved outcomes systematically “worse” (“better”) “comparable” observed outcomes. extensive discussion delta-adjustment methods, refer Cro et al. (2020). rbmi, marginal delta-adjustment approach implemented. means delta-adjustment applied dataset data imputation MAR reference-based missing data assumptions prior analysis imputed data. Sensitivity analysis using delta-adjustments can therefore performed without re-fit imputation model. rbmi, implemented via delta argument analyse() function.","code":""},{"path":"/articles/advanced.html","id":"simple-delta-adjustments-and-tipping-point-analyses","dir":"Articles","previous_headings":"8 Sensitivity analyses: Delta adjustments and tipping point analyses","what":"Simple delta adjustments and tipping point analyses","title":"rbmi: Advanced Functionality","text":"delta argument analyse() allows users modify outcome variable prior analysis. , user needs provide data.frame contains columns subject visit (identify observation adjusted) plus additional column called delta specifies value added outcomes prior analysis. delta_template() function supports user creating data.frame: creates skeleton data.frame containing one row per subject visit value delta set 0 observations: Note output delta_template() contains additional information can used properly re-set variable delta. example, assume user wants implement delta-adjustment imputed values CIR described section 3. Specifically, assume fixed “worsening adjustment” +5 points applied imputed values regardless treatment group. programmed follows: approach can used implement tipping point analysis. , apply different delta-adjustments imputed data control intervention group, respectively. Assume delta-adjustments less -5 points +15 points considered implausible clinical perspective. Therefore, vary delta-values group -5 +15 points investigate delta combinations lead “tipping” primary analysis result, defined analysis p-value \\(\\geq 0.05\\). According analysis, significant test result primary analysis CIR tipped non-significant result rather extreme delta-adjustments. Please note real analysis recommended use smaller step size grid used .","code":"dat_delta <- delta_template(imputations = impute_obj_CIR) head(dat_delta) #> id visit group is_mar is_missing is_post_ice strategy delta #> 1 id_1 1 Control TRUE TRUE TRUE MAR 0 #> 2 id_1 2 Control TRUE TRUE TRUE MAR 0 #> 3 id_1 3 Control TRUE TRUE TRUE MAR 0 #> 4 id_1 4 Control TRUE TRUE TRUE MAR 0 #> 5 id_1 5 Control TRUE TRUE TRUE MAR 0 #> 6 id_1 6 Control TRUE TRUE TRUE MAR 0 # Set delta-value to 5 for all imputed (previously missing) outcomes and 0 for all other outcomes dat_delta <- delta_template(imputations = impute_obj_CIR) %>% mutate(delta = is_missing * 5) # Repeat the analyses with the delta-adjusted values and pool results ana_delta <- analyse( impute_obj_CIR, delta = dat_delta, vars = vars_an ) pool(ana_delta) #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_1 -0.482 0.524 -1.516 0.552 0.359 #> lsm_ref_1 2.718 0.37 1.987 3.448 <0.001 #> lsm_alt_1 2.235 0.37 1.505 2.966 <0.001 #> trt_2 -0.016 0.56 -1.12 1.089 0.978 #> lsm_ref_2 3.907 0.396 3.125 4.688 <0.001 #> lsm_alt_2 3.891 0.395 3.111 4.671 <0.001 #> trt_3 -1.684 0.641 -2.948 -0.42 0.009 #> lsm_ref_3 6.092 0.452 5.201 6.983 <0.001 #> lsm_alt_3 4.408 0.452 3.515 5.3 <0.001 #> trt_4 -2.359 0.741 -3.821 -0.897 0.002 #> lsm_ref_4 7.951 0.526 6.913 8.99 <0.001 #> lsm_alt_4 5.593 0.522 4.563 6.623 <0.001 #> trt_5 -3.34 0.919 -5.153 -1.526 <0.001 #> lsm_ref_5 9.899 0.643 8.631 11.168 <0.001 #> lsm_alt_5 6.559 0.653 5.271 7.848 <0.001 #> trt_6 -4.21 1.026 -6.236 -2.184 <0.001 #> lsm_ref_6 11.435 0.718 10.019 12.851 <0.001 #> lsm_alt_6 7.225 0.725 5.793 8.656 <0.001 #> -------------------------------------------------- perform_tipp_analysis <- function(delta_control, delta_intervention) { # Derive delta offset based on control and intervention specific deltas delta_df <- delta_df_init %>% mutate( delta_ctl = (group == \"Control\") * is_missing * delta_control, delta_int = (group == \"Intervention\") * is_missing * delta_intervention, delta = delta_ctl + delta_int ) ana_delta <- analyse( impute_obj_CIR, fun = compare_change_lastvisit, vars = vars_an, delta = delta_df, ) pool_delta <- as.data.frame(pool(ana_delta)) list( trt_effect_6 = pool_delta[[\"est\"]], pval_6 = pool_delta[[\"pval\"]] ) } # Get initial delta template delta_df_init <- delta_template(impute_obj_CIR) tipp_frame_grid <- expand.grid( delta_control = seq(-5, 15, by = 2), delta_intervention = seq(-5, 15, by = 2) ) %>% as_tibble() tipp_frame <- tipp_frame_grid %>% mutate( results_list = map2(delta_control, delta_intervention, perform_tipp_analysis), trt_effect_6 = map_dbl(results_list, \"trt_effect_6\"), pval_6 = map_dbl(results_list, \"pval_6\") ) %>% select(-results_list) %>% mutate( pval = cut( pval_6, c(0, 0.001, 0.01, 0.05, 0.2, 1), right = FALSE, labels = c(\"<0.001\", \"0.001 - <0.01\", \"0.01- <0.05\", \"0.05 - <0.20\", \">= 0.20\") ) ) # Show delta values which lead to non-significant analysis results tipp_frame %>% filter(pval_6 >= 0.05) #> # A tibble: 3 × 5 #> delta_control delta_intervention trt_effect_6 pval_6 pval #> #> 1 -5 15 -1.99 0.0935 0.05 - <0.20 #> 2 -3 15 -2.15 0.0704 0.05 - <0.20 #> 3 -1 15 -2.31 0.0527 0.05 - <0.20 ggplot(tipp_frame, aes(delta_control, delta_intervention, fill = pval)) + geom_raster() + scale_fill_manual(values = c(\"darkgreen\", \"lightgreen\", \"lightyellow\", \"orange\", \"red\"))"},{"path":"/articles/advanced.html","id":"more-flexible-delta-adjustments-using-the-dlag-and-delta-arguments-of-delta_template","dir":"Articles","previous_headings":"8 Sensitivity analyses: Delta adjustments and tipping point analyses","what":"More flexible delta-adjustments using the dlag and delta arguments of delta_template()","title":"rbmi: Advanced Functionality","text":"far, discussed simple delta arguments add value imputed values. However, user may want apply flexible delta-adjustments missing values intercurrent event (ICE) vary magnitude delta adjustment depending far away visit question ICE visit. facilitate creation flexible delta-adjustments, delta_template() function two optional additional arguments delta dlag. delta argument specifies default amount delta applied post-ICE visit, whilst dlag specifies scaling coefficient applied based upon visits proximity first visit affected ICE. default, delta added unobserved (.e. imputed) post-ICE outcomes can changed setting optional argument missing_only = FALSE. usage delta dlag arguments best illustrated examples: Assume setting 4 visits user specified delta = c(5,6,7,8) dlag=c(1,2,3,4). subject first visit affected ICE visit 2, values delta dlag imply following delta offset: , subject delta offset 0 applied visit v1, 6 visit v2, 20 visit v3 44 visit v4. Assume instead, subject’s first visit affected ICE visit 3. , values delta dlag imply following delta offset: apply constant delta value +5 visits affected ICE regardless proximity first ICE visit, one set delta = c(5,5,5,5) dlag = c(1,0,0,0). Alternatively, may straightforward setting call delta_template() function without delta dlag arguments overwrite delta column resulting data.frame described previous section (additionally relying is_post_ice variable). Another way using arguments set delta difference time visits dlag amount delta per unit time. example, let’s say visits occur weeks 1, 5, 6 9 want delta 3 applied week ICE. simplicity, assume ICE occurs immediately subject’s last visit affected ICE. achieved setting delta = c(1,4,1,3) (difference weeks visit) dlag = c(3, 3, 3, 3). Assume subject’s first visit affected ICE visit v2, values delta dlag imply following delta offsets: wrap , show action simulated dataset section 2 imputed datasets based CIR assumption section 3. simulation setting specified follow-visits months 2, 4, 6, 8, 10, 12. Assume want apply delta-adjustment 1 every month ICE unobserved post-ICE visits intervention group . (E.g. ICE occurred immediately month 4 visit, total delta applied missing value month 10 visit 6.) program , first use delta dlag arguments delta_template() set corresponding template data.frame: Next, can use additional metadata variables provided delta_template() manually reset delta values control group back 0: Finally, can use delta data.frame apply desired delta offset analysis:","code":"v1 v2 v3 v4 -------------- 5 6 7 8 # delta assigned to each visit 0 1 2 3 # scaling starting from the first visit after the subjects ICE -------------- 0 6 14 24 # delta * scaling -------------- 0 6 20 44 # cumulative sum (i.e. delta) to be applied to each visit v1 v2 v3 v4 -------------- 5 6 7 8 # delta assigned to each visit 0 0 1 2 # scaling starting from the first visit after the subjects ICE -------------- 0 0 7 16 # delta * scaling -------------- 0 0 7 23 # cumulative sum (i.e. delta) to be applied to each visit v1 v2 v3 v4 -------------- 1 4 1 3 # delta assigned to each visit 0 3 3 3 # scaling starting from the first visit after the subjects ICE -------------- 0 12 3 9 # delta * scaling -------------- 0 12 15 24 # cumulative sum (i.e. delta) to be applied to each visit delta_df <- delta_template( impute_obj_CIR, delta = c(2, 2, 2, 2, 2, 2), dlag = c(1, 1, 1, 1, 1, 1) ) head(delta_df) #> id visit group is_mar is_missing is_post_ice strategy delta #> 1 id_1 1 Control TRUE TRUE TRUE MAR 2 #> 2 id_1 2 Control TRUE TRUE TRUE MAR 4 #> 3 id_1 3 Control TRUE TRUE TRUE MAR 6 #> 4 id_1 4 Control TRUE TRUE TRUE MAR 8 #> 5 id_1 5 Control TRUE TRUE TRUE MAR 10 #> 6 id_1 6 Control TRUE TRUE TRUE MAR 12 delta_df2 <- delta_df %>% mutate(delta = if_else(group == \"Control\", 0, delta)) head(delta_df2) #> id visit group is_mar is_missing is_post_ice strategy delta #> 1 id_1 1 Control TRUE TRUE TRUE MAR 0 #> 2 id_1 2 Control TRUE TRUE TRUE MAR 0 #> 3 id_1 3 Control TRUE TRUE TRUE MAR 0 #> 4 id_1 4 Control TRUE TRUE TRUE MAR 0 #> 5 id_1 5 Control TRUE TRUE TRUE MAR 0 #> 6 id_1 6 Control TRUE TRUE TRUE MAR 0 ana_delta <- analyse(impute_obj_CIR, delta = delta_df2, vars = vars_an) pool(ana_delta) #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_1 -0.446 0.514 -1.459 0.567 0.386 #> lsm_ref_1 2.62 0.363 1.904 3.335 <0.001 #> lsm_alt_1 2.173 0.363 1.458 2.889 <0.001 #> trt_2 0.072 0.546 -1.006 1.15 0.895 #> lsm_ref_2 3.708 0.387 2.945 4.471 <0.001 #> lsm_alt_2 3.78 0.386 3.018 4.542 <0.001 #> trt_3 -1.507 0.626 -2.743 -0.272 0.017 #> lsm_ref_3 5.844 0.441 4.973 6.714 <0.001 #> lsm_alt_3 4.336 0.442 3.464 5.209 <0.001 #> trt_4 -2.062 0.731 -3.504 -0.621 0.005 #> lsm_ref_4 7.658 0.519 6.634 8.682 <0.001 #> lsm_alt_4 5.596 0.515 4.58 6.612 <0.001 #> trt_5 -2.938 0.916 -4.746 -1.13 0.002 #> lsm_ref_5 9.558 0.641 8.293 10.823 <0.001 #> lsm_alt_5 6.62 0.651 5.335 7.905 <0.001 #> trt_6 -3.53 1.045 -5.591 -1.469 0.001 #> lsm_ref_6 11.045 0.73 9.604 12.486 <0.001 #> lsm_alt_6 7.515 0.738 6.058 8.971 <0.001 #> --------------------------------------------------"},{"path":[]},{"path":"/articles/quickstart.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"rbmi: Quickstart","text":"purpose vignette provide 15 minute quickstart guide core functions rbmi package. rbmi package consists 4 core functions (plus several helper functions) typically called sequence: draws() - fits imputation models stores parameters impute() - creates multiple imputed datasets analyse() - analyses multiple imputed datasets pool() - combines analysis results across imputed datasets single statistic","code":""},{"path":"/articles/quickstart.html","id":"the-data","dir":"Articles","previous_headings":"","what":"The Data","title":"rbmi: Quickstart","text":"use publicly available example dataset antidepressant clinical trial active drug versus placebo. relevant endpoint Hamilton 17-item depression rating scale (HAMD17) assessed baseline weeks 1, 2, 4, 6. Study drug discontinuation occurred 24% subjects active drug 26% subjects placebo. data study drug discontinuation missing single additional intermittent missing observation. consider imputation model mean change baseline HAMD17 score outcome (variable CHANGE dataset). following covariates included imputation model: treatment group (THERAPY), (categorical) visit (VISIT), treatment--visit interactions, baseline HAMD17 score (BASVAL), baseline HAMD17 score--visit interactions. common unstructured covariance matrix structure assumed groups. analysis model ANCOVA model treatment group primary factor adjustment baseline HAMD17 score. rbmi expects input dataset complete; , must one row per subject visit. Missing outcome values coded NA, missing covariate values allowed. dataset incomplete, expand_locf() helper function can used add missing rows, using LOCF imputation carry forward observed baseline covariate values visits missing outcomes. Rows corresponding missing outcomes present antidepressant trial dataset. address therefore use expand_locf() function follows:","code":"library(rbmi) library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union data(\"antidepressant_data\") dat <- antidepressant_data # Use expand_locf to add rows corresponding to visits with missing outcomes to the dataset dat <- expand_locf( dat, PATIENT = levels(dat$PATIENT), # expand by PATIENT and VISIT VISIT = levels(dat$VISIT), vars = c(\"BASVAL\", \"THERAPY\"), # fill with LOCF BASVAL and THERAPY group = c(\"PATIENT\"), order = c(\"PATIENT\", \"VISIT\") )"},{"path":"/articles/quickstart.html","id":"draws","dir":"Articles","previous_headings":"","what":"Draws","title":"rbmi: Quickstart","text":"draws() function fits imputation models stores corresponding parameter estimates Bayesian posterior parameter draws. three main inputs draws() function : data - primary longitudinal data.frame containing outcome variable covariates. data_ice - data.frame specifies first visit affected intercurrent event (ICE) imputation strategy handling missing outcome data ICE. one ICE imputed non-MAR strategy allowed per subject. method - statistical method used fit imputation models create imputed datasets. antidepressant trial data, dataset data_ice provided. However, can derived , dataset, subject’s first visit affected ICE “study drug discontinuation” corresponds first terminal missing observation. first derive dateset data_ice create 150 Bayesian posterior draws imputation model parameters. example, assume imputation strategy ICE Jump Reference (JR) subjects 150 multiple imputed datasets using Bayesian posterior draws imputation model created. Note use set_vars() specifies names key variables within dataset imputation model. Additionally, note whilst vars$group vars$visit added terms imputation model default, interaction , thus inclusion group * visit list covariates. Available imputation methods include: Bayesian multiple imputation - method_bayes() Approximate Bayesian multiple imputation - method_approxbayes() Conditional mean imputation (bootstrap) - method_condmean(type = \"bootstrap\") Conditional mean imputation (jackknife) - method_condmean(type = \"jackknife\") Bootstrapped multiple imputation - method = method_bmlmi() comparison methods, refer stat_specs vignette (Section 3.10). “statistical specifications” vignette (Section 3.10): vignette(\"stat_specs\",package=\"rbmi\"). Available imputation strategies include: Missing Random - \"MAR\" Jump Reference - \"JR\" Copy Reference - \"CR\" Copy Increments Reference - \"CIR\" Last Mean Carried Forward - \"LMCF\"","code":"# create data_ice and set the imputation strategy to JR for # each patient with at least one missing observation dat_ice <- dat %>% arrange(PATIENT, VISIT) %>% filter(is.na(CHANGE)) %>% group_by(PATIENT) %>% slice(1) %>% ungroup() %>% select(PATIENT, VISIT) %>% mutate(strategy = \"JR\") # In this dataset, subject 3618 has an intermittent missing values which does not correspond # to a study drug discontinuation. We therefore remove this subject from `dat_ice`. # (In the later imputation step, it will automatically be imputed under the default MAR assumption.) dat_ice <- dat_ice[-which(dat_ice$PATIENT == 3618),] dat_ice #> # A tibble: 43 × 3 #> PATIENT VISIT strategy #> #> 1 1513 5 JR #> 2 1514 5 JR #> 3 1517 5 JR #> 4 1804 7 JR #> 5 2104 7 JR #> 6 2118 5 JR #> 7 2218 6 JR #> 8 2230 6 JR #> 9 2721 5 JR #> 10 2729 5 JR #> # ℹ 33 more rows # Define the names of key variables in our dataset and # the covariates included in the imputation model using `set_vars()` # Note that the covariates argument can also include interaction terms vars <- set_vars( outcome = \"CHANGE\", visit = \"VISIT\", subjid = \"PATIENT\", group = \"THERAPY\", covariates = c(\"BASVAL*VISIT\", \"THERAPY*VISIT\") ) # Define which imputation method to use (here: Bayesian multiple imputation with 150 imputed datsets) method <- method_bayes( burn_in = 200, burn_between = 5, n_samples = 150, seed = 675442751 ) # Create samples for the imputation parameters by running the draws() function set.seed(987) drawObj <- draws( data = dat, data_ice = dat_ice, vars = vars, method = method, quiet = TRUE ) drawObj #> #> Draws Object #> ------------ #> Number of Samples: 150 #> Number of Failed Samples: 0 #> Model Formula: CHANGE ~ 1 + THERAPY + VISIT + BASVAL * VISIT + THERAPY * VISIT #> Imputation Type: random #> Method: #> name: Bayes #> burn_in: 200 #> burn_between: 5 #> same_cov: TRUE #> n_samples: 150 #> seed: 675442751"},{"path":"/articles/quickstart.html","id":"impute","dir":"Articles","previous_headings":"","what":"Impute","title":"rbmi: Quickstart","text":"next step use parameters imputation model generate imputed datasets. done via impute() function. function two key inputs: imputation model output draws() reference groups relevant reference-based imputation methods. ’s usage thus: instance, specifying PLACEBO group reference group well DRUG group (standard imputation using reference-based methods). Generally speaking, need see directly interact imputed datasets. However, wish inspect , can extracted imputation object using extract_imputed_dfs() helper function, .e.: Note case method_bayes() method_approxbayes(), imputed datasets correspond random imputations original dataset. method_condmean(), first imputed dataset always correspond completed original dataset containing subjects. method_condmean(type=\"jackknife\"), remaining datasets correspond conditional mean imputations leave-one-subject-datasets, whereas method_condmean(type=\"bootstrap\"), subsequent dataset corresponds conditional mean imputation bootstrapped datasets. method_bmlmi(), imputed datasets correspond sets random imputations bootstrapped datasets.","code":"imputeObj <- impute( drawObj, references = c(\"DRUG\" = \"PLACEBO\", \"PLACEBO\" = \"PLACEBO\") ) imputeObj #> #> Imputation Object #> ----------------- #> Number of Imputed Datasets: 150 #> Fraction of Missing Data (Original Dataset): #> 4: 0% #> 5: 8% #> 6: 13% #> 7: 25% #> References: #> DRUG -> PLACEBO #> PLACEBO -> PLACEBO imputed_dfs <- extract_imputed_dfs(imputeObj) head(imputed_dfs[[10]], 12) # first 12 rows of 10th imputed dataset #> PATIENT HAMATOTL PGIIMP RELDAYS VISIT THERAPY GENDER POOLINV BASVAL #> 1 new_pt_1 21 2 7 4 DRUG F 006 32 #> 2 new_pt_1 19 2 14 5 DRUG F 006 32 #> 3 new_pt_1 21 3 28 6 DRUG F 006 32 #> 4 new_pt_1 17 4 42 7 DRUG F 006 32 #> 5 new_pt_2 18 3 7 4 PLACEBO F 006 14 #> 6 new_pt_2 18 2 15 5 PLACEBO F 006 14 #> 7 new_pt_2 14 3 29 6 PLACEBO F 006 14 #> 8 new_pt_2 8 2 42 7 PLACEBO F 006 14 #> 9 new_pt_3 18 3 7 4 DRUG F 006 21 #> 10 new_pt_3 17 3 14 5 DRUG F 006 21 #> 11 new_pt_3 12 3 28 6 DRUG F 006 21 #> 12 new_pt_3 9 3 44 7 DRUG F 006 21 #> HAMDTL17 CHANGE #> 1 21 -11 #> 2 20 -12 #> 3 19 -13 #> 4 17 -15 #> 5 11 -3 #> 6 14 0 #> 7 9 -5 #> 8 5 -9 #> 9 20 -1 #> 10 18 -3 #> 11 16 -5 #> 12 13 -8"},{"path":"/articles/quickstart.html","id":"analyse","dir":"Articles","previous_headings":"","what":"Analyse","title":"rbmi: Quickstart","text":"next step run analysis model imputed dataset. done defining analysis function calling analyse() apply function imputed dataset. vignette use ancova() function provided rbmi package fits separate ANCOVA model outcomes visit returns treatment effect estimate corresponding least square means group per visit. Note , similar draws(), ancova() function uses set_vars() function determines names key variables within data covariates (addition treatment group) analysis model adjusted. Please also note names analysis estimates contain “ref” “alt” refer two treatment arms. particular “ref” refers first factor level vars$group necessarily coincide control arm. example, since levels(dat[[vars$group]]) = c(\"DRUG\", PLACEBO), results associated “ref” correspond intervention arm, associated “alt” correspond control arm. Additionally, can use delta argument analyse() perform delta adjustments imputed datasets prior analysis. brief, implemented specifying data.frame contains amount adjustment added longitudinal outcome subject visit, .e.  data.frame must contain columns subjid, visit, delta. appreciated carrying procedure potentially tedious, therefore delta_template() helper function provided simplify . particular, delta_template() returns shell data.frame delta-adjustment set 0 patients. Additionally delta_template() adds several meta-variables onto shell data.frame can used manual derivation manipulation delta-adjustment. example lets say want add delta-value 5 imputed values (.e. values missing original dataset) drug arm. implemented follows:","code":"anaObj <- analyse( imputeObj, ancova, vars = set_vars( subjid = \"PATIENT\", outcome = \"CHANGE\", visit = \"VISIT\", group = \"THERAPY\", covariates = c(\"BASVAL\") ) ) anaObj #> #> Analysis Object #> --------------- #> Number of Results: 150 #> Analysis Function: ancova #> Delta Applied: FALSE #> Analysis Estimates: #> trt_4 #> lsm_ref_4 #> lsm_alt_4 #> trt_5 #> lsm_ref_5 #> lsm_alt_5 #> trt_6 #> lsm_ref_6 #> lsm_alt_6 #> trt_7 #> lsm_ref_7 #> lsm_alt_7 # For reference show the additional meta variables provided delta_template(imputeObj) %>% as_tibble() #> # A tibble: 688 × 8 #> PATIENT VISIT THERAPY is_mar is_missing is_post_ice strategy delta #> #> 1 1503 4 DRUG TRUE FALSE FALSE NA 0 #> 2 1503 5 DRUG TRUE FALSE FALSE NA 0 #> 3 1503 6 DRUG TRUE FALSE FALSE NA 0 #> 4 1503 7 DRUG TRUE FALSE FALSE NA 0 #> 5 1507 4 PLACEBO TRUE FALSE FALSE NA 0 #> 6 1507 5 PLACEBO TRUE FALSE FALSE NA 0 #> 7 1507 6 PLACEBO TRUE FALSE FALSE NA 0 #> 8 1507 7 PLACEBO TRUE FALSE FALSE NA 0 #> 9 1509 4 DRUG TRUE FALSE FALSE NA 0 #> 10 1509 5 DRUG TRUE FALSE FALSE NA 0 #> # ℹ 678 more rows delta_df <- delta_template(imputeObj) %>% as_tibble() %>% mutate(delta = if_else(THERAPY == \"DRUG\" & is_missing , 5, 0)) %>% select(PATIENT, VISIT, delta) delta_df #> # A tibble: 688 × 3 #> PATIENT VISIT delta #> #> 1 1503 4 0 #> 2 1503 5 0 #> 3 1503 6 0 #> 4 1503 7 0 #> 5 1507 4 0 #> 6 1507 5 0 #> 7 1507 6 0 #> 8 1507 7 0 #> 9 1509 4 0 #> 10 1509 5 0 #> # ℹ 678 more rows anaObj_delta <- analyse( imputeObj, ancova, delta = delta_df, vars = set_vars( subjid = \"PATIENT\", outcome = \"CHANGE\", visit = \"VISIT\", group = \"THERAPY\", covariates = c(\"BASVAL\") ) )"},{"path":"/articles/quickstart.html","id":"pool","dir":"Articles","previous_headings":"","what":"Pool","title":"rbmi: Quickstart","text":"Finally, pool() function can used summarise analysis results across multiple imputed datasets provide overall statistic standard error, confidence intervals p-value hypothesis test null hypothesis effect equal 0. Note pooling method automatically derived based method specified original call draws(): method_bayes() method_approxbayes() pooling inference based Rubin’s rules. method_condmean(type = \"bootstrap\") inference either based normal approximation using bootstrap standard error (pool(..., type = \"normal\")) bootstrap percentiles (pool(..., type = \"percentile\")). method_condmean(type = \"jackknife\") inference based normal approximation using jackknife estimate standard error. method = method_bmlmi() inference according methods described von Hippel Bartlett (see stat_specs vignette details) Since used Bayesian multiple imputation vignette, pool() function automatically use Rubin’s rules. table values shown print message poolObj can also extracted using .data.frame() function: outputs gives estimated difference 2.079 (95% CI -0.138 4.296) two groups last visit associated p-value 0.066.","code":"poolObj <- pool( anaObj, conf.level = 0.95, alternative = \"two.sided\" ) poolObj #> #> Pool Object #> ----------- #> Number of Results Combined: 150 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_4 -0.092 0.683 -1.439 1.256 0.893 #> lsm_ref_4 -1.616 0.486 -2.576 -0.656 0.001 #> lsm_alt_4 -1.708 0.475 -2.645 -0.77 <0.001 #> trt_5 1.281 0.927 -0.55 3.112 0.169 #> lsm_ref_5 -4.112 0.661 -5.418 -2.807 <0.001 #> lsm_alt_5 -2.831 0.646 -4.107 -1.556 <0.001 #> trt_6 1.912 1.001 -0.066 3.89 0.058 #> lsm_ref_6 -6.097 0.714 -7.508 -4.686 <0.001 #> lsm_alt_6 -4.186 0.696 -5.561 -2.81 <0.001 #> trt_7 2.079 1.122 -0.138 4.296 0.066 #> lsm_ref_7 -6.946 0.815 -8.558 -5.335 <0.001 #> lsm_alt_7 -4.867 0.788 -6.426 -3.308 <0.001 #> -------------------------------------------------- as.data.frame(poolObj) #> parameter est se lci uci pval #> 1 trt_4 -0.09180645 0.6826279 -1.43949684 1.2558839 8.931772e-01 #> 2 lsm_ref_4 -1.61581996 0.4862316 -2.57577141 -0.6558685 1.093708e-03 #> 3 lsm_alt_4 -1.70762640 0.4749573 -2.64531931 -0.7699335 4.262148e-04 #> 4 trt_5 1.28107134 0.9269270 -0.54967136 3.1118141 1.689000e-01 #> 5 lsm_ref_5 -4.11245871 0.6608409 -5.41768364 -2.8072338 4.201381e-09 #> 6 lsm_alt_5 -2.83138737 0.6457744 -4.10686302 -1.5559117 2.114628e-05 #> 7 trt_6 1.91163968 1.0011368 -0.06637259 3.8896520 5.809419e-02 #> 8 lsm_ref_6 -6.09716631 0.7142461 -7.50839192 -4.6859407 1.384720e-14 #> 9 lsm_alt_6 -4.18552662 0.6963163 -5.56127560 -2.8097776 1.321956e-08 #> 10 trt_7 2.07945506 1.1216355 -0.13755657 4.2964667 6.579390e-02 #> 11 lsm_ref_7 -6.94648032 0.8150602 -8.55819661 -5.3347640 2.515736e-14 #> 12 lsm_alt_7 -4.86702525 0.7884953 -6.42588823 -3.3081623 6.801566e-09"},{"path":"/articles/quickstart.html","id":"code","dir":"Articles","previous_headings":"","what":"Code","title":"rbmi: Quickstart","text":"report code presented vignette.","code":"library(rbmi) library(dplyr) data(\"antidepressant_data\") dat <- antidepressant_data # Use expand_locf to add rows corresponding to visits with missing outcomes to the dataset dat <- expand_locf( dat, PATIENT = levels(dat$PATIENT), # expand by PATIENT and VISIT VISIT = levels(dat$VISIT), vars = c(\"BASVAL\", \"THERAPY\"), # fill with LOCF BASVAL and THERAPY group = c(\"PATIENT\"), order = c(\"PATIENT\", \"VISIT\") ) # Create data_ice and set the imputation strategy to JR for # each patient with at least one missing observation dat_ice <- dat %>% arrange(PATIENT, VISIT) %>% filter(is.na(CHANGE)) %>% group_by(PATIENT) %>% slice(1) %>% ungroup() %>% select(PATIENT, VISIT) %>% mutate(strategy = \"JR\") # In this dataset, subject 3618 has an intermittent missing values which does not correspond # to a study drug discontinuation. We therefore remove this subject from `dat_ice`. # (In the later imputation step, it will automatically be imputed under the default MAR assumption.) dat_ice <- dat_ice[-which(dat_ice$PATIENT == 3618),] # Define the names of key variables in our dataset using `set_vars()` # and the covariates included in the imputation model # Note that the covariates argument can also include interaction terms vars <- set_vars( outcome = \"CHANGE\", visit = \"VISIT\", subjid = \"PATIENT\", group = \"THERAPY\", covariates = c(\"BASVAL*VISIT\", \"THERAPY*VISIT\") ) # Define which imputation method to use (here: Bayesian multiple imputation with 150 imputed datsets) method <- method_bayes( burn_in = 200, burn_between = 5, n_samples = 150, seed = 675442751 ) # Create samples for the imputation parameters by running the draws() function set.seed(987) drawObj <- draws( data = dat, data_ice = dat_ice, vars = vars, method = method, quiet = TRUE ) # Impute the data imputeObj <- impute( drawObj, references = c(\"DRUG\" = \"PLACEBO\", \"PLACEBO\" = \"PLACEBO\") ) # Fit the analysis model on each imputed dataset anaObj <- analyse( imputeObj, ancova, vars = set_vars( subjid = \"PATIENT\", outcome = \"CHANGE\", visit = \"VISIT\", group = \"THERAPY\", covariates = c(\"BASVAL\") ) ) # Apply a delta adjustment # Add a delta-value of 5 to all imputed values (i.e. those values # which were missing in the original dataset) in the drug arm. delta_df <- delta_template(imputeObj) %>% as_tibble() %>% mutate(delta = if_else(THERAPY == \"DRUG\" & is_missing , 5, 0)) %>% select(PATIENT, VISIT, delta) # Repeat the analyses with the adjusted values anaObj_delta <- analyse( imputeObj, ancova, delta = delta_df, vars = set_vars( subjid = \"PATIENT\", outcome = \"CHANGE\", visit = \"VISIT\", group = \"THERAPY\", covariates = c(\"BASVAL\") ) ) # Pool the results poolObj <- pool( anaObj, conf.level = 0.95, alternative = \"two.sided\" )"},{"path":"/articles/stat_specs.html","id":"scope-of-this-document","dir":"Articles","previous_headings":"","what":"Scope of this document","title":"rbmi: Statistical Specifications","text":"document describes statistical methods implemented rbmi R package standard reference-based multiple imputation continuous longitudinal outcomes. package implements three classes multiple imputation (MI) approaches: Conventional MI methods based Bayesian (approximate Bayesian) posterior draws model parameters combined Rubin’s rules make inferences described Carpenter, Roger, Kenward (2013) Cro et al. (2020). Conditional mean imputation methods combined re-sampling techniques described Wolbers et al. (2022). Bootstrapped MI methods described von Hippel Bartlett (2021). document structured follows: first provide informal introduction estimands corresponding treatment effect estimation based MI (section 2). core document consists section 3 describes statistical methodology detail also contains comparison implemented approaches (section 3.10). link theory functions included package rbmi described section 4. conclude comparison package alternative software implementations reference-based imputation methods (section 5).","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"estimands","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods","what":"Estimands","title":"rbmi: Statistical Specifications","text":"ICH E9(R1) addendum estimands sensitivity analyses describes systematic approach ensure alignment among clinical trial objectives, trial execution/conduct, statistical analyses, interpretation results (ICH E9 working group (2019)). per addendum, estimand precise description treatment effect reflecting clinical question posed trial objective summarizes population-level outcomes patients different treatment conditions compared. One important attribute estimand list possible intercurrent events (ICEs), .e. events occurring treatment initiation affect either interpretation existence measurements associated clinical question interest, definition appropriate strategies deal ICEs. three relevant strategies purpose document hypothetical strategy, treatment policy strategy, composite strategy. hypothetical strategy, scenario envisaged ICE occur. scenario, endpoint values ICE directly observable treated using models missing data. treatment policy strategy, treatment effect presence ICEs targeted analyses based observed outcomes regardless whether subject ICE . composite strategy, ICE included component endpoint.","code":""},{"path":"/articles/stat_specs.html","id":"alignment-between-the-estimand-and-the-estimation-method","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods","what":"Alignment between the estimand and the estimation method","title":"rbmi: Statistical Specifications","text":"ICH E9(R1) addendum distinguishes ICEs missing data (ICH E9 working group (2019)). Whereas ICEs treatment discontinuations reflect clinical practice, amount missing data can minimized conduct clinical trial. However, many connections missing data ICEs. example, often difficult retain subjects clinical trial treatment discontinuation subject’s dropout trial leads missing data. another example, outcome values ICEs addressed using hypothetical strateg directly observable hypothetical scenario. Consequently, observed outcome values ICEs typically discarded treated missing data. addendum proposes estimation methods address problem presented missing data selected align estimand. recent overview methods align estimator estimand Mallinckrodt et al. (2020). short introduction estimation methods studies longitudinal endpoints can also found Wolbers et al. (2022). One prominent statistical method purpose multiple imputation (MI), target rbmi package.","code":""},{"path":"/articles/stat_specs.html","id":"missing-data-prior-to-ices","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods > 2.2 Alignment between the estimand and the estimation method","what":"Missing data prior to ICEs","title":"rbmi: Statistical Specifications","text":"Missing data may occur subjects without ICE prior occurrence ICE. missing outcomes associated ICE, often plausible impute missing--random (MAR) assumption using standard MMRM imputation model longitudinal outcomes. Informally, MAR occurs missing data can fully accounted baseline variables included model observed longitudinal outcomes, model correctly specified.","code":""},{"path":"/articles/stat_specs.html","id":"implementation-of-the-hypothetical-strategy","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods > 2.2 Alignment between the estimand and the estimation method","what":"Implementation of the hypothetical strategy","title":"rbmi: Statistical Specifications","text":"MAR imputation model described often also good starting point imputing data ICE handled using hypothetical strategy (Mallinckrodt et al. (2020)). Informally, assumes unobserved values ICE similar observed data subjects ICE remained follow-. However, situations, may reasonable assume missingness “informative” indicates systematically better worse outcome observed subjects. situations, MNAR imputation \\(\\delta\\)-adjustment explored sensitivity analysis. \\(\\delta\\)-adjustments add fixed random quantity imputations order make imputed outcomes systematically worse better observed described Cro et al. (2020). rbmi fixed \\(\\delta\\)-adjustments implemented.","code":""},{"path":"/articles/stat_specs.html","id":"implementation-of-the-treatment-policy-strategy","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods > 2.2 Alignment between the estimand and the estimation method","what":"Implementation of the treatment policy strategy","title":"rbmi: Statistical Specifications","text":"Ideally, data collection continues ICE handled treatment policy strategy missing data arises. Indeed, post-ICE data increasingly systematically collected RCTs. However, despite best efforts, missing data ICE study treatment discontinuation may still occur subject drops study discontinuation. difficult give definite recommendations regarding implementation treatment policy strategy presence missing data stage optimal method highly context dependent topic ongoing statistical research. ICEs thought negligible effect efficacy outcomes, standard MAR-based imputation may appropriate. contrast, ICE treatment discontinuation may expected substantial impact efficacy outcomes. settings, MAR assumption may still plausible conditioning subject’s time-varying treatment status (Guizzaro et al. (2021)). case, one option impute missing post-discontinuation data based subjects also discontinued treatment continued followed (Polverejan Dragalin (2020)). Another option may require somewhat less post-discontinuation data include subjects imputation procedure model post-discontinuation data using time-varying treatment status indicators (e.g. time-varying indicators treatment compliance, discontinuation, initiation rescue treatment) (Guizzaro et al. (2021)). approach, post-ICE outcomes included every step analysis, including fitting imputation model. assumes ICEs may impact post-ICE outcomes otherwise missingness non-informative. approach also assumes time-varying covariates contain missing values, deviations outcomes ICE correctly modeled time-varying covariates, sufficient post-ICE data available inform regression coefficients time-varying covariates. proposals relatively recent remain open questions regarding appropriate trade-model complexity (e.g. model account potentially differential effect post-ICE outcomes depending timing ICE?) variance resulting treatment effect estimate. generally, yet established much post-discontinuation data required implement methods robustly without risk substantial inflation variance. trial settings, subjects discontinue randomized treatment. settings, treatment discontinuation rates higher difficult retain subjects trial treatment discontinuation leading sparse data collection treatment discontinuation. settings, amount available data treatment discontinuation may insufficient inform imputation model explicitly models post-discontinuation data. Depending disease area anticipated mechanism action intervention, may plausible assume subjects intervention group behave similarly subjects control group ICE treatment discontinuation. case, reference-based imputation methods option (Mallinckrodt et al. (2020)). Reference-based imputation methods formalize idea impute missing data intervention group based data control reference group. general description review reference-based imputation methods, refer Carpenter, Roger, Kenward (2013), Cro et al. (2020), . White, Royes, Best (2020) Wolbers et al. (2022). technical description implemented statistical methodology reference-based imputation, refer section 3 (particular section 3.4).","code":""},{"path":"/articles/stat_specs.html","id":"implementation-of-the-composite-strategy","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods > 2.2 Alignment between the estimand and the estimation method","what":"Implementation of the composite strategy","title":"rbmi: Statistical Specifications","text":"composite strategy typically applied binary time--event outcomes can also used continuous outcomes ascribing suitably unfavorable value patients experience ICEs composite strategy defined. One possibility implement use MI \\(\\delta\\)-adjustment post-ICE data described Darken et al. (2020).","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"sec:methodsOverview","dir":"Articles","previous_headings":"3 Statistical methodology","what":"Overview of the imputation procedure","title":"rbmi: Statistical Specifications","text":"Analyses datasets missing data always rely missing data assumptions. methods described can used produce valid imputations MAR assumption reference-based imputation assumptions. MNAR imputation based fixed \\(\\delta\\)-adjustments typically used sensitivity analyses tipping-point analyses also supported. Three general imputation approaches implemented rbmi: Conventional MI based Bayesian (approximate Bayesian) posterior draws imputation model combined Rubin’s rules inference described Carpenter, Roger, Kenward (2013) Cro et al. (2020). Conditional mean imputation based REML estimate imputation model combined resampling techniques (jackknife bootstrap) inference described Wolbers et al. (2022). Bootstrapped MI methods based REML estimates imputation model described von Hippel Bartlett (2021).","code":""},{"path":"/articles/stat_specs.html","id":"conventional-mi","dir":"Articles","previous_headings":"3 Statistical methodology > 3.1 Overview of the imputation procedure","what":"Conventional MI","title":"rbmi: Statistical Specifications","text":"Conventional MI approaches include following steps: Base imputation model fitting step (Section 3.3) Fit Bayesian multivariate normal mixed model repeated measures (MMRM) observed longitudinal outcomes exclusion data ICEs reference-based missing data imputation desired (Section 3.3.3). Draw \\(M\\) posterior samples estimated parameters (regression coefficients covariance matrices) model. Alternatively, \\(M\\) approximate posterior draws posterior distribution can sampled repeatedly applying conventional restricted maximum-likelihood (REML) parameter estimation MMRM model nonparametric bootstrap samples original dataset (Section 3.3.4). Imputation step (Section 3.4) Take single sample \\(m\\) (\\(m\\1,\\ldots, M)\\) posterior distribution imputation model parameters. subject, use sampled parameters defined imputation strategy determine mean covariance matrix describing subject’s marginal outcome distribution longitudinal outcome assessments (.e. observed missing outcomes). subjects, construct conditional multivariate normal distribution missing outcomes given observed outcomes (including observed outcomes ICEs reference-based assumption desired). subject, draw single sample conditional distribution impute missing outcomes leading complete imputed dataset. sensitivity analyses, pre-defined \\(\\delta\\)-adjustment may applied imputed data prior analysis step. (Section 3.5). Analysis step (Section 3.6) Analyze imputed dataset using analysis model (e.g. ANCOVA) resulting point estimate standard error (corresponding degrees freedom) treatment effect. Pooling step inference (Section 3.7) Repeat steps 2. 3. posterior sample \\(m\\), resulting \\(M\\) complete datasets, \\(M\\) point estimates treatment effect, \\(M\\) standard errors (corresponding degrees freedom). Pool \\(M\\) treatment effect estimates, standard errors, degrees freedom using rules Barnard Rubin obtain final pooled treatment effect estimator, standard error, degrees freedom.","code":""},{"path":"/articles/stat_specs.html","id":"conditional-mean-imputation","dir":"Articles","previous_headings":"3 Statistical methodology > 3.1 Overview of the imputation procedure","what":"Conditional mean imputation","title":"rbmi: Statistical Specifications","text":"conditional mean imputation approach includes following steps: Base imputation model fitting step (Section 3.3) Fit conventional multivariate normal/MMRM model using restricted maximum likelihood (REML) observed longitudinal outcomes exclusion data ICEs reference-based missing data imputation desired (Section 3.3.2). Imputation step (Section 3.4) subject, use fitted parameters step 1. construct conditional distribution missing outcomes given observed outcomes (including observed outcomes ICEs reference-based missing data imputation desired) described . subject, impute missing data deterministically mean conditional distribution leading complete imputed dataset. sensitivity analyses, pre-defined \\(\\delta\\)-adjustment may applied imputed data prior analysis step. (Section 3.5). Analysis step (Section 3.6) Apply analysis model (e.g. ANCOVA) completed dataset resulting point estimate treatment effect. Jackknife bootstrap inference step (Section 3.8) Inference treatment effect estimate 3. based re-sampling techniques. jackknife bootstrap supported. Importantly, methods require repeating steps imputation procedure (.e. imputation, conditional mean imputation, analysis steps) resampled datasets.","code":""},{"path":"/articles/stat_specs.html","id":"bootstrapped-mi","dir":"Articles","previous_headings":"3 Statistical methodology > 3.1 Overview of the imputation procedure","what":"Bootstrapped MI","title":"rbmi: Statistical Specifications","text":"bootstrapped MI approach includes following steps: Base imputation model fitting step (Section 3.3) Apply conventional restricted maximum-likelihood (REML) parameter estimation MMRM model \\(B\\) nonparametric bootstrap samples original dataset using observed longitudinal outcomes exclusion data ICEs reference-based missing data imputation desired. Imputation step (Section 3.4) Take bootstrapped dataset \\(b\\) (\\(b\\1,\\ldots, B)\\) corresponding imputation model parameter estimates. subject (bootstrapped dataset), use parameter estimates defined strategy dealing ICEs determine mean covariance matrix describing subject’s marginal outcome distribution longitudinal outcome assessments (.e. observed missing outcomes). subjects (bootstrapped dataset), construct conditional multivariate normal distribution missing outcomes given observed outcomes (including observed outcomes ICEs reference-based missing data imputation desired). subject (bootstrapped dataset), draw \\(D\\) samples conditional distributions impute missing outcomes leading \\(D\\) complete imputed dataset bootstrap sample \\(b\\). sensitivity analyses, pre-defined \\(\\delta\\)-adjustment may applied imputed data prior analysis step. (Section 3.5). Analysis step (Section 3.6) Analyze \\(B\\times D\\) imputed datasets using analysis model (e.g. ANCOVA) resulting \\(B\\times D\\) point estimates treatment effect. Pooling step inference (Section 3.9) Pool \\(B\\times D\\) treatment effect estimates described von Hippel Bartlett (2021) obtain final pooled treatment effect estimate, standard error, degrees freedom.","code":""},{"path":"/articles/stat_specs.html","id":"setting-notation-and-missing-data-assumptions","dir":"Articles","previous_headings":"3 Statistical methodology","what":"Setting, notation, and missing data assumptions","title":"rbmi: Statistical Specifications","text":"Assume data study \\(n\\) subjects total subject \\(\\) (\\(=1,\\ldots,n\\)) \\(J\\) scheduled follow-visits outcome interest assessed. applications, data randomized trial intervention vs control group treatment effect interest comparison outcomes specific visit randomized groups. However, single-arm trials multi-arm trials principle also supported rbmi implementation. Denote observed outcome vector length \\(J\\) subject \\(\\) \\(Y_i\\) (missing assessments coded NA (available)) non-missing missing components \\(Y_{!}\\) \\(Y_{?}\\), respectively. default, imputation missing outcomes \\(Y_{}\\) performed MAR assumption rbmi. Therefore, missing data following ICE handled using MAR imputation, compatible default assumption. discussed Section 2, MAR assumption often good starting point implementing hypothetical strategy. also note observed outcome data ICE handled using hypothetical strategy compatible strategy. Therefore, assume post-ICE data ICEs handled using hypothetical strategy already set NA \\(Y_i\\) prior calling rbmi functions. However, observed outcomes ICEs handled using treatment policy strategy included \\(Y_i\\) compatible strategy. Subjects may also experience one ICE missing data imputation according reference-based imputation method foreseen. subject \\(\\) ICE, denote first visit affected ICE \\(\\tilde{t}_i \\\\{1,\\ldots,J\\}\\). subjects, set \\(\\tilde{t}_i=\\infty\\). subject’s outcome vector setting observed outcomes visit \\(\\tilde{t}_i\\) onwards missing (.e. NA) denoted \\(Y'_i\\) corresponding data vector removal NA elements \\(Y'_{!}\\). MNAR \\(\\delta\\)-adjustments added imputed datasets formal imputation steps. covered separate section (Section 3.5).","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"sec:imputationModelSpecs","dir":"Articles","previous_headings":"3 Statistical methodology > 3.3 The base imputation model","what":"Included data and model specification","title":"rbmi: Statistical Specifications","text":"purpose imputation model estimate (covariate-dependent) mean trajectories covariance matrices group absence ICEs handled using reference-based imputation methods. Conventionally, publications reference-based imputation methods implicitly assumed corresponding post-ICE data missing subjects (Carpenter, Roger, Kenward (2013)). also allow situation post-ICE data available subjects needs imputed using reference-based methods others. However, observed data ICEs reference-based imputation methods specified compatible imputation model described therefore removed considered missing purpose estimating imputation model, purpose . example, patient ICE addressed reference-based method outcomes ICE collected, post-ICE outcomes excluded fitting base imputation model (included following steps). , base imputation model fitted \\(Y'_{!}\\) \\(Y_{!}\\). exclude data, imputation model mistakenly estimate mean trajectories based mixture observed pre- post-ICE data relevant reference-based imputations. Observed post-ICE outcomes control reference group also excluded base imputation model user specifies reference-based imputation strategy ICEs. ensures ICE impact data included imputation model regardless whether ICE occurred control intervention group. hand, imputation reference group based MAR assumption even reference-based imputation methods may preferable settings include post-ICE data control group base imputation model. can implemented specifying MAR strategy ICE control group reference-based strategy ICE intervention group. base imputation model longitudinal outcomes \\(Y'_i\\) assumes mean structure linear function covariates. Full flexibility specification linear predictor model supported. minimum covariates include treatment group, (categorical) visit, treatment--visit interactions. Typically, covariates including baseline outcome also included. External time-varying covariates (e.g. calendar time visit) well internal time-varying (e.g. time-varying indicators treatment discontinuation initiation rescue treatment) may principle also included indicated (Guizzaro et al. (2021)). Missing covariate values allowed. means values time-varying covariates must non-missing every visit regardless whether outcome measured missing. Denote \\(J\\times p\\) design matrix subject \\(\\) corresponding mean structure model \\(X_i\\) matrix removal rows corresponding missing outcomes \\(Y'_{!}\\) \\(X'_{!}\\). \\(p\\) number parameters mean structure model elements \\(Y'_{!}\\). base imputation model observed outcomes defined : \\[ Y'_{!} = X'_{!}\\beta + \\epsilon_{!} \\mbox{ } \\epsilon_{!}\\sim N(0,\\Sigma_{!!})\\] \\(\\beta\\) vector regression coefficients \\(\\Sigma_{!!}\\) covariance matrix obtained complete-data \\(J\\times J\\)-covariance matrix \\(\\Sigma\\) omitting rows columns corresponding missing outcome assessments subject \\(\\). Typically, common unstructured covariance matrix subjects assumed \\(\\Sigma\\) separate covariate matrices per treatment group also supported. Indeed, implementation also supports specification separate covariate matrices according arbitrarily defined categorical variable groups subjects disjoint subset. example, useful different covariance matrices suspected different subject strata. Finally, imputation methods described rely Bayesian model fitting MCMC, flexibility choice covariance structure, .e. unstructured (default), heterogeneous Toeplitz, heterogeneous compound symmetry, AR(1) covariance structures supported.","code":""},{"path":"/articles/stat_specs.html","id":"sec:imputationModelREML","dir":"Articles","previous_headings":"3 Statistical methodology > 3.3 The base imputation model","what":"Restricted maximum likelihood estimation (REML)","title":"rbmi: Statistical Specifications","text":"Frequentist parameter estimation base imputation based REML. use REML improved alternative maximum likelihood (ML) covariance parameter estimation originally proposed Patterson Thompson (1971). Since , become default method parameter estimation linear mixed effects models. rbmi allows choose ML REML methods estimate model parameters, REML default option.","code":""},{"path":"/articles/stat_specs.html","id":"sec:imputationModelBayes","dir":"Articles","previous_headings":"3 Statistical methodology > 3.3 The base imputation model","what":"Bayesian model fitting","title":"rbmi: Statistical Specifications","text":"Bayesian imputation model fitted R package rstan (Stan Development Team (2020)). rstan R interface Stan. Stan powerful flexible statistical software developed dedicated team implements Bayesian inference state---art MCMC sampling procedures. multivariate normal model missing data specified section 3.3.1 can considered generalization models described Stan user’s guide (see Stan Development Team (2020, sec. 3.5)). prior distributions SAS implementation “five macros” used (Roger (2021)), .e. improper flat priors regression coefficients weakly informative inverse Wishart prior covariance matrix (matrices). Specifically, let \\(S \\\\mathbb{R}^{J \\times J}\\) symmetric positive definite matrix \\(\\nu \\(J-1, \\infty)\\). symmetric positive definite matrix \\(x \\\\mathbb{R}^{J \\times J}\\) density: \\[ \\text{InvWish}(x \\vert \\nu, S) = \\frac{1}{2^{\\nu J/2}} \\frac{1}{\\Gamma_J(\\frac{\\nu}{2})} \\vert S \\vert^{\\nu/2} \\vert x \\vert ^{-(\\nu + J + 1)/2} \\text{exp}(-\\frac{1}{2} \\text{tr}(Sx^{-1})). \\] \\(\\nu > J+1\\) mean given : \\[ E[x] = \\frac{S}{\\nu - J - 1}. \\] choose \\(S\\) equal estimated covariance matrix frequentist REML fit \\(\\nu = J+2\\) lowest degrees freedom guarantee finite mean. Setting degrees freedom low \\(\\nu\\) ensures prior little impact posterior. Moreover, choice allows interpret parameter \\(S\\) mean prior distribution. “five macros”, MCMC algorithm initialized parameters frequentist REML fit (see section 3.3.2). described , using weakly informative priors parameters. Therefore, Markov chain essentially starting targeted stationary posterior distribution minimal amount burn-chain required.","code":""},{"path":"/articles/stat_specs.html","id":"sec:imputationModelBoot","dir":"Articles","previous_headings":"3 Statistical methodology > 3.3 The base imputation model","what":"Approximate Bayesian posterior draws via the bootstrap","title":"rbmi: Statistical Specifications","text":"Several authors suggested stabler way get Bayesian posterior draws imputation model bootstrap incomplete data calculate REML estimates bootstrap sample (Little Rubin (2002), Efron (1994), Honaker King (2010), von Hippel Bartlett (2021)). method proper REML estimates bootstrap samples asymptotically equivalent sample posterior distribution may provide additional robustness model misspecification (Little Rubin (2002, sec. 10.2.3, part 6), Honaker King (2010)). order retain balance treatment groups stratification factors across bootstrap samples, user able provide stratification variables bootstrap rbmi implementation.","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"sec:imputatioMNAR","dir":"Articles","previous_headings":"3 Statistical methodology > 3.4 Imputation step","what":"Marginal imputation distribution for a subject - MAR case","title":"rbmi: Statistical Specifications","text":"subject \\(\\), marginal distribution complete \\(J\\)-dimensional outcome vector assessment visits according imputation model multivariate normal distribution. mean \\(\\tilde{\\mu}_i\\) given predicted mean imputation model conditional subject’s baseline characteristics, group, , optionally, time-varying covariates. covariance matrix \\(\\tilde{\\Sigma}_i\\) given overall estimated covariance matrix , different covariance matrices assumed different groups, covariance matrix corresponding subject \\(\\)’s group.","code":""},{"path":"/articles/stat_specs.html","id":"sec:imputationRefBased","dir":"Articles","previous_headings":"3 Statistical methodology > 3.4 Imputation step","what":"Marginal imputation distribution for a subject - reference-based imputation methods","title":"rbmi: Statistical Specifications","text":"subject \\(\\), calculate mean covariance matrix complete \\(J\\)-dimensional outcome vector assessment visits MAR case denote \\(\\mu_i\\) \\(\\Sigma_i\\). reference-based imputation methods, corresponding reference group also required group. Typically, reference group intervention group control group. reference mean \\(\\mu_{ref,}\\) defined predicted mean imputation model conditional reference group (rather actual group subject \\(\\) belongs ) subject’s baseline characteristics. reference covariance matrix \\(\\Sigma_{ref,}\\) overall estimated covariance matrix , different covariance matrices assumed different groups, estimated covariance matrix corresponding reference group. principle, time-varying covariates also included reference-based imputation methods. However, sensible external time-varying covariates (e.g. calendar time visit) internal time-varying covariates (e.g. treatment discontinuation) latter likely depend actual treatment group typically sensible assume trajectory time-varying covariate reference group. Based means covariance matrices, subject’s marginal imputation distribution reference-based imputation methods calculated detailed Carpenter, Roger, Kenward (2013, sec. 4.3). Denote mean covariance matrix marginal imputation distribution \\(\\tilde{\\mu}_i\\) \\(\\tilde{\\Sigma}_i\\). Recall subject’s first visit affected ICE denoted \\(\\tilde{t}_i \\\\{1,\\ldots,J\\}\\) (visit \\(\\tilde{t}_i-1\\) last visit unaffected ICE). marginal distribution patient \\(\\) built according specific assumption data post ICE follows: Jump reference (JR): patient’s outcome distribution normally distributed following mean: \\[\\tilde{\\mu}_i = (\\mu_i[1], \\dots, \\mu_i[\\tilde{t}_i-1], \\mu_{ref,}[\\tilde{t}_i], \\dots, \\mu_{ref,}[J])^T.\\] covariance matrix constructed follows. First, partition covariance matrices \\(\\Sigma_i\\) \\(\\Sigma_{ref,}\\) blocks according time ICE \\(\\tilde{t}_i\\): \\[ \\Sigma_{} = \\begin{bmatrix} \\Sigma_{, 11} & \\Sigma_{, 12} \\\\ \\Sigma_{, 21} & \\Sigma_{,22} \\\\ \\end{bmatrix} \\] \\[ \\Sigma_{ref,} = \\begin{bmatrix} \\Sigma_{ref, , 11} & \\Sigma_{ref, , 12} \\\\ \\Sigma_{ref, , 21} & \\Sigma_{ref, ,22} \\\\ \\end{bmatrix}. \\] want covariance matrix \\(\\tilde{\\Sigma}_i\\) match \\(\\Sigma_i\\) pre-deviation measurements, \\(\\Sigma_{ref,}\\) conditional components post-deviation given pre-deviation measurements. solution derived Carpenter, Roger, Kenward (2013, sec. 4.3) given : \\[ \\begin{matrix} \\tilde{\\Sigma}_{,11} = \\Sigma_{, 11} \\\\ \\tilde{\\Sigma}_{, 21} = \\Sigma_{ref,, 21} \\Sigma^{-1}_{ref,, 11} \\Sigma_{, 11} \\\\ \\tilde{\\Sigma}_{, 22} = \\Sigma_{ref, , 22} - \\Sigma_{ref,, 21} \\Sigma^{-1}_{ref,, 11} (\\Sigma_{ref,, 11} - \\Sigma_{,11}) \\Sigma^{-1}_{ref,, 11} \\Sigma_{ref,, 12}. \\end{matrix} \\] Copy increments reference (CIR): patient’s outcome distribution normally distributed following mean: \\[ \\begin{split} \\tilde{\\mu}_i =& (\\mu_i[1], \\dots, \\mu_i[\\tilde{t}_i-1], \\mu_i[\\tilde{t}_i-1] + (\\mu_{ref,}[\\tilde{t}_i] - \\mu_{ref,}[\\tilde{t}_i-1]), \\dots,\\\\ & \\mu_i[\\tilde{t}_i-1]+(\\mu_{ref,}[J] - \\mu_{ref,}[\\tilde{t}_i-1]))^T. \\end{split} \\] covariance matrix derived JR method. Copy reference (CR): patient’s outcome distribution normally distributed mean covariance matrix taken reference group: \\[ \\tilde{\\mu}_i = \\mu_{ref,} \\] \\[ \\tilde{\\Sigma}_i = \\Sigma_{ref,}. \\] Last mean carried forward (LMCF): patient’s outcome distribution normally distributed following mean: \\[ \\tilde{\\mu}_i = (\\mu_i[1], \\dots, \\mu_i[\\tilde{t}_i-1], \\mu_i[\\tilde{t}_i-1], \\dots, \\mu_i[\\tilde{t}_i-1])'\\] covariance matrix: \\[ \\tilde{\\Sigma}_i = \\Sigma_i.\\]","code":""},{"path":"/articles/stat_specs.html","id":"sec:imputationRandomConditionalMean","dir":"Articles","previous_headings":"3 Statistical methodology > 3.4 Imputation step","what":"Imputation of missing outcome data","title":"rbmi: Statistical Specifications","text":"joint marginal multivariate normal imputation distribution subject \\(\\)’s observed missing outcome data mean \\(\\tilde{\\mu}_i\\) covariance matrix \\(\\tilde{\\Sigma}_i\\) defined . actual imputation missing outcome data obtained conditioning marginal distribution subject’s observed outcome data. note, approach valid regardless whether subject intermittent terminal missing data. conditional distribution used imputation multivariate normal distribution explicit formulas conditional mean covariance readily available. completeness, report notation terminology setting. marginal distribution outcome patient \\(\\) \\(Y_i \\sim N(\\tilde{\\mu}_i, \\tilde{\\Sigma}_i)\\) outcome \\(Y_i\\) can decomposed observed (\\(Y_{,!}\\)) unobserved (\\(Y_{,?}\\)) components. Analogously mean \\(\\tilde{\\mu}_i\\) can decomposed \\((\\tilde{\\mu}_{,!},\\tilde{\\mu}_{,?})\\) covariance \\(\\tilde{\\Sigma}_i\\) : \\[ \\tilde{\\Sigma}_i = \\begin{bmatrix} \\tilde{\\Sigma}_{, !!} & \\tilde{\\Sigma}_{,!?} \\\\ \\tilde{\\Sigma}_{, ?!} & \\tilde{\\Sigma}_{, ??} \\end{bmatrix}. \\] conditional distribution \\(Y_{,?}\\) conditional \\(Y_{,!}\\) multivariate normal distribution expectation \\[ E(Y_{,?} \\vert Y_{,!})= \\tilde{\\mu}_{,?} + \\tilde{\\Sigma}_{, ?!} \\tilde{\\Sigma}_{,!!}^{-1} (Y_{,!} - \\tilde{\\mu}_{,!}) \\] covariance matrix \\[ Cov(Y_{,?} \\vert Y_{,!}) = \\tilde{\\Sigma}_{,??} - \\tilde{\\Sigma}_{,?!} \\tilde{\\Sigma}_{,!!}^{-1} \\tilde{\\Sigma}_{,!?}. \\] Conventional random imputation consists sampling conditional multivariate normal distribution. Conditional mean imputation imputes missing values deterministic conditional expectation \\(E(Y_{,?} \\vert Y_{,!})\\).","code":""},{"path":"/articles/stat_specs.html","id":"sec:deltaAdjustment","dir":"Articles","previous_headings":"3 Statistical methodology","what":"\\(\\delta\\)-adjustment","title":"rbmi: Statistical Specifications","text":"marginal \\(\\delta\\)-adjustment approach similar “five macros” SAS implemented (Roger (2021)), .e. fixed non-stochastic values added multivariate normal imputation step prior analysis. relevant sensitivity analyses order make imputed data systematically worse better, respectively, observed data. addition, authors suggested \\(\\delta\\)-type adjustments implement composite strategy continuous outcomes (Darken et al. (2020)). implementation provides full flexibility regarding specific implementation \\(\\delta\\)-adjustment, .e. value added may depend randomized treatment group, timing subject’s ICE, factors. suggestions case studies regarding topic, refer Cro et al. (2020).","code":""},{"path":"/articles/stat_specs.html","id":"sec:analysis","dir":"Articles","previous_headings":"3 Statistical methodology","what":"Analysis step","title":"rbmi: Statistical Specifications","text":"data imputation, standard analysis model can applied completed data resulting treatment effect estimate. imputed data longer contains missing values, analysis model often simple. example, can analysis covariance (ANCOVA) model outcome (change outcome baseline) specific visit j dependent variable, randomized treatment group primary covariate , typically, adjustment baseline covariates imputation model.","code":""},{"path":"/articles/stat_specs.html","id":"sec:pooling","dir":"Articles","previous_headings":"3 Statistical methodology","what":"Pooling step for inference of (approximate) Bayesian MI and Rubin’s rules","title":"rbmi: Statistical Specifications","text":"Assume analysis model applied \\(M\\) multiple imputed random datasets resulted \\(m\\) treatment effect estimates \\(\\hat{\\theta}_m\\) (\\(m=1,\\ldots,M\\)) corresponding standard error \\(SE_m\\) (available) degrees freedom \\(\\nu_{com}\\). degrees freedom available analysis model, set \\(\\nu_{com}=\\infty\\) inference based normal distribution. Rubin’s rules used pooling treatment effect estimates corresponding variances estimates analysis steps across \\(M\\) multiple imputed datasets. According Rubin’s rules, final estimate treatment effect calculated sample mean \\(M\\) treatment effect estimates: \\[ \\hat{\\theta} = \\frac{1}{M} \\sum_{m = 1}^M \\hat{\\theta}_m. \\] pooled variance based two components reflect within variance treatment effects across multiple imputed datasets: \\[ V(\\hat{\\theta}) = V_W(\\hat{\\theta}) + (1 + \\frac{1}{M}) V_B(\\hat{\\theta}) \\] \\(V_W(\\hat{\\theta}) = \\frac{1}{M}\\sum_{m = 1}^M SE^2_m\\) within-variance \\(V_B(\\hat{\\theta}) = \\frac{1}{M-1} \\sum_{m = 1}^M (\\hat{\\theta}_m - \\hat{\\theta})^2\\) -variance. Confidence intervals tests null hypothesis \\(H_0: \\theta=\\theta_0\\) based \\(t\\)-statistics \\(T\\): \\[ T= (\\hat{\\theta}-\\theta_0)/\\sqrt{V(\\hat{\\theta})}. \\] null hypothesis, \\(T\\) approximate \\(t\\)-distribution \\(\\nu\\) degrees freedom. \\(\\nu\\) calculated according Barnard Rubin approximation, see Barnard Rubin (1999) (formula 3) Little Rubin (2002) (formula (5.24), page 87): \\[ \\nu = \\frac{\\nu_{old}* \\nu_{obs}}{\\nu_{old} + \\nu_{obs}} \\] \\[ \\nu_{old} = \\frac{M-1}{\\lambda^2} \\quad\\mbox{}\\quad \\nu_{obs} = \\frac{\\nu_{com} + 1}{\\nu_{com} + 3} \\nu_{com} (1 - \\lambda) \\] \\(\\lambda = \\frac{(1 + \\frac{1}{M})V_B(\\hat{\\theta})}{V(\\hat{\\theta})}\\) fraction missing information.","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"point-estimate-of-the-treatment-effect","dir":"Articles","previous_headings":"3 Statistical methodology > 3.8 Bootstrap and jackknife inference for conditional mean imputation","what":"Point estimate of the treatment effect","title":"rbmi: Statistical Specifications","text":"point estimator obtained applying analysis model (Section 3.6) single conditional mean imputation missing data (see Section 3.4.3) based REML estimator parameters imputation model (see Section 3.3.2). denote treatment effect estimator \\(\\hat{\\theta}\\). demonstrated Wolbers et al. (2022) (Section 2.4), treatment effect estimator valid analysis model ANCOVA model , generally, treatment effect estimator linear function imputed outcome vector. Indeed, case, estimator identical pooled treatment effect across multiple random REML imputation infinite number imputations corresponds computationally efficient implementation proposal von Hippel Bartlett (2021). expect conditional mean imputation method also applicable analysis models (e.g. general MMRM analysis models) formally justified.","code":""},{"path":"/articles/stat_specs.html","id":"jackknife-standard-errors-confidence-intervals-ci-and-tests-for-the-treatment-effect","dir":"Articles","previous_headings":"3 Statistical methodology > 3.8 Bootstrap and jackknife inference for conditional mean imputation","what":"Jackknife standard errors, confidence intervals (CI) and tests for the treatment effect","title":"rbmi: Statistical Specifications","text":"dataset containing \\(n\\) subjects, jackknife standard error depends treatment effect estimates \\(\\hat{\\theta}_{(-b)}\\) (\\(b=1,\\ldots,n\\)) samples original dataset leave observation subject \\(b\\). described previously, obtain treatment effect estimates leave-one-subject-datasets, steps imputation procedure (.e. imputation, conditional mean imputation, analysis steps) need repeated new dataset. , jackknife standard error defined \\[\\hat{se}_{jack}=[\\frac{(n-1)}{n}\\cdot\\sum_{b=1}^{n} (\\hat{\\theta}_{(-b)}-\\bar{\\theta}_{(.)})^2]^{1/2}\\] \\(\\bar{\\theta}_{(.)}\\) denotes mean jackknife estimates (Efron Tibshirani (1994), chapter 10). corresponding two-sided normal approximation \\(1-\\alpha\\) CI defined \\(\\hat{\\theta}\\pm z^{1-\\alpha/2}\\cdot \\hat{se}_{jack}\\) \\(\\hat{\\theta}\\) treatment effect estimate original dataset. Tests null hypothesis \\(H_0: \\theta=\\theta_0\\) based \\(Z\\)-score \\(Z=(\\hat{\\theta}-\\theta_0)/\\hat{se}_{jack}\\) using standard normal approximation. simulation study reported Wolbers et al. (2022) demonstrated exact protection type error jackknife-based inference relatively low sample size (n = 100 per group) substantial amount missing data (>25% subjects ICE).","code":""},{"path":"/articles/stat_specs.html","id":"bootstrap-standard-errors-confidence-intervals-ci-and-tests-for-the-treatment-effect","dir":"Articles","previous_headings":"3 Statistical methodology > 3.8 Bootstrap and jackknife inference for conditional mean imputation","what":"Bootstrap standard errors, confidence intervals (CI) and tests for the treatment effect","title":"rbmi: Statistical Specifications","text":"alternative jackknife, bootstrap also implemented rbmi (Efron Tibshirani (1994), Davison Hinkley (1997)). Two different bootstrap methods implemented rbmi: Methods based bootstrap standard error normal approximation percentile bootstrap methods. Denote treatment effect estimates \\(B\\) bootstrap samples \\(\\hat{\\theta}^*_b\\) (\\(b=1,\\ldots,B\\)). bootstrap standard error \\(\\hat{se}_{boot}\\) defined empirical standard deviation bootstrapped treatment effect estimates. Confidence intervals tests based bootstrap standard error can constructed way jackknife. Confidence intervals using percentile bootstrap based empirical quantiles bootstrap distribution corresponding statistical tests implemented rbmi via inversion confidence interval. Explicit formulas bootstrap inference implemented rbmi package considerations regarding required number bootstrap samples included Appendix Wolbers et al. (2022). simulation study reported Wolbers et al. (2022) demonstrated small inflation type error rate inference based bootstrap standard error (\\(5.3\\%\\) nominal type error rate \\(5\\%\\)) sample size n = 100 per group substantial amount missing data (>25% subjects ICE). Based simulations, recommend jackknife bootstrap inference performed better simulation study typically much faster compute bootstrap.","code":""},{"path":"/articles/stat_specs.html","id":"sec:poolbmlmi","dir":"Articles","previous_headings":"3 Statistical methodology","what":"Pooling step for inference of the bootstrapped MI methods","title":"rbmi: Statistical Specifications","text":"Assume analysis model applied \\(B\\times D\\) multiple imputed random datasets resulted \\(B\\times D\\) treatment effect estimates \\(\\hat{\\theta}_{bd}\\) (\\(b=1,\\ldots,B\\); \\(d=1,\\ldots,D\\)). final estimate treatment effect calculated sample mean \\(B*D\\) treatment effect estimates: \\[ \\hat{\\theta} = \\frac{1}{BD} \\sum_{b = 1}^B \\sum_{d = 1}^D \\hat{\\theta}_{bd}. \\] pooled variance based two components reflect variability within imputed bootstrap samples (von Hippel Bartlett (2021), formula 8.4): \\[ V(\\hat{\\theta}) = (1 + \\frac{1}{B})\\frac{MSB - MSW}{D} + \\frac{MSW}{BD} \\] \\(MSB\\) mean square bootstrapped datasets, \\(MSW\\) mean square within bootstrapped datasets imputed datasets: \\[ \\begin{align*} MSB &= \\frac{D}{B-1} \\sum_{b = 1}^B (\\bar{\\theta_{b}} - \\hat{\\theta})^2 \\\\ MSW &= \\frac{1}{B(D-1)} \\sum_{b = 1}^B \\sum_{d = 1}^D (\\theta_{bd} - \\bar{\\theta_b})^2 \\end{align*} \\] \\(\\bar{\\theta_{b}}\\) mean across \\(D\\) estimates obtained random imputation \\(b\\)-th bootstrap sample. degrees freedom estimated following formula (von Hippel Bartlett (2021), formula 8.6): \\[ \\nu = \\frac{(MSB\\cdot (B+1) - MSW\\cdot B)^2}{\\frac{MSB^2\\cdot (B+1)^2}{B-1} + \\frac{MSW^2\\cdot B}{D-1}} \\] Confidence intervals tests null hypothesis \\(H_0: \\theta=\\theta_0\\) based \\(t\\)-statistics \\(T\\): \\[ T= (\\hat{\\theta}-\\theta_0)/\\sqrt{V(\\hat{\\theta})}. \\] null hypothesis, \\(T\\) approximate \\(t\\)-distribution \\(\\nu\\) degrees freedom.","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"treatment-effect-estimation","dir":"Articles","previous_headings":"3 Statistical methodology > 3.10 Comparison between the implemented approaches","what":"Treatment effect estimation","title":"rbmi: Statistical Specifications","text":"approaches provide consistent treatment effect estimates standard reference-based imputation methods case analysis model completed datasets general linear model ANCOVA. Methods conditional mean imputation also valid analysis models. validity conditional mean imputation formally demonstrated analyses using general linear model (Wolbers et al. (2022, sec. 2.4)) though may also applicable widely (e.g. general MMRM analysis models). Treatment effects based conditional mean imputation deterministic. methods affected Monte Carlo sampling error precision estimates depends number imputations bootstrap samples, respectively.","code":""},{"path":"/articles/stat_specs.html","id":"standard-errors-of-the-treatment-effect","dir":"Articles","previous_headings":"3 Statistical methodology > 3.10 Comparison between the implemented approaches","what":"Standard errors of the treatment effect","title":"rbmi: Statistical Specifications","text":"approaches provide frequentist consistent estimates standard error imputation MAR assumption. reference-based imputation methods, methods based conditional mean imputation bootstrapped MI provide frequentist consistent estimates standard error whereas Rubin’s rules applied conventional MI methods provides -called information anchored inference (Bartlett (2021), Cro, Carpenter, Kenward (2019), von Hippel Bartlett (2021), Wolbers et al. (2022)). Frequentist consistent estimates standard error lead confidence intervals tests (asymptotically) correct coverage type error control assumption reference-based assumption reflects true data-generating mechanism. finite samples, simulations sample size \\(n=100\\) per group reported Wolbers et al. (2022) demonstrated conditional mean imputation combined jackknife provided exact protection type one error rate whereas bootstrap associated small type error inflation (5.1% 5.3% nominal level 5%). well known Rubin’s rules provide frequentist consistent estimates standard error reference-based imputation methods (Seaman, White, Leacy (2014), Liu Pang (2016), Tang (2017), Cro, Carpenter, Kenward (2019), Bartlett (2021)). Standard errors Rubin’s rule typically larger frequentist standard error estimates leading conservative inference corresponding loss statistical power, see e.g. simulations reported Wolbers et al. (2022). Intuitively, occurs reference-based imputation methods borrow information reference group imputations intervention group leading reduction frequentist variance resulting treatment effect contrast captured Rubin’s variance estimator. Formally, occurs imputation analysis models uncongenial reference-based imputation methods (Meng (1994), Bartlett (2021)). Cro, Carpenter, Kenward (2019) argued Rubin’s rule nevertheless valid reference-based imputation methods approximately information-anchored, .e. proportion information lost due missing data MAR approximately preserved reference-based analyses. contrast, frequentist standard errors reference based imputation information anchored reference-based imputation standard errors reference-based assumptions typically smaller MAR imputation. Information anchoring sensible concept sensitivity analyses, whereas primary analyses, may important adhere principles frequentist inference. Analyses data missing observations generally rely unverifiable missing data assumptions assumptions reference-based imputation methods relatively strong. Therefore, assumptions need clinically justified appropriate least conservative considered disease area anticipated mechanism action intervention. Conditional mean imputation combined jackknife method leads deterministic standard error estimates , consequently, confidence intervals \\(p\\)-values also deterministic. particularly important regulatory setting important ascertain whether calculated \\(p\\)-value close critical boundary 5% truly threshold rather uncertain Monte Carlo error.","code":""},{"path":"/articles/stat_specs.html","id":"computational-complexity","dir":"Articles","previous_headings":"3 Statistical methodology > 3.10 Comparison between the implemented approaches","what":"Computational complexity","title":"rbmi: Statistical Specifications","text":"Bayesian MI methods rely specification prior distributions usage Markov chain Monte Carlo (MCMC) methods. methods based multiple imputation bootstrapping require tuning parameters specification number imputations \\(M\\) bootstrap samples \\(B\\) rely numerical optimization fitting MMRM imputation models via REML. Conditional mean imputation combined jackknife tuning parameters. rbmi implementation, fitting MMRM imputation model via REML computationally expensive. MCMC sampling using rstan (Stan Development Team (2020)) typically relatively fast setting requires small burn-burn-chains. addition, number random imputations reliable inference using Rubin’s rules often smaller number resamples required jackknife bootstrap (see e.g. discussions . R. White, Royston, Wood (2011, sec. 7) Bayesian MI Appendix Wolbers et al. (2022) bootstrap). Thus, many applications, expect conventional MI based Bayesian posterior draws fastest, followed conventional MI using approximate Bayesian posterior draws conditional mean imputation combined jackknife. Conditional mean imputation combined bootstrap bootstrapped MI methods typically computationally demanding. note, implemented methods conceptually straightforward parallelise parallelisation support provided rbmi.","code":""},{"path":"/articles/stat_specs.html","id":"sec:rbmiFunctions","dir":"Articles","previous_headings":"","what":"Mapping of statistical methods to rbmi functions","title":"rbmi: Statistical Specifications","text":"full documentation rbmi package functionality refer help pages functions package vignettes. give brief overview different steps imputation procedure mapped rbmi functions: Bayesian posterior parameter draws imputation model obtained via argument method = method_bayes(). Approximate Bayesian posterior parameter draws imputation model obtained via argument method = method_approxbayes(). ML REML parameter estimates imputation model parameters original dataset leave-one-subject-datasets (required jackknife) obtained via argument method = method_condmean(type = \"jackknife\"). ML REML parameter estimates imputation model parameters original dataset bootstrapped datasets obtained via argument method = method_condmean(type = \"bootstrap\"). Bootstrapped MI methods obtained via argument method = method_bmlmi(B=B, D=D) \\(B\\) refers number bootstrap samples \\(D\\) number random imputations bootstrap sample. imputation step using random imputation deterministic conditional mean imputation, respectively, implemented function impute(). Imputation can performed assuming already implemented imputation strategies presented section 3.4. Additionally, user-defined imputation strategies also supported. analysis step implemented function analyse() applies analysis model imputed datasets. default, analysis model (argument fun) ancova() function alternative analysis functions can also provided user. analyse() function also allows \\(\\delta\\)-adjustments imputed datasets prior analysis via argument delta. inference step implemented function pool() pools results across imputed datasets. Rubin Bernard rule applied case (approximate) Bayesian MI. conditional mean imputation, jackknife bootstrap (normal approximation percentile) inference supported. BMLMI, pooling inference steps performed via pool() case implements method described Section 3.9.","code":""},{"path":"/articles/stat_specs.html","id":"sec:otherSoftware","dir":"Articles","previous_headings":"","what":"Comparison to other software implementations","title":"rbmi: Statistical Specifications","text":"established software implementation reference-based imputation SAS -called “five macros” James Roger (Roger (2021)). alternative R implementation also currently development R package RefBasedMI (McGrath White (2021)). rbmi several features supported implementations: addition Bayesian MI approach implemented also packages, implementation provides three alternative MI approaches: approximate Bayesian MI, conditional mean imputation combined resampling, bootstrapped MI. rbmi allows usage data collected ICE. example, suppose want adopt treatment policy strategy ICE “treatment discontinuation”. possible implementation strategy use observed outcome data subjects remain study ICE use reference-based imputation case subject drops . implementation, implemented excluding observed post ICE data imputation model assumes MAR missingness including analysis model. knowledge, directly supported implementations. RefBasedMI fits imputation model data treatment group separately implies covariate-treatment group interactions covariates pooled data treatment groups. contrast, Roger’s five macros assume joint model including data randomized groups covariate-treatment interactions covariates allowed. also chose implement joint model use flexible model linear predictor may may include interaction term covariate treatment group. addition, imputation model also allows inclusion time-varying covariates. implementation, grouping subjects purpose imputation model (definition reference group) need correspond assigned treatment groups. provides additional flexibility imputation procedure. clear us whether feature supported Roger’s five macros RefBasedMI. believe R-based implementation modular RefBasedMI facilitate package enhancements. contrast, general causal model introduced . White, Royes, Best (2020) available implementations currently supported .","code":""},{"path":[]},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Craig Gower-Page. Author, maintainer. Alessandro Noci. Author. Marcel Wolbers. Contributor. Roche. Copyright holder, funder.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Gower-Page C, Noci (2024). rbmi: Reference Based Multiple Imputation. R package version 1.2.6, https://github.com/insightsengineering/rbmi, https://insightsengineering.github.io/rbmi/latest-tag/.","code":"@Manual{, title = {rbmi: Reference Based Multiple Imputation}, author = {Craig Gower-Page and Alessandro Noci}, year = {2024}, note = {R package version 1.2.6, https://github.com/insightsengineering/rbmi}, url = {https://insightsengineering.github.io/rbmi/latest-tag/}, }"},{"path":[]},{"path":"/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Reference Based Multiple Imputation","text":"rbmi R package imputation missing data clinical trials continuous multivariate normal longitudinal outcomes. supports imputation missing random (MAR) assumption, reference-based imputation methods, delta adjustments (required sensitivity analysis tipping point analyses). package implements Bayesian approximate Bayesian multiple imputation combined Rubin’s rules inference, frequentist conditional mean imputation combined (jackknife bootstrap) resampling.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Reference Based Multiple Imputation","text":"package can installed directly CRAN via:","code":"install.packages(\"rbmi\")"},{"path":"/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Reference Based Multiple Imputation","text":"package designed around 4 core functions: draws() - Fits multiple imputation models impute() - Imputes multiple datasets analyse() - Analyses multiple datasets pool() - Pools multiple results single statistic basic usage core functions described quickstart vignette:","code":"vignette(topic = \"quickstart\", package = \"rbmi\")"},{"path":"/index.html","id":"support","dir":"","previous_headings":"","what":"Support","title":"Reference Based Multiple Imputation","text":"help regards using package find bug please create GitHub issue","code":""},{"path":"/reference/QR_decomp.html","id":null,"dir":"Reference","previous_headings":"","what":"QR decomposition — QR_decomp","title":"QR decomposition — QR_decomp","text":"QR decomposition defined Stan user's guide (section 1.2).","code":""},{"path":"/reference/QR_decomp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"QR decomposition — QR_decomp","text":"","code":"QR_decomp(mat)"},{"path":"/reference/QR_decomp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"QR decomposition — QR_decomp","text":"mat matrix perform QR decomposition .","code":""},{"path":"/reference/Stack.html","id":null,"dir":"Reference","previous_headings":"","what":"R6 Class for a FIFO stack — Stack","title":"R6 Class for a FIFO stack — Stack","text":"simple stack object offering add / pop functionality","code":""},{"path":"/reference/Stack.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"R6 Class for a FIFO stack — Stack","text":"stack list containing current stack","code":""},{"path":[]},{"path":"/reference/Stack.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"R6 Class for a FIFO stack — Stack","text":"Stack$add() Stack$pop() Stack$clone()","code":""},{"path":"/reference/Stack.html","id":"method-add-","dir":"Reference","previous_headings":"","what":"Method add()","title":"R6 Class for a FIFO stack — Stack","text":"Adds content end stack (must list)","code":""},{"path":"/reference/Stack.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for a FIFO stack — Stack","text":"","code":"Stack$add(x)"},{"path":"/reference/Stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for a FIFO stack — Stack","text":"x content add stack","code":""},{"path":"/reference/Stack.html","id":"method-pop-","dir":"Reference","previous_headings":"","what":"Method pop()","title":"R6 Class for a FIFO stack — Stack","text":"Retrieve content stack","code":""},{"path":"/reference/Stack.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for a FIFO stack — Stack","text":"","code":"Stack$pop(i)"},{"path":"/reference/Stack.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for a FIFO stack — Stack","text":"number items retrieve stack. less items left stack just return everything left.","code":""},{"path":"/reference/Stack.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"R6 Class for a FIFO stack — Stack","text":"objects class cloneable method.","code":""},{"path":"/reference/Stack.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for a FIFO stack — Stack","text":"","code":"Stack$clone(deep = FALSE)"},{"path":"/reference/Stack.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for a FIFO stack — Stack","text":"deep Whether make deep clone.","code":""},{"path":"/reference/add_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a class — add_class","title":"Add a class — add_class","text":"Utility function add class object. Adds new class existing classes.","code":""},{"path":"/reference/add_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a class — add_class","text":"","code":"add_class(x, cls)"},{"path":"/reference/add_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a class — add_class","text":"x object add class . cls class added.","code":""},{"path":"/reference/adjust_trajectories.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories","title":"Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories","text":"Adjust trajectories due intercurrent event (ICE)","code":""},{"path":"/reference/adjust_trajectories.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories","text":"","code":"adjust_trajectories( distr_pars_group, outcome, ids, ind_ice, strategy_fun, distr_pars_ref = NULL )"},{"path":"/reference/adjust_trajectories.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories","text":"distr_pars_group Named list containing simulation parameters multivariate normal distribution assumed given treatment group. contains following elements: mu: Numeric vector indicating mean outcome trajectory. include outcome baseline. sigma Covariance matrix outcome trajectory. outcome Numeric variable specifies longitudinal outcome. ids Factor variable specifies id subject. ind_ice binary variable takes value 1 corresponding outcome affected ICE 0 otherwise. strategy_fun Function implementing trajectories intercurrent event (ICE). Must one getStrategies(). See getStrategies() details. distr_pars_ref Optional. Named list containing simulation parameters reference arm. contains following elements: mu: Numeric vector indicating mean outcome trajectory assuming ICEs. include outcome baseline. sigma Covariance matrix outcome trajectory assuming ICEs.","code":""},{"path":"/reference/adjust_trajectories.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories","text":"numeric vector containing adjusted trajectories.","code":""},{"path":[]},{"path":"/reference/adjust_trajectories_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","title":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","text":"Adjust trajectory subject's outcome due intercurrent event (ICE)","code":""},{"path":"/reference/adjust_trajectories_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","text":"","code":"adjust_trajectories_single( distr_pars_group, outcome, strategy_fun, distr_pars_ref = NULL )"},{"path":"/reference/adjust_trajectories_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","text":"distr_pars_group Named list containing simulation parameters multivariate normal distribution assumed given treatment group. contains following elements: mu: Numeric vector indicating mean outcome trajectory. include outcome baseline. sigma Covariance matrix outcome trajectory. outcome Numeric variable specifies longitudinal outcome. strategy_fun Function implementing trajectories intercurrent event (ICE). Must one getStrategies(). See getStrategies() details. distr_pars_ref Optional. Named list containing simulation parameters reference arm. contains following elements: mu: Numeric vector indicating mean outcome trajectory assuming ICEs. include outcome baseline. sigma Covariance matrix outcome trajectory assuming ICEs.","code":""},{"path":"/reference/adjust_trajectories_single.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","text":"numeric vector containing adjusted trajectory single subject.","code":""},{"path":"/reference/adjust_trajectories_single.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","text":"outcome specified --post-ICE observations (.e. observations adjusted) set NA.","code":""},{"path":"/reference/analyse.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyse Multiple Imputed Datasets — analyse","title":"Analyse Multiple Imputed Datasets — analyse","text":"function takes multiple imputed datasets (generated impute() function) runs analysis function .","code":""},{"path":"/reference/analyse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analyse Multiple Imputed Datasets — analyse","text":"","code":"analyse(imputations, fun = ancova, delta = NULL, ...)"},{"path":"/reference/analyse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analyse Multiple Imputed Datasets — analyse","text":"imputations imputations object created impute(). fun analysis function applied imputed dataset. See details. delta data.frame containing delta transformation applied imputed datasets prior running fun. See details. ... Additional arguments passed onto fun.","code":""},{"path":"/reference/analyse.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Analyse Multiple Imputed Datasets — analyse","text":"function works performing following steps: Extract dataset imputations object. Apply delta adjustments specified delta argument. Run analysis function fun dataset. Repeat steps 1-3 across datasets inside imputations object. Collect return analysis results. analysis function fun must take data.frame first argument. options analyse() passed onto fun via .... fun must return named list element list containing single numeric element called est (additionally se df originally specified method_bayes() method_approxbayes()) .e.: Please note vars$subjid column (defined original call draws()) scrambled data.frames provided fun. say contain original subject values hard coding subject ids strictly avoided. default fun ancova() function. Please note function requires vars object, created set_vars(), provided via vars argument e.g. analyse(imputeObj, vars = set_vars(...)). Please see documentation ancova() full details. Please also note theoretical justification conditional mean imputation method (method = method_condmean() draws()) relies fact ANCOVA linear transformation outcomes. Thus care required applying alternative analysis functions setting. delta argument can used specify offsets applied outcome variable imputed datasets prior analysis. typically used sensitivity tipping point analyses. delta dataset must contain columns vars$subjid, vars$visit (specified original call draws()) delta. Essentially data.frame merged onto imputed dataset vars$subjid vars$visit outcome variable modified : Please note order provide maximum flexibility, delta argument can used modify /outcome values including imputed. Care must taken defining offsets. recommend use helper function delta_template() define delta datasets provides utility variables is_missing can used identify exactly visits imputed.","code":"myfun <- function(dat, ...) { mod_1 <- lm(data = dat, outcome ~ group) mod_2 <- lm(data = dat, outcome ~ group + covar) x <- list( trt_1 = list( est = coef(mod_1)[[group]], se = sqrt(vcov(mod_1)[group, group]), df = df.residual(mod_1) ), trt_2 = list( est = coef(mod_2)[[group]], se = sqrt(vcov(mod_2)[group, group]), df = df.residual(mod_2) ) ) return(x) } imputed_data[[vars$outcome]] <- imputed_data[[vars$outcome]] + imputed_data[[\"delta\"]]"},{"path":[]},{"path":"/reference/analyse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Analyse Multiple Imputed Datasets — analyse","text":"","code":"if (FALSE) { # \\dontrun{ vars <- set_vars( subjid = \"subjid\", visit = \"visit\", outcome = \"outcome\", group = \"group\", covariates = c(\"sex\", \"age\", \"sex*age\") ) analyse( imputations = imputeObj, vars = vars ) deltadf <- data.frame( subjid = c(\"Pt1\", \"Pt1\", \"Pt2\"), visit = c(\"Visit_1\", \"Visit_2\", \"Visit_2\"), delta = c( 5, 9, -10) ) analyse( imputations = imputeObj, delta = deltadf, vars = vars ) } # }"},{"path":"/reference/ancova.html","id":null,"dir":"Reference","previous_headings":"","what":"Analysis of Covariance — ancova","title":"Analysis of Covariance — ancova","text":"Performs analysis covariance two groups returning estimated \"treatment effect\" (.e. contrast two treatment groups) least square means estimates group.","code":""},{"path":"/reference/ancova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analysis of Covariance — ancova","text":"","code":"ancova(data, vars, visits = NULL, weights = c(\"proportional\", \"equal\"))"},{"path":"/reference/ancova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analysis of Covariance — ancova","text":"data data.frame containing data used model. vars vars object generated set_vars(). group, visit, outcome covariates elements required. See details. visits optional character vector specifying visits fit ancova model . NULL, separate ancova model fit outcomes visit (determined unique(data[[vars$visit]])). See details. weights Character, either \"proportional\" (default) \"equal\". Specifies weighting strategy used categorical covariates calculating lsmeans. See details.","code":""},{"path":"/reference/ancova.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Analysis of Covariance — ancova","text":"function works follows: Select first value visits. Subset data observations occurred visit. Fit linear model vars$outcome ~ vars$group + vars$covariates. Extract \"treatment effect\" & least square means treatment group. Repeat points 2-3 values visits. value visits provided set unique(data[[vars$visit]]). order meet formatting standards set analyse() results collapsed single list suffixed visit name, e.g.: Please note \"ref\" refers first factor level vars$group necessarily coincide control arm. Analogously, \"alt\" refers second factor level vars$group. \"trt\" refers model contrast translating mean difference second level first level. want include interaction terms model can done providing covariates argument set_vars() e.g. set_vars(covariates = c(\"sex*age\")).","code":"list( trt_visit_1 = list(est = ...), lsm_ref_visit_1 = list(est = ...), lsm_alt_visit_1 = list(est = ...), trt_visit_2 = list(est = ...), lsm_ref_visit_2 = list(est = ...), lsm_alt_visit_2 = list(est = ...), ... )"},{"path":"/reference/ancova.html","id":"weighting","dir":"Reference","previous_headings":"","what":"Weighting","title":"Analysis of Covariance — ancova","text":"\"proportional\" default scheme used. equivalent standardization, .e. lsmeans group equal predicted mean outcome ancova model group based baseline characteristics subjects regardless assigned group. alternative weighting scheme, \"equal\", creates hypothetical patients expanding combinations models categorical covariates. lsmeans calculated average predicted mean outcome hypothetical patients assuming come group turn. short: \"proportional\" weights categorical covariates based upon frequency occurrence data. \"equal\" weights categorical covariates equally across theoretical combinations.","code":""},{"path":[]},{"path":"/reference/ancova_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","title":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","text":"Performance analysis covariance. See ancova() full details.","code":""},{"path":"/reference/ancova_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","text":"","code":"ancova_single( data, outcome, group, covariates, weights = c(\"proportional\", \"equal\") )"},{"path":"/reference/ancova_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","text":"data data.frame containing data required model. outcome Character, name outcome variable data. group Character, name group variable data. covariates Character vector containing name additional covariates included model well interaction terms. weights Character, specifies whether use \"proportional\" \"equal\" weighting categorical covariate combination calculating lsmeans.","code":""},{"path":"/reference/ancova_single.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","text":"group must factor variable 2 levels. outcome must continuous numeric variable.","code":""},{"path":[]},{"path":"/reference/ancova_single.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","text":"","code":"if (FALSE) { # \\dontrun{ iris2 <- iris[ iris$Species %in% c(\"versicolor\", \"virginica\"), ] iris2$Species <- factor(iris2$Species) ancova_single(iris2, \"Sepal.Length\", \"Species\", c(\"Petal.Length * Petal.Width\")) } # }"},{"path":"/reference/antidepressant_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Antidepressant trial data — antidepressant_data","title":"Antidepressant trial data — antidepressant_data","text":"dataset containing data publicly available example data set antidepressant clinical trial. dataset available website Drug Information Association Scientific Working Group Estimands Missing Data. per website, original data antidepressant clinical trial four treatments; two doses experimental medication, positive control, placebo published Goldstein et al (2004). mask real data, week 8 observations removed two arms created: original placebo arm \"drug arm\" created randomly selecting patients three non-placebo arms.","code":""},{"path":"/reference/antidepressant_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Antidepressant trial data — antidepressant_data","text":"","code":"antidepressant_data"},{"path":"/reference/antidepressant_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Antidepressant trial data — antidepressant_data","text":"data.frame 608 rows 11 variables: PATIENT: patients IDs. HAMATOTL: total score Hamilton Anxiety Rating Scale. PGIIMP: patient's Global Impression Improvement Rating Scale. RELDAYS: number days visit baseline. VISIT: post-baseline visit. levels 4,5,6,7. THERAPY: treatment group variable. equal PLACEBO observations placebo arm, DRUG observations active arm. GENDER: patient's gender. POOLINV: pooled investigator. BASVAL: baseline outcome value. HAMDTL17: Hamilton 17-item rating scale value. CHANGE: change baseline Hamilton 17-item rating scale.","code":""},{"path":"/reference/antidepressant_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Antidepressant trial data — antidepressant_data","text":"relevant endpoint Hamilton 17-item rating scale depression (HAMD17) baseline weeks 1, 2, 4, 6 assessments included. Study drug discontinuation occurred 24% subjects active drug 26% placebo. data study drug discontinuation missing single additional intermittent missing observation.","code":""},{"path":"/reference/antidepressant_data.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Antidepressant trial data — antidepressant_data","text":"Goldstein, Lu, Detke, Wiltse, Mallinckrodt, Demitrack. Duloxetine treatment depression: double-blind placebo-controlled comparison paroxetine. J Clin Psychopharmacol 2004;24: 389-399.","code":""},{"path":"/reference/apply_delta.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies delta adjustment — apply_delta","title":"Applies delta adjustment — apply_delta","text":"Takes delta dataset adjusts outcome variable adding corresponding delta.","code":""},{"path":"/reference/apply_delta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies delta adjustment — apply_delta","text":"","code":"apply_delta(data, delta = NULL, group = NULL, outcome = NULL)"},{"path":"/reference/apply_delta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies delta adjustment — apply_delta","text":"data data.frame outcome column adjusted. delta data.frame (must contain column called delta). group character vector variables data delta used merge 2 data.frames together . outcome character, name outcome variable data.","code":""},{"path":"/reference/as_analysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct an analysis object — as_analysis","title":"Construct an analysis object — as_analysis","text":"Creates analysis object ensuring components correctly defined.","code":""},{"path":"/reference/as_analysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct an analysis object — as_analysis","text":"","code":"as_analysis(results, method, delta = NULL, fun = NULL, fun_name = NULL)"},{"path":"/reference/as_analysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct an analysis object — as_analysis","text":"results list lists contain analysis results imputation See analyse() details object look like. method method object specified draws(). delta delta dataset used. See analyse() details specified. fun analysis function used. fun_name character name analysis function (used printing) purposes.","code":""},{"path":"/reference/as_ascii_table.html","id":null,"dir":"Reference","previous_headings":"","what":"as_ascii_table — as_ascii_table","title":"as_ascii_table — as_ascii_table","text":"function takes data.frame attempts convert simple ascii format suitable printing screen assumed variable values .character() method order cast character.","code":""},{"path":"/reference/as_ascii_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"as_ascii_table — as_ascii_table","text":"","code":"as_ascii_table(dat, line_prefix = \" \", pcol = NULL)"},{"path":"/reference/as_ascii_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"as_ascii_table — as_ascii_table","text":"dat Input dataset convert ascii table line_prefix Symbols prefix infront every line table pcol name column handled p-value. Sets value <0.001 value 0 rounding","code":""},{"path":"/reference/as_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Class — as_class","title":"Set Class — as_class","text":"Utility function set objects class.","code":""},{"path":"/reference/as_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Class — as_class","text":"","code":"as_class(x, cls)"},{"path":"/reference/as_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Class — as_class","text":"x object set class . cls class set.","code":""},{"path":"/reference/as_cropped_char.html","id":null,"dir":"Reference","previous_headings":"","what":"as_cropped_char — as_cropped_char","title":"as_cropped_char — as_cropped_char","text":"Makes character string x chars Reduce x char string ...","code":""},{"path":"/reference/as_cropped_char.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"as_cropped_char — as_cropped_char","text":"","code":"as_cropped_char(inval, crop_at = 30, ndp = 3)"},{"path":"/reference/as_cropped_char.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"as_cropped_char — as_cropped_char","text":"inval single element value crop_at character limit ndp Number decimal places display","code":""},{"path":"/reference/as_dataframe.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert object to dataframe — as_dataframe","title":"Convert object to dataframe — as_dataframe","text":"Convert object dataframe","code":""},{"path":"/reference/as_dataframe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert object to dataframe — as_dataframe","text":"","code":"as_dataframe(x)"},{"path":"/reference/as_dataframe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert object to dataframe — as_dataframe","text":"x data.frame like object Utility function convert \"data.frame-like\" object actual data.frame avoid issues inconsistency methods ( [() dplyr's grouped dataframes)","code":""},{"path":"/reference/as_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a draws object — as_draws","title":"Creates a draws object — as_draws","text":"Creates draws object final output call draws().","code":""},{"path":"/reference/as_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a draws object — as_draws","text":"","code":"as_draws(method, samples, data, formula, n_failures = NULL, fit = NULL)"},{"path":"/reference/as_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a draws object — as_draws","text":"method method object generated either method_bayes(), method_approxbayes(), method_condmean() method_bmlmi(). samples list sample_single objects. See sample_single(). data R6 longdata object containing relevant input data information. formula Fixed effects formula object used model specification. n_failures Absolute number failures model fit. fit method_bayes() chosen, returns MCMC Stan fit object. Otherwise NULL.","code":""},{"path":"/reference/as_draws.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a draws object — as_draws","text":"draws object named list containing following: data: R6 longdata object containing relevant input data information. method: method object generated either method_bayes(), method_approxbayes() method_condmean(). samples: list containing estimated parameters interest. element samples named list containing following: ids: vector characters containing ids subjects included original dataset. beta: numeric vector estimated regression coefficients. sigma: list estimated covariance matrices (one level vars$group). theta: numeric vector transformed covariances. failed: Logical. TRUE model fit failed. ids_samp: vector characters containing ids subjects included given sample. fit: method_bayes() chosen, returns MCMC Stan fit object. Otherwise NULL. n_failures: absolute number failures model fit. Relevant method_condmean(type = \"bootstrap\"), method_approxbayes() method_bmlmi(). formula: fixed effects formula object used model specification.","code":""},{"path":"/reference/as_imputation.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an imputation object — as_imputation","title":"Create an imputation object — as_imputation","text":"function creates object returned impute(). Essentially glorified wrapper around list() ensuring required elements set class added expected.","code":""},{"path":"/reference/as_imputation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an imputation object — as_imputation","text":"","code":"as_imputation(imputations, data, method, references)"},{"path":"/reference/as_imputation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an imputation object — as_imputation","text":"imputations list imputations_list's created imputation_df() data longdata object created longDataConstructor() method method object created method_condmean(), method_bayes() method_approxbayes() references named vector. Identifies references used generating imputed values. form c(\"Group\" = \"Reference\", \"Group\" = \"Reference\").","code":""},{"path":"/reference/as_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert indicator to index — as_indices","title":"Convert indicator to index — as_indices","text":"Converts string 0's 1's index positions 1's padding results 0's length","code":""},{"path":"/reference/as_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert indicator to index — as_indices","text":"","code":"as_indices(x)"},{"path":"/reference/as_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert indicator to index — as_indices","text":"x character vector whose values either \"0\" \"1\". elements vector must length","code":""},{"path":"/reference/as_indices.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert indicator to index — as_indices","text":".e.","code":"patmap(c(\"1101\", \"0001\")) -> list(c(1,2,4,999), c(4,999, 999, 999))"},{"path":"/reference/as_mmrm_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a ","title":"Creates a ","text":"Converts design matrix + key variables common format particular function following: Renames covariates V1, V2, etc avoid issues special characters variable names Ensures key variables right type Inserts outcome, visit subjid variables data.frame naming outcome, visit subjid provided also insert group variable data.frame named group","code":""},{"path":"/reference/as_mmrm_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a ","text":"","code":"as_mmrm_df(designmat, outcome, visit, subjid, group = NULL)"},{"path":"/reference/as_mmrm_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a ","text":"designmat data.frame matrix containing covariates use MMRM model. Dummy variables must already expanded , .e. via stats::model.matrix(). contain missing values outcome numeric vector. outcome value regressed MMRM model. visit character / factor vector. Indicates visit outcome value occurred . subjid character / factor vector. subject identifier used link separate visits belong subject. group character / factor vector. Indicates treatment group patient belongs .","code":""},{"path":"/reference/as_mmrm_formula.html","id":null,"dir":"Reference","previous_headings":"","what":"Create MMRM formula — as_mmrm_formula","title":"Create MMRM formula — as_mmrm_formula","text":"Derives MMRM model formula structure mmrm_df. returns formula object form:","code":""},{"path":"/reference/as_mmrm_formula.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create MMRM formula — as_mmrm_formula","text":"","code":"as_mmrm_formula(mmrm_df, cov_struct)"},{"path":"/reference/as_mmrm_formula.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create MMRM formula — as_mmrm_formula","text":"mmrm_df mmrm data.frame created as_mmrm_df() cov_struct Character - covariance structure used, must one \"us\", \"toep\", \"cs\", \"ar1\"","code":""},{"path":"/reference/as_mmrm_formula.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create MMRM formula — as_mmrm_formula","text":"","code":"outcome ~ 0 + V1 + V2 + V4 + ... + us(visit | group / subjid)"},{"path":"/reference/as_model_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand data.frame into a design matrix — as_model_df","title":"Expand data.frame into a design matrix — as_model_df","text":"Expands data.frame using formula create design matrix. Key details always place outcome variable first column return object.","code":""},{"path":"/reference/as_model_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand data.frame into a design matrix — as_model_df","text":"","code":"as_model_df(dat, frm)"},{"path":"/reference/as_model_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand data.frame into a design matrix — as_model_df","text":"dat data.frame frm formula","code":""},{"path":"/reference/as_model_df.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Expand data.frame into a design matrix — as_model_df","text":"outcome column may contain NA's none variables listed formula contain missing values","code":""},{"path":"/reference/as_simple_formula.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a simple formula object from a string — as_simple_formula","title":"Creates a simple formula object from a string — as_simple_formula","text":"Converts string list variables formula object","code":""},{"path":"/reference/as_simple_formula.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a simple formula object from a string — as_simple_formula","text":"","code":"as_simple_formula(outcome, covars)"},{"path":"/reference/as_simple_formula.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a simple formula object from a string — as_simple_formula","text":"outcome character (length 1 vector). Name outcome variable covars character (vector). Name covariates","code":""},{"path":"/reference/as_simple_formula.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a simple formula object from a string — as_simple_formula","text":"formula","code":""},{"path":"/reference/as_stan_array.html","id":null,"dir":"Reference","previous_headings":"","what":"As array — as_stan_array","title":"As array — as_stan_array","text":"Converts numeric value length 1 1 dimension array. avoid type errors thrown stan length 1 numeric vectors provided R stan::vector inputs","code":""},{"path":"/reference/as_stan_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"As array — as_stan_array","text":"","code":"as_stan_array(x)"},{"path":"/reference/as_stan_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"As array — as_stan_array","text":"x numeric vector","code":""},{"path":"/reference/as_strata.html","id":null,"dir":"Reference","previous_headings":"","what":"Create vector of Stratas — as_strata","title":"Create vector of Stratas — as_strata","text":"Collapse multiple categorical variables distinct unique categories. e.g. return","code":"as_strata(c(1,1,2,2,2,1), c(5,6,5,5,6,5)) c(1,2,3,3,4,1)"},{"path":"/reference/as_strata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create vector of Stratas — as_strata","text":"","code":"as_strata(...)"},{"path":"/reference/as_strata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create vector of Stratas — as_strata","text":"... numeric/character/factor vectors length","code":""},{"path":"/reference/as_strata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create vector of Stratas — as_strata","text":"","code":"if (FALSE) { # \\dontrun{ as_strata(c(1,1,2,2,2,1), c(5,6,5,5,6,5)) } # }"},{"path":"/reference/assert_variables_exist.html","id":null,"dir":"Reference","previous_headings":"","what":"Assert that all variables exist within a dataset — assert_variables_exist","title":"Assert that all variables exist within a dataset — assert_variables_exist","text":"Performs assertion check ensure vector variable exists within data.frame expected.","code":""},{"path":"/reference/assert_variables_exist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assert that all variables exist within a dataset — assert_variables_exist","text":"","code":"assert_variables_exist(data, vars)"},{"path":"/reference/assert_variables_exist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assert that all variables exist within a dataset — assert_variables_exist","text":"data data.frame vars character vector variable names","code":""},{"path":"/reference/char2fct.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert character variables to factor — char2fct","title":"Convert character variables to factor — char2fct","text":"Provided vector variable names function converts character variables factors. affect numeric existing factor variables","code":""},{"path":"/reference/char2fct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert character variables to factor — char2fct","text":"","code":"char2fct(data, vars = NULL)"},{"path":"/reference/char2fct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert character variables to factor — char2fct","text":"data data.frame vars character vector variables data","code":""},{"path":"/reference/check_ESS.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostics of the MCMC based on ESS — check_ESS","title":"Diagnostics of the MCMC based on ESS — check_ESS","text":"Check quality MCMC draws posterior distribution checking whether relative ESS sufficiently large.","code":""},{"path":"/reference/check_ESS.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostics of the MCMC based on ESS — check_ESS","text":"","code":"check_ESS(stan_fit, n_draws, threshold_lowESS = 0.4)"},{"path":"/reference/check_ESS.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostics of the MCMC based on ESS — check_ESS","text":"stan_fit stanfit object. n_draws Number MCMC draws. threshold_lowESS number [0,1] indicating minimum acceptable value relative ESS. See details.","code":""},{"path":"/reference/check_ESS.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostics of the MCMC based on ESS — check_ESS","text":"warning message case detected problems.","code":""},{"path":"/reference/check_ESS.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diagnostics of the MCMC based on ESS — check_ESS","text":"check_ESS() works follows: Extract ESS stan_fit parameter model. Compute relative ESS (.e. ESS divided number draws). Check whether parameter ESS lower threshold. least one parameter relative ESS threshold, warning thrown.","code":""},{"path":"/reference/check_hmc_diagn.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn","title":"Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn","text":"Check : divergent iterations. Bayesian Fraction Missing Information (BFMI) sufficiently low. number iterations saturated max treedepth zero. Please see rstan::check_hmc_diagnostics() details.","code":""},{"path":"/reference/check_hmc_diagn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn","text":"","code":"check_hmc_diagn(stan_fit)"},{"path":"/reference/check_hmc_diagn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn","text":"stan_fit stanfit object.","code":""},{"path":"/reference/check_hmc_diagn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn","text":"warning message case detected problems.","code":""},{"path":"/reference/check_mcmc.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostics of the MCMC — check_mcmc","title":"Diagnostics of the MCMC — check_mcmc","text":"Diagnostics MCMC","code":""},{"path":"/reference/check_mcmc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostics of the MCMC — check_mcmc","text":"","code":"check_mcmc(stan_fit, n_draws, threshold_lowESS = 0.4)"},{"path":"/reference/check_mcmc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostics of the MCMC — check_mcmc","text":"stan_fit stanfit object. n_draws Number MCMC draws. threshold_lowESS number [0,1] indicating minimum acceptable value relative ESS. See details.","code":""},{"path":"/reference/check_mcmc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostics of the MCMC — check_mcmc","text":"warning message case detected problems.","code":""},{"path":"/reference/check_mcmc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diagnostics of the MCMC — check_mcmc","text":"Performs checks quality MCMC. See check_ESS() check_hmc_diagn() details.","code":""},{"path":"/reference/compute_sigma.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma","title":"Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma","text":"Adapt covariance matrix reference-based methods. Used Copy Increments Reference (CIR) Jump Reference (JTR) methods, adapt covariance matrix different pre-deviation post deviation covariance structures. See Carpenter et al. (2013)","code":""},{"path":"/reference/compute_sigma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma","text":"","code":"compute_sigma(sigma_group, sigma_ref, index_mar)"},{"path":"/reference/compute_sigma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma","text":"sigma_group covariance matrix dimensions equal index_mar subjects original group sigma_ref covariance matrix dimensions equal index_mar subjects reference group index_mar logical vector indicating visits meet MAR assumption subject. .e. identifies observations non-MAR intercurrent event (ICE).","code":""},{"path":"/reference/compute_sigma.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma","text":"Carpenter, James R., James H. Roger, Michael G. Kenward. \"Analysis longitudinal trials protocol deviation: framework relevant, accessible assumptions, inference via multiple imputation.\" Journal Biopharmaceutical statistics 23.6 (2013): 1352-1371.","code":""},{"path":"/reference/convert_to_imputation_list_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's) — convert_to_imputation_list_df","title":"Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's) — convert_to_imputation_list_df","text":"Convert list imputation_list_single() objects imputation_list_df() object (.e. list imputation_df() objects's)","code":""},{"path":"/reference/convert_to_imputation_list_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's) — convert_to_imputation_list_df","text":"","code":"convert_to_imputation_list_df(imputes, sample_ids)"},{"path":"/reference/convert_to_imputation_list_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's) — convert_to_imputation_list_df","text":"imputes list imputation_list_single() objects sample_ids list 1 element per required imputation_df. element must contain vector \"ID\"'s correspond imputation_single() ID's required dataset. total number ID's must equal total number rows within imputes$imputations accommodate method_bmlmi() impute_data_individual() function returns list imputation_list_single() objects 1 object per subject. imputation_list_single() stores subjects imputations matrix columns matrix correspond D method_bmlmi(). Note methods (.e. methods_*()) special case D = 1. number rows matrix varies subject equal number times patient selected imputation (non-conditional mean methods 1 per subject per imputed dataset). function best illustrated example: convert_to_imputation_df(imputes, sample_ids) result : Note different repetitions (.e. value set D) grouped together sequentially.","code":"imputes = list( imputation_list_single( id = \"Tom\", imputations = matrix( imputation_single_t_1_1, imputation_single_t_1_2, imputation_single_t_2_1, imputation_single_t_2_2, imputation_single_t_3_1, imputation_single_t_3_2 ) ), imputation_list_single( id = \"Tom\", imputations = matrix( imputation_single_h_1_1, imputation_single_h_1_2, ) ) ) sample_ids <- list( c(\"Tom\", \"Harry\", \"Tom\"), c(\"Tom\") ) imputation_list_df( imputation_df( imputation_single_t_1_1, imputation_single_h_1_1, imputation_single_t_2_1 ), imputation_df( imputation_single_t_1_2, imputation_single_h_1_2, imputation_single_t_2_2 ), imputation_df( imputation_single_t_3_1 ), imputation_df( imputation_single_t_3_2 ) )"},{"path":"/reference/d_lagscale.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate delta from a lagged scale coefficient — d_lagscale","title":"Calculate delta from a lagged scale coefficient — d_lagscale","text":"Calculates delta value based upon baseline delta value post ICE scaling coefficient.","code":""},{"path":"/reference/d_lagscale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate delta from a lagged scale coefficient — d_lagscale","text":"","code":"d_lagscale(delta, dlag, is_post_ice)"},{"path":"/reference/d_lagscale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate delta from a lagged scale coefficient — d_lagscale","text":"delta numeric vector. Determines baseline amount delta applied visit. dlag numeric vector. Determines scaling applied delta based upon visit ICE occurred . Must length delta. is_post_ice logical vector. Indicates whether visit \"post-ICE\" .","code":""},{"path":"/reference/d_lagscale.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate delta from a lagged scale coefficient — d_lagscale","text":"See delta_template() full details calculation performed.","code":""},{"path":"/reference/delta_template.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a delta data.frame template — delta_template","title":"Create a delta data.frame template — delta_template","text":"Creates data.frame format required analyse() use applying delta adjustment.","code":""},{"path":"/reference/delta_template.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a delta data.frame template — delta_template","text":"","code":"delta_template(imputations, delta = NULL, dlag = NULL, missing_only = TRUE)"},{"path":"/reference/delta_template.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a delta data.frame template — delta_template","text":"imputations imputation object created impute(). delta NULL numeric vector. Determines baseline amount delta applied visit. See details. numeric vector must length number unique visits original dataset. dlag NULL numeric vector. Determines scaling applied delta based upon visit ICE occurred . See details. numeric vector must length number unique visits original dataset. missing_only Logical, TRUE non-missing post-ICE data delta value 0 assigned. Note calculation (described details section) performed first overwritten 0's end (.e. delta values missing post-ICE visits stay regardless option).","code":""},{"path":"/reference/delta_template.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a delta data.frame template — delta_template","text":"apply delta adjustment analyse() function expects delta data.frame 3 variables: vars$subjid, vars$visit delta (vars object supplied original call draws() created set_vars() function). function return data.frame aforementioned variables one row per subject per visit. delta argument function NULL delta column returned data.frame 0 observations. delta argument NULL delta calculated separately subject accumulative sum delta multiplied scaling coefficient dlag based upon many visits subject's intercurrent event (ICE) visit question . best illustrated example: Let delta = c(5,6,7,8) dlag=c(1,2,3,4) (.e. assuming 4 visits) lets say subject ICE visit 2. calculation follows: say subject delta offset 0 applied visit-1, 6 visit-2, 20 visit-3 44 visit-4. comparison, lets say subject instead ICE visit 3, calculation follows: terms practical usage, lets say wanted delta 5 used post ICE visits regardless proximity ICE visit. can achieved setting delta = c(5,5,5,5) dlag = c(1,0,0,0). example lets say subject ICE visit-1, calculation follows: Another way using arguments set delta difference time visits dlag amount delta per unit time. example lets say visit weeks 1, 5, 6 & 9 want delta 3 applied week ICE. can achieved setting delta = c(0,4,1,3) (difference weeks visit) dlag = c(3, 3, 3, 3). example lets say subject ICE week-5 (.e. visit-2) calculation : .e. week-6 (1 week ICE) delta 3 week-9 (4 weeks ICE) delta 12. Please note function also returns several utility variables user can create custom logic defining delta set . additional variables include: is_mar - observation missing regarded MAR? variable set FALSE observations occurred non-MAR ICE, otherwise set TRUE. is_missing - outcome variable observation missing. is_post_ice - observation occur patient's ICE defined data_ice dataset supplied draws(). strategy - imputation strategy assigned subject. design implementation function largely based upon functionality implemented called \"five marcos\" James Roger. See Roger (2021).","code":"v1 v2 v3 v4 -------------- 5 6 7 8 # delta assigned to each visit 0 1 2 3 # lagged scaling starting from the first visit after the subjects ICE -------------- 0 6 14 24 # delta * lagged scaling -------------- 0 6 20 44 # accumulative sum of delta to be applied to each visit v1 v2 v3 v4 -------------- 5 6 7 8 # delta assigned to each visit 0 0 1 2 # lagged scaling starting from the first visit after the subjects ICE -------------- 0 0 7 16 # delta * lagged scaling -------------- 0 0 7 23 # accumulative sum of delta to be applied to each visit v1 v2 v3 v4 -------------- 5 5 5 5 # delta assigned to each visit 1 0 0 0 # lagged scaling starting from the first visit after the subjects ICE -------------- 5 0 0 0 # delta * lagged scaling -------------- 5 5 5 5 # accumulative sum of delta to be applied to each visit v1 v2 v3 v4 -------------- 0 4 1 3 # delta assigned to each visit 0 0 3 3 # lagged scaling starting from the first visit after the subjects ICE -------------- 0 0 3 9 # delta * lagged scaling -------------- 0 0 3 12 # accumulative sum of delta to be applied to each visit"},{"path":"/reference/delta_template.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create a delta data.frame template — delta_template","text":"Roger, James. Reference-based mi via multivariate normal rm (“five macros” miwithd), 2021. URL https://www.lshtm.ac.uk/research/centres-projects-groups/missing-data#dia-missing-data.","code":""},{"path":[]},{"path":"/reference/delta_template.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a delta data.frame template — delta_template","text":"","code":"if (FALSE) { # \\dontrun{ delta_template(imputeObj) delta_template(imputeObj, delta = c(5,6,7,8), dlag = c(1,2,3,4)) } # }"},{"path":"/reference/do_not_run.html","id":null,"dir":"Reference","previous_headings":"","what":"Do not run this function — do_not_run","title":"Do not run this function — do_not_run","text":"function exists suppress false positive R CMD Check unused libraries","code":""},{"path":"/reference/do_not_run.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Do not run this function — do_not_run","text":"","code":"do_not_run()"},{"path":"/reference/do_not_run.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Do not run this function — do_not_run","text":"rstantools RcppParallel required used installation time. case RcppParallel used src/Makevars file created fly installation rstantools. rstantools used configure file.","code":""},{"path":"/reference/draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the base imputation model and get parameter estimates — draws","title":"Fit the base imputation model and get parameter estimates — draws","text":"draws fits base imputation model observed outcome data according given multiple imputation methodology. According user's method specification, returns either draws posterior distribution model parameters required Bayesian multiple imputation frequentist parameter estimates original data bootstrapped leave-one-datasets required conditional mean imputation. purpose imputation model estimate model parameters absence intercurrent events (ICEs) handled using reference-based imputation methods. reason, observed outcome data ICEs, reference-based imputation methods specified, removed considered missing purpose estimating imputation model, purpose . imputation model mixed model repeated measures (MMRM) valid missing--random (MAR) assumption. can fit using maximum likelihood (ML) restricted ML (REML) estimation, Bayesian approach, approximate Bayesian approach according user's method specification. ML/REML approaches approximate Bayesian approach support several possible covariance structures, Bayesian approach based MCMC sampling supports unstructured covariance structure. case covariance matrix can assumed different across group.","code":""},{"path":"/reference/draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the base imputation model and get parameter estimates — draws","text":"","code":"draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE) # S3 method for class 'approxbayes' draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE) # S3 method for class 'condmean' draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE) # S3 method for class 'bmlmi' draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE) # S3 method for class 'bayes' draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE)"},{"path":"/reference/draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the base imputation model and get parameter estimates — draws","text":"data data.frame containing data used model. See details. data_ice data.frame specifies information related ICEs imputation strategies. See details. vars vars object generated set_vars(). See details. method method object generated either method_bayes(), method_approxbayes(), method_condmean() method_bmlmi(). specifies multiple imputation methodology used. See details. ncores single numeric specifying number cores use creating draws object. Note parameter ignored method_bayes() (Default = 1). quiet Logical, TRUE suppress printing progress information printed console.","code":""},{"path":"/reference/draws.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the base imputation model and get parameter estimates — draws","text":"draws object named list containing following: data: R6 longdata object containing relevant input data information. method: method object generated either method_bayes(), method_approxbayes() method_condmean(). samples: list containing estimated parameters interest. element samples named list containing following: ids: vector characters containing ids subjects included original dataset. beta: numeric vector estimated regression coefficients. sigma: list estimated covariance matrices (one level vars$group). theta: numeric vector transformed covariances. failed: Logical. TRUE model fit failed. ids_samp: vector characters containing ids subjects included given sample. fit: method_bayes() chosen, returns MCMC Stan fit object. Otherwise NULL. n_failures: absolute number failures model fit. Relevant method_condmean(type = \"bootstrap\"), method_approxbayes() method_bmlmi(). formula: fixed effects formula object used model specification.","code":""},{"path":"/reference/draws.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit the base imputation model and get parameter estimates — draws","text":"draws performs first step multiple imputation (MI) procedure: fitting base imputation model. goal estimate parameters interest needed imputation phase (.e. regression coefficients covariance matrices MMRM model). function distinguishes following methods: Bayesian MI based MCMC sampling: draws returns draws posterior distribution parameters using Bayesian approach based MCMC sampling. method can specified using method = method_bayes(). Approximate Bayesian MI based bootstrapping: draws returns draws posterior distribution parameters using approximate Bayesian approach, sampling posterior distribution simulated fitting MMRM model bootstrap samples original dataset. method can specified using method = method_approxbayes()]. Conditional mean imputation bootstrap re-sampling: draws returns MMRM parameter estimates original dataset n_samples bootstrap samples. method can specified using method = method_condmean() argument type = \"bootstrap\". Conditional mean imputation jackknife re-sampling: draws returns MMRM parameter estimates original dataset leave-one-subject-sample. method can specified using method = method_condmean() argument type = \"jackknife\". Bootstrapped Maximum Likelihood MI: draws returns MMRM parameter estimates given number bootstrap samples needed perform random imputations bootstrapped samples. method can specified using method = method_bmlmi(). Bayesian MI based MCMC sampling proposed Carpenter, Roger, Kenward (2013) first introduced reference-based imputation methods. Approximate Bayesian MI discussed Little Rubin (2002). Conditional mean imputation methods discussed Wolbers et al (2022). Bootstrapped Maximum Likelihood MI described Von Hippel & Bartlett (2021). argument data contains longitudinal data. must least following variables: subjid: factor vector containing subject ids. visit: factor vector containing visit outcome observed . group: factor vector containing group subject belongs . outcome: numeric vector containing outcome variable. might contain missing values. Additional baseline time-varying covariates must included data. data must one row per visit per subject. means incomplete outcome data must set NA instead related row missing. Missing values covariates allowed. data incomplete expand_locf() helper function can used insert missing rows using Last Observation Carried Forward (LOCF) imputation impute covariates values. Note LOCF generally principled imputation method used appropriate specific covariate. Please note special provisioning baseline outcome values. want baseline observations included model part response variable removed advance outcome variable data. time want include baseline outcome covariate model, included separate column data (covariate). Character covariates explicitly cast factors. use custom analysis function requires specific reference levels character covariates (example computation least square means computation) advised manually cast character covariates factor advance running draws(). argument data_ice contains information occurrence ICEs. data.frame 3 columns: Subject ID: character vector containing ids subjects experienced ICE. column must named specified vars$subjid. Visit: character vector containing first visit occurrence ICE (.e. first visit affected ICE). visits must equal one levels data[[vars$visit]]. multiple ICEs happen subject, first non-MAR visit used. column must named specified vars$visit. Strategy: character vector specifying imputation strategy address ICE subject. column must named specified vars$strategy. Possible imputation strategies : \"MAR\": Missing Random. \"CIR\": Copy Increments Reference. \"CR\": Copy Reference. \"JR\": Jump Reference. \"LMCF\": Last Mean Carried Forward. explanations imputation strategies, see Carpenter, Roger, Kenward (2013), Cro et al (2021), Wolbers et al (2022). Please note user-defined imputation strategies can also set. data_ice argument necessary stage since (explained Wolbers et al (2022)), model fitted removing observations incompatible imputation model, .e. observed data data_ice[[vars$visit]] addressed imputation strategy different MAR excluded model fit. However observations discarded data imputation phase (performed function (impute()). summarize, stage pre-ICE data post-ICE data ICEs MAR imputation specified used. data_ice argument omitted, subject record within data_ice, assumed relevant subject's data pre-ICE missing visits imputed MAR assumption observed data used fit base imputation model. Please note ICE visit updated via update_strategy argument impute(); means subjects record data_ice always missing data imputed MAR assumption even strategy updated. vars argument named list specifies names key variables within data data_ice. list created set_vars() contains following named elements: subjid: name column data data_ice contains subject ids variable. visit: name column data data_ice contains visit variable. group: name column data contains group variable. outcome: name column data contains outcome variable. covariates: vector characters contains covariates included model (including interactions specified \"covariateName1*covariateName2\"``). covariates provided default model specification outcome ~ 1 + visit + groupwill used. Please note thegroup*visit` interaction included model default. strata: covariates used stratification variables bootstrap sampling. default vars$group set stratification variable. Needed method_condmean(type = \"bootstrap\") method_approxbayes(). strategy: name column data_ice contains subject-specific imputation strategy.","code":""},{"path":"/reference/draws.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit the base imputation model and get parameter estimates — draws","text":"James R Carpenter, James H Roger, Michael G Kenward. Analysis longitudinal trials protocol deviation: framework relevant, accessible assumptions, inference via multiple imputation. Journal Biopharmaceutical Statistics, 23(6):1352–1371, 2013. Suzie Cro, Tim P Morris, Michael G Kenward, James R Carpenter. Sensitivity analysis clinical trials missing continuous outcome data using controlled multiple imputation: practical guide. Statistics Medicine, 39(21):2815–2842, 2020. Roderick J. . Little Donald B. Rubin. Statistical Analysis Missing Data, Second Edition. John Wiley & Sons, Hoboken, New Jersey, 2002. [Section 10.2.3] Marcel Wolbers, Alessandro Noci, Paul Delmar, Craig Gower-Page, Sean Yiu, Jonathan W. Bartlett. Standard reference-based conditional mean imputation. https://arxiv.org/abs/2109.11162, 2022. Von Hippel, Paul T Bartlett, Jonathan W. Maximum likelihood multiple imputation: Faster imputations consistent standard errors without posterior draws. 2021.","code":""},{"path":[]},{"path":"/reference/encap_get_mmrm_sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Encapsulate get_mmrm_sample — encap_get_mmrm_sample","title":"Encapsulate get_mmrm_sample — encap_get_mmrm_sample","text":"Function creates new wrapper function around get_mmrm_sample() arguments get_mmrm_sample() enclosed within new function. makes running parallel single process calls function smoother. particular function takes care exporting arguments required parallel process cluster","code":""},{"path":"/reference/encap_get_mmrm_sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Encapsulate get_mmrm_sample — encap_get_mmrm_sample","text":"","code":"encap_get_mmrm_sample(cl, longdata, method)"},{"path":"/reference/encap_get_mmrm_sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Encapsulate get_mmrm_sample — encap_get_mmrm_sample","text":"cl Either cluster get_cluster() NULL longdata longdata object longDataConstructor$new() method method object","code":""},{"path":[]},{"path":"/reference/eval_mmrm.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate a call to mmrm — eval_mmrm","title":"Evaluate a call to mmrm — eval_mmrm","text":"utility function attempts evaluate call mmrm managing warnings errors thrown. particular function attempts catch warnings errors instead surfacing simply add additional element failed value TRUE. allows multiple calls made without program exiting.","code":""},{"path":"/reference/eval_mmrm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate a call to mmrm — eval_mmrm","text":"","code":"eval_mmrm(expr)"},{"path":"/reference/eval_mmrm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate a call to mmrm — eval_mmrm","text":"expr expression evaluated. call mmrm::mmrm().","code":""},{"path":"/reference/eval_mmrm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Evaluate a call to mmrm — eval_mmrm","text":"function originally developed use glmmTMB needed hand-holding dropping false-positive warnings. important now kept around encase need catch false-positive warnings future.","code":""},{"path":[]},{"path":"/reference/eval_mmrm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluate a call to mmrm — eval_mmrm","text":"","code":"if (FALSE) { # \\dontrun{ eval_mmrm({ mmrm::mmrm(formula, data) }) } # }"},{"path":"/reference/expand.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand and fill in missing data.frame rows — expand","title":"Expand and fill in missing data.frame rows — expand","text":"functions essentially wrappers around base::expand.grid() ensure missing combinations data inserted data.frame imputation/fill methods updating covariate values newly created rows.","code":""},{"path":"/reference/expand.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand and fill in missing data.frame rows — expand","text":"","code":"expand(data, ...) fill_locf(data, vars, group = NULL, order = NULL) expand_locf(data, ..., vars, group, order)"},{"path":"/reference/expand.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand and fill in missing data.frame rows — expand","text":"data dataset expand fill . ... variables levels expanded (note duplicate entries levels result multiple rows level). vars character vector containing names variables need filled . group character vector containing names variables group performing LOCF imputation var. order character vector containing names additional variables sort data.frame performing LOCF.","code":""},{"path":"/reference/expand.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Expand and fill in missing data.frame rows — expand","text":"draws() function makes assumption subjects visits present data.frame covariate values non missing; expand(), fill_locf() expand_locf() utility functions support users ensuring data.frame's conform assumptions. expand() takes vectors expected levels data.frame expands combinations inserting missing rows data.frame. Note \"expanded\" variables cast factors. fill_locf() applies LOCF imputation named covariates fill NAs created insertion new rows expand() (though note distinction made existing NAs newly created NAs). Note data.frame sorted c(group, order) performing LOCF imputation; data.frame returned original sort order however. expand_locf() simple composition function fill_locf() expand() .e. fill_locf(expand(...)).","code":""},{"path":"/reference/expand.html","id":"missing-first-values","dir":"Reference","previous_headings":"","what":"Missing First Values","title":"Expand and fill in missing data.frame rows — expand","text":"fill_locf() function performs last observation carried forward imputation. natural consequence unable impute missing observations observation first value given subject / grouping. values deliberately imputed risks silent errors case time varying covariates. One solution first use expand_locf() just visit variable time varying covariates merge baseline covariates afterwards .e.","code":"library(dplyr) dat_expanded <- expand( data = dat, subject = c(\"pt1\", \"pt2\", \"pt3\", \"pt4\"), visit = c(\"vis1\", \"vis2\", \"vis3\") ) dat_filled <- dat_expanded %>% left_join(baseline_covariates, by = \"subject\")"},{"path":"/reference/expand.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expand and fill in missing data.frame rows — expand","text":"","code":"if (FALSE) { # \\dontrun{ dat_expanded <- expand( data = dat, subject = c(\"pt1\", \"pt2\", \"pt3\", \"pt4\"), visit = c(\"vis1\", \"vis2\", \"vis3\") ) dat_filled <- fill_loc( data = dat_expanded, vars = c(\"Sex\", \"Age\"), group = \"subject\", order = \"visit\" ) ## Or dat_filled <- expand_locf( data = dat, subject = c(\"pt1\", \"pt2\", \"pt3\", \"pt4\"), visit = c(\"vis1\", \"vis2\", \"vis3\"), vars = c(\"Sex\", \"Age\"), group = \"subject\", order = \"visit\" ) } # }"},{"path":"/reference/extract_covariates.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Variables from string vector — extract_covariates","title":"Extract Variables from string vector — extract_covariates","text":"Takes string including potentially model terms like * : extracts individual variables","code":""},{"path":"/reference/extract_covariates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Variables from string vector — extract_covariates","text":"","code":"extract_covariates(x)"},{"path":"/reference/extract_covariates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Variables from string vector — extract_covariates","text":"x string variable names potentially including interaction terms","code":""},{"path":"/reference/extract_covariates.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract Variables from string vector — extract_covariates","text":".e. c(\"v1\", \"v2\", \"v2*v3\", \"v1:v2\") becomes c(\"v1\", \"v2\", \"v3\")","code":""},{"path":"/reference/extract_data_nmar_as_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na","title":"Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na","text":"Set NA outcome values MNAR missing (.e. occur ICE handled using reference-based imputation strategy)","code":""},{"path":"/reference/extract_data_nmar_as_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na","text":"","code":"extract_data_nmar_as_na(longdata)"},{"path":"/reference/extract_data_nmar_as_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na","text":"longdata R6 longdata object containing relevant input data information.","code":""},{"path":"/reference/extract_data_nmar_as_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na","text":"data.frame containing longdata$get_data(longdata$ids), MNAR outcome values set NA.","code":""},{"path":"/reference/extract_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract draws from a stanfit object — extract_draws","title":"Extract draws from a stanfit object — extract_draws","text":"Extract draws stanfit object convert lists. function rstan::extract() returns draws given parameter array. function calls rstan::extract() extract draws stanfit object convert arrays lists.","code":""},{"path":"/reference/extract_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract draws from a stanfit object — extract_draws","text":"","code":"extract_draws(stan_fit)"},{"path":"/reference/extract_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract draws from a stanfit object — extract_draws","text":"stan_fit stanfit object.","code":""},{"path":"/reference/extract_draws.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract draws from a stanfit object — extract_draws","text":"named list length 2 containing: beta: list length equal number draws containing draws posterior distribution regression coefficients. sigma: list length equal number draws containing draws posterior distribution covariance matrices. element list list length equal 1 same_cov = TRUE equal number groups same_cov = FALSE.","code":""},{"path":"/reference/extract_imputed_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract imputed dataset — extract_imputed_df","title":"Extract imputed dataset — extract_imputed_df","text":"Takes imputation object generated imputation_df() uses extract completed dataset longdata object created longDataConstructor(). Also applies delta transformation data.frame provided delta argument. See analyse() details structure data.frame. Subject IDs returned data.frame scrambled .e. original values.","code":""},{"path":"/reference/extract_imputed_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract imputed dataset — extract_imputed_df","text":"","code":"extract_imputed_df(imputation, ld, delta = NULL, idmap = FALSE)"},{"path":"/reference/extract_imputed_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract imputed dataset — extract_imputed_df","text":"imputation imputation object generated imputation_df(). ld longdata object generated longDataConstructor(). delta Either NULL data.frame. used offset outcome values imputed dataset. idmap Logical. TRUE attribute called \"idmap\" attached return object contains list maps old subject ids new subject ids.","code":""},{"path":"/reference/extract_imputed_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract imputed dataset — extract_imputed_df","text":"data.frame.","code":""},{"path":"/reference/extract_imputed_dfs.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract imputed datasets — extract_imputed_dfs","title":"Extract imputed datasets — extract_imputed_dfs","text":"Extracts imputed datasets contained within imputations object generated impute().","code":""},{"path":"/reference/extract_imputed_dfs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract imputed datasets — extract_imputed_dfs","text":"","code":"extract_imputed_dfs( imputations, index = seq_along(imputations$imputations), delta = NULL, idmap = FALSE )"},{"path":"/reference/extract_imputed_dfs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract imputed datasets — extract_imputed_dfs","text":"imputations imputations object created impute(). index indexes imputed datasets return. default, datasets within imputations object returned. delta data.frame containing delta transformation applied imputed dataset. See analyse() details format specification data.frame. idmap Logical. subject IDs imputed data.frame's replaced new IDs ensure unique. Setting argument TRUE attaches attribute, called idmap, returned data.frame's provide map new subject IDs old subject IDs.","code":""},{"path":"/reference/extract_imputed_dfs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract imputed datasets — extract_imputed_dfs","text":"list data.frames equal length index argument.","code":""},{"path":[]},{"path":"/reference/extract_imputed_dfs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract imputed datasets — extract_imputed_dfs","text":"","code":"if (FALSE) { # \\dontrun{ extract_imputed_dfs(imputeObj) extract_imputed_dfs(imputeObj, c(1:3)) } # }"},{"path":"/reference/extract_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract parameters from a MMRM model — extract_params","title":"Extract parameters from a MMRM model — extract_params","text":"Extracts beta sigma coefficients MMRM model created mmrm::mmrm().","code":""},{"path":"/reference/extract_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract parameters from a MMRM model — extract_params","text":"","code":"extract_params(fit)"},{"path":"/reference/extract_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract parameters from a MMRM model — extract_params","text":"fit object created mmrm::mmrm()","code":""},{"path":"/reference/fit_mcmc.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the base imputation model using a Bayesian approach — fit_mcmc","title":"Fit the base imputation model using a Bayesian approach — fit_mcmc","text":"fit_mcmc() fits base imputation model using Bayesian approach. done MCMC method implemented stan run using function rstan::sampling(). function returns draws posterior distribution model parameters stanfit object. Additionally performs multiple diagnostics checks chain returns warnings case detected issues.","code":""},{"path":"/reference/fit_mcmc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the base imputation model using a Bayesian approach — fit_mcmc","text":"","code":"fit_mcmc(designmat, outcome, group, subjid, visit, method, quiet = FALSE)"},{"path":"/reference/fit_mcmc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the base imputation model using a Bayesian approach — fit_mcmc","text":"designmat design matrix fixed effects. outcome response variable. Must numeric. group Character vector containing group variable. subjid Character vector containing subjects IDs. visit Character vector containing visit variable. method method object generated method_bayes(). quiet Specify whether stan sampling log printed console.","code":""},{"path":"/reference/fit_mcmc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the base imputation model using a Bayesian approach — fit_mcmc","text":"named list composed following: samples: named list containing draws parameter. corresponds output extract_draws(). fit: stanfit object.","code":""},{"path":"/reference/fit_mcmc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit the base imputation model using a Bayesian approach — fit_mcmc","text":"Bayesian model assumes multivariate normal likelihood function weakly-informative priors model parameters: particular, uniform priors assumed regression coefficients inverse-Wishart priors covariance matrices. chain initialized using REML parameter estimates MMRM starting values. function performs following steps: Fit MMRM using REML approach. Prepare input data MCMC fit described data{} block Stan file. See prepare_stan_data() details. Run MCMC according input arguments using starting values REML parameter estimates estimated point 1. Performs diagnostics checks MCMC. See check_mcmc() details. Extract draws model fit. chains perform method$n_samples draws keeping one every method$burn_between iterations. Additionally first method$burn_in iterations discarded. total number iterations method$burn_in + method$burn_between*method$n_samples. purpose method$burn_in ensure samples drawn stationary distribution Markov Chain. method$burn_between aims keep draws uncorrelated .","code":""},{"path":"/reference/fit_mmrm.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a MMRM model — fit_mmrm","title":"Fit a MMRM model — fit_mmrm","text":"Fits MMRM model allowing different covariance structures using mmrm::mmrm(). Returns list key model parameters beta, sigma additional element failed indicating whether fit failed converge. fit fail converge beta sigma present.","code":""},{"path":"/reference/fit_mmrm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a MMRM model — fit_mmrm","text":"","code":"fit_mmrm( designmat, outcome, subjid, visit, group, cov_struct = c(\"us\", \"toep\", \"cs\", \"ar1\"), REML = TRUE, same_cov = TRUE )"},{"path":"/reference/fit_mmrm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a MMRM model — fit_mmrm","text":"designmat data.frame matrix containing covariates use MMRM model. Dummy variables must already expanded , .e. via stats::model.matrix(). contain missing values outcome numeric vector. outcome value regressed MMRM model. subjid character / factor vector. subject identifier used link separate visits belong subject. visit character / factor vector. Indicates visit outcome value occurred . group character / factor vector. Indicates treatment group patient belongs . cov_struct character value. Specifies covariance structure use. Must one \"us\", \"toep\", \"cs\" \"ar1\" REML logical. Specifies whether restricted maximum likelihood used same_cov logical. Used specify shared individual covariance matrix used per group","code":""},{"path":"/reference/generate_data_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate data for a single group — generate_data_single","title":"Generate data for a single group — generate_data_single","text":"Generate data single group","code":""},{"path":"/reference/generate_data_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate data for a single group — generate_data_single","text":"","code":"generate_data_single(pars_group, strategy_fun = NULL, distr_pars_ref = NULL)"},{"path":"/reference/generate_data_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate data for a single group — generate_data_single","text":"pars_group simul_pars object generated set_simul_pars(). specifies simulation parameters given group. strategy_fun Function implementing trajectories intercurrent event (ICE). Must one getStrategies(). See getStrategies() details. NULL post-ICE outcomes untouched. distr_pars_ref Optional. Named list containing simulation parameters reference arm. contains following elements: mu: Numeric vector indicating mean outcome trajectory assuming ICEs. include outcome baseline. sigma Covariance matrix outcome trajectory assuming ICEs. NULL, parameters inherited pars_group.","code":""},{"path":"/reference/generate_data_single.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate data for a single group — generate_data_single","text":"data.frame containing simulated data. includes following variables: id: Factor variable specifies id subject. visit: Factor variable specifies visit assessment. Visit 0 denotes baseline visit. group: Factor variable specifies treatment group subject belongs . outcome_bl: Numeric variable specifies baseline outcome. outcome_noICE: Numeric variable specifies longitudinal outcome assuming ICEs. ind_ice1: Binary variable takes value 1 corresponding visit affected ICE1 0 otherwise. dropout_ice1: Binary variable takes value 1 corresponding visit affected drop-following ICE1 0 otherwise. ind_ice2: Binary variable takes value 1 corresponding visit affected ICE2. outcome: Numeric variable specifies longitudinal outcome including ICE1, ICE2 intermittent missing values.","code":""},{"path":[]},{"path":"/reference/getStrategies.html","id":null,"dir":"Reference","previous_headings":"","what":"Get imputation strategies — getStrategies","title":"Get imputation strategies — getStrategies","text":"Returns list defining imputation strategies used create multivariate normal distribution parameters merging source group reference group per patient.","code":""},{"path":"/reference/getStrategies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get imputation strategies — getStrategies","text":"","code":"getStrategies(...)"},{"path":"/reference/getStrategies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get imputation strategies — getStrategies","text":"... User defined methods added return list. Input must function.","code":""},{"path":"/reference/getStrategies.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get imputation strategies — getStrategies","text":"default Jump Reference (JR), Copy Reference (CR), Copy Increments Reference (CIR), Last Mean Carried Forward (LMCF) Missing Random (MAR) defined. user can define strategy functions (overwrite pre-defined ones) specifying named input function .e. NEW = function(...) .... exception MAR overwritten. user defined functions must take 3 inputs: pars_group, pars_ref index_mar. pars_group pars_ref lists elements mu sigma representing multivariate normal distribution parameters subject's current group reference group respectively. index_mar logical vector specifying visits subject met MAR assumption . function must return list elements mu sigma. See implementation strategy_JR() example.","code":""},{"path":"/reference/getStrategies.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get imputation strategies — getStrategies","text":"","code":"if (FALSE) { # \\dontrun{ getStrategies() getStrategies( NEW = function(pars_group, pars_ref, index_mar) code , JR = function(pars_group, pars_ref, index_mar) more_code ) } # }"},{"path":"/reference/get_ESS.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS","title":"Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS","text":"Extract Effective Sample Size (ESS) stanfit object","code":""},{"path":"/reference/get_ESS.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS","text":"","code":"get_ESS(stan_fit)"},{"path":"/reference/get_ESS.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS","text":"stan_fit stanfit object.","code":""},{"path":"/reference/get_ESS.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS","text":"named vector containing ESS parameter model.","code":""},{"path":"/reference/get_bootstrap_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a stack object populated with bootstrapped samples — get_bootstrap_stack","title":"Creates a stack object populated with bootstrapped samples — get_bootstrap_stack","text":"Function creates Stack() object populated stack bootstrap samples based upon method$n_samples","code":""},{"path":"/reference/get_bootstrap_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a stack object populated with bootstrapped samples — get_bootstrap_stack","text":"","code":"get_bootstrap_stack(longdata, method, stack = Stack$new())"},{"path":"/reference/get_bootstrap_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a stack object populated with bootstrapped samples — get_bootstrap_stack","text":"longdata longDataConstructor() object method method object stack Stack() object (exposed unit testing purposes)","code":""},{"path":"/reference/get_cluster.html","id":null,"dir":"Reference","previous_headings":"","what":"Create cluster — get_cluster","title":"Create cluster — get_cluster","text":"Create cluster","code":""},{"path":"/reference/get_cluster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create cluster — get_cluster","text":"","code":"get_cluster(ncores = 1)"},{"path":"/reference/get_cluster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create cluster — get_cluster","text":"ncores Number parallel processes use ncores 1 function return NULL function spawns PSOCK cluster. Ensures rbmi assert_that loaded sub-processes","code":""},{"path":"/reference/get_conditional_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive conditional multivariate normal parameters — get_conditional_parameters","title":"Derive conditional multivariate normal parameters — get_conditional_parameters","text":"Takes parameters multivariate normal distribution observed values calculate conditional distribution unobserved values.","code":""},{"path":"/reference/get_conditional_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive conditional multivariate normal parameters — get_conditional_parameters","text":"","code":"get_conditional_parameters(pars, values)"},{"path":"/reference/get_conditional_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive conditional multivariate normal parameters — get_conditional_parameters","text":"pars list elements mu sigma defining mean vector covariance matrix respectively. values vector observed values condition , must length pars$mu. Missing values must represented NA.","code":""},{"path":"/reference/get_conditional_parameters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive conditional multivariate normal parameters — get_conditional_parameters","text":"list conditional distribution parameters: mu - conditional mean vector. sigma - conditional covariance matrix.","code":""},{"path":"/reference/get_delta_template.html","id":null,"dir":"Reference","previous_headings":"","what":"Get delta utility variables — get_delta_template","title":"Get delta utility variables — get_delta_template","text":"function creates default delta template (1 row per subject per visit) extracts utility information users need define logic defining delta. See delta_template() full details.","code":""},{"path":"/reference/get_delta_template.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get delta utility variables — get_delta_template","text":"","code":"get_delta_template(imputations)"},{"path":"/reference/get_delta_template.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get delta utility variables — get_delta_template","text":"imputations imputations object created impute().","code":""},{"path":"/reference/get_draws_mle.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the base imputation model on bootstrap samples — get_draws_mle","title":"Fit the base imputation model on bootstrap samples — get_draws_mle","text":"Fit base imputation model using ML/REML approach given number bootstrap samples specified method$n_samples. Returns parameter estimates model fit.","code":""},{"path":"/reference/get_draws_mle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the base imputation model on bootstrap samples — get_draws_mle","text":"","code":"get_draws_mle( longdata, method, sample_stack, n_target_samples, first_sample_orig, use_samp_ids, failure_limit = 0, ncores = 1, quiet = FALSE )"},{"path":"/reference/get_draws_mle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the base imputation model on bootstrap samples — get_draws_mle","text":"longdata R6 longdata object containing relevant input data information. method method object generated either method_approxbayes() method_condmean() argument type = \"bootstrap\". sample_stack stack object containing subject ids used mmrm iteration. n_target_samples Number samples needed created first_sample_orig Logical. TRUE function returns method$n_samples + 1 samples first sample contains parameter estimates original dataset method$n_samples samples contain parameter estimates bootstrap samples. FALSE function returns method$n_samples samples containing parameter estimates bootstrap samples. use_samp_ids Logical. TRUE, sampled subject ids returned. Otherwise subject ids original dataset returned. values used tell impute() subjects used derive imputed dataset. failure_limit Number failed samples allowed throwing error ncores Number processes parallelise job quiet Logical, TRUE suppress printing progress information printed console.","code":""},{"path":"/reference/get_draws_mle.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the base imputation model on bootstrap samples — get_draws_mle","text":"draws object named list containing following: data: R6 longdata object containing relevant input data information. method: method object generated either method_bayes(), method_approxbayes() method_condmean(). samples: list containing estimated parameters interest. element samples named list containing following: ids: vector characters containing ids subjects included original dataset. beta: numeric vector estimated regression coefficients. sigma: list estimated covariance matrices (one level vars$group). theta: numeric vector transformed covariances. failed: Logical. TRUE model fit failed. ids_samp: vector characters containing ids subjects included given sample. fit: method_bayes() chosen, returns MCMC Stan fit object. Otherwise NULL. n_failures: absolute number failures model fit. Relevant method_condmean(type = \"bootstrap\"), method_approxbayes() method_bmlmi(). formula: fixed effects formula object used model specification.","code":""},{"path":"/reference/get_draws_mle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit the base imputation model on bootstrap samples — get_draws_mle","text":"function takes Stack object contains multiple lists patient ids. function takes Stack pulls set ids constructs dataset just consisting patients (.e. potentially bootstrap jackknife sample). function fits MMRM model dataset create sample object. function repeats process n_target_samples reached. failure_limit samples fail converge function throws error. reaching desired number samples function generates returns draws object.","code":""},{"path":"/reference/get_ests_bmlmi.html","id":null,"dir":"Reference","previous_headings":"","what":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"Compute pooled point estimates, standard error degrees freedom according Von Hippel Bartlett formula Bootstrapped Maximum Likelihood Multiple Imputation (BMLMI).","code":""},{"path":"/reference/get_ests_bmlmi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"","code":"get_ests_bmlmi(ests, D)"},{"path":"/reference/get_ests_bmlmi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"ests numeric vector containing estimates analysis imputed datasets. D numeric representing number imputations bootstrap sample BMLMI method.","code":""},{"path":"/reference/get_ests_bmlmi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"list containing point estimate, standard error degrees freedom.","code":""},{"path":"/reference/get_ests_bmlmi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"ests must provided following order: firsts D elements related analyses random imputation one bootstrap sample. second set D elements (.e. D+1 2*D) related second bootstrap sample .","code":""},{"path":"/reference/get_ests_bmlmi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"Von Hippel, Paul T Bartlett, Jonathan W8. Maximum likelihood multiple imputation: Faster imputations consistent standard errors without posterior draws. 2021","code":""},{"path":"/reference/get_example_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate a realistic example dataset — get_example_data","title":"Simulate a realistic example dataset — get_example_data","text":"Simulate realistic example dataset using simulate_data() hard-coded values input arguments.","code":""},{"path":"/reference/get_example_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate a realistic example dataset — get_example_data","text":"","code":"get_example_data()"},{"path":"/reference/get_example_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate a realistic example dataset — get_example_data","text":"get_example_data() simulates 1:1 randomized trial active drug (intervention) versus placebo (control) 100 subjects per group 6 post-baseline assessments (bi-monthly visits 12 months). One intercurrent event corresponding treatment discontinuation also simulated. Specifically, data simulated following assumptions: mean outcome trajectory placebo group increases linearly 50 baseline (visit 0) 60 visit 6, .e. slope 10 points/year. mean outcome trajectory intervention group identical placebo group visit 2. visit 2 onward, slope decreases 50% 5 points/year. covariance structure baseline follow-values groups implied random intercept slope model standard deviation 5 intercept slope, correlation 0.25. addition, independent residual error standard deviation 2.5 added assessment. probability study drug discontinuation visit calculated according logistic model depends observed outcome visit. Specifically, visit-wise discontinuation probability 2% 3% control intervention group, respectively, specified case observed outcome equal 50 (mean value baseline). odds discontinuation simulated increase +10% +1 point increase observed outcome. Study drug discontinuation simulated effect mean trajectory placebo group. intervention group, subjects discontinue follow slope mean trajectory placebo group time point onward. compatible copy increments reference (CIR) assumption. Study drop-study drug discontinuation visit occurs probability 50% leading missing outcome data time point onward.","code":""},{"path":[]},{"path":"/reference/get_jackknife_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a stack object populated with jackknife samples — get_jackknife_stack","title":"Creates a stack object populated with jackknife samples — get_jackknife_stack","text":"Function creates Stack() object populated stack jackknife samples based upon","code":""},{"path":"/reference/get_jackknife_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a stack object populated with jackknife samples — get_jackknife_stack","text":"","code":"get_jackknife_stack(longdata, method, stack = Stack$new())"},{"path":"/reference/get_jackknife_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a stack object populated with jackknife samples — get_jackknife_stack","text":"longdata longDataConstructor() object method method object stack Stack() object (exposed unit testing purposes)","code":""},{"path":"/reference/get_mmrm_sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit MMRM and returns parameter estimates — get_mmrm_sample","title":"Fit MMRM and returns parameter estimates — get_mmrm_sample","text":"get_mmrm_sample fits base imputation model using ML/REML approach. Returns parameter estimates fit.","code":""},{"path":"/reference/get_mmrm_sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit MMRM and returns parameter estimates — get_mmrm_sample","text":"","code":"get_mmrm_sample(ids, longdata, method)"},{"path":"/reference/get_mmrm_sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit MMRM and returns parameter estimates — get_mmrm_sample","text":"ids vector characters containing ids subjects. longdata R6 longdata object containing relevant input data information. method method object generated either method_approxbayes() method_condmean().","code":""},{"path":"/reference/get_mmrm_sample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit MMRM and returns parameter estimates — get_mmrm_sample","text":"named list class sample_single. contains following: ids vector characters containing ids subjects included original dataset. beta numeric vector estimated regression coefficients. sigma list estimated covariance matrices (one level vars$group). theta numeric vector transformed covariances. failed logical. TRUE model fit failed. ids_samp vector characters containing ids subjects included given sample.","code":""},{"path":"/reference/get_pattern_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine patients missingness group — get_pattern_groups","title":"Determine patients missingness group — get_pattern_groups","text":"Takes design matrix multiple rows per subject returns dataset 1 row per subject new column pgroup indicating group patient belongs (based upon missingness pattern treatment group)","code":""},{"path":"/reference/get_pattern_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine patients missingness group — get_pattern_groups","text":"","code":"get_pattern_groups(ddat)"},{"path":"/reference/get_pattern_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine patients missingness group — get_pattern_groups","text":"ddat data.frame columns subjid, visit, group, is_avail","code":""},{"path":"/reference/get_pattern_groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine patients missingness group — get_pattern_groups","text":"column is_avail must character numeric 0 1","code":""},{"path":"/reference/get_pattern_groups_unique.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Pattern Summary — get_pattern_groups_unique","title":"Get Pattern Summary — get_pattern_groups_unique","text":"Takes dataset pattern information creates summary dataset just 1 row per pattern","code":""},{"path":"/reference/get_pattern_groups_unique.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Pattern Summary — get_pattern_groups_unique","text":"","code":"get_pattern_groups_unique(patterns)"},{"path":"/reference/get_pattern_groups_unique.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Pattern Summary — get_pattern_groups_unique","text":"patterns data.frame columns pgroup, pattern group","code":""},{"path":"/reference/get_pattern_groups_unique.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Pattern Summary — get_pattern_groups_unique","text":"column pgroup must numeric vector indicating pattern group patient belongs column pattern must character string 0's 1's. must identical rows within pgroup column group must character / numeric vector indicating covariance group observation belongs . must identical within pgroup","code":""},{"path":"/reference/get_pool_components.html","id":null,"dir":"Reference","previous_headings":"","what":"Expected Pool Components — get_pool_components","title":"Expected Pool Components — get_pool_components","text":"Returns elements expected contained analyse object depending analysis method specified.","code":""},{"path":"/reference/get_pool_components.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expected Pool Components — get_pool_components","text":"","code":"get_pool_components(x)"},{"path":"/reference/get_pool_components.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expected Pool Components — get_pool_components","text":"x Character name analysis method, must one either \"rubin\", \"jackknife\", \"bootstrap\" \"bmlmi\".","code":""},{"path":"/reference/get_visit_distribution_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive visit distribution parameters — get_visit_distribution_parameters","title":"Derive visit distribution parameters — get_visit_distribution_parameters","text":"Takes patient level data beta coefficients expands get patient specific estimate visit distribution parameters mu sigma. Returns values specific format expected downstream functions imputation process (namely list(list(mu = ..., sigma = ...), list(mu = ..., sigma = ...))).","code":""},{"path":"/reference/get_visit_distribution_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive visit distribution parameters — get_visit_distribution_parameters","text":"","code":"get_visit_distribution_parameters(dat, beta, sigma)"},{"path":"/reference/get_visit_distribution_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive visit distribution parameters — get_visit_distribution_parameters","text":"dat Patient level dataset, must 1 row per visit. Column order must order beta. number columns must match length beta beta List model beta coefficients. 1 element sample e.g. 3 samples models 4 beta coefficients argument form list( c(1,2,3,4) , c(5,6,7,8), c(9,10,11,12)). elements beta must length must length order dat. sigma List sigma. Must number entries beta.","code":""},{"path":"/reference/has_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Does object have a class ? — has_class","title":"Does object have a class ? — has_class","text":"Utility function see object particular class. Useful know many classes object may .","code":""},{"path":"/reference/has_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Does object have a class ? — has_class","text":"","code":"has_class(x, cls)"},{"path":"/reference/has_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Does object have a class ? — has_class","text":"x object want check class . cls class want know .","code":""},{"path":"/reference/has_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Does object have a class ? — has_class","text":"TRUE object class. FALSE object class.","code":""},{"path":"/reference/ife.html","id":null,"dir":"Reference","previous_headings":"","what":"if else — ife","title":"if else — ife","text":"wrapper around () else() prevent unexpected interactions ifelse() factor variables","code":""},{"path":"/reference/ife.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"if else — ife","text":"","code":"ife(x, a, b)"},{"path":"/reference/ife.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"if else — ife","text":"x True / False value return True b value return False","code":""},{"path":"/reference/ife.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"if else — ife","text":"default ifelse() convert factor variables numeric values often undesirable. connivance function avoids problem","code":""},{"path":"/reference/imputation_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a valid imputation_df object — imputation_df","title":"Create a valid imputation_df object — imputation_df","text":"Create valid imputation_df object","code":""},{"path":"/reference/imputation_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a valid imputation_df object — imputation_df","text":"","code":"imputation_df(...)"},{"path":"/reference/imputation_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a valid imputation_df object — imputation_df","text":"... list imputation_single.","code":""},{"path":"/reference/imputation_list_df.html","id":null,"dir":"Reference","previous_headings":"","what":"List of imputations_df — imputation_list_df","title":"List of imputations_df — imputation_list_df","text":"container multiple imputation_df's","code":""},{"path":"/reference/imputation_list_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List of imputations_df — imputation_list_df","text":"","code":"imputation_list_df(...)"},{"path":"/reference/imputation_list_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List of imputations_df — imputation_list_df","text":"... objects class imputation_df","code":""},{"path":"/reference/imputation_list_single.html","id":null,"dir":"Reference","previous_headings":"","what":"A collection of imputation_singles() grouped by a single subjid ID — imputation_list_single","title":"A collection of imputation_singles() grouped by a single subjid ID — imputation_list_single","text":"collection imputation_singles() grouped single subjid ID","code":""},{"path":"/reference/imputation_list_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A collection of imputation_singles() grouped by a single subjid ID — imputation_list_single","text":"","code":"imputation_list_single(imputations, D = 1)"},{"path":"/reference/imputation_list_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A collection of imputation_singles() grouped by a single subjid ID — imputation_list_single","text":"imputations list imputation_single() objects ordered repetitions grouped sequentially D number repetitions performed determines many columns imputation matrix constructor function create imputation_list_single object contains matrix imputation_single() objects grouped single id. matrix split D columns (.e. non-bmlmi methods always 1) id attribute determined extracting id attribute contributing imputation_single() objects. error throw multiple id detected","code":""},{"path":"/reference/imputation_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a valid imputation_single object — imputation_single","title":"Create a valid imputation_single object — imputation_single","text":"Create valid imputation_single object","code":""},{"path":"/reference/imputation_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a valid imputation_single object — imputation_single","text":"","code":"imputation_single(id, values)"},{"path":"/reference/imputation_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a valid imputation_single object — imputation_single","text":"id character string specifying subject id. values numeric vector indicating imputed values.","code":""},{"path":"/reference/impute.html","id":null,"dir":"Reference","previous_headings":"","what":"Create imputed datasets — impute","title":"Create imputed datasets — impute","text":"impute() creates imputed datasets based upon data options specified call draws(). One imputed dataset created per \"sample\" created draws().","code":""},{"path":"/reference/impute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create imputed datasets — impute","text":"","code":"impute( draws, references = NULL, update_strategy = NULL, strategies = getStrategies() ) # S3 method for class 'random' impute( draws, references = NULL, update_strategy = NULL, strategies = getStrategies() ) # S3 method for class 'condmean' impute( draws, references = NULL, update_strategy = NULL, strategies = getStrategies() )"},{"path":"/reference/impute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create imputed datasets — impute","text":"draws draws object created draws(). references named vector. Identifies references used reference-based imputation methods. form c(\"Group1\" = \"Reference1\", \"Group2\" = \"Reference2\"). NULL (default), references assumed form c(\"Group1\" = \"Group1\", \"Group2\" = \"Group2\"). argument NULL imputation strategy (defined data_ice[[vars$strategy]] call draws) MAR set. update_strategy optional data.frame. Updates imputation method originally set via data_ice option draws(). See details section information. strategies named list functions. Defines imputation functions used. names list mirror values specified strategy column data_ice. Default = getStrategies(). See getStrategies() details.","code":""},{"path":"/reference/impute.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create imputed datasets — impute","text":"impute() uses imputation model parameter estimates, generated draws(), first calculate marginal (multivariate normal) distribution subject's longitudinal outcome variable depending covariate values. subjects intercurrent events (ICEs) handled using non-MAR methods, marginal distribution updated depending time first visit affected ICE, chosen imputation strategy chosen reference group described Carpenter, Roger, Kenward (2013) . subject's imputation distribution used imputing missing values defined marginal distribution conditional observed outcome values. One dataset generated per set parameter estimates provided draws(). exact manner missing values imputed conditional imputation distribution depends method object provided draws(), particular: Bayes & Approximate Bayes: imputed dataset contains 1 row per subject & visit original dataset missing values imputed taking single random sample conditional imputation distribution. Conditional Mean: imputed dataset contains 1 row per subject & visit bootstrapped jackknife dataset used generate corresponding parameter estimates draws(). Missing values imputed using mean conditional imputation distribution. Please note first imputed dataset refers conditional mean imputation original dataset whereas subsequent imputed datasets refer conditional mean imputations bootstrap jackknife samples, respectively, original data. Bootstrapped Maximum Likelihood MI (BMLMI): performs D random imputations bootstrapped dataset used generate corresponding parameter estimates draws(). total number B*D imputed datasets provided, B number bootstrapped datasets. Missing values imputed taking random sample conditional imputation distribution. update_strategy argument can used update imputation strategy originally set via data_ice option draws(). avoids re-run draws() function changing imputation strategy certain circumstances (detailed ). data.frame provided update_strategy argument must contain two columns, one subject ID another imputation strategy, whose names defined vars argument specified call draws(). Please note argument allows update imputation strategy arguments time first visit affected ICE. key limitation functionality one can switch MAR non-MAR strategy (vice versa) subjects without observed post-ICE data. reason change affect whether post-ICE data included base imputation model (explained help draws()). example, subject ICE \"Visit 2\" observed/known values \"Visit 3\" function throw error one tries switch strategy MAR non-MAR strategy. contrast, switching non-MAR MAR strategy, whilst valid, raise warning usable data utilised imputation model.","code":""},{"path":"/reference/impute.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create imputed datasets — impute","text":"James R Carpenter, James H Roger, Michael G Kenward. Analysis longitudinal trials protocol deviation: framework relevant, accessible assumptions, inference via multiple imputation. Journal Biopharmaceutical Statistics, 23(6):1352–1371, 2013. [Section 4.2 4.3]","code":""},{"path":"/reference/impute.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create imputed datasets — impute","text":"","code":"if (FALSE) { # \\dontrun{ impute( draws = drawobj, references = c(\"Trt\" = \"Placebo\", \"Placebo\" = \"Placebo\") ) new_strategy <- data.frame( subjid = c(\"Pt1\", \"Pt2\"), strategy = c(\"MAR\", \"JR\") ) impute( draws = drawobj, references = c(\"Trt\" = \"Placebo\", \"Placebo\" = \"Placebo\"), update_strategy = new_strategy ) } # }"},{"path":"/reference/impute_data_individual.html","id":null,"dir":"Reference","previous_headings":"","what":"Impute data for a single subject — impute_data_individual","title":"Impute data for a single subject — impute_data_individual","text":"function performs imputation single subject time implementing process detailed impute().","code":""},{"path":"/reference/impute_data_individual.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Impute data for a single subject — impute_data_individual","text":"","code":"impute_data_individual( id, index, beta, sigma, data, references, strategies, condmean, n_imputations = 1 )"},{"path":"/reference/impute_data_individual.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Impute data for a single subject — impute_data_individual","text":"id Character string identifying subject. index sample indexes subject belongs e.g c(1,1,1,2,2,4). beta list beta coefficients sample, .e. beta[[1]] set beta coefficients first sample. sigma list sigma coefficients sample split group .e. sigma[[1]][[\"\"]] give sigma coefficients group first sample. data longdata object created longDataConstructor() references named vector. Identifies references used generating imputed values. form c(\"Group\" = \"Reference\", \"Group\" = \"Reference\"). strategies named list functions. Defines imputation functions used. names list mirror values specified method column data_ice. Default = getStrategies(). See getStrategies() details. condmean Logical. TRUE impute using conditional mean values, FALSE impute taking random draw multivariate normal distribution. n_imputations condmean = FALSE numeric representing number random imputations performed sample. Default 1 (one random imputation per sample).","code":""},{"path":"/reference/impute_data_individual.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Impute data for a single subject — impute_data_individual","text":"Note function performs required imputations subject time. .e. subject included samples 1,3,5,9 imputations (using sample-dependent imputation model parameters) performed one step order avoid look subjects's covariates expanding design matrix multiple times (computationally expensive). function also supports subject belonging sample multiple times, .e. 1,1,2,3,5,5, typically occur bootstrapped datasets.","code":""},{"path":"/reference/impute_internal.html","id":null,"dir":"Reference","previous_headings":"","what":"Create imputed datasets — impute_internal","title":"Create imputed datasets — impute_internal","text":"work horse function implements functionality impute. See user level function impute() details.","code":""},{"path":"/reference/impute_internal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create imputed datasets — impute_internal","text":"","code":"impute_internal( draws, references = NULL, update_strategy, strategies, condmean )"},{"path":"/reference/impute_internal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create imputed datasets — impute_internal","text":"draws draws object created draws(). references named vector. Identifies references used reference-based imputation methods. form c(\"Group1\" = \"Reference1\", \"Group2\" = \"Reference2\"). NULL (default), references assumed form c(\"Group1\" = \"Group1\", \"Group2\" = \"Group2\"). argument NULL imputation strategy (defined data_ice[[vars$strategy]] call draws) MAR set. update_strategy optional data.frame. Updates imputation method originally set via data_ice option draws(). See details section information. strategies named list functions. Defines imputation functions used. names list mirror values specified strategy column data_ice. Default = getStrategies(). See getStrategies() details. condmean logical. TRUE impute using conditional mean values, values impute taking random draw multivariate normal distribution.","code":""},{"path":"/reference/impute_outcome.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample outcome value — impute_outcome","title":"Sample outcome value — impute_outcome","text":"Draws random sample multivariate normal distribution.","code":""},{"path":"/reference/impute_outcome.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample outcome value — impute_outcome","text":"","code":"impute_outcome(conditional_parameters, n_imputations = 1, condmean = FALSE)"},{"path":"/reference/impute_outcome.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample outcome value — impute_outcome","text":"conditional_parameters list elements mu sigma contain mean vector covariance matrix sample . n_imputations numeric representing number random samples multivariate normal distribution performed. Default 1. condmean conditional mean imputation performed (opposed random sampling)","code":""},{"path":"/reference/invert.html","id":null,"dir":"Reference","previous_headings":"","what":"invert — invert","title":"invert — invert","text":"Utility function used replicated purrr::transpose. Turns list inside .","code":""},{"path":"/reference/invert.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"invert — invert","text":"","code":"invert(x)"},{"path":"/reference/invert.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"invert — invert","text":"x list","code":""},{"path":"/reference/invert_indexes.html","id":null,"dir":"Reference","previous_headings":"","what":"Invert and derive indexes — invert_indexes","title":"Invert and derive indexes — invert_indexes","text":"Takes list elements creates new list containing 1 entry per unique element value containing indexes original elements occurred .","code":""},{"path":"/reference/invert_indexes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Invert and derive indexes — invert_indexes","text":"","code":"invert_indexes(x)"},{"path":"/reference/invert_indexes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Invert and derive indexes — invert_indexes","text":"x list elements invert calculate index (see details).","code":""},{"path":"/reference/invert_indexes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Invert and derive indexes — invert_indexes","text":"functions purpose best illustrated example: input: becomes:","code":"list( c(\"A\", \"B\", \"C\"), c(\"A\", \"A\", \"B\"))} list( \"A\" = c(1,2,2), \"B\" = c(1,2), \"C\" = 1 )"},{"path":"/reference/is_absent.html","id":null,"dir":"Reference","previous_headings":"","what":"Is value absent — is_absent","title":"Is value absent — is_absent","text":"Returns true value either NULL, NA \"\". case vector values must NULL/NA/\"\" x regarded absent.","code":""},{"path":"/reference/is_absent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is value absent — is_absent","text":"","code":"is_absent(x, na = TRUE, blank = TRUE)"},{"path":"/reference/is_absent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is value absent — is_absent","text":"x value check absent na NAs count absent blank blanks .e. \"\" count absent","code":""},{"path":"/reference/is_char_fact.html","id":null,"dir":"Reference","previous_headings":"","what":"Is character or factor — is_char_fact","title":"Is character or factor — is_char_fact","text":"returns true x character factor vector","code":""},{"path":"/reference/is_char_fact.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is character or factor — is_char_fact","text":"","code":"is_char_fact(x)"},{"path":"/reference/is_char_fact.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is character or factor — is_char_fact","text":"x character factor vector","code":""},{"path":"/reference/is_char_one.html","id":null,"dir":"Reference","previous_headings":"","what":"Is single character — is_char_one","title":"Is single character — is_char_one","text":"returns true x length 1 character vector","code":""},{"path":"/reference/is_char_one.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is single character — is_char_one","text":"","code":"is_char_one(x)"},{"path":"/reference/is_char_one.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is single character — is_char_one","text":"x character vector","code":""},{"path":"/reference/is_in_rbmi_development.html","id":null,"dir":"Reference","previous_headings":"","what":"Is package in development mode? — is_in_rbmi_development","title":"Is package in development mode? — is_in_rbmi_development","text":"Returns TRUE package developed .e. local copy source code actively editing Returns FALSE otherwise","code":""},{"path":"/reference/is_in_rbmi_development.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is package in development mode? — is_in_rbmi_development","text":"","code":"is_in_rbmi_development()"},{"path":"/reference/is_in_rbmi_development.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Is package in development mode? — is_in_rbmi_development","text":"Main use function parallel processing indicate whether sub-processes need load current development version code whether load main installed package system","code":""},{"path":"/reference/is_num_char_fact.html","id":null,"dir":"Reference","previous_headings":"","what":"Is character, factor or numeric — is_num_char_fact","title":"Is character, factor or numeric — is_num_char_fact","text":"returns true x character, numeric factor vector","code":""},{"path":"/reference/is_num_char_fact.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is character, factor or numeric — is_num_char_fact","text":"","code":"is_num_char_fact(x)"},{"path":"/reference/is_num_char_fact.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is character, factor or numeric — is_num_char_fact","text":"x character, numeric factor vector","code":""},{"path":"/reference/locf.html","id":null,"dir":"Reference","previous_headings":"","what":"Last Observation Carried Forward — locf","title":"Last Observation Carried Forward — locf","text":"Returns vector applied last observation carried forward imputation.","code":""},{"path":"/reference/locf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Last Observation Carried Forward — locf","text":"","code":"locf(x)"},{"path":"/reference/locf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Last Observation Carried Forward — locf","text":"x vector.","code":""},{"path":"/reference/locf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Last Observation Carried Forward — locf","text":"","code":"if (FALSE) { # \\dontrun{ locf(c(NA, 1, 2, 3, NA, 4)) # Returns c(NA, 1, 2, 3, 3, 4) } # }"},{"path":"/reference/longDataConstructor.html","id":null,"dir":"Reference","previous_headings":"","what":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"longdata object allows efficient storage recall longitudinal datasets use bootstrap sampling. object works de-constructing data lists based upon subject id thus enabling efficient lookup.","code":""},{"path":"/reference/longDataConstructor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"object also handles multiple operations specific rbmi defining whether outcome value MAR / Missing well tracking imputation strategy assigned subject. recognised objects functionality fairly overloaded hoped can split area specific objects / functions future. additions functionality object avoided possible.","code":""},{"path":"/reference/longDataConstructor.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"data original dataset passed constructor (sorted id visit) vars vars object (list key variables) passed constructor visits character vector containing distinct visit levels ids character vector containing unique ids subject self$data formula formula expressing design matrix data constructed strata numeric vector indicating strata corresponding value self$ids belongs . stratification variable defined default 1 subjects (.e. group). field used part self$sample_ids() function enable stratified bootstrap sampling ice_visit_index list indexed subject storing index number first visit affected ICE. ICE set equal number visits plus 1. values list indexed subject storing numeric vector original (unimputed) outcome values group list indexed subject storing single character indicating imputation group subject belongs defined self$data[id, self$ivars$group] used determine reference group used imputing subjects data. is_mar list indexed subject storing logical values indicating subjects outcome values MAR . list defaulted TRUE subjects & outcomes modified calls self$set_strategies(). Note indicate values missing, variable True outcome values either occurred ICE visit post ICE visit imputation strategy MAR strategies list indexed subject storing single character value indicating imputation strategy assigned subject. list defaulted \"MAR\" subjects modified calls either self$set_strategies() self$update_strategies() strategy_lock list indexed subject storing single logical value indicating whether patients imputation strategy locked . strategy locked means change MAR non-MAR. Strategies can changed non-MAR MAR though trigger warning. Strategies locked patient assigned MAR strategy non-missing ICE date. list populated call self$set_strategies(). indexes list indexed subject storing numeric vector indexes specify rows original dataset belong subject .e. recover full data subject \"pt3\" can use self$data[self$indexes[[\"pt3\"]],]. may seem redundant filtering data directly however enables efficient bootstrap sampling data .e. list populated object initialisation. is_missing list indexed subject storing logical vector indicating whether corresponding outcome subject missing. list populated object initialisation. is_post_ice list indexed subject storing logical vector indicating whether corresponding outcome subject post date ICE. ICE data provided defaults False observations. list populated call self$set_strategies().","code":"indexes <- unlist(self$indexes[c(\"pt3\", \"pt3\")]) self$data[indexes,]"},{"path":[]},{"path":"/reference/longDataConstructor.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"longDataConstructor$get_data() longDataConstructor$add_subject() longDataConstructor$validate_ids() longDataConstructor$sample_ids() longDataConstructor$extract_by_id() longDataConstructor$update_strategies() longDataConstructor$set_strategies() longDataConstructor$check_has_data_at_each_visit() longDataConstructor$set_strata() longDataConstructor$new() longDataConstructor$clone()","code":""},{"path":"/reference/longDataConstructor.html","id":"method-get-data-","dir":"Reference","previous_headings":"","what":"Method get_data()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Returns data.frame based upon required subject IDs. Replaces missing values new ones provided.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$get_data( obj = NULL, nmar.rm = FALSE, na.rm = FALSE, idmap = FALSE )"},{"path":"/reference/longDataConstructor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"obj Either NULL, character vector subjects IDs imputation list object. See details. nmar.rm Logical value. TRUE remove observations regarded MAR (determined self$is_mar). na.rm Logical value. TRUE remove outcome values missing (determined self$is_missing). idmap Logical value. TRUE add attribute idmap contains mapping new subject ids old subject ids. See details.","code":""},{"path":"/reference/longDataConstructor.html","id":"details-1","dir":"Reference","previous_headings":"","what":"Details","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"obj NULL full original dataset returned. obj character vector new dataset consisting just subjects returned; character vector contains duplicate entries subject returned multiple times. obj imputation_df object (created imputation_df()) subject ids specified object returned missing values filled specified imputation list object. .e. return data.frame consisting observations pt1 twice observations pt3 . first set observations pt1 missing values filled c(1,2,3) second set filled c(4,5,6). length values must equal sum(self$is_missing[[id]]). obj NULL subject IDs scrambled order ensure unique .e. pt2 requested twice process guarantees set observations unique subject ID number. idmap attribute (requested) can used map new ids back old ids.","code":"obj <- imputation_df( imputation_single( id = \"pt1\", values = c(1,2,3)), imputation_single( id = \"pt1\", values = c(4,5,6)), imputation_single( id = \"pt3\", values = c(7,8)) ) longdata$get_data(obj)"},{"path":"/reference/longDataConstructor.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"data.frame.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-add-subject-","dir":"Reference","previous_headings":"","what":"Method add_subject()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"function decomposes patient data self$data populates corresponding lists .e. self$is_missing, self$values, self$group, etc. function called upon objects initialization.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$add_subject(id)"},{"path":"/reference/longDataConstructor.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"id Character subject id exists within self$data.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-validate-ids-","dir":"Reference","previous_headings":"","what":"Method validate_ids()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Throws error element ids within source data self$data.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$validate_ids(ids)"},{"path":"/reference/longDataConstructor.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"ids character vector ids.","code":""},{"path":"/reference/longDataConstructor.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"TRUE","code":""},{"path":"/reference/longDataConstructor.html","id":"method-sample-ids-","dir":"Reference","previous_headings":"","what":"Method sample_ids()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Performs random stratified sampling patient ids (replacement) patient equal weight picked within strata (.e dependent many non-missing visits ).","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$sample_ids()"},{"path":"/reference/longDataConstructor.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Character vector ids.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-extract-by-id-","dir":"Reference","previous_headings":"","what":"Method extract_by_id()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Returns list key information given subject. convenience wrapper save manually grab element.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$extract_by_id(id)"},{"path":"/reference/longDataConstructor.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"id Character subject id exists within self$data.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-update-strategies-","dir":"Reference","previous_headings":"","what":"Method update_strategies()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Convenience function run self$set_strategies(dat_ice, update=TRUE) kept legacy reasons.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$update_strategies(dat_ice)"},{"path":"/reference/longDataConstructor.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"dat_ice data.frame containing ICE information see impute() format dataframe.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-set-strategies-","dir":"Reference","previous_headings":"","what":"Method set_strategies()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Updates self$strategies, self$is_mar, self$is_post_ice variables based upon provided ICE information.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$set_strategies(dat_ice = NULL, update = FALSE)"},{"path":"/reference/longDataConstructor.html","id":"arguments-5","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"dat_ice data.frame containing ICE information. See details. update Logical, indicates ICE data used update. See details.","code":""},{"path":"/reference/longDataConstructor.html","id":"details-2","dir":"Reference","previous_headings":"","what":"Details","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"See draws() specification dat_ice update=FALSE. See impute() format dat_ice update=TRUE. update=TRUE function ensures MAR strategies changed non-MAR presence post-ICE observations.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-check-has-data-at-each-visit-","dir":"Reference","previous_headings":"","what":"Method check_has_data_at_each_visit()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Ensures visits least 1 observed \"MAR\" observation. Throws error criteria met. ensure initial MMRM can resolved.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-7","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$check_has_data_at_each_visit()"},{"path":"/reference/longDataConstructor.html","id":"method-set-strata-","dir":"Reference","previous_headings":"","what":"Method set_strata()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Populates self$strata variable. user specified stratification variables first visit used determine value variables. stratification variables specified everyone defined strata 1.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-8","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$set_strata()"},{"path":"/reference/longDataConstructor.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Constructor function.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-9","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$new(data, vars)"},{"path":"/reference/longDataConstructor.html","id":"arguments-6","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"data longitudinal dataset. vars ivars object created set_vars().","code":""},{"path":"/reference/longDataConstructor.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"objects class cloneable method.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-10","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$clone(deep = FALSE)"},{"path":"/reference/longDataConstructor.html","id":"arguments-7","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"deep Whether make deep clone.","code":""},{"path":"/reference/ls_design.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate design vector for the lsmeans — ls_design","title":"Calculate design vector for the lsmeans — ls_design","text":"Calculates design vector required generate lsmean standard error. ls_design_equal calculates applying equal weight per covariate combination whilst ls_design_proportional applies weighting proportional frequency covariate combination occurred actual dataset.","code":""},{"path":"/reference/ls_design.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate design vector for the lsmeans — ls_design","text":"","code":"ls_design_equal(data, frm, covars, fix) ls_design_proportional(data, frm, covars, fix)"},{"path":"/reference/ls_design.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate design vector for the lsmeans — ls_design","text":"data data.frame frm Formula used fit original model covars character vector variables names exist data extracted (ls_design_equal ) fix named list variables fixed values","code":""},{"path":"/reference/lsmeans.html","id":null,"dir":"Reference","previous_headings":"","what":"Least Square Means — lsmeans","title":"Least Square Means — lsmeans","text":"Estimates least square means linear model. done generating prediction model using hypothetical observation constructed averaging data. See details information.","code":""},{"path":"/reference/lsmeans.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Least Square Means — lsmeans","text":"","code":"lsmeans(model, ..., .weights = c(\"proportional\", \"equal\"))"},{"path":"/reference/lsmeans.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Least Square Means — lsmeans","text":"model model created lm. ... Fixes specific variables specific values .e. trt = 1 age = 50. name argument must name variable within dataset. .weights Character, specifies whether use \"proportional\" \"equal\" weighting categorical covariate combination calculating lsmeans.","code":""},{"path":"/reference/lsmeans.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Least Square Means — lsmeans","text":"lsmeans obtained calculating hypothetical patients predicting expected values. hypothetical patients constructed expanding possible combinations categorical covariate setting numerical covariates equal mean. final lsmean value calculated averaging hypothetical patients. .weights equals \"proportional\" values weighted frequency occur full dataset. .weights equals \"equal\" hypothetical patient given equal weight regardless actually occurs dataset. Use ... argument fix specific variables specific values. See references identical implementations done SAS R via emmeans package. function attempts re-implement emmeans derivation standard linear models without include dependencies.","code":""},{"path":"/reference/lsmeans.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Least Square Means — lsmeans","text":"https://CRAN.R-project.org/package=emmeans https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.3/statug/statug_glm_details41.htm","code":""},{"path":"/reference/lsmeans.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Least Square Means — lsmeans","text":"","code":"if (FALSE) { # \\dontrun{ mod <- lm(Sepal.Length ~ Species + Petal.Length, data = iris) lsmeans(mod) lsmeans(mod, Species = \"virginica\") lsmeans(mod, Species = \"versicolor\") lsmeans(mod, Species = \"versicolor\", Petal.Length = 1) } # }"},{"path":"/reference/method.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the multiple imputation methodology — method","title":"Set the multiple imputation methodology — method","text":"functions determine methods rbmi use creating imputation models, generating imputed values pooling results.","code":""},{"path":"/reference/method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the multiple imputation methodology — method","text":"","code":"method_bayes( burn_in = 200, burn_between = 50, same_cov = TRUE, n_samples = 20, seed = sample.int(.Machine$integer.max, 1) ) method_approxbayes( covariance = c(\"us\", \"toep\", \"cs\", \"ar1\"), threshold = 0.01, same_cov = TRUE, REML = TRUE, n_samples = 20 ) method_condmean( covariance = c(\"us\", \"toep\", \"cs\", \"ar1\"), threshold = 0.01, same_cov = TRUE, REML = TRUE, n_samples = NULL, type = c(\"bootstrap\", \"jackknife\") ) method_bmlmi( covariance = c(\"us\", \"toep\", \"cs\", \"ar1\"), threshold = 0.01, same_cov = TRUE, REML = TRUE, B = 20, D = 2 )"},{"path":"/reference/method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the multiple imputation methodology — method","text":"burn_in numeric specifies many observations discarded prior extracting actual samples. Note sampler initialized maximum likelihood estimates weakly informative prior used thus theory value need high. burn_between numeric specifies \"thinning\" rate .e. many observations discarded sample. used prevent issues associated autocorrelation samples. same_cov logical, TRUE imputation model fitted using single shared covariance matrix observations. FALSE separate covariance matrix fit group determined group argument set_vars(). n_samples numeric determines many imputed datasets generated. case method_condmean(type = \"jackknife\") argument must set NULL. See details. seed numeric specifies seed used call Stan. argument passed onto seed argument rstan::sampling(). Note required method_bayes(), methods can achieve reproducible results setting seed via set.seed(). See details. covariance character string specifies structure covariance matrix used imputation model. Must one \"us\" (default), \"toep\", \"cs\" \"ar1\". See details. threshold numeric 0 1, specifies proportion bootstrap datasets can fail produce valid samples error thrown. See details. REML logical indicating whether use REML estimation rather maximum likelihood. type character string specifies resampling method used perform inference conditional mean imputation approach (set via method_condmean()) used. Must one \"bootstrap\" \"jackknife\". B numeric determines number bootstrap samples method_bmlmi. D numeric determines number random imputations bootstrap sample. Needed method_bmlmi().","code":""},{"path":"/reference/method.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Set the multiple imputation methodology — method","text":"case method_condmean(type = \"bootstrap\") n_samples + 1 imputation models datasets generated first sample based original dataset whilst n_samples samples bootstrapped datasets. Likewise, method_condmean(type = \"jackknife\") length(unique(data$subjid)) + 1 imputation models datasets generated. cases represented n + 1 displayed print message. user able specify different covariance structures using covariance argument. Currently supported structures include: Unstructured (\"us\") Toeplitz (\"toep\") Compound Symmetry (\"cs\") Autoregression-1 (\"ar1\") Note present Bayesian methods support unstructured. case method_condmean(type = \"bootstrap\"), method_approxbayes() method_bmlmi() repeated bootstrap samples original dataset taken MMRM fitted sample. Due randomness sampled datasets, well limitations optimisers used fit models, uncommon estimates particular dataset generated. instances rbmi designed throw bootstrapped dataset try another. However ensure errors due chance due underlying misspecification data /model tolerance limit set many samples can discarded. tolerance limit reached error thrown process aborted. tolerance limit defined ceiling(threshold * n_samples). Note jackknife method estimates need generated leave-one-datasets error thrown fail fit. Please note time writing (September 2021) Stan unable produce reproducible samples across different operating systems even seed used. care must taken using Stan across different machines. information limitation please consult Stan documentation https://mc-stan.org/docs/2_27/reference-manual/reproducibility-chapter.html","code":""},{"path":"/reference/parametric_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate parametric confidence intervals — parametric_ci","title":"Calculate parametric confidence intervals — parametric_ci","text":"Calculates confidence intervals based upon parametric distribution.","code":""},{"path":"/reference/parametric_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate parametric confidence intervals — parametric_ci","text":"","code":"parametric_ci(point, se, alpha, alternative, qfun, pfun, ...)"},{"path":"/reference/parametric_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate parametric confidence intervals — parametric_ci","text":"point point estimate. se standard error point estimate. using non-\"normal\" distribution set 1. alpha type 1 error rate, value 0 1. alternative character string specifying alternative hypothesis, must one \"two.sided\" (default), \"greater\" \"less\". qfun quantile function assumed distribution .e. qnorm. pfun CDF function assumed distribution .e. pnorm. ... additional arguments passed qfun pfun .e. df = 102.","code":""},{"path":"/reference/pool.html","id":null,"dir":"Reference","previous_headings":"","what":"Pool analysis results obtained from the imputed datasets — pool","title":"Pool analysis results obtained from the imputed datasets — pool","text":"Pool analysis results obtained imputed datasets","code":""},{"path":"/reference/pool.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pool analysis results obtained from the imputed datasets — pool","text":"","code":"pool( results, conf.level = 0.95, alternative = c(\"two.sided\", \"less\", \"greater\"), type = c(\"percentile\", \"normal\") ) # S3 method for class 'pool' as.data.frame(x, ...) # S3 method for class 'pool' print(x, ...)"},{"path":"/reference/pool.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pool analysis results obtained from the imputed datasets — pool","text":"results analysis object created analyse(). conf.level confidence level returned confidence interval. Must single number 0 1. Default 0.95. alternative character string specifying alternative hypothesis, must one \"two.sided\" (default), \"greater\" \"less\". type character string either \"percentile\" (default) \"normal\". Determines method used calculate bootstrap confidence intervals. See details. used method_condmean(type = \"bootstrap\") specified original call draws(). x pool object generated pool(). ... used.","code":""},{"path":"/reference/pool.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pool analysis results obtained from the imputed datasets — pool","text":"calculation used generate point estimate, standard errors confidence interval depends upon method specified original call draws(); particular: method_approxbayes() & method_bayes() use Rubin's rules pool estimates variances across multiple imputed datasets, Barnard-Rubin rule pool degree's freedom; see Little & Rubin (2002). method_condmean(type = \"bootstrap\") uses percentile normal approximation; see Efron & Tibshirani (1994). Note percentile bootstrap, standard error calculated, .e. standard errors NA object / data.frame. method_condmean(type = \"jackknife\") uses standard jackknife variance formula; see Efron & Tibshirani (1994). method_bmlmi uses pooling procedure Bootstrapped Maximum Likelihood MI (BMLMI). See Von Hippel & Bartlett (2021).","code":""},{"path":"/reference/pool.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pool analysis results obtained from the imputed datasets — pool","text":"Bradley Efron Robert J Tibshirani. introduction bootstrap. CRC press, 1994. [Section 11] Roderick J. . Little Donald B. Rubin. Statistical Analysis Missing Data, Second Edition. John Wiley & Sons, Hoboken, New Jersey, 2002. [Section 5.4] Von Hippel, Paul T Bartlett, Jonathan W. Maximum likelihood multiple imputation: Faster imputations consistent standard errors without posterior draws. 2021.","code":""},{"path":"/reference/pool_bootstrap_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Pooling via normal approximation — pool_bootstrap_normal","title":"Bootstrap Pooling via normal approximation — pool_bootstrap_normal","text":"Get point estimate, confidence interval p-value using normal approximation.","code":""},{"path":"/reference/pool_bootstrap_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Pooling via normal approximation — pool_bootstrap_normal","text":"","code":"pool_bootstrap_normal(est, conf.level, alternative)"},{"path":"/reference/pool_bootstrap_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Pooling via normal approximation — pool_bootstrap_normal","text":"est numeric vector point estimates bootstrap sample. conf.level confidence level returned confidence interval. Must single number 0 1. Default 0.95. alternative character string specifying alternative hypothesis, must one \"two.sided\" (default), \"greater\" \"less\".","code":""},{"path":"/reference/pool_bootstrap_normal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap Pooling via normal approximation — pool_bootstrap_normal","text":"point estimate taken first element est. remaining n-1 values est used generate confidence intervals.","code":""},{"path":"/reference/pool_bootstrap_percentile.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Pooling via Percentiles — pool_bootstrap_percentile","title":"Bootstrap Pooling via Percentiles — pool_bootstrap_percentile","text":"Get point estimate, confidence interval p-value using percentiles. Note quantile \"type=6\" used, see stats::quantile() details.","code":""},{"path":"/reference/pool_bootstrap_percentile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Pooling via Percentiles — pool_bootstrap_percentile","text":"","code":"pool_bootstrap_percentile(est, conf.level, alternative)"},{"path":"/reference/pool_bootstrap_percentile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Pooling via Percentiles — pool_bootstrap_percentile","text":"est numeric vector point estimates bootstrap sample. conf.level confidence level returned confidence interval. Must single number 0 1. Default 0.95. alternative character string specifying alternative hypothesis, must one \"two.sided\" (default), \"greater\" \"less\".","code":""},{"path":"/reference/pool_bootstrap_percentile.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap Pooling via Percentiles — pool_bootstrap_percentile","text":"point estimate taken first element est. remaining n-1 values est used generate confidence intervals.","code":""},{"path":"/reference/pool_internal.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal Pool Methods — pool_internal","title":"Internal Pool Methods — pool_internal","text":"Dispatches pool methods based upon results object class. See pool() details.","code":""},{"path":"/reference/pool_internal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal Pool Methods — pool_internal","text":"","code":"pool_internal(results, conf.level, alternative, type, D) # S3 method for class 'jackknife' pool_internal(results, conf.level, alternative, type, D) # S3 method for class 'bootstrap' pool_internal( results, conf.level, alternative, type = c(\"percentile\", \"normal\"), D ) # S3 method for class 'bmlmi' pool_internal(results, conf.level, alternative, type, D) # S3 method for class 'rubin' pool_internal(results, conf.level, alternative, type, D)"},{"path":"/reference/pool_internal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Internal Pool Methods — pool_internal","text":"results list results .e. x$results element analyse object created analyse()). conf.level confidence level returned confidence interval. Must single number 0 1. Default 0.95. alternative character string specifying alternative hypothesis, must one \"two.sided\" (default), \"greater\" \"less\". type character string either \"percentile\" (default) \"normal\". Determines method used calculate bootstrap confidence intervals. See details. used method_condmean(type = \"bootstrap\") specified original call draws(). D numeric representing number imputations bootstrap sample BMLMI method.","code":""},{"path":"/reference/prepare_stan_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare input data to run the Stan model — prepare_stan_data","title":"Prepare input data to run the Stan model — prepare_stan_data","text":"Prepare input data run Stan model. Creates / calculates required inputs required data{} block MMRM Stan program.","code":""},{"path":"/reference/prepare_stan_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare input data to run the Stan model — prepare_stan_data","text":"","code":"prepare_stan_data(ddat, subjid, visit, outcome, group)"},{"path":"/reference/prepare_stan_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare input data to run the Stan model — prepare_stan_data","text":"ddat design matrix subjid Character vector containing subjects IDs. visit Vector containing visits. outcome Numeric vector containing outcome variable. group Vector containing group variable.","code":""},{"path":"/reference/prepare_stan_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare input data to run the Stan model — prepare_stan_data","text":"stan_data object. named list per data{} block related Stan file. particular returns: N - number rows design matrix P - number columns design matrix G - number distinct covariance matrix groups (.e. length(unique(group))) n_visit - number unique outcome visits n_pat - total number pattern groups (defined missingness patterns & covariance group) pat_G - Index Sigma pattern group use pat_n_pt - number patients within pattern group pat_n_visit - number non-missing visits pattern group pat_sigma_index - rows/cols Sigma subset pattern group (padded 0's) y - outcome variable Q - design matrix (QR decomposition) R - R matrix QR decomposition design matrix","code":""},{"path":"/reference/prepare_stan_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prepare input data to run the Stan model — prepare_stan_data","text":"group argument determines covariance matrix group subject belongs . want subjects use shared covariance matrix set group \"1\" everyone.","code":""},{"path":"/reference/print.analysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Print analysis object — print.analysis","title":"Print analysis object — print.analysis","text":"Print analysis object","code":""},{"path":"/reference/print.analysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print analysis object — print.analysis","text":"","code":"# S3 method for class 'analysis' print(x, ...)"},{"path":"/reference/print.analysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print analysis object — print.analysis","text":"x analysis object generated analyse(). ... used.","code":""},{"path":"/reference/print.draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Print draws object — print.draws","title":"Print draws object — print.draws","text":"Print draws object","code":""},{"path":"/reference/print.draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print draws object — print.draws","text":"","code":"# S3 method for class 'draws' print(x, ...)"},{"path":"/reference/print.draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print draws object — print.draws","text":"x draws object generated draws(). ... used.","code":""},{"path":"/reference/print.imputation.html","id":null,"dir":"Reference","previous_headings":"","what":"Print imputation object — print.imputation","title":"Print imputation object — print.imputation","text":"Print imputation object","code":""},{"path":"/reference/print.imputation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print imputation object — print.imputation","text":"","code":"# S3 method for class 'imputation' print(x, ...)"},{"path":"/reference/print.imputation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print imputation object — print.imputation","text":"x imputation object generated impute(). ... used.","code":""},{"path":"/reference/progressLogger.html","id":null,"dir":"Reference","previous_headings":"","what":"R6 Class for printing current sampling progress — progressLogger","title":"R6 Class for printing current sampling progress — progressLogger","text":"Object initalised total number iterations expected occur. User can update object add method indicate many iterations just occurred. Every time step * 100 % iterations occurred message printed console. Use quiet argument prevent object printing anything ","code":""},{"path":"/reference/progressLogger.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"R6 Class for printing current sampling progress — progressLogger","text":"step real, percentage iterations allow printing progress console step_current integer, total number iterations completed since progress last printed console n integer, current number completed iterations n_max integer, total number expected iterations completed acts denominator calculating progress percentages quiet logical holds whether print anything","code":""},{"path":[]},{"path":"/reference/progressLogger.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"R6 Class for printing current sampling progress — progressLogger","text":"progressLogger$new() progressLogger$add() progressLogger$print_progress() progressLogger$clone()","code":""},{"path":"/reference/progressLogger.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"R6 Class for printing current sampling progress — progressLogger","text":"Create progressLogger object","code":""},{"path":"/reference/progressLogger.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for printing current sampling progress — progressLogger","text":"","code":"progressLogger$new(n_max, quiet = FALSE, step = 0.1)"},{"path":"/reference/progressLogger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for printing current sampling progress — progressLogger","text":"n_max integer, sets field n_max quiet logical, sets field quiet step real, sets field step","code":""},{"path":"/reference/progressLogger.html","id":"method-add-","dir":"Reference","previous_headings":"","what":"Method add()","title":"R6 Class for printing current sampling progress — progressLogger","text":"Records n iterations completed add number current step count (step_current) print progress message log step limit (step) reached. function nothing quiet set TRUE","code":""},{"path":"/reference/progressLogger.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for printing current sampling progress — progressLogger","text":"","code":"progressLogger$add(n)"},{"path":"/reference/progressLogger.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for printing current sampling progress — progressLogger","text":"n number successfully complete iterations since add() last called","code":""},{"path":"/reference/progressLogger.html","id":"method-print-progress-","dir":"Reference","previous_headings":"","what":"Method print_progress()","title":"R6 Class for printing current sampling progress — progressLogger","text":"method print current state progress","code":""},{"path":"/reference/progressLogger.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for printing current sampling progress — progressLogger","text":"","code":"progressLogger$print_progress()"},{"path":"/reference/progressLogger.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"R6 Class for printing current sampling progress — progressLogger","text":"objects class cloneable method.","code":""},{"path":"/reference/progressLogger.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for printing current sampling progress — progressLogger","text":"","code":"progressLogger$clone(deep = FALSE)"},{"path":"/reference/progressLogger.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for printing current sampling progress — progressLogger","text":"deep Whether make deep clone.","code":""},{"path":"/reference/pval_percentile.html","id":null,"dir":"Reference","previous_headings":"","what":"P-value of percentile bootstrap — pval_percentile","title":"P-value of percentile bootstrap — pval_percentile","text":"Determines (necessarily unique) quantile (type=6) \"est\" gives value 0 , derive p-value corresponding percentile bootstrap via inversion.","code":""},{"path":"/reference/pval_percentile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"P-value of percentile bootstrap — pval_percentile","text":"","code":"pval_percentile(est)"},{"path":"/reference/pval_percentile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"P-value of percentile bootstrap — pval_percentile","text":"est numeric vector point estimates bootstrap sample.","code":""},{"path":"/reference/pval_percentile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"P-value of percentile bootstrap — pval_percentile","text":"named numeric vector length 2 containing p-value H_0: theta=0 vs H_A: theta>0 (\"pval_greater\") p-value H_0: theta=0 vs H_A: theta<0 (\"pval_less\").","code":""},{"path":"/reference/pval_percentile.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"P-value of percentile bootstrap — pval_percentile","text":"p-value H_0: theta=0 vs H_A: theta>0 value alpha q_alpha = 0. least one estimate equal zero returns largest alpha q_alpha = 0. bootstrap estimates > 0 returns 0; bootstrap estimates < 0 returns 1. Analogous reasoning applied p-value H_0: theta=0 vs H_A: theta<0.","code":""},{"path":"/reference/random_effects_expr.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct random effects formula — random_effects_expr","title":"Construct random effects formula — random_effects_expr","text":"Constructs character representation random effects formula fitting MMRM subject visit format required mmrm::mmrm().","code":""},{"path":"/reference/random_effects_expr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct random effects formula — random_effects_expr","text":"","code":"random_effects_expr( cov_struct = c(\"us\", \"toep\", \"cs\", \"ar1\"), cov_by_group = FALSE )"},{"path":"/reference/random_effects_expr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct random effects formula — random_effects_expr","text":"cov_struct Character - covariance structure used, must one \"us\", \"toep\", \"cs\", \"ar1\" cov_by_group Boolean - Whenever use separate covariances per group level","code":""},{"path":"/reference/random_effects_expr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Construct random effects formula — random_effects_expr","text":"example assuming user specified covariance structure \"us\" groups provided return cov_by_group set FALSE indicates separate covariance matrices required per group following returned:","code":"us(visit | subjid) us( visit | group / subjid )"},{"path":"/reference/rbmi-package.html","id":null,"dir":"Reference","previous_headings":"","what":"rbmi: Reference Based Multiple Imputation — rbmi-package","title":"rbmi: Reference Based Multiple Imputation — rbmi-package","text":"rbmi package used perform reference based multiple imputation. package provides implementations common, patient-specific imputation strategies whilst allowing user select various standard Bayesian frequentist approaches. package designed around 4 core functions: draws() - Fits multiple imputation models impute() - Imputes multiple datasets analyse() - Analyses multiple datasets pool() - Pools multiple results single statistic learn rbmi, please see quickstart vignette: vignette(topic= \"quickstart\", package = \"rbmi\")","code":""},{"path":[]},{"path":"/reference/rbmi-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"rbmi: Reference Based Multiple Imputation — rbmi-package","text":"Maintainer: Craig Gower-Page craig.gower-page@roche.com Authors: Alessandro Noci alessandro.noci@roche.com contributors: Marcel Wolbers marcel.wolbers@roche.com [contributor] Roche [copyright holder, funder]","code":""},{"path":"/reference/record.html","id":null,"dir":"Reference","previous_headings":"","what":"Capture all Output — record","title":"Capture all Output — record","text":"function silences warnings, errors & messages instead returns list containing results (error) + warning error messages character vectors.","code":""},{"path":"/reference/record.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Capture all Output — record","text":"","code":"record(expr)"},{"path":"/reference/record.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Capture all Output — record","text":"expr expression executed","code":""},{"path":"/reference/record.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Capture all Output — record","text":"list containing results - object returned expr list() error thrown warnings - NULL character vector warnings thrown errors - NULL string error thrown messages - NULL character vector messages produced","code":""},{"path":"/reference/record.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Capture all Output — record","text":"","code":"if (FALSE) { # \\dontrun{ record({ x <- 1 y <- 2 warning(\"something went wrong\") message(\"O nearly done\") x + y }) } # }"},{"path":"/reference/recursive_reduce.html","id":null,"dir":"Reference","previous_headings":"","what":"recursive_reduce — recursive_reduce","title":"recursive_reduce — recursive_reduce","text":"Utility function used replicated purrr::reduce. Recursively applies function list elements 1 element remains","code":""},{"path":"/reference/recursive_reduce.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"recursive_reduce — recursive_reduce","text":"","code":"recursive_reduce(.l, .f)"},{"path":"/reference/recursive_reduce.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"recursive_reduce — recursive_reduce","text":".l list values apply function .f function apply element list turn .e. .l[[1]] <- .f( .l[[1]] , .l[[2]]) ; .l[[1]] <- .f( .l[[1]] , .l[[3]])","code":""},{"path":"/reference/remove_if_all_missing.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove subjects from dataset if they have no observed values — remove_if_all_missing","title":"Remove subjects from dataset if they have no observed values — remove_if_all_missing","text":"function takes data.frame variables visit, outcome & subjid. removes rows given subjid non-missing values outcome.","code":""},{"path":"/reference/remove_if_all_missing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove subjects from dataset if they have no observed values — remove_if_all_missing","text":"","code":"remove_if_all_missing(dat)"},{"path":"/reference/remove_if_all_missing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove subjects from dataset if they have no observed values — remove_if_all_missing","text":"dat data.frame","code":""},{"path":"/reference/rubin_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Barnard and Rubin degrees of freedom adjustment — rubin_df","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"Compute degrees freedom according Barnard-Rubin formula.","code":""},{"path":"/reference/rubin_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"","code":"rubin_df(v_com, var_b, var_t, M)"},{"path":"/reference/rubin_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"v_com Positive number representing degrees freedom complete-data analysis. var_b -variance point estimate across multiply imputed datasets. var_t Total-variance point estimate according Rubin's rules. M Number imputations.","code":""},{"path":"/reference/rubin_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"Degrees freedom according Barnard-Rubin formula. See Barnard-Rubin (1999).","code":""},{"path":"/reference/rubin_df.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"computation takes account limit cases missing data (.e. -variance var_b zero) complete-data degrees freedom set Inf. Moreover, v_com given NA, function returns Inf.","code":""},{"path":"/reference/rubin_df.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"Barnard, J. Rubin, D.B. (1999). Small sample degrees freedom multiple imputation. Biometrika, 86, 948-955.","code":""},{"path":"/reference/rubin_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine estimates using Rubin's rules — rubin_rules","title":"Combine estimates using Rubin's rules — rubin_rules","text":"Pool together results M complete-data analyses according Rubin's rules. See details.","code":""},{"path":"/reference/rubin_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine estimates using Rubin's rules — rubin_rules","text":"","code":"rubin_rules(ests, ses, v_com)"},{"path":"/reference/rubin_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine estimates using Rubin's rules — rubin_rules","text":"ests Numeric vector containing point estimates complete-data analyses. ses Numeric vector containing standard errors complete-data analyses. v_com Positive number representing degrees freedom complete-data analysis.","code":""},{"path":"/reference/rubin_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine estimates using Rubin's rules — rubin_rules","text":"list containing: est_point: pooled point estimate according Little-Rubin (2002). var_t: total variance according Little-Rubin (2002). df: degrees freedom according Barnard-Rubin (1999).","code":""},{"path":"/reference/rubin_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Combine estimates using Rubin's rules — rubin_rules","text":"rubin_rules applies Rubin's rules (Rubin, 1987) pooling together results multiple imputation procedure. pooled point estimate est_point average across point estimates complete-data analyses (given input argument ests). total variance var_t sum two terms representing within-variance -variance (see Little-Rubin (2002)). function also returns df, estimated pooled degrees freedom according Barnard-Rubin (1999) can used inference based t-distribution.","code":""},{"path":"/reference/rubin_rules.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Combine estimates using Rubin's rules — rubin_rules","text":"Barnard, J. Rubin, D.B. (1999). Small sample degrees freedom multiple imputation. Biometrika, 86, 948-955 Roderick J. . Little Donald B. Rubin. Statistical Analysis Missing Data, Second Edition. John Wiley & Sons, Hoboken, New Jersey, 2002. [Section 5.4]","code":""},{"path":[]},{"path":"/reference/sample_ids.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample Patient Ids — sample_ids","title":"Sample Patient Ids — sample_ids","text":"Performs stratified bootstrap sample IDS ensuring return vector length input vector","code":""},{"path":"/reference/sample_ids.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample Patient Ids — sample_ids","text":"","code":"sample_ids(ids, strata = rep(1, length(ids)))"},{"path":"/reference/sample_ids.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample Patient Ids — sample_ids","text":"ids vector sample strata strata indicator, ids sampled within strata ensuring numbers strata maintained","code":""},{"path":"/reference/sample_ids.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sample Patient Ids — sample_ids","text":"","code":"if (FALSE) { # \\dontrun{ sample_ids( c(\"a\", \"b\", \"c\", \"d\"), strata = c(1,1,2,2)) } # }"},{"path":"/reference/sample_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Create and validate a sample_list object — sample_list","title":"Create and validate a sample_list object — sample_list","text":"Given list sample_single objects generate sample_single(), creates sample_list objects validate .","code":""},{"path":"/reference/sample_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create and validate a sample_list object — sample_list","text":"","code":"sample_list(...)"},{"path":"/reference/sample_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create and validate a sample_list object — sample_list","text":"... list sample_single objects.","code":""},{"path":"/reference/sample_mvnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample random values from the multivariate normal distribution — sample_mvnorm","title":"Sample random values from the multivariate normal distribution — sample_mvnorm","text":"Sample random values multivariate normal distribution","code":""},{"path":"/reference/sample_mvnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample random values from the multivariate normal distribution — sample_mvnorm","text":"","code":"sample_mvnorm(mu, sigma)"},{"path":"/reference/sample_mvnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample random values from the multivariate normal distribution — sample_mvnorm","text":"mu mean vector sigma covariance matrix Samples multivariate normal variables multiplying univariate random normal variables cholesky decomposition covariance matrix. mu length 1 just uses rnorm instead.","code":""},{"path":"/reference/sample_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Create object of sample_single class — sample_single","title":"Create object of sample_single class — sample_single","text":"Creates object class sample_single named list containing input parameters validate .","code":""},{"path":"/reference/sample_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create object of sample_single class — sample_single","text":"","code":"sample_single( ids, beta = NA, sigma = NA, theta = NA, failed = any(is.na(beta)), ids_samp = ids )"},{"path":"/reference/sample_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create object of sample_single class — sample_single","text":"ids Vector characters containing ids subjects included original dataset. beta Numeric vector estimated regression coefficients. sigma List estimated covariance matrices (one level vars$group). theta Numeric vector transformed covariances. failed Logical. TRUE model fit failed. ids_samp Vector characters containing ids subjects included given sample.","code":""},{"path":"/reference/sample_single.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create object of sample_single class — sample_single","text":"named list class sample_single. contains following: ids vector characters containing ids subjects included original dataset. beta numeric vector estimated regression coefficients. sigma list estimated covariance matrices (one level vars$group). theta numeric vector transformed covariances. failed logical. TRUE model fit failed. ids_samp vector characters containing ids subjects included given sample.","code":""},{"path":"/reference/scalerConstructor.html","id":null,"dir":"Reference","previous_headings":"","what":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Scales design matrix non-categorical columns mean 0 standard deviation 1.","code":""},{"path":"/reference/scalerConstructor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"object initialisation used determine relevant mean SD's scale scaling (un-scaling) performed relevant object methods. Un-scaling done linear model Beta Sigma coefficients. purpose first column dataset scaled assumed outcome variable variables assumed post-transformation predictor variables (.e. dummy variables already expanded).","code":""},{"path":"/reference/scalerConstructor.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"centre Vector column means. first value outcome variable, variables predictors. scales Vector column standard deviations. first value outcome variable, variables predictors.","code":""},{"path":[]},{"path":"/reference/scalerConstructor.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"scalerConstructor$new() scalerConstructor$scale() scalerConstructor$unscale_sigma() scalerConstructor$unscale_beta() scalerConstructor$clone()","code":""},{"path":"/reference/scalerConstructor.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Uses dat determine relevant column means standard deviations use scaling un-scaling future datasets. Implicitly assumes new datasets column order dat","code":""},{"path":"/reference/scalerConstructor.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"","code":"scalerConstructor$new(dat)"},{"path":"/reference/scalerConstructor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"dat data.frame matrix. columns must numeric (.e dummy variables, must already expanded ).","code":""},{"path":"/reference/scalerConstructor.html","id":"details-1","dir":"Reference","previous_headings":"","what":"Details","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Categorical columns (determined values entirely 1 0) scaled. achieved setting corresponding values centre 0 scale 1.","code":""},{"path":"/reference/scalerConstructor.html","id":"method-scale-","dir":"Reference","previous_headings":"","what":"Method scale()","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Scales dataset continuous variables mean 0 standard deviation 1.","code":""},{"path":"/reference/scalerConstructor.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"","code":"scalerConstructor$scale(dat)"},{"path":"/reference/scalerConstructor.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"dat data.frame matrix whose columns numeric (.e. dummy variables expanded ) whose columns order dataset used initialization function.","code":""},{"path":"/reference/scalerConstructor.html","id":"method-unscale-sigma-","dir":"Reference","previous_headings":"","what":"Method unscale_sigma()","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Unscales sigma value (matrix) estimated linear model using design matrix scaled object. function works first column initialisation data.frame outcome variable.","code":""},{"path":"/reference/scalerConstructor.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"","code":"scalerConstructor$unscale_sigma(sigma)"},{"path":"/reference/scalerConstructor.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"sigma numeric value matrix.","code":""},{"path":"/reference/scalerConstructor.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"numeric value matrix","code":""},{"path":"/reference/scalerConstructor.html","id":"method-unscale-beta-","dir":"Reference","previous_headings":"","what":"Method unscale_beta()","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Unscales beta value (vector) estimated linear model using design matrix scaled object. function works first column initialization data.frame outcome variable.","code":""},{"path":"/reference/scalerConstructor.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"","code":"scalerConstructor$unscale_beta(beta)"},{"path":"/reference/scalerConstructor.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"beta numeric vector beta coefficients estimated linear model.","code":""},{"path":"/reference/scalerConstructor.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"numeric vector.","code":""},{"path":"/reference/scalerConstructor.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"objects class cloneable method.","code":""},{"path":"/reference/scalerConstructor.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"","code":"scalerConstructor$clone(deep = FALSE)"},{"path":"/reference/scalerConstructor.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"deep Whether make deep clone.","code":""},{"path":"/reference/set_simul_pars.html","id":null,"dir":"Reference","previous_headings":"","what":"Set simulation parameters of a study group. — set_simul_pars","title":"Set simulation parameters of a study group. — set_simul_pars","text":"function provides input arguments study group needed simulate data simulate_data(). simulate_data() generates data two-arms clinical trial longitudinal continuous outcomes two intercurrent events (ICEs). ICE1 may thought discontinuation study treatment due study drug condition related (SDCR) reasons. ICE2 may thought discontinuation study treatment due uninformative study drop-, .e. due study drug condition related (NSDRC) reasons outcome data ICE2 always missing.","code":""},{"path":"/reference/set_simul_pars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set simulation parameters of a study group. — set_simul_pars","text":"","code":"set_simul_pars( mu, sigma, n, prob_ice1 = 0, or_outcome_ice1 = 1, prob_post_ice1_dropout = 0, prob_ice2 = 0, prob_miss = 0 )"},{"path":"/reference/set_simul_pars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set simulation parameters of a study group. — set_simul_pars","text":"mu Numeric vector describing mean outcome trajectory visit (including baseline) assuming ICEs. sigma Covariance matrix outcome trajectory assuming ICEs. n Number subjects belonging group. prob_ice1 Numeric vector specifies probability experiencing ICE1 (discontinuation study treatment due SDCR reasons) visit subject observed outcome visit equal mean baseline (mu[1]). single numeric provided, probability applied visit. or_outcome_ice1 Numeric value specifies odds ratio experiencing ICE1 visit corresponding +1 higher value observed outcome visit. prob_post_ice1_dropout Numeric value specifies probability study drop-following ICE1. subject simulated drop-ICE1, outcomes ICE1 set missing. prob_ice2 Numeric specifies additional probability post-baseline visit affected study drop-. Outcome data subject's first simulated visit affected study drop-subsequent visits set missing. generates second intercurrent event ICE2, may thought treatment discontinuation due NSDRC reasons subsequent drop-. subject, ICE1 ICE2 simulated occur, assumed earlier counts. case ICEs simulated occur time, assumed ICE1 counts. means single subject can experience either ICE1 ICE2, . prob_miss Numeric value specifies additional probability given post-baseline observation missing. can used produce \"intermittent\" missing values associated ICE.","code":""},{"path":"/reference/set_simul_pars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set simulation parameters of a study group. — set_simul_pars","text":"simul_pars object named list containing simulation parameters.","code":""},{"path":"/reference/set_simul_pars.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Set simulation parameters of a study group. — set_simul_pars","text":"details, please see simulate_data().","code":""},{"path":[]},{"path":"/reference/set_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Set key variables — set_vars","title":"Set key variables — set_vars","text":"function used define names key variables within data.frame's provided input arguments draws() ancova().","code":""},{"path":"/reference/set_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set key variables — set_vars","text":"","code":"set_vars( subjid = \"subjid\", visit = \"visit\", outcome = \"outcome\", group = \"group\", covariates = character(0), strata = group, strategy = \"strategy\" )"},{"path":"/reference/set_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set key variables — set_vars","text":"subjid name \"Subject ID\" variable. length 1 character vector. visit name \"Visit\" variable. length 1 character vector. outcome name \"Outcome\" variable. length 1 character vector. group name \"Group\" variable. length 1 character vector. covariates name covariates used context modeling. See details. strata name stratification variable used context bootstrap sampling. See details. strategy name \"strategy\" variable. length 1 character vector.","code":""},{"path":"/reference/set_vars.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Set key variables — set_vars","text":"draws() ancova() covariates argument can specified indicate variables included imputation analysis models respectively. wish include interaction terms need manually specified .e. covariates = c(\"group*visit\", \"age*sex\"). Please note use () function inhibit interpretation/conversion objects supported. Currently strata used draws() combination method_condmean(type = \"bootstrap\") method_approxbayes() order allow specification stratified bootstrap sampling. default strata set equal value group assumed users want preserve group size samples. See draws() details. Likewise, currently strategy argument used draws() specify name strategy variable within data_ice data.frame. See draws() details.","code":""},{"path":[]},{"path":"/reference/set_vars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set key variables — set_vars","text":"","code":"if (FALSE) { # \\dontrun{ # Using CDISC variable names as an example set_vars( subjid = \"usubjid\", visit = \"avisit\", outcome = \"aval\", group = \"arm\", covariates = c(\"bwt\", \"bht\", \"arm * avisit\"), strategy = \"strat\" ) } # }"},{"path":"/reference/simulate_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate data — simulate_data","title":"Generate data — simulate_data","text":"Generate data two-arms clinical trial longitudinal continuous outcome two intercurrent events (ICEs). ICE1 may thought discontinuation study treatment due study drug condition related (SDCR) reasons. ICE2 may thought discontinuation study treatment due uninformative study drop-, .e. due study drug condition related (NSDRC) reasons outcome data ICE2 always missing.","code":""},{"path":"/reference/simulate_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate data — simulate_data","text":"","code":"simulate_data(pars_c, pars_t, post_ice1_traj, strategies = getStrategies())"},{"path":"/reference/simulate_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate data — simulate_data","text":"pars_c simul_pars object generated set_simul_pars(). specifies simulation parameters control arm. pars_t simul_pars object generated set_simul_pars(). specifies simulation parameters treatment arm. post_ice1_traj string specifies observed outcomes occurring ICE1 simulated. Must target function included strategies. Possible choices : Missing Random \"MAR\", Jump Reference \"JR\", Copy Reference \"CR\", Copy Increments Reference \"CIR\", Last Mean Carried Forward \"LMCF\". User-defined strategies also added. See getStrategies() details. strategies named list functions. Default equal getStrategies(). See getStrategies() details.","code":""},{"path":"/reference/simulate_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate data — simulate_data","text":"data.frame containing simulated data. includes following variables: id: Factor variable specifies id subject. visit: Factor variable specifies visit assessment. Visit 0 denotes baseline visit. group: Factor variable specifies treatment group subject belongs . outcome_bl: Numeric variable specifies baseline outcome. outcome_noICE: Numeric variable specifies longitudinal outcome assuming ICEs. ind_ice1: Binary variable takes value 1 corresponding visit affected ICE1 0 otherwise. dropout_ice1: Binary variable takes value 1 corresponding visit affected drop-following ICE1 0 otherwise. ind_ice2: Binary variable takes value 1 corresponding visit affected ICE2. outcome: Numeric variable specifies longitudinal outcome including ICE1, ICE2 intermittent missing values.","code":""},{"path":"/reference/simulate_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate data — simulate_data","text":"data generation works follows: Generate outcome data visits (including baseline) multivariate normal distribution parameters pars_c$mu pars_c$sigma control arm parameters pars_t$mu pars_t$sigma treatment arm, respectively. Note randomized trial, outcomes distribution baseline treatment groups, .e. one set pars_c$mu[1]=pars_t$mu[1] pars_c$sigma[1,1]=pars_t$sigma[1,1]. Simulate whether ICE1 (study treatment discontinuation due SDCR reasons) occurs visit according parameters pars_c$prob_ice1 pars_c$or_outcome_ice1 control arm pars_t$prob_ice1 pars_t$or_outcome_ice1 treatment arm, respectively. Simulate drop-following ICE1 according pars_c$prob_post_ice1_dropout pars_t$prob_post_ice1_dropout. Simulate additional uninformative study drop-probabilities pars_c$prob_ice2 pars_t$prob_ice2 visit. generates second intercurrent event ICE2, may thought treatment discontinuation due NSDRC reasons subsequent drop-. simulated time drop-subject's first visit affected drop-data visit subsequent visits consequently set missing. subject, ICE1 ICE2 simulated occur, assumed earlier counts. case ICEs simulated occur time, assumed ICE1 counts. means single subject can experience either ICE1 ICE2, . Adjust trajectories ICE1 according given assumption expressed post_ice1_traj argument. Note post-ICE1 outcomes intervention arm can adjusted. Post-ICE1 outcomes control arm adjusted. Simulate additional intermittent missing outcome data per arguments pars_c$prob_miss pars_t$prob_miss. probability ICE visit modeled according following logistic regression model: ~ 1 + (visit == 0) + ... + (visit == n_visits-1) + ((x-alpha)) : n_visits number visits (including baseline). alpha baseline outcome mean. term ((x-alpha)) specifies dependency probability ICE current outcome value. corresponding regression coefficients logistic model defined follows: intercept set 0, coefficients corresponding discontinuation visit subject outcome equal mean baseline set according parameters pars_c$prob_ice1 (pars_t$prob_ice1), regression coefficient associated covariate ((x-alpha)) set log(pars_c$or_outcome_ice1) (log(pars_t$or_outcome_ice1)). Please note baseline outcome missing affected ICEs.","code":""},{"path":"/reference/simulate_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate drop-out — simulate_dropout","title":"Simulate drop-out — simulate_dropout","text":"Simulate drop-","code":""},{"path":"/reference/simulate_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate drop-out — simulate_dropout","text":"","code":"simulate_dropout(prob_dropout, ids, subset = rep(1, length(ids)))"},{"path":"/reference/simulate_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate drop-out — simulate_dropout","text":"prob_dropout Numeric specifies probability post-baseline visit affected study drop-. ids Factor variable specifies id subject. subset Binary variable specifies subset affected drop-. .e. subset binary vector length equal length ids takes value 1 corresponding visit affected drop-0 otherwise.","code":""},{"path":"/reference/simulate_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate drop-out — simulate_dropout","text":"binary vector length equal length ids takes value 1 corresponding outcome affected study drop-.","code":""},{"path":"/reference/simulate_dropout.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate drop-out — simulate_dropout","text":"subset can used specify outcome values affected drop-. default subset set 1 values except values corresponding baseline outcome, since baseline supposed affected drop-. Even subset specified user, values corresponding baseline outcome still hard-coded 0.","code":""},{"path":"/reference/simulate_ice.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate intercurrent event — simulate_ice","title":"Simulate intercurrent event — simulate_ice","text":"Simulate intercurrent event","code":""},{"path":"/reference/simulate_ice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate intercurrent event — simulate_ice","text":"","code":"simulate_ice(outcome, visits, ids, prob_ice, or_outcome_ice, baseline_mean)"},{"path":"/reference/simulate_ice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate intercurrent event — simulate_ice","text":"outcome Numeric variable specifies longitudinal outcome single group. visits Factor variable specifies visit assessment. ids Factor variable specifies id subject. prob_ice Numeric vector specifies visit probability experiencing ICE current visit subject outcome equal mean baseline. single numeric provided, probability applied visit. or_outcome_ice Numeric value specifies odds ratio ICE corresponding +1 higher value outcome visit. baseline_mean Mean outcome value baseline.","code":""},{"path":"/reference/simulate_ice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate intercurrent event — simulate_ice","text":"binary variable takes value 1 corresponding outcome affected ICE 0 otherwise.","code":""},{"path":"/reference/simulate_ice.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate intercurrent event — simulate_ice","text":"probability ICE visit modeled according following logistic regression model: ~ 1 + (visit == 0) + ... + (visit == n_visits-1) + ((x-alpha)) : n_visits number visits (including baseline). alpha baseline outcome mean set via argument baseline_mean. term ((x-alpha)) specifies dependency probability ICE current outcome value. corresponding regression coefficients logistic model defined follows: intercept set 0, coefficients corresponding discontinuation visit subject outcome equal mean baseline set according parameter or_outcome_ice, regression coefficient associated covariate ((x-alpha)) set log(or_outcome_ice).","code":""},{"path":"/reference/simulate_test_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Create simulated datasets — simulate_test_data","title":"Create simulated datasets — simulate_test_data","text":"Creates longitudinal dataset format rbmi designed analyse.","code":""},{"path":"/reference/simulate_test_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create simulated datasets — simulate_test_data","text":"","code":"simulate_test_data( n = 200, sd = c(3, 5, 7), cor = c(0.1, 0.7, 0.4), mu = list(int = 10, age = 3, sex = 2, trt = c(0, 4, 8), visit = c(0, 1, 2)) ) as_vcov(sd, cor)"},{"path":"/reference/simulate_test_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create simulated datasets — simulate_test_data","text":"n number subjects sample. Total number observations returned thus n * length(sd) sd standard deviations outcome visit. .e. square root diagonal covariance matrix outcome cor correlation coefficients outcome values visit. See details. mu coefficients use construct mean outcome value visit. Must named list elements int, age, sex, trt & visit. See details.","code":""},{"path":"/reference/simulate_test_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create simulated datasets — simulate_test_data","text":"number visits determined size variance covariance matrix. .e. 3 standard deviation values provided 3 visits per patient created. covariates simulated dataset produced follows: Patients age sampled random N(0,1) distribution Patients sex sampled random 50/50 split Patients group sampled random fixed group n/2 patients outcome variable sampled multivariate normal distribution, see details mean outcome variable derived : coefficients intercept, age sex taken mu$int, mu$age mu$sex respectively, must length 1 numeric. Treatment visit coefficients taken mu$trt mu$visit respectively must either length 1 (.e. constant affect across visits) equal number visits (determined length sd). .e. wanted treatment slope 5 visit slope 1 specify: correlation matrix constructed cor follows. Let cor = c(, b, c, d, e, f) correlation matrix :","code":"outcome = Intercept + age + sex + visit + treatment mu = list(..., \"trt\" = c(0,5,10), \"visit\" = c(0,1,2)) 1 a b d a 1 c e b c 1 f d e f 1"},{"path":"/reference/sort_by.html","id":null,"dir":"Reference","previous_headings":"","what":"Sort data.frame — sort_by","title":"Sort data.frame — sort_by","text":"Sorts data.frame (ascending default) based upon variables within dataset","code":""},{"path":"/reference/sort_by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sort data.frame — sort_by","text":"","code":"sort_by(df, vars = NULL, decreasing = FALSE)"},{"path":"/reference/sort_by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sort data.frame — sort_by","text":"df data.frame vars character vector variables decreasing logical whether sort order descending ascending (default) order. Can either single logical value (case applied variables) vector length vars","code":""},{"path":"/reference/sort_by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sort data.frame — sort_by","text":"","code":"if (FALSE) { # \\dontrun{ sort_by(iris, c(\"Sepal.Length\", \"Sepal.Width\"), decreasing = c(TRUE, FALSE)) } # }"},{"path":"/reference/split_dim.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform array into list of arrays — split_dim","title":"Transform array into list of arrays — split_dim","text":"Transform array list arrays listing performed given dimension.","code":""},{"path":"/reference/split_dim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform array into list of arrays — split_dim","text":"","code":"split_dim(a, n)"},{"path":"/reference/split_dim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform array into list of arrays — split_dim","text":"Array number dimensions least 2. n Positive integer. Dimension listed.","code":""},{"path":"/reference/split_dim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform array into list of arrays — split_dim","text":"list length n arrays number dimensions equal number dimensions minus 1.","code":""},{"path":"/reference/split_dim.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform array into list of arrays — split_dim","text":"example, 3 dimensional array n = 1, split_dim(,n) returns list 2 dimensional arrays (.e. list matrices) element list [, , ], takes values 1 length first dimension array. Example: inputs: <- array( c(1,2,3,4,5,6,7,8,9,10,11,12), dim = c(3,2,2)), means : n <- 1 output res <- split_dim(,n) list 3 elements:","code":"a[1,,] a[2,,] a[3,,] [,1] [,2] [,1] [,2] [,1] [,2] --------- --------- --------- 1 7 2 8 3 9 4 10 5 11 6 12 res[[1]] res[[2]] res[[3]] [,1] [,2] [,1] [,2] [,1] [,2] --------- --------- --------- 1 7 2 8 3 9 4 10 5 11 6 12"},{"path":"/reference/split_imputations.html","id":null,"dir":"Reference","previous_headings":"","what":"Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations","title":"Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations","text":"Split flat list imputation_single() multiple imputation_df()'s ID","code":""},{"path":"/reference/split_imputations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations","text":"","code":"split_imputations(list_of_singles, split_ids)"},{"path":"/reference/split_imputations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations","text":"list_of_singles list imputation_single()'s split_ids list 1 element per required split. element must contain vector \"ID\"'s correspond imputation_single() ID's required within sample. total number ID's must equal length list_of_singles","code":""},{"path":"/reference/split_imputations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations","text":"function converts list imputations structured per patient structured per sample .e. converts :","code":"obj <- list( imputation_single(\"Ben\", numeric(0)), imputation_single(\"Ben\", numeric(0)), imputation_single(\"Ben\", numeric(0)), imputation_single(\"Harry\", c(1, 2)), imputation_single(\"Phil\", c(3, 4)), imputation_single(\"Phil\", c(5, 6)), imputation_single(\"Tom\", c(7, 8, 9)) ) index <- list( c(\"Ben\", \"Harry\", \"Phil\", \"Tom\"), c(\"Ben\", \"Ben\", \"Phil\") ) output <- list( imputation_df( imputation_single(id = \"Ben\", values = numeric(0)), imputation_single(id = \"Harry\", values = c(1, 2)), imputation_single(id = \"Phil\", values = c(3, 4)), imputation_single(id = \"Tom\", values = c(7, 8, 9)) ), imputation_df( imputation_single(id = \"Ben\", values = numeric(0)), imputation_single(id = \"Ben\", values = numeric(0)), imputation_single(id = \"Phil\", values = c(5, 6)) ) )"},{"path":"/reference/str_contains.html","id":null,"dir":"Reference","previous_headings":"","what":"Does a string contain a substring — str_contains","title":"Does a string contain a substring — str_contains","text":"Returns vector TRUE/FALSE element x contains element subs .e.","code":"str_contains( c(\"ben\", \"tom\", \"harry\"), c(\"e\", \"y\")) [1] TRUE FALSE TRUE"},{"path":"/reference/str_contains.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Does a string contain a substring — str_contains","text":"","code":"str_contains(x, subs)"},{"path":"/reference/str_contains.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Does a string contain a substring — str_contains","text":"x character vector subs character vector substrings look ","code":""},{"path":"/reference/strategies.html","id":null,"dir":"Reference","previous_headings":"","what":"Strategies — strategies","title":"Strategies — strategies","text":"functions used implement various reference based imputation strategies combining subjects distribution reference distribution based upon visits failed meet Missing--Random (MAR) assumption.","code":""},{"path":"/reference/strategies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Strategies — strategies","text":"","code":"strategy_MAR(pars_group, pars_ref, index_mar) strategy_JR(pars_group, pars_ref, index_mar) strategy_CR(pars_group, pars_ref, index_mar) strategy_CIR(pars_group, pars_ref, index_mar) strategy_LMCF(pars_group, pars_ref, index_mar)"},{"path":"/reference/strategies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Strategies — strategies","text":"pars_group list parameters subject's group. See details. pars_ref list parameters subject's reference group. See details. index_mar logical vector indicating visits meet MAR assumption subject. .e. identifies observations non-MAR intercurrent event (ICE).","code":""},{"path":"/reference/strategies.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Strategies — strategies","text":"pars_group pars_ref must list containing elements mu sigma. mu must numeric vector sigma must square matrix symmetric covariance matrix dimensions equal length mu index_mar. e.g. Users can define strategy functions include via strategies argument impute() using getStrategies(). said following strategies available \"box\": Missing Random (MAR) Jump Reference (JR) Copy Reference (CR) Copy Increments Reference (CIR) Last Mean Carried Forward (LMCF)","code":"list( mu = c(1,2,3), sigma = matrix(c(4,3,2,3,5,4,2,4,6), nrow = 3, ncol = 3) )"},{"path":"/reference/string_pad.html","id":null,"dir":"Reference","previous_headings":"","what":"string_pad — string_pad","title":"string_pad — string_pad","text":"Utility function used replicate str_pad. Adds white space either end string get equal desired length","code":""},{"path":"/reference/string_pad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"string_pad — string_pad","text":"","code":"string_pad(x, width)"},{"path":"/reference/string_pad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"string_pad — string_pad","text":"x string width desired length","code":""},{"path":"/reference/transpose_imputations.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose imputations — transpose_imputations","title":"Transpose imputations — transpose_imputations","text":"Takes imputation_df object transposes e.g.","code":"list( list(id = \"a\", values = c(1,2,3)), list(id = \"b\", values = c(4,5,6) ) )"},{"path":"/reference/transpose_imputations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose imputations — transpose_imputations","text":"","code":"transpose_imputations(imputations)"},{"path":"/reference/transpose_imputations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transpose imputations — transpose_imputations","text":"imputations imputation_df object created imputation_df()","code":""},{"path":"/reference/transpose_imputations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose imputations — transpose_imputations","text":"becomes","code":"list( ids = c(\"a\", \"b\"), values = c(1,2,3,4,5,6) )"},{"path":"/reference/transpose_results.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose results object — transpose_results","title":"Transpose results object — transpose_results","text":"Transposes Results object (created analyse()) order group estimates together vectors.","code":""},{"path":"/reference/transpose_results.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose results object — transpose_results","text":"","code":"transpose_results(results, components)"},{"path":"/reference/transpose_results.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transpose results object — transpose_results","text":"results list results. components character vector components extract (.e. \"est\", \"se\").","code":""},{"path":"/reference/transpose_results.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose results object — transpose_results","text":"Essentially function takes object format: produces:","code":"x <- list( list( \"trt1\" = list( est = 1, se = 2 ), \"trt2\" = list( est = 3, se = 4 ) ), list( \"trt1\" = list( est = 5, se = 6 ), \"trt2\" = list( est = 7, se = 8 ) ) ) list( trt1 = list( est = c(1,5), se = c(2,6) ), trt2 = list( est = c(3,7), se = c(4,8) ) )"},{"path":"/reference/transpose_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose samples — transpose_samples","title":"Transpose samples — transpose_samples","text":"Transposes samples generated draws() grouped subjid instead sample number.","code":""},{"path":"/reference/transpose_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose samples — transpose_samples","text":"","code":"transpose_samples(samples)"},{"path":"/reference/transpose_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transpose samples — transpose_samples","text":"samples list samples generated draws().","code":""},{"path":"/reference/validate.analysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate analysis objects — validate.analysis","title":"Validate analysis objects — validate.analysis","text":"Validates return object analyse() function.","code":""},{"path":"/reference/validate.analysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate analysis objects — validate.analysis","text":"","code":"# S3 method for class 'analysis' validate(x, ...)"},{"path":"/reference/validate.analysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate analysis objects — validate.analysis","text":"x analysis results object (class \"jackknife\", \"bootstrap\", \"rubin\"). ... used.","code":""},{"path":"/reference/validate.draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate draws object — validate.draws","title":"Validate draws object — validate.draws","text":"Validate draws object","code":""},{"path":"/reference/validate.draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate draws object — validate.draws","text":"","code":"# S3 method for class 'draws' validate(x, ...)"},{"path":"/reference/validate.draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate draws object — validate.draws","text":"x draws object generated as_draws(). ... used.","code":""},{"path":"/reference/validate.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic validation method — validate","title":"Generic validation method — validate","text":"function used perform assertions object conforms expected structure basic assumptions violated. throw error checks pass.","code":""},{"path":"/reference/validate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic validation method — validate","text":"","code":"validate(x, ...)"},{"path":"/reference/validate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic validation method — validate","text":"x object validated. ... additional arguments pass specific validation method.","code":""},{"path":"/reference/validate.is_mar.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate is_mar for a given subject — validate.is_mar","title":"Validate is_mar for a given subject — validate.is_mar","text":"Checks longitudinal data patient divided MAR followed non-MAR data; non-MAR observation followed MAR observation allowed.","code":""},{"path":"/reference/validate.is_mar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate is_mar for a given subject — validate.is_mar","text":"","code":"# S3 method for class 'is_mar' validate(x, ...)"},{"path":"/reference/validate.is_mar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate is_mar for a given subject — validate.is_mar","text":"x Object class is_mar. Logical vector indicating whether observations MAR. ... used.","code":""},{"path":"/reference/validate.is_mar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate is_mar for a given subject — validate.is_mar","text":"error issue otherwise return TRUE.","code":""},{"path":"/reference/validate.ivars.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate inputs for vars — validate.ivars","title":"Validate inputs for vars — validate.ivars","text":"Checks required variable names defined within vars appropriate datatypes","code":""},{"path":"/reference/validate.ivars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate inputs for vars — validate.ivars","text":"","code":"# S3 method for class 'ivars' validate(x, ...)"},{"path":"/reference/validate.ivars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate inputs for vars — validate.ivars","text":"x named list indicating names key variables source dataset ... used","code":""},{"path":"/reference/validate.references.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate user supplied references — validate.references","title":"Validate user supplied references — validate.references","text":"Checks ensure user specified references expect values (.e. found within source data).","code":""},{"path":"/reference/validate.references.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate user supplied references — validate.references","text":"","code":"# S3 method for class 'references' validate(x, control, ...)"},{"path":"/reference/validate.references.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate user supplied references — validate.references","text":"x named character vector. control factor variable (group variable source dataset). ... used.","code":""},{"path":"/reference/validate.references.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate user supplied references — validate.references","text":"error issue otherwise return TRUE.","code":""},{"path":"/reference/validate.sample_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate sample_list object — validate.sample_list","title":"Validate sample_list object — validate.sample_list","text":"Validate sample_list object","code":""},{"path":"/reference/validate.sample_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate sample_list object — validate.sample_list","text":"","code":"# S3 method for class 'sample_list' validate(x, ...)"},{"path":"/reference/validate.sample_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate sample_list object — validate.sample_list","text":"x sample_list object generated sample_list(). ... used.","code":""},{"path":"/reference/validate.sample_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate sample_single object — validate.sample_single","title":"Validate sample_single object — validate.sample_single","text":"Validate sample_single object","code":""},{"path":"/reference/validate.sample_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate sample_single object — validate.sample_single","text":"","code":"# S3 method for class 'sample_single' validate(x, ...)"},{"path":"/reference/validate.sample_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate sample_single object — validate.sample_single","text":"x sample_single object generated sample_single(). ... used.","code":""},{"path":"/reference/validate.simul_pars.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate a simul_pars object — validate.simul_pars","title":"Validate a simul_pars object — validate.simul_pars","text":"Validate simul_pars object","code":""},{"path":"/reference/validate.simul_pars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate a simul_pars object — validate.simul_pars","text":"","code":"# S3 method for class 'simul_pars' validate(x, ...)"},{"path":"/reference/validate.simul_pars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate a simul_pars object — validate.simul_pars","text":"x simul_pars object generated set_simul_pars(). ... used.","code":""},{"path":"/reference/validate.stan_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate a stan_data object — validate.stan_data","title":"Validate a stan_data object — validate.stan_data","text":"Validate stan_data object","code":""},{"path":"/reference/validate.stan_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate a stan_data object — validate.stan_data","text":"","code":"# S3 method for class 'stan_data' validate(x, ...)"},{"path":"/reference/validate.stan_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate a stan_data object — validate.stan_data","text":"x stan_data object. ... used.","code":""},{"path":"/reference/validate_analyse_pars.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate analysis results — validate_analyse_pars","title":"Validate analysis results — validate_analyse_pars","text":"Validates analysis results generated analyse().","code":""},{"path":"/reference/validate_analyse_pars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate analysis results — validate_analyse_pars","text":"","code":"validate_analyse_pars(results, pars)"},{"path":"/reference/validate_analyse_pars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate analysis results — validate_analyse_pars","text":"results list results generated analysis fun used analyse(). pars list expected parameters analysis. lists .e. c(\"est\", \"se\", \"df\").","code":""},{"path":"/reference/validate_datalong.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate a longdata object — validate_datalong","title":"Validate a longdata object — validate_datalong","text":"Validate longdata object","code":""},{"path":"/reference/validate_datalong.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate a longdata object — validate_datalong","text":"","code":"validate_datalong(data, vars) validate_datalong_varExists(data, vars) validate_datalong_types(data, vars) validate_datalong_notMissing(data, vars) validate_datalong_complete(data, vars) validate_datalong_unifromStrata(data, vars) validate_dataice(data, data_ice, vars, update = FALSE)"},{"path":"/reference/validate_datalong.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate a longdata object — validate_datalong","text":"data data.frame containing longitudinal outcome data + covariates multiple subjects vars vars object created set_vars() data_ice data.frame containing subjects ICE data. See draws() details. update logical, indicates ICE data set first time update applied","code":""},{"path":"/reference/validate_datalong.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Validate a longdata object — validate_datalong","text":"functions used validate various different parts longdata object used draws(), impute(), analyse() pool(). particular: validate_datalong_varExists - Checks variable listed vars actually exists data validate_datalong_types - Checks types key variable expected .e. visit factor variable validate_datalong_notMissing - Checks none key variables (except outcome variable) contain missing values validate_datalong_complete - Checks data complete .e. 1 row subject * visit combination. e.g. nrow(data) == length(unique(subjects)) * length(unique(visits)) validate_datalong_unifromStrata - Checks make sure variables listed stratification variables vary time. e.g. subjects switch stratification groups.","code":""},{"path":"/reference/validate_strategies.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate user specified strategies — validate_strategies","title":"Validate user specified strategies — validate_strategies","text":"Compares user provided strategies required (reference). throw error values reference defined.","code":""},{"path":"/reference/validate_strategies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate user specified strategies — validate_strategies","text":"","code":"validate_strategies(strategies, reference)"},{"path":"/reference/validate_strategies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate user specified strategies — validate_strategies","text":"strategies named list strategies. reference list character vector strategies need defined.","code":""},{"path":"/reference/validate_strategies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate user specified strategies — validate_strategies","text":"throw error issue otherwise return TRUE.","code":""},{"path":"/news/index.html","id":"rbmi-126","dir":"Changelog","previous_headings":"","what":"rbmi 1.2.6","title":"rbmi 1.2.6","text":"CRAN release: 2023-11-24 Updated unit tests fix false-positive error CRAN’s testing servers","code":""},{"path":"/news/index.html","id":"rbmi-125","dir":"Changelog","previous_headings":"","what":"rbmi 1.2.5","title":"rbmi 1.2.5","text":"CRAN release: 2023-09-20 Updated internal Stan code ensure future compatibility (@andrjohns, #390) Updated package description include relevant references (#393) Fixed documentation typos (#393)","code":""},{"path":"/news/index.html","id":"rbmi-123","dir":"Changelog","previous_headings":"","what":"rbmi 1.2.3","title":"rbmi 1.2.3","text":"CRAN release: 2022-11-14 Minor internal tweaks ensure compatibility packages rbmi depends ","code":""},{"path":"/news/index.html","id":"rbmi-121","dir":"Changelog","previous_headings":"","what":"rbmi 1.2.1","title":"rbmi 1.2.1","text":"CRAN release: 2022-10-25 Removed native pipes |> testing code package backwards compatible older servers Replaced glmmTMB dependency mmrm package. resulted package stable (less model fitting convergence issues) well speeding run times 3-fold.","code":""},{"path":"/news/index.html","id":"rbmi-114","dir":"Changelog","previous_headings":"","what":"rbmi 1.1.4","title":"rbmi 1.1.4","text":"CRAN release: 2022-05-18 Updated urls references vignettes Fixed bug visit factor levels re-constructed incorrectly delta_template() Fixed bug wrong visit displayed error message specific visit doesn’t data draws() Fixed bug wrong input parameter displayed error message simulate_data()","code":""},{"path":"/news/index.html","id":"rbmi-111--113","dir":"Changelog","previous_headings":"","what":"rbmi 1.1.1 & 1.1.3","title":"rbmi 1.1.1 & 1.1.3","text":"CRAN release: 2022-03-08 change functionality 1.1.0 Various minor tweaks address CRAN checks messages","code":""},{"path":"/news/index.html","id":"rbmi-110","dir":"Changelog","previous_headings":"","what":"rbmi 1.1.0","title":"rbmi 1.1.0","text":"CRAN release: 2022-03-02 Initial public release","code":""}] diff --git a/latest-tag/sitemap.xml b/latest-tag/sitemap.xml new file mode 100644 index 00000000..99d40340 --- /dev/null +++ b/latest-tag/sitemap.xml @@ -0,0 +1,149 @@ + +/404.html +/CONTRIBUTING.html +/LICENSE.html +/articles/advanced.html +/articles/index.html +/articles/quickstart.html +/articles/stat_specs.html +/authors.html +/index.html +/news/index.html +/reference/QR_decomp.html +/reference/Stack.html +/reference/add_class.html +/reference/adjust_trajectories.html +/reference/adjust_trajectories_single.html +/reference/analyse.html +/reference/ancova.html +/reference/ancova_single.html +/reference/antidepressant_data.html +/reference/apply_delta.html +/reference/as_analysis.html +/reference/as_ascii_table.html +/reference/as_class.html +/reference/as_cropped_char.html +/reference/as_dataframe.html +/reference/as_draws.html +/reference/as_imputation.html +/reference/as_indices.html +/reference/as_mmrm_df.html +/reference/as_mmrm_formula.html +/reference/as_model_df.html +/reference/as_simple_formula.html +/reference/as_stan_array.html +/reference/as_strata.html +/reference/assert_variables_exist.html +/reference/char2fct.html +/reference/check_ESS.html +/reference/check_hmc_diagn.html +/reference/check_mcmc.html +/reference/compute_sigma.html +/reference/convert_to_imputation_list_df.html +/reference/d_lagscale.html +/reference/delta_template.html +/reference/do_not_run.html +/reference/draws.html +/reference/encap_get_mmrm_sample.html +/reference/eval_mmrm.html +/reference/expand.html +/reference/extract_covariates.html +/reference/extract_data_nmar_as_na.html +/reference/extract_draws.html +/reference/extract_imputed_df.html +/reference/extract_imputed_dfs.html +/reference/extract_params.html +/reference/fit_mcmc.html +/reference/fit_mmrm.html +/reference/generate_data_single.html +/reference/getStrategies.html +/reference/get_ESS.html +/reference/get_bootstrap_stack.html +/reference/get_cluster.html +/reference/get_conditional_parameters.html +/reference/get_delta_template.html +/reference/get_draws_mle.html +/reference/get_ests_bmlmi.html +/reference/get_example_data.html +/reference/get_jackknife_stack.html +/reference/get_mmrm_sample.html +/reference/get_pattern_groups.html +/reference/get_pattern_groups_unique.html +/reference/get_pool_components.html +/reference/get_visit_distribution_parameters.html +/reference/has_class.html +/reference/ife.html +/reference/imputation_df.html +/reference/imputation_list_df.html +/reference/imputation_list_single.html +/reference/imputation_single.html +/reference/impute.html +/reference/impute_data_individual.html +/reference/impute_internal.html +/reference/impute_outcome.html +/reference/index.html +/reference/invert.html +/reference/invert_indexes.html +/reference/is_absent.html +/reference/is_char_fact.html +/reference/is_char_one.html +/reference/is_in_rbmi_development.html +/reference/is_num_char_fact.html +/reference/locf.html +/reference/longDataConstructor.html +/reference/ls_design.html +/reference/lsmeans.html +/reference/method.html +/reference/parametric_ci.html +/reference/pool.html +/reference/pool_bootstrap_normal.html +/reference/pool_bootstrap_percentile.html +/reference/pool_internal.html +/reference/prepare_stan_data.html +/reference/print.analysis.html +/reference/print.draws.html +/reference/print.imputation.html +/reference/progressLogger.html +/reference/pval_percentile.html +/reference/random_effects_expr.html +/reference/rbmi-package.html +/reference/record.html +/reference/recursive_reduce.html +/reference/remove_if_all_missing.html +/reference/rubin_df.html +/reference/rubin_rules.html +/reference/sample_ids.html +/reference/sample_list.html +/reference/sample_mvnorm.html +/reference/sample_single.html +/reference/scalerConstructor.html +/reference/set_simul_pars.html +/reference/set_vars.html +/reference/simulate_data.html +/reference/simulate_dropout.html +/reference/simulate_ice.html +/reference/simulate_test_data.html +/reference/sort_by.html +/reference/split_dim.html +/reference/split_imputations.html +/reference/str_contains.html +/reference/strategies.html +/reference/string_pad.html +/reference/transpose_imputations.html +/reference/transpose_results.html +/reference/transpose_samples.html +/reference/validate.analysis.html +/reference/validate.draws.html +/reference/validate.html +/reference/validate.is_mar.html +/reference/validate.ivars.html +/reference/validate.references.html +/reference/validate.sample_list.html +/reference/validate.sample_single.html +/reference/validate.simul_pars.html +/reference/validate.stan_data.html +/reference/validate_analyse_pars.html +/reference/validate_datalong.html +/reference/validate_strategies.html + + diff --git a/main/404.html b/main/404.html index 0944a653..030b8255 100644 --- a/main/404.html +++ b/main/404.html @@ -48,7 +48,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
      • + id="dropdown-versions">Versions
      main +latest-tag +v1.2.6
      • diff --git a/main/LICENSE.html b/main/LICENSE.html index 8cc24adc..90c61154 100644 --- a/main/LICENSE.html +++ b/main/LICENSE.html @@ -31,7 +31,9 @@
      main
      + id="dropdown-versions">Versions
      main +latest-tag +v1.2.6
      • diff --git a/main/articles/CondMean_Inference.html b/main/articles/CondMean_Inference.html index 68f5bb2f..098fd1c1 100644 --- a/main/articles/CondMean_Inference.html +++ b/main/articles/CondMean_Inference.html @@ -47,7 +47,9 @@
      main
      + id="dropdown-versions">Versions
      main +latest-tag +v1.2.6
        • + id="dropdown-versions">Versions
        main +latest-tag +v1.2.6
        • diff --git a/main/articles/advanced.html b/main/articles/advanced.html index 9cbcf9af..461e3af0 100644 --- a/main/articles/advanced.html +++ b/main/articles/advanced.html @@ -47,7 +47,9 @@
          main
          • + id="dropdown-versions">Versions
          main +latest-tag +v1.2.6
          • diff --git a/main/articles/index.html b/main/articles/index.html index 184b6ae1..5db06fd7 100644 --- a/main/articles/index.html +++ b/main/articles/index.html @@ -31,7 +31,9 @@
            main
            • + id="dropdown-versions">Versions
            main +latest-tag +v1.2.6
            • diff --git a/main/articles/quickstart.html b/main/articles/quickstart.html index c97c450b..c3535405 100644 --- a/main/articles/quickstart.html +++ b/main/articles/quickstart.html @@ -47,7 +47,9 @@
            main
            + id="dropdown-versions">Versions
            main +latest-tag +v1.2.6
              • + id="dropdown-versions">Versions
              main +latest-tag +v1.2.6
              • diff --git a/main/articles/stat_specs.html b/main/articles/stat_specs.html index dee496cc..bfc976d0 100644 --- a/main/articles/stat_specs.html +++ b/main/articles/stat_specs.html @@ -47,7 +47,9 @@
                main
                • + id="dropdown-versions">Versions
                main +latest-tag +v1.2.6
                • diff --git a/main/authors.html b/main/authors.html index 3973ca70..0d1d9735 100644 --- a/main/authors.html +++ b/main/authors.html @@ -31,7 +31,9 @@
                  main
                  • + id="dropdown-versions">Versions
                  main +latest-tag +v1.2.6
                  • diff --git a/main/index.html b/main/index.html index befafd7f..e2732806 100644 --- a/main/index.html +++ b/main/index.html @@ -50,7 +50,9 @@
                  main
                  + id="dropdown-versions">Versions
                  main +latest-tag +v1.2.6
                    • + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/QR_decomp.html b/main/reference/QR_decomp.html index 6457f95c..d8b7bf31 100644 --- a/main/reference/QR_decomp.html +++ b/main/reference/QR_decomp.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/Stack.html b/main/reference/Stack.html index 777be6ca..6500a232 100644 --- a/main/reference/Stack.html +++ b/main/reference/Stack.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/add_class.html b/main/reference/add_class.html index 71d32800..82783043 100644 --- a/main/reference/add_class.html +++ b/main/reference/add_class.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/adjust_trajectories.html b/main/reference/adjust_trajectories.html index ae7a8aee..81980b4d 100644 --- a/main/reference/adjust_trajectories.html +++ b/main/reference/adjust_trajectories.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/adjust_trajectories_single.html b/main/reference/adjust_trajectories_single.html index 1c8b71e2..3480831b 100644 --- a/main/reference/adjust_trajectories_single.html +++ b/main/reference/adjust_trajectories_single.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/analyse.html b/main/reference/analyse.html index bb8393f2..5fb5ab09 100644 --- a/main/reference/analyse.html +++ b/main/reference/analyse.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/ancova.html b/main/reference/ancova.html index bf819546..516643dd 100644 --- a/main/reference/ancova.html +++ b/main/reference/ancova.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/ancova_single.html b/main/reference/ancova_single.html index 484f6b27..b49bdcca 100644 --- a/main/reference/ancova_single.html +++ b/main/reference/ancova_single.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/antidepressant_data.html b/main/reference/antidepressant_data.html index a6b8a999..b4883acb 100644 --- a/main/reference/antidepressant_data.html +++ b/main/reference/antidepressant_data.html @@ -49,7 +49,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/apply_delta.html b/main/reference/apply_delta.html index e20d8055..05bb9285 100644 --- a/main/reference/apply_delta.html +++ b/main/reference/apply_delta.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/as_analysis.html b/main/reference/as_analysis.html index 0454df05..61d17cb0 100644 --- a/main/reference/as_analysis.html +++ b/main/reference/as_analysis.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/as_ascii_table.html b/main/reference/as_ascii_table.html index 1dc9f867..a3ae9045 100644 --- a/main/reference/as_ascii_table.html +++ b/main/reference/as_ascii_table.html @@ -37,7 +37,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/as_class.html b/main/reference/as_class.html index b477036b..745b1604 100644 --- a/main/reference/as_class.html +++ b/main/reference/as_class.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/as_cropped_char.html b/main/reference/as_cropped_char.html index 0e77c47e..cf621d8e 100644 --- a/main/reference/as_cropped_char.html +++ b/main/reference/as_cropped_char.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/as_dataframe.html b/main/reference/as_dataframe.html index 3b45dbfb..4533980b 100644 --- a/main/reference/as_dataframe.html +++ b/main/reference/as_dataframe.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/as_draws.html b/main/reference/as_draws.html index 6d4926a0..3c418d6e 100644 --- a/main/reference/as_draws.html +++ b/main/reference/as_draws.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/as_imputation.html b/main/reference/as_imputation.html index 4919affc..3656f6d3 100644 --- a/main/reference/as_imputation.html +++ b/main/reference/as_imputation.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/as_indices.html b/main/reference/as_indices.html index ef3d3054..2792c6da 100644 --- a/main/reference/as_indices.html +++ b/main/reference/as_indices.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/as_mmrm_df.html b/main/reference/as_mmrm_df.html index d23aeccd..9be14476 100644 --- a/main/reference/as_mmrm_df.html +++ b/main/reference/as_mmrm_df.html @@ -47,7 +47,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/as_mmrm_formula.html b/main/reference/as_mmrm_formula.html index 2296df78..f06e7c49 100644 --- a/main/reference/as_mmrm_formula.html +++ b/main/reference/as_mmrm_formula.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/as_model_df.html b/main/reference/as_model_df.html index 17b12735..b1fbf7d8 100644 --- a/main/reference/as_model_df.html +++ b/main/reference/as_model_df.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/as_simple_formula.html b/main/reference/as_simple_formula.html index 558d3321..7e55cf46 100644 --- a/main/reference/as_simple_formula.html +++ b/main/reference/as_simple_formula.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/as_stan_array.html b/main/reference/as_stan_array.html index 9984988d..c2dc7e2e 100644 --- a/main/reference/as_stan_array.html +++ b/main/reference/as_stan_array.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/as_strata.html b/main/reference/as_strata.html index db67452f..83997ed6 100644 --- a/main/reference/as_strata.html +++ b/main/reference/as_strata.html @@ -43,7 +43,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/assert_variables_exist.html b/main/reference/assert_variables_exist.html index 3b916138..e0755659 100644 --- a/main/reference/assert_variables_exist.html +++ b/main/reference/assert_variables_exist.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/char2fct.html b/main/reference/char2fct.html index 75a3694c..8e578c15 100644 --- a/main/reference/char2fct.html +++ b/main/reference/char2fct.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/check_ESS.html b/main/reference/check_ESS.html index ffec5f6a..b00bc5ab 100644 --- a/main/reference/check_ESS.html +++ b/main/reference/check_ESS.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/check_hmc_diagn.html b/main/reference/check_hmc_diagn.html index 8a1ae754..22e76785 100644 --- a/main/reference/check_hmc_diagn.html +++ b/main/reference/check_hmc_diagn.html @@ -43,7 +43,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/check_mcmc.html b/main/reference/check_mcmc.html index 2c215872..64723cb4 100644 --- a/main/reference/check_mcmc.html +++ b/main/reference/check_mcmc.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/compute_sigma.html b/main/reference/compute_sigma.html index 014804f2..84bbbb7e 100644 --- a/main/reference/compute_sigma.html +++ b/main/reference/compute_sigma.html @@ -37,7 +37,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/convert_to_imputation_list_df.html b/main/reference/convert_to_imputation_list_df.html index a84cfcc5..b3f0e25e 100644 --- a/main/reference/convert_to_imputation_list_df.html +++ b/main/reference/convert_to_imputation_list_df.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/d_lagscale.html b/main/reference/d_lagscale.html index df068c58..a95b3e84 100644 --- a/main/reference/d_lagscale.html +++ b/main/reference/d_lagscale.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/delta_template.html b/main/reference/delta_template.html index ebb96950..c54ea686 100644 --- a/main/reference/delta_template.html +++ b/main/reference/delta_template.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/do_not_run.html b/main/reference/do_not_run.html index d9568efc..534d98be 100644 --- a/main/reference/do_not_run.html +++ b/main/reference/do_not_run.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/draws.html b/main/reference/draws.html index 57727b06..34519e79 100644 --- a/main/reference/draws.html +++ b/main/reference/draws.html @@ -61,7 +61,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/encap_get_mmrm_sample.html b/main/reference/encap_get_mmrm_sample.html index ec6d1458..7427ffe9 100644 --- a/main/reference/encap_get_mmrm_sample.html +++ b/main/reference/encap_get_mmrm_sample.html @@ -39,7 +39,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/eval_mmrm.html b/main/reference/eval_mmrm.html index 2dda3ef0..26380d3e 100644 --- a/main/reference/eval_mmrm.html +++ b/main/reference/eval_mmrm.html @@ -41,7 +41,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/expand.html b/main/reference/expand.html index cc2cfb6e..6f3cdd11 100644 --- a/main/reference/expand.html +++ b/main/reference/expand.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/extract_covariates.html b/main/reference/extract_covariates.html index b0e18275..c8e5896e 100644 --- a/main/reference/extract_covariates.html +++ b/main/reference/extract_covariates.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/extract_data_nmar_as_na.html b/main/reference/extract_data_nmar_as_na.html index 3784668d..109a098e 100644 --- a/main/reference/extract_data_nmar_as_na.html +++ b/main/reference/extract_data_nmar_as_na.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/extract_draws.html b/main/reference/extract_draws.html index 6ef46665..7da6f58c 100644 --- a/main/reference/extract_draws.html +++ b/main/reference/extract_draws.html @@ -37,7 +37,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/extract_imputed_df.html b/main/reference/extract_imputed_df.html index 78148827..ca2cffa8 100644 --- a/main/reference/extract_imputed_df.html +++ b/main/reference/extract_imputed_df.html @@ -43,7 +43,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/extract_imputed_dfs.html b/main/reference/extract_imputed_dfs.html index 12c53858..bd489203 100644 --- a/main/reference/extract_imputed_dfs.html +++ b/main/reference/extract_imputed_dfs.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/extract_params.html b/main/reference/extract_params.html index 6f82b69d..ea252400 100644 --- a/main/reference/extract_params.html +++ b/main/reference/extract_params.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/fit_mcmc.html b/main/reference/fit_mcmc.html index 658c1835..18020dfd 100644 --- a/main/reference/fit_mcmc.html +++ b/main/reference/fit_mcmc.html @@ -41,7 +41,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/fit_mmrm.html b/main/reference/fit_mmrm.html index fd89f06e..681aec6f 100644 --- a/main/reference/fit_mmrm.html +++ b/main/reference/fit_mmrm.html @@ -37,7 +37,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/generate_data_single.html b/main/reference/generate_data_single.html index f190f8d9..3ca0a045 100644 --- a/main/reference/generate_data_single.html +++ b/main/reference/generate_data_single.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/getStrategies.html b/main/reference/getStrategies.html index 62e472df..6e14d61f 100644 --- a/main/reference/getStrategies.html +++ b/main/reference/getStrategies.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/get_ESS.html b/main/reference/get_ESS.html index 982809de..14ee219b 100644 --- a/main/reference/get_ESS.html +++ b/main/reference/get_ESS.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/get_bootstrap_stack.html b/main/reference/get_bootstrap_stack.html index b76ff4dc..c84b2855 100644 --- a/main/reference/get_bootstrap_stack.html +++ b/main/reference/get_bootstrap_stack.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/get_cluster.html b/main/reference/get_cluster.html index 5549a0e3..e42da7c0 100644 --- a/main/reference/get_cluster.html +++ b/main/reference/get_cluster.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/get_conditional_parameters.html b/main/reference/get_conditional_parameters.html index 83d3dedf..0de3475d 100644 --- a/main/reference/get_conditional_parameters.html +++ b/main/reference/get_conditional_parameters.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/get_delta_template.html b/main/reference/get_delta_template.html index 638beab9..6d09c3ec 100644 --- a/main/reference/get_delta_template.html +++ b/main/reference/get_delta_template.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/get_draws_mle.html b/main/reference/get_draws_mle.html index d0c274c4..77f0a3c4 100644 --- a/main/reference/get_draws_mle.html +++ b/main/reference/get_draws_mle.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/get_ests_bmlmi.html b/main/reference/get_ests_bmlmi.html index 154bebf9..3161c8f9 100644 --- a/main/reference/get_ests_bmlmi.html +++ b/main/reference/get_ests_bmlmi.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/get_example_data.html b/main/reference/get_example_data.html index 706db96a..374046ea 100644 --- a/main/reference/get_example_data.html +++ b/main/reference/get_example_data.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/get_jackknife_stack.html b/main/reference/get_jackknife_stack.html index d465bc42..f650cf4c 100644 --- a/main/reference/get_jackknife_stack.html +++ b/main/reference/get_jackknife_stack.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/get_mmrm_sample.html b/main/reference/get_mmrm_sample.html index 369ab94c..31954afc 100644 --- a/main/reference/get_mmrm_sample.html +++ b/main/reference/get_mmrm_sample.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/get_pattern_groups.html b/main/reference/get_pattern_groups.html index 56a117a7..4db6f515 100644 --- a/main/reference/get_pattern_groups.html +++ b/main/reference/get_pattern_groups.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/get_pattern_groups_unique.html b/main/reference/get_pattern_groups_unique.html index ec21d3ea..3c53984f 100644 --- a/main/reference/get_pattern_groups_unique.html +++ b/main/reference/get_pattern_groups_unique.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/get_pool_components.html b/main/reference/get_pool_components.html index 17de680a..87940961 100644 --- a/main/reference/get_pool_components.html +++ b/main/reference/get_pool_components.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/get_visit_distribution_parameters.html b/main/reference/get_visit_distribution_parameters.html index c8c2e5ba..8974f44e 100644 --- a/main/reference/get_visit_distribution_parameters.html +++ b/main/reference/get_visit_distribution_parameters.html @@ -39,7 +39,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/has_class.html b/main/reference/has_class.html index e928d8e1..8d300486 100644 --- a/main/reference/has_class.html +++ b/main/reference/has_class.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/ife.html b/main/reference/ife.html index a6bc2fb8..1d69602b 100644 --- a/main/reference/ife.html +++ b/main/reference/ife.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/imputation_df.html b/main/reference/imputation_df.html index 1b401c93..8e518df3 100644 --- a/main/reference/imputation_df.html +++ b/main/reference/imputation_df.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/imputation_list_df.html b/main/reference/imputation_list_df.html index 597ce296..f500cd42 100644 --- a/main/reference/imputation_list_df.html +++ b/main/reference/imputation_list_df.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/imputation_list_single.html b/main/reference/imputation_list_single.html index cd9d79bc..01d3908e 100644 --- a/main/reference/imputation_list_single.html +++ b/main/reference/imputation_list_single.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/imputation_single.html b/main/reference/imputation_single.html index e15efc79..37fd840e 100644 --- a/main/reference/imputation_single.html +++ b/main/reference/imputation_single.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/impute.html b/main/reference/impute.html index 691e010e..e9738462 100644 --- a/main/reference/impute.html +++ b/main/reference/impute.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/impute_data_individual.html b/main/reference/impute_data_individual.html index 24e6edfa..b4fd4853 100644 --- a/main/reference/impute_data_individual.html +++ b/main/reference/impute_data_individual.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/impute_internal.html b/main/reference/impute_internal.html index c47ba864..9180f5bf 100644 --- a/main/reference/impute_internal.html +++ b/main/reference/impute_internal.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/impute_outcome.html b/main/reference/impute_outcome.html index c505ce04..3ec68b6a 100644 --- a/main/reference/impute_outcome.html +++ b/main/reference/impute_outcome.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/index.html b/main/reference/index.html index 6d608f5c..1a1815dc 100644 --- a/main/reference/index.html +++ b/main/reference/index.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/invert.html b/main/reference/invert.html index b7208dd9..91224ccc 100644 --- a/main/reference/invert.html +++ b/main/reference/invert.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/invert_indexes.html b/main/reference/invert_indexes.html index b6f3687f..8fab5180 100644 --- a/main/reference/invert_indexes.html +++ b/main/reference/invert_indexes.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/is_absent.html b/main/reference/is_absent.html index db5c93a6..ea5f1370 100644 --- a/main/reference/is_absent.html +++ b/main/reference/is_absent.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/is_char_fact.html b/main/reference/is_char_fact.html index 0777296f..dbf6b686 100644 --- a/main/reference/is_char_fact.html +++ b/main/reference/is_char_fact.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/is_char_one.html b/main/reference/is_char_one.html index 821bf65a..510f37d9 100644 --- a/main/reference/is_char_one.html +++ b/main/reference/is_char_one.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/is_in_rbmi_development.html b/main/reference/is_in_rbmi_development.html index 09b09ac2..f8d2154e 100644 --- a/main/reference/is_in_rbmi_development.html +++ b/main/reference/is_in_rbmi_development.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/is_num_char_fact.html b/main/reference/is_num_char_fact.html index 8fcd0133..e7b39780 100644 --- a/main/reference/is_num_char_fact.html +++ b/main/reference/is_num_char_fact.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/locf.html b/main/reference/locf.html index fe74939b..a92321bb 100644 --- a/main/reference/locf.html +++ b/main/reference/locf.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/longDataConstructor.html b/main/reference/longDataConstructor.html index 842f6815..253cc211 100644 --- a/main/reference/longDataConstructor.html +++ b/main/reference/longDataConstructor.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/ls_design.html b/main/reference/ls_design.html index d213ce7d..badeb69f 100644 --- a/main/reference/ls_design.html +++ b/main/reference/ls_design.html @@ -41,7 +41,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/lsmeans.html b/main/reference/lsmeans.html index d6fc6e9e..cbaf2ab1 100644 --- a/main/reference/lsmeans.html +++ b/main/reference/lsmeans.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/method.html b/main/reference/method.html index e00768f9..a5a579f5 100644 --- a/main/reference/method.html +++ b/main/reference/method.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/parametric_ci.html b/main/reference/parametric_ci.html index bc3bbf96..e41e8ba0 100644 --- a/main/reference/parametric_ci.html +++ b/main/reference/parametric_ci.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/pool.html b/main/reference/pool.html index dddb065f..ad2b4c82 100644 --- a/main/reference/pool.html +++ b/main/reference/pool.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/pool_bootstrap_normal.html b/main/reference/pool_bootstrap_normal.html index 993c61d2..0558eb7c 100644 --- a/main/reference/pool_bootstrap_normal.html +++ b/main/reference/pool_bootstrap_normal.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/pool_bootstrap_percentile.html b/main/reference/pool_bootstrap_percentile.html index 17a1f081..19f30d09 100644 --- a/main/reference/pool_bootstrap_percentile.html +++ b/main/reference/pool_bootstrap_percentile.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/pool_internal.html b/main/reference/pool_internal.html index b8ad54c4..b64143ac 100644 --- a/main/reference/pool_internal.html +++ b/main/reference/pool_internal.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/prepare_stan_data.html b/main/reference/prepare_stan_data.html index 9526500c..049a7353 100644 --- a/main/reference/prepare_stan_data.html +++ b/main/reference/prepare_stan_data.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/print.analysis.html b/main/reference/print.analysis.html index 3d982095..5918b165 100644 --- a/main/reference/print.analysis.html +++ b/main/reference/print.analysis.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/print.draws.html b/main/reference/print.draws.html index 5f609186..a5a01942 100644 --- a/main/reference/print.draws.html +++ b/main/reference/print.draws.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/print.imputation.html b/main/reference/print.imputation.html index 82b3bdad..d3bbf507 100644 --- a/main/reference/print.imputation.html +++ b/main/reference/print.imputation.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/progressLogger.html b/main/reference/progressLogger.html index 1201f664..6987780c 100644 --- a/main/reference/progressLogger.html +++ b/main/reference/progressLogger.html @@ -39,7 +39,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/pval_percentile.html b/main/reference/pval_percentile.html index 4be73147..4b72bd4e 100644 --- a/main/reference/pval_percentile.html +++ b/main/reference/pval_percentile.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/random_effects_expr.html b/main/reference/random_effects_expr.html index 5fdd9fb8..f101d835 100644 --- a/main/reference/random_effects_expr.html +++ b/main/reference/random_effects_expr.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/rbmi-package.html b/main/reference/rbmi-package.html index 465392bd..4cf51ea8 100644 --- a/main/reference/rbmi-package.html +++ b/main/reference/rbmi-package.html @@ -53,7 +53,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/record.html b/main/reference/record.html index f9dfb0ce..bcf36849 100644 --- a/main/reference/record.html +++ b/main/reference/record.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/recursive_reduce.html b/main/reference/recursive_reduce.html index 08958996..562685d4 100644 --- a/main/reference/recursive_reduce.html +++ b/main/reference/recursive_reduce.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/remove_if_all_missing.html b/main/reference/remove_if_all_missing.html index 514f90b7..419fa76c 100644 --- a/main/reference/remove_if_all_missing.html +++ b/main/reference/remove_if_all_missing.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/rubin_df.html b/main/reference/rubin_df.html index 3e45b352..fc53b769 100644 --- a/main/reference/rubin_df.html +++ b/main/reference/rubin_df.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/rubin_rules.html b/main/reference/rubin_rules.html index d17bf9e4..91ce12d0 100644 --- a/main/reference/rubin_rules.html +++ b/main/reference/rubin_rules.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/sample_ids.html b/main/reference/sample_ids.html index d0a25430..942e1f76 100644 --- a/main/reference/sample_ids.html +++ b/main/reference/sample_ids.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/sample_list.html b/main/reference/sample_list.html index 9994526d..064523f8 100644 --- a/main/reference/sample_list.html +++ b/main/reference/sample_list.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/sample_mvnorm.html b/main/reference/sample_mvnorm.html index 786eedcb..900aa940 100644 --- a/main/reference/sample_mvnorm.html +++ b/main/reference/sample_mvnorm.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/sample_single.html b/main/reference/sample_single.html index fd723a2a..add1524f 100644 --- a/main/reference/sample_single.html +++ b/main/reference/sample_single.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/scalerConstructor.html b/main/reference/scalerConstructor.html index 5d52fc4c..cd069eec 100644 --- a/main/reference/scalerConstructor.html +++ b/main/reference/scalerConstructor.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/set_simul_pars.html b/main/reference/set_simul_pars.html index 4db2f137..a7d4baed 100644 --- a/main/reference/set_simul_pars.html +++ b/main/reference/set_simul_pars.html @@ -43,7 +43,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/set_vars.html b/main/reference/set_vars.html index cdccc0f9..89e5cb9b 100644 --- a/main/reference/set_vars.html +++ b/main/reference/set_vars.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/simulate_data.html b/main/reference/simulate_data.html index 348d06ee..6616095a 100644 --- a/main/reference/simulate_data.html +++ b/main/reference/simulate_data.html @@ -43,7 +43,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/simulate_dropout.html b/main/reference/simulate_dropout.html index 1aa93a33..5f466405 100644 --- a/main/reference/simulate_dropout.html +++ b/main/reference/simulate_dropout.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/simulate_ice.html b/main/reference/simulate_ice.html index e39ad579..e0df404d 100644 --- a/main/reference/simulate_ice.html +++ b/main/reference/simulate_ice.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/simulate_test_data.html b/main/reference/simulate_test_data.html index 3cf6f007..7336ff9b 100644 --- a/main/reference/simulate_test_data.html +++ b/main/reference/simulate_test_data.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/sort_by.html b/main/reference/sort_by.html index 9ffe2f95..db243f66 100644 --- a/main/reference/sort_by.html +++ b/main/reference/sort_by.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/split_dim.html b/main/reference/split_dim.html index 71a9b3c4..166b7312 100644 --- a/main/reference/split_dim.html +++ b/main/reference/split_dim.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/split_imputations.html b/main/reference/split_imputations.html index 21feca6f..7dfcfc81 100644 --- a/main/reference/split_imputations.html +++ b/main/reference/split_imputations.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/str_contains.html b/main/reference/str_contains.html index c24fa6e9..dfbdf011 100644 --- a/main/reference/str_contains.html +++ b/main/reference/str_contains.html @@ -41,7 +41,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/strategies.html b/main/reference/strategies.html index 40ff8677..55930fd2 100644 --- a/main/reference/strategies.html +++ b/main/reference/strategies.html @@ -37,7 +37,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/string_pad.html b/main/reference/string_pad.html index 1ab39882..9a02e84f 100644 --- a/main/reference/string_pad.html +++ b/main/reference/string_pad.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/transpose_imputations.html b/main/reference/transpose_imputations.html index 43a06abb..e4d06280 100644 --- a/main/reference/transpose_imputations.html +++ b/main/reference/transpose_imputations.html @@ -43,7 +43,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/transpose_results.html b/main/reference/transpose_results.html index fcb1f2fc..dfd25bce 100644 --- a/main/reference/transpose_results.html +++ b/main/reference/transpose_results.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/transpose_samples.html b/main/reference/transpose_samples.html index b2887de5..2c6a6ac5 100644 --- a/main/reference/transpose_samples.html +++ b/main/reference/transpose_samples.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/validate.analysis.html b/main/reference/validate.analysis.html index a51fb076..460666fe 100644 --- a/main/reference/validate.analysis.html +++ b/main/reference/validate.analysis.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/validate.draws.html b/main/reference/validate.draws.html index 6b115dad..e0a088c4 100644 --- a/main/reference/validate.draws.html +++ b/main/reference/validate.draws.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/validate.html b/main/reference/validate.html index 96397a40..eda4e034 100644 --- a/main/reference/validate.html +++ b/main/reference/validate.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/validate.is_mar.html b/main/reference/validate.is_mar.html index 76de6dd6..c4fb6f58 100644 --- a/main/reference/validate.is_mar.html +++ b/main/reference/validate.is_mar.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/validate.ivars.html b/main/reference/validate.ivars.html index a9273797..07d70766 100644 --- a/main/reference/validate.ivars.html +++ b/main/reference/validate.ivars.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/validate.references.html b/main/reference/validate.references.html index 6e2dd333..4f218d78 100644 --- a/main/reference/validate.references.html +++ b/main/reference/validate.references.html @@ -33,7 +33,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/validate.sample_list.html b/main/reference/validate.sample_list.html index 6a911d03..07444cf3 100644 --- a/main/reference/validate.sample_list.html +++ b/main/reference/validate.sample_list.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/validate.sample_single.html b/main/reference/validate.sample_single.html index b810a579..05d317f7 100644 --- a/main/reference/validate.sample_single.html +++ b/main/reference/validate.sample_single.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/validate.simul_pars.html b/main/reference/validate.simul_pars.html index 110252cd..b4b6296c 100644 --- a/main/reference/validate.simul_pars.html +++ b/main/reference/validate.simul_pars.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/validate.stan_data.html b/main/reference/validate.stan_data.html index e563ede6..0219cf04 100644 --- a/main/reference/validate.stan_data.html +++ b/main/reference/validate.stan_data.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/validate_analyse_pars.html b/main/reference/validate_analyse_pars.html index 33784a39..49f19f55 100644 --- a/main/reference/validate_analyse_pars.html +++ b/main/reference/validate_analyse_pars.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/validate_datalong.html b/main/reference/validate_datalong.html index a3e01536..d0a582f7 100644 --- a/main/reference/validate_datalong.html +++ b/main/reference/validate_datalong.html @@ -31,7 +31,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/main/reference/validate_strategies.html b/main/reference/validate_strategies.html index a00392fb..375b9403 100644 --- a/main/reference/validate_strategies.html +++ b/main/reference/validate_strategies.html @@ -35,7 +35,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                    • diff --git a/news/index.html b/news/index.html index de883d6a..415ff020 100644 --- a/news/index.html +++ b/news/index.html @@ -36,7 +36,9 @@
                    main
                    + id="dropdown-versions">Versions
                    main +latest-tag +v1.2.6
                  diff --git a/pkgdown.yml b/pkgdown.yml index ed033f73..62b16bc2 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -1,11 +1,8 @@ -pandoc: '3.2' +pandoc: '3.3' pkgdown: 2.1.0 pkgdown_sha: ~ articles: advanced: advanced.html - CondMean_Inference: CondMean_Inference.html - FAQ: FAQ.html quickstart: quickstart.html - retrieved_dropout: retrieved_dropout.html stat_specs: stat_specs.html -last_built: 2024-08-30T11:54Z +last_built: 2024-09-24T15:03Z diff --git a/reference/QR_decomp.html b/reference/QR_decomp.html index f53f0d13..6ab648dc 100644 --- a/reference/QR_decomp.html +++ b/reference/QR_decomp.html @@ -38,7 +38,9 @@
                  main
                  + id="dropdown-versions">Versions
                  main +latest-tag +v1.2.6
                diff --git a/reference/Stack.html b/reference/Stack.html index e41c121e..b8839501 100644 --- a/reference/Stack.html +++ b/reference/Stack.html @@ -36,7 +36,9 @@
                main
                + id="dropdown-versions">Versions
                main +latest-tag +v1.2.6
              diff --git a/reference/add_class.html b/reference/add_class.html index c250627c..c44141e7 100644 --- a/reference/add_class.html +++ b/reference/add_class.html @@ -38,7 +38,9 @@
              main
              + id="dropdown-versions">Versions
              main +latest-tag +v1.2.6
            diff --git a/reference/adjust_trajectories.html b/reference/adjust_trajectories.html index bfe2862b..38b21e3b 100644 --- a/reference/adjust_trajectories.html +++ b/reference/adjust_trajectories.html @@ -36,7 +36,9 @@
            main
            + id="dropdown-versions">Versions
            main +latest-tag +v1.2.6
          diff --git a/reference/adjust_trajectories_single.html b/reference/adjust_trajectories_single.html index 5a4a47a1..21f01403 100644 --- a/reference/adjust_trajectories_single.html +++ b/reference/adjust_trajectories_single.html @@ -36,7 +36,9 @@
          main
          + id="dropdown-versions">Versions
          main +latest-tag +v1.2.6
        diff --git a/reference/analyse.html b/reference/analyse.html index e9bf08d4..bc523f68 100644 --- a/reference/analyse.html +++ b/reference/analyse.html @@ -40,7 +40,9 @@
        main
        + id="dropdown-versions">Versions
        main +latest-tag +v1.2.6
      diff --git a/reference/ancova.html b/reference/ancova.html index 7f26f42e..402b81e7 100644 --- a/reference/ancova.html +++ b/reference/ancova.html @@ -40,7 +40,9 @@
      main
      + id="dropdown-versions">Versions
      main +latest-tag +v1.2.6
    diff --git a/reference/ancova_single.html b/reference/ancova_single.html index 8f4bb0c4..2a3084d2 100644 --- a/reference/ancova_single.html +++ b/reference/ancova_single.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/antidepressant_data.html b/reference/antidepressant_data.html index eadbc936..400b13eb 100644 --- a/reference/antidepressant_data.html +++ b/reference/antidepressant_data.html @@ -54,7 +54,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/apply_delta.html b/reference/apply_delta.html index e0ec1432..3c663ae7 100644 --- a/reference/apply_delta.html +++ b/reference/apply_delta.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/as_analysis.html b/reference/as_analysis.html index afc46fbc..669554a0 100644 --- a/reference/as_analysis.html +++ b/reference/as_analysis.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/as_ascii_table.html b/reference/as_ascii_table.html index 4f0cb48c..5e4c2c03 100644 --- a/reference/as_ascii_table.html +++ b/reference/as_ascii_table.html @@ -42,7 +42,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/as_class.html b/reference/as_class.html index 73a87568..df92e1d2 100644 --- a/reference/as_class.html +++ b/reference/as_class.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/as_cropped_char.html b/reference/as_cropped_char.html index a0bc2809..0f38650c 100644 --- a/reference/as_cropped_char.html +++ b/reference/as_cropped_char.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/as_dataframe.html b/reference/as_dataframe.html index d91abbdc..f764d33a 100644 --- a/reference/as_dataframe.html +++ b/reference/as_dataframe.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/as_draws.html b/reference/as_draws.html index eeff3a82..a5adf59f 100644 --- a/reference/as_draws.html +++ b/reference/as_draws.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/as_imputation.html b/reference/as_imputation.html index e9395435..9785535f 100644 --- a/reference/as_imputation.html +++ b/reference/as_imputation.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/as_indices.html b/reference/as_indices.html index cdffc8b9..8014f0ee 100644 --- a/reference/as_indices.html +++ b/reference/as_indices.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/as_mmrm_df.html b/reference/as_mmrm_df.html index 7d4b5fc5..4c6997aa 100644 --- a/reference/as_mmrm_df.html +++ b/reference/as_mmrm_df.html @@ -54,7 +54,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/as_mmrm_formula.html b/reference/as_mmrm_formula.html index 769921c3..256518ef 100644 --- a/reference/as_mmrm_formula.html +++ b/reference/as_mmrm_formula.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/as_model_df.html b/reference/as_model_df.html index 117ddacd..3f18cb3c 100644 --- a/reference/as_model_df.html +++ b/reference/as_model_df.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/as_simple_formula.html b/reference/as_simple_formula.html index c32d7f42..85bb4366 100644 --- a/reference/as_simple_formula.html +++ b/reference/as_simple_formula.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/as_stan_array.html b/reference/as_stan_array.html index f7facf41..d0a2e35f 100644 --- a/reference/as_stan_array.html +++ b/reference/as_stan_array.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/as_strata.html b/reference/as_strata.html index 87223919..f0ee229a 100644 --- a/reference/as_strata.html +++ b/reference/as_strata.html @@ -48,7 +48,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/assert_variables_exist.html b/reference/assert_variables_exist.html index 015204f9..2307f1e6 100644 --- a/reference/assert_variables_exist.html +++ b/reference/assert_variables_exist.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/char2fct.html b/reference/char2fct.html index ee109adb..e6c7f525 100644 --- a/reference/char2fct.html +++ b/reference/char2fct.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/check_ESS.html b/reference/check_ESS.html index c29f1f02..adeb7d2a 100644 --- a/reference/check_ESS.html +++ b/reference/check_ESS.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/check_hmc_diagn.html b/reference/check_hmc_diagn.html index 479d0a65..a99b8d54 100644 --- a/reference/check_hmc_diagn.html +++ b/reference/check_hmc_diagn.html @@ -48,7 +48,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/check_mcmc.html b/reference/check_mcmc.html index d310df3f..71fd0340 100644 --- a/reference/check_mcmc.html +++ b/reference/check_mcmc.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/compute_sigma.html b/reference/compute_sigma.html index 873fe5e1..175f2191 100644 --- a/reference/compute_sigma.html +++ b/reference/compute_sigma.html @@ -42,7 +42,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/convert_to_imputation_list_df.html b/reference/convert_to_imputation_list_df.html index 85f64632..cf3d5195 100644 --- a/reference/convert_to_imputation_list_df.html +++ b/reference/convert_to_imputation_list_df.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/d_lagscale.html b/reference/d_lagscale.html index 84765c84..f9ef7dea 100644 --- a/reference/d_lagscale.html +++ b/reference/d_lagscale.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/delta_template.html b/reference/delta_template.html index d9dc6156..ab4f1700 100644 --- a/reference/delta_template.html +++ b/reference/delta_template.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/draws.html b/reference/draws.html index 19f79331..7683f893 100644 --- a/reference/draws.html +++ b/reference/draws.html @@ -66,7 +66,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/encap_get_mmrm_sample.html b/reference/encap_get_mmrm_sample.html index 302fbabe..a29c91f3 100644 --- a/reference/encap_get_mmrm_sample.html +++ b/reference/encap_get_mmrm_sample.html @@ -44,7 +44,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/eval_glmmtmb.html b/reference/eval_glmmtmb.html index 7c038e0b..e30d68c7 100644 --- a/reference/eval_glmmtmb.html +++ b/reference/eval_glmmtmb.html @@ -48,7 +48,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/expand.html b/reference/expand.html index f4afa9ad..3dfd60f7 100644 --- a/reference/expand.html +++ b/reference/expand.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/extract_covariates.html b/reference/extract_covariates.html index b4c636a4..1f2eddb0 100644 --- a/reference/extract_covariates.html +++ b/reference/extract_covariates.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/extract_data_nmar_as_na.html b/reference/extract_data_nmar_as_na.html index 9c9c556a..5535a3bd 100644 --- a/reference/extract_data_nmar_as_na.html +++ b/reference/extract_data_nmar_as_na.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/extract_draws.html b/reference/extract_draws.html index 785af4f5..227c621a 100644 --- a/reference/extract_draws.html +++ b/reference/extract_draws.html @@ -42,7 +42,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/extract_imputed_df.html b/reference/extract_imputed_df.html index 8e7bc58c..2dfdc54a 100644 --- a/reference/extract_imputed_df.html +++ b/reference/extract_imputed_df.html @@ -48,7 +48,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/extract_imputed_dfs.html b/reference/extract_imputed_dfs.html index 85424021..02240e43 100644 --- a/reference/extract_imputed_dfs.html +++ b/reference/extract_imputed_dfs.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/extract_params.html b/reference/extract_params.html index b15f677c..031d67df 100644 --- a/reference/extract_params.html +++ b/reference/extract_params.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/fit_mcmc.html b/reference/fit_mcmc.html index 6cf0085d..7a8d4090 100644 --- a/reference/fit_mcmc.html +++ b/reference/fit_mcmc.html @@ -46,7 +46,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/fit_mmrm.html b/reference/fit_mmrm.html index cc488baf..96eb3b9d 100644 --- a/reference/fit_mmrm.html +++ b/reference/fit_mmrm.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/fit_mmrm_multiopt.html b/reference/fit_mmrm_multiopt.html index 03b5b2c5..ebd431ff 100644 --- a/reference/fit_mmrm_multiopt.html +++ b/reference/fit_mmrm_multiopt.html @@ -42,7 +42,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/generate_data_single.html b/reference/generate_data_single.html index 8df0f03a..80b2303a 100644 --- a/reference/generate_data_single.html +++ b/reference/generate_data_single.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/getStrategies.html b/reference/getStrategies.html index d10f08b1..aec9acaf 100644 --- a/reference/getStrategies.html +++ b/reference/getStrategies.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/get_ESS.html b/reference/get_ESS.html index c0b96b14..1aade89b 100644 --- a/reference/get_ESS.html +++ b/reference/get_ESS.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/get_bootstrap_stack.html b/reference/get_bootstrap_stack.html index ce4751b4..5709c9f4 100644 --- a/reference/get_bootstrap_stack.html +++ b/reference/get_bootstrap_stack.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/get_cluster.html b/reference/get_cluster.html index 8c4530c9..d709df19 100644 --- a/reference/get_cluster.html +++ b/reference/get_cluster.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/get_conditional_parameters.html b/reference/get_conditional_parameters.html index efe8e4a3..13fe287c 100644 --- a/reference/get_conditional_parameters.html +++ b/reference/get_conditional_parameters.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/get_delta_template.html b/reference/get_delta_template.html index 9c477eee..011cdb7f 100644 --- a/reference/get_delta_template.html +++ b/reference/get_delta_template.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/get_draws_mle.html b/reference/get_draws_mle.html index 8ce342ed..0248e500 100644 --- a/reference/get_draws_mle.html +++ b/reference/get_draws_mle.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/get_ests_bmlmi.html b/reference/get_ests_bmlmi.html index c7652dca..119721f4 100644 --- a/reference/get_ests_bmlmi.html +++ b/reference/get_ests_bmlmi.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/get_example_data.html b/reference/get_example_data.html index 8fa53abe..822c8c36 100644 --- a/reference/get_example_data.html +++ b/reference/get_example_data.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/get_jackknife_stack.html b/reference/get_jackknife_stack.html index 94c307cd..128d044a 100644 --- a/reference/get_jackknife_stack.html +++ b/reference/get_jackknife_stack.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/get_mmrm_sample.html b/reference/get_mmrm_sample.html index 9c0e1108..02b39323 100644 --- a/reference/get_mmrm_sample.html +++ b/reference/get_mmrm_sample.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/get_pattern_groups.html b/reference/get_pattern_groups.html index 21f6147f..9489dfa0 100644 --- a/reference/get_pattern_groups.html +++ b/reference/get_pattern_groups.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/get_pattern_groups_unique.html b/reference/get_pattern_groups_unique.html index 5f799c0d..60098295 100644 --- a/reference/get_pattern_groups_unique.html +++ b/reference/get_pattern_groups_unique.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/get_pool_components.html b/reference/get_pool_components.html index 088f7036..96a40a27 100644 --- a/reference/get_pool_components.html +++ b/reference/get_pool_components.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/get_visit_distribution_parameters.html b/reference/get_visit_distribution_parameters.html index cb1cf7de..174d7cb3 100644 --- a/reference/get_visit_distribution_parameters.html +++ b/reference/get_visit_distribution_parameters.html @@ -44,7 +44,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/has_class.html b/reference/has_class.html index 9601682b..6ee35940 100644 --- a/reference/has_class.html +++ b/reference/has_class.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/ife.html b/reference/ife.html index 8da48ab6..cf97bc2a 100644 --- a/reference/ife.html +++ b/reference/ife.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/imputation_df.html b/reference/imputation_df.html index dc1b0da6..d37d4fe8 100644 --- a/reference/imputation_df.html +++ b/reference/imputation_df.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/imputation_list_df.html b/reference/imputation_list_df.html index ad8e568e..c8a162f5 100644 --- a/reference/imputation_list_df.html +++ b/reference/imputation_list_df.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/imputation_list_single.html b/reference/imputation_list_single.html index 6fb3646c..1ddf0554 100644 --- a/reference/imputation_list_single.html +++ b/reference/imputation_list_single.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/imputation_single.html b/reference/imputation_single.html index 41145c99..08d4389a 100644 --- a/reference/imputation_single.html +++ b/reference/imputation_single.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/impute.html b/reference/impute.html index 7c662642..bb0c0342 100644 --- a/reference/impute.html +++ b/reference/impute.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/impute_data_individual.html b/reference/impute_data_individual.html index 577498d2..b2bd0d43 100644 --- a/reference/impute_data_individual.html +++ b/reference/impute_data_individual.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/impute_internal.html b/reference/impute_internal.html index d811f42a..4406de94 100644 --- a/reference/impute_internal.html +++ b/reference/impute_internal.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/impute_outcome.html b/reference/impute_outcome.html index 784b9ae8..df974ace 100644 --- a/reference/impute_outcome.html +++ b/reference/impute_outcome.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/index.html b/reference/index.html index efb7a577..af80b921 100644 --- a/reference/index.html +++ b/reference/index.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/invert.html b/reference/invert.html index 0f829400..42f10c83 100644 --- a/reference/invert.html +++ b/reference/invert.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/invert_indexes.html b/reference/invert_indexes.html index 83a04a2c..ee0de6e8 100644 --- a/reference/invert_indexes.html +++ b/reference/invert_indexes.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/is_absent.html b/reference/is_absent.html index 05ec1e64..120d1f0e 100644 --- a/reference/is_absent.html +++ b/reference/is_absent.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/is_char_fact.html b/reference/is_char_fact.html index 16427444..acce5a95 100644 --- a/reference/is_char_fact.html +++ b/reference/is_char_fact.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/is_char_one.html b/reference/is_char_one.html index a755a811..dd85848c 100644 --- a/reference/is_char_one.html +++ b/reference/is_char_one.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/is_num_char_fact.html b/reference/is_num_char_fact.html index b826abf9..c5419f16 100644 --- a/reference/is_num_char_fact.html +++ b/reference/is_num_char_fact.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/locf.html b/reference/locf.html index 2f96a495..aa5757e2 100644 --- a/reference/locf.html +++ b/reference/locf.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/longDataConstructor.html b/reference/longDataConstructor.html index a19a5169..7e214c36 100644 --- a/reference/longDataConstructor.html +++ b/reference/longDataConstructor.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/ls_design.html b/reference/ls_design.html index cfc1c733..2d10942f 100644 --- a/reference/ls_design.html +++ b/reference/ls_design.html @@ -46,7 +46,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/lsmeans.html b/reference/lsmeans.html index 3c1bb04c..287145bd 100644 --- a/reference/lsmeans.html +++ b/reference/lsmeans.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/method.html b/reference/method.html index 8537ccc5..5a2a07dd 100644 --- a/reference/method.html +++ b/reference/method.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/parametric_ci.html b/reference/parametric_ci.html index 191bc6b9..c1485b2c 100644 --- a/reference/parametric_ci.html +++ b/reference/parametric_ci.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/pool.html b/reference/pool.html index 9adee061..96bce6a8 100644 --- a/reference/pool.html +++ b/reference/pool.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/pool_bootstrap_normal.html b/reference/pool_bootstrap_normal.html index de19ec27..cc183ce9 100644 --- a/reference/pool_bootstrap_normal.html +++ b/reference/pool_bootstrap_normal.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/pool_bootstrap_percentile.html b/reference/pool_bootstrap_percentile.html index efd425ec..31a20bae 100644 --- a/reference/pool_bootstrap_percentile.html +++ b/reference/pool_bootstrap_percentile.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/pool_internal.html b/reference/pool_internal.html index 92db0633..57962e81 100644 --- a/reference/pool_internal.html +++ b/reference/pool_internal.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/prepare_stan_data.html b/reference/prepare_stan_data.html index d4d1d7b7..924e9482 100644 --- a/reference/prepare_stan_data.html +++ b/reference/prepare_stan_data.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/print.analysis.html b/reference/print.analysis.html index 0fb8fbad..cd435613 100644 --- a/reference/print.analysis.html +++ b/reference/print.analysis.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/print.draws.html b/reference/print.draws.html index 72f7a502..0d20773e 100644 --- a/reference/print.draws.html +++ b/reference/print.draws.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/print.imputation.html b/reference/print.imputation.html index ed57d622..5b83fa9b 100644 --- a/reference/print.imputation.html +++ b/reference/print.imputation.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/progressLogger.html b/reference/progressLogger.html index 7ef39a38..e6a08479 100644 --- a/reference/progressLogger.html +++ b/reference/progressLogger.html @@ -44,7 +44,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/pval_percentile.html b/reference/pval_percentile.html index 8c258736..68c2adca 100644 --- a/reference/pval_percentile.html +++ b/reference/pval_percentile.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/random_effects_expr.html b/reference/random_effects_expr.html index 9d068465..080e9195 100644 --- a/reference/random_effects_expr.html +++ b/reference/random_effects_expr.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/rbmi-package.html b/reference/rbmi-package.html index 3dd74d7f..92a66212 100644 --- a/reference/rbmi-package.html +++ b/reference/rbmi-package.html @@ -58,7 +58,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/record.html b/reference/record.html index 93718186..fd01c20d 100644 --- a/reference/record.html +++ b/reference/record.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/recursive_reduce.html b/reference/recursive_reduce.html index 460bb49d..12b78131 100644 --- a/reference/recursive_reduce.html +++ b/reference/recursive_reduce.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/remove_if_all_missing.html b/reference/remove_if_all_missing.html index bbf1b616..a771346a 100644 --- a/reference/remove_if_all_missing.html +++ b/reference/remove_if_all_missing.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/rubin_df.html b/reference/rubin_df.html index 21f9be9d..f06f8c28 100644 --- a/reference/rubin_df.html +++ b/reference/rubin_df.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/rubin_rules.html b/reference/rubin_rules.html index 00bdb9ad..64640b0b 100644 --- a/reference/rubin_rules.html +++ b/reference/rubin_rules.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/sample_ids.html b/reference/sample_ids.html index 2689f9a1..9841512e 100644 --- a/reference/sample_ids.html +++ b/reference/sample_ids.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/sample_list.html b/reference/sample_list.html index 5065b969..6c9294f9 100644 --- a/reference/sample_list.html +++ b/reference/sample_list.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/sample_mvnorm.html b/reference/sample_mvnorm.html index af7c6b3c..01af559d 100644 --- a/reference/sample_mvnorm.html +++ b/reference/sample_mvnorm.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/sample_single.html b/reference/sample_single.html index 007e85a5..fa2f637c 100644 --- a/reference/sample_single.html +++ b/reference/sample_single.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/scalerConstructor.html b/reference/scalerConstructor.html index 1843c80c..fb55ea50 100644 --- a/reference/scalerConstructor.html +++ b/reference/scalerConstructor.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/set_simul_pars.html b/reference/set_simul_pars.html index 3a994643..5377f901 100644 --- a/reference/set_simul_pars.html +++ b/reference/set_simul_pars.html @@ -48,7 +48,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/set_vars.html b/reference/set_vars.html index 82efcdef..7bf88c82 100644 --- a/reference/set_vars.html +++ b/reference/set_vars.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/simulate_data.html b/reference/simulate_data.html index 809dce62..ae10cea8 100644 --- a/reference/simulate_data.html +++ b/reference/simulate_data.html @@ -48,7 +48,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/simulate_dropout.html b/reference/simulate_dropout.html index 45a71f1d..b75d8b2a 100644 --- a/reference/simulate_dropout.html +++ b/reference/simulate_dropout.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/simulate_ice.html b/reference/simulate_ice.html index 78370ab7..243d09b8 100644 --- a/reference/simulate_ice.html +++ b/reference/simulate_ice.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/simulate_test_data.html b/reference/simulate_test_data.html index 1358faf4..1d70df5d 100644 --- a/reference/simulate_test_data.html +++ b/reference/simulate_test_data.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/sort_by.html b/reference/sort_by.html index de345e73..c8b97732 100644 --- a/reference/sort_by.html +++ b/reference/sort_by.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/split_dim.html b/reference/split_dim.html index f25b7a08..dc933dd6 100644 --- a/reference/split_dim.html +++ b/reference/split_dim.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/split_imputations.html b/reference/split_imputations.html index 141f18b1..7840622a 100644 --- a/reference/split_imputations.html +++ b/reference/split_imputations.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/str_contains.html b/reference/str_contains.html index 86cae2fd..085103c2 100644 --- a/reference/str_contains.html +++ b/reference/str_contains.html @@ -46,7 +46,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/strategies.html b/reference/strategies.html index bf95bda9..81c35c8f 100644 --- a/reference/strategies.html +++ b/reference/strategies.html @@ -42,7 +42,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/string_pad.html b/reference/string_pad.html index 17b2b054..ae9d4d41 100644 --- a/reference/string_pad.html +++ b/reference/string_pad.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/transpose_imputations.html b/reference/transpose_imputations.html index f07bbc38..fcefb653 100644 --- a/reference/transpose_imputations.html +++ b/reference/transpose_imputations.html @@ -48,7 +48,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/transpose_results.html b/reference/transpose_results.html index db00a3dd..9b586ba3 100644 --- a/reference/transpose_results.html +++ b/reference/transpose_results.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/transpose_samples.html b/reference/transpose_samples.html index 168e89bc..24983224 100644 --- a/reference/transpose_samples.html +++ b/reference/transpose_samples.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/validate.analysis.html b/reference/validate.analysis.html index e1549ae3..2009e45d 100644 --- a/reference/validate.analysis.html +++ b/reference/validate.analysis.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/validate.draws.html b/reference/validate.draws.html index 2eb3d381..fbf419c9 100644 --- a/reference/validate.draws.html +++ b/reference/validate.draws.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/validate.html b/reference/validate.html index eb7da24e..f487d33f 100644 --- a/reference/validate.html +++ b/reference/validate.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/validate.is_mar.html b/reference/validate.is_mar.html index e5c458f4..cc3d9fdf 100644 --- a/reference/validate.is_mar.html +++ b/reference/validate.is_mar.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/validate.ivars.html b/reference/validate.ivars.html index 6148371f..3b7bf3aa 100644 --- a/reference/validate.ivars.html +++ b/reference/validate.ivars.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/validate.references.html b/reference/validate.references.html index 220bab11..9c18842f 100644 --- a/reference/validate.references.html +++ b/reference/validate.references.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/validate.sample_list.html b/reference/validate.sample_list.html index 353260e6..915951f8 100644 --- a/reference/validate.sample_list.html +++ b/reference/validate.sample_list.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/validate.sample_single.html b/reference/validate.sample_single.html index 2c923dd4..069b3b5e 100644 --- a/reference/validate.sample_single.html +++ b/reference/validate.sample_single.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/validate.simul_pars.html b/reference/validate.simul_pars.html index 6dc72a60..0ce522cc 100644 --- a/reference/validate.simul_pars.html +++ b/reference/validate.simul_pars.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/validate.stan_data.html b/reference/validate.stan_data.html index 93e2e25d..238b9d91 100644 --- a/reference/validate.stan_data.html +++ b/reference/validate.stan_data.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/validate_analyse_pars.html b/reference/validate_analyse_pars.html index c409b1a3..faf31032 100644 --- a/reference/validate_analyse_pars.html +++ b/reference/validate_analyse_pars.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/validate_datalong.html b/reference/validate_datalong.html index a18410d7..a6104f7a 100644 --- a/reference/validate_datalong.html +++ b/reference/validate_datalong.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/reference/validate_strategies.html b/reference/validate_strategies.html index 11c6457f..b9a70116 100644 --- a/reference/validate_strategies.html +++ b/reference/validate_strategies.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/404.html b/test-pkgdown-workflow/404.html index a82f9efc..414b1a9f 100644 --- a/test-pkgdown-workflow/404.html +++ b/test-pkgdown-workflow/404.html @@ -51,7 +51,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/LICENSE.html b/test-pkgdown-workflow/LICENSE.html index 04500a5b..0c68fb8a 100644 --- a/test-pkgdown-workflow/LICENSE.html +++ b/test-pkgdown-workflow/LICENSE.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/articles/advanced.html b/test-pkgdown-workflow/articles/advanced.html index 2032917b..9288663a 100644 --- a/test-pkgdown-workflow/articles/advanced.html +++ b/test-pkgdown-workflow/articles/advanced.html @@ -53,7 +53,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/articles/quickstart.html b/test-pkgdown-workflow/articles/quickstart.html index 412f3521..d52324af 100644 --- a/test-pkgdown-workflow/articles/quickstart.html +++ b/test-pkgdown-workflow/articles/quickstart.html @@ -53,7 +53,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/index.html b/test-pkgdown-workflow/index.html index 44227310..a841ec4e 100644 --- a/test-pkgdown-workflow/index.html +++ b/test-pkgdown-workflow/index.html @@ -63,7 +63,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/QR_decomp.html b/test-pkgdown-workflow/reference/QR_decomp.html index e4fd790c..44889e6d 100644 --- a/test-pkgdown-workflow/reference/QR_decomp.html +++ b/test-pkgdown-workflow/reference/QR_decomp.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/Stack.html b/test-pkgdown-workflow/reference/Stack.html index 5bd9960b..72916d47 100644 --- a/test-pkgdown-workflow/reference/Stack.html +++ b/test-pkgdown-workflow/reference/Stack.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/add_class.html b/test-pkgdown-workflow/reference/add_class.html index 3fed597a..9e158f21 100644 --- a/test-pkgdown-workflow/reference/add_class.html +++ b/test-pkgdown-workflow/reference/add_class.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/adjust_trajectories.html b/test-pkgdown-workflow/reference/adjust_trajectories.html index 62a8450f..e7666973 100644 --- a/test-pkgdown-workflow/reference/adjust_trajectories.html +++ b/test-pkgdown-workflow/reference/adjust_trajectories.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/adjust_trajectories_single.html b/test-pkgdown-workflow/reference/adjust_trajectories_single.html index 5e45f122..5823adc8 100644 --- a/test-pkgdown-workflow/reference/adjust_trajectories_single.html +++ b/test-pkgdown-workflow/reference/adjust_trajectories_single.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/analyse.html b/test-pkgdown-workflow/reference/analyse.html index 6b50fe99..74750faf 100644 --- a/test-pkgdown-workflow/reference/analyse.html +++ b/test-pkgdown-workflow/reference/analyse.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/ancova.html b/test-pkgdown-workflow/reference/ancova.html index 7ec86231..dbc65969 100644 --- a/test-pkgdown-workflow/reference/ancova.html +++ b/test-pkgdown-workflow/reference/ancova.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/ancova_single.html b/test-pkgdown-workflow/reference/ancova_single.html index 16587854..15180fca 100644 --- a/test-pkgdown-workflow/reference/ancova_single.html +++ b/test-pkgdown-workflow/reference/ancova_single.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/antidepressant_data.html b/test-pkgdown-workflow/reference/antidepressant_data.html index 71f1a0c4..189e02d1 100644 --- a/test-pkgdown-workflow/reference/antidepressant_data.html +++ b/test-pkgdown-workflow/reference/antidepressant_data.html @@ -54,7 +54,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/apply_delta.html b/test-pkgdown-workflow/reference/apply_delta.html index 8ff84b75..4a639f27 100644 --- a/test-pkgdown-workflow/reference/apply_delta.html +++ b/test-pkgdown-workflow/reference/apply_delta.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/as_analysis.html b/test-pkgdown-workflow/reference/as_analysis.html index 058b2c1b..2c1daa43 100644 --- a/test-pkgdown-workflow/reference/as_analysis.html +++ b/test-pkgdown-workflow/reference/as_analysis.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/as_ascii_table.html b/test-pkgdown-workflow/reference/as_ascii_table.html index b9534b8c..7b16d3a2 100644 --- a/test-pkgdown-workflow/reference/as_ascii_table.html +++ b/test-pkgdown-workflow/reference/as_ascii_table.html @@ -42,7 +42,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/as_class.html b/test-pkgdown-workflow/reference/as_class.html index 7be29fc6..5f7568b8 100644 --- a/test-pkgdown-workflow/reference/as_class.html +++ b/test-pkgdown-workflow/reference/as_class.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/as_cropped_char.html b/test-pkgdown-workflow/reference/as_cropped_char.html index 9204e5c2..b01aacc9 100644 --- a/test-pkgdown-workflow/reference/as_cropped_char.html +++ b/test-pkgdown-workflow/reference/as_cropped_char.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/as_dataframe.html b/test-pkgdown-workflow/reference/as_dataframe.html index d835ae86..2ebb96c9 100644 --- a/test-pkgdown-workflow/reference/as_dataframe.html +++ b/test-pkgdown-workflow/reference/as_dataframe.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/as_draws.html b/test-pkgdown-workflow/reference/as_draws.html index d4086e47..144e076a 100644 --- a/test-pkgdown-workflow/reference/as_draws.html +++ b/test-pkgdown-workflow/reference/as_draws.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/as_imputation.html b/test-pkgdown-workflow/reference/as_imputation.html index 06e31e57..61fc86fd 100644 --- a/test-pkgdown-workflow/reference/as_imputation.html +++ b/test-pkgdown-workflow/reference/as_imputation.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/as_indices.html b/test-pkgdown-workflow/reference/as_indices.html index f63ab138..8f8ddddd 100644 --- a/test-pkgdown-workflow/reference/as_indices.html +++ b/test-pkgdown-workflow/reference/as_indices.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/as_mmrm_df.html b/test-pkgdown-workflow/reference/as_mmrm_df.html index bacb5872..ab75f989 100644 --- a/test-pkgdown-workflow/reference/as_mmrm_df.html +++ b/test-pkgdown-workflow/reference/as_mmrm_df.html @@ -52,7 +52,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/as_mmrm_formula.html b/test-pkgdown-workflow/reference/as_mmrm_formula.html index a145a227..79a0909e 100644 --- a/test-pkgdown-workflow/reference/as_mmrm_formula.html +++ b/test-pkgdown-workflow/reference/as_mmrm_formula.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/as_model_df.html b/test-pkgdown-workflow/reference/as_model_df.html index 76edafee..099db6e3 100644 --- a/test-pkgdown-workflow/reference/as_model_df.html +++ b/test-pkgdown-workflow/reference/as_model_df.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/as_simple_formula.html b/test-pkgdown-workflow/reference/as_simple_formula.html index 5ba069f1..39df1926 100644 --- a/test-pkgdown-workflow/reference/as_simple_formula.html +++ b/test-pkgdown-workflow/reference/as_simple_formula.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/as_stan_array.html b/test-pkgdown-workflow/reference/as_stan_array.html index 3a1e5806..0b926c5d 100644 --- a/test-pkgdown-workflow/reference/as_stan_array.html +++ b/test-pkgdown-workflow/reference/as_stan_array.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/as_strata.html b/test-pkgdown-workflow/reference/as_strata.html index a69fc20c..5ffc8ec4 100644 --- a/test-pkgdown-workflow/reference/as_strata.html +++ b/test-pkgdown-workflow/reference/as_strata.html @@ -48,7 +48,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/assert_variables_exist.html b/test-pkgdown-workflow/reference/assert_variables_exist.html index 6c5f26a1..8f9ec4bf 100644 --- a/test-pkgdown-workflow/reference/assert_variables_exist.html +++ b/test-pkgdown-workflow/reference/assert_variables_exist.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/char2fct.html b/test-pkgdown-workflow/reference/char2fct.html index 03b502af..b9cb6f7c 100644 --- a/test-pkgdown-workflow/reference/char2fct.html +++ b/test-pkgdown-workflow/reference/char2fct.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/check_ESS.html b/test-pkgdown-workflow/reference/check_ESS.html index f00bf943..4029de9c 100644 --- a/test-pkgdown-workflow/reference/check_ESS.html +++ b/test-pkgdown-workflow/reference/check_ESS.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/check_hmc_diagn.html b/test-pkgdown-workflow/reference/check_hmc_diagn.html index 66a7c43d..0da3e28c 100644 --- a/test-pkgdown-workflow/reference/check_hmc_diagn.html +++ b/test-pkgdown-workflow/reference/check_hmc_diagn.html @@ -48,7 +48,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/check_mcmc.html b/test-pkgdown-workflow/reference/check_mcmc.html index cca616c2..437ef33a 100644 --- a/test-pkgdown-workflow/reference/check_mcmc.html +++ b/test-pkgdown-workflow/reference/check_mcmc.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/compute_sigma.html b/test-pkgdown-workflow/reference/compute_sigma.html index d7f6c950..4129ddad 100644 --- a/test-pkgdown-workflow/reference/compute_sigma.html +++ b/test-pkgdown-workflow/reference/compute_sigma.html @@ -42,7 +42,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/convert_to_imputation_list_df.html b/test-pkgdown-workflow/reference/convert_to_imputation_list_df.html index f0eb133b..a3486890 100644 --- a/test-pkgdown-workflow/reference/convert_to_imputation_list_df.html +++ b/test-pkgdown-workflow/reference/convert_to_imputation_list_df.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/d_lagscale.html b/test-pkgdown-workflow/reference/d_lagscale.html index 12525e6d..08ee6227 100644 --- a/test-pkgdown-workflow/reference/d_lagscale.html +++ b/test-pkgdown-workflow/reference/d_lagscale.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/delta_template.html b/test-pkgdown-workflow/reference/delta_template.html index 3363061a..867cc7af 100644 --- a/test-pkgdown-workflow/reference/delta_template.html +++ b/test-pkgdown-workflow/reference/delta_template.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/do_not_run.html b/test-pkgdown-workflow/reference/do_not_run.html index 3f2bb885..de9f5316 100644 --- a/test-pkgdown-workflow/reference/do_not_run.html +++ b/test-pkgdown-workflow/reference/do_not_run.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/draws.html b/test-pkgdown-workflow/reference/draws.html index 63b4784a..1417d122 100644 --- a/test-pkgdown-workflow/reference/draws.html +++ b/test-pkgdown-workflow/reference/draws.html @@ -66,7 +66,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/encap_get_mmrm_sample.html b/test-pkgdown-workflow/reference/encap_get_mmrm_sample.html index ae1931e2..a19e426e 100644 --- a/test-pkgdown-workflow/reference/encap_get_mmrm_sample.html +++ b/test-pkgdown-workflow/reference/encap_get_mmrm_sample.html @@ -44,7 +44,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/eval_mmrm.html b/test-pkgdown-workflow/reference/eval_mmrm.html index 62c3204f..e6b82751 100644 --- a/test-pkgdown-workflow/reference/eval_mmrm.html +++ b/test-pkgdown-workflow/reference/eval_mmrm.html @@ -46,7 +46,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/expand.html b/test-pkgdown-workflow/reference/expand.html index 0fcc5da0..0cd02786 100644 --- a/test-pkgdown-workflow/reference/expand.html +++ b/test-pkgdown-workflow/reference/expand.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/extract_covariates.html b/test-pkgdown-workflow/reference/extract_covariates.html index f6ec9434..8fad8777 100644 --- a/test-pkgdown-workflow/reference/extract_covariates.html +++ b/test-pkgdown-workflow/reference/extract_covariates.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/extract_data_nmar_as_na.html b/test-pkgdown-workflow/reference/extract_data_nmar_as_na.html index 547adf9d..7f86c2ce 100644 --- a/test-pkgdown-workflow/reference/extract_data_nmar_as_na.html +++ b/test-pkgdown-workflow/reference/extract_data_nmar_as_na.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/extract_draws.html b/test-pkgdown-workflow/reference/extract_draws.html index 21f566ba..de629143 100644 --- a/test-pkgdown-workflow/reference/extract_draws.html +++ b/test-pkgdown-workflow/reference/extract_draws.html @@ -42,7 +42,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/extract_imputed_df.html b/test-pkgdown-workflow/reference/extract_imputed_df.html index 26292918..93d279cd 100644 --- a/test-pkgdown-workflow/reference/extract_imputed_df.html +++ b/test-pkgdown-workflow/reference/extract_imputed_df.html @@ -48,7 +48,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/extract_imputed_dfs.html b/test-pkgdown-workflow/reference/extract_imputed_dfs.html index 491ee697..719cdcf1 100644 --- a/test-pkgdown-workflow/reference/extract_imputed_dfs.html +++ b/test-pkgdown-workflow/reference/extract_imputed_dfs.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/extract_params.html b/test-pkgdown-workflow/reference/extract_params.html index 99bf81cb..b2a96466 100644 --- a/test-pkgdown-workflow/reference/extract_params.html +++ b/test-pkgdown-workflow/reference/extract_params.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/fit_mcmc.html b/test-pkgdown-workflow/reference/fit_mcmc.html index fd920265..377d8401 100644 --- a/test-pkgdown-workflow/reference/fit_mcmc.html +++ b/test-pkgdown-workflow/reference/fit_mcmc.html @@ -46,7 +46,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/fit_mmrm.html b/test-pkgdown-workflow/reference/fit_mmrm.html index ffb431a2..9541177a 100644 --- a/test-pkgdown-workflow/reference/fit_mmrm.html +++ b/test-pkgdown-workflow/reference/fit_mmrm.html @@ -42,7 +42,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/generate_data_single.html b/test-pkgdown-workflow/reference/generate_data_single.html index 44bca781..f162fa62 100644 --- a/test-pkgdown-workflow/reference/generate_data_single.html +++ b/test-pkgdown-workflow/reference/generate_data_single.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/getStrategies.html b/test-pkgdown-workflow/reference/getStrategies.html index 1e18eb37..7c216d67 100644 --- a/test-pkgdown-workflow/reference/getStrategies.html +++ b/test-pkgdown-workflow/reference/getStrategies.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/get_ESS.html b/test-pkgdown-workflow/reference/get_ESS.html index 3d5dd3ed..65fa7310 100644 --- a/test-pkgdown-workflow/reference/get_ESS.html +++ b/test-pkgdown-workflow/reference/get_ESS.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/get_bootstrap_stack.html b/test-pkgdown-workflow/reference/get_bootstrap_stack.html index d7b87fc9..c73bda23 100644 --- a/test-pkgdown-workflow/reference/get_bootstrap_stack.html +++ b/test-pkgdown-workflow/reference/get_bootstrap_stack.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/get_cluster.html b/test-pkgdown-workflow/reference/get_cluster.html index 499c90ba..9989c69a 100644 --- a/test-pkgdown-workflow/reference/get_cluster.html +++ b/test-pkgdown-workflow/reference/get_cluster.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/get_conditional_parameters.html b/test-pkgdown-workflow/reference/get_conditional_parameters.html index b555f9ac..2e36240a 100644 --- a/test-pkgdown-workflow/reference/get_conditional_parameters.html +++ b/test-pkgdown-workflow/reference/get_conditional_parameters.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/get_delta_template.html b/test-pkgdown-workflow/reference/get_delta_template.html index 54cd83e6..3e781d14 100644 --- a/test-pkgdown-workflow/reference/get_delta_template.html +++ b/test-pkgdown-workflow/reference/get_delta_template.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/get_draws_mle.html b/test-pkgdown-workflow/reference/get_draws_mle.html index 56ba05a4..f0b4e010 100644 --- a/test-pkgdown-workflow/reference/get_draws_mle.html +++ b/test-pkgdown-workflow/reference/get_draws_mle.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/get_ests_bmlmi.html b/test-pkgdown-workflow/reference/get_ests_bmlmi.html index 0a434f73..21946341 100644 --- a/test-pkgdown-workflow/reference/get_ests_bmlmi.html +++ b/test-pkgdown-workflow/reference/get_ests_bmlmi.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/get_example_data.html b/test-pkgdown-workflow/reference/get_example_data.html index 59d6ecc4..36c30c02 100644 --- a/test-pkgdown-workflow/reference/get_example_data.html +++ b/test-pkgdown-workflow/reference/get_example_data.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/get_jackknife_stack.html b/test-pkgdown-workflow/reference/get_jackknife_stack.html index 778a317c..b4489a48 100644 --- a/test-pkgdown-workflow/reference/get_jackknife_stack.html +++ b/test-pkgdown-workflow/reference/get_jackknife_stack.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/get_mmrm_sample.html b/test-pkgdown-workflow/reference/get_mmrm_sample.html index 36cf6dad..315b01a6 100644 --- a/test-pkgdown-workflow/reference/get_mmrm_sample.html +++ b/test-pkgdown-workflow/reference/get_mmrm_sample.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/get_pattern_groups.html b/test-pkgdown-workflow/reference/get_pattern_groups.html index 7092ab41..56d148b6 100644 --- a/test-pkgdown-workflow/reference/get_pattern_groups.html +++ b/test-pkgdown-workflow/reference/get_pattern_groups.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/get_pattern_groups_unique.html b/test-pkgdown-workflow/reference/get_pattern_groups_unique.html index 44ace4dd..582b9c66 100644 --- a/test-pkgdown-workflow/reference/get_pattern_groups_unique.html +++ b/test-pkgdown-workflow/reference/get_pattern_groups_unique.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/get_pool_components.html b/test-pkgdown-workflow/reference/get_pool_components.html index 3bcfaba4..d8ba6f11 100644 --- a/test-pkgdown-workflow/reference/get_pool_components.html +++ b/test-pkgdown-workflow/reference/get_pool_components.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/get_visit_distribution_parameters.html b/test-pkgdown-workflow/reference/get_visit_distribution_parameters.html index 6c117b73..69155d2e 100644 --- a/test-pkgdown-workflow/reference/get_visit_distribution_parameters.html +++ b/test-pkgdown-workflow/reference/get_visit_distribution_parameters.html @@ -44,7 +44,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/has_class.html b/test-pkgdown-workflow/reference/has_class.html index 8dc5b2e2..a00ed8d1 100644 --- a/test-pkgdown-workflow/reference/has_class.html +++ b/test-pkgdown-workflow/reference/has_class.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/ife.html b/test-pkgdown-workflow/reference/ife.html index 6ee3c292..a1e149dd 100644 --- a/test-pkgdown-workflow/reference/ife.html +++ b/test-pkgdown-workflow/reference/ife.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/imputation_df.html b/test-pkgdown-workflow/reference/imputation_df.html index e8bb96d6..4b77fa2c 100644 --- a/test-pkgdown-workflow/reference/imputation_df.html +++ b/test-pkgdown-workflow/reference/imputation_df.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/imputation_list_df.html b/test-pkgdown-workflow/reference/imputation_list_df.html index 94ce26b9..872f46f6 100644 --- a/test-pkgdown-workflow/reference/imputation_list_df.html +++ b/test-pkgdown-workflow/reference/imputation_list_df.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/imputation_list_single.html b/test-pkgdown-workflow/reference/imputation_list_single.html index 4a68a553..ad7d2cd8 100644 --- a/test-pkgdown-workflow/reference/imputation_list_single.html +++ b/test-pkgdown-workflow/reference/imputation_list_single.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/imputation_single.html b/test-pkgdown-workflow/reference/imputation_single.html index d6aac2dd..0f954ba1 100644 --- a/test-pkgdown-workflow/reference/imputation_single.html +++ b/test-pkgdown-workflow/reference/imputation_single.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/impute.html b/test-pkgdown-workflow/reference/impute.html index 69cec469..5940573d 100644 --- a/test-pkgdown-workflow/reference/impute.html +++ b/test-pkgdown-workflow/reference/impute.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/impute_data_individual.html b/test-pkgdown-workflow/reference/impute_data_individual.html index aee51ed2..bc6d1733 100644 --- a/test-pkgdown-workflow/reference/impute_data_individual.html +++ b/test-pkgdown-workflow/reference/impute_data_individual.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/impute_internal.html b/test-pkgdown-workflow/reference/impute_internal.html index 3e9a63dd..df9e01e1 100644 --- a/test-pkgdown-workflow/reference/impute_internal.html +++ b/test-pkgdown-workflow/reference/impute_internal.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/impute_outcome.html b/test-pkgdown-workflow/reference/impute_outcome.html index e229f370..38403e15 100644 --- a/test-pkgdown-workflow/reference/impute_outcome.html +++ b/test-pkgdown-workflow/reference/impute_outcome.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/index.html b/test-pkgdown-workflow/reference/index.html index 50a7a909..fc35d530 100644 --- a/test-pkgdown-workflow/reference/index.html +++ b/test-pkgdown-workflow/reference/index.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/invert.html b/test-pkgdown-workflow/reference/invert.html index 116e9c06..67f32423 100644 --- a/test-pkgdown-workflow/reference/invert.html +++ b/test-pkgdown-workflow/reference/invert.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/invert_indexes.html b/test-pkgdown-workflow/reference/invert_indexes.html index 8d6fbf3a..92faf8f8 100644 --- a/test-pkgdown-workflow/reference/invert_indexes.html +++ b/test-pkgdown-workflow/reference/invert_indexes.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/is_absent.html b/test-pkgdown-workflow/reference/is_absent.html index 0c5c9d74..9302efde 100644 --- a/test-pkgdown-workflow/reference/is_absent.html +++ b/test-pkgdown-workflow/reference/is_absent.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/is_char_fact.html b/test-pkgdown-workflow/reference/is_char_fact.html index 4ed127e5..dd9a9de5 100644 --- a/test-pkgdown-workflow/reference/is_char_fact.html +++ b/test-pkgdown-workflow/reference/is_char_fact.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/is_char_one.html b/test-pkgdown-workflow/reference/is_char_one.html index 8be0c196..853d507d 100644 --- a/test-pkgdown-workflow/reference/is_char_one.html +++ b/test-pkgdown-workflow/reference/is_char_one.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/is_in_rbmi_development.html b/test-pkgdown-workflow/reference/is_in_rbmi_development.html index dd7ba58e..baa36596 100644 --- a/test-pkgdown-workflow/reference/is_in_rbmi_development.html +++ b/test-pkgdown-workflow/reference/is_in_rbmi_development.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/is_num_char_fact.html b/test-pkgdown-workflow/reference/is_num_char_fact.html index c561b615..0ad1dc1a 100644 --- a/test-pkgdown-workflow/reference/is_num_char_fact.html +++ b/test-pkgdown-workflow/reference/is_num_char_fact.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/locf.html b/test-pkgdown-workflow/reference/locf.html index b2dbc9f2..83746477 100644 --- a/test-pkgdown-workflow/reference/locf.html +++ b/test-pkgdown-workflow/reference/locf.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/longDataConstructor.html b/test-pkgdown-workflow/reference/longDataConstructor.html index 908428c7..f8939b70 100644 --- a/test-pkgdown-workflow/reference/longDataConstructor.html +++ b/test-pkgdown-workflow/reference/longDataConstructor.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/ls_design.html b/test-pkgdown-workflow/reference/ls_design.html index 20b498df..500ab519 100644 --- a/test-pkgdown-workflow/reference/ls_design.html +++ b/test-pkgdown-workflow/reference/ls_design.html @@ -46,7 +46,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/lsmeans.html b/test-pkgdown-workflow/reference/lsmeans.html index 18eefa19..2ec6c940 100644 --- a/test-pkgdown-workflow/reference/lsmeans.html +++ b/test-pkgdown-workflow/reference/lsmeans.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/method.html b/test-pkgdown-workflow/reference/method.html index 86da4cbb..e4201a38 100644 --- a/test-pkgdown-workflow/reference/method.html +++ b/test-pkgdown-workflow/reference/method.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/parametric_ci.html b/test-pkgdown-workflow/reference/parametric_ci.html index 0174c1b7..23760e4b 100644 --- a/test-pkgdown-workflow/reference/parametric_ci.html +++ b/test-pkgdown-workflow/reference/parametric_ci.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/pool.html b/test-pkgdown-workflow/reference/pool.html index 2fa1d1db..8d8b0780 100644 --- a/test-pkgdown-workflow/reference/pool.html +++ b/test-pkgdown-workflow/reference/pool.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/pool_bootstrap_normal.html b/test-pkgdown-workflow/reference/pool_bootstrap_normal.html index 4b1c90ca..89238f40 100644 --- a/test-pkgdown-workflow/reference/pool_bootstrap_normal.html +++ b/test-pkgdown-workflow/reference/pool_bootstrap_normal.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/pool_bootstrap_percentile.html b/test-pkgdown-workflow/reference/pool_bootstrap_percentile.html index 8d793d16..b0d479b1 100644 --- a/test-pkgdown-workflow/reference/pool_bootstrap_percentile.html +++ b/test-pkgdown-workflow/reference/pool_bootstrap_percentile.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/pool_internal.html b/test-pkgdown-workflow/reference/pool_internal.html index e7245372..d7907e80 100644 --- a/test-pkgdown-workflow/reference/pool_internal.html +++ b/test-pkgdown-workflow/reference/pool_internal.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/prepare_stan_data.html b/test-pkgdown-workflow/reference/prepare_stan_data.html index bbb6316e..655e7ddb 100644 --- a/test-pkgdown-workflow/reference/prepare_stan_data.html +++ b/test-pkgdown-workflow/reference/prepare_stan_data.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/print.analysis.html b/test-pkgdown-workflow/reference/print.analysis.html index b29f6b46..e903266d 100644 --- a/test-pkgdown-workflow/reference/print.analysis.html +++ b/test-pkgdown-workflow/reference/print.analysis.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/print.draws.html b/test-pkgdown-workflow/reference/print.draws.html index ee85ef8a..b9a237df 100644 --- a/test-pkgdown-workflow/reference/print.draws.html +++ b/test-pkgdown-workflow/reference/print.draws.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/print.imputation.html b/test-pkgdown-workflow/reference/print.imputation.html index 513939e3..2578131c 100644 --- a/test-pkgdown-workflow/reference/print.imputation.html +++ b/test-pkgdown-workflow/reference/print.imputation.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/progressLogger.html b/test-pkgdown-workflow/reference/progressLogger.html index 9518df19..ea5fd449 100644 --- a/test-pkgdown-workflow/reference/progressLogger.html +++ b/test-pkgdown-workflow/reference/progressLogger.html @@ -44,7 +44,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/pval_percentile.html b/test-pkgdown-workflow/reference/pval_percentile.html index 8c18477d..f0fd5cb9 100644 --- a/test-pkgdown-workflow/reference/pval_percentile.html +++ b/test-pkgdown-workflow/reference/pval_percentile.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/random_effects_expr.html b/test-pkgdown-workflow/reference/random_effects_expr.html index acb540bd..a358a5f0 100644 --- a/test-pkgdown-workflow/reference/random_effects_expr.html +++ b/test-pkgdown-workflow/reference/random_effects_expr.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/rbmi-package.html b/test-pkgdown-workflow/reference/rbmi-package.html index e97ee286..2af08a65 100644 --- a/test-pkgdown-workflow/reference/rbmi-package.html +++ b/test-pkgdown-workflow/reference/rbmi-package.html @@ -58,7 +58,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/record.html b/test-pkgdown-workflow/reference/record.html index 6fa1d150..9f134fa8 100644 --- a/test-pkgdown-workflow/reference/record.html +++ b/test-pkgdown-workflow/reference/record.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/recursive_reduce.html b/test-pkgdown-workflow/reference/recursive_reduce.html index a597ce26..3c3aafd8 100644 --- a/test-pkgdown-workflow/reference/recursive_reduce.html +++ b/test-pkgdown-workflow/reference/recursive_reduce.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/remove_if_all_missing.html b/test-pkgdown-workflow/reference/remove_if_all_missing.html index 20b68c9a..13373d8d 100644 --- a/test-pkgdown-workflow/reference/remove_if_all_missing.html +++ b/test-pkgdown-workflow/reference/remove_if_all_missing.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/rubin_df.html b/test-pkgdown-workflow/reference/rubin_df.html index 0b3803a3..607d4034 100644 --- a/test-pkgdown-workflow/reference/rubin_df.html +++ b/test-pkgdown-workflow/reference/rubin_df.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/rubin_rules.html b/test-pkgdown-workflow/reference/rubin_rules.html index 337a7f2c..07fddd72 100644 --- a/test-pkgdown-workflow/reference/rubin_rules.html +++ b/test-pkgdown-workflow/reference/rubin_rules.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/sample_ids.html b/test-pkgdown-workflow/reference/sample_ids.html index d1999ade..848f5292 100644 --- a/test-pkgdown-workflow/reference/sample_ids.html +++ b/test-pkgdown-workflow/reference/sample_ids.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/sample_list.html b/test-pkgdown-workflow/reference/sample_list.html index e1d1016f..6f86c072 100644 --- a/test-pkgdown-workflow/reference/sample_list.html +++ b/test-pkgdown-workflow/reference/sample_list.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/sample_mvnorm.html b/test-pkgdown-workflow/reference/sample_mvnorm.html index 21641851..8cc7725b 100644 --- a/test-pkgdown-workflow/reference/sample_mvnorm.html +++ b/test-pkgdown-workflow/reference/sample_mvnorm.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/sample_single.html b/test-pkgdown-workflow/reference/sample_single.html index 4d441302..9c752748 100644 --- a/test-pkgdown-workflow/reference/sample_single.html +++ b/test-pkgdown-workflow/reference/sample_single.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/scalerConstructor.html b/test-pkgdown-workflow/reference/scalerConstructor.html index 704179b6..5ed9916b 100644 --- a/test-pkgdown-workflow/reference/scalerConstructor.html +++ b/test-pkgdown-workflow/reference/scalerConstructor.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/set_simul_pars.html b/test-pkgdown-workflow/reference/set_simul_pars.html index 5af56292..ef3316f0 100644 --- a/test-pkgdown-workflow/reference/set_simul_pars.html +++ b/test-pkgdown-workflow/reference/set_simul_pars.html @@ -48,7 +48,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/set_vars.html b/test-pkgdown-workflow/reference/set_vars.html index 58e76b46..ebfdea25 100644 --- a/test-pkgdown-workflow/reference/set_vars.html +++ b/test-pkgdown-workflow/reference/set_vars.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/simulate_data.html b/test-pkgdown-workflow/reference/simulate_data.html index 084a46f4..7696e25d 100644 --- a/test-pkgdown-workflow/reference/simulate_data.html +++ b/test-pkgdown-workflow/reference/simulate_data.html @@ -48,7 +48,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/simulate_dropout.html b/test-pkgdown-workflow/reference/simulate_dropout.html index 2509dd87..c195b8e1 100644 --- a/test-pkgdown-workflow/reference/simulate_dropout.html +++ b/test-pkgdown-workflow/reference/simulate_dropout.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/simulate_ice.html b/test-pkgdown-workflow/reference/simulate_ice.html index ae6fd4ac..c389747c 100644 --- a/test-pkgdown-workflow/reference/simulate_ice.html +++ b/test-pkgdown-workflow/reference/simulate_ice.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/simulate_test_data.html b/test-pkgdown-workflow/reference/simulate_test_data.html index 5cde7c7f..5a8f1253 100644 --- a/test-pkgdown-workflow/reference/simulate_test_data.html +++ b/test-pkgdown-workflow/reference/simulate_test_data.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/sort_by.html b/test-pkgdown-workflow/reference/sort_by.html index 031c7369..a6a99058 100644 --- a/test-pkgdown-workflow/reference/sort_by.html +++ b/test-pkgdown-workflow/reference/sort_by.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/split_dim.html b/test-pkgdown-workflow/reference/split_dim.html index 53c3d225..4db27418 100644 --- a/test-pkgdown-workflow/reference/split_dim.html +++ b/test-pkgdown-workflow/reference/split_dim.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/split_imputations.html b/test-pkgdown-workflow/reference/split_imputations.html index 3fc0aa09..e9ee8682 100644 --- a/test-pkgdown-workflow/reference/split_imputations.html +++ b/test-pkgdown-workflow/reference/split_imputations.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/str_contains.html b/test-pkgdown-workflow/reference/str_contains.html index 605dbf48..9040e536 100644 --- a/test-pkgdown-workflow/reference/str_contains.html +++ b/test-pkgdown-workflow/reference/str_contains.html @@ -46,7 +46,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/strategies.html b/test-pkgdown-workflow/reference/strategies.html index 40cc8116..b331f821 100644 --- a/test-pkgdown-workflow/reference/strategies.html +++ b/test-pkgdown-workflow/reference/strategies.html @@ -42,7 +42,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/string_pad.html b/test-pkgdown-workflow/reference/string_pad.html index b223cd08..75500bd8 100644 --- a/test-pkgdown-workflow/reference/string_pad.html +++ b/test-pkgdown-workflow/reference/string_pad.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/transpose_imputations.html b/test-pkgdown-workflow/reference/transpose_imputations.html index 9f973ee9..4b50a57d 100644 --- a/test-pkgdown-workflow/reference/transpose_imputations.html +++ b/test-pkgdown-workflow/reference/transpose_imputations.html @@ -48,7 +48,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/transpose_results.html b/test-pkgdown-workflow/reference/transpose_results.html index 6df521df..5665d7f1 100644 --- a/test-pkgdown-workflow/reference/transpose_results.html +++ b/test-pkgdown-workflow/reference/transpose_results.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/transpose_samples.html b/test-pkgdown-workflow/reference/transpose_samples.html index f1f72c88..842e7bd9 100644 --- a/test-pkgdown-workflow/reference/transpose_samples.html +++ b/test-pkgdown-workflow/reference/transpose_samples.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/validate.analysis.html b/test-pkgdown-workflow/reference/validate.analysis.html index 7187806c..36a5971c 100644 --- a/test-pkgdown-workflow/reference/validate.analysis.html +++ b/test-pkgdown-workflow/reference/validate.analysis.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/validate.draws.html b/test-pkgdown-workflow/reference/validate.draws.html index 8f1f984f..b4e11178 100644 --- a/test-pkgdown-workflow/reference/validate.draws.html +++ b/test-pkgdown-workflow/reference/validate.draws.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/validate.html b/test-pkgdown-workflow/reference/validate.html index 86bfdd8c..447470b5 100644 --- a/test-pkgdown-workflow/reference/validate.html +++ b/test-pkgdown-workflow/reference/validate.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/validate.is_mar.html b/test-pkgdown-workflow/reference/validate.is_mar.html index f5ccdb99..a6bdeccb 100644 --- a/test-pkgdown-workflow/reference/validate.is_mar.html +++ b/test-pkgdown-workflow/reference/validate.is_mar.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/validate.ivars.html b/test-pkgdown-workflow/reference/validate.ivars.html index b4a34a1b..649db3c8 100644 --- a/test-pkgdown-workflow/reference/validate.ivars.html +++ b/test-pkgdown-workflow/reference/validate.ivars.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/validate.references.html b/test-pkgdown-workflow/reference/validate.references.html index fcf7a795..0ef7ca12 100644 --- a/test-pkgdown-workflow/reference/validate.references.html +++ b/test-pkgdown-workflow/reference/validate.references.html @@ -38,7 +38,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/validate.sample_list.html b/test-pkgdown-workflow/reference/validate.sample_list.html index c275d537..8ebd28fc 100644 --- a/test-pkgdown-workflow/reference/validate.sample_list.html +++ b/test-pkgdown-workflow/reference/validate.sample_list.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/validate.sample_single.html b/test-pkgdown-workflow/reference/validate.sample_single.html index 2668560f..f713b6ee 100644 --- a/test-pkgdown-workflow/reference/validate.sample_single.html +++ b/test-pkgdown-workflow/reference/validate.sample_single.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/validate.simul_pars.html b/test-pkgdown-workflow/reference/validate.simul_pars.html index 06b87f7f..8e378fd9 100644 --- a/test-pkgdown-workflow/reference/validate.simul_pars.html +++ b/test-pkgdown-workflow/reference/validate.simul_pars.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/validate.stan_data.html b/test-pkgdown-workflow/reference/validate.stan_data.html index b7714212..73d14478 100644 --- a/test-pkgdown-workflow/reference/validate.stan_data.html +++ b/test-pkgdown-workflow/reference/validate.stan_data.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/validate_analyse_pars.html b/test-pkgdown-workflow/reference/validate_analyse_pars.html index c797c6f1..935da22f 100644 --- a/test-pkgdown-workflow/reference/validate_analyse_pars.html +++ b/test-pkgdown-workflow/reference/validate_analyse_pars.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/validate_datalong.html b/test-pkgdown-workflow/reference/validate_datalong.html index 2ccf0747..1f429d99 100644 --- a/test-pkgdown-workflow/reference/validate_datalong.html +++ b/test-pkgdown-workflow/reference/validate_datalong.html @@ -36,7 +36,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/test-pkgdown-workflow/reference/validate_strategies.html b/test-pkgdown-workflow/reference/validate_strategies.html index d99dde5d..40cb31ce 100644 --- a/test-pkgdown-workflow/reference/validate_strategies.html +++ b/test-pkgdown-workflow/reference/validate_strategies.html @@ -40,7 +40,9 @@
    main
    + id="dropdown-versions">Versions
    main +latest-tag +v1.2.6
    diff --git a/v1.2.6/404.html b/v1.2.6/404.html index 84eae5e6..d67e68de 100644 --- a/v1.2.6/404.html +++ b/v1.2.6/404.html @@ -40,6 +40,14 @@
  • Changelog
    • +
  • +
    main +latest-tag +v1.2.6
    • +
  • Changelog
    • diff --git a/v1.2.6/LICENSE.html b/v1.2.6/LICENSE.html index 83411048..8bb1dd24 100644 --- a/v1.2.6/LICENSE.html +++ b/v1.2.6/LICENSE.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/articles/advanced.html b/v1.2.6/articles/advanced.html index 25d5d22f..d403241c 100644 --- a/v1.2.6/articles/advanced.html +++ b/v1.2.6/articles/advanced.html @@ -40,6 +40,14 @@
  • Changelog
    • +
  • +
    main +latest-tag +v1.2.6
    • +
  • Changelog
    • diff --git a/v1.2.6/articles/quickstart.html b/v1.2.6/articles/quickstart.html index 6ac72971..a65178c5 100644 --- a/v1.2.6/articles/quickstart.html +++ b/v1.2.6/articles/quickstart.html @@ -40,6 +40,14 @@
  • Changelog
    • +
  • +
    main +latest-tag +v1.2.6
    • +
    • Changelog
    +
  • +
    main +latest-tag +v1.2.6
    • +
    • diff --git a/v1.2.6/authors.html b/v1.2.6/authors.html index 244dbdad..f3e59cdf 100644 --- a/v1.2.6/authors.html +++ b/v1.2.6/authors.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/index.html b/v1.2.6/index.html index 06d0846d..24d23702 100644 --- a/v1.2.6/index.html +++ b/v1.2.6/index.html @@ -42,6 +42,14 @@
  • Changelog
    • +
  • +
    main +latest-tag +v1.2.6
    • +
  • Changelog
    • diff --git a/v1.2.6/reference/QR_decomp.html b/v1.2.6/reference/QR_decomp.html index 28f3ebc3..cbc5d0f3 100644 --- a/v1.2.6/reference/QR_decomp.html +++ b/v1.2.6/reference/QR_decomp.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/Stack.html b/v1.2.6/reference/Stack.html index 9bb1cff4..40ca4c6a 100644 --- a/v1.2.6/reference/Stack.html +++ b/v1.2.6/reference/Stack.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/add_class.html b/v1.2.6/reference/add_class.html index c3d763d1..38b948ea 100644 --- a/v1.2.6/reference/add_class.html +++ b/v1.2.6/reference/add_class.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/adjust_trajectories.html b/v1.2.6/reference/adjust_trajectories.html index ba553004..01827594 100644 --- a/v1.2.6/reference/adjust_trajectories.html +++ b/v1.2.6/reference/adjust_trajectories.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/adjust_trajectories_single.html b/v1.2.6/reference/adjust_trajectories_single.html index 5989bcf9..d3dff856 100644 --- a/v1.2.6/reference/adjust_trajectories_single.html +++ b/v1.2.6/reference/adjust_trajectories_single.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/analyse.html b/v1.2.6/reference/analyse.html index ba8e8e2d..4881cb19 100644 --- a/v1.2.6/reference/analyse.html +++ b/v1.2.6/reference/analyse.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/ancova.html b/v1.2.6/reference/ancova.html index 97bc113d..324f76e8 100644 --- a/v1.2.6/reference/ancova.html +++ b/v1.2.6/reference/ancova.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/ancova_single.html b/v1.2.6/reference/ancova_single.html index 759f88b2..64073f44 100644 --- a/v1.2.6/reference/ancova_single.html +++ b/v1.2.6/reference/ancova_single.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/antidepressant_data.html b/v1.2.6/reference/antidepressant_data.html index 7f79e224..e75a78ab 100644 --- a/v1.2.6/reference/antidepressant_data.html +++ b/v1.2.6/reference/antidepressant_data.html @@ -42,6 +42,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/apply_delta.html b/v1.2.6/reference/apply_delta.html index 323d4939..aa63ddfe 100644 --- a/v1.2.6/reference/apply_delta.html +++ b/v1.2.6/reference/apply_delta.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/as_analysis.html b/v1.2.6/reference/as_analysis.html index 69c5a310..552567d6 100644 --- a/v1.2.6/reference/as_analysis.html +++ b/v1.2.6/reference/as_analysis.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/as_ascii_table.html b/v1.2.6/reference/as_ascii_table.html index b877a42f..a83ee31b 100644 --- a/v1.2.6/reference/as_ascii_table.html +++ b/v1.2.6/reference/as_ascii_table.html @@ -30,6 +30,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/as_class.html b/v1.2.6/reference/as_class.html index 61de9ff4..ab1d85e4 100644 --- a/v1.2.6/reference/as_class.html +++ b/v1.2.6/reference/as_class.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/as_cropped_char.html b/v1.2.6/reference/as_cropped_char.html index 08edb025..0d1cacc5 100644 --- a/v1.2.6/reference/as_cropped_char.html +++ b/v1.2.6/reference/as_cropped_char.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/as_dataframe.html b/v1.2.6/reference/as_dataframe.html index 16a2c8ab..0e7de733 100644 --- a/v1.2.6/reference/as_dataframe.html +++ b/v1.2.6/reference/as_dataframe.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/as_draws.html b/v1.2.6/reference/as_draws.html index d24df925..f3a16b4d 100644 --- a/v1.2.6/reference/as_draws.html +++ b/v1.2.6/reference/as_draws.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/as_imputation.html b/v1.2.6/reference/as_imputation.html index 78f44d46..0e484c12 100644 --- a/v1.2.6/reference/as_imputation.html +++ b/v1.2.6/reference/as_imputation.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/as_indices.html b/v1.2.6/reference/as_indices.html index b9e059ec..d5b28c1d 100644 --- a/v1.2.6/reference/as_indices.html +++ b/v1.2.6/reference/as_indices.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/as_mmrm_df.html b/v1.2.6/reference/as_mmrm_df.html index 93836ca5..8ac24e3b 100644 --- a/v1.2.6/reference/as_mmrm_df.html +++ b/v1.2.6/reference/as_mmrm_df.html @@ -40,6 +40,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/as_mmrm_formula.html b/v1.2.6/reference/as_mmrm_formula.html index c5bd279a..240d0ec1 100644 --- a/v1.2.6/reference/as_mmrm_formula.html +++ b/v1.2.6/reference/as_mmrm_formula.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/as_model_df.html b/v1.2.6/reference/as_model_df.html index 538e774f..dc58c7d0 100644 --- a/v1.2.6/reference/as_model_df.html +++ b/v1.2.6/reference/as_model_df.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/as_simple_formula.html b/v1.2.6/reference/as_simple_formula.html index c4f7dd7f..85446771 100644 --- a/v1.2.6/reference/as_simple_formula.html +++ b/v1.2.6/reference/as_simple_formula.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/as_stan_array.html b/v1.2.6/reference/as_stan_array.html index 5ce9eea3..21f95be4 100644 --- a/v1.2.6/reference/as_stan_array.html +++ b/v1.2.6/reference/as_stan_array.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/as_strata.html b/v1.2.6/reference/as_strata.html index 52454df2..7803b671 100644 --- a/v1.2.6/reference/as_strata.html +++ b/v1.2.6/reference/as_strata.html @@ -36,6 +36,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/assert_variables_exist.html b/v1.2.6/reference/assert_variables_exist.html index 20a5496e..eba66688 100644 --- a/v1.2.6/reference/assert_variables_exist.html +++ b/v1.2.6/reference/assert_variables_exist.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/char2fct.html b/v1.2.6/reference/char2fct.html index 3db00064..f31d125e 100644 --- a/v1.2.6/reference/char2fct.html +++ b/v1.2.6/reference/char2fct.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/check_ESS.html b/v1.2.6/reference/check_ESS.html index f6bb6671..48d93510 100644 --- a/v1.2.6/reference/check_ESS.html +++ b/v1.2.6/reference/check_ESS.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/check_hmc_diagn.html b/v1.2.6/reference/check_hmc_diagn.html index f89ce16e..2fd9751a 100644 --- a/v1.2.6/reference/check_hmc_diagn.html +++ b/v1.2.6/reference/check_hmc_diagn.html @@ -36,6 +36,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/check_mcmc.html b/v1.2.6/reference/check_mcmc.html index 31d1266a..062ddaea 100644 --- a/v1.2.6/reference/check_mcmc.html +++ b/v1.2.6/reference/check_mcmc.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/compute_sigma.html b/v1.2.6/reference/compute_sigma.html index 171c2965..e1f4f848 100644 --- a/v1.2.6/reference/compute_sigma.html +++ b/v1.2.6/reference/compute_sigma.html @@ -30,6 +30,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/convert_to_imputation_list_df.html b/v1.2.6/reference/convert_to_imputation_list_df.html index f13901ad..a80c720a 100644 --- a/v1.2.6/reference/convert_to_imputation_list_df.html +++ b/v1.2.6/reference/convert_to_imputation_list_df.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/d_lagscale.html b/v1.2.6/reference/d_lagscale.html index e0b35590..30cc9e0d 100644 --- a/v1.2.6/reference/d_lagscale.html +++ b/v1.2.6/reference/d_lagscale.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/delta_template.html b/v1.2.6/reference/delta_template.html index f2ae3b06..517e09d2 100644 --- a/v1.2.6/reference/delta_template.html +++ b/v1.2.6/reference/delta_template.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/do_not_run.html b/v1.2.6/reference/do_not_run.html index 13b82ef1..b1791c1f 100644 --- a/v1.2.6/reference/do_not_run.html +++ b/v1.2.6/reference/do_not_run.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/draws.html b/v1.2.6/reference/draws.html index 29b2d4da..39fe2875 100644 --- a/v1.2.6/reference/draws.html +++ b/v1.2.6/reference/draws.html @@ -54,6 +54,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/encap_get_mmrm_sample.html b/v1.2.6/reference/encap_get_mmrm_sample.html index d211208c..7f2ea581 100644 --- a/v1.2.6/reference/encap_get_mmrm_sample.html +++ b/v1.2.6/reference/encap_get_mmrm_sample.html @@ -32,6 +32,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/eval_mmrm.html b/v1.2.6/reference/eval_mmrm.html index 623e958b..686ed79b 100644 --- a/v1.2.6/reference/eval_mmrm.html +++ b/v1.2.6/reference/eval_mmrm.html @@ -34,6 +34,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/expand.html b/v1.2.6/reference/expand.html index 20030c66..b433b881 100644 --- a/v1.2.6/reference/expand.html +++ b/v1.2.6/reference/expand.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/extract_covariates.html b/v1.2.6/reference/extract_covariates.html index cdf9a299..139ba4d3 100644 --- a/v1.2.6/reference/extract_covariates.html +++ b/v1.2.6/reference/extract_covariates.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/extract_data_nmar_as_na.html b/v1.2.6/reference/extract_data_nmar_as_na.html index 02b66a66..790820eb 100644 --- a/v1.2.6/reference/extract_data_nmar_as_na.html +++ b/v1.2.6/reference/extract_data_nmar_as_na.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/extract_draws.html b/v1.2.6/reference/extract_draws.html index 23a3c732..5096dba0 100644 --- a/v1.2.6/reference/extract_draws.html +++ b/v1.2.6/reference/extract_draws.html @@ -30,6 +30,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/extract_imputed_df.html b/v1.2.6/reference/extract_imputed_df.html index cb5e9d7a..0dcf1747 100644 --- a/v1.2.6/reference/extract_imputed_df.html +++ b/v1.2.6/reference/extract_imputed_df.html @@ -36,6 +36,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/extract_imputed_dfs.html b/v1.2.6/reference/extract_imputed_dfs.html index 39a617be..de0ca0f2 100644 --- a/v1.2.6/reference/extract_imputed_dfs.html +++ b/v1.2.6/reference/extract_imputed_dfs.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/extract_params.html b/v1.2.6/reference/extract_params.html index 06b67f37..295316d9 100644 --- a/v1.2.6/reference/extract_params.html +++ b/v1.2.6/reference/extract_params.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/fit_mcmc.html b/v1.2.6/reference/fit_mcmc.html index 72516b02..e16b03af 100644 --- a/v1.2.6/reference/fit_mcmc.html +++ b/v1.2.6/reference/fit_mcmc.html @@ -34,6 +34,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/fit_mmrm.html b/v1.2.6/reference/fit_mmrm.html index 94b5f2a3..e8b0b9d8 100644 --- a/v1.2.6/reference/fit_mmrm.html +++ b/v1.2.6/reference/fit_mmrm.html @@ -30,6 +30,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/generate_data_single.html b/v1.2.6/reference/generate_data_single.html index 45a9219a..b551b294 100644 --- a/v1.2.6/reference/generate_data_single.html +++ b/v1.2.6/reference/generate_data_single.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/getStrategies.html b/v1.2.6/reference/getStrategies.html index acd57c20..ed445259 100644 --- a/v1.2.6/reference/getStrategies.html +++ b/v1.2.6/reference/getStrategies.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/get_ESS.html b/v1.2.6/reference/get_ESS.html index 9bfde14f..230bf3b4 100644 --- a/v1.2.6/reference/get_ESS.html +++ b/v1.2.6/reference/get_ESS.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/get_bootstrap_stack.html b/v1.2.6/reference/get_bootstrap_stack.html index 3ee7c981..9c20c125 100644 --- a/v1.2.6/reference/get_bootstrap_stack.html +++ b/v1.2.6/reference/get_bootstrap_stack.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/get_cluster.html b/v1.2.6/reference/get_cluster.html index c00b8f70..2b1eb92b 100644 --- a/v1.2.6/reference/get_cluster.html +++ b/v1.2.6/reference/get_cluster.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/get_conditional_parameters.html b/v1.2.6/reference/get_conditional_parameters.html index ce49c075..c86b17ec 100644 --- a/v1.2.6/reference/get_conditional_parameters.html +++ b/v1.2.6/reference/get_conditional_parameters.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/get_delta_template.html b/v1.2.6/reference/get_delta_template.html index 819fd1a1..373a77fa 100644 --- a/v1.2.6/reference/get_delta_template.html +++ b/v1.2.6/reference/get_delta_template.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/get_draws_mle.html b/v1.2.6/reference/get_draws_mle.html index 15b9c32c..97768bc7 100644 --- a/v1.2.6/reference/get_draws_mle.html +++ b/v1.2.6/reference/get_draws_mle.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/get_ests_bmlmi.html b/v1.2.6/reference/get_ests_bmlmi.html index a130c886..c42917a0 100644 --- a/v1.2.6/reference/get_ests_bmlmi.html +++ b/v1.2.6/reference/get_ests_bmlmi.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/get_example_data.html b/v1.2.6/reference/get_example_data.html index f4b9e17e..f8c348e6 100644 --- a/v1.2.6/reference/get_example_data.html +++ b/v1.2.6/reference/get_example_data.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/get_jackknife_stack.html b/v1.2.6/reference/get_jackknife_stack.html index c6f973d5..3d693b5e 100644 --- a/v1.2.6/reference/get_jackknife_stack.html +++ b/v1.2.6/reference/get_jackknife_stack.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/get_mmrm_sample.html b/v1.2.6/reference/get_mmrm_sample.html index af1e500c..e27c0069 100644 --- a/v1.2.6/reference/get_mmrm_sample.html +++ b/v1.2.6/reference/get_mmrm_sample.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/get_pattern_groups.html b/v1.2.6/reference/get_pattern_groups.html index 3341bbda..b09df765 100644 --- a/v1.2.6/reference/get_pattern_groups.html +++ b/v1.2.6/reference/get_pattern_groups.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/get_pattern_groups_unique.html b/v1.2.6/reference/get_pattern_groups_unique.html index fd145b6e..97c8029f 100644 --- a/v1.2.6/reference/get_pattern_groups_unique.html +++ b/v1.2.6/reference/get_pattern_groups_unique.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/get_pool_components.html b/v1.2.6/reference/get_pool_components.html index 8bcb7bc6..28c8371f 100644 --- a/v1.2.6/reference/get_pool_components.html +++ b/v1.2.6/reference/get_pool_components.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/get_visit_distribution_parameters.html b/v1.2.6/reference/get_visit_distribution_parameters.html index a5e77551..a842d70d 100644 --- a/v1.2.6/reference/get_visit_distribution_parameters.html +++ b/v1.2.6/reference/get_visit_distribution_parameters.html @@ -32,6 +32,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/has_class.html b/v1.2.6/reference/has_class.html index 1c4d0c3e..c771957c 100644 --- a/v1.2.6/reference/has_class.html +++ b/v1.2.6/reference/has_class.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/ife.html b/v1.2.6/reference/ife.html index d8c44683..ea2409e7 100644 --- a/v1.2.6/reference/ife.html +++ b/v1.2.6/reference/ife.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/imputation_df.html b/v1.2.6/reference/imputation_df.html index c8da443f..e9976606 100644 --- a/v1.2.6/reference/imputation_df.html +++ b/v1.2.6/reference/imputation_df.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/imputation_list_df.html b/v1.2.6/reference/imputation_list_df.html index 4c50a2b1..3d15eb19 100644 --- a/v1.2.6/reference/imputation_list_df.html +++ b/v1.2.6/reference/imputation_list_df.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/imputation_list_single.html b/v1.2.6/reference/imputation_list_single.html index 24ead800..ffd84fb4 100644 --- a/v1.2.6/reference/imputation_list_single.html +++ b/v1.2.6/reference/imputation_list_single.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/imputation_single.html b/v1.2.6/reference/imputation_single.html index a3bbffd9..67088147 100644 --- a/v1.2.6/reference/imputation_single.html +++ b/v1.2.6/reference/imputation_single.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/impute.html b/v1.2.6/reference/impute.html index bb5f7887..81f8b31e 100644 --- a/v1.2.6/reference/impute.html +++ b/v1.2.6/reference/impute.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/impute_data_individual.html b/v1.2.6/reference/impute_data_individual.html index 2add59eb..b9bb5b95 100644 --- a/v1.2.6/reference/impute_data_individual.html +++ b/v1.2.6/reference/impute_data_individual.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/impute_internal.html b/v1.2.6/reference/impute_internal.html index e50a88c1..b986198e 100644 --- a/v1.2.6/reference/impute_internal.html +++ b/v1.2.6/reference/impute_internal.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/impute_outcome.html b/v1.2.6/reference/impute_outcome.html index c84a894e..a3e1cfa8 100644 --- a/v1.2.6/reference/impute_outcome.html +++ b/v1.2.6/reference/impute_outcome.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/index.html b/v1.2.6/reference/index.html index dd605966..13b62008 100644 --- a/v1.2.6/reference/index.html +++ b/v1.2.6/reference/index.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/invert.html b/v1.2.6/reference/invert.html index 1f8adcd2..f1680ea4 100644 --- a/v1.2.6/reference/invert.html +++ b/v1.2.6/reference/invert.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/invert_indexes.html b/v1.2.6/reference/invert_indexes.html index 2e84a765..d802106c 100644 --- a/v1.2.6/reference/invert_indexes.html +++ b/v1.2.6/reference/invert_indexes.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/is_absent.html b/v1.2.6/reference/is_absent.html index 894e12a8..6ea45975 100644 --- a/v1.2.6/reference/is_absent.html +++ b/v1.2.6/reference/is_absent.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/is_char_fact.html b/v1.2.6/reference/is_char_fact.html index c630cfdd..d989683d 100644 --- a/v1.2.6/reference/is_char_fact.html +++ b/v1.2.6/reference/is_char_fact.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/is_char_one.html b/v1.2.6/reference/is_char_one.html index c07c4179..0e78ca91 100644 --- a/v1.2.6/reference/is_char_one.html +++ b/v1.2.6/reference/is_char_one.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/is_in_rbmi_development.html b/v1.2.6/reference/is_in_rbmi_development.html index 3fd753ac..f2ffa392 100644 --- a/v1.2.6/reference/is_in_rbmi_development.html +++ b/v1.2.6/reference/is_in_rbmi_development.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/is_num_char_fact.html b/v1.2.6/reference/is_num_char_fact.html index 73ae25ee..f63bceea 100644 --- a/v1.2.6/reference/is_num_char_fact.html +++ b/v1.2.6/reference/is_num_char_fact.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/locf.html b/v1.2.6/reference/locf.html index 0d7f4dcd..57afb9b6 100644 --- a/v1.2.6/reference/locf.html +++ b/v1.2.6/reference/locf.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/longDataConstructor.html b/v1.2.6/reference/longDataConstructor.html index aea144fe..2d743767 100644 --- a/v1.2.6/reference/longDataConstructor.html +++ b/v1.2.6/reference/longDataConstructor.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/ls_design.html b/v1.2.6/reference/ls_design.html index dcb8713b..487ce781 100644 --- a/v1.2.6/reference/ls_design.html +++ b/v1.2.6/reference/ls_design.html @@ -34,6 +34,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/lsmeans.html b/v1.2.6/reference/lsmeans.html index 890d29e7..5abc7489 100644 --- a/v1.2.6/reference/lsmeans.html +++ b/v1.2.6/reference/lsmeans.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/method.html b/v1.2.6/reference/method.html index 5be7da98..0528a501 100644 --- a/v1.2.6/reference/method.html +++ b/v1.2.6/reference/method.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/parametric_ci.html b/v1.2.6/reference/parametric_ci.html index 526e7fc0..01f4bf47 100644 --- a/v1.2.6/reference/parametric_ci.html +++ b/v1.2.6/reference/parametric_ci.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/pool.html b/v1.2.6/reference/pool.html index db902b76..237e2ce9 100644 --- a/v1.2.6/reference/pool.html +++ b/v1.2.6/reference/pool.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/pool_bootstrap_normal.html b/v1.2.6/reference/pool_bootstrap_normal.html index bf1efa45..c2bf0bc5 100644 --- a/v1.2.6/reference/pool_bootstrap_normal.html +++ b/v1.2.6/reference/pool_bootstrap_normal.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/pool_bootstrap_percentile.html b/v1.2.6/reference/pool_bootstrap_percentile.html index 76b0f7f7..31256cb6 100644 --- a/v1.2.6/reference/pool_bootstrap_percentile.html +++ b/v1.2.6/reference/pool_bootstrap_percentile.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/pool_internal.html b/v1.2.6/reference/pool_internal.html index 4f573716..65f530b7 100644 --- a/v1.2.6/reference/pool_internal.html +++ b/v1.2.6/reference/pool_internal.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/prepare_stan_data.html b/v1.2.6/reference/prepare_stan_data.html index 3e7f3860..6c643204 100644 --- a/v1.2.6/reference/prepare_stan_data.html +++ b/v1.2.6/reference/prepare_stan_data.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/print.analysis.html b/v1.2.6/reference/print.analysis.html index ec313857..a57b555d 100644 --- a/v1.2.6/reference/print.analysis.html +++ b/v1.2.6/reference/print.analysis.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/print.draws.html b/v1.2.6/reference/print.draws.html index c74e8625..d8291cab 100644 --- a/v1.2.6/reference/print.draws.html +++ b/v1.2.6/reference/print.draws.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/print.imputation.html b/v1.2.6/reference/print.imputation.html index 024e571b..b8c93d4d 100644 --- a/v1.2.6/reference/print.imputation.html +++ b/v1.2.6/reference/print.imputation.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/progressLogger.html b/v1.2.6/reference/progressLogger.html index 2455db68..db3871d5 100644 --- a/v1.2.6/reference/progressLogger.html +++ b/v1.2.6/reference/progressLogger.html @@ -32,6 +32,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/pval_percentile.html b/v1.2.6/reference/pval_percentile.html index 0bf916ca..fb1954c5 100644 --- a/v1.2.6/reference/pval_percentile.html +++ b/v1.2.6/reference/pval_percentile.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/random_effects_expr.html b/v1.2.6/reference/random_effects_expr.html index 87075fec..aadde329 100644 --- a/v1.2.6/reference/random_effects_expr.html +++ b/v1.2.6/reference/random_effects_expr.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/rbmi-package.html b/v1.2.6/reference/rbmi-package.html index c5a3c0b1..f5a29193 100644 --- a/v1.2.6/reference/rbmi-package.html +++ b/v1.2.6/reference/rbmi-package.html @@ -46,6 +46,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/record.html b/v1.2.6/reference/record.html index ab190eaf..af879568 100644 --- a/v1.2.6/reference/record.html +++ b/v1.2.6/reference/record.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/recursive_reduce.html b/v1.2.6/reference/recursive_reduce.html index 6eab47f5..1b89a690 100644 --- a/v1.2.6/reference/recursive_reduce.html +++ b/v1.2.6/reference/recursive_reduce.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/remove_if_all_missing.html b/v1.2.6/reference/remove_if_all_missing.html index 31925678..845e2ef6 100644 --- a/v1.2.6/reference/remove_if_all_missing.html +++ b/v1.2.6/reference/remove_if_all_missing.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/rubin_df.html b/v1.2.6/reference/rubin_df.html index 461ec972..7c815a24 100644 --- a/v1.2.6/reference/rubin_df.html +++ b/v1.2.6/reference/rubin_df.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/rubin_rules.html b/v1.2.6/reference/rubin_rules.html index 0eda8c6e..1d9411ea 100644 --- a/v1.2.6/reference/rubin_rules.html +++ b/v1.2.6/reference/rubin_rules.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/sample_ids.html b/v1.2.6/reference/sample_ids.html index ccdd9cf9..b5ce5917 100644 --- a/v1.2.6/reference/sample_ids.html +++ b/v1.2.6/reference/sample_ids.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/sample_list.html b/v1.2.6/reference/sample_list.html index eff97ad0..08b564d5 100644 --- a/v1.2.6/reference/sample_list.html +++ b/v1.2.6/reference/sample_list.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/sample_mvnorm.html b/v1.2.6/reference/sample_mvnorm.html index 91b2aca1..a93c7808 100644 --- a/v1.2.6/reference/sample_mvnorm.html +++ b/v1.2.6/reference/sample_mvnorm.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/sample_single.html b/v1.2.6/reference/sample_single.html index 65157beb..6ac8d09d 100644 --- a/v1.2.6/reference/sample_single.html +++ b/v1.2.6/reference/sample_single.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/scalerConstructor.html b/v1.2.6/reference/scalerConstructor.html index 99d8379c..146ee08c 100644 --- a/v1.2.6/reference/scalerConstructor.html +++ b/v1.2.6/reference/scalerConstructor.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/set_simul_pars.html b/v1.2.6/reference/set_simul_pars.html index 36e754b4..418bc464 100644 --- a/v1.2.6/reference/set_simul_pars.html +++ b/v1.2.6/reference/set_simul_pars.html @@ -36,6 +36,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/set_vars.html b/v1.2.6/reference/set_vars.html index 91065238..eff72f22 100644 --- a/v1.2.6/reference/set_vars.html +++ b/v1.2.6/reference/set_vars.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/simulate_data.html b/v1.2.6/reference/simulate_data.html index 1896af5c..a1327b8b 100644 --- a/v1.2.6/reference/simulate_data.html +++ b/v1.2.6/reference/simulate_data.html @@ -36,6 +36,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/simulate_dropout.html b/v1.2.6/reference/simulate_dropout.html index 82da81b9..15887b17 100644 --- a/v1.2.6/reference/simulate_dropout.html +++ b/v1.2.6/reference/simulate_dropout.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/simulate_ice.html b/v1.2.6/reference/simulate_ice.html index e87f116a..e8e69c16 100644 --- a/v1.2.6/reference/simulate_ice.html +++ b/v1.2.6/reference/simulate_ice.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/simulate_test_data.html b/v1.2.6/reference/simulate_test_data.html index 1f6dae3b..8c0c29b8 100644 --- a/v1.2.6/reference/simulate_test_data.html +++ b/v1.2.6/reference/simulate_test_data.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/sort_by.html b/v1.2.6/reference/sort_by.html index 73ed3379..fa22559e 100644 --- a/v1.2.6/reference/sort_by.html +++ b/v1.2.6/reference/sort_by.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/split_dim.html b/v1.2.6/reference/split_dim.html index 7f3c35b3..ea06f98a 100644 --- a/v1.2.6/reference/split_dim.html +++ b/v1.2.6/reference/split_dim.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/split_imputations.html b/v1.2.6/reference/split_imputations.html index b7ea7445..abb29fe4 100644 --- a/v1.2.6/reference/split_imputations.html +++ b/v1.2.6/reference/split_imputations.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/str_contains.html b/v1.2.6/reference/str_contains.html index 4ca06776..d03eac51 100644 --- a/v1.2.6/reference/str_contains.html +++ b/v1.2.6/reference/str_contains.html @@ -34,6 +34,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/strategies.html b/v1.2.6/reference/strategies.html index ab888815..5c3f54d4 100644 --- a/v1.2.6/reference/strategies.html +++ b/v1.2.6/reference/strategies.html @@ -30,6 +30,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/string_pad.html b/v1.2.6/reference/string_pad.html index d296ec49..73e11efd 100644 --- a/v1.2.6/reference/string_pad.html +++ b/v1.2.6/reference/string_pad.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/transpose_imputations.html b/v1.2.6/reference/transpose_imputations.html index 572e8a28..cb4fb627 100644 --- a/v1.2.6/reference/transpose_imputations.html +++ b/v1.2.6/reference/transpose_imputations.html @@ -36,6 +36,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/transpose_results.html b/v1.2.6/reference/transpose_results.html index 9ec04812..078a807d 100644 --- a/v1.2.6/reference/transpose_results.html +++ b/v1.2.6/reference/transpose_results.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/transpose_samples.html b/v1.2.6/reference/transpose_samples.html index 3e1d7ef4..27a783c3 100644 --- a/v1.2.6/reference/transpose_samples.html +++ b/v1.2.6/reference/transpose_samples.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/validate.analysis.html b/v1.2.6/reference/validate.analysis.html index ee37653d..2dac1005 100644 --- a/v1.2.6/reference/validate.analysis.html +++ b/v1.2.6/reference/validate.analysis.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/validate.draws.html b/v1.2.6/reference/validate.draws.html index 285a69cd..9fec8702 100644 --- a/v1.2.6/reference/validate.draws.html +++ b/v1.2.6/reference/validate.draws.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/validate.html b/v1.2.6/reference/validate.html index 3808130b..5f1fb82b 100644 --- a/v1.2.6/reference/validate.html +++ b/v1.2.6/reference/validate.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/validate.is_mar.html b/v1.2.6/reference/validate.is_mar.html index 3fb39a9d..6ee7f6c5 100644 --- a/v1.2.6/reference/validate.is_mar.html +++ b/v1.2.6/reference/validate.is_mar.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/validate.ivars.html b/v1.2.6/reference/validate.ivars.html index 41d9441e..c393c350 100644 --- a/v1.2.6/reference/validate.ivars.html +++ b/v1.2.6/reference/validate.ivars.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/validate.references.html b/v1.2.6/reference/validate.references.html index 16b00455..6711a5d1 100644 --- a/v1.2.6/reference/validate.references.html +++ b/v1.2.6/reference/validate.references.html @@ -26,6 +26,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/validate.sample_list.html b/v1.2.6/reference/validate.sample_list.html index bb8c8657..67d86ec6 100644 --- a/v1.2.6/reference/validate.sample_list.html +++ b/v1.2.6/reference/validate.sample_list.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/validate.sample_single.html b/v1.2.6/reference/validate.sample_single.html index cba93bce..082e2360 100644 --- a/v1.2.6/reference/validate.sample_single.html +++ b/v1.2.6/reference/validate.sample_single.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/validate.simul_pars.html b/v1.2.6/reference/validate.simul_pars.html index ccfa0190..3cb41060 100644 --- a/v1.2.6/reference/validate.simul_pars.html +++ b/v1.2.6/reference/validate.simul_pars.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/validate.stan_data.html b/v1.2.6/reference/validate.stan_data.html index 1e2c15b0..389af110 100644 --- a/v1.2.6/reference/validate.stan_data.html +++ b/v1.2.6/reference/validate.stan_data.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/validate_analyse_pars.html b/v1.2.6/reference/validate_analyse_pars.html index 1a605a78..32b270b6 100644 --- a/v1.2.6/reference/validate_analyse_pars.html +++ b/v1.2.6/reference/validate_analyse_pars.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/validate_datalong.html b/v1.2.6/reference/validate_datalong.html index 757c2a67..53e40662 100644 --- a/v1.2.6/reference/validate_datalong.html +++ b/v1.2.6/reference/validate_datalong.html @@ -24,6 +24,14 @@
  • Changelog
    • diff --git a/v1.2.6/reference/validate_strategies.html b/v1.2.6/reference/validate_strategies.html index e139face..5e2d6939 100644 --- a/v1.2.6/reference/validate_strategies.html +++ b/v1.2.6/reference/validate_strategies.html @@ -28,6 +28,14 @@
  • Changelog
    • diff --git a/v1.2.6/search.json b/v1.2.6/search.json index 6f53ad6c..d0324c95 100644 --- a/v1.2.6/search.json +++ b/v1.2.6/search.json @@ -1 +1 @@ -[{"path":"/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to rbmi","title":"Contributing to rbmi","text":"file outlines propose make changes rbmi well providing details obscure aspects package’s development process.","code":""},{"path":"/CONTRIBUTING.html","id":"setup","dir":"","previous_headings":"","what":"Setup","title":"Contributing to rbmi","text":"order develop contribute rbmi need access C/C++ compiler. Windows install rtools macOS install Xcode. Likewise, also need install package’s development dependencies. can done launching R within project root executing:","code":"devtools::install_dev_deps()"},{"path":"/CONTRIBUTING.html","id":"code-changes","dir":"","previous_headings":"","what":"Code changes","title":"Contributing to rbmi","text":"want make code contribution, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed).","code":""},{"path":"/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Code changes","what":"Pull request process","title":"Contributing to rbmi","text":"project uses simple GitHub flow model development. , code changes done feature branch based main branch merged back main branch complete. Pull Requests accepted unless CI/CD checks passed. (See CI/CD section information). Pull Requests relating package’s core R code must accompanied corresponding unit test. pull requests containing changes core R code contain unit test demonstrate working intended accepted. (See Unit Testing section information). Pull Requests add lines changed NEWS.md file.","code":""},{"path":"/CONTRIBUTING.html","id":"coding-considerations","dir":"","previous_headings":"Code changes","what":"Coding Considerations","title":"Contributing to rbmi","text":"use roxygen2, Markdown syntax, documentation. Please ensure code conforms lintr. can check running lintr::lint(\"FILE NAME\") files modified ensuring findings kept possible. hard requirements following lintr’s conventions encourage developers follow guidance closely possible. project uses 4 space indents, contributions following accepted. project makes use S3 R6 OOP. Usage S4 OOP systems avoided unless absolutely necessary ensure consistency. said recommended stick S3 unless modification place R6 specific features required. current desire package keep dependency tree small possible. end discouraged adding additional packages “Depends” / “Imports” section unless absolutely essential. importing package just use single function consider just copying source code function instead, though please check licence include proper attribution/notices. expectations “Suggests” free use package vignettes / unit tests, though please mindful unnecessarily excessive .","code":""},{"path":"/CONTRIBUTING.html","id":"unit-testing--cicd","dir":"","previous_headings":"","what":"Unit Testing & CI/CD","title":"Contributing to rbmi","text":"project uses testthat perform unit testing combination GitHub Actions CI/CD.","code":""},{"path":"/CONTRIBUTING.html","id":"scheduled-testing","dir":"","previous_headings":"Unit Testing & CI/CD","what":"Scheduled Testing","title":"Contributing to rbmi","text":"Due stochastic nature package unit tests take considerable amount time execute. avoid issues usability, unit tests take couple seconds run deferred scheduled testing. tests run occasionally periodic basis (currently twice month) every pull request / push event. defer test scheduled build simply include skip_if_not(is_full_test()) top test_that() block .e. scheduled tests can also manually activated going “https://github.com/insightsengineering/rbmi” -> “Actions” -> “Bi-Weekly” -> “Run Workflow”. advisable releasing CRAN.","code":"test_that(\"some unit test\", { skip_if_not(is_full_test()) expect_equal(1,1) })"},{"path":"/CONTRIBUTING.html","id":"cran-releases","dir":"","previous_headings":"Unit Testing & CI/CD","what":"CRAN Releases","title":"Contributing to rbmi","text":"order release package CRAN needs tested across multiple different OS’s versions R. implemented project via GitHub Action Workflow titled “Check CRAN” needs manually activated. go “https://github.com/insightsengineering/rbmi” -> “Actions” -> “Check CRAN” -> “Run Workflow”. tests pass package can safely released CRAN (updating relevant cran-comments.md file)","code":""},{"path":"/CONTRIBUTING.html","id":"docker-images","dir":"","previous_headings":"Unit Testing & CI/CD","what":"Docker Images","title":"Contributing to rbmi","text":"support CI/CD terms reducing installation time, several Docker images pre-built contain packages system dependencies project needs. current relevant images can found : ghcr.io/insightsengineering/rbmi:r404 ghcr.io/insightsengineering/rbmi:r410 ghcr.io/insightsengineering/rbmi:latest latest image automatically re-built month contain latest version R packages. versions built older versions R (indicated tag number) contain package versions version R released. important ensure package works older versions R many companies typically run due delays validation processes. code create images can found misc/docker. legacy images (.e. everything excluding “latest” image) built manual request running corresponding GitHub Actions Workflow.","code":""},{"path":"/CONTRIBUTING.html","id":"reproducibility-print-tests--snaps","dir":"","previous_headings":"Unit Testing & CI/CD","what":"Reproducibility, Print Tests & Snaps","title":"Contributing to rbmi","text":"particular issue testing package reproducibility. part handled well via set.seed() however stan/rstan guarantee reproducibility even seed run different hardware. issue surfaces testing print messages pool object displays treatment estimates thus identical run different machines. address issue pre-made pool objects generated stored R/sysdata.rda (generated data-raw/create_print_test_data.R). generated print messages compared expected values stored tests/testthat/_snaps/ (automatically created testthat::expect_snapshot())","code":""},{"path":"/CONTRIBUTING.html","id":"fitting-mmrms","dir":"","previous_headings":"","what":"Fitting MMRM’s","title":"Contributing to rbmi","text":"package currently uses mmrm package fit MMRM models. package still fairly new far proven stable, fast reliable. spot issues MMRM package please raise corresponding GitHub Repository - link mmrm package uses TMB uncommon see warnings either inconsistent versions TMB Matrix package compiled . order resolve may wish re-compile packages source using: Note need rtools installed Windows machine Xcode running macOS (somehow else access C/C++ compiler).","code":"install.packages(c(\"TMB\", \"mmrm\"), type = \"source\")"},{"path":"/CONTRIBUTING.html","id":"rstan","dir":"","previous_headings":"","what":"rstan","title":"Contributing to rbmi","text":"Bayesian models fitted package implemented via stan/rstan. code can found inst/stan/MMRM.stan. Note package automatically take care compiling code install run devtools::load_all(). Please note package won’t recompile code unless changed source code delete src directory.","code":""},{"path":"/CONTRIBUTING.html","id":"vignettes","dir":"","previous_headings":"","what":"Vignettes","title":"Contributing to rbmi","text":"CRAN imposes 10-minute run limit building, compiling testing package. keep limit vignettes pre-built; say simply changing source code automatically update vignettes, need manually re-build . need run: re-built need commit updated *.html files git repository. reference static vignette process works using “asis” vignette engine provided R.rsp. works getting R recognise vignettes files ending *.html.asis; builds simply copying corresponding files ending *.html relevent docs/ folder built package.","code":"Rscript vignettes/build.R"},{"path":"/CONTRIBUTING.html","id":"misc--local-folders","dir":"","previous_headings":"","what":"Misc & Local Folders","title":"Contributing to rbmi","text":"misc/ folder project used hold useful scripts, analyses, simulations & infrastructure code wish keep isn’t essential build deployment package. Feel free store additional stuff feel worth keeping. Likewise, local/ added .gitignore file meaning anything stored folder won’t committed repository. example, may find useful storing personal scripts testing generally exploring package development.","code":""},{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"Apache License","title":"Apache License","text":"Version 2.0, January 2004 ","code":""},{"path":[]},{"path":"/LICENSE.html","id":"id_1-definitions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"1. Definitions","title":"Apache License","text":"“License” shall mean terms conditions use, reproduction, distribution defined Sections 1 9 document. “Licensor” shall mean copyright owner entity authorized copyright owner granting License. “Legal Entity” shall mean union acting entity entities control, controlled , common control entity. purposes definition, “control” means () power, direct indirect, cause direction management entity, whether contract otherwise, (ii) ownership fifty percent (50%) outstanding shares, (iii) beneficial ownership entity. “” (“”) shall mean individual Legal Entity exercising permissions granted License. “Source” form shall mean preferred form making modifications, including limited software source code, documentation source, configuration files. “Object” form shall mean form resulting mechanical transformation translation Source form, including limited compiled object code, generated documentation, conversions media types. “Work” shall mean work authorship, whether Source Object form, made available License, indicated copyright notice included attached work (example provided Appendix ). “Derivative Works” shall mean work, whether Source Object form, based (derived ) Work editorial revisions, annotations, elaborations, modifications represent, whole, original work authorship. purposes License, Derivative Works shall include works remain separable , merely link (bind name) interfaces , Work Derivative Works thereof. “Contribution” shall mean work authorship, including original version Work modifications additions Work Derivative Works thereof, intentionally submitted Licensor inclusion Work copyright owner individual Legal Entity authorized submit behalf copyright owner. purposes definition, “submitted” means form electronic, verbal, written communication sent Licensor representatives, including limited communication electronic mailing lists, source code control systems, issue tracking systems managed , behalf , Licensor purpose discussing improving Work, excluding communication conspicuously marked otherwise designated writing copyright owner “Contribution.” “Contributor” shall mean Licensor individual Legal Entity behalf Contribution received Licensor subsequently incorporated within Work.","code":""},{"path":"/LICENSE.html","id":"id_2-grant-of-copyright-license","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"2. Grant of Copyright License","title":"Apache License","text":"Subject terms conditions License, Contributor hereby grants perpetual, worldwide, non-exclusive, -charge, royalty-free, irrevocable copyright license reproduce, prepare Derivative Works , publicly display, publicly perform, sublicense, distribute Work Derivative Works Source Object form.","code":""},{"path":"/LICENSE.html","id":"id_3-grant-of-patent-license","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"3. Grant of Patent License","title":"Apache License","text":"Subject terms conditions License, Contributor hereby grants perpetual, worldwide, non-exclusive, -charge, royalty-free, irrevocable (except stated section) patent license make, made, use, offer sell, sell, import, otherwise transfer Work, license applies patent claims licensable Contributor necessarily infringed Contribution(s) alone combination Contribution(s) Work Contribution(s) submitted. institute patent litigation entity (including cross-claim counterclaim lawsuit) alleging Work Contribution incorporated within Work constitutes direct contributory patent infringement, patent licenses granted License Work shall terminate date litigation filed.","code":""},{"path":"/LICENSE.html","id":"id_4-redistribution","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"4. Redistribution","title":"Apache License","text":"may reproduce distribute copies Work Derivative Works thereof medium, without modifications, Source Object form, provided meet following conditions: () must give recipients Work Derivative Works copy License; (b) must cause modified files carry prominent notices stating changed files; (c) must retain, Source form Derivative Works distribute, copyright, patent, trademark, attribution notices Source form Work, excluding notices pertain part Derivative Works; (d) Work includes “NOTICE” text file part distribution, Derivative Works distribute must include readable copy attribution notices contained within NOTICE file, excluding notices pertain part Derivative Works, least one following places: within NOTICE text file distributed part Derivative Works; within Source form documentation, provided along Derivative Works; , within display generated Derivative Works, wherever third-party notices normally appear. contents NOTICE file informational purposes modify License. may add attribution notices within Derivative Works distribute, alongside addendum NOTICE text Work, provided additional attribution notices construed modifying License. may add copyright statement modifications may provide additional different license terms conditions use, reproduction, distribution modifications, Derivative Works whole, provided use, reproduction, distribution Work otherwise complies conditions stated License.","code":""},{"path":"/LICENSE.html","id":"id_5-submission-of-contributions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"5. Submission of Contributions","title":"Apache License","text":"Unless explicitly state otherwise, Contribution intentionally submitted inclusion Work Licensor shall terms conditions License, without additional terms conditions. Notwithstanding , nothing herein shall supersede modify terms separate license agreement may executed Licensor regarding Contributions.","code":""},{"path":"/LICENSE.html","id":"id_6-trademarks","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"6. Trademarks","title":"Apache License","text":"License grant permission use trade names, trademarks, service marks, product names Licensor, except required reasonable customary use describing origin Work reproducing content NOTICE file.","code":""},{"path":"/LICENSE.html","id":"id_7-disclaimer-of-warranty","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"7. Disclaimer of Warranty","title":"Apache License","text":"Unless required applicable law agreed writing, Licensor provides Work (Contributor provides Contributions) “” BASIS, WITHOUT WARRANTIES CONDITIONS KIND, either express implied, including, without limitation, warranties conditions TITLE, NON-INFRINGEMENT, MERCHANTABILITY, FITNESS PARTICULAR PURPOSE. solely responsible determining appropriateness using redistributing Work assume risks associated exercise permissions License.","code":""},{"path":"/LICENSE.html","id":"id_8-limitation-of-liability","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"8. Limitation of Liability","title":"Apache License","text":"event legal theory, whether tort (including negligence), contract, otherwise, unless required applicable law (deliberate grossly negligent acts) agreed writing, shall Contributor liable damages, including direct, indirect, special, incidental, consequential damages character arising result License use inability use Work (including limited damages loss goodwill, work stoppage, computer failure malfunction, commercial damages losses), even Contributor advised possibility damages.","code":""},{"path":"/LICENSE.html","id":"id_9-accepting-warranty-or-additional-liability","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"9. Accepting Warranty or Additional Liability","title":"Apache License","text":"redistributing Work Derivative Works thereof, may choose offer, charge fee , acceptance support, warranty, indemnity, liability obligations /rights consistent License. However, accepting obligations, may act behalf sole responsibility, behalf Contributor, agree indemnify, defend, hold Contributor harmless liability incurred , claims asserted , Contributor reason accepting warranty additional liability. END TERMS CONDITIONS","code":""},{"path":"/LICENSE.html","id":"appendix-how-to-apply-the-apache-license-to-your-work","dir":"","previous_headings":"","what":"APPENDIX: How to apply the Apache License to your work","title":"Apache License","text":"apply Apache License work, attach following boilerplate notice, fields enclosed brackets [] replaced identifying information. (Don’t include brackets!) text enclosed appropriate comment syntax file format. also recommend file class name description purpose included “printed page” copyright notice easier identification within third-party archives.","code":"Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."},{"path":"/articles/advanced.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"rbmi: Advanced Functionality","text":"purpose vignette provide overview advanced features rbmi package. sections vignette relatively self-contained, .e. readers able jump directly section covers functionality interested .","code":""},{"path":"/articles/advanced.html","id":"sec:dataSimul","dir":"Articles","previous_headings":"","what":"Data simulation using function simulate_data()","title":"rbmi: Advanced Functionality","text":"order demonstrate advanced functions first create simulated dataset rbmi function simulate_data(). simulate_data() function generates data randomized clinical trial longitudinal continuous outcomes two different types intercurrent events (ICEs). One intercurrent event (ICE1) may thought discontinuation study treatment due study drug condition related (SDCR) reasons. event (ICE2) may thought discontinuation study treatment due study drug condition related (NSDCR) reasons. purpose vignette, simulate data similarly simulation study reported Wolbers et al. (2022) (though change simulation parameters) include one ICE type (ICE1). Specifically, simulate 1:1 randomized trial active drug (intervention) versus placebo (control) 100 subjects per group 6 post-baseline assessments (bi-monthly visits 12 months) following assumptions: mean outcome trajectory placebo group increases linearly 50 baseline (visit 0) 60 visit 6, .e. slope 10 points/year. mean outcome trajectory intervention group identical placebo group visit 2. visit 2 onward, slope decreases 50% 5 points/year. covariance structure baseline follow-values groups implied random intercept slope model standard deviation 5 intercept slope, correlation 0.25. addition, independent residual error standard deviation 2.5 added assessment. probability study drug discontinuation visit calculated according logistic model depends observed outcome visit. Specifically, visit-wise discontinuation probability 2% 3% control intervention group, respectively, specified case observed outcome equal 50 (mean value baseline). odds discontinuation simulated increase +10% +1 point increase observed outcome. Study drug discontinuation simulated effect mean trajectory placebo group. intervention group, subjects discontinue follow slope mean trajectory placebo group time point onward. compatible copy increments reference (CIR) assumption. Study drop-study drug discontinuation visit occurs probability 50% leading missing outcome data time point onward. function simulate_data() requires 3 arguments (see function documentation help(simulate_data) details): pars_c: simulation parameters control group pars_t: simulation parameters intervention group post_ice1_traj: Specifies observed outcomes ICE1 simulated , report data according specifications can simulated function simulate_data():","code":"library(rbmi) library(dplyr) library(ggplot2) library(purrr) set.seed(122) n <- 100 time <- c(0, 2, 4, 6, 8, 10, 12) # Mean trajectory control muC <- c(50.0, 51.66667, 53.33333, 55.0, 56.66667, 58.33333, 60.0) # Mean trajectory intervention muT <- c(50.0, 51.66667, 53.33333, 54.16667, 55.0, 55.83333, 56.66667) # Create Sigma sd_error <- 2.5 covRE <- rbind( c(25.0, 6.25), c(6.25, 25.0) ) Sigma <- cbind(1, time / 12) %*% covRE %*% rbind(1, time / 12) + diag(sd_error^2, nrow = length(time)) # Set probability of discontinuation probDisc_C <- 0.02 probDisc_T <- 0.03 or_outcome <- 1.10 # +1 point increase => +10% odds of discontinuation # Set drop-out rate following discontinuation prob_dropout <- 0.5 # Set simulation parameters of the control group parsC <- set_simul_pars( mu = muC, sigma = Sigma, n = n, prob_ice1 = probDisc_C, or_outcome_ice1 = or_outcome, prob_post_ice1_dropout = prob_dropout ) # Set simulation parameters of the intervention group parsT <- parsC parsT$mu <- muT parsT$prob_ice1 <- probDisc_T # Set assumption about post-ice trajectory post_ice_traj <- \"CIR\" # Simulate data data <- simulate_data( pars_c = parsC, pars_t = parsT, post_ice1_traj = post_ice_traj ) head(data) #> id visit group outcome_bl outcome_noICE ind_ice1 ind_ice2 dropout_ice1 #> 1 id_1 0 Control 57.32704 57.32704 0 0 0 #> 2 id_1 1 Control 57.32704 54.69751 1 0 1 #> 3 id_1 2 Control 57.32704 58.60702 1 0 1 #> 4 id_1 3 Control 57.32704 61.50119 1 0 1 #> 5 id_1 4 Control 57.32704 56.68363 1 0 1 #> 6 id_1 5 Control 57.32704 66.14799 1 0 1 #> outcome #> 1 57.32704 #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA # As a simple descriptive of the simulated data, summarize the number of subjects with ICEs and missing data data %>% group_by(id) %>% summarise( group = group[1], any_ICE = (any(ind_ice1 == 1)), any_NA = any(is.na(outcome))) %>% group_by(group) %>% summarise( subjects_with_ICE = sum(any_ICE), subjects_with_missings = sum(any_NA) ) #> # A tibble: 2 × 3 #> group subjects_with_ICE subjects_with_missings #> #> 1 Control 18 8 #> 2 Intervention 25 14"},{"path":"/articles/advanced.html","id":"sec:postICEobs","dir":"Articles","previous_headings":"","what":"Handling of observed post-ICE data in rbmi under reference-based imputation","title":"rbmi: Advanced Functionality","text":"rbmi always uses non-missing outcome data input data set, .e. data never overwritten imputation step removed analysis step. implies data considered irrelevant treatment effect estimation (e.g. data ICE estimand specified hypothetical strategy), data need removed input data set user prior calling rbmi functions. imputation missing random (MAR) strategy, observed outcome data also included fitting base imputation model. However, ICEs handled using reference-based imputation methods (CIR, CR, JR), rbmi excludes observed post-ICE data base imputation model. data excluded, base imputation model mistakenly estimate mean trajectories based mixture observed pre- post-ICE data relevant reference-based imputations. However, observed post-ICE data added back data set fitting base imputation model included subsequent imputation analysis steps. Post-ICE data control reference group also excluded base imputation model user specifies reference-based imputation strategy ICEs. ensures ICE impact data included base imputation model regardless whether ICE occurred control intervention group. hand, imputation reference group based MAR assumption even reference-based imputation methods may preferable settings include post-ICE data control group base imputation model. can implemented specifying MAR strategy ICE control group reference-based strategy ICE intervention group. use latter approach example . simulated trial data section 2 assumed outcomes intervention group observed ICE “treatment discontinuation” follow increments observed control group. Thus imputation missing data intervention group treatment discontinuation might performed reference-based copy increments reference (CIR) assumption. Specifically, implement estimator following assumptions: endpoint interest change outcome baseline visit. imputation model includes treatment group, (categorical) visit, treatment--visit interactions, baseline outcome, baseline outcome--visit interactions covariates. imputation model assumes common unstructured covariance matrix treatment groups control group, missing data imputed MAR whereas intervention group, missing post-ICE data imputed CIR assumption analysis model endpoint imputed datasets separate ANCOVA model visit treatment group primary covariate adjustment baseline outcome value. illustration purposes, chose MI based approximate Bayesian posterior draws 20 random imputations demanding computational perspective. practical applications, number random imputations may need increased. Moreover, imputations also supported rbmi. guidance regarding choice imputation approach, refer user comparison implemented approaches Section 3.9 “Statistical Specifications” vignette (vignette(\"stat_specs\", package = \"rbmi\")). first report code set variables imputation analysis models. yet familiar syntax, recommend first check “quickstart” vignette (vignette(\"quickstart\", package = \"rbmi\")). chosen imputation method can set function method_approxbayes() follows: can now sequentially call 4 key functions rbmi perform multiple imputation. Please note management observed post-ICE data performed without additional complexity user. draws() automatically excludes post-ICE data handled reference-based method (keeps post-ICE data handled using MAR) using information provided argument data_ice. impute() impute truly missing data data[[vars$outcome]]. last output gives estimated difference -4.537 (95% CI -6.420 -2.655) two groups last visit associated p-value lower 0.001.","code":"# Create data_ice including the subject's first visit affected by the ICE and the imputation strategy # Imputation strategy for post-ICE data is CIR in the intervention group and MAR for the control group # (note that ICEs which are handled using MAR are optional and do not impact the analysis # because imputation of missing data under MAR is the default) data_ice_CIR <- data %>% group_by(id) %>% filter(ind_ice1 == 1) %>% # select visits with ICEs mutate(strategy = ifelse(group == \"Intervention\", \"CIR\", \"MAR\")) %>% summarise( visit = visit[1], # Select first visit affected by the ICE strategy = strategy[1] ) # Compute endpoint of interest: change from baseline and # remove rows corresponding to baseline visits data <- data %>% filter(visit != 0) %>% mutate( change = outcome - outcome_bl, visit = factor(visit, levels = unique(visit)) ) # Define key variables for the imputation and analysis models vars <- set_vars( subjid = \"id\", visit = \"visit\", outcome = \"change\", group = \"group\", covariates = c(\"visit*outcome_bl\", \"visit*group\"), strategy = \"strategy\" ) vars_an <- vars vars_an$covariates <- \"outcome_bl\" method <- method_approxbayes(n_sample = 20) draw_obj <- draws( data = data, data_ice = data_ice_CIR, vars = vars, method = method, quiet = TRUE, ncores = 2 ) impute_obj_CIR <- impute( draw_obj, references = c(\"Control\" = \"Control\", \"Intervention\" = \"Control\") ) ana_obj_CIR <- analyse( impute_obj_CIR, vars = vars_an ) pool_obj_CIR <- pool(ana_obj_CIR) pool_obj_CIR #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_1 -0.486 0.512 -1.496 0.524 0.343 #> lsm_ref_1 2.62 0.362 1.907 3.333 <0.001 #> lsm_alt_1 2.133 0.362 1.42 2.847 <0.001 #> trt_2 -0.066 0.542 -1.135 1.004 0.904 #> lsm_ref_2 3.707 0.384 2.95 4.464 <0.001 #> lsm_alt_2 3.641 0.383 2.885 4.397 <0.001 #> trt_3 -1.782 0.607 -2.979 -0.585 0.004 #> lsm_ref_3 5.841 0.428 4.997 6.685 <0.001 #> lsm_alt_3 4.059 0.428 3.214 4.904 <0.001 #> trt_4 -2.518 0.692 -3.884 -1.152 <0.001 #> lsm_ref_4 7.656 0.492 6.685 8.627 <0.001 #> lsm_alt_4 5.138 0.488 4.176 6.1 <0.001 #> trt_5 -3.658 0.856 -5.346 -1.97 <0.001 #> lsm_ref_5 9.558 0.598 8.379 10.737 <0.001 #> lsm_alt_5 5.9 0.608 4.699 7.101 <0.001 #> trt_6 -4.537 0.954 -6.42 -2.655 <0.001 #> lsm_ref_6 11.048 0.666 9.735 12.362 <0.001 #> lsm_alt_6 6.511 0.674 5.181 7.841 <0.001 #> --------------------------------------------------"},{"path":"/articles/advanced.html","id":"efficiently-changing-reference-based-imputation-strategies","dir":"Articles","previous_headings":"","what":"Efficiently changing reference-based imputation strategies","title":"rbmi: Advanced Functionality","text":"draws() function far computationally intensive function rbmi. settings, may important explore impact change reference-based imputation strategy results. change affect imputation model affect subsequent imputation step. order allow changes imputation strategy without re-run draws() function, function impute() additional argument update_strategies. However, please note functionality comes important limitations: described beginning Section 3, post-ICE outcomes included input dataset base imputation model imputation method MAR excluded reference-based imputation methods (CIR, CR, JR). Therefore, updata_strategies applied imputation strategy changed MAR non-MAR strategy presence observed post-ICE outcomes. Similarly, change non-MAR strategy MAR triggers warning presence observed post-ICE outcomes base imputation model fitted relevant data MAR. Finally, update_strategies applied timing ICEs changed (argument data_ice) addition imputation strategy. example, described analysis copy increments reference (CIR) assumption previous section. Let’s assume want change strategy jump reference imputation strategy sensitivity analysis. can efficiently implemented using update_strategies follows: imputations jump reference assumption, get estimated difference -4.360 (95% CI -6.238 -2.482) two groups last visit associated p-value <0.001.","code":"# Change ICE strategy from CIR to JR data_ice_JR <- data_ice_CIR %>% mutate(strategy = ifelse(strategy == \"CIR\", \"JR\", strategy)) impute_obj_JR <- impute( draw_obj, references = c(\"Control\" = \"Control\", \"Intervention\" = \"Control\"), update_strategy = data_ice_JR ) ana_obj_JR <- analyse( impute_obj_JR, vars = vars_an ) pool_obj_JR <- pool(ana_obj_JR) pool_obj_JR #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_1 -0.485 0.513 -1.496 0.526 0.346 #> lsm_ref_1 2.609 0.363 1.892 3.325 <0.001 #> lsm_alt_1 2.124 0.361 1.412 2.836 <0.001 #> trt_2 -0.06 0.535 -1.115 0.995 0.911 #> lsm_ref_2 3.694 0.378 2.948 4.441 <0.001 #> lsm_alt_2 3.634 0.381 2.882 4.387 <0.001 #> trt_3 -1.767 0.598 -2.948 -0.587 0.004 #> lsm_ref_3 5.845 0.422 5.012 6.677 <0.001 #> lsm_alt_3 4.077 0.432 3.225 4.93 <0.001 #> trt_4 -2.529 0.686 -3.883 -1.175 <0.001 #> lsm_ref_4 7.637 0.495 6.659 8.614 <0.001 #> lsm_alt_4 5.108 0.492 4.138 6.078 <0.001 #> trt_5 -3.523 0.856 -5.212 -1.833 <0.001 #> lsm_ref_5 9.554 0.61 8.351 10.758 <0.001 #> lsm_alt_5 6.032 0.611 4.827 7.237 <0.001 #> trt_6 -4.36 0.952 -6.238 -2.482 <0.001 #> lsm_ref_6 11.003 0.676 9.669 12.337 <0.001 #> lsm_alt_6 6.643 0.687 5.287 8 <0.001 #> --------------------------------------------------"},{"path":"/articles/advanced.html","id":"imputation-under-mar-with-time-varying-covariates","dir":"Articles","previous_headings":"","what":"Imputation under MAR with time-varying covariates","title":"rbmi: Advanced Functionality","text":"Guizzaro et al. (2021) suggested implement treatment policy strategy via imputation MAR assumption conditioning subject’s ICE status, .e. impute missing post-ICE data based observed post-ICE data. One possible implementation proposal add time-varying covariates imputation model. case study implements proposal compares reference-based imputation methods estimators early Parkinson’s disease can found Noci et al. (2021). settings, may carried including binary time-varying indicator subject’s ICE status visit (defined 0 pre-ICE visits 1 post-ICE visits) imputation model. However, simulated data introduced section 2, may plausible assume treatment discontinuation leads change “slope” mean outcome trajectory. can implemented including time-varying covariate equal 0 visits prior treatment discontinuation equal time treatment discontinuation subsequent visits. regression coefficient corresponding change post-ICE “slope” allowed depend assigned treatment group, .e. imputation model include interaction time-varying covariate treatment group. Let’s first define time-varying covariate: can include time-varying covariate imputation model, crossed group variable: now sequentially call 4 key rbmi functions:","code":"data <- data %>% group_by(id) %>% mutate(time_from_ice1 = cumsum(ind_ice1)*2/12 ) # multiplication by 2/12 because visits are bi-monthly vars_tv <- set_vars( subjid = \"id\", visit = \"visit\", outcome = \"change\", group = \"group\", covariates = c(\"visit*outcome_bl\", \"visit*group\", \"time_from_ice1*group\"), strategy = \"strategy\" ) draw_obj <- draws( data = data, data_ice = NULL, # if NULL, MAR is assumed for all missing data vars = vars_tv, method = method, quiet = TRUE ) impute_obj_tv <- impute( draw_obj, references = c(\"Control\" = \"Control\", \"Intervention\" = \"Intervention\") ) ana_obj_tv <- analyse( impute_obj_tv, vars = vars_an ) pool(ana_obj_tv) #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_1 -0.492 0.515 -1.507 0.524 0.341 #> lsm_ref_1 2.623 0.362 1.908 3.338 <0.001 #> lsm_alt_1 2.131 0.366 1.409 2.854 <0.001 #> trt_2 0.018 0.55 -1.067 1.103 0.974 #> lsm_ref_2 3.697 0.382 2.943 4.45 <0.001 #> lsm_alt_2 3.715 0.394 2.936 4.493 <0.001 #> trt_3 -1.802 0.614 -3.015 -0.59 0.004 #> lsm_ref_3 5.815 0.429 4.97 6.661 <0.001 #> lsm_alt_3 4.013 0.441 3.142 4.884 <0.001 #> trt_4 -2.543 0.704 -3.932 -1.154 <0.001 #> lsm_ref_4 7.609 0.486 6.65 8.568 <0.001 #> lsm_alt_4 5.066 0.516 4.046 6.086 <0.001 #> trt_5 -3.739 0.879 -5.475 -2.004 <0.001 #> lsm_ref_5 9.499 0.606 8.302 10.695 <0.001 #> lsm_alt_5 5.759 0.636 4.502 7.017 <0.001 #> trt_6 -4.685 0.98 -6.622 -2.748 <0.001 #> lsm_ref_6 10.988 0.667 9.67 12.305 <0.001 #> lsm_alt_6 6.302 0.712 4.894 7.711 <0.001 #> --------------------------------------------------"},{"path":"/articles/advanced.html","id":"custom-imputation-strategies","dir":"Articles","previous_headings":"","what":"Custom imputation strategies","title":"rbmi: Advanced Functionality","text":"following imputation strategies implemented rbmi: Missing Random (MAR) Jump Reference (JR) Copy Reference (CR) Copy Increments Reference (CIR) Last Mean Carried Forward (LMCF) addition, rbmi allows user implement imputation strategy. , user needs three things: Define function implementing new imputation strategy. Specify patients use strategy data_ice dataset provided draws(). Provide imputation strategy function impute(). imputation strategy function must take 3 arguments (pars_group, pars_ref, index_mar) calculates mean covariance matrix subject’s marginal imputation distribution applied subjects strategy applies. , pars_group contains predicted mean trajectory (pars_group$mu, numeric vector) covariance matrix (pars_group$sigma) subject conditional assigned treatment group covariates. pars_ref contains corresponding mean trajectory covariance matrix conditional reference group subject’s covariates. index_mar logical vector specifies visit whether visit unaffected ICE handled using non-MAR method . example, user can check CIR strategy implemented looking function strategy_CIR(). illustrate simple example, assume new strategy implemented follows: - marginal mean imputation distribution equal marginal mean trajectory subject according assigned group covariates ICE. - ICE marginal mean imputation distribution equal average visit-wise marginal means based subjects covariates assigned group reference group, respectively. - covariance matrix marginal imputation distribution, covariance matrix assigned group taken. , first need define imputation function example coded follows: example showing use: incorporate rbmi, data_ice needs updated strategy AVG specified visits affected ICE. Additionally, function needs provided impute() via getStrategies() function shown : , analysis proceed calling analyse() pool() .","code":"strategy_CIR #> function (pars_group, pars_ref, index_mar) #> { #> if (all(index_mar)) { #> return(pars_group) #> } #> else if (all(!index_mar)) { #> return(pars_ref) #> } #> mu <- pars_group$mu #> last_mar <- which(!index_mar)[1] - 1 #> increments_from_last_mar_ref <- pars_ref$mu[!index_mar] - #> pars_ref$mu[last_mar] #> mu[!index_mar] <- mu[last_mar] + increments_from_last_mar_ref #> sigma <- compute_sigma(sigma_group = pars_group$sigma, sigma_ref = pars_ref$sigma, #> index_mar = index_mar) #> pars <- list(mu = mu, sigma = sigma) #> return(pars) #> } #> #> strategy_AVG <- function(pars_group, pars_ref, index_mar) { mu_mean <- (pars_group$mu + pars_ref$mu) / 2 x <- pars_group x$mu[!index_mar] <- mu_mean[!index_mar] return(x) } pars_group <- list( mu = c(1, 2, 3), sigma = as_vcov(c(1, 3, 2), c(0.4, 0.5, 0.45)) ) pars_ref <- list( mu = c(5, 6, 7), sigma = as_vcov(c(2, 1, 1), c(0.7, 0.8, 0.5)) ) index_mar <- c(TRUE, TRUE, FALSE) strategy_AVG(pars_group, pars_ref, index_mar) #> $mu #> [1] 1 2 5 #> #> $sigma #> [,1] [,2] [,3] #> [1,] 1.0 1.2 1.0 #> [2,] 1.2 9.0 2.7 #> [3,] 1.0 2.7 4.0 data_ice_AVG <- data_ice_CIR %>% mutate(strategy = ifelse(strategy == \"CIR\", \"AVG\", strategy)) draw_obj <- draws( data = data, data_ice = data_ice_AVG, vars = vars, method = method, quiet = TRUE ) impute_obj <- impute( draw_obj, references = c(\"Control\" = \"Control\", \"Intervention\" = \"Control\"), strategies = getStrategies(AVG = strategy_AVG) )"},{"path":"/articles/advanced.html","id":"custom-analysis-functions","dir":"Articles","previous_headings":"","what":"Custom analysis functions","title":"rbmi: Advanced Functionality","text":"default rbmi analyse data using ancova() function. analysis function fits ANCOVA model outcomes visit separately, returns “treatment effect” estimate well corresponding least square means group. user wants perform different analysis, return different statistics analysis, can done using custom analysis function. Beware validity conditional mean imputation method formally established analysis functions corresponding linear models (ANCOVA) caution required applying alternative analysis functions method. custom analysis function must take data.frame first argument return named list element list containing minimum point estimate, called est. method method_bayes() method_approxbayes(), list must additionally contain standard error (element se) , available, degrees freedom complete-data analysis model (element df). simple example, replicate ANCOVA analysis last visit CIR-based imputations user-defined analysis function : second example, assume supplementary analysis user wants compare proportion subjects change baseline >10 points last visit treatment groups baseline outcome additional covariate. lead following basic analysis function: Note user wants rbmi use normal approximation pooled test statistics, degrees freedom need set df = NA (per example). degrees freedom complete data test statistics known degrees freedom set df = Inf, rbmi pools degrees freedom across imputed datasets according rule Barnard Rubin (see “Statistical Specifications” vignette (vignette(\"stat_specs\", package = \"rbmi\") details). According rule, infinite degrees freedom complete data analysis imply pooled degrees freedom also infinite. Rather, case pooled degrees freedom (M-1)/lambda^2, M number imputations lambda fraction missing information (see Barnard Rubin (1999) details).","code":"compare_change_lastvisit <- function(data, ...) { fit <- lm(change ~ group + outcome_bl, data = data, subset = (visit == 6) ) res <- list( trt = list( est = coef(fit)[\"groupIntervention\"], se = sqrt(vcov(fit)[\"groupIntervention\", \"groupIntervention\"]), df = df.residual(fit) ) ) return(res) } ana_obj_CIR6 <- analyse( impute_obj_CIR, fun = compare_change_lastvisit, vars = vars_an ) pool(ana_obj_CIR6) #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================= #> parameter est se lci uci pval #> ------------------------------------------------- #> trt -4.537 0.954 -6.42 -2.655 <0.001 #> ------------------------------------------------- compare_prop_lastvisit <- function(data, ...) { fit <- glm( I(change > 10) ~ group + outcome_bl, family = binomial(), data = data, subset = (visit == 6) ) res <- list( trt = list( est = coef(fit)[\"groupIntervention\"], se = sqrt(vcov(fit)[\"groupIntervention\", \"groupIntervention\"]), df = NA ) ) return(res) } ana_obj_prop <- analyse( impute_obj_CIR, fun = compare_prop_lastvisit, vars = vars_an ) pool_obj_prop <- pool(ana_obj_prop) pool_obj_prop #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================= #> parameter est se lci uci pval #> ------------------------------------------------- #> trt -1.052 0.314 -1.667 -0.438 0.001 #> ------------------------------------------------- tmp <- as.data.frame(pool_obj_prop) %>% mutate( OR = exp(est), OR.lci = exp(lci), OR.uci = exp(uci) ) %>% select(parameter, OR, OR.lci, OR.uci) tmp #> parameter OR OR.lci OR.uci #> 1 trt 0.3491078 0.188807 0.6455073"},{"path":"/articles/advanced.html","id":"sensitivity-analyses-delta-adjustments-and-tipping-point-analyses","dir":"Articles","previous_headings":"","what":"Sensitivity analyses: Delta adjustments and tipping point analyses","title":"rbmi: Advanced Functionality","text":"Delta-adjustments used impute missing data missing random (NMAR) assumption. reflects belief unobserved outcomes systematically “worse” (“better”) “comparable” observed outcomes. extensive discussion delta-adjustment methods, refer Cro et al. (2020). rbmi, marginal delta-adjustment approach implemented. means delta-adjustment applied dataset data imputation MAR reference-based missing data assumptions prior analysis imputed data. Sensitivity analysis using delta-adjustments can therefore performed without re-fit imputation model. rbmi, implemented via delta argument analyse() function.","code":""},{"path":"/articles/advanced.html","id":"simple-delta-adjustments-and-tipping-point-analyses","dir":"Articles","previous_headings":"8 Sensitivity analyses: Delta adjustments and tipping point analyses","what":"Simple delta adjustments and tipping point analyses","title":"rbmi: Advanced Functionality","text":"delta argument analyse() allows users modify outcome variable prior analysis. , user needs provide data.frame contains columns subject visit (identify observation adjusted) plus additional column called delta specifies value added outcomes prior analysis. delta_template() function supports user creating data.frame: creates skeleton data.frame containing one row per subject visit value delta set 0 observations: Note output delta_template() contains additional information can used properly re-set variable delta. example, assume user wants implement delta-adjustment imputed values CIR described section 3. Specifically, assume fixed “worsening adjustment” +5 points applied imputed values regardless treatment group. programmed follows: approach can used implement tipping point analysis. , apply different delta-adjustments imputed data control intervention group, respectively. Assume delta-adjustments less -5 points +15 points considered implausible clinical perspective. Therefore, vary delta-values group -5 +15 points investigate delta combinations lead “tipping” primary analysis result, defined analysis p-value \\(\\geq 0.05\\). According analysis, significant test result primary analysis CIR tipped non-significant result rather extreme delta-adjustments. Please note real analysis recommended use smaller step size grid used .","code":"dat_delta <- delta_template(imputations = impute_obj_CIR) head(dat_delta) #> id visit group is_mar is_missing is_post_ice strategy delta #> 1 id_1 1 Control TRUE TRUE TRUE MAR 0 #> 2 id_1 2 Control TRUE TRUE TRUE MAR 0 #> 3 id_1 3 Control TRUE TRUE TRUE MAR 0 #> 4 id_1 4 Control TRUE TRUE TRUE MAR 0 #> 5 id_1 5 Control TRUE TRUE TRUE MAR 0 #> 6 id_1 6 Control TRUE TRUE TRUE MAR 0 # Set delta-value to 5 for all imputed (previously missing) outcomes and 0 for all other outcomes dat_delta <- delta_template(imputations = impute_obj_CIR) %>% mutate(delta = is_missing * 5) # Repeat the analyses with the delta-adjusted values and pool results ana_delta <- analyse( impute_obj_CIR, delta = dat_delta, vars = vars_an ) pool(ana_delta) #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_1 -0.482 0.524 -1.516 0.552 0.359 #> lsm_ref_1 2.718 0.37 1.987 3.448 <0.001 #> lsm_alt_1 2.235 0.37 1.505 2.966 <0.001 #> trt_2 -0.016 0.56 -1.12 1.089 0.978 #> lsm_ref_2 3.907 0.396 3.125 4.688 <0.001 #> lsm_alt_2 3.891 0.395 3.111 4.671 <0.001 #> trt_3 -1.684 0.641 -2.948 -0.42 0.009 #> lsm_ref_3 6.092 0.452 5.201 6.983 <0.001 #> lsm_alt_3 4.408 0.452 3.515 5.3 <0.001 #> trt_4 -2.359 0.741 -3.821 -0.897 0.002 #> lsm_ref_4 7.951 0.526 6.913 8.99 <0.001 #> lsm_alt_4 5.593 0.522 4.563 6.623 <0.001 #> trt_5 -3.34 0.919 -5.153 -1.526 <0.001 #> lsm_ref_5 9.899 0.643 8.631 11.168 <0.001 #> lsm_alt_5 6.559 0.653 5.271 7.848 <0.001 #> trt_6 -4.21 1.026 -6.236 -2.184 <0.001 #> lsm_ref_6 11.435 0.718 10.019 12.851 <0.001 #> lsm_alt_6 7.225 0.725 5.793 8.656 <0.001 #> -------------------------------------------------- perform_tipp_analysis <- function(delta_control, delta_intervention) { # Derive delta offset based on control and intervention specific deltas delta_df <- delta_df_init %>% mutate( delta_ctl = (group == \"Control\") * is_missing * delta_control, delta_int = (group == \"Intervention\") * is_missing * delta_intervention, delta = delta_ctl + delta_int ) ana_delta <- analyse( impute_obj_CIR, fun = compare_change_lastvisit, vars = vars_an, delta = delta_df, ) pool_delta <- as.data.frame(pool(ana_delta)) list( trt_effect_6 = pool_delta[[\"est\"]], pval_6 = pool_delta[[\"pval\"]] ) } # Get initial delta template delta_df_init <- delta_template(impute_obj_CIR) tipp_frame_grid <- expand.grid( delta_control = seq(-5, 15, by = 2), delta_intervention = seq(-5, 15, by = 2) ) %>% as_tibble() tipp_frame <- tipp_frame_grid %>% mutate( results_list = map2(delta_control, delta_intervention, perform_tipp_analysis), trt_effect_6 = map_dbl(results_list, \"trt_effect_6\"), pval_6 = map_dbl(results_list, \"pval_6\") ) %>% select(-results_list) %>% mutate( pval = cut( pval_6, c(0, 0.001, 0.01, 0.05, 0.2, 1), right = FALSE, labels = c(\"<0.001\", \"0.001 - <0.01\", \"0.01- <0.05\", \"0.05 - <0.20\", \">= 0.20\") ) ) # Show delta values which lead to non-significant analysis results tipp_frame %>% filter(pval_6 >= 0.05) #> # A tibble: 3 × 5 #> delta_control delta_intervention trt_effect_6 pval_6 pval #> #> 1 -5 15 -1.99 0.0935 0.05 - <0.20 #> 2 -3 15 -2.15 0.0704 0.05 - <0.20 #> 3 -1 15 -2.31 0.0527 0.05 - <0.20 ggplot(tipp_frame, aes(delta_control, delta_intervention, fill = pval)) + geom_raster() + scale_fill_manual(values = c(\"darkgreen\", \"lightgreen\", \"lightyellow\", \"orange\", \"red\"))"},{"path":"/articles/advanced.html","id":"more-flexible-delta-adjustments-using-the-dlag-and-delta-arguments-of-delta_template","dir":"Articles","previous_headings":"8 Sensitivity analyses: Delta adjustments and tipping point analyses","what":"More flexible delta-adjustments using the dlag and delta arguments of delta_template()","title":"rbmi: Advanced Functionality","text":"far, discussed simple delta arguments add value imputed values. However, user may want apply flexible delta-adjustments missing values intercurrent event (ICE) vary magnitude delta adjustment depending far away visit question ICE visit. facilitate creation flexible delta-adjustments, delta_template() function two optional additional arguments delta dlag. delta argument specifies default amount delta applied post-ICE visit, whilst dlag specifies scaling coefficient applied based upon visits proximity first visit affected ICE. default, delta added unobserved (.e. imputed) post-ICE outcomes can changed setting optional argument missing_only = FALSE. usage delta dlag arguments best illustrated examples: Assume setting 4 visits user specified delta = c(5,6,7,8) dlag=c(1,2,3,4). subject first visit affected ICE visit 2, values delta dlag imply following delta offset: , subject delta offset 0 applied visit v1, 6 visit v2, 20 visit v3 44 visit v4. Assume instead, subject’s first visit affected ICE visit 3. , values delta dlag imply following delta offset: apply constant delta value +5 visits affected ICE regardless proximity first ICE visit, one set delta = c(5,5,5,5) dlag = c(1,0,0,0). Alternatively, may straightforward setting call delta_template() function without delta dlag arguments overwrite delta column resulting data.frame described previous section (additionally relying is_post_ice variable). Another way using arguments set delta difference time visits dlag amount delta per unit time. example, let’s say visits occur weeks 1, 5, 6 9 want delta 3 applied week ICE. simplicity, assume ICE occurs immediately subject’s last visit affected ICE. achieved setting delta = c(1,4,1,3) (difference weeks visit) dlag = c(3, 3, 3, 3). Assume subject’s first visit affected ICE visit v2, values delta dlag imply following delta offsets: wrap , show action simulated dataset section 2 imputed datasets based CIR assumption section 3. simulation setting specified follow-visits months 2, 4, 6, 8, 10, 12. Assume want apply delta-adjustment 1 every month ICE unobserved post-ICE visits intervention group . (E.g. ICE occurred immediately month 4 visit, total delta applied missing value month 10 visit 6.) program , first use delta dlag arguments delta_template() set corresponding template data.frame: Next, can use additional metadata variables provided delta_template() manually reset delta values control group back 0: Finally, can use delta data.frame apply desired delta offset analysis:","code":"v1 v2 v3 v4 -------------- 5 6 7 8 # delta assigned to each visit 0 1 2 3 # scaling starting from the first visit after the subjects ICE -------------- 0 6 14 24 # delta * scaling -------------- 0 6 20 44 # cumulative sum (i.e. delta) to be applied to each visit v1 v2 v3 v4 -------------- 5 6 7 8 # delta assigned to each visit 0 0 1 2 # scaling starting from the first visit after the subjects ICE -------------- 0 0 7 16 # delta * scaling -------------- 0 0 7 23 # cumulative sum (i.e. delta) to be applied to each visit v1 v2 v3 v4 -------------- 1 4 1 3 # delta assigned to each visit 0 3 3 3 # scaling starting from the first visit after the subjects ICE -------------- 0 12 3 9 # delta * scaling -------------- 0 12 15 24 # cumulative sum (i.e. delta) to be applied to each visit delta_df <- delta_template( impute_obj_CIR, delta = c(2, 2, 2, 2, 2, 2), dlag = c(1, 1, 1, 1, 1, 1) ) head(delta_df) #> id visit group is_mar is_missing is_post_ice strategy delta #> 1 id_1 1 Control TRUE TRUE TRUE MAR 2 #> 2 id_1 2 Control TRUE TRUE TRUE MAR 4 #> 3 id_1 3 Control TRUE TRUE TRUE MAR 6 #> 4 id_1 4 Control TRUE TRUE TRUE MAR 8 #> 5 id_1 5 Control TRUE TRUE TRUE MAR 10 #> 6 id_1 6 Control TRUE TRUE TRUE MAR 12 delta_df2 <- delta_df %>% mutate(delta = if_else(group == \"Control\", 0, delta)) head(delta_df2) #> id visit group is_mar is_missing is_post_ice strategy delta #> 1 id_1 1 Control TRUE TRUE TRUE MAR 0 #> 2 id_1 2 Control TRUE TRUE TRUE MAR 0 #> 3 id_1 3 Control TRUE TRUE TRUE MAR 0 #> 4 id_1 4 Control TRUE TRUE TRUE MAR 0 #> 5 id_1 5 Control TRUE TRUE TRUE MAR 0 #> 6 id_1 6 Control TRUE TRUE TRUE MAR 0 ana_delta <- analyse(impute_obj_CIR, delta = delta_df2, vars = vars_an) pool(ana_delta) #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_1 -0.446 0.514 -1.459 0.567 0.386 #> lsm_ref_1 2.62 0.363 1.904 3.335 <0.001 #> lsm_alt_1 2.173 0.363 1.458 2.889 <0.001 #> trt_2 0.072 0.546 -1.006 1.15 0.895 #> lsm_ref_2 3.708 0.387 2.945 4.471 <0.001 #> lsm_alt_2 3.78 0.386 3.018 4.542 <0.001 #> trt_3 -1.507 0.626 -2.743 -0.272 0.017 #> lsm_ref_3 5.844 0.441 4.973 6.714 <0.001 #> lsm_alt_3 4.336 0.442 3.464 5.209 <0.001 #> trt_4 -2.062 0.731 -3.504 -0.621 0.005 #> lsm_ref_4 7.658 0.519 6.634 8.682 <0.001 #> lsm_alt_4 5.596 0.515 4.58 6.612 <0.001 #> trt_5 -2.938 0.916 -4.746 -1.13 0.002 #> lsm_ref_5 9.558 0.641 8.293 10.823 <0.001 #> lsm_alt_5 6.62 0.651 5.335 7.905 <0.001 #> trt_6 -3.53 1.045 -5.591 -1.469 0.001 #> lsm_ref_6 11.045 0.73 9.604 12.486 <0.001 #> lsm_alt_6 7.515 0.738 6.058 8.971 <0.001 #> --------------------------------------------------"},{"path":[]},{"path":"/articles/quickstart.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"rbmi: Quickstart","text":"purpose vignette provide 15 minute quickstart guide core functions rbmi package. rbmi package consists 4 core functions (plus several helper functions) typically called sequence: draws() - fits imputation models stores parameters impute() - creates multiple imputed datasets analyse() - analyses multiple imputed datasets pool() - combines analysis results across imputed datasets single statistic","code":""},{"path":"/articles/quickstart.html","id":"the-data","dir":"Articles","previous_headings":"","what":"The Data","title":"rbmi: Quickstart","text":"use publicly available example dataset antidepressant clinical trial active drug versus placebo. relevant endpoint Hamilton 17-item depression rating scale (HAMD17) assessed baseline weeks 1, 2, 4, 6. Study drug discontinuation occurred 24% subjects active drug 26% subjects placebo. data study drug discontinuation missing single additional intermittent missing observation. consider imputation model mean change baseline HAMD17 score outcome (variable CHANGE dataset). following covariates included imputation model: treatment group (THERAPY), (categorical) visit (VISIT), treatment--visit interactions, baseline HAMD17 score (BASVAL), baseline HAMD17 score--visit interactions. common unstructured covariance matrix structure assumed groups. analysis model ANCOVA model treatment group primary factor adjustment baseline HAMD17 score. rbmi expects input dataset complete; , must one row per subject visit. Missing outcome values coded NA, missing covariate values allowed. dataset incomplete, expand_locf() helper function can used add missing rows, using LOCF imputation carry forward observed baseline covariate values visits missing outcomes. Rows corresponding missing outcomes present antidepressant trial dataset. address therefore use expand_locf() function follows:","code":"library(rbmi) library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union data(\"antidepressant_data\") dat <- antidepressant_data # Use expand_locf to add rows corresponding to visits with missing outcomes to the dataset dat <- expand_locf( dat, PATIENT = levels(dat$PATIENT), # expand by PATIENT and VISIT VISIT = levels(dat$VISIT), vars = c(\"BASVAL\", \"THERAPY\"), # fill with LOCF BASVAL and THERAPY group = c(\"PATIENT\"), order = c(\"PATIENT\", \"VISIT\") )"},{"path":"/articles/quickstart.html","id":"draws","dir":"Articles","previous_headings":"","what":"Draws","title":"rbmi: Quickstart","text":"draws() function fits imputation models stores corresponding parameter estimates Bayesian posterior parameter draws. three main inputs draws() function : data - primary longitudinal data.frame containing outcome variable covariates. data_ice - data.frame specifies first visit affected intercurrent event (ICE) imputation strategy handling missing outcome data ICE. one ICE imputed non-MAR strategy allowed per subject. method - statistical method used fit imputation models create imputed datasets. antidepressant trial data, dataset data_ice provided. However, can derived , dataset, subject’s first visit affected ICE “study drug discontinuation” corresponds first terminal missing observation. first derive dateset data_ice create 150 Bayesian posterior draws imputation model parameters. example, assume imputation strategy ICE Jump Reference (JR) subjects 150 multiple imputed datasets using Bayesian posterior draws imputation model created. Note use set_vars() specifies names key variables within dataset imputation model. Additionally, note whilst vars$group vars$visit added terms imputation model default, interaction , thus inclusion group * visit list covariates. Available imputation methods include: Bayesian multiple imputation - method_bayes() Approximate Bayesian multiple imputation - method_approxbayes() Conditional mean imputation (bootstrap) - method_condmean(type = \"bootstrap\") Conditional mean imputation (jackknife) - method_condmean(type = \"jackknife\") Bootstrapped multiple imputation - method = method_bmlmi() comparison methods, refer stat_specs vignette (Section 3.10). “statistical specifications” vignette (Section 3.10): vignette(\"stat_specs\",package=\"rbmi\"). Available imputation strategies include: Missing Random - \"MAR\" Jump Reference - \"JR\" Copy Reference - \"CR\" Copy Increments Reference - \"CIR\" Last Mean Carried Forward - \"LMCF\"","code":"# create data_ice and set the imputation strategy to JR for # each patient with at least one missing observation dat_ice <- dat %>% arrange(PATIENT, VISIT) %>% filter(is.na(CHANGE)) %>% group_by(PATIENT) %>% slice(1) %>% ungroup() %>% select(PATIENT, VISIT) %>% mutate(strategy = \"JR\") # In this dataset, subject 3618 has an intermittent missing values which does not correspond # to a study drug discontinuation. We therefore remove this subject from `dat_ice`. # (In the later imputation step, it will automatically be imputed under the default MAR assumption.) dat_ice <- dat_ice[-which(dat_ice$PATIENT == 3618),] dat_ice #> # A tibble: 43 × 3 #> PATIENT VISIT strategy #> #> 1 1513 5 JR #> 2 1514 5 JR #> 3 1517 5 JR #> 4 1804 7 JR #> 5 2104 7 JR #> 6 2118 5 JR #> 7 2218 6 JR #> 8 2230 6 JR #> 9 2721 5 JR #> 10 2729 5 JR #> # ℹ 33 more rows # Define the names of key variables in our dataset and # the covariates included in the imputation model using `set_vars()` # Note that the covariates argument can also include interaction terms vars <- set_vars( outcome = \"CHANGE\", visit = \"VISIT\", subjid = \"PATIENT\", group = \"THERAPY\", covariates = c(\"BASVAL*VISIT\", \"THERAPY*VISIT\") ) # Define which imputation method to use (here: Bayesian multiple imputation with 150 imputed datsets) method <- method_bayes( burn_in = 200, burn_between = 5, n_samples = 150, seed = 675442751 ) # Create samples for the imputation parameters by running the draws() function set.seed(987) drawObj <- draws( data = dat, data_ice = dat_ice, vars = vars, method = method, quiet = TRUE ) drawObj #> #> Draws Object #> ------------ #> Number of Samples: 150 #> Number of Failed Samples: 0 #> Model Formula: CHANGE ~ 1 + THERAPY + VISIT + BASVAL * VISIT + THERAPY * VISIT #> Imputation Type: random #> Method: #> name: Bayes #> burn_in: 200 #> burn_between: 5 #> same_cov: TRUE #> n_samples: 150 #> seed: 675442751"},{"path":"/articles/quickstart.html","id":"impute","dir":"Articles","previous_headings":"","what":"Impute","title":"rbmi: Quickstart","text":"next step use parameters imputation model generate imputed datasets. done via impute() function. function two key inputs: imputation model output draws() reference groups relevant reference-based imputation methods. ’s usage thus: instance, specifying PLACEBO group reference group well DRUG group (standard imputation using reference-based methods). Generally speaking, need see directly interact imputed datasets. However, wish inspect , can extracted imputation object using extract_imputed_dfs() helper function, .e.: Note case method_bayes() method_approxbayes(), imputed datasets correspond random imputations original dataset. method_condmean(), first imputed dataset always correspond completed original dataset containing subjects. method_condmean(type=\"jackknife\"), remaining datasets correspond conditional mean imputations leave-one-subject-datasets, whereas method_condmean(type=\"bootstrap\"), subsequent dataset corresponds conditional mean imputation bootstrapped datasets. method_bmlmi(), imputed datasets correspond sets random imputations bootstrapped datasets.","code":"imputeObj <- impute( drawObj, references = c(\"DRUG\" = \"PLACEBO\", \"PLACEBO\" = \"PLACEBO\") ) imputeObj #> #> Imputation Object #> ----------------- #> Number of Imputed Datasets: 150 #> Fraction of Missing Data (Original Dataset): #> 4: 0% #> 5: 8% #> 6: 13% #> 7: 25% #> References: #> DRUG -> PLACEBO #> PLACEBO -> PLACEBO imputed_dfs <- extract_imputed_dfs(imputeObj) head(imputed_dfs[[10]], 12) # first 12 rows of 10th imputed dataset #> PATIENT HAMATOTL PGIIMP RELDAYS VISIT THERAPY GENDER POOLINV BASVAL #> 1 new_pt_1 21 2 7 4 DRUG F 006 32 #> 2 new_pt_1 19 2 14 5 DRUG F 006 32 #> 3 new_pt_1 21 3 28 6 DRUG F 006 32 #> 4 new_pt_1 17 4 42 7 DRUG F 006 32 #> 5 new_pt_2 18 3 7 4 PLACEBO F 006 14 #> 6 new_pt_2 18 2 15 5 PLACEBO F 006 14 #> 7 new_pt_2 14 3 29 6 PLACEBO F 006 14 #> 8 new_pt_2 8 2 42 7 PLACEBO F 006 14 #> 9 new_pt_3 18 3 7 4 DRUG F 006 21 #> 10 new_pt_3 17 3 14 5 DRUG F 006 21 #> 11 new_pt_3 12 3 28 6 DRUG F 006 21 #> 12 new_pt_3 9 3 44 7 DRUG F 006 21 #> HAMDTL17 CHANGE #> 1 21 -11 #> 2 20 -12 #> 3 19 -13 #> 4 17 -15 #> 5 11 -3 #> 6 14 0 #> 7 9 -5 #> 8 5 -9 #> 9 20 -1 #> 10 18 -3 #> 11 16 -5 #> 12 13 -8"},{"path":"/articles/quickstart.html","id":"analyse","dir":"Articles","previous_headings":"","what":"Analyse","title":"rbmi: Quickstart","text":"next step run analysis model imputed dataset. done defining analysis function calling analyse() apply function imputed dataset. vignette use ancova() function provided rbmi package fits separate ANCOVA model outcomes visit returns treatment effect estimate corresponding least square means group per visit. Note , similar draws(), ancova() function uses set_vars() function determines names key variables within data covariates (addition treatment group) analysis model adjusted. Please also note names analysis estimates contain “ref” “alt” refer two treatment arms. particular “ref” refers first factor level vars$group necessarily coincide control arm. example, since levels(dat[[vars$group]]) = c(\"DRUG\", PLACEBO), results associated “ref” correspond intervention arm, associated “alt” correspond control arm. Additionally, can use delta argument analyse() perform delta adjustments imputed datasets prior analysis. brief, implemented specifying data.frame contains amount adjustment added longitudinal outcome subject visit, .e.  data.frame must contain columns subjid, visit, delta. appreciated carrying procedure potentially tedious, therefore delta_template() helper function provided simplify . particular, delta_template() returns shell data.frame delta-adjustment set 0 patients. Additionally delta_template() adds several meta-variables onto shell data.frame can used manual derivation manipulation delta-adjustment. example lets say want add delta-value 5 imputed values (.e. values missing original dataset) drug arm. implemented follows:","code":"anaObj <- analyse( imputeObj, ancova, vars = set_vars( subjid = \"PATIENT\", outcome = \"CHANGE\", visit = \"VISIT\", group = \"THERAPY\", covariates = c(\"BASVAL\") ) ) anaObj #> #> Analysis Object #> --------------- #> Number of Results: 150 #> Analysis Function: ancova #> Delta Applied: FALSE #> Analysis Estimates: #> trt_4 #> lsm_ref_4 #> lsm_alt_4 #> trt_5 #> lsm_ref_5 #> lsm_alt_5 #> trt_6 #> lsm_ref_6 #> lsm_alt_6 #> trt_7 #> lsm_ref_7 #> lsm_alt_7 # For reference show the additional meta variables provided delta_template(imputeObj) %>% as_tibble() #> # A tibble: 688 × 8 #> PATIENT VISIT THERAPY is_mar is_missing is_post_ice strategy delta #> #> 1 1503 4 DRUG TRUE FALSE FALSE NA 0 #> 2 1503 5 DRUG TRUE FALSE FALSE NA 0 #> 3 1503 6 DRUG TRUE FALSE FALSE NA 0 #> 4 1503 7 DRUG TRUE FALSE FALSE NA 0 #> 5 1507 4 PLACEBO TRUE FALSE FALSE NA 0 #> 6 1507 5 PLACEBO TRUE FALSE FALSE NA 0 #> 7 1507 6 PLACEBO TRUE FALSE FALSE NA 0 #> 8 1507 7 PLACEBO TRUE FALSE FALSE NA 0 #> 9 1509 4 DRUG TRUE FALSE FALSE NA 0 #> 10 1509 5 DRUG TRUE FALSE FALSE NA 0 #> # ℹ 678 more rows delta_df <- delta_template(imputeObj) %>% as_tibble() %>% mutate(delta = if_else(THERAPY == \"DRUG\" & is_missing , 5, 0)) %>% select(PATIENT, VISIT, delta) delta_df #> # A tibble: 688 × 3 #> PATIENT VISIT delta #> #> 1 1503 4 0 #> 2 1503 5 0 #> 3 1503 6 0 #> 4 1503 7 0 #> 5 1507 4 0 #> 6 1507 5 0 #> 7 1507 6 0 #> 8 1507 7 0 #> 9 1509 4 0 #> 10 1509 5 0 #> # ℹ 678 more rows anaObj_delta <- analyse( imputeObj, ancova, delta = delta_df, vars = set_vars( subjid = \"PATIENT\", outcome = \"CHANGE\", visit = \"VISIT\", group = \"THERAPY\", covariates = c(\"BASVAL\") ) )"},{"path":"/articles/quickstart.html","id":"pool","dir":"Articles","previous_headings":"","what":"Pool","title":"rbmi: Quickstart","text":"Finally, pool() function can used summarise analysis results across multiple imputed datasets provide overall statistic standard error, confidence intervals p-value hypothesis test null hypothesis effect equal 0. Note pooling method automatically derived based method specified original call draws(): method_bayes() method_approxbayes() pooling inference based Rubin’s rules. method_condmean(type = \"bootstrap\") inference either based normal approximation using bootstrap standard error (pool(..., type = \"normal\")) bootstrap percentiles (pool(..., type = \"percentile\")). method_condmean(type = \"jackknife\") inference based normal approximation using jackknife estimate standard error. method = method_bmlmi() inference according methods described von Hippel Bartlett (see stat_specs vignette details) Since used Bayesian multiple imputation vignette, pool() function automatically use Rubin’s rules. table values shown print message poolObj can also extracted using .data.frame() function: outputs gives estimated difference 2.079 (95% CI -0.138 4.296) two groups last visit associated p-value 0.066.","code":"poolObj <- pool( anaObj, conf.level = 0.95, alternative = \"two.sided\" ) poolObj #> #> Pool Object #> ----------- #> Number of Results Combined: 150 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_4 -0.092 0.683 -1.439 1.256 0.893 #> lsm_ref_4 -1.616 0.486 -2.576 -0.656 0.001 #> lsm_alt_4 -1.708 0.475 -2.645 -0.77 <0.001 #> trt_5 1.281 0.927 -0.55 3.112 0.169 #> lsm_ref_5 -4.112 0.661 -5.418 -2.807 <0.001 #> lsm_alt_5 -2.831 0.646 -4.107 -1.556 <0.001 #> trt_6 1.912 1.001 -0.066 3.89 0.058 #> lsm_ref_6 -6.097 0.714 -7.508 -4.686 <0.001 #> lsm_alt_6 -4.186 0.696 -5.561 -2.81 <0.001 #> trt_7 2.079 1.122 -0.138 4.296 0.066 #> lsm_ref_7 -6.946 0.815 -8.558 -5.335 <0.001 #> lsm_alt_7 -4.867 0.788 -6.426 -3.308 <0.001 #> -------------------------------------------------- as.data.frame(poolObj) #> parameter est se lci uci pval #> 1 trt_4 -0.09180645 0.6826279 -1.43949684 1.2558839 8.931772e-01 #> 2 lsm_ref_4 -1.61581996 0.4862316 -2.57577141 -0.6558685 1.093708e-03 #> 3 lsm_alt_4 -1.70762640 0.4749573 -2.64531931 -0.7699335 4.262148e-04 #> 4 trt_5 1.28107134 0.9269270 -0.54967136 3.1118141 1.689000e-01 #> 5 lsm_ref_5 -4.11245871 0.6608409 -5.41768364 -2.8072338 4.201381e-09 #> 6 lsm_alt_5 -2.83138737 0.6457744 -4.10686302 -1.5559117 2.114628e-05 #> 7 trt_6 1.91163968 1.0011368 -0.06637259 3.8896520 5.809419e-02 #> 8 lsm_ref_6 -6.09716631 0.7142461 -7.50839192 -4.6859407 1.384720e-14 #> 9 lsm_alt_6 -4.18552662 0.6963163 -5.56127560 -2.8097776 1.321956e-08 #> 10 trt_7 2.07945506 1.1216355 -0.13755657 4.2964667 6.579390e-02 #> 11 lsm_ref_7 -6.94648032 0.8150602 -8.55819661 -5.3347640 2.515736e-14 #> 12 lsm_alt_7 -4.86702525 0.7884953 -6.42588823 -3.3081623 6.801566e-09"},{"path":"/articles/quickstart.html","id":"code","dir":"Articles","previous_headings":"","what":"Code","title":"rbmi: Quickstart","text":"report code presented vignette.","code":"library(rbmi) library(dplyr) data(\"antidepressant_data\") dat <- antidepressant_data # Use expand_locf to add rows corresponding to visits with missing outcomes to the dataset dat <- expand_locf( dat, PATIENT = levels(dat$PATIENT), # expand by PATIENT and VISIT VISIT = levels(dat$VISIT), vars = c(\"BASVAL\", \"THERAPY\"), # fill with LOCF BASVAL and THERAPY group = c(\"PATIENT\"), order = c(\"PATIENT\", \"VISIT\") ) # Create data_ice and set the imputation strategy to JR for # each patient with at least one missing observation dat_ice <- dat %>% arrange(PATIENT, VISIT) %>% filter(is.na(CHANGE)) %>% group_by(PATIENT) %>% slice(1) %>% ungroup() %>% select(PATIENT, VISIT) %>% mutate(strategy = \"JR\") # In this dataset, subject 3618 has an intermittent missing values which does not correspond # to a study drug discontinuation. We therefore remove this subject from `dat_ice`. # (In the later imputation step, it will automatically be imputed under the default MAR assumption.) dat_ice <- dat_ice[-which(dat_ice$PATIENT == 3618),] # Define the names of key variables in our dataset using `set_vars()` # and the covariates included in the imputation model # Note that the covariates argument can also include interaction terms vars <- set_vars( outcome = \"CHANGE\", visit = \"VISIT\", subjid = \"PATIENT\", group = \"THERAPY\", covariates = c(\"BASVAL*VISIT\", \"THERAPY*VISIT\") ) # Define which imputation method to use (here: Bayesian multiple imputation with 150 imputed datsets) method <- method_bayes( burn_in = 200, burn_between = 5, n_samples = 150, seed = 675442751 ) # Create samples for the imputation parameters by running the draws() function set.seed(987) drawObj <- draws( data = dat, data_ice = dat_ice, vars = vars, method = method, quiet = TRUE ) # Impute the data imputeObj <- impute( drawObj, references = c(\"DRUG\" = \"PLACEBO\", \"PLACEBO\" = \"PLACEBO\") ) # Fit the analysis model on each imputed dataset anaObj <- analyse( imputeObj, ancova, vars = set_vars( subjid = \"PATIENT\", outcome = \"CHANGE\", visit = \"VISIT\", group = \"THERAPY\", covariates = c(\"BASVAL\") ) ) # Apply a delta adjustment # Add a delta-value of 5 to all imputed values (i.e. those values # which were missing in the original dataset) in the drug arm. delta_df <- delta_template(imputeObj) %>% as_tibble() %>% mutate(delta = if_else(THERAPY == \"DRUG\" & is_missing , 5, 0)) %>% select(PATIENT, VISIT, delta) # Repeat the analyses with the adjusted values anaObj_delta <- analyse( imputeObj, ancova, delta = delta_df, vars = set_vars( subjid = \"PATIENT\", outcome = \"CHANGE\", visit = \"VISIT\", group = \"THERAPY\", covariates = c(\"BASVAL\") ) ) # Pool the results poolObj <- pool( anaObj, conf.level = 0.95, alternative = \"two.sided\" )"},{"path":"/articles/stat_specs.html","id":"scope-of-this-document","dir":"Articles","previous_headings":"","what":"Scope of this document","title":"rbmi: Statistical Specifications","text":"document describes statistical methods implemented rbmi R package standard reference-based multiple imputation continuous longitudinal outcomes. package implements three classes multiple imputation (MI) approaches: Conventional MI methods based Bayesian (approximate Bayesian) posterior draws model parameters combined Rubin’s rules make inferences described Carpenter, Roger, Kenward (2013) Cro et al. (2020). Conditional mean imputation methods combined re-sampling techniques described Wolbers et al. (2022). Bootstrapped MI methods described von Hippel Bartlett (2021). document structured follows: first provide informal introduction estimands corresponding treatment effect estimation based MI (section 2). core document consists section 3 describes statistical methodology detail also contains comparison implemented approaches (section 3.10). link theory functions included package rbmi described section 4. conclude comparison package alternative software implementations reference-based imputation methods (section 5).","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"estimands","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods","what":"Estimands","title":"rbmi: Statistical Specifications","text":"ICH E9(R1) addendum estimands sensitivity analyses describes systematic approach ensure alignment among clinical trial objectives, trial execution/conduct, statistical analyses, interpretation results (ICH E9 working group (2019)). per addendum, estimand precise description treatment effect reflecting clinical question posed trial objective summarizes population-level outcomes patients different treatment conditions compared. One important attribute estimand list possible intercurrent events (ICEs), .e. events occurring treatment initiation affect either interpretation existence measurements associated clinical question interest, definition appropriate strategies deal ICEs. three relevant strategies purpose document hypothetical strategy, treatment policy strategy, composite strategy. hypothetical strategy, scenario envisaged ICE occur. scenario, endpoint values ICE directly observable treated using models missing data. treatment policy strategy, treatment effect presence ICEs targeted analyses based observed outcomes regardless whether subject ICE . composite strategy, ICE included component endpoint.","code":""},{"path":"/articles/stat_specs.html","id":"alignment-between-the-estimand-and-the-estimation-method","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods","what":"Alignment between the estimand and the estimation method","title":"rbmi: Statistical Specifications","text":"ICH E9(R1) addendum distinguishes ICEs missing data (ICH E9 working group (2019)). Whereas ICEs treatment discontinuations reflect clinical practice, amount missing data can minimized conduct clinical trial. However, many connections missing data ICEs. example, often difficult retain subjects clinical trial treatment discontinuation subject’s dropout trial leads missing data. another example, outcome values ICEs addressed using hypothetical strateg directly observable hypothetical scenario. Consequently, observed outcome values ICEs typically discarded treated missing data. addendum proposes estimation methods address problem presented missing data selected align estimand. recent overview methods align estimator estimand Mallinckrodt et al. (2020). short introduction estimation methods studies longitudinal endpoints can also found Wolbers et al. (2022). One prominent statistical method purpose multiple imputation (MI), target rbmi package.","code":""},{"path":"/articles/stat_specs.html","id":"missing-data-prior-to-ices","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods > 2.2 Alignment between the estimand and the estimation method","what":"Missing data prior to ICEs","title":"rbmi: Statistical Specifications","text":"Missing data may occur subjects without ICE prior occurrence ICE. missing outcomes associated ICE, often plausible impute missing--random (MAR) assumption using standard MMRM imputation model longitudinal outcomes. Informally, MAR occurs missing data can fully accounted baseline variables included model observed longitudinal outcomes, model correctly specified.","code":""},{"path":"/articles/stat_specs.html","id":"implementation-of-the-hypothetical-strategy","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods > 2.2 Alignment between the estimand and the estimation method","what":"Implementation of the hypothetical strategy","title":"rbmi: Statistical Specifications","text":"MAR imputation model described often also good starting point imputing data ICE handled using hypothetical strategy (Mallinckrodt et al. (2020)). Informally, assumes unobserved values ICE similar observed data subjects ICE remained follow-. However, situations, may reasonable assume missingness “informative” indicates systematically better worse outcome observed subjects. situations, MNAR imputation \\(\\delta\\)-adjustment explored sensitivity analysis. \\(\\delta\\)-adjustments add fixed random quantity imputations order make imputed outcomes systematically worse better observed described Cro et al. (2020). rbmi fixed \\(\\delta\\)-adjustments implemented.","code":""},{"path":"/articles/stat_specs.html","id":"implementation-of-the-treatment-policy-strategy","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods > 2.2 Alignment between the estimand and the estimation method","what":"Implementation of the treatment policy strategy","title":"rbmi: Statistical Specifications","text":"Ideally, data collection continues ICE handled treatment policy strategy missing data arises. Indeed, post-ICE data increasingly systematically collected RCTs. However, despite best efforts, missing data ICE study treatment discontinuation may still occur subject drops study discontinuation. difficult give definite recommendations regarding implementation treatment policy strategy presence missing data stage optimal method highly context dependent topic ongoing statistical research. ICEs thought negligible effect efficacy outcomes, standard MAR-based imputation may appropriate. contrast, ICE treatment discontinuation may expected substantial impact efficacy outcomes. settings, MAR assumption may still plausible conditioning subject’s time-varying treatment status (Guizzaro et al. (2021)). case, one option impute missing post-discontinuation data based subjects also discontinued treatment continued followed (Polverejan Dragalin (2020)). Another option may require somewhat less post-discontinuation data include subjects imputation procedure model post-discontinuation data using time-varying treatment status indicators (e.g. time-varying indicators treatment compliance, discontinuation, initiation rescue treatment) (Guizzaro et al. (2021)). approach, post-ICE outcomes included every step analysis, including fitting imputation model. assumes ICEs may impact post-ICE outcomes otherwise missingness non-informative. approach also assumes time-varying covariates contain missing values, deviations outcomes ICE correctly modeled time-varying covariates, sufficient post-ICE data available inform regression coefficients time-varying covariates. proposals relatively recent remain open questions regarding appropriate trade-model complexity (e.g. model account potentially differential effect post-ICE outcomes depending timing ICE?) variance resulting treatment effect estimate. generally, yet established much post-discontinuation data required implement methods robustly without risk substantial inflation variance. trial settings, subjects discontinue randomized treatment. settings, treatment discontinuation rates higher difficult retain subjects trial treatment discontinuation leading sparse data collection treatment discontinuation. settings, amount available data treatment discontinuation may insufficient inform imputation model explicitly models post-discontinuation data. Depending disease area anticipated mechanism action intervention, may plausible assume subjects intervention group behave similarly subjects control group ICE treatment discontinuation. case, reference-based imputation methods option (Mallinckrodt et al. (2020)). Reference-based imputation methods formalize idea impute missing data intervention group based data control reference group. general description review reference-based imputation methods, refer Carpenter, Roger, Kenward (2013), Cro et al. (2020), . White, Royes, Best (2020) Wolbers et al. (2022). technical description implemented statistical methodology reference-based imputation, refer section 3 (particular section 3.4).","code":""},{"path":"/articles/stat_specs.html","id":"implementation-of-the-composite-strategy","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods > 2.2 Alignment between the estimand and the estimation method","what":"Implementation of the composite strategy","title":"rbmi: Statistical Specifications","text":"composite strategy typically applied binary time--event outcomes can also used continuous outcomes ascribing suitably unfavorable value patients experience ICEs composite strategy defined. One possibility implement use MI \\(\\delta\\)-adjustment post-ICE data described Darken et al. (2020).","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"sec:methodsOverview","dir":"Articles","previous_headings":"3 Statistical methodology","what":"Overview of the imputation procedure","title":"rbmi: Statistical Specifications","text":"Analyses datasets missing data always rely missing data assumptions. methods described can used produce valid imputations MAR assumption reference-based imputation assumptions. MNAR imputation based fixed \\(\\delta\\)-adjustments typically used sensitivity analyses tipping-point analyses also supported. Three general imputation approaches implemented rbmi: Conventional MI based Bayesian (approximate Bayesian) posterior draws imputation model combined Rubin’s rules inference described Carpenter, Roger, Kenward (2013) Cro et al. (2020). Conditional mean imputation based REML estimate imputation model combined resampling techniques (jackknife bootstrap) inference described Wolbers et al. (2022). Bootstrapped MI methods based REML estimates imputation model described von Hippel Bartlett (2021).","code":""},{"path":"/articles/stat_specs.html","id":"conventional-mi","dir":"Articles","previous_headings":"3 Statistical methodology > 3.1 Overview of the imputation procedure","what":"Conventional MI","title":"rbmi: Statistical Specifications","text":"Conventional MI approaches include following steps: Base imputation model fitting step (Section 3.3) Fit Bayesian multivariate normal mixed model repeated measures (MMRM) observed longitudinal outcomes exclusion data ICEs reference-based missing data imputation desired (Section 3.3.3). Draw \\(M\\) posterior samples estimated parameters (regression coefficients covariance matrices) model. Alternatively, \\(M\\) approximate posterior draws posterior distribution can sampled repeatedly applying conventional restricted maximum-likelihood (REML) parameter estimation MMRM model nonparametric bootstrap samples original dataset (Section 3.3.4). Imputation step (Section 3.4) Take single sample \\(m\\) (\\(m\\1,\\ldots, M)\\) posterior distribution imputation model parameters. subject, use sampled parameters defined imputation strategy determine mean covariance matrix describing subject’s marginal outcome distribution longitudinal outcome assessments (.e. observed missing outcomes). subjects, construct conditional multivariate normal distribution missing outcomes given observed outcomes (including observed outcomes ICEs reference-based assumption desired). subject, draw single sample conditional distribution impute missing outcomes leading complete imputed dataset. sensitivity analyses, pre-defined \\(\\delta\\)-adjustment may applied imputed data prior analysis step. (Section 3.5). Analysis step (Section 3.6) Analyze imputed dataset using analysis model (e.g. ANCOVA) resulting point estimate standard error (corresponding degrees freedom) treatment effect. Pooling step inference (Section 3.7) Repeat steps 2. 3. posterior sample \\(m\\), resulting \\(M\\) complete datasets, \\(M\\) point estimates treatment effect, \\(M\\) standard errors (corresponding degrees freedom). Pool \\(M\\) treatment effect estimates, standard errors, degrees freedom using rules Barnard Rubin obtain final pooled treatment effect estimator, standard error, degrees freedom.","code":""},{"path":"/articles/stat_specs.html","id":"conditional-mean-imputation","dir":"Articles","previous_headings":"3 Statistical methodology > 3.1 Overview of the imputation procedure","what":"Conditional mean imputation","title":"rbmi: Statistical Specifications","text":"conditional mean imputation approach includes following steps: Base imputation model fitting step (Section 3.3) Fit conventional multivariate normal/MMRM model using restricted maximum likelihood (REML) observed longitudinal outcomes exclusion data ICEs reference-based missing data imputation desired (Section 3.3.2). Imputation step (Section 3.4) subject, use fitted parameters step 1. construct conditional distribution missing outcomes given observed outcomes (including observed outcomes ICEs reference-based missing data imputation desired) described . subject, impute missing data deterministically mean conditional distribution leading complete imputed dataset. sensitivity analyses, pre-defined \\(\\delta\\)-adjustment may applied imputed data prior analysis step. (Section 3.5). Analysis step (Section 3.6) Apply analysis model (e.g. ANCOVA) completed dataset resulting point estimate treatment effect. Jackknife bootstrap inference step (Section 3.8) Inference treatment effect estimate 3. based re-sampling techniques. jackknife bootstrap supported. Importantly, methods require repeating steps imputation procedure (.e. imputation, conditional mean imputation, analysis steps) resampled datasets.","code":""},{"path":"/articles/stat_specs.html","id":"bootstrapped-mi","dir":"Articles","previous_headings":"3 Statistical methodology > 3.1 Overview of the imputation procedure","what":"Bootstrapped MI","title":"rbmi: Statistical Specifications","text":"bootstrapped MI approach includes following steps: Base imputation model fitting step (Section 3.3) Apply conventional restricted maximum-likelihood (REML) parameter estimation MMRM model \\(B\\) nonparametric bootstrap samples original dataset using observed longitudinal outcomes exclusion data ICEs reference-based missing data imputation desired. Imputation step (Section 3.4) Take bootstrapped dataset \\(b\\) (\\(b\\1,\\ldots, B)\\) corresponding imputation model parameter estimates. subject (bootstrapped dataset), use parameter estimates defined strategy dealing ICEs determine mean covariance matrix describing subject’s marginal outcome distribution longitudinal outcome assessments (.e. observed missing outcomes). subjects (bootstrapped dataset), construct conditional multivariate normal distribution missing outcomes given observed outcomes (including observed outcomes ICEs reference-based missing data imputation desired). subject (bootstrapped dataset), draw \\(D\\) samples conditional distributions impute missing outcomes leading \\(D\\) complete imputed dataset bootstrap sample \\(b\\). sensitivity analyses, pre-defined \\(\\delta\\)-adjustment may applied imputed data prior analysis step. (Section 3.5). Analysis step (Section 3.6) Analyze \\(B\\times D\\) imputed datasets using analysis model (e.g. ANCOVA) resulting \\(B\\times D\\) point estimates treatment effect. Pooling step inference (Section 3.9) Pool \\(B\\times D\\) treatment effect estimates described von Hippel Bartlett (2021) obtain final pooled treatment effect estimate, standard error, degrees freedom.","code":""},{"path":"/articles/stat_specs.html","id":"setting-notation-and-missing-data-assumptions","dir":"Articles","previous_headings":"3 Statistical methodology","what":"Setting, notation, and missing data assumptions","title":"rbmi: Statistical Specifications","text":"Assume data study \\(n\\) subjects total subject \\(\\) (\\(=1,\\ldots,n\\)) \\(J\\) scheduled follow-visits outcome interest assessed. applications, data randomized trial intervention vs control group treatment effect interest comparison outcomes specific visit randomized groups. However, single-arm trials multi-arm trials principle also supported rbmi implementation. Denote observed outcome vector length \\(J\\) subject \\(\\) \\(Y_i\\) (missing assessments coded NA (available)) non-missing missing components \\(Y_{!}\\) \\(Y_{?}\\), respectively. default, imputation missing outcomes \\(Y_{}\\) performed MAR assumption rbmi. Therefore, missing data following ICE handled using MAR imputation, compatible default assumption. discussed Section 2, MAR assumption often good starting point implementing hypothetical strategy. also note observed outcome data ICE handled using hypothetical strategy compatible strategy. Therefore, assume post-ICE data ICEs handled using hypothetical strategy already set NA \\(Y_i\\) prior calling rbmi functions. However, observed outcomes ICEs handled using treatment policy strategy included \\(Y_i\\) compatible strategy. Subjects may also experience one ICE missing data imputation according reference-based imputation method foreseen. subject \\(\\) ICE, denote first visit affected ICE \\(\\tilde{t}_i \\\\{1,\\ldots,J\\}\\). subjects, set \\(\\tilde{t}_i=\\infty\\). subject’s outcome vector setting observed outcomes visit \\(\\tilde{t}_i\\) onwards missing (.e. NA) denoted \\(Y'_i\\) corresponding data vector removal NA elements \\(Y'_{!}\\). MNAR \\(\\delta\\)-adjustments added imputed datasets formal imputation steps. covered separate section (Section 3.5).","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"sec:imputationModelSpecs","dir":"Articles","previous_headings":"3 Statistical methodology > 3.3 The base imputation model","what":"Included data and model specification","title":"rbmi: Statistical Specifications","text":"purpose imputation model estimate (covariate-dependent) mean trajectories covariance matrices group absence ICEs handled using reference-based imputation methods. Conventionally, publications reference-based imputation methods implicitly assumed corresponding post-ICE data missing subjects (Carpenter, Roger, Kenward (2013)). also allow situation post-ICE data available subjects needs imputed using reference-based methods others. However, observed data ICEs reference-based imputation methods specified compatible imputation model described therefore removed considered missing purpose estimating imputation model, purpose . example, patient ICE addressed reference-based method outcomes ICE collected, post-ICE outcomes excluded fitting base imputation model (included following steps). , base imputation model fitted \\(Y'_{!}\\) \\(Y_{!}\\). exclude data, imputation model mistakenly estimate mean trajectories based mixture observed pre- post-ICE data relevant reference-based imputations. Observed post-ICE outcomes control reference group also excluded base imputation model user specifies reference-based imputation strategy ICEs. ensures ICE impact data included imputation model regardless whether ICE occurred control intervention group. hand, imputation reference group based MAR assumption even reference-based imputation methods may preferable settings include post-ICE data control group base imputation model. can implemented specifying MAR strategy ICE control group reference-based strategy ICE intervention group. base imputation model longitudinal outcomes \\(Y'_i\\) assumes mean structure linear function covariates. Full flexibility specification linear predictor model supported. minimum covariates include treatment group, (categorical) visit, treatment--visit interactions. Typically, covariates including baseline outcome also included. External time-varying covariates (e.g. calendar time visit) well internal time-varying (e.g. time-varying indicators treatment discontinuation initiation rescue treatment) may principle also included indicated (Guizzaro et al. (2021)). Missing covariate values allowed. means values time-varying covariates must non-missing every visit regardless whether outcome measured missing. Denote \\(J\\times p\\) design matrix subject \\(\\) corresponding mean structure model \\(X_i\\) matrix removal rows corresponding missing outcomes \\(Y'_{!}\\) \\(X'_{!}\\). \\(p\\) number parameters mean structure model elements \\(Y'_{!}\\). base imputation model observed outcomes defined : \\[ Y'_{!} = X'_{!}\\beta + \\epsilon_{!} \\mbox{ } \\epsilon_{!}\\sim N(0,\\Sigma_{!!})\\] \\(\\beta\\) vector regression coefficients \\(\\Sigma_{!!}\\) covariance matrix obtained complete-data \\(J\\times J\\)-covariance matrix \\(\\Sigma\\) omitting rows columns corresponding missing outcome assessments subject \\(\\). Typically, common unstructured covariance matrix subjects assumed \\(\\Sigma\\) separate covariate matrices per treatment group also supported. Indeed, implementation also supports specification separate covariate matrices according arbitrarily defined categorical variable groups subjects disjoint subset. example, useful different covariance matrices suspected different subject strata. Finally, imputation methods described rely Bayesian model fitting MCMC, flexibility choice covariance structure, .e. unstructured (default), heterogeneous Toeplitz, heterogeneous compound symmetry, AR(1) covariance structures supported.","code":""},{"path":"/articles/stat_specs.html","id":"sec:imputationModelREML","dir":"Articles","previous_headings":"3 Statistical methodology > 3.3 The base imputation model","what":"Restricted maximum likelihood estimation (REML)","title":"rbmi: Statistical Specifications","text":"Frequentist parameter estimation base imputation based REML. use REML improved alternative maximum likelihood (ML) covariance parameter estimation originally proposed Patterson Thompson (1971). Since , become default method parameter estimation linear mixed effects models. rbmi allows choose ML REML methods estimate model parameters, REML default option.","code":""},{"path":"/articles/stat_specs.html","id":"sec:imputationModelBayes","dir":"Articles","previous_headings":"3 Statistical methodology > 3.3 The base imputation model","what":"Bayesian model fitting","title":"rbmi: Statistical Specifications","text":"Bayesian imputation model fitted R package rstan (Stan Development Team (2020)). rstan R interface Stan. Stan powerful flexible statistical software developed dedicated team implements Bayesian inference state---art MCMC sampling procedures. multivariate normal model missing data specified section 3.3.1 can considered generalization models described Stan user’s guide (see Stan Development Team (2020, sec. 3.5)). prior distributions SAS implementation “five macros” used (Roger (2021)), .e. improper flat priors regression coefficients weakly informative inverse Wishart prior covariance matrix (matrices). Specifically, let \\(S \\\\mathbb{R}^{J \\times J}\\) symmetric positive definite matrix \\(\\nu \\(J-1, \\infty)\\). symmetric positive definite matrix \\(x \\\\mathbb{R}^{J \\times J}\\) density: \\[ \\text{InvWish}(x \\vert \\nu, S) = \\frac{1}{2^{\\nu J/2}} \\frac{1}{\\Gamma_J(\\frac{\\nu}{2})} \\vert S \\vert^{\\nu/2} \\vert x \\vert ^{-(\\nu + J + 1)/2} \\text{exp}(-\\frac{1}{2} \\text{tr}(Sx^{-1})). \\] \\(\\nu > J+1\\) mean given : \\[ E[x] = \\frac{S}{\\nu - J - 1}. \\] choose \\(S\\) equal estimated covariance matrix frequentist REML fit \\(\\nu = J+2\\) lowest degrees freedom guarantee finite mean. Setting degrees freedom low \\(\\nu\\) ensures prior little impact posterior. Moreover, choice allows interpret parameter \\(S\\) mean prior distribution. “five macros”, MCMC algorithm initialized parameters frequentist REML fit (see section 3.3.2). described , using weakly informative priors parameters. Therefore, Markov chain essentially starting targeted stationary posterior distribution minimal amount burn-chain required.","code":""},{"path":"/articles/stat_specs.html","id":"sec:imputationModelBoot","dir":"Articles","previous_headings":"3 Statistical methodology > 3.3 The base imputation model","what":"Approximate Bayesian posterior draws via the bootstrap","title":"rbmi: Statistical Specifications","text":"Several authors suggested stabler way get Bayesian posterior draws imputation model bootstrap incomplete data calculate REML estimates bootstrap sample (Little Rubin (2002), Efron (1994), Honaker King (2010), von Hippel Bartlett (2021)). method proper REML estimates bootstrap samples asymptotically equivalent sample posterior distribution may provide additional robustness model misspecification (Little Rubin (2002, sec. 10.2.3, part 6), Honaker King (2010)). order retain balance treatment groups stratification factors across bootstrap samples, user able provide stratification variables bootstrap rbmi implementation.","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"sec:imputatioMNAR","dir":"Articles","previous_headings":"3 Statistical methodology > 3.4 Imputation step","what":"Marginal imputation distribution for a subject - MAR case","title":"rbmi: Statistical Specifications","text":"subject \\(\\), marginal distribution complete \\(J\\)-dimensional outcome vector assessment visits according imputation model multivariate normal distribution. mean \\(\\tilde{\\mu}_i\\) given predicted mean imputation model conditional subject’s baseline characteristics, group, , optionally, time-varying covariates. covariance matrix \\(\\tilde{\\Sigma}_i\\) given overall estimated covariance matrix , different covariance matrices assumed different groups, covariance matrix corresponding subject \\(\\)’s group.","code":""},{"path":"/articles/stat_specs.html","id":"sec:imputationRefBased","dir":"Articles","previous_headings":"3 Statistical methodology > 3.4 Imputation step","what":"Marginal imputation distribution for a subject - reference-based imputation methods","title":"rbmi: Statistical Specifications","text":"subject \\(\\), calculate mean covariance matrix complete \\(J\\)-dimensional outcome vector assessment visits MAR case denote \\(\\mu_i\\) \\(\\Sigma_i\\). reference-based imputation methods, corresponding reference group also required group. Typically, reference group intervention group control group. reference mean \\(\\mu_{ref,}\\) defined predicted mean imputation model conditional reference group (rather actual group subject \\(\\) belongs ) subject’s baseline characteristics. reference covariance matrix \\(\\Sigma_{ref,}\\) overall estimated covariance matrix , different covariance matrices assumed different groups, estimated covariance matrix corresponding reference group. principle, time-varying covariates also included reference-based imputation methods. However, sensible external time-varying covariates (e.g. calendar time visit) internal time-varying covariates (e.g. treatment discontinuation) latter likely depend actual treatment group typically sensible assume trajectory time-varying covariate reference group. Based means covariance matrices, subject’s marginal imputation distribution reference-based imputation methods calculated detailed Carpenter, Roger, Kenward (2013, sec. 4.3). Denote mean covariance matrix marginal imputation distribution \\(\\tilde{\\mu}_i\\) \\(\\tilde{\\Sigma}_i\\). Recall subject’s first visit affected ICE denoted \\(\\tilde{t}_i \\\\{1,\\ldots,J\\}\\) (visit \\(\\tilde{t}_i-1\\) last visit unaffected ICE). marginal distribution patient \\(\\) built according specific assumption data post ICE follows: Jump reference (JR): patient’s outcome distribution normally distributed following mean: \\[\\tilde{\\mu}_i = (\\mu_i[1], \\dots, \\mu_i[\\tilde{t}_i-1], \\mu_{ref,}[\\tilde{t}_i], \\dots, \\mu_{ref,}[J])^T.\\] covariance matrix constructed follows. First, partition covariance matrices \\(\\Sigma_i\\) \\(\\Sigma_{ref,}\\) blocks according time ICE \\(\\tilde{t}_i\\): \\[ \\Sigma_{} = \\begin{bmatrix} \\Sigma_{, 11} & \\Sigma_{, 12} \\\\ \\Sigma_{, 21} & \\Sigma_{,22} \\\\ \\end{bmatrix} \\] \\[ \\Sigma_{ref,} = \\begin{bmatrix} \\Sigma_{ref, , 11} & \\Sigma_{ref, , 12} \\\\ \\Sigma_{ref, , 21} & \\Sigma_{ref, ,22} \\\\ \\end{bmatrix}. \\] want covariance matrix \\(\\tilde{\\Sigma}_i\\) match \\(\\Sigma_i\\) pre-deviation measurements, \\(\\Sigma_{ref,}\\) conditional components post-deviation given pre-deviation measurements. solution derived Carpenter, Roger, Kenward (2013, sec. 4.3) given : \\[ \\begin{matrix} \\tilde{\\Sigma}_{,11} = \\Sigma_{, 11} \\\\ \\tilde{\\Sigma}_{, 21} = \\Sigma_{ref,, 21} \\Sigma^{-1}_{ref,, 11} \\Sigma_{, 11} \\\\ \\tilde{\\Sigma}_{, 22} = \\Sigma_{ref, , 22} - \\Sigma_{ref,, 21} \\Sigma^{-1}_{ref,, 11} (\\Sigma_{ref,, 11} - \\Sigma_{,11}) \\Sigma^{-1}_{ref,, 11} \\Sigma_{ref,, 12}. \\end{matrix} \\] Copy increments reference (CIR): patient’s outcome distribution normally distributed following mean: \\[ \\begin{split} \\tilde{\\mu}_i =& (\\mu_i[1], \\dots, \\mu_i[\\tilde{t}_i-1], \\mu_i[\\tilde{t}_i-1] + (\\mu_{ref,}[\\tilde{t}_i] - \\mu_{ref,}[\\tilde{t}_i-1]), \\dots,\\\\ & \\mu_i[\\tilde{t}_i-1]+(\\mu_{ref,}[J] - \\mu_{ref,}[\\tilde{t}_i-1]))^T. \\end{split} \\] covariance matrix derived JR method. Copy reference (CR): patient’s outcome distribution normally distributed mean covariance matrix taken reference group: \\[ \\tilde{\\mu}_i = \\mu_{ref,} \\] \\[ \\tilde{\\Sigma}_i = \\Sigma_{ref,}. \\] Last mean carried forward (LMCF): patient’s outcome distribution normally distributed following mean: \\[ \\tilde{\\mu}_i = (\\mu_i[1], \\dots, \\mu_i[\\tilde{t}_i-1], \\mu_i[\\tilde{t}_i-1], \\dots, \\mu_i[\\tilde{t}_i-1])'\\] covariance matrix: \\[ \\tilde{\\Sigma}_i = \\Sigma_i.\\]","code":""},{"path":"/articles/stat_specs.html","id":"sec:imputationRandomConditionalMean","dir":"Articles","previous_headings":"3 Statistical methodology > 3.4 Imputation step","what":"Imputation of missing outcome data","title":"rbmi: Statistical Specifications","text":"joint marginal multivariate normal imputation distribution subject \\(\\)’s observed missing outcome data mean \\(\\tilde{\\mu}_i\\) covariance matrix \\(\\tilde{\\Sigma}_i\\) defined . actual imputation missing outcome data obtained conditioning marginal distribution subject’s observed outcome data. note, approach valid regardless whether subject intermittent terminal missing data. conditional distribution used imputation multivariate normal distribution explicit formulas conditional mean covariance readily available. completeness, report notation terminology setting. marginal distribution outcome patient \\(\\) \\(Y_i \\sim N(\\tilde{\\mu}_i, \\tilde{\\Sigma}_i)\\) outcome \\(Y_i\\) can decomposed observed (\\(Y_{,!}\\)) unobserved (\\(Y_{,?}\\)) components. Analogously mean \\(\\tilde{\\mu}_i\\) can decomposed \\((\\tilde{\\mu}_{,!},\\tilde{\\mu}_{,?})\\) covariance \\(\\tilde{\\Sigma}_i\\) : \\[ \\tilde{\\Sigma}_i = \\begin{bmatrix} \\tilde{\\Sigma}_{, !!} & \\tilde{\\Sigma}_{,!?} \\\\ \\tilde{\\Sigma}_{, ?!} & \\tilde{\\Sigma}_{, ??} \\end{bmatrix}. \\] conditional distribution \\(Y_{,?}\\) conditional \\(Y_{,!}\\) multivariate normal distribution expectation \\[ E(Y_{,?} \\vert Y_{,!})= \\tilde{\\mu}_{,?} + \\tilde{\\Sigma}_{, ?!} \\tilde{\\Sigma}_{,!!}^{-1} (Y_{,!} - \\tilde{\\mu}_{,!}) \\] covariance matrix \\[ Cov(Y_{,?} \\vert Y_{,!}) = \\tilde{\\Sigma}_{,??} - \\tilde{\\Sigma}_{,?!} \\tilde{\\Sigma}_{,!!}^{-1} \\tilde{\\Sigma}_{,!?}. \\] Conventional random imputation consists sampling conditional multivariate normal distribution. Conditional mean imputation imputes missing values deterministic conditional expectation \\(E(Y_{,?} \\vert Y_{,!})\\).","code":""},{"path":"/articles/stat_specs.html","id":"sec:deltaAdjustment","dir":"Articles","previous_headings":"3 Statistical methodology","what":"\\(\\delta\\)-adjustment","title":"rbmi: Statistical Specifications","text":"marginal \\(\\delta\\)-adjustment approach similar “five macros” SAS implemented (Roger (2021)), .e. fixed non-stochastic values added multivariate normal imputation step prior analysis. relevant sensitivity analyses order make imputed data systematically worse better, respectively, observed data. addition, authors suggested \\(\\delta\\)-type adjustments implement composite strategy continuous outcomes (Darken et al. (2020)). implementation provides full flexibility regarding specific implementation \\(\\delta\\)-adjustment, .e. value added may depend randomized treatment group, timing subject’s ICE, factors. suggestions case studies regarding topic, refer Cro et al. (2020).","code":""},{"path":"/articles/stat_specs.html","id":"sec:analysis","dir":"Articles","previous_headings":"3 Statistical methodology","what":"Analysis step","title":"rbmi: Statistical Specifications","text":"data imputation, standard analysis model can applied completed data resulting treatment effect estimate. imputed data longer contains missing values, analysis model often simple. example, can analysis covariance (ANCOVA) model outcome (change outcome baseline) specific visit j dependent variable, randomized treatment group primary covariate , typically, adjustment baseline covariates imputation model.","code":""},{"path":"/articles/stat_specs.html","id":"sec:pooling","dir":"Articles","previous_headings":"3 Statistical methodology","what":"Pooling step for inference of (approximate) Bayesian MI and Rubin’s rules","title":"rbmi: Statistical Specifications","text":"Assume analysis model applied \\(M\\) multiple imputed random datasets resulted \\(m\\) treatment effect estimates \\(\\hat{\\theta}_m\\) (\\(m=1,\\ldots,M\\)) corresponding standard error \\(SE_m\\) (available) degrees freedom \\(\\nu_{com}\\). degrees freedom available analysis model, set \\(\\nu_{com}=\\infty\\) inference based normal distribution. Rubin’s rules used pooling treatment effect estimates corresponding variances estimates analysis steps across \\(M\\) multiple imputed datasets. According Rubin’s rules, final estimate treatment effect calculated sample mean \\(M\\) treatment effect estimates: \\[ \\hat{\\theta} = \\frac{1}{M} \\sum_{m = 1}^M \\hat{\\theta}_m. \\] pooled variance based two components reflect within variance treatment effects across multiple imputed datasets: \\[ V(\\hat{\\theta}) = V_W(\\hat{\\theta}) + (1 + \\frac{1}{M}) V_B(\\hat{\\theta}) \\] \\(V_W(\\hat{\\theta}) = \\frac{1}{M}\\sum_{m = 1}^M SE^2_m\\) within-variance \\(V_B(\\hat{\\theta}) = \\frac{1}{M-1} \\sum_{m = 1}^M (\\hat{\\theta}_m - \\hat{\\theta})^2\\) -variance. Confidence intervals tests null hypothesis \\(H_0: \\theta=\\theta_0\\) based \\(t\\)-statistics \\(T\\): \\[ T= (\\hat{\\theta}-\\theta_0)/\\sqrt{V(\\hat{\\theta})}. \\] null hypothesis, \\(T\\) approximate \\(t\\)-distribution \\(\\nu\\) degrees freedom. \\(\\nu\\) calculated according Barnard Rubin approximation, see Barnard Rubin (1999) (formula 3) Little Rubin (2002) (formula (5.24), page 87): \\[ \\nu = \\frac{\\nu_{old}* \\nu_{obs}}{\\nu_{old} + \\nu_{obs}} \\] \\[ \\nu_{old} = \\frac{M-1}{\\lambda^2} \\quad\\mbox{}\\quad \\nu_{obs} = \\frac{\\nu_{com} + 1}{\\nu_{com} + 3} \\nu_{com} (1 - \\lambda) \\] \\(\\lambda = \\frac{(1 + \\frac{1}{M})V_B(\\hat{\\theta})}{V(\\hat{\\theta})}\\) fraction missing information.","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"point-estimate-of-the-treatment-effect","dir":"Articles","previous_headings":"3 Statistical methodology > 3.8 Bootstrap and jackknife inference for conditional mean imputation","what":"Point estimate of the treatment effect","title":"rbmi: Statistical Specifications","text":"point estimator obtained applying analysis model (Section 3.6) single conditional mean imputation missing data (see Section 3.4.3) based REML estimator parameters imputation model (see Section 3.3.2). denote treatment effect estimator \\(\\hat{\\theta}\\). demonstrated Wolbers et al. (2022) (Section 2.4), treatment effect estimator valid analysis model ANCOVA model , generally, treatment effect estimator linear function imputed outcome vector. Indeed, case, estimator identical pooled treatment effect across multiple random REML imputation infinite number imputations corresponds computationally efficient implementation proposal von Hippel Bartlett (2021). expect conditional mean imputation method also applicable analysis models (e.g. general MMRM analysis models) formally justified.","code":""},{"path":"/articles/stat_specs.html","id":"jackknife-standard-errors-confidence-intervals-ci-and-tests-for-the-treatment-effect","dir":"Articles","previous_headings":"3 Statistical methodology > 3.8 Bootstrap and jackknife inference for conditional mean imputation","what":"Jackknife standard errors, confidence intervals (CI) and tests for the treatment effect","title":"rbmi: Statistical Specifications","text":"dataset containing \\(n\\) subjects, jackknife standard error depends treatment effect estimates \\(\\hat{\\theta}_{(-b)}\\) (\\(b=1,\\ldots,n\\)) samples original dataset leave observation subject \\(b\\). described previously, obtain treatment effect estimates leave-one-subject-datasets, steps imputation procedure (.e. imputation, conditional mean imputation, analysis steps) need repeated new dataset. , jackknife standard error defined \\[\\hat{se}_{jack}=[\\frac{(n-1)}{n}\\cdot\\sum_{b=1}^{n} (\\hat{\\theta}_{(-b)}-\\bar{\\theta}_{(.)})^2]^{1/2}\\] \\(\\bar{\\theta}_{(.)}\\) denotes mean jackknife estimates (Efron Tibshirani (1994), chapter 10). corresponding two-sided normal approximation \\(1-\\alpha\\) CI defined \\(\\hat{\\theta}\\pm z^{1-\\alpha/2}\\cdot \\hat{se}_{jack}\\) \\(\\hat{\\theta}\\) treatment effect estimate original dataset. Tests null hypothesis \\(H_0: \\theta=\\theta_0\\) based \\(Z\\)-score \\(Z=(\\hat{\\theta}-\\theta_0)/\\hat{se}_{jack}\\) using standard normal approximation. simulation study reported Wolbers et al. (2022) demonstrated exact protection type error jackknife-based inference relatively low sample size (n = 100 per group) substantial amount missing data (>25% subjects ICE).","code":""},{"path":"/articles/stat_specs.html","id":"bootstrap-standard-errors-confidence-intervals-ci-and-tests-for-the-treatment-effect","dir":"Articles","previous_headings":"3 Statistical methodology > 3.8 Bootstrap and jackknife inference for conditional mean imputation","what":"Bootstrap standard errors, confidence intervals (CI) and tests for the treatment effect","title":"rbmi: Statistical Specifications","text":"alternative jackknife, bootstrap also implemented rbmi (Efron Tibshirani (1994), Davison Hinkley (1997)). Two different bootstrap methods implemented rbmi: Methods based bootstrap standard error normal approximation percentile bootstrap methods. Denote treatment effect estimates \\(B\\) bootstrap samples \\(\\hat{\\theta}^*_b\\) (\\(b=1,\\ldots,B\\)). bootstrap standard error \\(\\hat{se}_{boot}\\) defined empirical standard deviation bootstrapped treatment effect estimates. Confidence intervals tests based bootstrap standard error can constructed way jackknife. Confidence intervals using percentile bootstrap based empirical quantiles bootstrap distribution corresponding statistical tests implemented rbmi via inversion confidence interval. Explicit formulas bootstrap inference implemented rbmi package considerations regarding required number bootstrap samples included Appendix Wolbers et al. (2022). simulation study reported Wolbers et al. (2022) demonstrated small inflation type error rate inference based bootstrap standard error (\\(5.3\\%\\) nominal type error rate \\(5\\%\\)) sample size n = 100 per group substantial amount missing data (>25% subjects ICE). Based simulations, recommend jackknife bootstrap inference performed better simulation study typically much faster compute bootstrap.","code":""},{"path":"/articles/stat_specs.html","id":"sec:poolbmlmi","dir":"Articles","previous_headings":"3 Statistical methodology","what":"Pooling step for inference of the bootstrapped MI methods","title":"rbmi: Statistical Specifications","text":"Assume analysis model applied \\(B\\times D\\) multiple imputed random datasets resulted \\(B\\times D\\) treatment effect estimates \\(\\hat{\\theta}_{bd}\\) (\\(b=1,\\ldots,B\\); \\(d=1,\\ldots,D\\)). final estimate treatment effect calculated sample mean \\(B*D\\) treatment effect estimates: \\[ \\hat{\\theta} = \\frac{1}{BD} \\sum_{b = 1}^B \\sum_{d = 1}^D \\hat{\\theta}_{bd}. \\] pooled variance based two components reflect variability within imputed bootstrap samples (von Hippel Bartlett (2021), formula 8.4): \\[ V(\\hat{\\theta}) = (1 + \\frac{1}{B})\\frac{MSB - MSW}{D} + \\frac{MSW}{BD} \\] \\(MSB\\) mean square bootstrapped datasets, \\(MSW\\) mean square within bootstrapped datasets imputed datasets: \\[ \\begin{align*} MSB &= \\frac{D}{B-1} \\sum_{b = 1}^B (\\bar{\\theta_{b}} - \\hat{\\theta})^2 \\\\ MSW &= \\frac{1}{B(D-1)} \\sum_{b = 1}^B \\sum_{d = 1}^D (\\theta_{bd} - \\bar{\\theta_b})^2 \\end{align*} \\] \\(\\bar{\\theta_{b}}\\) mean across \\(D\\) estimates obtained random imputation \\(b\\)-th bootstrap sample. degrees freedom estimated following formula (von Hippel Bartlett (2021), formula 8.6): \\[ \\nu = \\frac{(MSB\\cdot (B+1) - MSW\\cdot B)^2}{\\frac{MSB^2\\cdot (B+1)^2}{B-1} + \\frac{MSW^2\\cdot B}{D-1}} \\] Confidence intervals tests null hypothesis \\(H_0: \\theta=\\theta_0\\) based \\(t\\)-statistics \\(T\\): \\[ T= (\\hat{\\theta}-\\theta_0)/\\sqrt{V(\\hat{\\theta})}. \\] null hypothesis, \\(T\\) approximate \\(t\\)-distribution \\(\\nu\\) degrees freedom.","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"treatment-effect-estimation","dir":"Articles","previous_headings":"3 Statistical methodology > 3.10 Comparison between the implemented approaches","what":"Treatment effect estimation","title":"rbmi: Statistical Specifications","text":"approaches provide consistent treatment effect estimates standard reference-based imputation methods case analysis model completed datasets general linear model ANCOVA. Methods conditional mean imputation also valid analysis models. validity conditional mean imputation formally demonstrated analyses using general linear model (Wolbers et al. (2022, sec. 2.4)) though may also applicable widely (e.g. general MMRM analysis models). Treatment effects based conditional mean imputation deterministic. methods affected Monte Carlo sampling error precision estimates depends number imputations bootstrap samples, respectively.","code":""},{"path":"/articles/stat_specs.html","id":"standard-errors-of-the-treatment-effect","dir":"Articles","previous_headings":"3 Statistical methodology > 3.10 Comparison between the implemented approaches","what":"Standard errors of the treatment effect","title":"rbmi: Statistical Specifications","text":"approaches provide frequentist consistent estimates standard error imputation MAR assumption. reference-based imputation methods, methods based conditional mean imputation bootstrapped MI provide frequentist consistent estimates standard error whereas Rubin’s rules applied conventional MI methods provides -called information anchored inference (Bartlett (2021), Cro, Carpenter, Kenward (2019), von Hippel Bartlett (2021), Wolbers et al. (2022)). Frequentist consistent estimates standard error lead confidence intervals tests (asymptotically) correct coverage type error control assumption reference-based assumption reflects true data-generating mechanism. finite samples, simulations sample size \\(n=100\\) per group reported Wolbers et al. (2022) demonstrated conditional mean imputation combined jackknife provided exact protection type one error rate whereas bootstrap associated small type error inflation (5.1% 5.3% nominal level 5%). well known Rubin’s rules provide frequentist consistent estimates standard error reference-based imputation methods (Seaman, White, Leacy (2014), Liu Pang (2016), Tang (2017), Cro, Carpenter, Kenward (2019), Bartlett (2021)). Standard errors Rubin’s rule typically larger frequentist standard error estimates leading conservative inference corresponding loss statistical power, see e.g. simulations reported Wolbers et al. (2022). Intuitively, occurs reference-based imputation methods borrow information reference group imputations intervention group leading reduction frequentist variance resulting treatment effect contrast captured Rubin’s variance estimator. Formally, occurs imputation analysis models uncongenial reference-based imputation methods (Meng (1994), Bartlett (2021)). Cro, Carpenter, Kenward (2019) argued Rubin’s rule nevertheless valid reference-based imputation methods approximately information-anchored, .e. proportion information lost due missing data MAR approximately preserved reference-based analyses. contrast, frequentist standard errors reference based imputation information anchored reference-based imputation standard errors reference-based assumptions typically smaller MAR imputation. Information anchoring sensible concept sensitivity analyses, whereas primary analyses, may important adhere principles frequentist inference. Analyses data missing observations generally rely unverifiable missing data assumptions assumptions reference-based imputation methods relatively strong. Therefore, assumptions need clinically justified appropriate least conservative considered disease area anticipated mechanism action intervention. Conditional mean imputation combined jackknife method leads deterministic standard error estimates , consequently, confidence intervals \\(p\\)-values also deterministic. particularly important regulatory setting important ascertain whether calculated \\(p\\)-value close critical boundary 5% truly threshold rather uncertain Monte Carlo error.","code":""},{"path":"/articles/stat_specs.html","id":"computational-complexity","dir":"Articles","previous_headings":"3 Statistical methodology > 3.10 Comparison between the implemented approaches","what":"Computational complexity","title":"rbmi: Statistical Specifications","text":"Bayesian MI methods rely specification prior distributions usage Markov chain Monte Carlo (MCMC) methods. methods based multiple imputation bootstrapping require tuning parameters specification number imputations \\(M\\) bootstrap samples \\(B\\) rely numerical optimization fitting MMRM imputation models via REML. Conditional mean imputation combined jackknife tuning parameters. rbmi implementation, fitting MMRM imputation model via REML computationally expensive. MCMC sampling using rstan (Stan Development Team (2020)) typically relatively fast setting requires small burn-burn-chains. addition, number random imputations reliable inference using Rubin’s rules often smaller number resamples required jackknife bootstrap (see e.g. discussions . R. White, Royston, Wood (2011, sec. 7) Bayesian MI Appendix Wolbers et al. (2022) bootstrap). Thus, many applications, expect conventional MI based Bayesian posterior draws fastest, followed conventional MI using approximate Bayesian posterior draws conditional mean imputation combined jackknife. Conditional mean imputation combined bootstrap bootstrapped MI methods typically computationally demanding. note, implemented methods conceptually straightforward parallelise parallelisation support provided rbmi.","code":""},{"path":"/articles/stat_specs.html","id":"sec:rbmiFunctions","dir":"Articles","previous_headings":"","what":"Mapping of statistical methods to rbmi functions","title":"rbmi: Statistical Specifications","text":"full documentation rbmi package functionality refer help pages functions package vignettes. give brief overview different steps imputation procedure mapped rbmi functions: Bayesian posterior parameter draws imputation model obtained via argument method = method_bayes(). Approximate Bayesian posterior parameter draws imputation model obtained via argument method = method_approxbayes(). ML REML parameter estimates imputation model parameters original dataset leave-one-subject-datasets (required jackknife) obtained via argument method = method_condmean(type = \"jackknife\"). ML REML parameter estimates imputation model parameters original dataset bootstrapped datasets obtained via argument method = method_condmean(type = \"bootstrap\"). Bootstrapped MI methods obtained via argument method = method_bmlmi(B=B, D=D) \\(B\\) refers number bootstrap samples \\(D\\) number random imputations bootstrap sample. imputation step using random imputation deterministic conditional mean imputation, respectively, implemented function impute(). Imputation can performed assuming already implemented imputation strategies presented section 3.4. Additionally, user-defined imputation strategies also supported. analysis step implemented function analyse() applies analysis model imputed datasets. default, analysis model (argument fun) ancova() function alternative analysis functions can also provided user. analyse() function also allows \\(\\delta\\)-adjustments imputed datasets prior analysis via argument delta. inference step implemented function pool() pools results across imputed datasets. Rubin Bernard rule applied case (approximate) Bayesian MI. conditional mean imputation, jackknife bootstrap (normal approximation percentile) inference supported. BMLMI, pooling inference steps performed via pool() case implements method described Section 3.9.","code":""},{"path":"/articles/stat_specs.html","id":"sec:otherSoftware","dir":"Articles","previous_headings":"","what":"Comparison to other software implementations","title":"rbmi: Statistical Specifications","text":"established software implementation reference-based imputation SAS -called “five macros” James Roger (Roger (2021)). alternative R implementation also currently development R package RefBasedMI (McGrath White (2021)). rbmi several features supported implementations: addition Bayesian MI approach implemented also packages, implementation provides three alternative MI approaches: approximate Bayesian MI, conditional mean imputation combined resampling, bootstrapped MI. rbmi allows usage data collected ICE. example, suppose want adopt treatment policy strategy ICE “treatment discontinuation”. possible implementation strategy use observed outcome data subjects remain study ICE use reference-based imputation case subject drops . implementation, implemented excluding observed post ICE data imputation model assumes MAR missingness including analysis model. knowledge, directly supported implementations. RefBasedMI fits imputation model data treatment group separately implies covariate-treatment group interactions covariates pooled data treatment groups. contrast, Roger’s five macros assume joint model including data randomized groups covariate-treatment interactions covariates allowed. also chose implement joint model use flexible model linear predictor may may include interaction term covariate treatment group. addition, imputation model also allows inclusion time-varying covariates. implementation, grouping subjects purpose imputation model (definition reference group) need correspond assigned treatment groups. provides additional flexibility imputation procedure. clear us whether feature supported Roger’s five macros RefBasedMI. believe R-based implementation modular RefBasedMI facilitate package enhancements. contrast, general causal model introduced . White, Royes, Best (2020) available implementations currently supported .","code":""},{"path":[]},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Craig Gower-Page. Author, maintainer. Alessandro Noci. Author. Marcel Wolbers. Contributor. Roche. Copyright holder, funder.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Gower-Page C, Noci (2024). rbmi: Reference Based Multiple Imputation. R package version 1.2.6, https://github.com/insightsengineering/rbmi, https://insightsengineering.github.io/rbmi/.","code":"@Manual{, title = {rbmi: Reference Based Multiple Imputation}, author = {Craig Gower-Page and Alessandro Noci}, year = {2024}, note = {R package version 1.2.6, https://github.com/insightsengineering/rbmi}, url = {https://insightsengineering.github.io/rbmi/}, }"},{"path":[]},{"path":"/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Reference Based Multiple Imputation","text":"rbmi R package imputation missing data clinical trials continuous multivariate normal longitudinal outcomes. supports imputation missing random (MAR) assumption, reference-based imputation methods, delta adjustments (required sensitivity analysis tipping point analyses). package implements Bayesian approximate Bayesian multiple imputation combined Rubin’s rules inference, frequentist conditional mean imputation combined (jackknife bootstrap) resampling.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Reference Based Multiple Imputation","text":"package can installed directly CRAN via:","code":"install.packages(\"rbmi\")"},{"path":"/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Reference Based Multiple Imputation","text":"package designed around 4 core functions: draws() - Fits multiple imputation models impute() - Imputes multiple datasets analyse() - Analyses multiple datasets pool() - Pools multiple results single statistic basic usage core functions described quickstart vignette:","code":"vignette(topic = \"quickstart\", package = \"rbmi\")"},{"path":"/index.html","id":"support","dir":"","previous_headings":"","what":"Support","title":"Reference Based Multiple Imputation","text":"help regards using package find bug please create GitHub issue","code":""},{"path":"/reference/QR_decomp.html","id":null,"dir":"Reference","previous_headings":"","what":"QR decomposition — QR_decomp","title":"QR decomposition — QR_decomp","text":"QR decomposition defined Stan user's guide (section 1.2).","code":""},{"path":"/reference/QR_decomp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"QR decomposition — QR_decomp","text":"","code":"QR_decomp(mat)"},{"path":"/reference/QR_decomp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"QR decomposition — QR_decomp","text":"mat matrix perform QR decomposition .","code":""},{"path":"/reference/Stack.html","id":null,"dir":"Reference","previous_headings":"","what":"R6 Class for a FIFO stack — Stack","title":"R6 Class for a FIFO stack — Stack","text":"simple stack object offering add / pop functionality","code":""},{"path":"/reference/Stack.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"R6 Class for a FIFO stack — Stack","text":"stack list containing current stack","code":""},{"path":[]},{"path":"/reference/Stack.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"R6 Class for a FIFO stack — Stack","text":"Stack$add() Stack$pop() Stack$clone()","code":""},{"path":"/reference/Stack.html","id":"method-add-","dir":"Reference","previous_headings":"","what":"Method add()","title":"R6 Class for a FIFO stack — Stack","text":"Adds content end stack (must list)","code":""},{"path":"/reference/Stack.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for a FIFO stack — Stack","text":"","code":"Stack$add(x)"},{"path":"/reference/Stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for a FIFO stack — Stack","text":"x content add stack","code":""},{"path":"/reference/Stack.html","id":"method-pop-","dir":"Reference","previous_headings":"","what":"Method pop()","title":"R6 Class for a FIFO stack — Stack","text":"Retrieve content stack","code":""},{"path":"/reference/Stack.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for a FIFO stack — Stack","text":"","code":"Stack$pop(i)"},{"path":"/reference/Stack.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for a FIFO stack — Stack","text":"number items retrieve stack. less items left stack just return everything left.","code":""},{"path":"/reference/Stack.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"R6 Class for a FIFO stack — Stack","text":"objects class cloneable method.","code":""},{"path":"/reference/Stack.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for a FIFO stack — Stack","text":"","code":"Stack$clone(deep = FALSE)"},{"path":"/reference/Stack.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for a FIFO stack — Stack","text":"deep Whether make deep clone.","code":""},{"path":"/reference/add_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a class — add_class","title":"Add a class — add_class","text":"Utility function add class object. Adds new class existing classes.","code":""},{"path":"/reference/add_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a class — add_class","text":"","code":"add_class(x, cls)"},{"path":"/reference/add_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a class — add_class","text":"x object add class . cls class added.","code":""},{"path":"/reference/adjust_trajectories.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories","title":"Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories","text":"Adjust trajectories due intercurrent event (ICE)","code":""},{"path":"/reference/adjust_trajectories.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories","text":"","code":"adjust_trajectories( distr_pars_group, outcome, ids, ind_ice, strategy_fun, distr_pars_ref = NULL )"},{"path":"/reference/adjust_trajectories.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories","text":"distr_pars_group Named list containing simulation parameters multivariate normal distribution assumed given treatment group. contains following elements: mu: Numeric vector indicating mean outcome trajectory. include outcome baseline. sigma Covariance matrix outcome trajectory. outcome Numeric variable specifies longitudinal outcome. ids Factor variable specifies id subject. ind_ice binary variable takes value 1 corresponding outcome affected ICE 0 otherwise. strategy_fun Function implementing trajectories intercurrent event (ICE). Must one getStrategies(). See getStrategies() details. distr_pars_ref Optional. Named list containing simulation parameters reference arm. contains following elements: mu: Numeric vector indicating mean outcome trajectory assuming ICEs. include outcome baseline. sigma Covariance matrix outcome trajectory assuming ICEs.","code":""},{"path":"/reference/adjust_trajectories.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories","text":"numeric vector containing adjusted trajectories.","code":""},{"path":[]},{"path":"/reference/adjust_trajectories_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","title":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","text":"Adjust trajectory subject's outcome due intercurrent event (ICE)","code":""},{"path":"/reference/adjust_trajectories_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","text":"","code":"adjust_trajectories_single( distr_pars_group, outcome, strategy_fun, distr_pars_ref = NULL )"},{"path":"/reference/adjust_trajectories_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","text":"distr_pars_group Named list containing simulation parameters multivariate normal distribution assumed given treatment group. contains following elements: mu: Numeric vector indicating mean outcome trajectory. include outcome baseline. sigma Covariance matrix outcome trajectory. outcome Numeric variable specifies longitudinal outcome. strategy_fun Function implementing trajectories intercurrent event (ICE). Must one getStrategies(). See getStrategies() details. distr_pars_ref Optional. Named list containing simulation parameters reference arm. contains following elements: mu: Numeric vector indicating mean outcome trajectory assuming ICEs. include outcome baseline. sigma Covariance matrix outcome trajectory assuming ICEs.","code":""},{"path":"/reference/adjust_trajectories_single.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","text":"numeric vector containing adjusted trajectory single subject.","code":""},{"path":"/reference/adjust_trajectories_single.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","text":"outcome specified --post-ICE observations (.e. observations adjusted) set NA.","code":""},{"path":"/reference/analyse.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyse Multiple Imputed Datasets — analyse","title":"Analyse Multiple Imputed Datasets — analyse","text":"function takes multiple imputed datasets (generated impute() function) runs analysis function .","code":""},{"path":"/reference/analyse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analyse Multiple Imputed Datasets — analyse","text":"","code":"analyse(imputations, fun = ancova, delta = NULL, ...)"},{"path":"/reference/analyse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analyse Multiple Imputed Datasets — analyse","text":"imputations imputations object created impute(). fun analysis function applied imputed dataset. See details. delta data.frame containing delta transformation applied imputed datasets prior running fun. See details. ... Additional arguments passed onto fun.","code":""},{"path":"/reference/analyse.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Analyse Multiple Imputed Datasets — analyse","text":"function works performing following steps: Extract dataset imputations object. Apply delta adjustments specified delta argument. Run analysis function fun dataset. Repeat steps 1-3 across datasets inside imputations object. Collect return analysis results. analysis function fun must take data.frame first argument. options analyse() passed onto fun via .... fun must return named list element list containing single numeric element called est (additionally se df originally specified method_bayes() method_approxbayes()) .e.: Please note vars$subjid column (defined original call draws()) scrambled data.frames provided fun. say contain original subject values hard coding subject ids strictly avoided. default fun ancova() function. Please note function requires vars object, created set_vars(), provided via vars argument e.g. analyse(imputeObj, vars = set_vars(...)). Please see documentation ancova() full details. Please also note theoretical justification conditional mean imputation method (method = method_condmean() draws()) relies fact ANCOVA linear transformation outcomes. Thus care required applying alternative analysis functions setting. delta argument can used specify offsets applied outcome variable imputed datasets prior analysis. typically used sensitivity tipping point analyses. delta dataset must contain columns vars$subjid, vars$visit (specified original call draws()) delta. Essentially data.frame merged onto imputed dataset vars$subjid vars$visit outcome variable modified : Please note order provide maximum flexibility, delta argument can used modify /outcome values including imputed. Care must taken defining offsets. recommend use helper function delta_template() define delta datasets provides utility variables is_missing can used identify exactly visits imputed.","code":"myfun <- function(dat, ...) { mod_1 <- lm(data = dat, outcome ~ group) mod_2 <- lm(data = dat, outcome ~ group + covar) x <- list( trt_1 = list( est = coef(mod_1)[[group]], se = sqrt(vcov(mod_1)[group, group]), df = df.residual(mod_1) ), trt_2 = list( est = coef(mod_2)[[group]], se = sqrt(vcov(mod_2)[group, group]), df = df.residual(mod_2) ) ) return(x) } imputed_data[[vars$outcome]] <- imputed_data[[vars$outcome]] + imputed_data[[\"delta\"]]"},{"path":[]},{"path":"/reference/analyse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Analyse Multiple Imputed Datasets — analyse","text":"","code":"if (FALSE) { # \\dontrun{ vars <- set_vars( subjid = \"subjid\", visit = \"visit\", outcome = \"outcome\", group = \"group\", covariates = c(\"sex\", \"age\", \"sex*age\") ) analyse( imputations = imputeObj, vars = vars ) deltadf <- data.frame( subjid = c(\"Pt1\", \"Pt1\", \"Pt2\"), visit = c(\"Visit_1\", \"Visit_2\", \"Visit_2\"), delta = c( 5, 9, -10) ) analyse( imputations = imputeObj, delta = deltadf, vars = vars ) } # }"},{"path":"/reference/ancova.html","id":null,"dir":"Reference","previous_headings":"","what":"Analysis of Covariance — ancova","title":"Analysis of Covariance — ancova","text":"Performs analysis covariance two groups returning estimated \"treatment effect\" (.e. contrast two treatment groups) least square means estimates group.","code":""},{"path":"/reference/ancova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analysis of Covariance — ancova","text":"","code":"ancova(data, vars, visits = NULL, weights = c(\"proportional\", \"equal\"))"},{"path":"/reference/ancova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analysis of Covariance — ancova","text":"data data.frame containing data used model. vars vars object generated set_vars(). group, visit, outcome covariates elements required. See details. visits optional character vector specifying visits fit ancova model . NULL, separate ancova model fit outcomes visit (determined unique(data[[vars$visit]])). See details. weights Character, either \"proportional\" (default) \"equal\". Specifies weighting strategy used categorical covariates calculating lsmeans. See details.","code":""},{"path":"/reference/ancova.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Analysis of Covariance — ancova","text":"function works follows: Select first value visits. Subset data observations occurred visit. Fit linear model vars$outcome ~ vars$group + vars$covariates. Extract \"treatment effect\" & least square means treatment group. Repeat points 2-3 values visits. value visits provided set unique(data[[vars$visit]]). order meet formatting standards set analyse() results collapsed single list suffixed visit name, e.g.: Please note \"ref\" refers first factor level vars$group necessarily coincide control arm. Analogously, \"alt\" refers second factor level vars$group. \"trt\" refers model contrast translating mean difference second level first level. want include interaction terms model can done providing covariates argument set_vars() e.g. set_vars(covariates = c(\"sex*age\")).","code":"list( trt_visit_1 = list(est = ...), lsm_ref_visit_1 = list(est = ...), lsm_alt_visit_1 = list(est = ...), trt_visit_2 = list(est = ...), lsm_ref_visit_2 = list(est = ...), lsm_alt_visit_2 = list(est = ...), ... )"},{"path":"/reference/ancova.html","id":"weighting","dir":"Reference","previous_headings":"","what":"Weighting","title":"Analysis of Covariance — ancova","text":"\"proportional\" default scheme used. equivalent standardization, .e. lsmeans group equal predicted mean outcome ancova model group based baseline characteristics subjects regardless assigned group. alternative weighting scheme, \"equal\", creates hypothetical patients expanding combinations models categorical covariates. lsmeans calculated average predicted mean outcome hypothetical patients assuming come group turn. short: \"proportional\" weights categorical covariates based upon frequency occurrence data. \"equal\" weights categorical covariates equally across theoretical combinations.","code":""},{"path":[]},{"path":"/reference/ancova_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","title":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","text":"Performance analysis covariance. See ancova() full details.","code":""},{"path":"/reference/ancova_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","text":"","code":"ancova_single( data, outcome, group, covariates, weights = c(\"proportional\", \"equal\") )"},{"path":"/reference/ancova_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","text":"data data.frame containing data required model. outcome Character, name outcome variable data. group Character, name group variable data. covariates Character vector containing name additional covariates included model well interaction terms. weights Character, specifies whether use \"proportional\" \"equal\" weighting categorical covariate combination calculating lsmeans.","code":""},{"path":"/reference/ancova_single.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","text":"group must factor variable 2 levels. outcome must continuous numeric variable.","code":""},{"path":[]},{"path":"/reference/ancova_single.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","text":"","code":"if (FALSE) { # \\dontrun{ iris2 <- iris[ iris$Species %in% c(\"versicolor\", \"virginica\"), ] iris2$Species <- factor(iris2$Species) ancova_single(iris2, \"Sepal.Length\", \"Species\", c(\"Petal.Length * Petal.Width\")) } # }"},{"path":"/reference/antidepressant_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Antidepressant trial data — antidepressant_data","title":"Antidepressant trial data — antidepressant_data","text":"dataset containing data publicly available example data set antidepressant clinical trial. dataset available website Drug Information Association Scientific Working Group Estimands Missing Data. per website, original data antidepressant clinical trial four treatments; two doses experimental medication, positive control, placebo published Goldstein et al (2004). mask real data, week 8 observations removed two arms created: original placebo arm \"drug arm\" created randomly selecting patients three non-placebo arms.","code":""},{"path":"/reference/antidepressant_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Antidepressant trial data — antidepressant_data","text":"","code":"antidepressant_data"},{"path":"/reference/antidepressant_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Antidepressant trial data — antidepressant_data","text":"data.frame 608 rows 11 variables: PATIENT: patients IDs. HAMATOTL: total score Hamilton Anxiety Rating Scale. PGIIMP: patient's Global Impression Improvement Rating Scale. RELDAYS: number days visit baseline. VISIT: post-baseline visit. levels 4,5,6,7. THERAPY: treatment group variable. equal PLACEBO observations placebo arm, DRUG observations active arm. GENDER: patient's gender. POOLINV: pooled investigator. BASVAL: baseline outcome value. HAMDTL17: Hamilton 17-item rating scale value. CHANGE: change baseline Hamilton 17-item rating scale.","code":""},{"path":"/reference/antidepressant_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Antidepressant trial data — antidepressant_data","text":"relevant endpoint Hamilton 17-item rating scale depression (HAMD17) baseline weeks 1, 2, 4, 6 assessments included. Study drug discontinuation occurred 24% subjects active drug 26% placebo. data study drug discontinuation missing single additional intermittent missing observation.","code":""},{"path":"/reference/antidepressant_data.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Antidepressant trial data — antidepressant_data","text":"Goldstein, Lu, Detke, Wiltse, Mallinckrodt, Demitrack. Duloxetine treatment depression: double-blind placebo-controlled comparison paroxetine. J Clin Psychopharmacol 2004;24: 389-399.","code":""},{"path":"/reference/apply_delta.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies delta adjustment — apply_delta","title":"Applies delta adjustment — apply_delta","text":"Takes delta dataset adjusts outcome variable adding corresponding delta.","code":""},{"path":"/reference/apply_delta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies delta adjustment — apply_delta","text":"","code":"apply_delta(data, delta = NULL, group = NULL, outcome = NULL)"},{"path":"/reference/apply_delta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies delta adjustment — apply_delta","text":"data data.frame outcome column adjusted. delta data.frame (must contain column called delta). group character vector variables data delta used merge 2 data.frames together . outcome character, name outcome variable data.","code":""},{"path":"/reference/as_analysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct an analysis object — as_analysis","title":"Construct an analysis object — as_analysis","text":"Creates analysis object ensuring components correctly defined.","code":""},{"path":"/reference/as_analysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct an analysis object — as_analysis","text":"","code":"as_analysis(results, method, delta = NULL, fun = NULL, fun_name = NULL)"},{"path":"/reference/as_analysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct an analysis object — as_analysis","text":"results list lists contain analysis results imputation See analyse() details object look like. method method object specified draws(). delta delta dataset used. See analyse() details specified. fun analysis function used. fun_name character name analysis function (used printing) purposes.","code":""},{"path":"/reference/as_ascii_table.html","id":null,"dir":"Reference","previous_headings":"","what":"as_ascii_table — as_ascii_table","title":"as_ascii_table — as_ascii_table","text":"function takes data.frame attempts convert simple ascii format suitable printing screen assumed variable values .character() method order cast character.","code":""},{"path":"/reference/as_ascii_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"as_ascii_table — as_ascii_table","text":"","code":"as_ascii_table(dat, line_prefix = \" \", pcol = NULL)"},{"path":"/reference/as_ascii_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"as_ascii_table — as_ascii_table","text":"dat Input dataset convert ascii table line_prefix Symbols prefix infront every line table pcol name column handled p-value. Sets value <0.001 value 0 rounding","code":""},{"path":"/reference/as_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Class — as_class","title":"Set Class — as_class","text":"Utility function set objects class.","code":""},{"path":"/reference/as_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Class — as_class","text":"","code":"as_class(x, cls)"},{"path":"/reference/as_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Class — as_class","text":"x object set class . cls class set.","code":""},{"path":"/reference/as_cropped_char.html","id":null,"dir":"Reference","previous_headings":"","what":"as_cropped_char — as_cropped_char","title":"as_cropped_char — as_cropped_char","text":"Makes character string x chars Reduce x char string ...","code":""},{"path":"/reference/as_cropped_char.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"as_cropped_char — as_cropped_char","text":"","code":"as_cropped_char(inval, crop_at = 30, ndp = 3)"},{"path":"/reference/as_cropped_char.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"as_cropped_char — as_cropped_char","text":"inval single element value crop_at character limit ndp Number decimal places display","code":""},{"path":"/reference/as_dataframe.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert object to dataframe — as_dataframe","title":"Convert object to dataframe — as_dataframe","text":"Convert object dataframe","code":""},{"path":"/reference/as_dataframe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert object to dataframe — as_dataframe","text":"","code":"as_dataframe(x)"},{"path":"/reference/as_dataframe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert object to dataframe — as_dataframe","text":"x data.frame like object Utility function convert \"data.frame-like\" object actual data.frame avoid issues inconsistency methods ( [() dplyr's grouped dataframes)","code":""},{"path":"/reference/as_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a draws object — as_draws","title":"Creates a draws object — as_draws","text":"Creates draws object final output call draws().","code":""},{"path":"/reference/as_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a draws object — as_draws","text":"","code":"as_draws(method, samples, data, formula, n_failures = NULL, fit = NULL)"},{"path":"/reference/as_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a draws object — as_draws","text":"method method object generated either method_bayes(), method_approxbayes(), method_condmean() method_bmlmi(). samples list sample_single objects. See sample_single(). data R6 longdata object containing relevant input data information. formula Fixed effects formula object used model specification. n_failures Absolute number failures model fit. fit method_bayes() chosen, returns MCMC Stan fit object. Otherwise NULL.","code":""},{"path":"/reference/as_draws.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a draws object — as_draws","text":"draws object named list containing following: data: R6 longdata object containing relevant input data information. method: method object generated either method_bayes(), method_approxbayes() method_condmean(). samples: list containing estimated parameters interest. element samples named list containing following: ids: vector characters containing ids subjects included original dataset. beta: numeric vector estimated regression coefficients. sigma: list estimated covariance matrices (one level vars$group). theta: numeric vector transformed covariances. failed: Logical. TRUE model fit failed. ids_samp: vector characters containing ids subjects included given sample. fit: method_bayes() chosen, returns MCMC Stan fit object. Otherwise NULL. n_failures: absolute number failures model fit. Relevant method_condmean(type = \"bootstrap\"), method_approxbayes() method_bmlmi(). formula: fixed effects formula object used model specification.","code":""},{"path":"/reference/as_imputation.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an imputation object — as_imputation","title":"Create an imputation object — as_imputation","text":"function creates object returned impute(). Essentially glorified wrapper around list() ensuring required elements set class added expected.","code":""},{"path":"/reference/as_imputation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an imputation object — as_imputation","text":"","code":"as_imputation(imputations, data, method, references)"},{"path":"/reference/as_imputation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an imputation object — as_imputation","text":"imputations list imputations_list's created imputation_df() data longdata object created longDataConstructor() method method object created method_condmean(), method_bayes() method_approxbayes() references named vector. Identifies references used generating imputed values. form c(\"Group\" = \"Reference\", \"Group\" = \"Reference\").","code":""},{"path":"/reference/as_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert indicator to index — as_indices","title":"Convert indicator to index — as_indices","text":"Converts string 0's 1's index positions 1's padding results 0's length","code":""},{"path":"/reference/as_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert indicator to index — as_indices","text":"","code":"as_indices(x)"},{"path":"/reference/as_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert indicator to index — as_indices","text":"x character vector whose values either \"0\" \"1\". elements vector must length","code":""},{"path":"/reference/as_indices.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert indicator to index — as_indices","text":".e.","code":"patmap(c(\"1101\", \"0001\")) -> list(c(1,2,4,999), c(4,999, 999, 999))"},{"path":"/reference/as_mmrm_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a ","title":"Creates a ","text":"Converts design matrix + key variables common format particular function following: Renames covariates V1, V2, etc avoid issues special characters variable names Ensures key variables right type Inserts outcome, visit subjid variables data.frame naming outcome, visit subjid provided also insert group variable data.frame named group","code":""},{"path":"/reference/as_mmrm_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a ","text":"","code":"as_mmrm_df(designmat, outcome, visit, subjid, group = NULL)"},{"path":"/reference/as_mmrm_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a ","text":"designmat data.frame matrix containing covariates use MMRM model. Dummy variables must already expanded , .e. via stats::model.matrix(). contain missing values outcome numeric vector. outcome value regressed MMRM model. visit character / factor vector. Indicates visit outcome value occurred . subjid character / factor vector. subject identifier used link separate visits belong subject. group character / factor vector. Indicates treatment group patient belongs .","code":""},{"path":"/reference/as_mmrm_formula.html","id":null,"dir":"Reference","previous_headings":"","what":"Create MMRM formula — as_mmrm_formula","title":"Create MMRM formula — as_mmrm_formula","text":"Derives MMRM model formula structure mmrm_df. returns formula object form:","code":""},{"path":"/reference/as_mmrm_formula.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create MMRM formula — as_mmrm_formula","text":"","code":"as_mmrm_formula(mmrm_df, cov_struct)"},{"path":"/reference/as_mmrm_formula.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create MMRM formula — as_mmrm_formula","text":"mmrm_df mmrm data.frame created as_mmrm_df() cov_struct Character - covariance structure used, must one \"us\", \"toep\", \"cs\", \"ar1\"","code":""},{"path":"/reference/as_mmrm_formula.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create MMRM formula — as_mmrm_formula","text":"","code":"outcome ~ 0 + V1 + V2 + V4 + ... + us(visit | group / subjid)"},{"path":"/reference/as_model_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand data.frame into a design matrix — as_model_df","title":"Expand data.frame into a design matrix — as_model_df","text":"Expands data.frame using formula create design matrix. Key details always place outcome variable first column return object.","code":""},{"path":"/reference/as_model_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand data.frame into a design matrix — as_model_df","text":"","code":"as_model_df(dat, frm)"},{"path":"/reference/as_model_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand data.frame into a design matrix — as_model_df","text":"dat data.frame frm formula","code":""},{"path":"/reference/as_model_df.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Expand data.frame into a design matrix — as_model_df","text":"outcome column may contain NA's none variables listed formula contain missing values","code":""},{"path":"/reference/as_simple_formula.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a simple formula object from a string — as_simple_formula","title":"Creates a simple formula object from a string — as_simple_formula","text":"Converts string list variables formula object","code":""},{"path":"/reference/as_simple_formula.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a simple formula object from a string — as_simple_formula","text":"","code":"as_simple_formula(outcome, covars)"},{"path":"/reference/as_simple_formula.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a simple formula object from a string — as_simple_formula","text":"outcome character (length 1 vector). Name outcome variable covars character (vector). Name covariates","code":""},{"path":"/reference/as_simple_formula.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a simple formula object from a string — as_simple_formula","text":"formula","code":""},{"path":"/reference/as_stan_array.html","id":null,"dir":"Reference","previous_headings":"","what":"As array — as_stan_array","title":"As array — as_stan_array","text":"Converts numeric value length 1 1 dimension array. avoid type errors thrown stan length 1 numeric vectors provided R stan::vector inputs","code":""},{"path":"/reference/as_stan_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"As array — as_stan_array","text":"","code":"as_stan_array(x)"},{"path":"/reference/as_stan_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"As array — as_stan_array","text":"x numeric vector","code":""},{"path":"/reference/as_strata.html","id":null,"dir":"Reference","previous_headings":"","what":"Create vector of Stratas — as_strata","title":"Create vector of Stratas — as_strata","text":"Collapse multiple categorical variables distinct unique categories. e.g. return","code":"as_strata(c(1,1,2,2,2,1), c(5,6,5,5,6,5)) c(1,2,3,3,4,1)"},{"path":"/reference/as_strata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create vector of Stratas — as_strata","text":"","code":"as_strata(...)"},{"path":"/reference/as_strata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create vector of Stratas — as_strata","text":"... numeric/character/factor vectors length","code":""},{"path":"/reference/as_strata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create vector of Stratas — as_strata","text":"","code":"if (FALSE) { # \\dontrun{ as_strata(c(1,1,2,2,2,1), c(5,6,5,5,6,5)) } # }"},{"path":"/reference/assert_variables_exist.html","id":null,"dir":"Reference","previous_headings":"","what":"Assert that all variables exist within a dataset — assert_variables_exist","title":"Assert that all variables exist within a dataset — assert_variables_exist","text":"Performs assertion check ensure vector variable exists within data.frame expected.","code":""},{"path":"/reference/assert_variables_exist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assert that all variables exist within a dataset — assert_variables_exist","text":"","code":"assert_variables_exist(data, vars)"},{"path":"/reference/assert_variables_exist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assert that all variables exist within a dataset — assert_variables_exist","text":"data data.frame vars character vector variable names","code":""},{"path":"/reference/char2fct.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert character variables to factor — char2fct","title":"Convert character variables to factor — char2fct","text":"Provided vector variable names function converts character variables factors. affect numeric existing factor variables","code":""},{"path":"/reference/char2fct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert character variables to factor — char2fct","text":"","code":"char2fct(data, vars = NULL)"},{"path":"/reference/char2fct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert character variables to factor — char2fct","text":"data data.frame vars character vector variables data","code":""},{"path":"/reference/check_ESS.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostics of the MCMC based on ESS — check_ESS","title":"Diagnostics of the MCMC based on ESS — check_ESS","text":"Check quality MCMC draws posterior distribution checking whether relative ESS sufficiently large.","code":""},{"path":"/reference/check_ESS.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostics of the MCMC based on ESS — check_ESS","text":"","code":"check_ESS(stan_fit, n_draws, threshold_lowESS = 0.4)"},{"path":"/reference/check_ESS.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostics of the MCMC based on ESS — check_ESS","text":"stan_fit stanfit object. n_draws Number MCMC draws. threshold_lowESS number [0,1] indicating minimum acceptable value relative ESS. See details.","code":""},{"path":"/reference/check_ESS.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostics of the MCMC based on ESS — check_ESS","text":"warning message case detected problems.","code":""},{"path":"/reference/check_ESS.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diagnostics of the MCMC based on ESS — check_ESS","text":"check_ESS() works follows: Extract ESS stan_fit parameter model. Compute relative ESS (.e. ESS divided number draws). Check whether parameter ESS lower threshold. least one parameter relative ESS threshold, warning thrown.","code":""},{"path":"/reference/check_hmc_diagn.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn","title":"Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn","text":"Check : divergent iterations. Bayesian Fraction Missing Information (BFMI) sufficiently low. number iterations saturated max treedepth zero. Please see rstan::check_hmc_diagnostics() details.","code":""},{"path":"/reference/check_hmc_diagn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn","text":"","code":"check_hmc_diagn(stan_fit)"},{"path":"/reference/check_hmc_diagn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn","text":"stan_fit stanfit object.","code":""},{"path":"/reference/check_hmc_diagn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn","text":"warning message case detected problems.","code":""},{"path":"/reference/check_mcmc.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostics of the MCMC — check_mcmc","title":"Diagnostics of the MCMC — check_mcmc","text":"Diagnostics MCMC","code":""},{"path":"/reference/check_mcmc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostics of the MCMC — check_mcmc","text":"","code":"check_mcmc(stan_fit, n_draws, threshold_lowESS = 0.4)"},{"path":"/reference/check_mcmc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostics of the MCMC — check_mcmc","text":"stan_fit stanfit object. n_draws Number MCMC draws. threshold_lowESS number [0,1] indicating minimum acceptable value relative ESS. See details.","code":""},{"path":"/reference/check_mcmc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostics of the MCMC — check_mcmc","text":"warning message case detected problems.","code":""},{"path":"/reference/check_mcmc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diagnostics of the MCMC — check_mcmc","text":"Performs checks quality MCMC. See check_ESS() check_hmc_diagn() details.","code":""},{"path":"/reference/compute_sigma.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma","title":"Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma","text":"Adapt covariance matrix reference-based methods. Used Copy Increments Reference (CIR) Jump Reference (JTR) methods, adapt covariance matrix different pre-deviation post deviation covariance structures. See Carpenter et al. (2013)","code":""},{"path":"/reference/compute_sigma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma","text":"","code":"compute_sigma(sigma_group, sigma_ref, index_mar)"},{"path":"/reference/compute_sigma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma","text":"sigma_group covariance matrix dimensions equal index_mar subjects original group sigma_ref covariance matrix dimensions equal index_mar subjects reference group index_mar logical vector indicating visits meet MAR assumption subject. .e. identifies observations non-MAR intercurrent event (ICE).","code":""},{"path":"/reference/compute_sigma.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma","text":"Carpenter, James R., James H. Roger, Michael G. Kenward. \"Analysis longitudinal trials protocol deviation: framework relevant, accessible assumptions, inference via multiple imputation.\" Journal Biopharmaceutical statistics 23.6 (2013): 1352-1371.","code":""},{"path":"/reference/convert_to_imputation_list_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's) — convert_to_imputation_list_df","title":"Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's) — convert_to_imputation_list_df","text":"Convert list imputation_list_single() objects imputation_list_df() object (.e. list imputation_df() objects's)","code":""},{"path":"/reference/convert_to_imputation_list_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's) — convert_to_imputation_list_df","text":"","code":"convert_to_imputation_list_df(imputes, sample_ids)"},{"path":"/reference/convert_to_imputation_list_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's) — convert_to_imputation_list_df","text":"imputes list imputation_list_single() objects sample_ids list 1 element per required imputation_df. element must contain vector \"ID\"'s correspond imputation_single() ID's required dataset. total number ID's must equal total number rows within imputes$imputations accommodate method_bmlmi() impute_data_individual() function returns list imputation_list_single() objects 1 object per subject. imputation_list_single() stores subjects imputations matrix columns matrix correspond D method_bmlmi(). Note methods (.e. methods_*()) special case D = 1. number rows matrix varies subject equal number times patient selected imputation (non-conditional mean methods 1 per subject per imputed dataset). function best illustrated example: convert_to_imputation_df(imputes, sample_ids) result : Note different repetitions (.e. value set D) grouped together sequentially.","code":"imputes = list( imputation_list_single( id = \"Tom\", imputations = matrix( imputation_single_t_1_1, imputation_single_t_1_2, imputation_single_t_2_1, imputation_single_t_2_2, imputation_single_t_3_1, imputation_single_t_3_2 ) ), imputation_list_single( id = \"Tom\", imputations = matrix( imputation_single_h_1_1, imputation_single_h_1_2, ) ) ) sample_ids <- list( c(\"Tom\", \"Harry\", \"Tom\"), c(\"Tom\") ) imputation_list_df( imputation_df( imputation_single_t_1_1, imputation_single_h_1_1, imputation_single_t_2_1 ), imputation_df( imputation_single_t_1_2, imputation_single_h_1_2, imputation_single_t_2_2 ), imputation_df( imputation_single_t_3_1 ), imputation_df( imputation_single_t_3_2 ) )"},{"path":"/reference/d_lagscale.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate delta from a lagged scale coefficient — d_lagscale","title":"Calculate delta from a lagged scale coefficient — d_lagscale","text":"Calculates delta value based upon baseline delta value post ICE scaling coefficient.","code":""},{"path":"/reference/d_lagscale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate delta from a lagged scale coefficient — d_lagscale","text":"","code":"d_lagscale(delta, dlag, is_post_ice)"},{"path":"/reference/d_lagscale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate delta from a lagged scale coefficient — d_lagscale","text":"delta numeric vector. Determines baseline amount delta applied visit. dlag numeric vector. Determines scaling applied delta based upon visit ICE occurred . Must length delta. is_post_ice logical vector. Indicates whether visit \"post-ICE\" .","code":""},{"path":"/reference/d_lagscale.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate delta from a lagged scale coefficient — d_lagscale","text":"See delta_template() full details calculation performed.","code":""},{"path":"/reference/delta_template.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a delta data.frame template — delta_template","title":"Create a delta data.frame template — delta_template","text":"Creates data.frame format required analyse() use applying delta adjustment.","code":""},{"path":"/reference/delta_template.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a delta data.frame template — delta_template","text":"","code":"delta_template(imputations, delta = NULL, dlag = NULL, missing_only = TRUE)"},{"path":"/reference/delta_template.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a delta data.frame template — delta_template","text":"imputations imputation object created impute(). delta NULL numeric vector. Determines baseline amount delta applied visit. See details. numeric vector must length number unique visits original dataset. dlag NULL numeric vector. Determines scaling applied delta based upon visit ICE occurred . See details. numeric vector must length number unique visits original dataset. missing_only Logical, TRUE non-missing post-ICE data delta value 0 assigned. Note calculation (described details section) performed first overwritten 0's end (.e. delta values missing post-ICE visits stay regardless option).","code":""},{"path":"/reference/delta_template.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a delta data.frame template — delta_template","text":"apply delta adjustment analyse() function expects delta data.frame 3 variables: vars$subjid, vars$visit delta (vars object supplied original call draws() created set_vars() function). function return data.frame aforementioned variables one row per subject per visit. delta argument function NULL delta column returned data.frame 0 observations. delta argument NULL delta calculated separately subject accumulative sum delta multiplied scaling coefficient dlag based upon many visits subject's intercurrent event (ICE) visit question . best illustrated example: Let delta = c(5,6,7,8) dlag=c(1,2,3,4) (.e. assuming 4 visits) lets say subject ICE visit 2. calculation follows: say subject delta offset 0 applied visit-1, 6 visit-2, 20 visit-3 44 visit-4. comparison, lets say subject instead ICE visit 3, calculation follows: terms practical usage, lets say wanted delta 5 used post ICE visits regardless proximity ICE visit. can achieved setting delta = c(5,5,5,5) dlag = c(1,0,0,0). example lets say subject ICE visit-1, calculation follows: Another way using arguments set delta difference time visits dlag amount delta per unit time. example lets say visit weeks 1, 5, 6 & 9 want delta 3 applied week ICE. can achieved setting delta = c(0,4,1,3) (difference weeks visit) dlag = c(3, 3, 3, 3). example lets say subject ICE week-5 (.e. visit-2) calculation : .e. week-6 (1 week ICE) delta 3 week-9 (4 weeks ICE) delta 12. Please note function also returns several utility variables user can create custom logic defining delta set . additional variables include: is_mar - observation missing regarded MAR? variable set FALSE observations occurred non-MAR ICE, otherwise set TRUE. is_missing - outcome variable observation missing. is_post_ice - observation occur patient's ICE defined data_ice dataset supplied draws(). strategy - imputation strategy assigned subject. design implementation function largely based upon functionality implemented called \"five marcos\" James Roger. See Roger (2021).","code":"v1 v2 v3 v4 -------------- 5 6 7 8 # delta assigned to each visit 0 1 2 3 # lagged scaling starting from the first visit after the subjects ICE -------------- 0 6 14 24 # delta * lagged scaling -------------- 0 6 20 44 # accumulative sum of delta to be applied to each visit v1 v2 v3 v4 -------------- 5 6 7 8 # delta assigned to each visit 0 0 1 2 # lagged scaling starting from the first visit after the subjects ICE -------------- 0 0 7 16 # delta * lagged scaling -------------- 0 0 7 23 # accumulative sum of delta to be applied to each visit v1 v2 v3 v4 -------------- 5 5 5 5 # delta assigned to each visit 1 0 0 0 # lagged scaling starting from the first visit after the subjects ICE -------------- 5 0 0 0 # delta * lagged scaling -------------- 5 5 5 5 # accumulative sum of delta to be applied to each visit v1 v2 v3 v4 -------------- 0 4 1 3 # delta assigned to each visit 0 0 3 3 # lagged scaling starting from the first visit after the subjects ICE -------------- 0 0 3 9 # delta * lagged scaling -------------- 0 0 3 12 # accumulative sum of delta to be applied to each visit"},{"path":"/reference/delta_template.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create a delta data.frame template — delta_template","text":"Roger, James. Reference-based mi via multivariate normal rm (“five macros” miwithd), 2021. URL https://www.lshtm.ac.uk/research/centres-projects-groups/missing-data#dia-missing-data.","code":""},{"path":[]},{"path":"/reference/delta_template.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a delta data.frame template — delta_template","text":"","code":"if (FALSE) { # \\dontrun{ delta_template(imputeObj) delta_template(imputeObj, delta = c(5,6,7,8), dlag = c(1,2,3,4)) } # }"},{"path":"/reference/do_not_run.html","id":null,"dir":"Reference","previous_headings":"","what":"Do not run this function — do_not_run","title":"Do not run this function — do_not_run","text":"function exists suppress false positive R CMD Check unused libraries","code":""},{"path":"/reference/do_not_run.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Do not run this function — do_not_run","text":"","code":"do_not_run()"},{"path":"/reference/do_not_run.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Do not run this function — do_not_run","text":"rstantools RcppParallel required used installation time. case RcppParallel used src/Makevars file created fly installation rstantools. rstantools used configure file.","code":""},{"path":"/reference/draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the base imputation model and get parameter estimates — draws","title":"Fit the base imputation model and get parameter estimates — draws","text":"draws fits base imputation model observed outcome data according given multiple imputation methodology. According user's method specification, returns either draws posterior distribution model parameters required Bayesian multiple imputation frequentist parameter estimates original data bootstrapped leave-one-datasets required conditional mean imputation. purpose imputation model estimate model parameters absence intercurrent events (ICEs) handled using reference-based imputation methods. reason, observed outcome data ICEs, reference-based imputation methods specified, removed considered missing purpose estimating imputation model, purpose . imputation model mixed model repeated measures (MMRM) valid missing--random (MAR) assumption. can fit using maximum likelihood (ML) restricted ML (REML) estimation, Bayesian approach, approximate Bayesian approach according user's method specification. ML/REML approaches approximate Bayesian approach support several possible covariance structures, Bayesian approach based MCMC sampling supports unstructured covariance structure. case covariance matrix can assumed different across group.","code":""},{"path":"/reference/draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the base imputation model and get parameter estimates — draws","text":"","code":"draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE) # S3 method for class 'approxbayes' draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE) # S3 method for class 'condmean' draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE) # S3 method for class 'bmlmi' draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE) # S3 method for class 'bayes' draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE)"},{"path":"/reference/draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the base imputation model and get parameter estimates — draws","text":"data data.frame containing data used model. See details. data_ice data.frame specifies information related ICEs imputation strategies. See details. vars vars object generated set_vars(). See details. method method object generated either method_bayes(), method_approxbayes(), method_condmean() method_bmlmi(). specifies multiple imputation methodology used. See details. ncores single numeric specifying number cores use creating draws object. Note parameter ignored method_bayes() (Default = 1). quiet Logical, TRUE suppress printing progress information printed console.","code":""},{"path":"/reference/draws.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the base imputation model and get parameter estimates — draws","text":"draws object named list containing following: data: R6 longdata object containing relevant input data information. method: method object generated either method_bayes(), method_approxbayes() method_condmean(). samples: list containing estimated parameters interest. element samples named list containing following: ids: vector characters containing ids subjects included original dataset. beta: numeric vector estimated regression coefficients. sigma: list estimated covariance matrices (one level vars$group). theta: numeric vector transformed covariances. failed: Logical. TRUE model fit failed. ids_samp: vector characters containing ids subjects included given sample. fit: method_bayes() chosen, returns MCMC Stan fit object. Otherwise NULL. n_failures: absolute number failures model fit. Relevant method_condmean(type = \"bootstrap\"), method_approxbayes() method_bmlmi(). formula: fixed effects formula object used model specification.","code":""},{"path":"/reference/draws.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit the base imputation model and get parameter estimates — draws","text":"draws performs first step multiple imputation (MI) procedure: fitting base imputation model. goal estimate parameters interest needed imputation phase (.e. regression coefficients covariance matrices MMRM model). function distinguishes following methods: Bayesian MI based MCMC sampling: draws returns draws posterior distribution parameters using Bayesian approach based MCMC sampling. method can specified using method = method_bayes(). Approximate Bayesian MI based bootstrapping: draws returns draws posterior distribution parameters using approximate Bayesian approach, sampling posterior distribution simulated fitting MMRM model bootstrap samples original dataset. method can specified using method = method_approxbayes()]. Conditional mean imputation bootstrap re-sampling: draws returns MMRM parameter estimates original dataset n_samples bootstrap samples. method can specified using method = method_condmean() argument type = \"bootstrap\". Conditional mean imputation jackknife re-sampling: draws returns MMRM parameter estimates original dataset leave-one-subject-sample. method can specified using method = method_condmean() argument type = \"jackknife\". Bootstrapped Maximum Likelihood MI: draws returns MMRM parameter estimates given number bootstrap samples needed perform random imputations bootstrapped samples. method can specified using method = method_bmlmi(). Bayesian MI based MCMC sampling proposed Carpenter, Roger, Kenward (2013) first introduced reference-based imputation methods. Approximate Bayesian MI discussed Little Rubin (2002). Conditional mean imputation methods discussed Wolbers et al (2022). Bootstrapped Maximum Likelihood MI described Von Hippel & Bartlett (2021). argument data contains longitudinal data. must least following variables: subjid: factor vector containing subject ids. visit: factor vector containing visit outcome observed . group: factor vector containing group subject belongs . outcome: numeric vector containing outcome variable. might contain missing values. Additional baseline time-varying covariates must included data. data must one row per visit per subject. means incomplete outcome data must set NA instead related row missing. Missing values covariates allowed. data incomplete expand_locf() helper function can used insert missing rows using Last Observation Carried Forward (LOCF) imputation impute covariates values. Note LOCF generally principled imputation method used appropriate specific covariate. Please note special provisioning baseline outcome values. want baseline observations included model part response variable removed advance outcome variable data. time want include baseline outcome covariate model, included separate column data (covariate). Character covariates explicitly cast factors. use custom analysis function requires specific reference levels character covariates (example computation least square means computation) advised manually cast character covariates factor advance running draws(). argument data_ice contains information occurrence ICEs. data.frame 3 columns: Subject ID: character vector containing ids subjects experienced ICE. column must named specified vars$subjid. Visit: character vector containing first visit occurrence ICE (.e. first visit affected ICE). visits must equal one levels data[[vars$visit]]. multiple ICEs happen subject, first non-MAR visit used. column must named specified vars$visit. Strategy: character vector specifying imputation strategy address ICE subject. column must named specified vars$strategy. Possible imputation strategies : \"MAR\": Missing Random. \"CIR\": Copy Increments Reference. \"CR\": Copy Reference. \"JR\": Jump Reference. \"LMCF\": Last Mean Carried Forward. explanations imputation strategies, see Carpenter, Roger, Kenward (2013), Cro et al (2021), Wolbers et al (2022). Please note user-defined imputation strategies can also set. data_ice argument necessary stage since (explained Wolbers et al (2022)), model fitted removing observations incompatible imputation model, .e. observed data data_ice[[vars$visit]] addressed imputation strategy different MAR excluded model fit. However observations discarded data imputation phase (performed function (impute()). summarize, stage pre-ICE data post-ICE data ICEs MAR imputation specified used. data_ice argument omitted, subject record within data_ice, assumed relevant subject's data pre-ICE missing visits imputed MAR assumption observed data used fit base imputation model. Please note ICE visit updated via update_strategy argument impute(); means subjects record data_ice always missing data imputed MAR assumption even strategy updated. vars argument named list specifies names key variables within data data_ice. list created set_vars() contains following named elements: subjid: name column data data_ice contains subject ids variable. visit: name column data data_ice contains visit variable. group: name column data contains group variable. outcome: name column data contains outcome variable. covariates: vector characters contains covariates included model (including interactions specified \"covariateName1*covariateName2\"``). covariates provided default model specification outcome ~ 1 + visit + groupwill used. Please note thegroup*visit` interaction included model default. strata: covariates used stratification variables bootstrap sampling. default vars$group set stratification variable. Needed method_condmean(type = \"bootstrap\") method_approxbayes(). strategy: name column data_ice contains subject-specific imputation strategy.","code":""},{"path":"/reference/draws.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit the base imputation model and get parameter estimates — draws","text":"James R Carpenter, James H Roger, Michael G Kenward. Analysis longitudinal trials protocol deviation: framework relevant, accessible assumptions, inference via multiple imputation. Journal Biopharmaceutical Statistics, 23(6):1352–1371, 2013. Suzie Cro, Tim P Morris, Michael G Kenward, James R Carpenter. Sensitivity analysis clinical trials missing continuous outcome data using controlled multiple imputation: practical guide. Statistics Medicine, 39(21):2815–2842, 2020. Roderick J. . Little Donald B. Rubin. Statistical Analysis Missing Data, Second Edition. John Wiley & Sons, Hoboken, New Jersey, 2002. [Section 10.2.3] Marcel Wolbers, Alessandro Noci, Paul Delmar, Craig Gower-Page, Sean Yiu, Jonathan W. Bartlett. Standard reference-based conditional mean imputation. https://arxiv.org/abs/2109.11162, 2022. Von Hippel, Paul T Bartlett, Jonathan W. Maximum likelihood multiple imputation: Faster imputations consistent standard errors without posterior draws. 2021.","code":""},{"path":[]},{"path":"/reference/encap_get_mmrm_sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Encapsulate get_mmrm_sample — encap_get_mmrm_sample","title":"Encapsulate get_mmrm_sample — encap_get_mmrm_sample","text":"Function creates new wrapper function around get_mmrm_sample() arguments get_mmrm_sample() enclosed within new function. makes running parallel single process calls function smoother. particular function takes care exporting arguments required parallel process cluster","code":""},{"path":"/reference/encap_get_mmrm_sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Encapsulate get_mmrm_sample — encap_get_mmrm_sample","text":"","code":"encap_get_mmrm_sample(cl, longdata, method)"},{"path":"/reference/encap_get_mmrm_sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Encapsulate get_mmrm_sample — encap_get_mmrm_sample","text":"cl Either cluster get_cluster() NULL longdata longdata object longDataConstructor$new() method method object","code":""},{"path":[]},{"path":"/reference/eval_mmrm.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate a call to mmrm — eval_mmrm","title":"Evaluate a call to mmrm — eval_mmrm","text":"utility function attempts evaluate call mmrm managing warnings errors thrown. particular function attempts catch warnings errors instead surfacing simply add additional element failed value TRUE. allows multiple calls made without program exiting.","code":""},{"path":"/reference/eval_mmrm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate a call to mmrm — eval_mmrm","text":"","code":"eval_mmrm(expr)"},{"path":"/reference/eval_mmrm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate a call to mmrm — eval_mmrm","text":"expr expression evaluated. call mmrm::mmrm().","code":""},{"path":"/reference/eval_mmrm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Evaluate a call to mmrm — eval_mmrm","text":"function originally developed use glmmTMB needed hand-holding dropping false-positive warnings. important now kept around encase need catch false-positive warnings future.","code":""},{"path":[]},{"path":"/reference/eval_mmrm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluate a call to mmrm — eval_mmrm","text":"","code":"if (FALSE) { # \\dontrun{ eval_mmrm({ mmrm::mmrm(formula, data) }) } # }"},{"path":"/reference/expand.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand and fill in missing data.frame rows — expand","title":"Expand and fill in missing data.frame rows — expand","text":"functions essentially wrappers around base::expand.grid() ensure missing combinations data inserted data.frame imputation/fill methods updating covariate values newly created rows.","code":""},{"path":"/reference/expand.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand and fill in missing data.frame rows — expand","text":"","code":"expand(data, ...) fill_locf(data, vars, group = NULL, order = NULL) expand_locf(data, ..., vars, group, order)"},{"path":"/reference/expand.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand and fill in missing data.frame rows — expand","text":"data dataset expand fill . ... variables levels expanded (note duplicate entries levels result multiple rows level). vars character vector containing names variables need filled . group character vector containing names variables group performing LOCF imputation var. order character vector containing names additional variables sort data.frame performing LOCF.","code":""},{"path":"/reference/expand.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Expand and fill in missing data.frame rows — expand","text":"draws() function makes assumption subjects visits present data.frame covariate values non missing; expand(), fill_locf() expand_locf() utility functions support users ensuring data.frame's conform assumptions. expand() takes vectors expected levels data.frame expands combinations inserting missing rows data.frame. Note \"expanded\" variables cast factors. fill_locf() applies LOCF imputation named covariates fill NAs created insertion new rows expand() (though note distinction made existing NAs newly created NAs). Note data.frame sorted c(group, order) performing LOCF imputation; data.frame returned original sort order however. expand_locf() simple composition function fill_locf() expand() .e. fill_locf(expand(...)).","code":""},{"path":"/reference/expand.html","id":"missing-first-values","dir":"Reference","previous_headings":"","what":"Missing First Values","title":"Expand and fill in missing data.frame rows — expand","text":"fill_locf() function performs last observation carried forward imputation. natural consequence unable impute missing observations observation first value given subject / grouping. values deliberately imputed risks silent errors case time varying covariates. One solution first use expand_locf() just visit variable time varying covariates merge baseline covariates afterwards .e.","code":"library(dplyr) dat_expanded <- expand( data = dat, subject = c(\"pt1\", \"pt2\", \"pt3\", \"pt4\"), visit = c(\"vis1\", \"vis2\", \"vis3\") ) dat_filled <- dat_expanded %>% left_join(baseline_covariates, by = \"subject\")"},{"path":"/reference/expand.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expand and fill in missing data.frame rows — expand","text":"","code":"if (FALSE) { # \\dontrun{ dat_expanded <- expand( data = dat, subject = c(\"pt1\", \"pt2\", \"pt3\", \"pt4\"), visit = c(\"vis1\", \"vis2\", \"vis3\") ) dat_filled <- fill_loc( data = dat_expanded, vars = c(\"Sex\", \"Age\"), group = \"subject\", order = \"visit\" ) ## Or dat_filled <- expand_locf( data = dat, subject = c(\"pt1\", \"pt2\", \"pt3\", \"pt4\"), visit = c(\"vis1\", \"vis2\", \"vis3\"), vars = c(\"Sex\", \"Age\"), group = \"subject\", order = \"visit\" ) } # }"},{"path":"/reference/extract_covariates.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Variables from string vector — extract_covariates","title":"Extract Variables from string vector — extract_covariates","text":"Takes string including potentially model terms like * : extracts individual variables","code":""},{"path":"/reference/extract_covariates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Variables from string vector — extract_covariates","text":"","code":"extract_covariates(x)"},{"path":"/reference/extract_covariates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Variables from string vector — extract_covariates","text":"x string variable names potentially including interaction terms","code":""},{"path":"/reference/extract_covariates.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract Variables from string vector — extract_covariates","text":".e. c(\"v1\", \"v2\", \"v2*v3\", \"v1:v2\") becomes c(\"v1\", \"v2\", \"v3\")","code":""},{"path":"/reference/extract_data_nmar_as_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na","title":"Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na","text":"Set NA outcome values MNAR missing (.e. occur ICE handled using reference-based imputation strategy)","code":""},{"path":"/reference/extract_data_nmar_as_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na","text":"","code":"extract_data_nmar_as_na(longdata)"},{"path":"/reference/extract_data_nmar_as_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na","text":"longdata R6 longdata object containing relevant input data information.","code":""},{"path":"/reference/extract_data_nmar_as_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na","text":"data.frame containing longdata$get_data(longdata$ids), MNAR outcome values set NA.","code":""},{"path":"/reference/extract_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract draws from a stanfit object — extract_draws","title":"Extract draws from a stanfit object — extract_draws","text":"Extract draws stanfit object convert lists. function rstan::extract() returns draws given parameter array. function calls rstan::extract() extract draws stanfit object convert arrays lists.","code":""},{"path":"/reference/extract_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract draws from a stanfit object — extract_draws","text":"","code":"extract_draws(stan_fit)"},{"path":"/reference/extract_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract draws from a stanfit object — extract_draws","text":"stan_fit stanfit object.","code":""},{"path":"/reference/extract_draws.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract draws from a stanfit object — extract_draws","text":"named list length 2 containing: beta: list length equal number draws containing draws posterior distribution regression coefficients. sigma: list length equal number draws containing draws posterior distribution covariance matrices. element list list length equal 1 same_cov = TRUE equal number groups same_cov = FALSE.","code":""},{"path":"/reference/extract_imputed_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract imputed dataset — extract_imputed_df","title":"Extract imputed dataset — extract_imputed_df","text":"Takes imputation object generated imputation_df() uses extract completed dataset longdata object created longDataConstructor(). Also applies delta transformation data.frame provided delta argument. See analyse() details structure data.frame. Subject IDs returned data.frame scrambled .e. original values.","code":""},{"path":"/reference/extract_imputed_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract imputed dataset — extract_imputed_df","text":"","code":"extract_imputed_df(imputation, ld, delta = NULL, idmap = FALSE)"},{"path":"/reference/extract_imputed_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract imputed dataset — extract_imputed_df","text":"imputation imputation object generated imputation_df(). ld longdata object generated longDataConstructor(). delta Either NULL data.frame. used offset outcome values imputed dataset. idmap Logical. TRUE attribute called \"idmap\" attached return object contains list maps old subject ids new subject ids.","code":""},{"path":"/reference/extract_imputed_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract imputed dataset — extract_imputed_df","text":"data.frame.","code":""},{"path":"/reference/extract_imputed_dfs.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract imputed datasets — extract_imputed_dfs","title":"Extract imputed datasets — extract_imputed_dfs","text":"Extracts imputed datasets contained within imputations object generated impute().","code":""},{"path":"/reference/extract_imputed_dfs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract imputed datasets — extract_imputed_dfs","text":"","code":"extract_imputed_dfs( imputations, index = seq_along(imputations$imputations), delta = NULL, idmap = FALSE )"},{"path":"/reference/extract_imputed_dfs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract imputed datasets — extract_imputed_dfs","text":"imputations imputations object created impute(). index indexes imputed datasets return. default, datasets within imputations object returned. delta data.frame containing delta transformation applied imputed dataset. See analyse() details format specification data.frame. idmap Logical. subject IDs imputed data.frame's replaced new IDs ensure unique. Setting argument TRUE attaches attribute, called idmap, returned data.frame's provide map new subject IDs old subject IDs.","code":""},{"path":"/reference/extract_imputed_dfs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract imputed datasets — extract_imputed_dfs","text":"list data.frames equal length index argument.","code":""},{"path":[]},{"path":"/reference/extract_imputed_dfs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract imputed datasets — extract_imputed_dfs","text":"","code":"if (FALSE) { # \\dontrun{ extract_imputed_dfs(imputeObj) extract_imputed_dfs(imputeObj, c(1:3)) } # }"},{"path":"/reference/extract_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract parameters from a MMRM model — extract_params","title":"Extract parameters from a MMRM model — extract_params","text":"Extracts beta sigma coefficients MMRM model created mmrm::mmrm().","code":""},{"path":"/reference/extract_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract parameters from a MMRM model — extract_params","text":"","code":"extract_params(fit)"},{"path":"/reference/extract_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract parameters from a MMRM model — extract_params","text":"fit object created mmrm::mmrm()","code":""},{"path":"/reference/fit_mcmc.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the base imputation model using a Bayesian approach — fit_mcmc","title":"Fit the base imputation model using a Bayesian approach — fit_mcmc","text":"fit_mcmc() fits base imputation model using Bayesian approach. done MCMC method implemented stan run using function rstan::sampling(). function returns draws posterior distribution model parameters stanfit object. Additionally performs multiple diagnostics checks chain returns warnings case detected issues.","code":""},{"path":"/reference/fit_mcmc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the base imputation model using a Bayesian approach — fit_mcmc","text":"","code":"fit_mcmc(designmat, outcome, group, subjid, visit, method, quiet = FALSE)"},{"path":"/reference/fit_mcmc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the base imputation model using a Bayesian approach — fit_mcmc","text":"designmat design matrix fixed effects. outcome response variable. Must numeric. group Character vector containing group variable. subjid Character vector containing subjects IDs. visit Character vector containing visit variable. method method object generated method_bayes(). quiet Specify whether stan sampling log printed console.","code":""},{"path":"/reference/fit_mcmc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the base imputation model using a Bayesian approach — fit_mcmc","text":"named list composed following: samples: named list containing draws parameter. corresponds output extract_draws(). fit: stanfit object.","code":""},{"path":"/reference/fit_mcmc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit the base imputation model using a Bayesian approach — fit_mcmc","text":"Bayesian model assumes multivariate normal likelihood function weakly-informative priors model parameters: particular, uniform priors assumed regression coefficients inverse-Wishart priors covariance matrices. chain initialized using REML parameter estimates MMRM starting values. function performs following steps: Fit MMRM using REML approach. Prepare input data MCMC fit described data{} block Stan file. See prepare_stan_data() details. Run MCMC according input arguments using starting values REML parameter estimates estimated point 1. Performs diagnostics checks MCMC. See check_mcmc() details. Extract draws model fit. chains perform method$n_samples draws keeping one every method$burn_between iterations. Additionally first method$burn_in iterations discarded. total number iterations method$burn_in + method$burn_between*method$n_samples. purpose method$burn_in ensure samples drawn stationary distribution Markov Chain. method$burn_between aims keep draws uncorrelated .","code":""},{"path":"/reference/fit_mmrm.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a MMRM model — fit_mmrm","title":"Fit a MMRM model — fit_mmrm","text":"Fits MMRM model allowing different covariance structures using mmrm::mmrm(). Returns list key model parameters beta, sigma additional element failed indicating whether fit failed converge. fit fail converge beta sigma present.","code":""},{"path":"/reference/fit_mmrm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a MMRM model — fit_mmrm","text":"","code":"fit_mmrm( designmat, outcome, subjid, visit, group, cov_struct = c(\"us\", \"toep\", \"cs\", \"ar1\"), REML = TRUE, same_cov = TRUE )"},{"path":"/reference/fit_mmrm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a MMRM model — fit_mmrm","text":"designmat data.frame matrix containing covariates use MMRM model. Dummy variables must already expanded , .e. via stats::model.matrix(). contain missing values outcome numeric vector. outcome value regressed MMRM model. subjid character / factor vector. subject identifier used link separate visits belong subject. visit character / factor vector. Indicates visit outcome value occurred . group character / factor vector. Indicates treatment group patient belongs . cov_struct character value. Specifies covariance structure use. Must one \"us\", \"toep\", \"cs\" \"ar1\" REML logical. Specifies whether restricted maximum likelihood used same_cov logical. Used specify shared individual covariance matrix used per group","code":""},{"path":"/reference/generate_data_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate data for a single group — generate_data_single","title":"Generate data for a single group — generate_data_single","text":"Generate data single group","code":""},{"path":"/reference/generate_data_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate data for a single group — generate_data_single","text":"","code":"generate_data_single(pars_group, strategy_fun = NULL, distr_pars_ref = NULL)"},{"path":"/reference/generate_data_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate data for a single group — generate_data_single","text":"pars_group simul_pars object generated set_simul_pars(). specifies simulation parameters given group. strategy_fun Function implementing trajectories intercurrent event (ICE). Must one getStrategies(). See getStrategies() details. NULL post-ICE outcomes untouched. distr_pars_ref Optional. Named list containing simulation parameters reference arm. contains following elements: mu: Numeric vector indicating mean outcome trajectory assuming ICEs. include outcome baseline. sigma Covariance matrix outcome trajectory assuming ICEs. NULL, parameters inherited pars_group.","code":""},{"path":"/reference/generate_data_single.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate data for a single group — generate_data_single","text":"data.frame containing simulated data. includes following variables: id: Factor variable specifies id subject. visit: Factor variable specifies visit assessment. Visit 0 denotes baseline visit. group: Factor variable specifies treatment group subject belongs . outcome_bl: Numeric variable specifies baseline outcome. outcome_noICE: Numeric variable specifies longitudinal outcome assuming ICEs. ind_ice1: Binary variable takes value 1 corresponding visit affected ICE1 0 otherwise. dropout_ice1: Binary variable takes value 1 corresponding visit affected drop-following ICE1 0 otherwise. ind_ice2: Binary variable takes value 1 corresponding visit affected ICE2. outcome: Numeric variable specifies longitudinal outcome including ICE1, ICE2 intermittent missing values.","code":""},{"path":[]},{"path":"/reference/getStrategies.html","id":null,"dir":"Reference","previous_headings":"","what":"Get imputation strategies — getStrategies","title":"Get imputation strategies — getStrategies","text":"Returns list defining imputation strategies used create multivariate normal distribution parameters merging source group reference group per patient.","code":""},{"path":"/reference/getStrategies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get imputation strategies — getStrategies","text":"","code":"getStrategies(...)"},{"path":"/reference/getStrategies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get imputation strategies — getStrategies","text":"... User defined methods added return list. Input must function.","code":""},{"path":"/reference/getStrategies.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get imputation strategies — getStrategies","text":"default Jump Reference (JR), Copy Reference (CR), Copy Increments Reference (CIR), Last Mean Carried Forward (LMCF) Missing Random (MAR) defined. user can define strategy functions (overwrite pre-defined ones) specifying named input function .e. NEW = function(...) .... exception MAR overwritten. user defined functions must take 3 inputs: pars_group, pars_ref index_mar. pars_group pars_ref lists elements mu sigma representing multivariate normal distribution parameters subject's current group reference group respectively. index_mar logical vector specifying visits subject met MAR assumption . function must return list elements mu sigma. See implementation strategy_JR() example.","code":""},{"path":"/reference/getStrategies.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get imputation strategies — getStrategies","text":"","code":"if (FALSE) { # \\dontrun{ getStrategies() getStrategies( NEW = function(pars_group, pars_ref, index_mar) code , JR = function(pars_group, pars_ref, index_mar) more_code ) } # }"},{"path":"/reference/get_ESS.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS","title":"Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS","text":"Extract Effective Sample Size (ESS) stanfit object","code":""},{"path":"/reference/get_ESS.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS","text":"","code":"get_ESS(stan_fit)"},{"path":"/reference/get_ESS.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS","text":"stan_fit stanfit object.","code":""},{"path":"/reference/get_ESS.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS","text":"named vector containing ESS parameter model.","code":""},{"path":"/reference/get_bootstrap_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a stack object populated with bootstrapped samples — get_bootstrap_stack","title":"Creates a stack object populated with bootstrapped samples — get_bootstrap_stack","text":"Function creates Stack() object populated stack bootstrap samples based upon method$n_samples","code":""},{"path":"/reference/get_bootstrap_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a stack object populated with bootstrapped samples — get_bootstrap_stack","text":"","code":"get_bootstrap_stack(longdata, method, stack = Stack$new())"},{"path":"/reference/get_bootstrap_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a stack object populated with bootstrapped samples — get_bootstrap_stack","text":"longdata longDataConstructor() object method method object stack Stack() object (exposed unit testing purposes)","code":""},{"path":"/reference/get_cluster.html","id":null,"dir":"Reference","previous_headings":"","what":"Create cluster — get_cluster","title":"Create cluster — get_cluster","text":"Create cluster","code":""},{"path":"/reference/get_cluster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create cluster — get_cluster","text":"","code":"get_cluster(ncores = 1)"},{"path":"/reference/get_cluster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create cluster — get_cluster","text":"ncores Number parallel processes use ncores 1 function return NULL function spawns PSOCK cluster. Ensures rbmi assert_that loaded sub-processes","code":""},{"path":"/reference/get_conditional_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive conditional multivariate normal parameters — get_conditional_parameters","title":"Derive conditional multivariate normal parameters — get_conditional_parameters","text":"Takes parameters multivariate normal distribution observed values calculate conditional distribution unobserved values.","code":""},{"path":"/reference/get_conditional_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive conditional multivariate normal parameters — get_conditional_parameters","text":"","code":"get_conditional_parameters(pars, values)"},{"path":"/reference/get_conditional_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive conditional multivariate normal parameters — get_conditional_parameters","text":"pars list elements mu sigma defining mean vector covariance matrix respectively. values vector observed values condition , must length pars$mu. Missing values must represented NA.","code":""},{"path":"/reference/get_conditional_parameters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive conditional multivariate normal parameters — get_conditional_parameters","text":"list conditional distribution parameters: mu - conditional mean vector. sigma - conditional covariance matrix.","code":""},{"path":"/reference/get_delta_template.html","id":null,"dir":"Reference","previous_headings":"","what":"Get delta utility variables — get_delta_template","title":"Get delta utility variables — get_delta_template","text":"function creates default delta template (1 row per subject per visit) extracts utility information users need define logic defining delta. See delta_template() full details.","code":""},{"path":"/reference/get_delta_template.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get delta utility variables — get_delta_template","text":"","code":"get_delta_template(imputations)"},{"path":"/reference/get_delta_template.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get delta utility variables — get_delta_template","text":"imputations imputations object created impute().","code":""},{"path":"/reference/get_draws_mle.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the base imputation model on bootstrap samples — get_draws_mle","title":"Fit the base imputation model on bootstrap samples — get_draws_mle","text":"Fit base imputation model using ML/REML approach given number bootstrap samples specified method$n_samples. Returns parameter estimates model fit.","code":""},{"path":"/reference/get_draws_mle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the base imputation model on bootstrap samples — get_draws_mle","text":"","code":"get_draws_mle( longdata, method, sample_stack, n_target_samples, first_sample_orig, use_samp_ids, failure_limit = 0, ncores = 1, quiet = FALSE )"},{"path":"/reference/get_draws_mle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the base imputation model on bootstrap samples — get_draws_mle","text":"longdata R6 longdata object containing relevant input data information. method method object generated either method_approxbayes() method_condmean() argument type = \"bootstrap\". sample_stack stack object containing subject ids used mmrm iteration. n_target_samples Number samples needed created first_sample_orig Logical. TRUE function returns method$n_samples + 1 samples first sample contains parameter estimates original dataset method$n_samples samples contain parameter estimates bootstrap samples. FALSE function returns method$n_samples samples containing parameter estimates bootstrap samples. use_samp_ids Logical. TRUE, sampled subject ids returned. Otherwise subject ids original dataset returned. values used tell impute() subjects used derive imputed dataset. failure_limit Number failed samples allowed throwing error ncores Number processes parallelise job quiet Logical, TRUE suppress printing progress information printed console.","code":""},{"path":"/reference/get_draws_mle.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the base imputation model on bootstrap samples — get_draws_mle","text":"draws object named list containing following: data: R6 longdata object containing relevant input data information. method: method object generated either method_bayes(), method_approxbayes() method_condmean(). samples: list containing estimated parameters interest. element samples named list containing following: ids: vector characters containing ids subjects included original dataset. beta: numeric vector estimated regression coefficients. sigma: list estimated covariance matrices (one level vars$group). theta: numeric vector transformed covariances. failed: Logical. TRUE model fit failed. ids_samp: vector characters containing ids subjects included given sample. fit: method_bayes() chosen, returns MCMC Stan fit object. Otherwise NULL. n_failures: absolute number failures model fit. Relevant method_condmean(type = \"bootstrap\"), method_approxbayes() method_bmlmi(). formula: fixed effects formula object used model specification.","code":""},{"path":"/reference/get_draws_mle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit the base imputation model on bootstrap samples — get_draws_mle","text":"function takes Stack object contains multiple lists patient ids. function takes Stack pulls set ids constructs dataset just consisting patients (.e. potentially bootstrap jackknife sample). function fits MMRM model dataset create sample object. function repeats process n_target_samples reached. failure_limit samples fail converge function throws error. reaching desired number samples function generates returns draws object.","code":""},{"path":"/reference/get_ests_bmlmi.html","id":null,"dir":"Reference","previous_headings":"","what":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"Compute pooled point estimates, standard error degrees freedom according Von Hippel Bartlett formula Bootstrapped Maximum Likelihood Multiple Imputation (BMLMI).","code":""},{"path":"/reference/get_ests_bmlmi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"","code":"get_ests_bmlmi(ests, D)"},{"path":"/reference/get_ests_bmlmi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"ests numeric vector containing estimates analysis imputed datasets. D numeric representing number imputations bootstrap sample BMLMI method.","code":""},{"path":"/reference/get_ests_bmlmi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"list containing point estimate, standard error degrees freedom.","code":""},{"path":"/reference/get_ests_bmlmi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"ests must provided following order: firsts D elements related analyses random imputation one bootstrap sample. second set D elements (.e. D+1 2*D) related second bootstrap sample .","code":""},{"path":"/reference/get_ests_bmlmi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"Von Hippel, Paul T Bartlett, Jonathan W8. Maximum likelihood multiple imputation: Faster imputations consistent standard errors without posterior draws. 2021","code":""},{"path":"/reference/get_example_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate a realistic example dataset — get_example_data","title":"Simulate a realistic example dataset — get_example_data","text":"Simulate realistic example dataset using simulate_data() hard-coded values input arguments.","code":""},{"path":"/reference/get_example_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate a realistic example dataset — get_example_data","text":"","code":"get_example_data()"},{"path":"/reference/get_example_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate a realistic example dataset — get_example_data","text":"get_example_data() simulates 1:1 randomized trial active drug (intervention) versus placebo (control) 100 subjects per group 6 post-baseline assessments (bi-monthly visits 12 months). One intercurrent event corresponding treatment discontinuation also simulated. Specifically, data simulated following assumptions: mean outcome trajectory placebo group increases linearly 50 baseline (visit 0) 60 visit 6, .e. slope 10 points/year. mean outcome trajectory intervention group identical placebo group visit 2. visit 2 onward, slope decreases 50% 5 points/year. covariance structure baseline follow-values groups implied random intercept slope model standard deviation 5 intercept slope, correlation 0.25. addition, independent residual error standard deviation 2.5 added assessment. probability study drug discontinuation visit calculated according logistic model depends observed outcome visit. Specifically, visit-wise discontinuation probability 2% 3% control intervention group, respectively, specified case observed outcome equal 50 (mean value baseline). odds discontinuation simulated increase +10% +1 point increase observed outcome. Study drug discontinuation simulated effect mean trajectory placebo group. intervention group, subjects discontinue follow slope mean trajectory placebo group time point onward. compatible copy increments reference (CIR) assumption. Study drop-study drug discontinuation visit occurs probability 50% leading missing outcome data time point onward.","code":""},{"path":[]},{"path":"/reference/get_jackknife_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a stack object populated with jackknife samples — get_jackknife_stack","title":"Creates a stack object populated with jackknife samples — get_jackknife_stack","text":"Function creates Stack() object populated stack jackknife samples based upon","code":""},{"path":"/reference/get_jackknife_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a stack object populated with jackknife samples — get_jackknife_stack","text":"","code":"get_jackknife_stack(longdata, method, stack = Stack$new())"},{"path":"/reference/get_jackknife_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a stack object populated with jackknife samples — get_jackknife_stack","text":"longdata longDataConstructor() object method method object stack Stack() object (exposed unit testing purposes)","code":""},{"path":"/reference/get_mmrm_sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit MMRM and returns parameter estimates — get_mmrm_sample","title":"Fit MMRM and returns parameter estimates — get_mmrm_sample","text":"get_mmrm_sample fits base imputation model using ML/REML approach. Returns parameter estimates fit.","code":""},{"path":"/reference/get_mmrm_sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit MMRM and returns parameter estimates — get_mmrm_sample","text":"","code":"get_mmrm_sample(ids, longdata, method)"},{"path":"/reference/get_mmrm_sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit MMRM and returns parameter estimates — get_mmrm_sample","text":"ids vector characters containing ids subjects. longdata R6 longdata object containing relevant input data information. method method object generated either method_approxbayes() method_condmean().","code":""},{"path":"/reference/get_mmrm_sample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit MMRM and returns parameter estimates — get_mmrm_sample","text":"named list class sample_single. contains following: ids vector characters containing ids subjects included original dataset. beta numeric vector estimated regression coefficients. sigma list estimated covariance matrices (one level vars$group). theta numeric vector transformed covariances. failed logical. TRUE model fit failed. ids_samp vector characters containing ids subjects included given sample.","code":""},{"path":"/reference/get_pattern_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine patients missingness group — get_pattern_groups","title":"Determine patients missingness group — get_pattern_groups","text":"Takes design matrix multiple rows per subject returns dataset 1 row per subject new column pgroup indicating group patient belongs (based upon missingness pattern treatment group)","code":""},{"path":"/reference/get_pattern_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine patients missingness group — get_pattern_groups","text":"","code":"get_pattern_groups(ddat)"},{"path":"/reference/get_pattern_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine patients missingness group — get_pattern_groups","text":"ddat data.frame columns subjid, visit, group, is_avail","code":""},{"path":"/reference/get_pattern_groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine patients missingness group — get_pattern_groups","text":"column is_avail must character numeric 0 1","code":""},{"path":"/reference/get_pattern_groups_unique.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Pattern Summary — get_pattern_groups_unique","title":"Get Pattern Summary — get_pattern_groups_unique","text":"Takes dataset pattern information creates summary dataset just 1 row per pattern","code":""},{"path":"/reference/get_pattern_groups_unique.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Pattern Summary — get_pattern_groups_unique","text":"","code":"get_pattern_groups_unique(patterns)"},{"path":"/reference/get_pattern_groups_unique.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Pattern Summary — get_pattern_groups_unique","text":"patterns data.frame columns pgroup, pattern group","code":""},{"path":"/reference/get_pattern_groups_unique.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Pattern Summary — get_pattern_groups_unique","text":"column pgroup must numeric vector indicating pattern group patient belongs column pattern must character string 0's 1's. must identical rows within pgroup column group must character / numeric vector indicating covariance group observation belongs . must identical within pgroup","code":""},{"path":"/reference/get_pool_components.html","id":null,"dir":"Reference","previous_headings":"","what":"Expected Pool Components — get_pool_components","title":"Expected Pool Components — get_pool_components","text":"Returns elements expected contained analyse object depending analysis method specified.","code":""},{"path":"/reference/get_pool_components.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expected Pool Components — get_pool_components","text":"","code":"get_pool_components(x)"},{"path":"/reference/get_pool_components.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expected Pool Components — get_pool_components","text":"x Character name analysis method, must one either \"rubin\", \"jackknife\", \"bootstrap\" \"bmlmi\".","code":""},{"path":"/reference/get_visit_distribution_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive visit distribution parameters — get_visit_distribution_parameters","title":"Derive visit distribution parameters — get_visit_distribution_parameters","text":"Takes patient level data beta coefficients expands get patient specific estimate visit distribution parameters mu sigma. Returns values specific format expected downstream functions imputation process (namely list(list(mu = ..., sigma = ...), list(mu = ..., sigma = ...))).","code":""},{"path":"/reference/get_visit_distribution_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive visit distribution parameters — get_visit_distribution_parameters","text":"","code":"get_visit_distribution_parameters(dat, beta, sigma)"},{"path":"/reference/get_visit_distribution_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive visit distribution parameters — get_visit_distribution_parameters","text":"dat Patient level dataset, must 1 row per visit. Column order must order beta. number columns must match length beta beta List model beta coefficients. 1 element sample e.g. 3 samples models 4 beta coefficients argument form list( c(1,2,3,4) , c(5,6,7,8), c(9,10,11,12)). elements beta must length must length order dat. sigma List sigma. Must number entries beta.","code":""},{"path":"/reference/has_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Does object have a class ? — has_class","title":"Does object have a class ? — has_class","text":"Utility function see object particular class. Useful know many classes object may .","code":""},{"path":"/reference/has_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Does object have a class ? — has_class","text":"","code":"has_class(x, cls)"},{"path":"/reference/has_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Does object have a class ? — has_class","text":"x object want check class . cls class want know .","code":""},{"path":"/reference/has_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Does object have a class ? — has_class","text":"TRUE object class. FALSE object class.","code":""},{"path":"/reference/ife.html","id":null,"dir":"Reference","previous_headings":"","what":"if else — ife","title":"if else — ife","text":"wrapper around () else() prevent unexpected interactions ifelse() factor variables","code":""},{"path":"/reference/ife.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"if else — ife","text":"","code":"ife(x, a, b)"},{"path":"/reference/ife.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"if else — ife","text":"x True / False value return True b value return False","code":""},{"path":"/reference/ife.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"if else — ife","text":"default ifelse() convert factor variables numeric values often undesirable. connivance function avoids problem","code":""},{"path":"/reference/imputation_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a valid imputation_df object — imputation_df","title":"Create a valid imputation_df object — imputation_df","text":"Create valid imputation_df object","code":""},{"path":"/reference/imputation_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a valid imputation_df object — imputation_df","text":"","code":"imputation_df(...)"},{"path":"/reference/imputation_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a valid imputation_df object — imputation_df","text":"... list imputation_single.","code":""},{"path":"/reference/imputation_list_df.html","id":null,"dir":"Reference","previous_headings":"","what":"List of imputations_df — imputation_list_df","title":"List of imputations_df — imputation_list_df","text":"container multiple imputation_df's","code":""},{"path":"/reference/imputation_list_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List of imputations_df — imputation_list_df","text":"","code":"imputation_list_df(...)"},{"path":"/reference/imputation_list_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List of imputations_df — imputation_list_df","text":"... objects class imputation_df","code":""},{"path":"/reference/imputation_list_single.html","id":null,"dir":"Reference","previous_headings":"","what":"A collection of imputation_singles() grouped by a single subjid ID — imputation_list_single","title":"A collection of imputation_singles() grouped by a single subjid ID — imputation_list_single","text":"collection imputation_singles() grouped single subjid ID","code":""},{"path":"/reference/imputation_list_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A collection of imputation_singles() grouped by a single subjid ID — imputation_list_single","text":"","code":"imputation_list_single(imputations, D = 1)"},{"path":"/reference/imputation_list_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A collection of imputation_singles() grouped by a single subjid ID — imputation_list_single","text":"imputations list imputation_single() objects ordered repetitions grouped sequentially D number repetitions performed determines many columns imputation matrix constructor function create imputation_list_single object contains matrix imputation_single() objects grouped single id. matrix split D columns (.e. non-bmlmi methods always 1) id attribute determined extracting id attribute contributing imputation_single() objects. error throw multiple id detected","code":""},{"path":"/reference/imputation_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a valid imputation_single object — imputation_single","title":"Create a valid imputation_single object — imputation_single","text":"Create valid imputation_single object","code":""},{"path":"/reference/imputation_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a valid imputation_single object — imputation_single","text":"","code":"imputation_single(id, values)"},{"path":"/reference/imputation_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a valid imputation_single object — imputation_single","text":"id character string specifying subject id. values numeric vector indicating imputed values.","code":""},{"path":"/reference/impute.html","id":null,"dir":"Reference","previous_headings":"","what":"Create imputed datasets — impute","title":"Create imputed datasets — impute","text":"impute() creates imputed datasets based upon data options specified call draws(). One imputed dataset created per \"sample\" created draws().","code":""},{"path":"/reference/impute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create imputed datasets — impute","text":"","code":"impute( draws, references = NULL, update_strategy = NULL, strategies = getStrategies() ) # S3 method for class 'random' impute( draws, references = NULL, update_strategy = NULL, strategies = getStrategies() ) # S3 method for class 'condmean' impute( draws, references = NULL, update_strategy = NULL, strategies = getStrategies() )"},{"path":"/reference/impute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create imputed datasets — impute","text":"draws draws object created draws(). references named vector. Identifies references used reference-based imputation methods. form c(\"Group1\" = \"Reference1\", \"Group2\" = \"Reference2\"). NULL (default), references assumed form c(\"Group1\" = \"Group1\", \"Group2\" = \"Group2\"). argument NULL imputation strategy (defined data_ice[[vars$strategy]] call draws) MAR set. update_strategy optional data.frame. Updates imputation method originally set via data_ice option draws(). See details section information. strategies named list functions. Defines imputation functions used. names list mirror values specified strategy column data_ice. Default = getStrategies(). See getStrategies() details.","code":""},{"path":"/reference/impute.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create imputed datasets — impute","text":"impute() uses imputation model parameter estimates, generated draws(), first calculate marginal (multivariate normal) distribution subject's longitudinal outcome variable depending covariate values. subjects intercurrent events (ICEs) handled using non-MAR methods, marginal distribution updated depending time first visit affected ICE, chosen imputation strategy chosen reference group described Carpenter, Roger, Kenward (2013) . subject's imputation distribution used imputing missing values defined marginal distribution conditional observed outcome values. One dataset generated per set parameter estimates provided draws(). exact manner missing values imputed conditional imputation distribution depends method object provided draws(), particular: Bayes & Approximate Bayes: imputed dataset contains 1 row per subject & visit original dataset missing values imputed taking single random sample conditional imputation distribution. Conditional Mean: imputed dataset contains 1 row per subject & visit bootstrapped jackknife dataset used generate corresponding parameter estimates draws(). Missing values imputed using mean conditional imputation distribution. Please note first imputed dataset refers conditional mean imputation original dataset whereas subsequent imputed datasets refer conditional mean imputations bootstrap jackknife samples, respectively, original data. Bootstrapped Maximum Likelihood MI (BMLMI): performs D random imputations bootstrapped dataset used generate corresponding parameter estimates draws(). total number B*D imputed datasets provided, B number bootstrapped datasets. Missing values imputed taking random sample conditional imputation distribution. update_strategy argument can used update imputation strategy originally set via data_ice option draws(). avoids re-run draws() function changing imputation strategy certain circumstances (detailed ). data.frame provided update_strategy argument must contain two columns, one subject ID another imputation strategy, whose names defined vars argument specified call draws(). Please note argument allows update imputation strategy arguments time first visit affected ICE. key limitation functionality one can switch MAR non-MAR strategy (vice versa) subjects without observed post-ICE data. reason change affect whether post-ICE data included base imputation model (explained help draws()). example, subject ICE \"Visit 2\" observed/known values \"Visit 3\" function throw error one tries switch strategy MAR non-MAR strategy. contrast, switching non-MAR MAR strategy, whilst valid, raise warning usable data utilised imputation model.","code":""},{"path":"/reference/impute.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create imputed datasets — impute","text":"James R Carpenter, James H Roger, Michael G Kenward. Analysis longitudinal trials protocol deviation: framework relevant, accessible assumptions, inference via multiple imputation. Journal Biopharmaceutical Statistics, 23(6):1352–1371, 2013. [Section 4.2 4.3]","code":""},{"path":"/reference/impute.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create imputed datasets — impute","text":"","code":"if (FALSE) { # \\dontrun{ impute( draws = drawobj, references = c(\"Trt\" = \"Placebo\", \"Placebo\" = \"Placebo\") ) new_strategy <- data.frame( subjid = c(\"Pt1\", \"Pt2\"), strategy = c(\"MAR\", \"JR\") ) impute( draws = drawobj, references = c(\"Trt\" = \"Placebo\", \"Placebo\" = \"Placebo\"), update_strategy = new_strategy ) } # }"},{"path":"/reference/impute_data_individual.html","id":null,"dir":"Reference","previous_headings":"","what":"Impute data for a single subject — impute_data_individual","title":"Impute data for a single subject — impute_data_individual","text":"function performs imputation single subject time implementing process detailed impute().","code":""},{"path":"/reference/impute_data_individual.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Impute data for a single subject — impute_data_individual","text":"","code":"impute_data_individual( id, index, beta, sigma, data, references, strategies, condmean, n_imputations = 1 )"},{"path":"/reference/impute_data_individual.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Impute data for a single subject — impute_data_individual","text":"id Character string identifying subject. index sample indexes subject belongs e.g c(1,1,1,2,2,4). beta list beta coefficients sample, .e. beta[[1]] set beta coefficients first sample. sigma list sigma coefficients sample split group .e. sigma[[1]][[\"\"]] give sigma coefficients group first sample. data longdata object created longDataConstructor() references named vector. Identifies references used generating imputed values. form c(\"Group\" = \"Reference\", \"Group\" = \"Reference\"). strategies named list functions. Defines imputation functions used. names list mirror values specified method column data_ice. Default = getStrategies(). See getStrategies() details. condmean Logical. TRUE impute using conditional mean values, FALSE impute taking random draw multivariate normal distribution. n_imputations condmean = FALSE numeric representing number random imputations performed sample. Default 1 (one random imputation per sample).","code":""},{"path":"/reference/impute_data_individual.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Impute data for a single subject — impute_data_individual","text":"Note function performs required imputations subject time. .e. subject included samples 1,3,5,9 imputations (using sample-dependent imputation model parameters) performed one step order avoid look subjects's covariates expanding design matrix multiple times (computationally expensive). function also supports subject belonging sample multiple times, .e. 1,1,2,3,5,5, typically occur bootstrapped datasets.","code":""},{"path":"/reference/impute_internal.html","id":null,"dir":"Reference","previous_headings":"","what":"Create imputed datasets — impute_internal","title":"Create imputed datasets — impute_internal","text":"work horse function implements functionality impute. See user level function impute() details.","code":""},{"path":"/reference/impute_internal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create imputed datasets — impute_internal","text":"","code":"impute_internal( draws, references = NULL, update_strategy, strategies, condmean )"},{"path":"/reference/impute_internal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create imputed datasets — impute_internal","text":"draws draws object created draws(). references named vector. Identifies references used reference-based imputation methods. form c(\"Group1\" = \"Reference1\", \"Group2\" = \"Reference2\"). NULL (default), references assumed form c(\"Group1\" = \"Group1\", \"Group2\" = \"Group2\"). argument NULL imputation strategy (defined data_ice[[vars$strategy]] call draws) MAR set. update_strategy optional data.frame. Updates imputation method originally set via data_ice option draws(). See details section information. strategies named list functions. Defines imputation functions used. names list mirror values specified strategy column data_ice. Default = getStrategies(). See getStrategies() details. condmean logical. TRUE impute using conditional mean values, values impute taking random draw multivariate normal distribution.","code":""},{"path":"/reference/impute_outcome.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample outcome value — impute_outcome","title":"Sample outcome value — impute_outcome","text":"Draws random sample multivariate normal distribution.","code":""},{"path":"/reference/impute_outcome.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample outcome value — impute_outcome","text":"","code":"impute_outcome(conditional_parameters, n_imputations = 1, condmean = FALSE)"},{"path":"/reference/impute_outcome.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample outcome value — impute_outcome","text":"conditional_parameters list elements mu sigma contain mean vector covariance matrix sample . n_imputations numeric representing number random samples multivariate normal distribution performed. Default 1. condmean conditional mean imputation performed (opposed random sampling)","code":""},{"path":"/reference/invert.html","id":null,"dir":"Reference","previous_headings":"","what":"invert — invert","title":"invert — invert","text":"Utility function used replicated purrr::transpose. Turns list inside .","code":""},{"path":"/reference/invert.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"invert — invert","text":"","code":"invert(x)"},{"path":"/reference/invert.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"invert — invert","text":"x list","code":""},{"path":"/reference/invert_indexes.html","id":null,"dir":"Reference","previous_headings":"","what":"Invert and derive indexes — invert_indexes","title":"Invert and derive indexes — invert_indexes","text":"Takes list elements creates new list containing 1 entry per unique element value containing indexes original elements occurred .","code":""},{"path":"/reference/invert_indexes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Invert and derive indexes — invert_indexes","text":"","code":"invert_indexes(x)"},{"path":"/reference/invert_indexes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Invert and derive indexes — invert_indexes","text":"x list elements invert calculate index (see details).","code":""},{"path":"/reference/invert_indexes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Invert and derive indexes — invert_indexes","text":"functions purpose best illustrated example: input: becomes:","code":"list( c(\"A\", \"B\", \"C\"), c(\"A\", \"A\", \"B\"))} list( \"A\" = c(1,2,2), \"B\" = c(1,2), \"C\" = 1 )"},{"path":"/reference/is_absent.html","id":null,"dir":"Reference","previous_headings":"","what":"Is value absent — is_absent","title":"Is value absent — is_absent","text":"Returns true value either NULL, NA \"\". case vector values must NULL/NA/\"\" x regarded absent.","code":""},{"path":"/reference/is_absent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is value absent — is_absent","text":"","code":"is_absent(x, na = TRUE, blank = TRUE)"},{"path":"/reference/is_absent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is value absent — is_absent","text":"x value check absent na NAs count absent blank blanks .e. \"\" count absent","code":""},{"path":"/reference/is_char_fact.html","id":null,"dir":"Reference","previous_headings":"","what":"Is character or factor — is_char_fact","title":"Is character or factor — is_char_fact","text":"returns true x character factor vector","code":""},{"path":"/reference/is_char_fact.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is character or factor — is_char_fact","text":"","code":"is_char_fact(x)"},{"path":"/reference/is_char_fact.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is character or factor — is_char_fact","text":"x character factor vector","code":""},{"path":"/reference/is_char_one.html","id":null,"dir":"Reference","previous_headings":"","what":"Is single character — is_char_one","title":"Is single character — is_char_one","text":"returns true x length 1 character vector","code":""},{"path":"/reference/is_char_one.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is single character — is_char_one","text":"","code":"is_char_one(x)"},{"path":"/reference/is_char_one.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is single character — is_char_one","text":"x character vector","code":""},{"path":"/reference/is_in_rbmi_development.html","id":null,"dir":"Reference","previous_headings":"","what":"Is package in development mode? — is_in_rbmi_development","title":"Is package in development mode? — is_in_rbmi_development","text":"Returns TRUE package developed .e. local copy source code actively editing Returns FALSE otherwise","code":""},{"path":"/reference/is_in_rbmi_development.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is package in development mode? — is_in_rbmi_development","text":"","code":"is_in_rbmi_development()"},{"path":"/reference/is_in_rbmi_development.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Is package in development mode? — is_in_rbmi_development","text":"Main use function parallel processing indicate whether sub-processes need load current development version code whether load main installed package system","code":""},{"path":"/reference/is_num_char_fact.html","id":null,"dir":"Reference","previous_headings":"","what":"Is character, factor or numeric — is_num_char_fact","title":"Is character, factor or numeric — is_num_char_fact","text":"returns true x character, numeric factor vector","code":""},{"path":"/reference/is_num_char_fact.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is character, factor or numeric — is_num_char_fact","text":"","code":"is_num_char_fact(x)"},{"path":"/reference/is_num_char_fact.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is character, factor or numeric — is_num_char_fact","text":"x character, numeric factor vector","code":""},{"path":"/reference/locf.html","id":null,"dir":"Reference","previous_headings":"","what":"Last Observation Carried Forward — locf","title":"Last Observation Carried Forward — locf","text":"Returns vector applied last observation carried forward imputation.","code":""},{"path":"/reference/locf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Last Observation Carried Forward — locf","text":"","code":"locf(x)"},{"path":"/reference/locf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Last Observation Carried Forward — locf","text":"x vector.","code":""},{"path":"/reference/locf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Last Observation Carried Forward — locf","text":"","code":"if (FALSE) { # \\dontrun{ locf(c(NA, 1, 2, 3, NA, 4)) # Returns c(NA, 1, 2, 3, 3, 4) } # }"},{"path":"/reference/longDataConstructor.html","id":null,"dir":"Reference","previous_headings":"","what":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"longdata object allows efficient storage recall longitudinal datasets use bootstrap sampling. object works de-constructing data lists based upon subject id thus enabling efficient lookup.","code":""},{"path":"/reference/longDataConstructor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"object also handles multiple operations specific rbmi defining whether outcome value MAR / Missing well tracking imputation strategy assigned subject. recognised objects functionality fairly overloaded hoped can split area specific objects / functions future. additions functionality object avoided possible.","code":""},{"path":"/reference/longDataConstructor.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"data original dataset passed constructor (sorted id visit) vars vars object (list key variables) passed constructor visits character vector containing distinct visit levels ids character vector containing unique ids subject self$data formula formula expressing design matrix data constructed strata numeric vector indicating strata corresponding value self$ids belongs . stratification variable defined default 1 subjects (.e. group). field used part self$sample_ids() function enable stratified bootstrap sampling ice_visit_index list indexed subject storing index number first visit affected ICE. ICE set equal number visits plus 1. values list indexed subject storing numeric vector original (unimputed) outcome values group list indexed subject storing single character indicating imputation group subject belongs defined self$data[id, self$ivars$group] used determine reference group used imputing subjects data. is_mar list indexed subject storing logical values indicating subjects outcome values MAR . list defaulted TRUE subjects & outcomes modified calls self$set_strategies(). Note indicate values missing, variable True outcome values either occurred ICE visit post ICE visit imputation strategy MAR strategies list indexed subject storing single character value indicating imputation strategy assigned subject. list defaulted \"MAR\" subjects modified calls either self$set_strategies() self$update_strategies() strategy_lock list indexed subject storing single logical value indicating whether patients imputation strategy locked . strategy locked means change MAR non-MAR. Strategies can changed non-MAR MAR though trigger warning. Strategies locked patient assigned MAR strategy non-missing ICE date. list populated call self$set_strategies(). indexes list indexed subject storing numeric vector indexes specify rows original dataset belong subject .e. recover full data subject \"pt3\" can use self$data[self$indexes[[\"pt3\"]],]. may seem redundant filtering data directly however enables efficient bootstrap sampling data .e. list populated object initialisation. is_missing list indexed subject storing logical vector indicating whether corresponding outcome subject missing. list populated object initialisation. is_post_ice list indexed subject storing logical vector indicating whether corresponding outcome subject post date ICE. ICE data provided defaults False observations. list populated call self$set_strategies().","code":"indexes <- unlist(self$indexes[c(\"pt3\", \"pt3\")]) self$data[indexes,]"},{"path":[]},{"path":"/reference/longDataConstructor.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"longDataConstructor$get_data() longDataConstructor$add_subject() longDataConstructor$validate_ids() longDataConstructor$sample_ids() longDataConstructor$extract_by_id() longDataConstructor$update_strategies() longDataConstructor$set_strategies() longDataConstructor$check_has_data_at_each_visit() longDataConstructor$set_strata() longDataConstructor$new() longDataConstructor$clone()","code":""},{"path":"/reference/longDataConstructor.html","id":"method-get-data-","dir":"Reference","previous_headings":"","what":"Method get_data()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Returns data.frame based upon required subject IDs. Replaces missing values new ones provided.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$get_data( obj = NULL, nmar.rm = FALSE, na.rm = FALSE, idmap = FALSE )"},{"path":"/reference/longDataConstructor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"obj Either NULL, character vector subjects IDs imputation list object. See details. nmar.rm Logical value. TRUE remove observations regarded MAR (determined self$is_mar). na.rm Logical value. TRUE remove outcome values missing (determined self$is_missing). idmap Logical value. TRUE add attribute idmap contains mapping new subject ids old subject ids. See details.","code":""},{"path":"/reference/longDataConstructor.html","id":"details-1","dir":"Reference","previous_headings":"","what":"Details","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"obj NULL full original dataset returned. obj character vector new dataset consisting just subjects returned; character vector contains duplicate entries subject returned multiple times. obj imputation_df object (created imputation_df()) subject ids specified object returned missing values filled specified imputation list object. .e. return data.frame consisting observations pt1 twice observations pt3 . first set observations pt1 missing values filled c(1,2,3) second set filled c(4,5,6). length values must equal sum(self$is_missing[[id]]). obj NULL subject IDs scrambled order ensure unique .e. pt2 requested twice process guarantees set observations unique subject ID number. idmap attribute (requested) can used map new ids back old ids.","code":"obj <- imputation_df( imputation_single( id = \"pt1\", values = c(1,2,3)), imputation_single( id = \"pt1\", values = c(4,5,6)), imputation_single( id = \"pt3\", values = c(7,8)) ) longdata$get_data(obj)"},{"path":"/reference/longDataConstructor.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"data.frame.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-add-subject-","dir":"Reference","previous_headings":"","what":"Method add_subject()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"function decomposes patient data self$data populates corresponding lists .e. self$is_missing, self$values, self$group, etc. function called upon objects initialization.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$add_subject(id)"},{"path":"/reference/longDataConstructor.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"id Character subject id exists within self$data.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-validate-ids-","dir":"Reference","previous_headings":"","what":"Method validate_ids()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Throws error element ids within source data self$data.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$validate_ids(ids)"},{"path":"/reference/longDataConstructor.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"ids character vector ids.","code":""},{"path":"/reference/longDataConstructor.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"TRUE","code":""},{"path":"/reference/longDataConstructor.html","id":"method-sample-ids-","dir":"Reference","previous_headings":"","what":"Method sample_ids()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Performs random stratified sampling patient ids (replacement) patient equal weight picked within strata (.e dependent many non-missing visits ).","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$sample_ids()"},{"path":"/reference/longDataConstructor.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Character vector ids.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-extract-by-id-","dir":"Reference","previous_headings":"","what":"Method extract_by_id()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Returns list key information given subject. convenience wrapper save manually grab element.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$extract_by_id(id)"},{"path":"/reference/longDataConstructor.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"id Character subject id exists within self$data.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-update-strategies-","dir":"Reference","previous_headings":"","what":"Method update_strategies()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Convenience function run self$set_strategies(dat_ice, update=TRUE) kept legacy reasons.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$update_strategies(dat_ice)"},{"path":"/reference/longDataConstructor.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"dat_ice data.frame containing ICE information see impute() format dataframe.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-set-strategies-","dir":"Reference","previous_headings":"","what":"Method set_strategies()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Updates self$strategies, self$is_mar, self$is_post_ice variables based upon provided ICE information.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$set_strategies(dat_ice = NULL, update = FALSE)"},{"path":"/reference/longDataConstructor.html","id":"arguments-5","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"dat_ice data.frame containing ICE information. See details. update Logical, indicates ICE data used update. See details.","code":""},{"path":"/reference/longDataConstructor.html","id":"details-2","dir":"Reference","previous_headings":"","what":"Details","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"See draws() specification dat_ice update=FALSE. See impute() format dat_ice update=TRUE. update=TRUE function ensures MAR strategies changed non-MAR presence post-ICE observations.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-check-has-data-at-each-visit-","dir":"Reference","previous_headings":"","what":"Method check_has_data_at_each_visit()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Ensures visits least 1 observed \"MAR\" observation. Throws error criteria met. ensure initial MMRM can resolved.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-7","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$check_has_data_at_each_visit()"},{"path":"/reference/longDataConstructor.html","id":"method-set-strata-","dir":"Reference","previous_headings":"","what":"Method set_strata()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Populates self$strata variable. user specified stratification variables first visit used determine value variables. stratification variables specified everyone defined strata 1.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-8","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$set_strata()"},{"path":"/reference/longDataConstructor.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Constructor function.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-9","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$new(data, vars)"},{"path":"/reference/longDataConstructor.html","id":"arguments-6","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"data longitudinal dataset. vars ivars object created set_vars().","code":""},{"path":"/reference/longDataConstructor.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"objects class cloneable method.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-10","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$clone(deep = FALSE)"},{"path":"/reference/longDataConstructor.html","id":"arguments-7","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"deep Whether make deep clone.","code":""},{"path":"/reference/ls_design.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate design vector for the lsmeans — ls_design","title":"Calculate design vector for the lsmeans — ls_design","text":"Calculates design vector required generate lsmean standard error. ls_design_equal calculates applying equal weight per covariate combination whilst ls_design_proportional applies weighting proportional frequency covariate combination occurred actual dataset.","code":""},{"path":"/reference/ls_design.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate design vector for the lsmeans — ls_design","text":"","code":"ls_design_equal(data, frm, covars, fix) ls_design_proportional(data, frm, covars, fix)"},{"path":"/reference/ls_design.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate design vector for the lsmeans — ls_design","text":"data data.frame frm Formula used fit original model covars character vector variables names exist data extracted (ls_design_equal ) fix named list variables fixed values","code":""},{"path":"/reference/lsmeans.html","id":null,"dir":"Reference","previous_headings":"","what":"Least Square Means — lsmeans","title":"Least Square Means — lsmeans","text":"Estimates least square means linear model. done generating prediction model using hypothetical observation constructed averaging data. See details information.","code":""},{"path":"/reference/lsmeans.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Least Square Means — lsmeans","text":"","code":"lsmeans(model, ..., .weights = c(\"proportional\", \"equal\"))"},{"path":"/reference/lsmeans.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Least Square Means — lsmeans","text":"model model created lm. ... Fixes specific variables specific values .e. trt = 1 age = 50. name argument must name variable within dataset. .weights Character, specifies whether use \"proportional\" \"equal\" weighting categorical covariate combination calculating lsmeans.","code":""},{"path":"/reference/lsmeans.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Least Square Means — lsmeans","text":"lsmeans obtained calculating hypothetical patients predicting expected values. hypothetical patients constructed expanding possible combinations categorical covariate setting numerical covariates equal mean. final lsmean value calculated averaging hypothetical patients. .weights equals \"proportional\" values weighted frequency occur full dataset. .weights equals \"equal\" hypothetical patient given equal weight regardless actually occurs dataset. Use ... argument fix specific variables specific values. See references identical implementations done SAS R via emmeans package. function attempts re-implement emmeans derivation standard linear models without include dependencies.","code":""},{"path":"/reference/lsmeans.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Least Square Means — lsmeans","text":"https://CRAN.R-project.org/package=emmeans https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.3/statug/statug_glm_details41.htm","code":""},{"path":"/reference/lsmeans.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Least Square Means — lsmeans","text":"","code":"if (FALSE) { # \\dontrun{ mod <- lm(Sepal.Length ~ Species + Petal.Length, data = iris) lsmeans(mod) lsmeans(mod, Species = \"virginica\") lsmeans(mod, Species = \"versicolor\") lsmeans(mod, Species = \"versicolor\", Petal.Length = 1) } # }"},{"path":"/reference/method.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the multiple imputation methodology — method","title":"Set the multiple imputation methodology — method","text":"functions determine methods rbmi use creating imputation models, generating imputed values pooling results.","code":""},{"path":"/reference/method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the multiple imputation methodology — method","text":"","code":"method_bayes( burn_in = 200, burn_between = 50, same_cov = TRUE, n_samples = 20, seed = sample.int(.Machine$integer.max, 1) ) method_approxbayes( covariance = c(\"us\", \"toep\", \"cs\", \"ar1\"), threshold = 0.01, same_cov = TRUE, REML = TRUE, n_samples = 20 ) method_condmean( covariance = c(\"us\", \"toep\", \"cs\", \"ar1\"), threshold = 0.01, same_cov = TRUE, REML = TRUE, n_samples = NULL, type = c(\"bootstrap\", \"jackknife\") ) method_bmlmi( covariance = c(\"us\", \"toep\", \"cs\", \"ar1\"), threshold = 0.01, same_cov = TRUE, REML = TRUE, B = 20, D = 2 )"},{"path":"/reference/method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the multiple imputation methodology — method","text":"burn_in numeric specifies many observations discarded prior extracting actual samples. Note sampler initialized maximum likelihood estimates weakly informative prior used thus theory value need high. burn_between numeric specifies \"thinning\" rate .e. many observations discarded sample. used prevent issues associated autocorrelation samples. same_cov logical, TRUE imputation model fitted using single shared covariance matrix observations. FALSE separate covariance matrix fit group determined group argument set_vars(). n_samples numeric determines many imputed datasets generated. case method_condmean(type = \"jackknife\") argument must set NULL. See details. seed numeric specifies seed used call Stan. argument passed onto seed argument rstan::sampling(). Note required method_bayes(), methods can achieve reproducible results setting seed via set.seed(). See details. covariance character string specifies structure covariance matrix used imputation model. Must one \"us\" (default), \"toep\", \"cs\" \"ar1\". See details. threshold numeric 0 1, specifies proportion bootstrap datasets can fail produce valid samples error thrown. See details. REML logical indicating whether use REML estimation rather maximum likelihood. type character string specifies resampling method used perform inference conditional mean imputation approach (set via method_condmean()) used. Must one \"bootstrap\" \"jackknife\". B numeric determines number bootstrap samples method_bmlmi. D numeric determines number random imputations bootstrap sample. Needed method_bmlmi().","code":""},{"path":"/reference/method.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Set the multiple imputation methodology — method","text":"case method_condmean(type = \"bootstrap\") n_samples + 1 imputation models datasets generated first sample based original dataset whilst n_samples samples bootstrapped datasets. Likewise, method_condmean(type = \"jackknife\") length(unique(data$subjid)) + 1 imputation models datasets generated. cases represented n + 1 displayed print message. user able specify different covariance structures using covariance argument. Currently supported structures include: Unstructured (\"us\") Toeplitz (\"toep\") Compound Symmetry (\"cs\") Autoregression-1 (\"ar1\") Note present Bayesian methods support unstructured. case method_condmean(type = \"bootstrap\"), method_approxbayes() method_bmlmi() repeated bootstrap samples original dataset taken MMRM fitted sample. Due randomness sampled datasets, well limitations optimisers used fit models, uncommon estimates particular dataset generated. instances rbmi designed throw bootstrapped dataset try another. However ensure errors due chance due underlying misspecification data /model tolerance limit set many samples can discarded. tolerance limit reached error thrown process aborted. tolerance limit defined ceiling(threshold * n_samples). Note jackknife method estimates need generated leave-one-datasets error thrown fail fit. Please note time writing (September 2021) Stan unable produce reproducible samples across different operating systems even seed used. care must taken using Stan across different machines. information limitation please consult Stan documentation https://mc-stan.org/docs/2_27/reference-manual/reproducibility-chapter.html","code":""},{"path":"/reference/parametric_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate parametric confidence intervals — parametric_ci","title":"Calculate parametric confidence intervals — parametric_ci","text":"Calculates confidence intervals based upon parametric distribution.","code":""},{"path":"/reference/parametric_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate parametric confidence intervals — parametric_ci","text":"","code":"parametric_ci(point, se, alpha, alternative, qfun, pfun, ...)"},{"path":"/reference/parametric_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate parametric confidence intervals — parametric_ci","text":"point point estimate. se standard error point estimate. using non-\"normal\" distribution set 1. alpha type 1 error rate, value 0 1. alternative character string specifying alternative hypothesis, must one \"two.sided\" (default), \"greater\" \"less\". qfun quantile function assumed distribution .e. qnorm. pfun CDF function assumed distribution .e. pnorm. ... additional arguments passed qfun pfun .e. df = 102.","code":""},{"path":"/reference/pool.html","id":null,"dir":"Reference","previous_headings":"","what":"Pool analysis results obtained from the imputed datasets — pool","title":"Pool analysis results obtained from the imputed datasets — pool","text":"Pool analysis results obtained imputed datasets","code":""},{"path":"/reference/pool.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pool analysis results obtained from the imputed datasets — pool","text":"","code":"pool( results, conf.level = 0.95, alternative = c(\"two.sided\", \"less\", \"greater\"), type = c(\"percentile\", \"normal\") ) # S3 method for class 'pool' as.data.frame(x, ...) # S3 method for class 'pool' print(x, ...)"},{"path":"/reference/pool.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pool analysis results obtained from the imputed datasets — pool","text":"results analysis object created analyse(). conf.level confidence level returned confidence interval. Must single number 0 1. Default 0.95. alternative character string specifying alternative hypothesis, must one \"two.sided\" (default), \"greater\" \"less\". type character string either \"percentile\" (default) \"normal\". Determines method used calculate bootstrap confidence intervals. See details. used method_condmean(type = \"bootstrap\") specified original call draws(). x pool object generated pool(). ... used.","code":""},{"path":"/reference/pool.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pool analysis results obtained from the imputed datasets — pool","text":"calculation used generate point estimate, standard errors confidence interval depends upon method specified original call draws(); particular: method_approxbayes() & method_bayes() use Rubin's rules pool estimates variances across multiple imputed datasets, Barnard-Rubin rule pool degree's freedom; see Little & Rubin (2002). method_condmean(type = \"bootstrap\") uses percentile normal approximation; see Efron & Tibshirani (1994). Note percentile bootstrap, standard error calculated, .e. standard errors NA object / data.frame. method_condmean(type = \"jackknife\") uses standard jackknife variance formula; see Efron & Tibshirani (1994). method_bmlmi uses pooling procedure Bootstrapped Maximum Likelihood MI (BMLMI). See Von Hippel & Bartlett (2021).","code":""},{"path":"/reference/pool.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pool analysis results obtained from the imputed datasets — pool","text":"Bradley Efron Robert J Tibshirani. introduction bootstrap. CRC press, 1994. [Section 11] Roderick J. . Little Donald B. Rubin. Statistical Analysis Missing Data, Second Edition. John Wiley & Sons, Hoboken, New Jersey, 2002. [Section 5.4] Von Hippel, Paul T Bartlett, Jonathan W. Maximum likelihood multiple imputation: Faster imputations consistent standard errors without posterior draws. 2021.","code":""},{"path":"/reference/pool_bootstrap_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Pooling via normal approximation — pool_bootstrap_normal","title":"Bootstrap Pooling via normal approximation — pool_bootstrap_normal","text":"Get point estimate, confidence interval p-value using normal approximation.","code":""},{"path":"/reference/pool_bootstrap_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Pooling via normal approximation — pool_bootstrap_normal","text":"","code":"pool_bootstrap_normal(est, conf.level, alternative)"},{"path":"/reference/pool_bootstrap_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Pooling via normal approximation — pool_bootstrap_normal","text":"est numeric vector point estimates bootstrap sample. conf.level confidence level returned confidence interval. Must single number 0 1. Default 0.95. alternative character string specifying alternative hypothesis, must one \"two.sided\" (default), \"greater\" \"less\".","code":""},{"path":"/reference/pool_bootstrap_normal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap Pooling via normal approximation — pool_bootstrap_normal","text":"point estimate taken first element est. remaining n-1 values est used generate confidence intervals.","code":""},{"path":"/reference/pool_bootstrap_percentile.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Pooling via Percentiles — pool_bootstrap_percentile","title":"Bootstrap Pooling via Percentiles — pool_bootstrap_percentile","text":"Get point estimate, confidence interval p-value using percentiles. Note quantile \"type=6\" used, see stats::quantile() details.","code":""},{"path":"/reference/pool_bootstrap_percentile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Pooling via Percentiles — pool_bootstrap_percentile","text":"","code":"pool_bootstrap_percentile(est, conf.level, alternative)"},{"path":"/reference/pool_bootstrap_percentile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Pooling via Percentiles — pool_bootstrap_percentile","text":"est numeric vector point estimates bootstrap sample. conf.level confidence level returned confidence interval. Must single number 0 1. Default 0.95. alternative character string specifying alternative hypothesis, must one \"two.sided\" (default), \"greater\" \"less\".","code":""},{"path":"/reference/pool_bootstrap_percentile.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap Pooling via Percentiles — pool_bootstrap_percentile","text":"point estimate taken first element est. remaining n-1 values est used generate confidence intervals.","code":""},{"path":"/reference/pool_internal.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal Pool Methods — pool_internal","title":"Internal Pool Methods — pool_internal","text":"Dispatches pool methods based upon results object class. See pool() details.","code":""},{"path":"/reference/pool_internal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal Pool Methods — pool_internal","text":"","code":"pool_internal(results, conf.level, alternative, type, D) # S3 method for class 'jackknife' pool_internal(results, conf.level, alternative, type, D) # S3 method for class 'bootstrap' pool_internal( results, conf.level, alternative, type = c(\"percentile\", \"normal\"), D ) # S3 method for class 'bmlmi' pool_internal(results, conf.level, alternative, type, D) # S3 method for class 'rubin' pool_internal(results, conf.level, alternative, type, D)"},{"path":"/reference/pool_internal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Internal Pool Methods — pool_internal","text":"results list results .e. x$results element analyse object created analyse()). conf.level confidence level returned confidence interval. Must single number 0 1. Default 0.95. alternative character string specifying alternative hypothesis, must one \"two.sided\" (default), \"greater\" \"less\". type character string either \"percentile\" (default) \"normal\". Determines method used calculate bootstrap confidence intervals. See details. used method_condmean(type = \"bootstrap\") specified original call draws(). D numeric representing number imputations bootstrap sample BMLMI method.","code":""},{"path":"/reference/prepare_stan_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare input data to run the Stan model — prepare_stan_data","title":"Prepare input data to run the Stan model — prepare_stan_data","text":"Prepare input data run Stan model. Creates / calculates required inputs required data{} block MMRM Stan program.","code":""},{"path":"/reference/prepare_stan_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare input data to run the Stan model — prepare_stan_data","text":"","code":"prepare_stan_data(ddat, subjid, visit, outcome, group)"},{"path":"/reference/prepare_stan_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare input data to run the Stan model — prepare_stan_data","text":"ddat design matrix subjid Character vector containing subjects IDs. visit Vector containing visits. outcome Numeric vector containing outcome variable. group Vector containing group variable.","code":""},{"path":"/reference/prepare_stan_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare input data to run the Stan model — prepare_stan_data","text":"stan_data object. named list per data{} block related Stan file. particular returns: N - number rows design matrix P - number columns design matrix G - number distinct covariance matrix groups (.e. length(unique(group))) n_visit - number unique outcome visits n_pat - total number pattern groups (defined missingness patterns & covariance group) pat_G - Index Sigma pattern group use pat_n_pt - number patients within pattern group pat_n_visit - number non-missing visits pattern group pat_sigma_index - rows/cols Sigma subset pattern group (padded 0's) y - outcome variable Q - design matrix (QR decomposition) R - R matrix QR decomposition design matrix","code":""},{"path":"/reference/prepare_stan_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prepare input data to run the Stan model — prepare_stan_data","text":"group argument determines covariance matrix group subject belongs . want subjects use shared covariance matrix set group \"1\" everyone.","code":""},{"path":"/reference/print.analysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Print analysis object — print.analysis","title":"Print analysis object — print.analysis","text":"Print analysis object","code":""},{"path":"/reference/print.analysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print analysis object — print.analysis","text":"","code":"# S3 method for class 'analysis' print(x, ...)"},{"path":"/reference/print.analysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print analysis object — print.analysis","text":"x analysis object generated analyse(). ... used.","code":""},{"path":"/reference/print.draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Print draws object — print.draws","title":"Print draws object — print.draws","text":"Print draws object","code":""},{"path":"/reference/print.draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print draws object — print.draws","text":"","code":"# S3 method for class 'draws' print(x, ...)"},{"path":"/reference/print.draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print draws object — print.draws","text":"x draws object generated draws(). ... used.","code":""},{"path":"/reference/print.imputation.html","id":null,"dir":"Reference","previous_headings":"","what":"Print imputation object — print.imputation","title":"Print imputation object — print.imputation","text":"Print imputation object","code":""},{"path":"/reference/print.imputation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print imputation object — print.imputation","text":"","code":"# S3 method for class 'imputation' print(x, ...)"},{"path":"/reference/print.imputation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print imputation object — print.imputation","text":"x imputation object generated impute(). ... used.","code":""},{"path":"/reference/progressLogger.html","id":null,"dir":"Reference","previous_headings":"","what":"R6 Class for printing current sampling progress — progressLogger","title":"R6 Class for printing current sampling progress — progressLogger","text":"Object initalised total number iterations expected occur. User can update object add method indicate many iterations just occurred. Every time step * 100 % iterations occurred message printed console. Use quiet argument prevent object printing anything ","code":""},{"path":"/reference/progressLogger.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"R6 Class for printing current sampling progress — progressLogger","text":"step real, percentage iterations allow printing progress console step_current integer, total number iterations completed since progress last printed console n integer, current number completed iterations n_max integer, total number expected iterations completed acts denominator calculating progress percentages quiet logical holds whether print anything","code":""},{"path":[]},{"path":"/reference/progressLogger.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"R6 Class for printing current sampling progress — progressLogger","text":"progressLogger$new() progressLogger$add() progressLogger$print_progress() progressLogger$clone()","code":""},{"path":"/reference/progressLogger.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"R6 Class for printing current sampling progress — progressLogger","text":"Create progressLogger object","code":""},{"path":"/reference/progressLogger.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for printing current sampling progress — progressLogger","text":"","code":"progressLogger$new(n_max, quiet = FALSE, step = 0.1)"},{"path":"/reference/progressLogger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for printing current sampling progress — progressLogger","text":"n_max integer, sets field n_max quiet logical, sets field quiet step real, sets field step","code":""},{"path":"/reference/progressLogger.html","id":"method-add-","dir":"Reference","previous_headings":"","what":"Method add()","title":"R6 Class for printing current sampling progress — progressLogger","text":"Records n iterations completed add number current step count (step_current) print progress message log step limit (step) reached. function nothing quiet set TRUE","code":""},{"path":"/reference/progressLogger.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for printing current sampling progress — progressLogger","text":"","code":"progressLogger$add(n)"},{"path":"/reference/progressLogger.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for printing current sampling progress — progressLogger","text":"n number successfully complete iterations since add() last called","code":""},{"path":"/reference/progressLogger.html","id":"method-print-progress-","dir":"Reference","previous_headings":"","what":"Method print_progress()","title":"R6 Class for printing current sampling progress — progressLogger","text":"method print current state progress","code":""},{"path":"/reference/progressLogger.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for printing current sampling progress — progressLogger","text":"","code":"progressLogger$print_progress()"},{"path":"/reference/progressLogger.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"R6 Class for printing current sampling progress — progressLogger","text":"objects class cloneable method.","code":""},{"path":"/reference/progressLogger.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for printing current sampling progress — progressLogger","text":"","code":"progressLogger$clone(deep = FALSE)"},{"path":"/reference/progressLogger.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for printing current sampling progress — progressLogger","text":"deep Whether make deep clone.","code":""},{"path":"/reference/pval_percentile.html","id":null,"dir":"Reference","previous_headings":"","what":"P-value of percentile bootstrap — pval_percentile","title":"P-value of percentile bootstrap — pval_percentile","text":"Determines (necessarily unique) quantile (type=6) \"est\" gives value 0 , derive p-value corresponding percentile bootstrap via inversion.","code":""},{"path":"/reference/pval_percentile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"P-value of percentile bootstrap — pval_percentile","text":"","code":"pval_percentile(est)"},{"path":"/reference/pval_percentile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"P-value of percentile bootstrap — pval_percentile","text":"est numeric vector point estimates bootstrap sample.","code":""},{"path":"/reference/pval_percentile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"P-value of percentile bootstrap — pval_percentile","text":"named numeric vector length 2 containing p-value H_0: theta=0 vs H_A: theta>0 (\"pval_greater\") p-value H_0: theta=0 vs H_A: theta<0 (\"pval_less\").","code":""},{"path":"/reference/pval_percentile.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"P-value of percentile bootstrap — pval_percentile","text":"p-value H_0: theta=0 vs H_A: theta>0 value alpha q_alpha = 0. least one estimate equal zero returns largest alpha q_alpha = 0. bootstrap estimates > 0 returns 0; bootstrap estimates < 0 returns 1. Analogous reasoning applied p-value H_0: theta=0 vs H_A: theta<0.","code":""},{"path":"/reference/random_effects_expr.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct random effects formula — random_effects_expr","title":"Construct random effects formula — random_effects_expr","text":"Constructs character representation random effects formula fitting MMRM subject visit format required mmrm::mmrm().","code":""},{"path":"/reference/random_effects_expr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct random effects formula — random_effects_expr","text":"","code":"random_effects_expr( cov_struct = c(\"us\", \"toep\", \"cs\", \"ar1\"), cov_by_group = FALSE )"},{"path":"/reference/random_effects_expr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct random effects formula — random_effects_expr","text":"cov_struct Character - covariance structure used, must one \"us\", \"toep\", \"cs\", \"ar1\" cov_by_group Boolean - Whenever use separate covariances per group level","code":""},{"path":"/reference/random_effects_expr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Construct random effects formula — random_effects_expr","text":"example assuming user specified covariance structure \"us\" groups provided return cov_by_group set FALSE indicates separate covariance matrices required per group following returned:","code":"us(visit | subjid) us( visit | group / subjid )"},{"path":"/reference/rbmi-package.html","id":null,"dir":"Reference","previous_headings":"","what":"rbmi: Reference Based Multiple Imputation — rbmi-package","title":"rbmi: Reference Based Multiple Imputation — rbmi-package","text":"rbmi package used perform reference based multiple imputation. package provides implementations common, patient-specific imputation strategies whilst allowing user select various standard Bayesian frequentist approaches. package designed around 4 core functions: draws() - Fits multiple imputation models impute() - Imputes multiple datasets analyse() - Analyses multiple datasets pool() - Pools multiple results single statistic learn rbmi, please see quickstart vignette: vignette(topic= \"quickstart\", package = \"rbmi\")","code":""},{"path":[]},{"path":"/reference/rbmi-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"rbmi: Reference Based Multiple Imputation — rbmi-package","text":"Maintainer: Craig Gower-Page craig.gower-page@roche.com Authors: Alessandro Noci alessandro.noci@roche.com contributors: Marcel Wolbers marcel.wolbers@roche.com [contributor] Roche [copyright holder, funder]","code":""},{"path":"/reference/record.html","id":null,"dir":"Reference","previous_headings":"","what":"Capture all Output — record","title":"Capture all Output — record","text":"function silences warnings, errors & messages instead returns list containing results (error) + warning error messages character vectors.","code":""},{"path":"/reference/record.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Capture all Output — record","text":"","code":"record(expr)"},{"path":"/reference/record.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Capture all Output — record","text":"expr expression executed","code":""},{"path":"/reference/record.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Capture all Output — record","text":"list containing results - object returned expr list() error thrown warnings - NULL character vector warnings thrown errors - NULL string error thrown messages - NULL character vector messages produced","code":""},{"path":"/reference/record.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Capture all Output — record","text":"","code":"if (FALSE) { # \\dontrun{ record({ x <- 1 y <- 2 warning(\"something went wrong\") message(\"O nearly done\") x + y }) } # }"},{"path":"/reference/recursive_reduce.html","id":null,"dir":"Reference","previous_headings":"","what":"recursive_reduce — recursive_reduce","title":"recursive_reduce — recursive_reduce","text":"Utility function used replicated purrr::reduce. Recursively applies function list elements 1 element remains","code":""},{"path":"/reference/recursive_reduce.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"recursive_reduce — recursive_reduce","text":"","code":"recursive_reduce(.l, .f)"},{"path":"/reference/recursive_reduce.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"recursive_reduce — recursive_reduce","text":".l list values apply function .f function apply element list turn .e. .l[[1]] <- .f( .l[[1]] , .l[[2]]) ; .l[[1]] <- .f( .l[[1]] , .l[[3]])","code":""},{"path":"/reference/remove_if_all_missing.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove subjects from dataset if they have no observed values — remove_if_all_missing","title":"Remove subjects from dataset if they have no observed values — remove_if_all_missing","text":"function takes data.frame variables visit, outcome & subjid. removes rows given subjid non-missing values outcome.","code":""},{"path":"/reference/remove_if_all_missing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove subjects from dataset if they have no observed values — remove_if_all_missing","text":"","code":"remove_if_all_missing(dat)"},{"path":"/reference/remove_if_all_missing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove subjects from dataset if they have no observed values — remove_if_all_missing","text":"dat data.frame","code":""},{"path":"/reference/rubin_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Barnard and Rubin degrees of freedom adjustment — rubin_df","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"Compute degrees freedom according Barnard-Rubin formula.","code":""},{"path":"/reference/rubin_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"","code":"rubin_df(v_com, var_b, var_t, M)"},{"path":"/reference/rubin_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"v_com Positive number representing degrees freedom complete-data analysis. var_b -variance point estimate across multiply imputed datasets. var_t Total-variance point estimate according Rubin's rules. M Number imputations.","code":""},{"path":"/reference/rubin_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"Degrees freedom according Barnard-Rubin formula. See Barnard-Rubin (1999).","code":""},{"path":"/reference/rubin_df.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"computation takes account limit cases missing data (.e. -variance var_b zero) complete-data degrees freedom set Inf. Moreover, v_com given NA, function returns Inf.","code":""},{"path":"/reference/rubin_df.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"Barnard, J. Rubin, D.B. (1999). Small sample degrees freedom multiple imputation. Biometrika, 86, 948-955.","code":""},{"path":"/reference/rubin_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine estimates using Rubin's rules — rubin_rules","title":"Combine estimates using Rubin's rules — rubin_rules","text":"Pool together results M complete-data analyses according Rubin's rules. See details.","code":""},{"path":"/reference/rubin_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine estimates using Rubin's rules — rubin_rules","text":"","code":"rubin_rules(ests, ses, v_com)"},{"path":"/reference/rubin_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine estimates using Rubin's rules — rubin_rules","text":"ests Numeric vector containing point estimates complete-data analyses. ses Numeric vector containing standard errors complete-data analyses. v_com Positive number representing degrees freedom complete-data analysis.","code":""},{"path":"/reference/rubin_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine estimates using Rubin's rules — rubin_rules","text":"list containing: est_point: pooled point estimate according Little-Rubin (2002). var_t: total variance according Little-Rubin (2002). df: degrees freedom according Barnard-Rubin (1999).","code":""},{"path":"/reference/rubin_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Combine estimates using Rubin's rules — rubin_rules","text":"rubin_rules applies Rubin's rules (Rubin, 1987) pooling together results multiple imputation procedure. pooled point estimate est_point average across point estimates complete-data analyses (given input argument ests). total variance var_t sum two terms representing within-variance -variance (see Little-Rubin (2002)). function also returns df, estimated pooled degrees freedom according Barnard-Rubin (1999) can used inference based t-distribution.","code":""},{"path":"/reference/rubin_rules.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Combine estimates using Rubin's rules — rubin_rules","text":"Barnard, J. Rubin, D.B. (1999). Small sample degrees freedom multiple imputation. Biometrika, 86, 948-955 Roderick J. . Little Donald B. Rubin. Statistical Analysis Missing Data, Second Edition. John Wiley & Sons, Hoboken, New Jersey, 2002. [Section 5.4]","code":""},{"path":[]},{"path":"/reference/sample_ids.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample Patient Ids — sample_ids","title":"Sample Patient Ids — sample_ids","text":"Performs stratified bootstrap sample IDS ensuring return vector length input vector","code":""},{"path":"/reference/sample_ids.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample Patient Ids — sample_ids","text":"","code":"sample_ids(ids, strata = rep(1, length(ids)))"},{"path":"/reference/sample_ids.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample Patient Ids — sample_ids","text":"ids vector sample strata strata indicator, ids sampled within strata ensuring numbers strata maintained","code":""},{"path":"/reference/sample_ids.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sample Patient Ids — sample_ids","text":"","code":"if (FALSE) { # \\dontrun{ sample_ids( c(\"a\", \"b\", \"c\", \"d\"), strata = c(1,1,2,2)) } # }"},{"path":"/reference/sample_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Create and validate a sample_list object — sample_list","title":"Create and validate a sample_list object — sample_list","text":"Given list sample_single objects generate sample_single(), creates sample_list objects validate .","code":""},{"path":"/reference/sample_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create and validate a sample_list object — sample_list","text":"","code":"sample_list(...)"},{"path":"/reference/sample_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create and validate a sample_list object — sample_list","text":"... list sample_single objects.","code":""},{"path":"/reference/sample_mvnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample random values from the multivariate normal distribution — sample_mvnorm","title":"Sample random values from the multivariate normal distribution — sample_mvnorm","text":"Sample random values multivariate normal distribution","code":""},{"path":"/reference/sample_mvnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample random values from the multivariate normal distribution — sample_mvnorm","text":"","code":"sample_mvnorm(mu, sigma)"},{"path":"/reference/sample_mvnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample random values from the multivariate normal distribution — sample_mvnorm","text":"mu mean vector sigma covariance matrix Samples multivariate normal variables multiplying univariate random normal variables cholesky decomposition covariance matrix. mu length 1 just uses rnorm instead.","code":""},{"path":"/reference/sample_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Create object of sample_single class — sample_single","title":"Create object of sample_single class — sample_single","text":"Creates object class sample_single named list containing input parameters validate .","code":""},{"path":"/reference/sample_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create object of sample_single class — sample_single","text":"","code":"sample_single( ids, beta = NA, sigma = NA, theta = NA, failed = any(is.na(beta)), ids_samp = ids )"},{"path":"/reference/sample_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create object of sample_single class — sample_single","text":"ids Vector characters containing ids subjects included original dataset. beta Numeric vector estimated regression coefficients. sigma List estimated covariance matrices (one level vars$group). theta Numeric vector transformed covariances. failed Logical. TRUE model fit failed. ids_samp Vector characters containing ids subjects included given sample.","code":""},{"path":"/reference/sample_single.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create object of sample_single class — sample_single","text":"named list class sample_single. contains following: ids vector characters containing ids subjects included original dataset. beta numeric vector estimated regression coefficients. sigma list estimated covariance matrices (one level vars$group). theta numeric vector transformed covariances. failed logical. TRUE model fit failed. ids_samp vector characters containing ids subjects included given sample.","code":""},{"path":"/reference/scalerConstructor.html","id":null,"dir":"Reference","previous_headings":"","what":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Scales design matrix non-categorical columns mean 0 standard deviation 1.","code":""},{"path":"/reference/scalerConstructor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"object initialisation used determine relevant mean SD's scale scaling (un-scaling) performed relevant object methods. Un-scaling done linear model Beta Sigma coefficients. purpose first column dataset scaled assumed outcome variable variables assumed post-transformation predictor variables (.e. dummy variables already expanded).","code":""},{"path":"/reference/scalerConstructor.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"centre Vector column means. first value outcome variable, variables predictors. scales Vector column standard deviations. first value outcome variable, variables predictors.","code":""},{"path":[]},{"path":"/reference/scalerConstructor.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"scalerConstructor$new() scalerConstructor$scale() scalerConstructor$unscale_sigma() scalerConstructor$unscale_beta() scalerConstructor$clone()","code":""},{"path":"/reference/scalerConstructor.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Uses dat determine relevant column means standard deviations use scaling un-scaling future datasets. Implicitly assumes new datasets column order dat","code":""},{"path":"/reference/scalerConstructor.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"","code":"scalerConstructor$new(dat)"},{"path":"/reference/scalerConstructor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"dat data.frame matrix. columns must numeric (.e dummy variables, must already expanded ).","code":""},{"path":"/reference/scalerConstructor.html","id":"details-1","dir":"Reference","previous_headings":"","what":"Details","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Categorical columns (determined values entirely 1 0) scaled. achieved setting corresponding values centre 0 scale 1.","code":""},{"path":"/reference/scalerConstructor.html","id":"method-scale-","dir":"Reference","previous_headings":"","what":"Method scale()","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Scales dataset continuous variables mean 0 standard deviation 1.","code":""},{"path":"/reference/scalerConstructor.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"","code":"scalerConstructor$scale(dat)"},{"path":"/reference/scalerConstructor.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"dat data.frame matrix whose columns numeric (.e. dummy variables expanded ) whose columns order dataset used initialization function.","code":""},{"path":"/reference/scalerConstructor.html","id":"method-unscale-sigma-","dir":"Reference","previous_headings":"","what":"Method unscale_sigma()","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Unscales sigma value (matrix) estimated linear model using design matrix scaled object. function works first column initialisation data.frame outcome variable.","code":""},{"path":"/reference/scalerConstructor.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"","code":"scalerConstructor$unscale_sigma(sigma)"},{"path":"/reference/scalerConstructor.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"sigma numeric value matrix.","code":""},{"path":"/reference/scalerConstructor.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"numeric value matrix","code":""},{"path":"/reference/scalerConstructor.html","id":"method-unscale-beta-","dir":"Reference","previous_headings":"","what":"Method unscale_beta()","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Unscales beta value (vector) estimated linear model using design matrix scaled object. function works first column initialization data.frame outcome variable.","code":""},{"path":"/reference/scalerConstructor.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"","code":"scalerConstructor$unscale_beta(beta)"},{"path":"/reference/scalerConstructor.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"beta numeric vector beta coefficients estimated linear model.","code":""},{"path":"/reference/scalerConstructor.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"numeric vector.","code":""},{"path":"/reference/scalerConstructor.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"objects class cloneable method.","code":""},{"path":"/reference/scalerConstructor.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"","code":"scalerConstructor$clone(deep = FALSE)"},{"path":"/reference/scalerConstructor.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"deep Whether make deep clone.","code":""},{"path":"/reference/set_simul_pars.html","id":null,"dir":"Reference","previous_headings":"","what":"Set simulation parameters of a study group. — set_simul_pars","title":"Set simulation parameters of a study group. — set_simul_pars","text":"function provides input arguments study group needed simulate data simulate_data(). simulate_data() generates data two-arms clinical trial longitudinal continuous outcomes two intercurrent events (ICEs). ICE1 may thought discontinuation study treatment due study drug condition related (SDCR) reasons. ICE2 may thought discontinuation study treatment due uninformative study drop-, .e. due study drug condition related (NSDRC) reasons outcome data ICE2 always missing.","code":""},{"path":"/reference/set_simul_pars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set simulation parameters of a study group. — set_simul_pars","text":"","code":"set_simul_pars( mu, sigma, n, prob_ice1 = 0, or_outcome_ice1 = 1, prob_post_ice1_dropout = 0, prob_ice2 = 0, prob_miss = 0 )"},{"path":"/reference/set_simul_pars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set simulation parameters of a study group. — set_simul_pars","text":"mu Numeric vector describing mean outcome trajectory visit (including baseline) assuming ICEs. sigma Covariance matrix outcome trajectory assuming ICEs. n Number subjects belonging group. prob_ice1 Numeric vector specifies probability experiencing ICE1 (discontinuation study treatment due SDCR reasons) visit subject observed outcome visit equal mean baseline (mu[1]). single numeric provided, probability applied visit. or_outcome_ice1 Numeric value specifies odds ratio experiencing ICE1 visit corresponding +1 higher value observed outcome visit. prob_post_ice1_dropout Numeric value specifies probability study drop-following ICE1. subject simulated drop-ICE1, outcomes ICE1 set missing. prob_ice2 Numeric specifies additional probability post-baseline visit affected study drop-. Outcome data subject's first simulated visit affected study drop-subsequent visits set missing. generates second intercurrent event ICE2, may thought treatment discontinuation due NSDRC reasons subsequent drop-. subject, ICE1 ICE2 simulated occur, assumed earlier counts. case ICEs simulated occur time, assumed ICE1 counts. means single subject can experience either ICE1 ICE2, . prob_miss Numeric value specifies additional probability given post-baseline observation missing. can used produce \"intermittent\" missing values associated ICE.","code":""},{"path":"/reference/set_simul_pars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set simulation parameters of a study group. — set_simul_pars","text":"simul_pars object named list containing simulation parameters.","code":""},{"path":"/reference/set_simul_pars.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Set simulation parameters of a study group. — set_simul_pars","text":"details, please see simulate_data().","code":""},{"path":[]},{"path":"/reference/set_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Set key variables — set_vars","title":"Set key variables — set_vars","text":"function used define names key variables within data.frame's provided input arguments draws() ancova().","code":""},{"path":"/reference/set_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set key variables — set_vars","text":"","code":"set_vars( subjid = \"subjid\", visit = \"visit\", outcome = \"outcome\", group = \"group\", covariates = character(0), strata = group, strategy = \"strategy\" )"},{"path":"/reference/set_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set key variables — set_vars","text":"subjid name \"Subject ID\" variable. length 1 character vector. visit name \"Visit\" variable. length 1 character vector. outcome name \"Outcome\" variable. length 1 character vector. group name \"Group\" variable. length 1 character vector. covariates name covariates used context modeling. See details. strata name stratification variable used context bootstrap sampling. See details. strategy name \"strategy\" variable. length 1 character vector.","code":""},{"path":"/reference/set_vars.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Set key variables — set_vars","text":"draws() ancova() covariates argument can specified indicate variables included imputation analysis models respectively. wish include interaction terms need manually specified .e. covariates = c(\"group*visit\", \"age*sex\"). Please note use () function inhibit interpretation/conversion objects supported. Currently strata used draws() combination method_condmean(type = \"bootstrap\") method_approxbayes() order allow specification stratified bootstrap sampling. default strata set equal value group assumed users want preserve group size samples. See draws() details. Likewise, currently strategy argument used draws() specify name strategy variable within data_ice data.frame. See draws() details.","code":""},{"path":[]},{"path":"/reference/set_vars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set key variables — set_vars","text":"","code":"if (FALSE) { # \\dontrun{ # Using CDISC variable names as an example set_vars( subjid = \"usubjid\", visit = \"avisit\", outcome = \"aval\", group = \"arm\", covariates = c(\"bwt\", \"bht\", \"arm * avisit\"), strategy = \"strat\" ) } # }"},{"path":"/reference/simulate_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate data — simulate_data","title":"Generate data — simulate_data","text":"Generate data two-arms clinical trial longitudinal continuous outcome two intercurrent events (ICEs). ICE1 may thought discontinuation study treatment due study drug condition related (SDCR) reasons. ICE2 may thought discontinuation study treatment due uninformative study drop-, .e. due study drug condition related (NSDRC) reasons outcome data ICE2 always missing.","code":""},{"path":"/reference/simulate_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate data — simulate_data","text":"","code":"simulate_data(pars_c, pars_t, post_ice1_traj, strategies = getStrategies())"},{"path":"/reference/simulate_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate data — simulate_data","text":"pars_c simul_pars object generated set_simul_pars(). specifies simulation parameters control arm. pars_t simul_pars object generated set_simul_pars(). specifies simulation parameters treatment arm. post_ice1_traj string specifies observed outcomes occurring ICE1 simulated. Must target function included strategies. Possible choices : Missing Random \"MAR\", Jump Reference \"JR\", Copy Reference \"CR\", Copy Increments Reference \"CIR\", Last Mean Carried Forward \"LMCF\". User-defined strategies also added. See getStrategies() details. strategies named list functions. Default equal getStrategies(). See getStrategies() details.","code":""},{"path":"/reference/simulate_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate data — simulate_data","text":"data.frame containing simulated data. includes following variables: id: Factor variable specifies id subject. visit: Factor variable specifies visit assessment. Visit 0 denotes baseline visit. group: Factor variable specifies treatment group subject belongs . outcome_bl: Numeric variable specifies baseline outcome. outcome_noICE: Numeric variable specifies longitudinal outcome assuming ICEs. ind_ice1: Binary variable takes value 1 corresponding visit affected ICE1 0 otherwise. dropout_ice1: Binary variable takes value 1 corresponding visit affected drop-following ICE1 0 otherwise. ind_ice2: Binary variable takes value 1 corresponding visit affected ICE2. outcome: Numeric variable specifies longitudinal outcome including ICE1, ICE2 intermittent missing values.","code":""},{"path":"/reference/simulate_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate data — simulate_data","text":"data generation works follows: Generate outcome data visits (including baseline) multivariate normal distribution parameters pars_c$mu pars_c$sigma control arm parameters pars_t$mu pars_t$sigma treatment arm, respectively. Note randomized trial, outcomes distribution baseline treatment groups, .e. one set pars_c$mu[1]=pars_t$mu[1] pars_c$sigma[1,1]=pars_t$sigma[1,1]. Simulate whether ICE1 (study treatment discontinuation due SDCR reasons) occurs visit according parameters pars_c$prob_ice1 pars_c$or_outcome_ice1 control arm pars_t$prob_ice1 pars_t$or_outcome_ice1 treatment arm, respectively. Simulate drop-following ICE1 according pars_c$prob_post_ice1_dropout pars_t$prob_post_ice1_dropout. Simulate additional uninformative study drop-probabilities pars_c$prob_ice2 pars_t$prob_ice2 visit. generates second intercurrent event ICE2, may thought treatment discontinuation due NSDRC reasons subsequent drop-. simulated time drop-subject's first visit affected drop-data visit subsequent visits consequently set missing. subject, ICE1 ICE2 simulated occur, assumed earlier counts. case ICEs simulated occur time, assumed ICE1 counts. means single subject can experience either ICE1 ICE2, . Adjust trajectories ICE1 according given assumption expressed post_ice1_traj argument. Note post-ICE1 outcomes intervention arm can adjusted. Post-ICE1 outcomes control arm adjusted. Simulate additional intermittent missing outcome data per arguments pars_c$prob_miss pars_t$prob_miss. probability ICE visit modeled according following logistic regression model: ~ 1 + (visit == 0) + ... + (visit == n_visits-1) + ((x-alpha)) : n_visits number visits (including baseline). alpha baseline outcome mean. term ((x-alpha)) specifies dependency probability ICE current outcome value. corresponding regression coefficients logistic model defined follows: intercept set 0, coefficients corresponding discontinuation visit subject outcome equal mean baseline set according parameters pars_c$prob_ice1 (pars_t$prob_ice1), regression coefficient associated covariate ((x-alpha)) set log(pars_c$or_outcome_ice1) (log(pars_t$or_outcome_ice1)). Please note baseline outcome missing affected ICEs.","code":""},{"path":"/reference/simulate_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate drop-out — simulate_dropout","title":"Simulate drop-out — simulate_dropout","text":"Simulate drop-","code":""},{"path":"/reference/simulate_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate drop-out — simulate_dropout","text":"","code":"simulate_dropout(prob_dropout, ids, subset = rep(1, length(ids)))"},{"path":"/reference/simulate_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate drop-out — simulate_dropout","text":"prob_dropout Numeric specifies probability post-baseline visit affected study drop-. ids Factor variable specifies id subject. subset Binary variable specifies subset affected drop-. .e. subset binary vector length equal length ids takes value 1 corresponding visit affected drop-0 otherwise.","code":""},{"path":"/reference/simulate_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate drop-out — simulate_dropout","text":"binary vector length equal length ids takes value 1 corresponding outcome affected study drop-.","code":""},{"path":"/reference/simulate_dropout.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate drop-out — simulate_dropout","text":"subset can used specify outcome values affected drop-. default subset set 1 values except values corresponding baseline outcome, since baseline supposed affected drop-. Even subset specified user, values corresponding baseline outcome still hard-coded 0.","code":""},{"path":"/reference/simulate_ice.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate intercurrent event — simulate_ice","title":"Simulate intercurrent event — simulate_ice","text":"Simulate intercurrent event","code":""},{"path":"/reference/simulate_ice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate intercurrent event — simulate_ice","text":"","code":"simulate_ice(outcome, visits, ids, prob_ice, or_outcome_ice, baseline_mean)"},{"path":"/reference/simulate_ice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate intercurrent event — simulate_ice","text":"outcome Numeric variable specifies longitudinal outcome single group. visits Factor variable specifies visit assessment. ids Factor variable specifies id subject. prob_ice Numeric vector specifies visit probability experiencing ICE current visit subject outcome equal mean baseline. single numeric provided, probability applied visit. or_outcome_ice Numeric value specifies odds ratio ICE corresponding +1 higher value outcome visit. baseline_mean Mean outcome value baseline.","code":""},{"path":"/reference/simulate_ice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate intercurrent event — simulate_ice","text":"binary variable takes value 1 corresponding outcome affected ICE 0 otherwise.","code":""},{"path":"/reference/simulate_ice.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate intercurrent event — simulate_ice","text":"probability ICE visit modeled according following logistic regression model: ~ 1 + (visit == 0) + ... + (visit == n_visits-1) + ((x-alpha)) : n_visits number visits (including baseline). alpha baseline outcome mean set via argument baseline_mean. term ((x-alpha)) specifies dependency probability ICE current outcome value. corresponding regression coefficients logistic model defined follows: intercept set 0, coefficients corresponding discontinuation visit subject outcome equal mean baseline set according parameter or_outcome_ice, regression coefficient associated covariate ((x-alpha)) set log(or_outcome_ice).","code":""},{"path":"/reference/simulate_test_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Create simulated datasets — simulate_test_data","title":"Create simulated datasets — simulate_test_data","text":"Creates longitudinal dataset format rbmi designed analyse.","code":""},{"path":"/reference/simulate_test_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create simulated datasets — simulate_test_data","text":"","code":"simulate_test_data( n = 200, sd = c(3, 5, 7), cor = c(0.1, 0.7, 0.4), mu = list(int = 10, age = 3, sex = 2, trt = c(0, 4, 8), visit = c(0, 1, 2)) ) as_vcov(sd, cor)"},{"path":"/reference/simulate_test_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create simulated datasets — simulate_test_data","text":"n number subjects sample. Total number observations returned thus n * length(sd) sd standard deviations outcome visit. .e. square root diagonal covariance matrix outcome cor correlation coefficients outcome values visit. See details. mu coefficients use construct mean outcome value visit. Must named list elements int, age, sex, trt & visit. See details.","code":""},{"path":"/reference/simulate_test_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create simulated datasets — simulate_test_data","text":"number visits determined size variance covariance matrix. .e. 3 standard deviation values provided 3 visits per patient created. covariates simulated dataset produced follows: Patients age sampled random N(0,1) distribution Patients sex sampled random 50/50 split Patients group sampled random fixed group n/2 patients outcome variable sampled multivariate normal distribution, see details mean outcome variable derived : coefficients intercept, age sex taken mu$int, mu$age mu$sex respectively, must length 1 numeric. Treatment visit coefficients taken mu$trt mu$visit respectively must either length 1 (.e. constant affect across visits) equal number visits (determined length sd). .e. wanted treatment slope 5 visit slope 1 specify: correlation matrix constructed cor follows. Let cor = c(, b, c, d, e, f) correlation matrix :","code":"outcome = Intercept + age + sex + visit + treatment mu = list(..., \"trt\" = c(0,5,10), \"visit\" = c(0,1,2)) 1 a b d a 1 c e b c 1 f d e f 1"},{"path":"/reference/sort_by.html","id":null,"dir":"Reference","previous_headings":"","what":"Sort data.frame — sort_by","title":"Sort data.frame — sort_by","text":"Sorts data.frame (ascending default) based upon variables within dataset","code":""},{"path":"/reference/sort_by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sort data.frame — sort_by","text":"","code":"sort_by(df, vars = NULL, decreasing = FALSE)"},{"path":"/reference/sort_by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sort data.frame — sort_by","text":"df data.frame vars character vector variables decreasing logical whether sort order descending ascending (default) order. Can either single logical value (case applied variables) vector length vars","code":""},{"path":"/reference/sort_by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sort data.frame — sort_by","text":"","code":"if (FALSE) { # \\dontrun{ sort_by(iris, c(\"Sepal.Length\", \"Sepal.Width\"), decreasing = c(TRUE, FALSE)) } # }"},{"path":"/reference/split_dim.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform array into list of arrays — split_dim","title":"Transform array into list of arrays — split_dim","text":"Transform array list arrays listing performed given dimension.","code":""},{"path":"/reference/split_dim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform array into list of arrays — split_dim","text":"","code":"split_dim(a, n)"},{"path":"/reference/split_dim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform array into list of arrays — split_dim","text":"Array number dimensions least 2. n Positive integer. Dimension listed.","code":""},{"path":"/reference/split_dim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform array into list of arrays — split_dim","text":"list length n arrays number dimensions equal number dimensions minus 1.","code":""},{"path":"/reference/split_dim.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform array into list of arrays — split_dim","text":"example, 3 dimensional array n = 1, split_dim(,n) returns list 2 dimensional arrays (.e. list matrices) element list [, , ], takes values 1 length first dimension array. Example: inputs: <- array( c(1,2,3,4,5,6,7,8,9,10,11,12), dim = c(3,2,2)), means : n <- 1 output res <- split_dim(,n) list 3 elements:","code":"a[1,,] a[2,,] a[3,,] [,1] [,2] [,1] [,2] [,1] [,2] --------- --------- --------- 1 7 2 8 3 9 4 10 5 11 6 12 res[[1]] res[[2]] res[[3]] [,1] [,2] [,1] [,2] [,1] [,2] --------- --------- --------- 1 7 2 8 3 9 4 10 5 11 6 12"},{"path":"/reference/split_imputations.html","id":null,"dir":"Reference","previous_headings":"","what":"Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations","title":"Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations","text":"Split flat list imputation_single() multiple imputation_df()'s ID","code":""},{"path":"/reference/split_imputations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations","text":"","code":"split_imputations(list_of_singles, split_ids)"},{"path":"/reference/split_imputations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations","text":"list_of_singles list imputation_single()'s split_ids list 1 element per required split. element must contain vector \"ID\"'s correspond imputation_single() ID's required within sample. total number ID's must equal length list_of_singles","code":""},{"path":"/reference/split_imputations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations","text":"function converts list imputations structured per patient structured per sample .e. converts :","code":"obj <- list( imputation_single(\"Ben\", numeric(0)), imputation_single(\"Ben\", numeric(0)), imputation_single(\"Ben\", numeric(0)), imputation_single(\"Harry\", c(1, 2)), imputation_single(\"Phil\", c(3, 4)), imputation_single(\"Phil\", c(5, 6)), imputation_single(\"Tom\", c(7, 8, 9)) ) index <- list( c(\"Ben\", \"Harry\", \"Phil\", \"Tom\"), c(\"Ben\", \"Ben\", \"Phil\") ) output <- list( imputation_df( imputation_single(id = \"Ben\", values = numeric(0)), imputation_single(id = \"Harry\", values = c(1, 2)), imputation_single(id = \"Phil\", values = c(3, 4)), imputation_single(id = \"Tom\", values = c(7, 8, 9)) ), imputation_df( imputation_single(id = \"Ben\", values = numeric(0)), imputation_single(id = \"Ben\", values = numeric(0)), imputation_single(id = \"Phil\", values = c(5, 6)) ) )"},{"path":"/reference/str_contains.html","id":null,"dir":"Reference","previous_headings":"","what":"Does a string contain a substring — str_contains","title":"Does a string contain a substring — str_contains","text":"Returns vector TRUE/FALSE element x contains element subs .e.","code":"str_contains( c(\"ben\", \"tom\", \"harry\"), c(\"e\", \"y\")) [1] TRUE FALSE TRUE"},{"path":"/reference/str_contains.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Does a string contain a substring — str_contains","text":"","code":"str_contains(x, subs)"},{"path":"/reference/str_contains.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Does a string contain a substring — str_contains","text":"x character vector subs character vector substrings look ","code":""},{"path":"/reference/strategies.html","id":null,"dir":"Reference","previous_headings":"","what":"Strategies — strategies","title":"Strategies — strategies","text":"functions used implement various reference based imputation strategies combining subjects distribution reference distribution based upon visits failed meet Missing--Random (MAR) assumption.","code":""},{"path":"/reference/strategies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Strategies — strategies","text":"","code":"strategy_MAR(pars_group, pars_ref, index_mar) strategy_JR(pars_group, pars_ref, index_mar) strategy_CR(pars_group, pars_ref, index_mar) strategy_CIR(pars_group, pars_ref, index_mar) strategy_LMCF(pars_group, pars_ref, index_mar)"},{"path":"/reference/strategies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Strategies — strategies","text":"pars_group list parameters subject's group. See details. pars_ref list parameters subject's reference group. See details. index_mar logical vector indicating visits meet MAR assumption subject. .e. identifies observations non-MAR intercurrent event (ICE).","code":""},{"path":"/reference/strategies.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Strategies — strategies","text":"pars_group pars_ref must list containing elements mu sigma. mu must numeric vector sigma must square matrix symmetric covariance matrix dimensions equal length mu index_mar. e.g. Users can define strategy functions include via strategies argument impute() using getStrategies(). said following strategies available \"box\": Missing Random (MAR) Jump Reference (JR) Copy Reference (CR) Copy Increments Reference (CIR) Last Mean Carried Forward (LMCF)","code":"list( mu = c(1,2,3), sigma = matrix(c(4,3,2,3,5,4,2,4,6), nrow = 3, ncol = 3) )"},{"path":"/reference/string_pad.html","id":null,"dir":"Reference","previous_headings":"","what":"string_pad — string_pad","title":"string_pad — string_pad","text":"Utility function used replicate str_pad. Adds white space either end string get equal desired length","code":""},{"path":"/reference/string_pad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"string_pad — string_pad","text":"","code":"string_pad(x, width)"},{"path":"/reference/string_pad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"string_pad — string_pad","text":"x string width desired length","code":""},{"path":"/reference/transpose_imputations.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose imputations — transpose_imputations","title":"Transpose imputations — transpose_imputations","text":"Takes imputation_df object transposes e.g.","code":"list( list(id = \"a\", values = c(1,2,3)), list(id = \"b\", values = c(4,5,6) ) )"},{"path":"/reference/transpose_imputations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose imputations — transpose_imputations","text":"","code":"transpose_imputations(imputations)"},{"path":"/reference/transpose_imputations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transpose imputations — transpose_imputations","text":"imputations imputation_df object created imputation_df()","code":""},{"path":"/reference/transpose_imputations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose imputations — transpose_imputations","text":"becomes","code":"list( ids = c(\"a\", \"b\"), values = c(1,2,3,4,5,6) )"},{"path":"/reference/transpose_results.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose results object — transpose_results","title":"Transpose results object — transpose_results","text":"Transposes Results object (created analyse()) order group estimates together vectors.","code":""},{"path":"/reference/transpose_results.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose results object — transpose_results","text":"","code":"transpose_results(results, components)"},{"path":"/reference/transpose_results.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transpose results object — transpose_results","text":"results list results. components character vector components extract (.e. \"est\", \"se\").","code":""},{"path":"/reference/transpose_results.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose results object — transpose_results","text":"Essentially function takes object format: produces:","code":"x <- list( list( \"trt1\" = list( est = 1, se = 2 ), \"trt2\" = list( est = 3, se = 4 ) ), list( \"trt1\" = list( est = 5, se = 6 ), \"trt2\" = list( est = 7, se = 8 ) ) ) list( trt1 = list( est = c(1,5), se = c(2,6) ), trt2 = list( est = c(3,7), se = c(4,8) ) )"},{"path":"/reference/transpose_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose samples — transpose_samples","title":"Transpose samples — transpose_samples","text":"Transposes samples generated draws() grouped subjid instead sample number.","code":""},{"path":"/reference/transpose_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose samples — transpose_samples","text":"","code":"transpose_samples(samples)"},{"path":"/reference/transpose_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transpose samples — transpose_samples","text":"samples list samples generated draws().","code":""},{"path":"/reference/validate.analysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate analysis objects — validate.analysis","title":"Validate analysis objects — validate.analysis","text":"Validates return object analyse() function.","code":""},{"path":"/reference/validate.analysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate analysis objects — validate.analysis","text":"","code":"# S3 method for class 'analysis' validate(x, ...)"},{"path":"/reference/validate.analysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate analysis objects — validate.analysis","text":"x analysis results object (class \"jackknife\", \"bootstrap\", \"rubin\"). ... used.","code":""},{"path":"/reference/validate.draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate draws object — validate.draws","title":"Validate draws object — validate.draws","text":"Validate draws object","code":""},{"path":"/reference/validate.draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate draws object — validate.draws","text":"","code":"# S3 method for class 'draws' validate(x, ...)"},{"path":"/reference/validate.draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate draws object — validate.draws","text":"x draws object generated as_draws(). ... used.","code":""},{"path":"/reference/validate.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic validation method — validate","title":"Generic validation method — validate","text":"function used perform assertions object conforms expected structure basic assumptions violated. throw error checks pass.","code":""},{"path":"/reference/validate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic validation method — validate","text":"","code":"validate(x, ...)"},{"path":"/reference/validate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic validation method — validate","text":"x object validated. ... additional arguments pass specific validation method.","code":""},{"path":"/reference/validate.is_mar.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate is_mar for a given subject — validate.is_mar","title":"Validate is_mar for a given subject — validate.is_mar","text":"Checks longitudinal data patient divided MAR followed non-MAR data; non-MAR observation followed MAR observation allowed.","code":""},{"path":"/reference/validate.is_mar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate is_mar for a given subject — validate.is_mar","text":"","code":"# S3 method for class 'is_mar' validate(x, ...)"},{"path":"/reference/validate.is_mar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate is_mar for a given subject — validate.is_mar","text":"x Object class is_mar. Logical vector indicating whether observations MAR. ... used.","code":""},{"path":"/reference/validate.is_mar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate is_mar for a given subject — validate.is_mar","text":"error issue otherwise return TRUE.","code":""},{"path":"/reference/validate.ivars.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate inputs for vars — validate.ivars","title":"Validate inputs for vars — validate.ivars","text":"Checks required variable names defined within vars appropriate datatypes","code":""},{"path":"/reference/validate.ivars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate inputs for vars — validate.ivars","text":"","code":"# S3 method for class 'ivars' validate(x, ...)"},{"path":"/reference/validate.ivars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate inputs for vars — validate.ivars","text":"x named list indicating names key variables source dataset ... used","code":""},{"path":"/reference/validate.references.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate user supplied references — validate.references","title":"Validate user supplied references — validate.references","text":"Checks ensure user specified references expect values (.e. found within source data).","code":""},{"path":"/reference/validate.references.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate user supplied references — validate.references","text":"","code":"# S3 method for class 'references' validate(x, control, ...)"},{"path":"/reference/validate.references.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate user supplied references — validate.references","text":"x named character vector. control factor variable (group variable source dataset). ... used.","code":""},{"path":"/reference/validate.references.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate user supplied references — validate.references","text":"error issue otherwise return TRUE.","code":""},{"path":"/reference/validate.sample_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate sample_list object — validate.sample_list","title":"Validate sample_list object — validate.sample_list","text":"Validate sample_list object","code":""},{"path":"/reference/validate.sample_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate sample_list object — validate.sample_list","text":"","code":"# S3 method for class 'sample_list' validate(x, ...)"},{"path":"/reference/validate.sample_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate sample_list object — validate.sample_list","text":"x sample_list object generated sample_list(). ... used.","code":""},{"path":"/reference/validate.sample_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate sample_single object — validate.sample_single","title":"Validate sample_single object — validate.sample_single","text":"Validate sample_single object","code":""},{"path":"/reference/validate.sample_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate sample_single object — validate.sample_single","text":"","code":"# S3 method for class 'sample_single' validate(x, ...)"},{"path":"/reference/validate.sample_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate sample_single object — validate.sample_single","text":"x sample_single object generated sample_single(). ... used.","code":""},{"path":"/reference/validate.simul_pars.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate a simul_pars object — validate.simul_pars","title":"Validate a simul_pars object — validate.simul_pars","text":"Validate simul_pars object","code":""},{"path":"/reference/validate.simul_pars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate a simul_pars object — validate.simul_pars","text":"","code":"# S3 method for class 'simul_pars' validate(x, ...)"},{"path":"/reference/validate.simul_pars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate a simul_pars object — validate.simul_pars","text":"x simul_pars object generated set_simul_pars(). ... used.","code":""},{"path":"/reference/validate.stan_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate a stan_data object — validate.stan_data","title":"Validate a stan_data object — validate.stan_data","text":"Validate stan_data object","code":""},{"path":"/reference/validate.stan_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate a stan_data object — validate.stan_data","text":"","code":"# S3 method for class 'stan_data' validate(x, ...)"},{"path":"/reference/validate.stan_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate a stan_data object — validate.stan_data","text":"x stan_data object. ... used.","code":""},{"path":"/reference/validate_analyse_pars.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate analysis results — validate_analyse_pars","title":"Validate analysis results — validate_analyse_pars","text":"Validates analysis results generated analyse().","code":""},{"path":"/reference/validate_analyse_pars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate analysis results — validate_analyse_pars","text":"","code":"validate_analyse_pars(results, pars)"},{"path":"/reference/validate_analyse_pars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate analysis results — validate_analyse_pars","text":"results list results generated analysis fun used analyse(). pars list expected parameters analysis. lists .e. c(\"est\", \"se\", \"df\").","code":""},{"path":"/reference/validate_datalong.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate a longdata object — validate_datalong","title":"Validate a longdata object — validate_datalong","text":"Validate longdata object","code":""},{"path":"/reference/validate_datalong.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate a longdata object — validate_datalong","text":"","code":"validate_datalong(data, vars) validate_datalong_varExists(data, vars) validate_datalong_types(data, vars) validate_datalong_notMissing(data, vars) validate_datalong_complete(data, vars) validate_datalong_unifromStrata(data, vars) validate_dataice(data, data_ice, vars, update = FALSE)"},{"path":"/reference/validate_datalong.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate a longdata object — validate_datalong","text":"data data.frame containing longitudinal outcome data + covariates multiple subjects vars vars object created set_vars() data_ice data.frame containing subjects ICE data. See draws() details. update logical, indicates ICE data set first time update applied","code":""},{"path":"/reference/validate_datalong.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Validate a longdata object — validate_datalong","text":"functions used validate various different parts longdata object used draws(), impute(), analyse() pool(). particular: validate_datalong_varExists - Checks variable listed vars actually exists data validate_datalong_types - Checks types key variable expected .e. visit factor variable validate_datalong_notMissing - Checks none key variables (except outcome variable) contain missing values validate_datalong_complete - Checks data complete .e. 1 row subject * visit combination. e.g. nrow(data) == length(unique(subjects)) * length(unique(visits)) validate_datalong_unifromStrata - Checks make sure variables listed stratification variables vary time. e.g. subjects switch stratification groups.","code":""},{"path":"/reference/validate_strategies.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate user specified strategies — validate_strategies","title":"Validate user specified strategies — validate_strategies","text":"Compares user provided strategies required (reference). throw error values reference defined.","code":""},{"path":"/reference/validate_strategies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate user specified strategies — validate_strategies","text":"","code":"validate_strategies(strategies, reference)"},{"path":"/reference/validate_strategies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate user specified strategies — validate_strategies","text":"strategies named list strategies. reference list character vector strategies need defined.","code":""},{"path":"/reference/validate_strategies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate user specified strategies — validate_strategies","text":"throw error issue otherwise return TRUE.","code":""},{"path":"/news/index.html","id":"rbmi-126","dir":"Changelog","previous_headings":"","what":"rbmi 1.2.6","title":"rbmi 1.2.6","text":"CRAN release: 2023-11-24 Updated unit tests fix false-positive error CRAN’s testing servers","code":""},{"path":"/news/index.html","id":"rbmi-125","dir":"Changelog","previous_headings":"","what":"rbmi 1.2.5","title":"rbmi 1.2.5","text":"CRAN release: 2023-09-20 Updated internal Stan code ensure future compatibility (@andrjohns, #390) Updated package description include relevant references (#393) Fixed documentation typos (#393)","code":""},{"path":"/news/index.html","id":"rbmi-123","dir":"Changelog","previous_headings":"","what":"rbmi 1.2.3","title":"rbmi 1.2.3","text":"CRAN release: 2022-11-14 Minor internal tweaks ensure compatibility packages rbmi depends ","code":""},{"path":"/news/index.html","id":"rbmi-121","dir":"Changelog","previous_headings":"","what":"rbmi 1.2.1","title":"rbmi 1.2.1","text":"CRAN release: 2022-10-25 Removed native pipes |> testing code package backwards compatible older servers Replaced glmmTMB dependency mmrm package. resulted package stable (less model fitting convergence issues) well speeding run times 3-fold.","code":""},{"path":"/news/index.html","id":"rbmi-114","dir":"Changelog","previous_headings":"","what":"rbmi 1.1.4","title":"rbmi 1.1.4","text":"CRAN release: 2022-05-18 Updated urls references vignettes Fixed bug visit factor levels re-constructed incorrectly delta_template() Fixed bug wrong visit displayed error message specific visit doesn’t data draws() Fixed bug wrong input parameter displayed error message simulate_data()","code":""},{"path":"/news/index.html","id":"rbmi-111--113","dir":"Changelog","previous_headings":"","what":"rbmi 1.1.1 & 1.1.3","title":"rbmi 1.1.1 & 1.1.3","text":"CRAN release: 2022-03-08 change functionality 1.1.0 Various minor tweaks address CRAN checks messages","code":""},{"path":"/news/index.html","id":"rbmi-110","dir":"Changelog","previous_headings":"","what":"rbmi 1.1.0","title":"rbmi 1.1.0","text":"CRAN release: 2022-03-02 Initial public release","code":""}] +[{"path":"/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to rbmi","title":"Contributing to rbmi","text":"file outlines propose make changes rbmi well providing details obscure aspects package’s development process.","code":""},{"path":"/CONTRIBUTING.html","id":"setup","dir":"","previous_headings":"","what":"Setup","title":"Contributing to rbmi","text":"order develop contribute rbmi need access C/C++ compiler. Windows install rtools macOS install Xcode. Likewise, also need install package’s development dependencies. can done launching R within project root executing:","code":"devtools::install_dev_deps()"},{"path":"/CONTRIBUTING.html","id":"code-changes","dir":"","previous_headings":"","what":"Code changes","title":"Contributing to rbmi","text":"want make code contribution, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed).","code":""},{"path":"/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Code changes","what":"Pull request process","title":"Contributing to rbmi","text":"project uses simple GitHub flow model development. , code changes done feature branch based main branch merged back main branch complete. Pull Requests accepted unless CI/CD checks passed. (See CI/CD section information). Pull Requests relating package’s core R code must accompanied corresponding unit test. pull requests containing changes core R code contain unit test demonstrate working intended accepted. (See Unit Testing section information). Pull Requests add lines changed NEWS.md file.","code":""},{"path":"/CONTRIBUTING.html","id":"coding-considerations","dir":"","previous_headings":"Code changes","what":"Coding Considerations","title":"Contributing to rbmi","text":"use roxygen2, Markdown syntax, documentation. Please ensure code conforms lintr. can check running lintr::lint(\"FILE NAME\") files modified ensuring findings kept possible. hard requirements following lintr’s conventions encourage developers follow guidance closely possible. project uses 4 space indents, contributions following accepted. project makes use S3 R6 OOP. Usage S4 OOP systems avoided unless absolutely necessary ensure consistency. said recommended stick S3 unless modification place R6 specific features required. current desire package keep dependency tree small possible. end discouraged adding additional packages “Depends” / “Imports” section unless absolutely essential. importing package just use single function consider just copying source code function instead, though please check licence include proper attribution/notices. expectations “Suggests” free use package vignettes / unit tests, though please mindful unnecessarily excessive .","code":""},{"path":"/CONTRIBUTING.html","id":"unit-testing--cicd","dir":"","previous_headings":"","what":"Unit Testing & CI/CD","title":"Contributing to rbmi","text":"project uses testthat perform unit testing combination GitHub Actions CI/CD.","code":""},{"path":"/CONTRIBUTING.html","id":"scheduled-testing","dir":"","previous_headings":"Unit Testing & CI/CD","what":"Scheduled Testing","title":"Contributing to rbmi","text":"Due stochastic nature package unit tests take considerable amount time execute. avoid issues usability, unit tests take couple seconds run deferred scheduled testing. tests run occasionally periodic basis (currently twice month) every pull request / push event. defer test scheduled build simply include skip_if_not(is_full_test()) top test_that() block .e. scheduled tests can also manually activated going “https://github.com/insightsengineering/rbmi” -> “Actions” -> “Bi-Weekly” -> “Run Workflow”. advisable releasing CRAN.","code":"test_that(\"some unit test\", { skip_if_not(is_full_test()) expect_equal(1,1) })"},{"path":"/CONTRIBUTING.html","id":"cran-releases","dir":"","previous_headings":"Unit Testing & CI/CD","what":"CRAN Releases","title":"Contributing to rbmi","text":"order release package CRAN needs tested across multiple different OS’s versions R. implemented project via GitHub Action Workflow titled “Check CRAN” needs manually activated. go “https://github.com/insightsengineering/rbmi” -> “Actions” -> “Check CRAN” -> “Run Workflow”. tests pass package can safely released CRAN (updating relevant cran-comments.md file)","code":""},{"path":"/CONTRIBUTING.html","id":"docker-images","dir":"","previous_headings":"Unit Testing & CI/CD","what":"Docker Images","title":"Contributing to rbmi","text":"support CI/CD terms reducing installation time, several Docker images pre-built contain packages system dependencies project needs. current relevant images can found : ghcr.io/insightsengineering/rbmi:r404 ghcr.io/insightsengineering/rbmi:r410 ghcr.io/insightsengineering/rbmi:latest latest image automatically re-built month contain latest version R packages. versions built older versions R (indicated tag number) contain package versions version R released. important ensure package works older versions R many companies typically run due delays validation processes. code create images can found misc/docker. legacy images (.e. everything excluding “latest” image) built manual request running corresponding GitHub Actions Workflow.","code":""},{"path":"/CONTRIBUTING.html","id":"reproducibility-print-tests--snaps","dir":"","previous_headings":"Unit Testing & CI/CD","what":"Reproducibility, Print Tests & Snaps","title":"Contributing to rbmi","text":"particular issue testing package reproducibility. part handled well via set.seed() however stan/rstan guarantee reproducibility even seed run different hardware. issue surfaces testing print messages pool object displays treatment estimates thus identical run different machines. address issue pre-made pool objects generated stored R/sysdata.rda (generated data-raw/create_print_test_data.R). generated print messages compared expected values stored tests/testthat/_snaps/ (automatically created testthat::expect_snapshot())","code":""},{"path":"/CONTRIBUTING.html","id":"fitting-mmrms","dir":"","previous_headings":"","what":"Fitting MMRM’s","title":"Contributing to rbmi","text":"package currently uses mmrm package fit MMRM models. package still fairly new far proven stable, fast reliable. spot issues MMRM package please raise corresponding GitHub Repository - link mmrm package uses TMB uncommon see warnings either inconsistent versions TMB Matrix package compiled . order resolve may wish re-compile packages source using: Note need rtools installed Windows machine Xcode running macOS (somehow else access C/C++ compiler).","code":"install.packages(c(\"TMB\", \"mmrm\"), type = \"source\")"},{"path":"/CONTRIBUTING.html","id":"rstan","dir":"","previous_headings":"","what":"rstan","title":"Contributing to rbmi","text":"Bayesian models fitted package implemented via stan/rstan. code can found inst/stan/MMRM.stan. Note package automatically take care compiling code install run devtools::load_all(). Please note package won’t recompile code unless changed source code delete src directory.","code":""},{"path":"/CONTRIBUTING.html","id":"vignettes","dir":"","previous_headings":"","what":"Vignettes","title":"Contributing to rbmi","text":"CRAN imposes 10-minute run limit building, compiling testing package. keep limit vignettes pre-built; say simply changing source code automatically update vignettes, need manually re-build . need run: re-built need commit updated *.html files git repository. reference static vignette process works using “asis” vignette engine provided R.rsp. works getting R recognise vignettes files ending *.html.asis; builds simply copying corresponding files ending *.html relevent docs/ folder built package.","code":"Rscript vignettes/build.R"},{"path":"/CONTRIBUTING.html","id":"misc--local-folders","dir":"","previous_headings":"","what":"Misc & Local Folders","title":"Contributing to rbmi","text":"misc/ folder project used hold useful scripts, analyses, simulations & infrastructure code wish keep isn’t essential build deployment package. Feel free store additional stuff feel worth keeping. Likewise, local/ added .gitignore file meaning anything stored folder won’t committed repository. example, may find useful storing personal scripts testing generally exploring package development.","code":""},{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"Apache License","title":"Apache License","text":"Version 2.0, January 2004 ","code":""},{"path":[]},{"path":"/LICENSE.html","id":"id_1-definitions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"1. Definitions","title":"Apache License","text":"“License” shall mean terms conditions use, reproduction, distribution defined Sections 1 9 document. “Licensor” shall mean copyright owner entity authorized copyright owner granting License. “Legal Entity” shall mean union acting entity entities control, controlled , common control entity. purposes definition, “control” means () power, direct indirect, cause direction management entity, whether contract otherwise, (ii) ownership fifty percent (50%) outstanding shares, (iii) beneficial ownership entity. “” (“”) shall mean individual Legal Entity exercising permissions granted License. “Source” form shall mean preferred form making modifications, including limited software source code, documentation source, configuration files. “Object” form shall mean form resulting mechanical transformation translation Source form, including limited compiled object code, generated documentation, conversions media types. “Work” shall mean work authorship, whether Source Object form, made available License, indicated copyright notice included attached work (example provided Appendix ). “Derivative Works” shall mean work, whether Source Object form, based (derived ) Work editorial revisions, annotations, elaborations, modifications represent, whole, original work authorship. purposes License, Derivative Works shall include works remain separable , merely link (bind name) interfaces , Work Derivative Works thereof. “Contribution” shall mean work authorship, including original version Work modifications additions Work Derivative Works thereof, intentionally submitted Licensor inclusion Work copyright owner individual Legal Entity authorized submit behalf copyright owner. purposes definition, “submitted” means form electronic, verbal, written communication sent Licensor representatives, including limited communication electronic mailing lists, source code control systems, issue tracking systems managed , behalf , Licensor purpose discussing improving Work, excluding communication conspicuously marked otherwise designated writing copyright owner “Contribution.” “Contributor” shall mean Licensor individual Legal Entity behalf Contribution received Licensor subsequently incorporated within Work.","code":""},{"path":"/LICENSE.html","id":"id_2-grant-of-copyright-license","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"2. Grant of Copyright License","title":"Apache License","text":"Subject terms conditions License, Contributor hereby grants perpetual, worldwide, non-exclusive, -charge, royalty-free, irrevocable copyright license reproduce, prepare Derivative Works , publicly display, publicly perform, sublicense, distribute Work Derivative Works Source Object form.","code":""},{"path":"/LICENSE.html","id":"id_3-grant-of-patent-license","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"3. Grant of Patent License","title":"Apache License","text":"Subject terms conditions License, Contributor hereby grants perpetual, worldwide, non-exclusive, -charge, royalty-free, irrevocable (except stated section) patent license make, made, use, offer sell, sell, import, otherwise transfer Work, license applies patent claims licensable Contributor necessarily infringed Contribution(s) alone combination Contribution(s) Work Contribution(s) submitted. institute patent litigation entity (including cross-claim counterclaim lawsuit) alleging Work Contribution incorporated within Work constitutes direct contributory patent infringement, patent licenses granted License Work shall terminate date litigation filed.","code":""},{"path":"/LICENSE.html","id":"id_4-redistribution","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"4. Redistribution","title":"Apache License","text":"may reproduce distribute copies Work Derivative Works thereof medium, without modifications, Source Object form, provided meet following conditions: () must give recipients Work Derivative Works copy License; (b) must cause modified files carry prominent notices stating changed files; (c) must retain, Source form Derivative Works distribute, copyright, patent, trademark, attribution notices Source form Work, excluding notices pertain part Derivative Works; (d) Work includes “NOTICE” text file part distribution, Derivative Works distribute must include readable copy attribution notices contained within NOTICE file, excluding notices pertain part Derivative Works, least one following places: within NOTICE text file distributed part Derivative Works; within Source form documentation, provided along Derivative Works; , within display generated Derivative Works, wherever third-party notices normally appear. contents NOTICE file informational purposes modify License. may add attribution notices within Derivative Works distribute, alongside addendum NOTICE text Work, provided additional attribution notices construed modifying License. may add copyright statement modifications may provide additional different license terms conditions use, reproduction, distribution modifications, Derivative Works whole, provided use, reproduction, distribution Work otherwise complies conditions stated License.","code":""},{"path":"/LICENSE.html","id":"id_5-submission-of-contributions","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"5. Submission of Contributions","title":"Apache License","text":"Unless explicitly state otherwise, Contribution intentionally submitted inclusion Work Licensor shall terms conditions License, without additional terms conditions. Notwithstanding , nothing herein shall supersede modify terms separate license agreement may executed Licensor regarding Contributions.","code":""},{"path":"/LICENSE.html","id":"id_6-trademarks","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"6. Trademarks","title":"Apache License","text":"License grant permission use trade names, trademarks, service marks, product names Licensor, except required reasonable customary use describing origin Work reproducing content NOTICE file.","code":""},{"path":"/LICENSE.html","id":"id_7-disclaimer-of-warranty","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"7. Disclaimer of Warranty","title":"Apache License","text":"Unless required applicable law agreed writing, Licensor provides Work (Contributor provides Contributions) “” BASIS, WITHOUT WARRANTIES CONDITIONS KIND, either express implied, including, without limitation, warranties conditions TITLE, NON-INFRINGEMENT, MERCHANTABILITY, FITNESS PARTICULAR PURPOSE. solely responsible determining appropriateness using redistributing Work assume risks associated exercise permissions License.","code":""},{"path":"/LICENSE.html","id":"id_8-limitation-of-liability","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"8. Limitation of Liability","title":"Apache License","text":"event legal theory, whether tort (including negligence), contract, otherwise, unless required applicable law (deliberate grossly negligent acts) agreed writing, shall Contributor liable damages, including direct, indirect, special, incidental, consequential damages character arising result License use inability use Work (including limited damages loss goodwill, work stoppage, computer failure malfunction, commercial damages losses), even Contributor advised possibility damages.","code":""},{"path":"/LICENSE.html","id":"id_9-accepting-warranty-or-additional-liability","dir":"","previous_headings":"Terms and Conditions for use, reproduction, and distribution","what":"9. Accepting Warranty or Additional Liability","title":"Apache License","text":"redistributing Work Derivative Works thereof, may choose offer, charge fee , acceptance support, warranty, indemnity, liability obligations /rights consistent License. However, accepting obligations, may act behalf sole responsibility, behalf Contributor, agree indemnify, defend, hold Contributor harmless liability incurred , claims asserted , Contributor reason accepting warranty additional liability. END TERMS CONDITIONS","code":""},{"path":"/LICENSE.html","id":"appendix-how-to-apply-the-apache-license-to-your-work","dir":"","previous_headings":"","what":"APPENDIX: How to apply the Apache License to your work","title":"Apache License","text":"apply Apache License work, attach following boilerplate notice, fields enclosed brackets [] replaced identifying information. (Don’t include brackets!) text enclosed appropriate comment syntax file format. also recommend file class name description purpose included “printed page” copyright notice easier identification within third-party archives.","code":"Copyright [yyyy] [name of copyright owner] Licensed under the Apache License, Version 2.0 (the \"License\"); you may not use this file except in compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0 Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License."},{"path":"/articles/advanced.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"rbmi: Advanced Functionality","text":"purpose vignette provide overview advanced features rbmi package. sections vignette relatively self-contained, .e. readers able jump directly section covers functionality interested .","code":""},{"path":"/articles/advanced.html","id":"sec:dataSimul","dir":"Articles","previous_headings":"","what":"Data simulation using function simulate_data()","title":"rbmi: Advanced Functionality","text":"order demonstrate advanced functions first create simulated dataset rbmi function simulate_data(). simulate_data() function generates data randomized clinical trial longitudinal continuous outcomes two different types intercurrent events (ICEs). One intercurrent event (ICE1) may thought discontinuation study treatment due study drug condition related (SDCR) reasons. event (ICE2) may thought discontinuation study treatment due study drug condition related (NSDCR) reasons. purpose vignette, simulate data similarly simulation study reported Wolbers et al. (2022) (though change simulation parameters) include one ICE type (ICE1). Specifically, simulate 1:1 randomized trial active drug (intervention) versus placebo (control) 100 subjects per group 6 post-baseline assessments (bi-monthly visits 12 months) following assumptions: mean outcome trajectory placebo group increases linearly 50 baseline (visit 0) 60 visit 6, .e. slope 10 points/year. mean outcome trajectory intervention group identical placebo group visit 2. visit 2 onward, slope decreases 50% 5 points/year. covariance structure baseline follow-values groups implied random intercept slope model standard deviation 5 intercept slope, correlation 0.25. addition, independent residual error standard deviation 2.5 added assessment. probability study drug discontinuation visit calculated according logistic model depends observed outcome visit. Specifically, visit-wise discontinuation probability 2% 3% control intervention group, respectively, specified case observed outcome equal 50 (mean value baseline). odds discontinuation simulated increase +10% +1 point increase observed outcome. Study drug discontinuation simulated effect mean trajectory placebo group. intervention group, subjects discontinue follow slope mean trajectory placebo group time point onward. compatible copy increments reference (CIR) assumption. Study drop-study drug discontinuation visit occurs probability 50% leading missing outcome data time point onward. function simulate_data() requires 3 arguments (see function documentation help(simulate_data) details): pars_c: simulation parameters control group pars_t: simulation parameters intervention group post_ice1_traj: Specifies observed outcomes ICE1 simulated , report data according specifications can simulated function simulate_data():","code":"library(rbmi) library(dplyr) library(ggplot2) library(purrr) set.seed(122) n <- 100 time <- c(0, 2, 4, 6, 8, 10, 12) # Mean trajectory control muC <- c(50.0, 51.66667, 53.33333, 55.0, 56.66667, 58.33333, 60.0) # Mean trajectory intervention muT <- c(50.0, 51.66667, 53.33333, 54.16667, 55.0, 55.83333, 56.66667) # Create Sigma sd_error <- 2.5 covRE <- rbind( c(25.0, 6.25), c(6.25, 25.0) ) Sigma <- cbind(1, time / 12) %*% covRE %*% rbind(1, time / 12) + diag(sd_error^2, nrow = length(time)) # Set probability of discontinuation probDisc_C <- 0.02 probDisc_T <- 0.03 or_outcome <- 1.10 # +1 point increase => +10% odds of discontinuation # Set drop-out rate following discontinuation prob_dropout <- 0.5 # Set simulation parameters of the control group parsC <- set_simul_pars( mu = muC, sigma = Sigma, n = n, prob_ice1 = probDisc_C, or_outcome_ice1 = or_outcome, prob_post_ice1_dropout = prob_dropout ) # Set simulation parameters of the intervention group parsT <- parsC parsT$mu <- muT parsT$prob_ice1 <- probDisc_T # Set assumption about post-ice trajectory post_ice_traj <- \"CIR\" # Simulate data data <- simulate_data( pars_c = parsC, pars_t = parsT, post_ice1_traj = post_ice_traj ) head(data) #> id visit group outcome_bl outcome_noICE ind_ice1 ind_ice2 dropout_ice1 #> 1 id_1 0 Control 57.32704 57.32704 0 0 0 #> 2 id_1 1 Control 57.32704 54.69751 1 0 1 #> 3 id_1 2 Control 57.32704 58.60702 1 0 1 #> 4 id_1 3 Control 57.32704 61.50119 1 0 1 #> 5 id_1 4 Control 57.32704 56.68363 1 0 1 #> 6 id_1 5 Control 57.32704 66.14799 1 0 1 #> outcome #> 1 57.32704 #> 2 NA #> 3 NA #> 4 NA #> 5 NA #> 6 NA # As a simple descriptive of the simulated data, summarize the number of subjects with ICEs and missing data data %>% group_by(id) %>% summarise( group = group[1], any_ICE = (any(ind_ice1 == 1)), any_NA = any(is.na(outcome))) %>% group_by(group) %>% summarise( subjects_with_ICE = sum(any_ICE), subjects_with_missings = sum(any_NA) ) #> # A tibble: 2 × 3 #> group subjects_with_ICE subjects_with_missings #> #> 1 Control 18 8 #> 2 Intervention 25 14"},{"path":"/articles/advanced.html","id":"sec:postICEobs","dir":"Articles","previous_headings":"","what":"Handling of observed post-ICE data in rbmi under reference-based imputation","title":"rbmi: Advanced Functionality","text":"rbmi always uses non-missing outcome data input data set, .e. data never overwritten imputation step removed analysis step. implies data considered irrelevant treatment effect estimation (e.g. data ICE estimand specified hypothetical strategy), data need removed input data set user prior calling rbmi functions. imputation missing random (MAR) strategy, observed outcome data also included fitting base imputation model. However, ICEs handled using reference-based imputation methods (CIR, CR, JR), rbmi excludes observed post-ICE data base imputation model. data excluded, base imputation model mistakenly estimate mean trajectories based mixture observed pre- post-ICE data relevant reference-based imputations. However, observed post-ICE data added back data set fitting base imputation model included subsequent imputation analysis steps. Post-ICE data control reference group also excluded base imputation model user specifies reference-based imputation strategy ICEs. ensures ICE impact data included base imputation model regardless whether ICE occurred control intervention group. hand, imputation reference group based MAR assumption even reference-based imputation methods may preferable settings include post-ICE data control group base imputation model. can implemented specifying MAR strategy ICE control group reference-based strategy ICE intervention group. use latter approach example . simulated trial data section 2 assumed outcomes intervention group observed ICE “treatment discontinuation” follow increments observed control group. Thus imputation missing data intervention group treatment discontinuation might performed reference-based copy increments reference (CIR) assumption. Specifically, implement estimator following assumptions: endpoint interest change outcome baseline visit. imputation model includes treatment group, (categorical) visit, treatment--visit interactions, baseline outcome, baseline outcome--visit interactions covariates. imputation model assumes common unstructured covariance matrix treatment groups control group, missing data imputed MAR whereas intervention group, missing post-ICE data imputed CIR assumption analysis model endpoint imputed datasets separate ANCOVA model visit treatment group primary covariate adjustment baseline outcome value. illustration purposes, chose MI based approximate Bayesian posterior draws 20 random imputations demanding computational perspective. practical applications, number random imputations may need increased. Moreover, imputations also supported rbmi. guidance regarding choice imputation approach, refer user comparison implemented approaches Section 3.9 “Statistical Specifications” vignette (vignette(\"stat_specs\", package = \"rbmi\")). first report code set variables imputation analysis models. yet familiar syntax, recommend first check “quickstart” vignette (vignette(\"quickstart\", package = \"rbmi\")). chosen imputation method can set function method_approxbayes() follows: can now sequentially call 4 key functions rbmi perform multiple imputation. Please note management observed post-ICE data performed without additional complexity user. draws() automatically excludes post-ICE data handled reference-based method (keeps post-ICE data handled using MAR) using information provided argument data_ice. impute() impute truly missing data data[[vars$outcome]]. last output gives estimated difference -4.537 (95% CI -6.420 -2.655) two groups last visit associated p-value lower 0.001.","code":"# Create data_ice including the subject's first visit affected by the ICE and the imputation strategy # Imputation strategy for post-ICE data is CIR in the intervention group and MAR for the control group # (note that ICEs which are handled using MAR are optional and do not impact the analysis # because imputation of missing data under MAR is the default) data_ice_CIR <- data %>% group_by(id) %>% filter(ind_ice1 == 1) %>% # select visits with ICEs mutate(strategy = ifelse(group == \"Intervention\", \"CIR\", \"MAR\")) %>% summarise( visit = visit[1], # Select first visit affected by the ICE strategy = strategy[1] ) # Compute endpoint of interest: change from baseline and # remove rows corresponding to baseline visits data <- data %>% filter(visit != 0) %>% mutate( change = outcome - outcome_bl, visit = factor(visit, levels = unique(visit)) ) # Define key variables for the imputation and analysis models vars <- set_vars( subjid = \"id\", visit = \"visit\", outcome = \"change\", group = \"group\", covariates = c(\"visit*outcome_bl\", \"visit*group\"), strategy = \"strategy\" ) vars_an <- vars vars_an$covariates <- \"outcome_bl\" method <- method_approxbayes(n_sample = 20) draw_obj <- draws( data = data, data_ice = data_ice_CIR, vars = vars, method = method, quiet = TRUE, ncores = 2 ) impute_obj_CIR <- impute( draw_obj, references = c(\"Control\" = \"Control\", \"Intervention\" = \"Control\") ) ana_obj_CIR <- analyse( impute_obj_CIR, vars = vars_an ) pool_obj_CIR <- pool(ana_obj_CIR) pool_obj_CIR #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_1 -0.486 0.512 -1.496 0.524 0.343 #> lsm_ref_1 2.62 0.362 1.907 3.333 <0.001 #> lsm_alt_1 2.133 0.362 1.42 2.847 <0.001 #> trt_2 -0.066 0.542 -1.135 1.004 0.904 #> lsm_ref_2 3.707 0.384 2.95 4.464 <0.001 #> lsm_alt_2 3.641 0.383 2.885 4.397 <0.001 #> trt_3 -1.782 0.607 -2.979 -0.585 0.004 #> lsm_ref_3 5.841 0.428 4.997 6.685 <0.001 #> lsm_alt_3 4.059 0.428 3.214 4.904 <0.001 #> trt_4 -2.518 0.692 -3.884 -1.152 <0.001 #> lsm_ref_4 7.656 0.492 6.685 8.627 <0.001 #> lsm_alt_4 5.138 0.488 4.176 6.1 <0.001 #> trt_5 -3.658 0.856 -5.346 -1.97 <0.001 #> lsm_ref_5 9.558 0.598 8.379 10.737 <0.001 #> lsm_alt_5 5.9 0.608 4.699 7.101 <0.001 #> trt_6 -4.537 0.954 -6.42 -2.655 <0.001 #> lsm_ref_6 11.048 0.666 9.735 12.362 <0.001 #> lsm_alt_6 6.511 0.674 5.181 7.841 <0.001 #> --------------------------------------------------"},{"path":"/articles/advanced.html","id":"efficiently-changing-reference-based-imputation-strategies","dir":"Articles","previous_headings":"","what":"Efficiently changing reference-based imputation strategies","title":"rbmi: Advanced Functionality","text":"draws() function far computationally intensive function rbmi. settings, may important explore impact change reference-based imputation strategy results. change affect imputation model affect subsequent imputation step. order allow changes imputation strategy without re-run draws() function, function impute() additional argument update_strategies. However, please note functionality comes important limitations: described beginning Section 3, post-ICE outcomes included input dataset base imputation model imputation method MAR excluded reference-based imputation methods (CIR, CR, JR). Therefore, updata_strategies applied imputation strategy changed MAR non-MAR strategy presence observed post-ICE outcomes. Similarly, change non-MAR strategy MAR triggers warning presence observed post-ICE outcomes base imputation model fitted relevant data MAR. Finally, update_strategies applied timing ICEs changed (argument data_ice) addition imputation strategy. example, described analysis copy increments reference (CIR) assumption previous section. Let’s assume want change strategy jump reference imputation strategy sensitivity analysis. can efficiently implemented using update_strategies follows: imputations jump reference assumption, get estimated difference -4.360 (95% CI -6.238 -2.482) two groups last visit associated p-value <0.001.","code":"# Change ICE strategy from CIR to JR data_ice_JR <- data_ice_CIR %>% mutate(strategy = ifelse(strategy == \"CIR\", \"JR\", strategy)) impute_obj_JR <- impute( draw_obj, references = c(\"Control\" = \"Control\", \"Intervention\" = \"Control\"), update_strategy = data_ice_JR ) ana_obj_JR <- analyse( impute_obj_JR, vars = vars_an ) pool_obj_JR <- pool(ana_obj_JR) pool_obj_JR #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_1 -0.485 0.513 -1.496 0.526 0.346 #> lsm_ref_1 2.609 0.363 1.892 3.325 <0.001 #> lsm_alt_1 2.124 0.361 1.412 2.836 <0.001 #> trt_2 -0.06 0.535 -1.115 0.995 0.911 #> lsm_ref_2 3.694 0.378 2.948 4.441 <0.001 #> lsm_alt_2 3.634 0.381 2.882 4.387 <0.001 #> trt_3 -1.767 0.598 -2.948 -0.587 0.004 #> lsm_ref_3 5.845 0.422 5.012 6.677 <0.001 #> lsm_alt_3 4.077 0.432 3.225 4.93 <0.001 #> trt_4 -2.529 0.686 -3.883 -1.175 <0.001 #> lsm_ref_4 7.637 0.495 6.659 8.614 <0.001 #> lsm_alt_4 5.108 0.492 4.138 6.078 <0.001 #> trt_5 -3.523 0.856 -5.212 -1.833 <0.001 #> lsm_ref_5 9.554 0.61 8.351 10.758 <0.001 #> lsm_alt_5 6.032 0.611 4.827 7.237 <0.001 #> trt_6 -4.36 0.952 -6.238 -2.482 <0.001 #> lsm_ref_6 11.003 0.676 9.669 12.337 <0.001 #> lsm_alt_6 6.643 0.687 5.287 8 <0.001 #> --------------------------------------------------"},{"path":"/articles/advanced.html","id":"imputation-under-mar-with-time-varying-covariates","dir":"Articles","previous_headings":"","what":"Imputation under MAR with time-varying covariates","title":"rbmi: Advanced Functionality","text":"Guizzaro et al. (2021) suggested implement treatment policy strategy via imputation MAR assumption conditioning subject’s ICE status, .e. impute missing post-ICE data based observed post-ICE data. One possible implementation proposal add time-varying covariates imputation model. case study implements proposal compares reference-based imputation methods estimators early Parkinson’s disease can found Noci et al. (2021). settings, may carried including binary time-varying indicator subject’s ICE status visit (defined 0 pre-ICE visits 1 post-ICE visits) imputation model. However, simulated data introduced section 2, may plausible assume treatment discontinuation leads change “slope” mean outcome trajectory. can implemented including time-varying covariate equal 0 visits prior treatment discontinuation equal time treatment discontinuation subsequent visits. regression coefficient corresponding change post-ICE “slope” allowed depend assigned treatment group, .e. imputation model include interaction time-varying covariate treatment group. Let’s first define time-varying covariate: can include time-varying covariate imputation model, crossed group variable: now sequentially call 4 key rbmi functions:","code":"data <- data %>% group_by(id) %>% mutate(time_from_ice1 = cumsum(ind_ice1)*2/12 ) # multiplication by 2/12 because visits are bi-monthly vars_tv <- set_vars( subjid = \"id\", visit = \"visit\", outcome = \"change\", group = \"group\", covariates = c(\"visit*outcome_bl\", \"visit*group\", \"time_from_ice1*group\"), strategy = \"strategy\" ) draw_obj <- draws( data = data, data_ice = NULL, # if NULL, MAR is assumed for all missing data vars = vars_tv, method = method, quiet = TRUE ) impute_obj_tv <- impute( draw_obj, references = c(\"Control\" = \"Control\", \"Intervention\" = \"Intervention\") ) ana_obj_tv <- analyse( impute_obj_tv, vars = vars_an ) pool(ana_obj_tv) #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_1 -0.492 0.515 -1.507 0.524 0.341 #> lsm_ref_1 2.623 0.362 1.908 3.338 <0.001 #> lsm_alt_1 2.131 0.366 1.409 2.854 <0.001 #> trt_2 0.018 0.55 -1.067 1.103 0.974 #> lsm_ref_2 3.697 0.382 2.943 4.45 <0.001 #> lsm_alt_2 3.715 0.394 2.936 4.493 <0.001 #> trt_3 -1.802 0.614 -3.015 -0.59 0.004 #> lsm_ref_3 5.815 0.429 4.97 6.661 <0.001 #> lsm_alt_3 4.013 0.441 3.142 4.884 <0.001 #> trt_4 -2.543 0.704 -3.932 -1.154 <0.001 #> lsm_ref_4 7.609 0.486 6.65 8.568 <0.001 #> lsm_alt_4 5.066 0.516 4.046 6.086 <0.001 #> trt_5 -3.739 0.879 -5.475 -2.004 <0.001 #> lsm_ref_5 9.499 0.606 8.302 10.695 <0.001 #> lsm_alt_5 5.759 0.636 4.502 7.017 <0.001 #> trt_6 -4.685 0.98 -6.622 -2.748 <0.001 #> lsm_ref_6 10.988 0.667 9.67 12.305 <0.001 #> lsm_alt_6 6.302 0.712 4.894 7.711 <0.001 #> --------------------------------------------------"},{"path":"/articles/advanced.html","id":"custom-imputation-strategies","dir":"Articles","previous_headings":"","what":"Custom imputation strategies","title":"rbmi: Advanced Functionality","text":"following imputation strategies implemented rbmi: Missing Random (MAR) Jump Reference (JR) Copy Reference (CR) Copy Increments Reference (CIR) Last Mean Carried Forward (LMCF) addition, rbmi allows user implement imputation strategy. , user needs three things: Define function implementing new imputation strategy. Specify patients use strategy data_ice dataset provided draws(). Provide imputation strategy function impute(). imputation strategy function must take 3 arguments (pars_group, pars_ref, index_mar) calculates mean covariance matrix subject’s marginal imputation distribution applied subjects strategy applies. , pars_group contains predicted mean trajectory (pars_group$mu, numeric vector) covariance matrix (pars_group$sigma) subject conditional assigned treatment group covariates. pars_ref contains corresponding mean trajectory covariance matrix conditional reference group subject’s covariates. index_mar logical vector specifies visit whether visit unaffected ICE handled using non-MAR method . example, user can check CIR strategy implemented looking function strategy_CIR(). illustrate simple example, assume new strategy implemented follows: - marginal mean imputation distribution equal marginal mean trajectory subject according assigned group covariates ICE. - ICE marginal mean imputation distribution equal average visit-wise marginal means based subjects covariates assigned group reference group, respectively. - covariance matrix marginal imputation distribution, covariance matrix assigned group taken. , first need define imputation function example coded follows: example showing use: incorporate rbmi, data_ice needs updated strategy AVG specified visits affected ICE. Additionally, function needs provided impute() via getStrategies() function shown : , analysis proceed calling analyse() pool() .","code":"strategy_CIR #> function (pars_group, pars_ref, index_mar) #> { #> if (all(index_mar)) { #> return(pars_group) #> } #> else if (all(!index_mar)) { #> return(pars_ref) #> } #> mu <- pars_group$mu #> last_mar <- which(!index_mar)[1] - 1 #> increments_from_last_mar_ref <- pars_ref$mu[!index_mar] - #> pars_ref$mu[last_mar] #> mu[!index_mar] <- mu[last_mar] + increments_from_last_mar_ref #> sigma <- compute_sigma(sigma_group = pars_group$sigma, sigma_ref = pars_ref$sigma, #> index_mar = index_mar) #> pars <- list(mu = mu, sigma = sigma) #> return(pars) #> } #> #> strategy_AVG <- function(pars_group, pars_ref, index_mar) { mu_mean <- (pars_group$mu + pars_ref$mu) / 2 x <- pars_group x$mu[!index_mar] <- mu_mean[!index_mar] return(x) } pars_group <- list( mu = c(1, 2, 3), sigma = as_vcov(c(1, 3, 2), c(0.4, 0.5, 0.45)) ) pars_ref <- list( mu = c(5, 6, 7), sigma = as_vcov(c(2, 1, 1), c(0.7, 0.8, 0.5)) ) index_mar <- c(TRUE, TRUE, FALSE) strategy_AVG(pars_group, pars_ref, index_mar) #> $mu #> [1] 1 2 5 #> #> $sigma #> [,1] [,2] [,3] #> [1,] 1.0 1.2 1.0 #> [2,] 1.2 9.0 2.7 #> [3,] 1.0 2.7 4.0 data_ice_AVG <- data_ice_CIR %>% mutate(strategy = ifelse(strategy == \"CIR\", \"AVG\", strategy)) draw_obj <- draws( data = data, data_ice = data_ice_AVG, vars = vars, method = method, quiet = TRUE ) impute_obj <- impute( draw_obj, references = c(\"Control\" = \"Control\", \"Intervention\" = \"Control\"), strategies = getStrategies(AVG = strategy_AVG) )"},{"path":"/articles/advanced.html","id":"custom-analysis-functions","dir":"Articles","previous_headings":"","what":"Custom analysis functions","title":"rbmi: Advanced Functionality","text":"default rbmi analyse data using ancova() function. analysis function fits ANCOVA model outcomes visit separately, returns “treatment effect” estimate well corresponding least square means group. user wants perform different analysis, return different statistics analysis, can done using custom analysis function. Beware validity conditional mean imputation method formally established analysis functions corresponding linear models (ANCOVA) caution required applying alternative analysis functions method. custom analysis function must take data.frame first argument return named list element list containing minimum point estimate, called est. method method_bayes() method_approxbayes(), list must additionally contain standard error (element se) , available, degrees freedom complete-data analysis model (element df). simple example, replicate ANCOVA analysis last visit CIR-based imputations user-defined analysis function : second example, assume supplementary analysis user wants compare proportion subjects change baseline >10 points last visit treatment groups baseline outcome additional covariate. lead following basic analysis function: Note user wants rbmi use normal approximation pooled test statistics, degrees freedom need set df = NA (per example). degrees freedom complete data test statistics known degrees freedom set df = Inf, rbmi pools degrees freedom across imputed datasets according rule Barnard Rubin (see “Statistical Specifications” vignette (vignette(\"stat_specs\", package = \"rbmi\") details). According rule, infinite degrees freedom complete data analysis imply pooled degrees freedom also infinite. Rather, case pooled degrees freedom (M-1)/lambda^2, M number imputations lambda fraction missing information (see Barnard Rubin (1999) details).","code":"compare_change_lastvisit <- function(data, ...) { fit <- lm(change ~ group + outcome_bl, data = data, subset = (visit == 6) ) res <- list( trt = list( est = coef(fit)[\"groupIntervention\"], se = sqrt(vcov(fit)[\"groupIntervention\", \"groupIntervention\"]), df = df.residual(fit) ) ) return(res) } ana_obj_CIR6 <- analyse( impute_obj_CIR, fun = compare_change_lastvisit, vars = vars_an ) pool(ana_obj_CIR6) #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================= #> parameter est se lci uci pval #> ------------------------------------------------- #> trt -4.537 0.954 -6.42 -2.655 <0.001 #> ------------------------------------------------- compare_prop_lastvisit <- function(data, ...) { fit <- glm( I(change > 10) ~ group + outcome_bl, family = binomial(), data = data, subset = (visit == 6) ) res <- list( trt = list( est = coef(fit)[\"groupIntervention\"], se = sqrt(vcov(fit)[\"groupIntervention\", \"groupIntervention\"]), df = NA ) ) return(res) } ana_obj_prop <- analyse( impute_obj_CIR, fun = compare_prop_lastvisit, vars = vars_an ) pool_obj_prop <- pool(ana_obj_prop) pool_obj_prop #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================= #> parameter est se lci uci pval #> ------------------------------------------------- #> trt -1.052 0.314 -1.667 -0.438 0.001 #> ------------------------------------------------- tmp <- as.data.frame(pool_obj_prop) %>% mutate( OR = exp(est), OR.lci = exp(lci), OR.uci = exp(uci) ) %>% select(parameter, OR, OR.lci, OR.uci) tmp #> parameter OR OR.lci OR.uci #> 1 trt 0.3491078 0.188807 0.6455073"},{"path":"/articles/advanced.html","id":"sensitivity-analyses-delta-adjustments-and-tipping-point-analyses","dir":"Articles","previous_headings":"","what":"Sensitivity analyses: Delta adjustments and tipping point analyses","title":"rbmi: Advanced Functionality","text":"Delta-adjustments used impute missing data missing random (NMAR) assumption. reflects belief unobserved outcomes systematically “worse” (“better”) “comparable” observed outcomes. extensive discussion delta-adjustment methods, refer Cro et al. (2020). rbmi, marginal delta-adjustment approach implemented. means delta-adjustment applied dataset data imputation MAR reference-based missing data assumptions prior analysis imputed data. Sensitivity analysis using delta-adjustments can therefore performed without re-fit imputation model. rbmi, implemented via delta argument analyse() function.","code":""},{"path":"/articles/advanced.html","id":"simple-delta-adjustments-and-tipping-point-analyses","dir":"Articles","previous_headings":"8 Sensitivity analyses: Delta adjustments and tipping point analyses","what":"Simple delta adjustments and tipping point analyses","title":"rbmi: Advanced Functionality","text":"delta argument analyse() allows users modify outcome variable prior analysis. , user needs provide data.frame contains columns subject visit (identify observation adjusted) plus additional column called delta specifies value added outcomes prior analysis. delta_template() function supports user creating data.frame: creates skeleton data.frame containing one row per subject visit value delta set 0 observations: Note output delta_template() contains additional information can used properly re-set variable delta. example, assume user wants implement delta-adjustment imputed values CIR described section 3. Specifically, assume fixed “worsening adjustment” +5 points applied imputed values regardless treatment group. programmed follows: approach can used implement tipping point analysis. , apply different delta-adjustments imputed data control intervention group, respectively. Assume delta-adjustments less -5 points +15 points considered implausible clinical perspective. Therefore, vary delta-values group -5 +15 points investigate delta combinations lead “tipping” primary analysis result, defined analysis p-value \\(\\geq 0.05\\). According analysis, significant test result primary analysis CIR tipped non-significant result rather extreme delta-adjustments. Please note real analysis recommended use smaller step size grid used .","code":"dat_delta <- delta_template(imputations = impute_obj_CIR) head(dat_delta) #> id visit group is_mar is_missing is_post_ice strategy delta #> 1 id_1 1 Control TRUE TRUE TRUE MAR 0 #> 2 id_1 2 Control TRUE TRUE TRUE MAR 0 #> 3 id_1 3 Control TRUE TRUE TRUE MAR 0 #> 4 id_1 4 Control TRUE TRUE TRUE MAR 0 #> 5 id_1 5 Control TRUE TRUE TRUE MAR 0 #> 6 id_1 6 Control TRUE TRUE TRUE MAR 0 # Set delta-value to 5 for all imputed (previously missing) outcomes and 0 for all other outcomes dat_delta <- delta_template(imputations = impute_obj_CIR) %>% mutate(delta = is_missing * 5) # Repeat the analyses with the delta-adjusted values and pool results ana_delta <- analyse( impute_obj_CIR, delta = dat_delta, vars = vars_an ) pool(ana_delta) #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_1 -0.482 0.524 -1.516 0.552 0.359 #> lsm_ref_1 2.718 0.37 1.987 3.448 <0.001 #> lsm_alt_1 2.235 0.37 1.505 2.966 <0.001 #> trt_2 -0.016 0.56 -1.12 1.089 0.978 #> lsm_ref_2 3.907 0.396 3.125 4.688 <0.001 #> lsm_alt_2 3.891 0.395 3.111 4.671 <0.001 #> trt_3 -1.684 0.641 -2.948 -0.42 0.009 #> lsm_ref_3 6.092 0.452 5.201 6.983 <0.001 #> lsm_alt_3 4.408 0.452 3.515 5.3 <0.001 #> trt_4 -2.359 0.741 -3.821 -0.897 0.002 #> lsm_ref_4 7.951 0.526 6.913 8.99 <0.001 #> lsm_alt_4 5.593 0.522 4.563 6.623 <0.001 #> trt_5 -3.34 0.919 -5.153 -1.526 <0.001 #> lsm_ref_5 9.899 0.643 8.631 11.168 <0.001 #> lsm_alt_5 6.559 0.653 5.271 7.848 <0.001 #> trt_6 -4.21 1.026 -6.236 -2.184 <0.001 #> lsm_ref_6 11.435 0.718 10.019 12.851 <0.001 #> lsm_alt_6 7.225 0.725 5.793 8.656 <0.001 #> -------------------------------------------------- perform_tipp_analysis <- function(delta_control, delta_intervention) { # Derive delta offset based on control and intervention specific deltas delta_df <- delta_df_init %>% mutate( delta_ctl = (group == \"Control\") * is_missing * delta_control, delta_int = (group == \"Intervention\") * is_missing * delta_intervention, delta = delta_ctl + delta_int ) ana_delta <- analyse( impute_obj_CIR, fun = compare_change_lastvisit, vars = vars_an, delta = delta_df, ) pool_delta <- as.data.frame(pool(ana_delta)) list( trt_effect_6 = pool_delta[[\"est\"]], pval_6 = pool_delta[[\"pval\"]] ) } # Get initial delta template delta_df_init <- delta_template(impute_obj_CIR) tipp_frame_grid <- expand.grid( delta_control = seq(-5, 15, by = 2), delta_intervention = seq(-5, 15, by = 2) ) %>% as_tibble() tipp_frame <- tipp_frame_grid %>% mutate( results_list = map2(delta_control, delta_intervention, perform_tipp_analysis), trt_effect_6 = map_dbl(results_list, \"trt_effect_6\"), pval_6 = map_dbl(results_list, \"pval_6\") ) %>% select(-results_list) %>% mutate( pval = cut( pval_6, c(0, 0.001, 0.01, 0.05, 0.2, 1), right = FALSE, labels = c(\"<0.001\", \"0.001 - <0.01\", \"0.01- <0.05\", \"0.05 - <0.20\", \">= 0.20\") ) ) # Show delta values which lead to non-significant analysis results tipp_frame %>% filter(pval_6 >= 0.05) #> # A tibble: 3 × 5 #> delta_control delta_intervention trt_effect_6 pval_6 pval #> #> 1 -5 15 -1.99 0.0935 0.05 - <0.20 #> 2 -3 15 -2.15 0.0704 0.05 - <0.20 #> 3 -1 15 -2.31 0.0527 0.05 - <0.20 ggplot(tipp_frame, aes(delta_control, delta_intervention, fill = pval)) + geom_raster() + scale_fill_manual(values = c(\"darkgreen\", \"lightgreen\", \"lightyellow\", \"orange\", \"red\"))"},{"path":"/articles/advanced.html","id":"more-flexible-delta-adjustments-using-the-dlag-and-delta-arguments-of-delta_template","dir":"Articles","previous_headings":"8 Sensitivity analyses: Delta adjustments and tipping point analyses","what":"More flexible delta-adjustments using the dlag and delta arguments of delta_template()","title":"rbmi: Advanced Functionality","text":"far, discussed simple delta arguments add value imputed values. However, user may want apply flexible delta-adjustments missing values intercurrent event (ICE) vary magnitude delta adjustment depending far away visit question ICE visit. facilitate creation flexible delta-adjustments, delta_template() function two optional additional arguments delta dlag. delta argument specifies default amount delta applied post-ICE visit, whilst dlag specifies scaling coefficient applied based upon visits proximity first visit affected ICE. default, delta added unobserved (.e. imputed) post-ICE outcomes can changed setting optional argument missing_only = FALSE. usage delta dlag arguments best illustrated examples: Assume setting 4 visits user specified delta = c(5,6,7,8) dlag=c(1,2,3,4). subject first visit affected ICE visit 2, values delta dlag imply following delta offset: , subject delta offset 0 applied visit v1, 6 visit v2, 20 visit v3 44 visit v4. Assume instead, subject’s first visit affected ICE visit 3. , values delta dlag imply following delta offset: apply constant delta value +5 visits affected ICE regardless proximity first ICE visit, one set delta = c(5,5,5,5) dlag = c(1,0,0,0). Alternatively, may straightforward setting call delta_template() function without delta dlag arguments overwrite delta column resulting data.frame described previous section (additionally relying is_post_ice variable). Another way using arguments set delta difference time visits dlag amount delta per unit time. example, let’s say visits occur weeks 1, 5, 6 9 want delta 3 applied week ICE. simplicity, assume ICE occurs immediately subject’s last visit affected ICE. achieved setting delta = c(1,4,1,3) (difference weeks visit) dlag = c(3, 3, 3, 3). Assume subject’s first visit affected ICE visit v2, values delta dlag imply following delta offsets: wrap , show action simulated dataset section 2 imputed datasets based CIR assumption section 3. simulation setting specified follow-visits months 2, 4, 6, 8, 10, 12. Assume want apply delta-adjustment 1 every month ICE unobserved post-ICE visits intervention group . (E.g. ICE occurred immediately month 4 visit, total delta applied missing value month 10 visit 6.) program , first use delta dlag arguments delta_template() set corresponding template data.frame: Next, can use additional metadata variables provided delta_template() manually reset delta values control group back 0: Finally, can use delta data.frame apply desired delta offset analysis:","code":"v1 v2 v3 v4 -------------- 5 6 7 8 # delta assigned to each visit 0 1 2 3 # scaling starting from the first visit after the subjects ICE -------------- 0 6 14 24 # delta * scaling -------------- 0 6 20 44 # cumulative sum (i.e. delta) to be applied to each visit v1 v2 v3 v4 -------------- 5 6 7 8 # delta assigned to each visit 0 0 1 2 # scaling starting from the first visit after the subjects ICE -------------- 0 0 7 16 # delta * scaling -------------- 0 0 7 23 # cumulative sum (i.e. delta) to be applied to each visit v1 v2 v3 v4 -------------- 1 4 1 3 # delta assigned to each visit 0 3 3 3 # scaling starting from the first visit after the subjects ICE -------------- 0 12 3 9 # delta * scaling -------------- 0 12 15 24 # cumulative sum (i.e. delta) to be applied to each visit delta_df <- delta_template( impute_obj_CIR, delta = c(2, 2, 2, 2, 2, 2), dlag = c(1, 1, 1, 1, 1, 1) ) head(delta_df) #> id visit group is_mar is_missing is_post_ice strategy delta #> 1 id_1 1 Control TRUE TRUE TRUE MAR 2 #> 2 id_1 2 Control TRUE TRUE TRUE MAR 4 #> 3 id_1 3 Control TRUE TRUE TRUE MAR 6 #> 4 id_1 4 Control TRUE TRUE TRUE MAR 8 #> 5 id_1 5 Control TRUE TRUE TRUE MAR 10 #> 6 id_1 6 Control TRUE TRUE TRUE MAR 12 delta_df2 <- delta_df %>% mutate(delta = if_else(group == \"Control\", 0, delta)) head(delta_df2) #> id visit group is_mar is_missing is_post_ice strategy delta #> 1 id_1 1 Control TRUE TRUE TRUE MAR 0 #> 2 id_1 2 Control TRUE TRUE TRUE MAR 0 #> 3 id_1 3 Control TRUE TRUE TRUE MAR 0 #> 4 id_1 4 Control TRUE TRUE TRUE MAR 0 #> 5 id_1 5 Control TRUE TRUE TRUE MAR 0 #> 6 id_1 6 Control TRUE TRUE TRUE MAR 0 ana_delta <- analyse(impute_obj_CIR, delta = delta_df2, vars = vars_an) pool(ana_delta) #> #> Pool Object #> ----------- #> Number of Results Combined: 20 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_1 -0.446 0.514 -1.459 0.567 0.386 #> lsm_ref_1 2.62 0.363 1.904 3.335 <0.001 #> lsm_alt_1 2.173 0.363 1.458 2.889 <0.001 #> trt_2 0.072 0.546 -1.006 1.15 0.895 #> lsm_ref_2 3.708 0.387 2.945 4.471 <0.001 #> lsm_alt_2 3.78 0.386 3.018 4.542 <0.001 #> trt_3 -1.507 0.626 -2.743 -0.272 0.017 #> lsm_ref_3 5.844 0.441 4.973 6.714 <0.001 #> lsm_alt_3 4.336 0.442 3.464 5.209 <0.001 #> trt_4 -2.062 0.731 -3.504 -0.621 0.005 #> lsm_ref_4 7.658 0.519 6.634 8.682 <0.001 #> lsm_alt_4 5.596 0.515 4.58 6.612 <0.001 #> trt_5 -2.938 0.916 -4.746 -1.13 0.002 #> lsm_ref_5 9.558 0.641 8.293 10.823 <0.001 #> lsm_alt_5 6.62 0.651 5.335 7.905 <0.001 #> trt_6 -3.53 1.045 -5.591 -1.469 0.001 #> lsm_ref_6 11.045 0.73 9.604 12.486 <0.001 #> lsm_alt_6 7.515 0.738 6.058 8.971 <0.001 #> --------------------------------------------------"},{"path":[]},{"path":"/articles/quickstart.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"rbmi: Quickstart","text":"purpose vignette provide 15 minute quickstart guide core functions rbmi package. rbmi package consists 4 core functions (plus several helper functions) typically called sequence: draws() - fits imputation models stores parameters impute() - creates multiple imputed datasets analyse() - analyses multiple imputed datasets pool() - combines analysis results across imputed datasets single statistic","code":""},{"path":"/articles/quickstart.html","id":"the-data","dir":"Articles","previous_headings":"","what":"The Data","title":"rbmi: Quickstart","text":"use publicly available example dataset antidepressant clinical trial active drug versus placebo. relevant endpoint Hamilton 17-item depression rating scale (HAMD17) assessed baseline weeks 1, 2, 4, 6. Study drug discontinuation occurred 24% subjects active drug 26% subjects placebo. data study drug discontinuation missing single additional intermittent missing observation. consider imputation model mean change baseline HAMD17 score outcome (variable CHANGE dataset). following covariates included imputation model: treatment group (THERAPY), (categorical) visit (VISIT), treatment--visit interactions, baseline HAMD17 score (BASVAL), baseline HAMD17 score--visit interactions. common unstructured covariance matrix structure assumed groups. analysis model ANCOVA model treatment group primary factor adjustment baseline HAMD17 score. rbmi expects input dataset complete; , must one row per subject visit. Missing outcome values coded NA, missing covariate values allowed. dataset incomplete, expand_locf() helper function can used add missing rows, using LOCF imputation carry forward observed baseline covariate values visits missing outcomes. Rows corresponding missing outcomes present antidepressant trial dataset. address therefore use expand_locf() function follows:","code":"library(rbmi) library(dplyr) #> #> Attaching package: 'dplyr' #> The following objects are masked from 'package:stats': #> #> filter, lag #> The following objects are masked from 'package:base': #> #> intersect, setdiff, setequal, union data(\"antidepressant_data\") dat <- antidepressant_data # Use expand_locf to add rows corresponding to visits with missing outcomes to the dataset dat <- expand_locf( dat, PATIENT = levels(dat$PATIENT), # expand by PATIENT and VISIT VISIT = levels(dat$VISIT), vars = c(\"BASVAL\", \"THERAPY\"), # fill with LOCF BASVAL and THERAPY group = c(\"PATIENT\"), order = c(\"PATIENT\", \"VISIT\") )"},{"path":"/articles/quickstart.html","id":"draws","dir":"Articles","previous_headings":"","what":"Draws","title":"rbmi: Quickstart","text":"draws() function fits imputation models stores corresponding parameter estimates Bayesian posterior parameter draws. three main inputs draws() function : data - primary longitudinal data.frame containing outcome variable covariates. data_ice - data.frame specifies first visit affected intercurrent event (ICE) imputation strategy handling missing outcome data ICE. one ICE imputed non-MAR strategy allowed per subject. method - statistical method used fit imputation models create imputed datasets. antidepressant trial data, dataset data_ice provided. However, can derived , dataset, subject’s first visit affected ICE “study drug discontinuation” corresponds first terminal missing observation. first derive dateset data_ice create 150 Bayesian posterior draws imputation model parameters. example, assume imputation strategy ICE Jump Reference (JR) subjects 150 multiple imputed datasets using Bayesian posterior draws imputation model created. Note use set_vars() specifies names key variables within dataset imputation model. Additionally, note whilst vars$group vars$visit added terms imputation model default, interaction , thus inclusion group * visit list covariates. Available imputation methods include: Bayesian multiple imputation - method_bayes() Approximate Bayesian multiple imputation - method_approxbayes() Conditional mean imputation (bootstrap) - method_condmean(type = \"bootstrap\") Conditional mean imputation (jackknife) - method_condmean(type = \"jackknife\") Bootstrapped multiple imputation - method = method_bmlmi() comparison methods, refer stat_specs vignette (Section 3.10). “statistical specifications” vignette (Section 3.10): vignette(\"stat_specs\",package=\"rbmi\"). Available imputation strategies include: Missing Random - \"MAR\" Jump Reference - \"JR\" Copy Reference - \"CR\" Copy Increments Reference - \"CIR\" Last Mean Carried Forward - \"LMCF\"","code":"# create data_ice and set the imputation strategy to JR for # each patient with at least one missing observation dat_ice <- dat %>% arrange(PATIENT, VISIT) %>% filter(is.na(CHANGE)) %>% group_by(PATIENT) %>% slice(1) %>% ungroup() %>% select(PATIENT, VISIT) %>% mutate(strategy = \"JR\") # In this dataset, subject 3618 has an intermittent missing values which does not correspond # to a study drug discontinuation. We therefore remove this subject from `dat_ice`. # (In the later imputation step, it will automatically be imputed under the default MAR assumption.) dat_ice <- dat_ice[-which(dat_ice$PATIENT == 3618),] dat_ice #> # A tibble: 43 × 3 #> PATIENT VISIT strategy #> #> 1 1513 5 JR #> 2 1514 5 JR #> 3 1517 5 JR #> 4 1804 7 JR #> 5 2104 7 JR #> 6 2118 5 JR #> 7 2218 6 JR #> 8 2230 6 JR #> 9 2721 5 JR #> 10 2729 5 JR #> # ℹ 33 more rows # Define the names of key variables in our dataset and # the covariates included in the imputation model using `set_vars()` # Note that the covariates argument can also include interaction terms vars <- set_vars( outcome = \"CHANGE\", visit = \"VISIT\", subjid = \"PATIENT\", group = \"THERAPY\", covariates = c(\"BASVAL*VISIT\", \"THERAPY*VISIT\") ) # Define which imputation method to use (here: Bayesian multiple imputation with 150 imputed datsets) method <- method_bayes( burn_in = 200, burn_between = 5, n_samples = 150, seed = 675442751 ) # Create samples for the imputation parameters by running the draws() function set.seed(987) drawObj <- draws( data = dat, data_ice = dat_ice, vars = vars, method = method, quiet = TRUE ) drawObj #> #> Draws Object #> ------------ #> Number of Samples: 150 #> Number of Failed Samples: 0 #> Model Formula: CHANGE ~ 1 + THERAPY + VISIT + BASVAL * VISIT + THERAPY * VISIT #> Imputation Type: random #> Method: #> name: Bayes #> burn_in: 200 #> burn_between: 5 #> same_cov: TRUE #> n_samples: 150 #> seed: 675442751"},{"path":"/articles/quickstart.html","id":"impute","dir":"Articles","previous_headings":"","what":"Impute","title":"rbmi: Quickstart","text":"next step use parameters imputation model generate imputed datasets. done via impute() function. function two key inputs: imputation model output draws() reference groups relevant reference-based imputation methods. ’s usage thus: instance, specifying PLACEBO group reference group well DRUG group (standard imputation using reference-based methods). Generally speaking, need see directly interact imputed datasets. However, wish inspect , can extracted imputation object using extract_imputed_dfs() helper function, .e.: Note case method_bayes() method_approxbayes(), imputed datasets correspond random imputations original dataset. method_condmean(), first imputed dataset always correspond completed original dataset containing subjects. method_condmean(type=\"jackknife\"), remaining datasets correspond conditional mean imputations leave-one-subject-datasets, whereas method_condmean(type=\"bootstrap\"), subsequent dataset corresponds conditional mean imputation bootstrapped datasets. method_bmlmi(), imputed datasets correspond sets random imputations bootstrapped datasets.","code":"imputeObj <- impute( drawObj, references = c(\"DRUG\" = \"PLACEBO\", \"PLACEBO\" = \"PLACEBO\") ) imputeObj #> #> Imputation Object #> ----------------- #> Number of Imputed Datasets: 150 #> Fraction of Missing Data (Original Dataset): #> 4: 0% #> 5: 8% #> 6: 13% #> 7: 25% #> References: #> DRUG -> PLACEBO #> PLACEBO -> PLACEBO imputed_dfs <- extract_imputed_dfs(imputeObj) head(imputed_dfs[[10]], 12) # first 12 rows of 10th imputed dataset #> PATIENT HAMATOTL PGIIMP RELDAYS VISIT THERAPY GENDER POOLINV BASVAL #> 1 new_pt_1 21 2 7 4 DRUG F 006 32 #> 2 new_pt_1 19 2 14 5 DRUG F 006 32 #> 3 new_pt_1 21 3 28 6 DRUG F 006 32 #> 4 new_pt_1 17 4 42 7 DRUG F 006 32 #> 5 new_pt_2 18 3 7 4 PLACEBO F 006 14 #> 6 new_pt_2 18 2 15 5 PLACEBO F 006 14 #> 7 new_pt_2 14 3 29 6 PLACEBO F 006 14 #> 8 new_pt_2 8 2 42 7 PLACEBO F 006 14 #> 9 new_pt_3 18 3 7 4 DRUG F 006 21 #> 10 new_pt_3 17 3 14 5 DRUG F 006 21 #> 11 new_pt_3 12 3 28 6 DRUG F 006 21 #> 12 new_pt_3 9 3 44 7 DRUG F 006 21 #> HAMDTL17 CHANGE #> 1 21 -11 #> 2 20 -12 #> 3 19 -13 #> 4 17 -15 #> 5 11 -3 #> 6 14 0 #> 7 9 -5 #> 8 5 -9 #> 9 20 -1 #> 10 18 -3 #> 11 16 -5 #> 12 13 -8"},{"path":"/articles/quickstart.html","id":"analyse","dir":"Articles","previous_headings":"","what":"Analyse","title":"rbmi: Quickstart","text":"next step run analysis model imputed dataset. done defining analysis function calling analyse() apply function imputed dataset. vignette use ancova() function provided rbmi package fits separate ANCOVA model outcomes visit returns treatment effect estimate corresponding least square means group per visit. Note , similar draws(), ancova() function uses set_vars() function determines names key variables within data covariates (addition treatment group) analysis model adjusted. Please also note names analysis estimates contain “ref” “alt” refer two treatment arms. particular “ref” refers first factor level vars$group necessarily coincide control arm. example, since levels(dat[[vars$group]]) = c(\"DRUG\", PLACEBO), results associated “ref” correspond intervention arm, associated “alt” correspond control arm. Additionally, can use delta argument analyse() perform delta adjustments imputed datasets prior analysis. brief, implemented specifying data.frame contains amount adjustment added longitudinal outcome subject visit, .e.  data.frame must contain columns subjid, visit, delta. appreciated carrying procedure potentially tedious, therefore delta_template() helper function provided simplify . particular, delta_template() returns shell data.frame delta-adjustment set 0 patients. Additionally delta_template() adds several meta-variables onto shell data.frame can used manual derivation manipulation delta-adjustment. example lets say want add delta-value 5 imputed values (.e. values missing original dataset) drug arm. implemented follows:","code":"anaObj <- analyse( imputeObj, ancova, vars = set_vars( subjid = \"PATIENT\", outcome = \"CHANGE\", visit = \"VISIT\", group = \"THERAPY\", covariates = c(\"BASVAL\") ) ) anaObj #> #> Analysis Object #> --------------- #> Number of Results: 150 #> Analysis Function: ancova #> Delta Applied: FALSE #> Analysis Estimates: #> trt_4 #> lsm_ref_4 #> lsm_alt_4 #> trt_5 #> lsm_ref_5 #> lsm_alt_5 #> trt_6 #> lsm_ref_6 #> lsm_alt_6 #> trt_7 #> lsm_ref_7 #> lsm_alt_7 # For reference show the additional meta variables provided delta_template(imputeObj) %>% as_tibble() #> # A tibble: 688 × 8 #> PATIENT VISIT THERAPY is_mar is_missing is_post_ice strategy delta #> #> 1 1503 4 DRUG TRUE FALSE FALSE NA 0 #> 2 1503 5 DRUG TRUE FALSE FALSE NA 0 #> 3 1503 6 DRUG TRUE FALSE FALSE NA 0 #> 4 1503 7 DRUG TRUE FALSE FALSE NA 0 #> 5 1507 4 PLACEBO TRUE FALSE FALSE NA 0 #> 6 1507 5 PLACEBO TRUE FALSE FALSE NA 0 #> 7 1507 6 PLACEBO TRUE FALSE FALSE NA 0 #> 8 1507 7 PLACEBO TRUE FALSE FALSE NA 0 #> 9 1509 4 DRUG TRUE FALSE FALSE NA 0 #> 10 1509 5 DRUG TRUE FALSE FALSE NA 0 #> # ℹ 678 more rows delta_df <- delta_template(imputeObj) %>% as_tibble() %>% mutate(delta = if_else(THERAPY == \"DRUG\" & is_missing , 5, 0)) %>% select(PATIENT, VISIT, delta) delta_df #> # A tibble: 688 × 3 #> PATIENT VISIT delta #> #> 1 1503 4 0 #> 2 1503 5 0 #> 3 1503 6 0 #> 4 1503 7 0 #> 5 1507 4 0 #> 6 1507 5 0 #> 7 1507 6 0 #> 8 1507 7 0 #> 9 1509 4 0 #> 10 1509 5 0 #> # ℹ 678 more rows anaObj_delta <- analyse( imputeObj, ancova, delta = delta_df, vars = set_vars( subjid = \"PATIENT\", outcome = \"CHANGE\", visit = \"VISIT\", group = \"THERAPY\", covariates = c(\"BASVAL\") ) )"},{"path":"/articles/quickstart.html","id":"pool","dir":"Articles","previous_headings":"","what":"Pool","title":"rbmi: Quickstart","text":"Finally, pool() function can used summarise analysis results across multiple imputed datasets provide overall statistic standard error, confidence intervals p-value hypothesis test null hypothesis effect equal 0. Note pooling method automatically derived based method specified original call draws(): method_bayes() method_approxbayes() pooling inference based Rubin’s rules. method_condmean(type = \"bootstrap\") inference either based normal approximation using bootstrap standard error (pool(..., type = \"normal\")) bootstrap percentiles (pool(..., type = \"percentile\")). method_condmean(type = \"jackknife\") inference based normal approximation using jackknife estimate standard error. method = method_bmlmi() inference according methods described von Hippel Bartlett (see stat_specs vignette details) Since used Bayesian multiple imputation vignette, pool() function automatically use Rubin’s rules. table values shown print message poolObj can also extracted using .data.frame() function: outputs gives estimated difference 2.079 (95% CI -0.138 4.296) two groups last visit associated p-value 0.066.","code":"poolObj <- pool( anaObj, conf.level = 0.95, alternative = \"two.sided\" ) poolObj #> #> Pool Object #> ----------- #> Number of Results Combined: 150 #> Method: rubin #> Confidence Level: 0.95 #> Alternative: two.sided #> #> Results: #> #> ================================================== #> parameter est se lci uci pval #> -------------------------------------------------- #> trt_4 -0.092 0.683 -1.439 1.256 0.893 #> lsm_ref_4 -1.616 0.486 -2.576 -0.656 0.001 #> lsm_alt_4 -1.708 0.475 -2.645 -0.77 <0.001 #> trt_5 1.281 0.927 -0.55 3.112 0.169 #> lsm_ref_5 -4.112 0.661 -5.418 -2.807 <0.001 #> lsm_alt_5 -2.831 0.646 -4.107 -1.556 <0.001 #> trt_6 1.912 1.001 -0.066 3.89 0.058 #> lsm_ref_6 -6.097 0.714 -7.508 -4.686 <0.001 #> lsm_alt_6 -4.186 0.696 -5.561 -2.81 <0.001 #> trt_7 2.079 1.122 -0.138 4.296 0.066 #> lsm_ref_7 -6.946 0.815 -8.558 -5.335 <0.001 #> lsm_alt_7 -4.867 0.788 -6.426 -3.308 <0.001 #> -------------------------------------------------- as.data.frame(poolObj) #> parameter est se lci uci pval #> 1 trt_4 -0.09180645 0.6826279 -1.43949684 1.2558839 8.931772e-01 #> 2 lsm_ref_4 -1.61581996 0.4862316 -2.57577141 -0.6558685 1.093708e-03 #> 3 lsm_alt_4 -1.70762640 0.4749573 -2.64531931 -0.7699335 4.262148e-04 #> 4 trt_5 1.28107134 0.9269270 -0.54967136 3.1118141 1.689000e-01 #> 5 lsm_ref_5 -4.11245871 0.6608409 -5.41768364 -2.8072338 4.201381e-09 #> 6 lsm_alt_5 -2.83138737 0.6457744 -4.10686302 -1.5559117 2.114628e-05 #> 7 trt_6 1.91163968 1.0011368 -0.06637259 3.8896520 5.809419e-02 #> 8 lsm_ref_6 -6.09716631 0.7142461 -7.50839192 -4.6859407 1.384720e-14 #> 9 lsm_alt_6 -4.18552662 0.6963163 -5.56127560 -2.8097776 1.321956e-08 #> 10 trt_7 2.07945506 1.1216355 -0.13755657 4.2964667 6.579390e-02 #> 11 lsm_ref_7 -6.94648032 0.8150602 -8.55819661 -5.3347640 2.515736e-14 #> 12 lsm_alt_7 -4.86702525 0.7884953 -6.42588823 -3.3081623 6.801566e-09"},{"path":"/articles/quickstart.html","id":"code","dir":"Articles","previous_headings":"","what":"Code","title":"rbmi: Quickstart","text":"report code presented vignette.","code":"library(rbmi) library(dplyr) data(\"antidepressant_data\") dat <- antidepressant_data # Use expand_locf to add rows corresponding to visits with missing outcomes to the dataset dat <- expand_locf( dat, PATIENT = levels(dat$PATIENT), # expand by PATIENT and VISIT VISIT = levels(dat$VISIT), vars = c(\"BASVAL\", \"THERAPY\"), # fill with LOCF BASVAL and THERAPY group = c(\"PATIENT\"), order = c(\"PATIENT\", \"VISIT\") ) # Create data_ice and set the imputation strategy to JR for # each patient with at least one missing observation dat_ice <- dat %>% arrange(PATIENT, VISIT) %>% filter(is.na(CHANGE)) %>% group_by(PATIENT) %>% slice(1) %>% ungroup() %>% select(PATIENT, VISIT) %>% mutate(strategy = \"JR\") # In this dataset, subject 3618 has an intermittent missing values which does not correspond # to a study drug discontinuation. We therefore remove this subject from `dat_ice`. # (In the later imputation step, it will automatically be imputed under the default MAR assumption.) dat_ice <- dat_ice[-which(dat_ice$PATIENT == 3618),] # Define the names of key variables in our dataset using `set_vars()` # and the covariates included in the imputation model # Note that the covariates argument can also include interaction terms vars <- set_vars( outcome = \"CHANGE\", visit = \"VISIT\", subjid = \"PATIENT\", group = \"THERAPY\", covariates = c(\"BASVAL*VISIT\", \"THERAPY*VISIT\") ) # Define which imputation method to use (here: Bayesian multiple imputation with 150 imputed datsets) method <- method_bayes( burn_in = 200, burn_between = 5, n_samples = 150, seed = 675442751 ) # Create samples for the imputation parameters by running the draws() function set.seed(987) drawObj <- draws( data = dat, data_ice = dat_ice, vars = vars, method = method, quiet = TRUE ) # Impute the data imputeObj <- impute( drawObj, references = c(\"DRUG\" = \"PLACEBO\", \"PLACEBO\" = \"PLACEBO\") ) # Fit the analysis model on each imputed dataset anaObj <- analyse( imputeObj, ancova, vars = set_vars( subjid = \"PATIENT\", outcome = \"CHANGE\", visit = \"VISIT\", group = \"THERAPY\", covariates = c(\"BASVAL\") ) ) # Apply a delta adjustment # Add a delta-value of 5 to all imputed values (i.e. those values # which were missing in the original dataset) in the drug arm. delta_df <- delta_template(imputeObj) %>% as_tibble() %>% mutate(delta = if_else(THERAPY == \"DRUG\" & is_missing , 5, 0)) %>% select(PATIENT, VISIT, delta) # Repeat the analyses with the adjusted values anaObj_delta <- analyse( imputeObj, ancova, delta = delta_df, vars = set_vars( subjid = \"PATIENT\", outcome = \"CHANGE\", visit = \"VISIT\", group = \"THERAPY\", covariates = c(\"BASVAL\") ) ) # Pool the results poolObj <- pool( anaObj, conf.level = 0.95, alternative = \"two.sided\" )"},{"path":"/articles/stat_specs.html","id":"scope-of-this-document","dir":"Articles","previous_headings":"","what":"Scope of this document","title":"rbmi: Statistical Specifications","text":"document describes statistical methods implemented rbmi R package standard reference-based multiple imputation continuous longitudinal outcomes. package implements three classes multiple imputation (MI) approaches: Conventional MI methods based Bayesian (approximate Bayesian) posterior draws model parameters combined Rubin’s rules make inferences described Carpenter, Roger, Kenward (2013) Cro et al. (2020). Conditional mean imputation methods combined re-sampling techniques described Wolbers et al. (2022). Bootstrapped MI methods described von Hippel Bartlett (2021). document structured follows: first provide informal introduction estimands corresponding treatment effect estimation based MI (section 2). core document consists section 3 describes statistical methodology detail also contains comparison implemented approaches (section 3.10). link theory functions included package rbmi described section 4. conclude comparison package alternative software implementations reference-based imputation methods (section 5).","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"estimands","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods","what":"Estimands","title":"rbmi: Statistical Specifications","text":"ICH E9(R1) addendum estimands sensitivity analyses describes systematic approach ensure alignment among clinical trial objectives, trial execution/conduct, statistical analyses, interpretation results (ICH E9 working group (2019)). per addendum, estimand precise description treatment effect reflecting clinical question posed trial objective summarizes population-level outcomes patients different treatment conditions compared. One important attribute estimand list possible intercurrent events (ICEs), .e. events occurring treatment initiation affect either interpretation existence measurements associated clinical question interest, definition appropriate strategies deal ICEs. three relevant strategies purpose document hypothetical strategy, treatment policy strategy, composite strategy. hypothetical strategy, scenario envisaged ICE occur. scenario, endpoint values ICE directly observable treated using models missing data. treatment policy strategy, treatment effect presence ICEs targeted analyses based observed outcomes regardless whether subject ICE . composite strategy, ICE included component endpoint.","code":""},{"path":"/articles/stat_specs.html","id":"alignment-between-the-estimand-and-the-estimation-method","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods","what":"Alignment between the estimand and the estimation method","title":"rbmi: Statistical Specifications","text":"ICH E9(R1) addendum distinguishes ICEs missing data (ICH E9 working group (2019)). Whereas ICEs treatment discontinuations reflect clinical practice, amount missing data can minimized conduct clinical trial. However, many connections missing data ICEs. example, often difficult retain subjects clinical trial treatment discontinuation subject’s dropout trial leads missing data. another example, outcome values ICEs addressed using hypothetical strateg directly observable hypothetical scenario. Consequently, observed outcome values ICEs typically discarded treated missing data. addendum proposes estimation methods address problem presented missing data selected align estimand. recent overview methods align estimator estimand Mallinckrodt et al. (2020). short introduction estimation methods studies longitudinal endpoints can also found Wolbers et al. (2022). One prominent statistical method purpose multiple imputation (MI), target rbmi package.","code":""},{"path":"/articles/stat_specs.html","id":"missing-data-prior-to-ices","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods > 2.2 Alignment between the estimand and the estimation method","what":"Missing data prior to ICEs","title":"rbmi: Statistical Specifications","text":"Missing data may occur subjects without ICE prior occurrence ICE. missing outcomes associated ICE, often plausible impute missing--random (MAR) assumption using standard MMRM imputation model longitudinal outcomes. Informally, MAR occurs missing data can fully accounted baseline variables included model observed longitudinal outcomes, model correctly specified.","code":""},{"path":"/articles/stat_specs.html","id":"implementation-of-the-hypothetical-strategy","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods > 2.2 Alignment between the estimand and the estimation method","what":"Implementation of the hypothetical strategy","title":"rbmi: Statistical Specifications","text":"MAR imputation model described often also good starting point imputing data ICE handled using hypothetical strategy (Mallinckrodt et al. (2020)). Informally, assumes unobserved values ICE similar observed data subjects ICE remained follow-. However, situations, may reasonable assume missingness “informative” indicates systematically better worse outcome observed subjects. situations, MNAR imputation \\(\\delta\\)-adjustment explored sensitivity analysis. \\(\\delta\\)-adjustments add fixed random quantity imputations order make imputed outcomes systematically worse better observed described Cro et al. (2020). rbmi fixed \\(\\delta\\)-adjustments implemented.","code":""},{"path":"/articles/stat_specs.html","id":"implementation-of-the-treatment-policy-strategy","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods > 2.2 Alignment between the estimand and the estimation method","what":"Implementation of the treatment policy strategy","title":"rbmi: Statistical Specifications","text":"Ideally, data collection continues ICE handled treatment policy strategy missing data arises. Indeed, post-ICE data increasingly systematically collected RCTs. However, despite best efforts, missing data ICE study treatment discontinuation may still occur subject drops study discontinuation. difficult give definite recommendations regarding implementation treatment policy strategy presence missing data stage optimal method highly context dependent topic ongoing statistical research. ICEs thought negligible effect efficacy outcomes, standard MAR-based imputation may appropriate. contrast, ICE treatment discontinuation may expected substantial impact efficacy outcomes. settings, MAR assumption may still plausible conditioning subject’s time-varying treatment status (Guizzaro et al. (2021)). case, one option impute missing post-discontinuation data based subjects also discontinued treatment continued followed (Polverejan Dragalin (2020)). Another option may require somewhat less post-discontinuation data include subjects imputation procedure model post-discontinuation data using time-varying treatment status indicators (e.g. time-varying indicators treatment compliance, discontinuation, initiation rescue treatment) (Guizzaro et al. (2021)). approach, post-ICE outcomes included every step analysis, including fitting imputation model. assumes ICEs may impact post-ICE outcomes otherwise missingness non-informative. approach also assumes time-varying covariates contain missing values, deviations outcomes ICE correctly modeled time-varying covariates, sufficient post-ICE data available inform regression coefficients time-varying covariates. proposals relatively recent remain open questions regarding appropriate trade-model complexity (e.g. model account potentially differential effect post-ICE outcomes depending timing ICE?) variance resulting treatment effect estimate. generally, yet established much post-discontinuation data required implement methods robustly without risk substantial inflation variance. trial settings, subjects discontinue randomized treatment. settings, treatment discontinuation rates higher difficult retain subjects trial treatment discontinuation leading sparse data collection treatment discontinuation. settings, amount available data treatment discontinuation may insufficient inform imputation model explicitly models post-discontinuation data. Depending disease area anticipated mechanism action intervention, may plausible assume subjects intervention group behave similarly subjects control group ICE treatment discontinuation. case, reference-based imputation methods option (Mallinckrodt et al. (2020)). Reference-based imputation methods formalize idea impute missing data intervention group based data control reference group. general description review reference-based imputation methods, refer Carpenter, Roger, Kenward (2013), Cro et al. (2020), . White, Royes, Best (2020) Wolbers et al. (2022). technical description implemented statistical methodology reference-based imputation, refer section 3 (particular section 3.4).","code":""},{"path":"/articles/stat_specs.html","id":"implementation-of-the-composite-strategy","dir":"Articles","previous_headings":"2 Introduction to estimands and estimation methods > 2.2 Alignment between the estimand and the estimation method","what":"Implementation of the composite strategy","title":"rbmi: Statistical Specifications","text":"composite strategy typically applied binary time--event outcomes can also used continuous outcomes ascribing suitably unfavorable value patients experience ICEs composite strategy defined. One possibility implement use MI \\(\\delta\\)-adjustment post-ICE data described Darken et al. (2020).","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"sec:methodsOverview","dir":"Articles","previous_headings":"3 Statistical methodology","what":"Overview of the imputation procedure","title":"rbmi: Statistical Specifications","text":"Analyses datasets missing data always rely missing data assumptions. methods described can used produce valid imputations MAR assumption reference-based imputation assumptions. MNAR imputation based fixed \\(\\delta\\)-adjustments typically used sensitivity analyses tipping-point analyses also supported. Three general imputation approaches implemented rbmi: Conventional MI based Bayesian (approximate Bayesian) posterior draws imputation model combined Rubin’s rules inference described Carpenter, Roger, Kenward (2013) Cro et al. (2020). Conditional mean imputation based REML estimate imputation model combined resampling techniques (jackknife bootstrap) inference described Wolbers et al. (2022). Bootstrapped MI methods based REML estimates imputation model described von Hippel Bartlett (2021).","code":""},{"path":"/articles/stat_specs.html","id":"conventional-mi","dir":"Articles","previous_headings":"3 Statistical methodology > 3.1 Overview of the imputation procedure","what":"Conventional MI","title":"rbmi: Statistical Specifications","text":"Conventional MI approaches include following steps: Base imputation model fitting step (Section 3.3) Fit Bayesian multivariate normal mixed model repeated measures (MMRM) observed longitudinal outcomes exclusion data ICEs reference-based missing data imputation desired (Section 3.3.3). Draw \\(M\\) posterior samples estimated parameters (regression coefficients covariance matrices) model. Alternatively, \\(M\\) approximate posterior draws posterior distribution can sampled repeatedly applying conventional restricted maximum-likelihood (REML) parameter estimation MMRM model nonparametric bootstrap samples original dataset (Section 3.3.4). Imputation step (Section 3.4) Take single sample \\(m\\) (\\(m\\1,\\ldots, M)\\) posterior distribution imputation model parameters. subject, use sampled parameters defined imputation strategy determine mean covariance matrix describing subject’s marginal outcome distribution longitudinal outcome assessments (.e. observed missing outcomes). subjects, construct conditional multivariate normal distribution missing outcomes given observed outcomes (including observed outcomes ICEs reference-based assumption desired). subject, draw single sample conditional distribution impute missing outcomes leading complete imputed dataset. sensitivity analyses, pre-defined \\(\\delta\\)-adjustment may applied imputed data prior analysis step. (Section 3.5). Analysis step (Section 3.6) Analyze imputed dataset using analysis model (e.g. ANCOVA) resulting point estimate standard error (corresponding degrees freedom) treatment effect. Pooling step inference (Section 3.7) Repeat steps 2. 3. posterior sample \\(m\\), resulting \\(M\\) complete datasets, \\(M\\) point estimates treatment effect, \\(M\\) standard errors (corresponding degrees freedom). Pool \\(M\\) treatment effect estimates, standard errors, degrees freedom using rules Barnard Rubin obtain final pooled treatment effect estimator, standard error, degrees freedom.","code":""},{"path":"/articles/stat_specs.html","id":"conditional-mean-imputation","dir":"Articles","previous_headings":"3 Statistical methodology > 3.1 Overview of the imputation procedure","what":"Conditional mean imputation","title":"rbmi: Statistical Specifications","text":"conditional mean imputation approach includes following steps: Base imputation model fitting step (Section 3.3) Fit conventional multivariate normal/MMRM model using restricted maximum likelihood (REML) observed longitudinal outcomes exclusion data ICEs reference-based missing data imputation desired (Section 3.3.2). Imputation step (Section 3.4) subject, use fitted parameters step 1. construct conditional distribution missing outcomes given observed outcomes (including observed outcomes ICEs reference-based missing data imputation desired) described . subject, impute missing data deterministically mean conditional distribution leading complete imputed dataset. sensitivity analyses, pre-defined \\(\\delta\\)-adjustment may applied imputed data prior analysis step. (Section 3.5). Analysis step (Section 3.6) Apply analysis model (e.g. ANCOVA) completed dataset resulting point estimate treatment effect. Jackknife bootstrap inference step (Section 3.8) Inference treatment effect estimate 3. based re-sampling techniques. jackknife bootstrap supported. Importantly, methods require repeating steps imputation procedure (.e. imputation, conditional mean imputation, analysis steps) resampled datasets.","code":""},{"path":"/articles/stat_specs.html","id":"bootstrapped-mi","dir":"Articles","previous_headings":"3 Statistical methodology > 3.1 Overview of the imputation procedure","what":"Bootstrapped MI","title":"rbmi: Statistical Specifications","text":"bootstrapped MI approach includes following steps: Base imputation model fitting step (Section 3.3) Apply conventional restricted maximum-likelihood (REML) parameter estimation MMRM model \\(B\\) nonparametric bootstrap samples original dataset using observed longitudinal outcomes exclusion data ICEs reference-based missing data imputation desired. Imputation step (Section 3.4) Take bootstrapped dataset \\(b\\) (\\(b\\1,\\ldots, B)\\) corresponding imputation model parameter estimates. subject (bootstrapped dataset), use parameter estimates defined strategy dealing ICEs determine mean covariance matrix describing subject’s marginal outcome distribution longitudinal outcome assessments (.e. observed missing outcomes). subjects (bootstrapped dataset), construct conditional multivariate normal distribution missing outcomes given observed outcomes (including observed outcomes ICEs reference-based missing data imputation desired). subject (bootstrapped dataset), draw \\(D\\) samples conditional distributions impute missing outcomes leading \\(D\\) complete imputed dataset bootstrap sample \\(b\\). sensitivity analyses, pre-defined \\(\\delta\\)-adjustment may applied imputed data prior analysis step. (Section 3.5). Analysis step (Section 3.6) Analyze \\(B\\times D\\) imputed datasets using analysis model (e.g. ANCOVA) resulting \\(B\\times D\\) point estimates treatment effect. Pooling step inference (Section 3.9) Pool \\(B\\times D\\) treatment effect estimates described von Hippel Bartlett (2021) obtain final pooled treatment effect estimate, standard error, degrees freedom.","code":""},{"path":"/articles/stat_specs.html","id":"setting-notation-and-missing-data-assumptions","dir":"Articles","previous_headings":"3 Statistical methodology","what":"Setting, notation, and missing data assumptions","title":"rbmi: Statistical Specifications","text":"Assume data study \\(n\\) subjects total subject \\(\\) (\\(=1,\\ldots,n\\)) \\(J\\) scheduled follow-visits outcome interest assessed. applications, data randomized trial intervention vs control group treatment effect interest comparison outcomes specific visit randomized groups. However, single-arm trials multi-arm trials principle also supported rbmi implementation. Denote observed outcome vector length \\(J\\) subject \\(\\) \\(Y_i\\) (missing assessments coded NA (available)) non-missing missing components \\(Y_{!}\\) \\(Y_{?}\\), respectively. default, imputation missing outcomes \\(Y_{}\\) performed MAR assumption rbmi. Therefore, missing data following ICE handled using MAR imputation, compatible default assumption. discussed Section 2, MAR assumption often good starting point implementing hypothetical strategy. also note observed outcome data ICE handled using hypothetical strategy compatible strategy. Therefore, assume post-ICE data ICEs handled using hypothetical strategy already set NA \\(Y_i\\) prior calling rbmi functions. However, observed outcomes ICEs handled using treatment policy strategy included \\(Y_i\\) compatible strategy. Subjects may also experience one ICE missing data imputation according reference-based imputation method foreseen. subject \\(\\) ICE, denote first visit affected ICE \\(\\tilde{t}_i \\\\{1,\\ldots,J\\}\\). subjects, set \\(\\tilde{t}_i=\\infty\\). subject’s outcome vector setting observed outcomes visit \\(\\tilde{t}_i\\) onwards missing (.e. NA) denoted \\(Y'_i\\) corresponding data vector removal NA elements \\(Y'_{!}\\). MNAR \\(\\delta\\)-adjustments added imputed datasets formal imputation steps. covered separate section (Section 3.5).","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"sec:imputationModelSpecs","dir":"Articles","previous_headings":"3 Statistical methodology > 3.3 The base imputation model","what":"Included data and model specification","title":"rbmi: Statistical Specifications","text":"purpose imputation model estimate (covariate-dependent) mean trajectories covariance matrices group absence ICEs handled using reference-based imputation methods. Conventionally, publications reference-based imputation methods implicitly assumed corresponding post-ICE data missing subjects (Carpenter, Roger, Kenward (2013)). also allow situation post-ICE data available subjects needs imputed using reference-based methods others. However, observed data ICEs reference-based imputation methods specified compatible imputation model described therefore removed considered missing purpose estimating imputation model, purpose . example, patient ICE addressed reference-based method outcomes ICE collected, post-ICE outcomes excluded fitting base imputation model (included following steps). , base imputation model fitted \\(Y'_{!}\\) \\(Y_{!}\\). exclude data, imputation model mistakenly estimate mean trajectories based mixture observed pre- post-ICE data relevant reference-based imputations. Observed post-ICE outcomes control reference group also excluded base imputation model user specifies reference-based imputation strategy ICEs. ensures ICE impact data included imputation model regardless whether ICE occurred control intervention group. hand, imputation reference group based MAR assumption even reference-based imputation methods may preferable settings include post-ICE data control group base imputation model. can implemented specifying MAR strategy ICE control group reference-based strategy ICE intervention group. base imputation model longitudinal outcomes \\(Y'_i\\) assumes mean structure linear function covariates. Full flexibility specification linear predictor model supported. minimum covariates include treatment group, (categorical) visit, treatment--visit interactions. Typically, covariates including baseline outcome also included. External time-varying covariates (e.g. calendar time visit) well internal time-varying (e.g. time-varying indicators treatment discontinuation initiation rescue treatment) may principle also included indicated (Guizzaro et al. (2021)). Missing covariate values allowed. means values time-varying covariates must non-missing every visit regardless whether outcome measured missing. Denote \\(J\\times p\\) design matrix subject \\(\\) corresponding mean structure model \\(X_i\\) matrix removal rows corresponding missing outcomes \\(Y'_{!}\\) \\(X'_{!}\\). \\(p\\) number parameters mean structure model elements \\(Y'_{!}\\). base imputation model observed outcomes defined : \\[ Y'_{!} = X'_{!}\\beta + \\epsilon_{!} \\mbox{ } \\epsilon_{!}\\sim N(0,\\Sigma_{!!})\\] \\(\\beta\\) vector regression coefficients \\(\\Sigma_{!!}\\) covariance matrix obtained complete-data \\(J\\times J\\)-covariance matrix \\(\\Sigma\\) omitting rows columns corresponding missing outcome assessments subject \\(\\). Typically, common unstructured covariance matrix subjects assumed \\(\\Sigma\\) separate covariate matrices per treatment group also supported. Indeed, implementation also supports specification separate covariate matrices according arbitrarily defined categorical variable groups subjects disjoint subset. example, useful different covariance matrices suspected different subject strata. Finally, imputation methods described rely Bayesian model fitting MCMC, flexibility choice covariance structure, .e. unstructured (default), heterogeneous Toeplitz, heterogeneous compound symmetry, AR(1) covariance structures supported.","code":""},{"path":"/articles/stat_specs.html","id":"sec:imputationModelREML","dir":"Articles","previous_headings":"3 Statistical methodology > 3.3 The base imputation model","what":"Restricted maximum likelihood estimation (REML)","title":"rbmi: Statistical Specifications","text":"Frequentist parameter estimation base imputation based REML. use REML improved alternative maximum likelihood (ML) covariance parameter estimation originally proposed Patterson Thompson (1971). Since , become default method parameter estimation linear mixed effects models. rbmi allows choose ML REML methods estimate model parameters, REML default option.","code":""},{"path":"/articles/stat_specs.html","id":"sec:imputationModelBayes","dir":"Articles","previous_headings":"3 Statistical methodology > 3.3 The base imputation model","what":"Bayesian model fitting","title":"rbmi: Statistical Specifications","text":"Bayesian imputation model fitted R package rstan (Stan Development Team (2020)). rstan R interface Stan. Stan powerful flexible statistical software developed dedicated team implements Bayesian inference state---art MCMC sampling procedures. multivariate normal model missing data specified section 3.3.1 can considered generalization models described Stan user’s guide (see Stan Development Team (2020, sec. 3.5)). prior distributions SAS implementation “five macros” used (Roger (2021)), .e. improper flat priors regression coefficients weakly informative inverse Wishart prior covariance matrix (matrices). Specifically, let \\(S \\\\mathbb{R}^{J \\times J}\\) symmetric positive definite matrix \\(\\nu \\(J-1, \\infty)\\). symmetric positive definite matrix \\(x \\\\mathbb{R}^{J \\times J}\\) density: \\[ \\text{InvWish}(x \\vert \\nu, S) = \\frac{1}{2^{\\nu J/2}} \\frac{1}{\\Gamma_J(\\frac{\\nu}{2})} \\vert S \\vert^{\\nu/2} \\vert x \\vert ^{-(\\nu + J + 1)/2} \\text{exp}(-\\frac{1}{2} \\text{tr}(Sx^{-1})). \\] \\(\\nu > J+1\\) mean given : \\[ E[x] = \\frac{S}{\\nu - J - 1}. \\] choose \\(S\\) equal estimated covariance matrix frequentist REML fit \\(\\nu = J+2\\) lowest degrees freedom guarantee finite mean. Setting degrees freedom low \\(\\nu\\) ensures prior little impact posterior. Moreover, choice allows interpret parameter \\(S\\) mean prior distribution. “five macros”, MCMC algorithm initialized parameters frequentist REML fit (see section 3.3.2). described , using weakly informative priors parameters. Therefore, Markov chain essentially starting targeted stationary posterior distribution minimal amount burn-chain required.","code":""},{"path":"/articles/stat_specs.html","id":"sec:imputationModelBoot","dir":"Articles","previous_headings":"3 Statistical methodology > 3.3 The base imputation model","what":"Approximate Bayesian posterior draws via the bootstrap","title":"rbmi: Statistical Specifications","text":"Several authors suggested stabler way get Bayesian posterior draws imputation model bootstrap incomplete data calculate REML estimates bootstrap sample (Little Rubin (2002), Efron (1994), Honaker King (2010), von Hippel Bartlett (2021)). method proper REML estimates bootstrap samples asymptotically equivalent sample posterior distribution may provide additional robustness model misspecification (Little Rubin (2002, sec. 10.2.3, part 6), Honaker King (2010)). order retain balance treatment groups stratification factors across bootstrap samples, user able provide stratification variables bootstrap rbmi implementation.","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"sec:imputatioMNAR","dir":"Articles","previous_headings":"3 Statistical methodology > 3.4 Imputation step","what":"Marginal imputation distribution for a subject - MAR case","title":"rbmi: Statistical Specifications","text":"subject \\(\\), marginal distribution complete \\(J\\)-dimensional outcome vector assessment visits according imputation model multivariate normal distribution. mean \\(\\tilde{\\mu}_i\\) given predicted mean imputation model conditional subject’s baseline characteristics, group, , optionally, time-varying covariates. covariance matrix \\(\\tilde{\\Sigma}_i\\) given overall estimated covariance matrix , different covariance matrices assumed different groups, covariance matrix corresponding subject \\(\\)’s group.","code":""},{"path":"/articles/stat_specs.html","id":"sec:imputationRefBased","dir":"Articles","previous_headings":"3 Statistical methodology > 3.4 Imputation step","what":"Marginal imputation distribution for a subject - reference-based imputation methods","title":"rbmi: Statistical Specifications","text":"subject \\(\\), calculate mean covariance matrix complete \\(J\\)-dimensional outcome vector assessment visits MAR case denote \\(\\mu_i\\) \\(\\Sigma_i\\). reference-based imputation methods, corresponding reference group also required group. Typically, reference group intervention group control group. reference mean \\(\\mu_{ref,}\\) defined predicted mean imputation model conditional reference group (rather actual group subject \\(\\) belongs ) subject’s baseline characteristics. reference covariance matrix \\(\\Sigma_{ref,}\\) overall estimated covariance matrix , different covariance matrices assumed different groups, estimated covariance matrix corresponding reference group. principle, time-varying covariates also included reference-based imputation methods. However, sensible external time-varying covariates (e.g. calendar time visit) internal time-varying covariates (e.g. treatment discontinuation) latter likely depend actual treatment group typically sensible assume trajectory time-varying covariate reference group. Based means covariance matrices, subject’s marginal imputation distribution reference-based imputation methods calculated detailed Carpenter, Roger, Kenward (2013, sec. 4.3). Denote mean covariance matrix marginal imputation distribution \\(\\tilde{\\mu}_i\\) \\(\\tilde{\\Sigma}_i\\). Recall subject’s first visit affected ICE denoted \\(\\tilde{t}_i \\\\{1,\\ldots,J\\}\\) (visit \\(\\tilde{t}_i-1\\) last visit unaffected ICE). marginal distribution patient \\(\\) built according specific assumption data post ICE follows: Jump reference (JR): patient’s outcome distribution normally distributed following mean: \\[\\tilde{\\mu}_i = (\\mu_i[1], \\dots, \\mu_i[\\tilde{t}_i-1], \\mu_{ref,}[\\tilde{t}_i], \\dots, \\mu_{ref,}[J])^T.\\] covariance matrix constructed follows. First, partition covariance matrices \\(\\Sigma_i\\) \\(\\Sigma_{ref,}\\) blocks according time ICE \\(\\tilde{t}_i\\): \\[ \\Sigma_{} = \\begin{bmatrix} \\Sigma_{, 11} & \\Sigma_{, 12} \\\\ \\Sigma_{, 21} & \\Sigma_{,22} \\\\ \\end{bmatrix} \\] \\[ \\Sigma_{ref,} = \\begin{bmatrix} \\Sigma_{ref, , 11} & \\Sigma_{ref, , 12} \\\\ \\Sigma_{ref, , 21} & \\Sigma_{ref, ,22} \\\\ \\end{bmatrix}. \\] want covariance matrix \\(\\tilde{\\Sigma}_i\\) match \\(\\Sigma_i\\) pre-deviation measurements, \\(\\Sigma_{ref,}\\) conditional components post-deviation given pre-deviation measurements. solution derived Carpenter, Roger, Kenward (2013, sec. 4.3) given : \\[ \\begin{matrix} \\tilde{\\Sigma}_{,11} = \\Sigma_{, 11} \\\\ \\tilde{\\Sigma}_{, 21} = \\Sigma_{ref,, 21} \\Sigma^{-1}_{ref,, 11} \\Sigma_{, 11} \\\\ \\tilde{\\Sigma}_{, 22} = \\Sigma_{ref, , 22} - \\Sigma_{ref,, 21} \\Sigma^{-1}_{ref,, 11} (\\Sigma_{ref,, 11} - \\Sigma_{,11}) \\Sigma^{-1}_{ref,, 11} \\Sigma_{ref,, 12}. \\end{matrix} \\] Copy increments reference (CIR): patient’s outcome distribution normally distributed following mean: \\[ \\begin{split} \\tilde{\\mu}_i =& (\\mu_i[1], \\dots, \\mu_i[\\tilde{t}_i-1], \\mu_i[\\tilde{t}_i-1] + (\\mu_{ref,}[\\tilde{t}_i] - \\mu_{ref,}[\\tilde{t}_i-1]), \\dots,\\\\ & \\mu_i[\\tilde{t}_i-1]+(\\mu_{ref,}[J] - \\mu_{ref,}[\\tilde{t}_i-1]))^T. \\end{split} \\] covariance matrix derived JR method. Copy reference (CR): patient’s outcome distribution normally distributed mean covariance matrix taken reference group: \\[ \\tilde{\\mu}_i = \\mu_{ref,} \\] \\[ \\tilde{\\Sigma}_i = \\Sigma_{ref,}. \\] Last mean carried forward (LMCF): patient’s outcome distribution normally distributed following mean: \\[ \\tilde{\\mu}_i = (\\mu_i[1], \\dots, \\mu_i[\\tilde{t}_i-1], \\mu_i[\\tilde{t}_i-1], \\dots, \\mu_i[\\tilde{t}_i-1])'\\] covariance matrix: \\[ \\tilde{\\Sigma}_i = \\Sigma_i.\\]","code":""},{"path":"/articles/stat_specs.html","id":"sec:imputationRandomConditionalMean","dir":"Articles","previous_headings":"3 Statistical methodology > 3.4 Imputation step","what":"Imputation of missing outcome data","title":"rbmi: Statistical Specifications","text":"joint marginal multivariate normal imputation distribution subject \\(\\)’s observed missing outcome data mean \\(\\tilde{\\mu}_i\\) covariance matrix \\(\\tilde{\\Sigma}_i\\) defined . actual imputation missing outcome data obtained conditioning marginal distribution subject’s observed outcome data. note, approach valid regardless whether subject intermittent terminal missing data. conditional distribution used imputation multivariate normal distribution explicit formulas conditional mean covariance readily available. completeness, report notation terminology setting. marginal distribution outcome patient \\(\\) \\(Y_i \\sim N(\\tilde{\\mu}_i, \\tilde{\\Sigma}_i)\\) outcome \\(Y_i\\) can decomposed observed (\\(Y_{,!}\\)) unobserved (\\(Y_{,?}\\)) components. Analogously mean \\(\\tilde{\\mu}_i\\) can decomposed \\((\\tilde{\\mu}_{,!},\\tilde{\\mu}_{,?})\\) covariance \\(\\tilde{\\Sigma}_i\\) : \\[ \\tilde{\\Sigma}_i = \\begin{bmatrix} \\tilde{\\Sigma}_{, !!} & \\tilde{\\Sigma}_{,!?} \\\\ \\tilde{\\Sigma}_{, ?!} & \\tilde{\\Sigma}_{, ??} \\end{bmatrix}. \\] conditional distribution \\(Y_{,?}\\) conditional \\(Y_{,!}\\) multivariate normal distribution expectation \\[ E(Y_{,?} \\vert Y_{,!})= \\tilde{\\mu}_{,?} + \\tilde{\\Sigma}_{, ?!} \\tilde{\\Sigma}_{,!!}^{-1} (Y_{,!} - \\tilde{\\mu}_{,!}) \\] covariance matrix \\[ Cov(Y_{,?} \\vert Y_{,!}) = \\tilde{\\Sigma}_{,??} - \\tilde{\\Sigma}_{,?!} \\tilde{\\Sigma}_{,!!}^{-1} \\tilde{\\Sigma}_{,!?}. \\] Conventional random imputation consists sampling conditional multivariate normal distribution. Conditional mean imputation imputes missing values deterministic conditional expectation \\(E(Y_{,?} \\vert Y_{,!})\\).","code":""},{"path":"/articles/stat_specs.html","id":"sec:deltaAdjustment","dir":"Articles","previous_headings":"3 Statistical methodology","what":"\\(\\delta\\)-adjustment","title":"rbmi: Statistical Specifications","text":"marginal \\(\\delta\\)-adjustment approach similar “five macros” SAS implemented (Roger (2021)), .e. fixed non-stochastic values added multivariate normal imputation step prior analysis. relevant sensitivity analyses order make imputed data systematically worse better, respectively, observed data. addition, authors suggested \\(\\delta\\)-type adjustments implement composite strategy continuous outcomes (Darken et al. (2020)). implementation provides full flexibility regarding specific implementation \\(\\delta\\)-adjustment, .e. value added may depend randomized treatment group, timing subject’s ICE, factors. suggestions case studies regarding topic, refer Cro et al. (2020).","code":""},{"path":"/articles/stat_specs.html","id":"sec:analysis","dir":"Articles","previous_headings":"3 Statistical methodology","what":"Analysis step","title":"rbmi: Statistical Specifications","text":"data imputation, standard analysis model can applied completed data resulting treatment effect estimate. imputed data longer contains missing values, analysis model often simple. example, can analysis covariance (ANCOVA) model outcome (change outcome baseline) specific visit j dependent variable, randomized treatment group primary covariate , typically, adjustment baseline covariates imputation model.","code":""},{"path":"/articles/stat_specs.html","id":"sec:pooling","dir":"Articles","previous_headings":"3 Statistical methodology","what":"Pooling step for inference of (approximate) Bayesian MI and Rubin’s rules","title":"rbmi: Statistical Specifications","text":"Assume analysis model applied \\(M\\) multiple imputed random datasets resulted \\(m\\) treatment effect estimates \\(\\hat{\\theta}_m\\) (\\(m=1,\\ldots,M\\)) corresponding standard error \\(SE_m\\) (available) degrees freedom \\(\\nu_{com}\\). degrees freedom available analysis model, set \\(\\nu_{com}=\\infty\\) inference based normal distribution. Rubin’s rules used pooling treatment effect estimates corresponding variances estimates analysis steps across \\(M\\) multiple imputed datasets. According Rubin’s rules, final estimate treatment effect calculated sample mean \\(M\\) treatment effect estimates: \\[ \\hat{\\theta} = \\frac{1}{M} \\sum_{m = 1}^M \\hat{\\theta}_m. \\] pooled variance based two components reflect within variance treatment effects across multiple imputed datasets: \\[ V(\\hat{\\theta}) = V_W(\\hat{\\theta}) + (1 + \\frac{1}{M}) V_B(\\hat{\\theta}) \\] \\(V_W(\\hat{\\theta}) = \\frac{1}{M}\\sum_{m = 1}^M SE^2_m\\) within-variance \\(V_B(\\hat{\\theta}) = \\frac{1}{M-1} \\sum_{m = 1}^M (\\hat{\\theta}_m - \\hat{\\theta})^2\\) -variance. Confidence intervals tests null hypothesis \\(H_0: \\theta=\\theta_0\\) based \\(t\\)-statistics \\(T\\): \\[ T= (\\hat{\\theta}-\\theta_0)/\\sqrt{V(\\hat{\\theta})}. \\] null hypothesis, \\(T\\) approximate \\(t\\)-distribution \\(\\nu\\) degrees freedom. \\(\\nu\\) calculated according Barnard Rubin approximation, see Barnard Rubin (1999) (formula 3) Little Rubin (2002) (formula (5.24), page 87): \\[ \\nu = \\frac{\\nu_{old}* \\nu_{obs}}{\\nu_{old} + \\nu_{obs}} \\] \\[ \\nu_{old} = \\frac{M-1}{\\lambda^2} \\quad\\mbox{}\\quad \\nu_{obs} = \\frac{\\nu_{com} + 1}{\\nu_{com} + 3} \\nu_{com} (1 - \\lambda) \\] \\(\\lambda = \\frac{(1 + \\frac{1}{M})V_B(\\hat{\\theta})}{V(\\hat{\\theta})}\\) fraction missing information.","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"point-estimate-of-the-treatment-effect","dir":"Articles","previous_headings":"3 Statistical methodology > 3.8 Bootstrap and jackknife inference for conditional mean imputation","what":"Point estimate of the treatment effect","title":"rbmi: Statistical Specifications","text":"point estimator obtained applying analysis model (Section 3.6) single conditional mean imputation missing data (see Section 3.4.3) based REML estimator parameters imputation model (see Section 3.3.2). denote treatment effect estimator \\(\\hat{\\theta}\\). demonstrated Wolbers et al. (2022) (Section 2.4), treatment effect estimator valid analysis model ANCOVA model , generally, treatment effect estimator linear function imputed outcome vector. Indeed, case, estimator identical pooled treatment effect across multiple random REML imputation infinite number imputations corresponds computationally efficient implementation proposal von Hippel Bartlett (2021). expect conditional mean imputation method also applicable analysis models (e.g. general MMRM analysis models) formally justified.","code":""},{"path":"/articles/stat_specs.html","id":"jackknife-standard-errors-confidence-intervals-ci-and-tests-for-the-treatment-effect","dir":"Articles","previous_headings":"3 Statistical methodology > 3.8 Bootstrap and jackknife inference for conditional mean imputation","what":"Jackknife standard errors, confidence intervals (CI) and tests for the treatment effect","title":"rbmi: Statistical Specifications","text":"dataset containing \\(n\\) subjects, jackknife standard error depends treatment effect estimates \\(\\hat{\\theta}_{(-b)}\\) (\\(b=1,\\ldots,n\\)) samples original dataset leave observation subject \\(b\\). described previously, obtain treatment effect estimates leave-one-subject-datasets, steps imputation procedure (.e. imputation, conditional mean imputation, analysis steps) need repeated new dataset. , jackknife standard error defined \\[\\hat{se}_{jack}=[\\frac{(n-1)}{n}\\cdot\\sum_{b=1}^{n} (\\hat{\\theta}_{(-b)}-\\bar{\\theta}_{(.)})^2]^{1/2}\\] \\(\\bar{\\theta}_{(.)}\\) denotes mean jackknife estimates (Efron Tibshirani (1994), chapter 10). corresponding two-sided normal approximation \\(1-\\alpha\\) CI defined \\(\\hat{\\theta}\\pm z^{1-\\alpha/2}\\cdot \\hat{se}_{jack}\\) \\(\\hat{\\theta}\\) treatment effect estimate original dataset. Tests null hypothesis \\(H_0: \\theta=\\theta_0\\) based \\(Z\\)-score \\(Z=(\\hat{\\theta}-\\theta_0)/\\hat{se}_{jack}\\) using standard normal approximation. simulation study reported Wolbers et al. (2022) demonstrated exact protection type error jackknife-based inference relatively low sample size (n = 100 per group) substantial amount missing data (>25% subjects ICE).","code":""},{"path":"/articles/stat_specs.html","id":"bootstrap-standard-errors-confidence-intervals-ci-and-tests-for-the-treatment-effect","dir":"Articles","previous_headings":"3 Statistical methodology > 3.8 Bootstrap and jackknife inference for conditional mean imputation","what":"Bootstrap standard errors, confidence intervals (CI) and tests for the treatment effect","title":"rbmi: Statistical Specifications","text":"alternative jackknife, bootstrap also implemented rbmi (Efron Tibshirani (1994), Davison Hinkley (1997)). Two different bootstrap methods implemented rbmi: Methods based bootstrap standard error normal approximation percentile bootstrap methods. Denote treatment effect estimates \\(B\\) bootstrap samples \\(\\hat{\\theta}^*_b\\) (\\(b=1,\\ldots,B\\)). bootstrap standard error \\(\\hat{se}_{boot}\\) defined empirical standard deviation bootstrapped treatment effect estimates. Confidence intervals tests based bootstrap standard error can constructed way jackknife. Confidence intervals using percentile bootstrap based empirical quantiles bootstrap distribution corresponding statistical tests implemented rbmi via inversion confidence interval. Explicit formulas bootstrap inference implemented rbmi package considerations regarding required number bootstrap samples included Appendix Wolbers et al. (2022). simulation study reported Wolbers et al. (2022) demonstrated small inflation type error rate inference based bootstrap standard error (\\(5.3\\%\\) nominal type error rate \\(5\\%\\)) sample size n = 100 per group substantial amount missing data (>25% subjects ICE). Based simulations, recommend jackknife bootstrap inference performed better simulation study typically much faster compute bootstrap.","code":""},{"path":"/articles/stat_specs.html","id":"sec:poolbmlmi","dir":"Articles","previous_headings":"3 Statistical methodology","what":"Pooling step for inference of the bootstrapped MI methods","title":"rbmi: Statistical Specifications","text":"Assume analysis model applied \\(B\\times D\\) multiple imputed random datasets resulted \\(B\\times D\\) treatment effect estimates \\(\\hat{\\theta}_{bd}\\) (\\(b=1,\\ldots,B\\); \\(d=1,\\ldots,D\\)). final estimate treatment effect calculated sample mean \\(B*D\\) treatment effect estimates: \\[ \\hat{\\theta} = \\frac{1}{BD} \\sum_{b = 1}^B \\sum_{d = 1}^D \\hat{\\theta}_{bd}. \\] pooled variance based two components reflect variability within imputed bootstrap samples (von Hippel Bartlett (2021), formula 8.4): \\[ V(\\hat{\\theta}) = (1 + \\frac{1}{B})\\frac{MSB - MSW}{D} + \\frac{MSW}{BD} \\] \\(MSB\\) mean square bootstrapped datasets, \\(MSW\\) mean square within bootstrapped datasets imputed datasets: \\[ \\begin{align*} MSB &= \\frac{D}{B-1} \\sum_{b = 1}^B (\\bar{\\theta_{b}} - \\hat{\\theta})^2 \\\\ MSW &= \\frac{1}{B(D-1)} \\sum_{b = 1}^B \\sum_{d = 1}^D (\\theta_{bd} - \\bar{\\theta_b})^2 \\end{align*} \\] \\(\\bar{\\theta_{b}}\\) mean across \\(D\\) estimates obtained random imputation \\(b\\)-th bootstrap sample. degrees freedom estimated following formula (von Hippel Bartlett (2021), formula 8.6): \\[ \\nu = \\frac{(MSB\\cdot (B+1) - MSW\\cdot B)^2}{\\frac{MSB^2\\cdot (B+1)^2}{B-1} + \\frac{MSW^2\\cdot B}{D-1}} \\] Confidence intervals tests null hypothesis \\(H_0: \\theta=\\theta_0\\) based \\(t\\)-statistics \\(T\\): \\[ T= (\\hat{\\theta}-\\theta_0)/\\sqrt{V(\\hat{\\theta})}. \\] null hypothesis, \\(T\\) approximate \\(t\\)-distribution \\(\\nu\\) degrees freedom.","code":""},{"path":[]},{"path":"/articles/stat_specs.html","id":"treatment-effect-estimation","dir":"Articles","previous_headings":"3 Statistical methodology > 3.10 Comparison between the implemented approaches","what":"Treatment effect estimation","title":"rbmi: Statistical Specifications","text":"approaches provide consistent treatment effect estimates standard reference-based imputation methods case analysis model completed datasets general linear model ANCOVA. Methods conditional mean imputation also valid analysis models. validity conditional mean imputation formally demonstrated analyses using general linear model (Wolbers et al. (2022, sec. 2.4)) though may also applicable widely (e.g. general MMRM analysis models). Treatment effects based conditional mean imputation deterministic. methods affected Monte Carlo sampling error precision estimates depends number imputations bootstrap samples, respectively.","code":""},{"path":"/articles/stat_specs.html","id":"standard-errors-of-the-treatment-effect","dir":"Articles","previous_headings":"3 Statistical methodology > 3.10 Comparison between the implemented approaches","what":"Standard errors of the treatment effect","title":"rbmi: Statistical Specifications","text":"approaches provide frequentist consistent estimates standard error imputation MAR assumption. reference-based imputation methods, methods based conditional mean imputation bootstrapped MI provide frequentist consistent estimates standard error whereas Rubin’s rules applied conventional MI methods provides -called information anchored inference (Bartlett (2021), Cro, Carpenter, Kenward (2019), von Hippel Bartlett (2021), Wolbers et al. (2022)). Frequentist consistent estimates standard error lead confidence intervals tests (asymptotically) correct coverage type error control assumption reference-based assumption reflects true data-generating mechanism. finite samples, simulations sample size \\(n=100\\) per group reported Wolbers et al. (2022) demonstrated conditional mean imputation combined jackknife provided exact protection type one error rate whereas bootstrap associated small type error inflation (5.1% 5.3% nominal level 5%). well known Rubin’s rules provide frequentist consistent estimates standard error reference-based imputation methods (Seaman, White, Leacy (2014), Liu Pang (2016), Tang (2017), Cro, Carpenter, Kenward (2019), Bartlett (2021)). Standard errors Rubin’s rule typically larger frequentist standard error estimates leading conservative inference corresponding loss statistical power, see e.g. simulations reported Wolbers et al. (2022). Intuitively, occurs reference-based imputation methods borrow information reference group imputations intervention group leading reduction frequentist variance resulting treatment effect contrast captured Rubin’s variance estimator. Formally, occurs imputation analysis models uncongenial reference-based imputation methods (Meng (1994), Bartlett (2021)). Cro, Carpenter, Kenward (2019) argued Rubin’s rule nevertheless valid reference-based imputation methods approximately information-anchored, .e. proportion information lost due missing data MAR approximately preserved reference-based analyses. contrast, frequentist standard errors reference based imputation information anchored reference-based imputation standard errors reference-based assumptions typically smaller MAR imputation. Information anchoring sensible concept sensitivity analyses, whereas primary analyses, may important adhere principles frequentist inference. Analyses data missing observations generally rely unverifiable missing data assumptions assumptions reference-based imputation methods relatively strong. Therefore, assumptions need clinically justified appropriate least conservative considered disease area anticipated mechanism action intervention. Conditional mean imputation combined jackknife method leads deterministic standard error estimates , consequently, confidence intervals \\(p\\)-values also deterministic. particularly important regulatory setting important ascertain whether calculated \\(p\\)-value close critical boundary 5% truly threshold rather uncertain Monte Carlo error.","code":""},{"path":"/articles/stat_specs.html","id":"computational-complexity","dir":"Articles","previous_headings":"3 Statistical methodology > 3.10 Comparison between the implemented approaches","what":"Computational complexity","title":"rbmi: Statistical Specifications","text":"Bayesian MI methods rely specification prior distributions usage Markov chain Monte Carlo (MCMC) methods. methods based multiple imputation bootstrapping require tuning parameters specification number imputations \\(M\\) bootstrap samples \\(B\\) rely numerical optimization fitting MMRM imputation models via REML. Conditional mean imputation combined jackknife tuning parameters. rbmi implementation, fitting MMRM imputation model via REML computationally expensive. MCMC sampling using rstan (Stan Development Team (2020)) typically relatively fast setting requires small burn-burn-chains. addition, number random imputations reliable inference using Rubin’s rules often smaller number resamples required jackknife bootstrap (see e.g. discussions . R. White, Royston, Wood (2011, sec. 7) Bayesian MI Appendix Wolbers et al. (2022) bootstrap). Thus, many applications, expect conventional MI based Bayesian posterior draws fastest, followed conventional MI using approximate Bayesian posterior draws conditional mean imputation combined jackknife. Conditional mean imputation combined bootstrap bootstrapped MI methods typically computationally demanding. note, implemented methods conceptually straightforward parallelise parallelisation support provided rbmi.","code":""},{"path":"/articles/stat_specs.html","id":"sec:rbmiFunctions","dir":"Articles","previous_headings":"","what":"Mapping of statistical methods to rbmi functions","title":"rbmi: Statistical Specifications","text":"full documentation rbmi package functionality refer help pages functions package vignettes. give brief overview different steps imputation procedure mapped rbmi functions: Bayesian posterior parameter draws imputation model obtained via argument method = method_bayes(). Approximate Bayesian posterior parameter draws imputation model obtained via argument method = method_approxbayes(). ML REML parameter estimates imputation model parameters original dataset leave-one-subject-datasets (required jackknife) obtained via argument method = method_condmean(type = \"jackknife\"). ML REML parameter estimates imputation model parameters original dataset bootstrapped datasets obtained via argument method = method_condmean(type = \"bootstrap\"). Bootstrapped MI methods obtained via argument method = method_bmlmi(B=B, D=D) \\(B\\) refers number bootstrap samples \\(D\\) number random imputations bootstrap sample. imputation step using random imputation deterministic conditional mean imputation, respectively, implemented function impute(). Imputation can performed assuming already implemented imputation strategies presented section 3.4. Additionally, user-defined imputation strategies also supported. analysis step implemented function analyse() applies analysis model imputed datasets. default, analysis model (argument fun) ancova() function alternative analysis functions can also provided user. analyse() function also allows \\(\\delta\\)-adjustments imputed datasets prior analysis via argument delta. inference step implemented function pool() pools results across imputed datasets. Rubin Bernard rule applied case (approximate) Bayesian MI. conditional mean imputation, jackknife bootstrap (normal approximation percentile) inference supported. BMLMI, pooling inference steps performed via pool() case implements method described Section 3.9.","code":""},{"path":"/articles/stat_specs.html","id":"sec:otherSoftware","dir":"Articles","previous_headings":"","what":"Comparison to other software implementations","title":"rbmi: Statistical Specifications","text":"established software implementation reference-based imputation SAS -called “five macros” James Roger (Roger (2021)). alternative R implementation also currently development R package RefBasedMI (McGrath White (2021)). rbmi several features supported implementations: addition Bayesian MI approach implemented also packages, implementation provides three alternative MI approaches: approximate Bayesian MI, conditional mean imputation combined resampling, bootstrapped MI. rbmi allows usage data collected ICE. example, suppose want adopt treatment policy strategy ICE “treatment discontinuation”. possible implementation strategy use observed outcome data subjects remain study ICE use reference-based imputation case subject drops . implementation, implemented excluding observed post ICE data imputation model assumes MAR missingness including analysis model. knowledge, directly supported implementations. RefBasedMI fits imputation model data treatment group separately implies covariate-treatment group interactions covariates pooled data treatment groups. contrast, Roger’s five macros assume joint model including data randomized groups covariate-treatment interactions covariates allowed. also chose implement joint model use flexible model linear predictor may may include interaction term covariate treatment group. addition, imputation model also allows inclusion time-varying covariates. implementation, grouping subjects purpose imputation model (definition reference group) need correspond assigned treatment groups. provides additional flexibility imputation procedure. clear us whether feature supported Roger’s five macros RefBasedMI. believe R-based implementation modular RefBasedMI facilitate package enhancements. contrast, general causal model introduced . White, Royes, Best (2020) available implementations currently supported .","code":""},{"path":[]},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Craig Gower-Page. Author, maintainer. Alessandro Noci. Author. Marcel Wolbers. Contributor. Roche. Copyright holder, funder.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Gower-Page C, Noci (2024). rbmi: Reference Based Multiple Imputation. R package version 1.2.6, https://github.com/insightsengineering/rbmi, https://insightsengineering.github.io/rbmi/v1.2.6/.","code":"@Manual{, title = {rbmi: Reference Based Multiple Imputation}, author = {Craig Gower-Page and Alessandro Noci}, year = {2024}, note = {R package version 1.2.6, https://github.com/insightsengineering/rbmi}, url = {https://insightsengineering.github.io/rbmi/v1.2.6/}, }"},{"path":[]},{"path":"/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Reference Based Multiple Imputation","text":"rbmi R package imputation missing data clinical trials continuous multivariate normal longitudinal outcomes. supports imputation missing random (MAR) assumption, reference-based imputation methods, delta adjustments (required sensitivity analysis tipping point analyses). package implements Bayesian approximate Bayesian multiple imputation combined Rubin’s rules inference, frequentist conditional mean imputation combined (jackknife bootstrap) resampling.","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Reference Based Multiple Imputation","text":"package can installed directly CRAN via:","code":"install.packages(\"rbmi\")"},{"path":"/index.html","id":"usage","dir":"","previous_headings":"","what":"Usage","title":"Reference Based Multiple Imputation","text":"package designed around 4 core functions: draws() - Fits multiple imputation models impute() - Imputes multiple datasets analyse() - Analyses multiple datasets pool() - Pools multiple results single statistic basic usage core functions described quickstart vignette:","code":"vignette(topic = \"quickstart\", package = \"rbmi\")"},{"path":"/index.html","id":"support","dir":"","previous_headings":"","what":"Support","title":"Reference Based Multiple Imputation","text":"help regards using package find bug please create GitHub issue","code":""},{"path":"/reference/QR_decomp.html","id":null,"dir":"Reference","previous_headings":"","what":"QR decomposition — QR_decomp","title":"QR decomposition — QR_decomp","text":"QR decomposition defined Stan user's guide (section 1.2).","code":""},{"path":"/reference/QR_decomp.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"QR decomposition — QR_decomp","text":"","code":"QR_decomp(mat)"},{"path":"/reference/QR_decomp.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"QR decomposition — QR_decomp","text":"mat matrix perform QR decomposition .","code":""},{"path":"/reference/Stack.html","id":null,"dir":"Reference","previous_headings":"","what":"R6 Class for a FIFO stack — Stack","title":"R6 Class for a FIFO stack — Stack","text":"simple stack object offering add / pop functionality","code":""},{"path":"/reference/Stack.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"R6 Class for a FIFO stack — Stack","text":"stack list containing current stack","code":""},{"path":[]},{"path":"/reference/Stack.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"R6 Class for a FIFO stack — Stack","text":"Stack$add() Stack$pop() Stack$clone()","code":""},{"path":"/reference/Stack.html","id":"method-add-","dir":"Reference","previous_headings":"","what":"Method add()","title":"R6 Class for a FIFO stack — Stack","text":"Adds content end stack (must list)","code":""},{"path":"/reference/Stack.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for a FIFO stack — Stack","text":"","code":"Stack$add(x)"},{"path":"/reference/Stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for a FIFO stack — Stack","text":"x content add stack","code":""},{"path":"/reference/Stack.html","id":"method-pop-","dir":"Reference","previous_headings":"","what":"Method pop()","title":"R6 Class for a FIFO stack — Stack","text":"Retrieve content stack","code":""},{"path":"/reference/Stack.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for a FIFO stack — Stack","text":"","code":"Stack$pop(i)"},{"path":"/reference/Stack.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for a FIFO stack — Stack","text":"number items retrieve stack. less items left stack just return everything left.","code":""},{"path":"/reference/Stack.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"R6 Class for a FIFO stack — Stack","text":"objects class cloneable method.","code":""},{"path":"/reference/Stack.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for a FIFO stack — Stack","text":"","code":"Stack$clone(deep = FALSE)"},{"path":"/reference/Stack.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for a FIFO stack — Stack","text":"deep Whether make deep clone.","code":""},{"path":"/reference/add_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Add a class — add_class","title":"Add a class — add_class","text":"Utility function add class object. Adds new class existing classes.","code":""},{"path":"/reference/add_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Add a class — add_class","text":"","code":"add_class(x, cls)"},{"path":"/reference/add_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Add a class — add_class","text":"x object add class . cls class added.","code":""},{"path":"/reference/adjust_trajectories.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories","title":"Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories","text":"Adjust trajectories due intercurrent event (ICE)","code":""},{"path":"/reference/adjust_trajectories.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories","text":"","code":"adjust_trajectories( distr_pars_group, outcome, ids, ind_ice, strategy_fun, distr_pars_ref = NULL )"},{"path":"/reference/adjust_trajectories.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories","text":"distr_pars_group Named list containing simulation parameters multivariate normal distribution assumed given treatment group. contains following elements: mu: Numeric vector indicating mean outcome trajectory. include outcome baseline. sigma Covariance matrix outcome trajectory. outcome Numeric variable specifies longitudinal outcome. ids Factor variable specifies id subject. ind_ice binary variable takes value 1 corresponding outcome affected ICE 0 otherwise. strategy_fun Function implementing trajectories intercurrent event (ICE). Must one getStrategies(). See getStrategies() details. distr_pars_ref Optional. Named list containing simulation parameters reference arm. contains following elements: mu: Numeric vector indicating mean outcome trajectory assuming ICEs. include outcome baseline. sigma Covariance matrix outcome trajectory assuming ICEs.","code":""},{"path":"/reference/adjust_trajectories.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust trajectories due to the intercurrent event (ICE) — adjust_trajectories","text":"numeric vector containing adjusted trajectories.","code":""},{"path":[]},{"path":"/reference/adjust_trajectories_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","title":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","text":"Adjust trajectory subject's outcome due intercurrent event (ICE)","code":""},{"path":"/reference/adjust_trajectories_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","text":"","code":"adjust_trajectories_single( distr_pars_group, outcome, strategy_fun, distr_pars_ref = NULL )"},{"path":"/reference/adjust_trajectories_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","text":"distr_pars_group Named list containing simulation parameters multivariate normal distribution assumed given treatment group. contains following elements: mu: Numeric vector indicating mean outcome trajectory. include outcome baseline. sigma Covariance matrix outcome trajectory. outcome Numeric variable specifies longitudinal outcome. strategy_fun Function implementing trajectories intercurrent event (ICE). Must one getStrategies(). See getStrategies() details. distr_pars_ref Optional. Named list containing simulation parameters reference arm. contains following elements: mu: Numeric vector indicating mean outcome trajectory assuming ICEs. include outcome baseline. sigma Covariance matrix outcome trajectory assuming ICEs.","code":""},{"path":"/reference/adjust_trajectories_single.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","text":"numeric vector containing adjusted trajectory single subject.","code":""},{"path":"/reference/adjust_trajectories_single.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Adjust trajectory of a subject's outcome due to the intercurrent event (ICE) — adjust_trajectories_single","text":"outcome specified --post-ICE observations (.e. observations adjusted) set NA.","code":""},{"path":"/reference/analyse.html","id":null,"dir":"Reference","previous_headings":"","what":"Analyse Multiple Imputed Datasets — analyse","title":"Analyse Multiple Imputed Datasets — analyse","text":"function takes multiple imputed datasets (generated impute() function) runs analysis function .","code":""},{"path":"/reference/analyse.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analyse Multiple Imputed Datasets — analyse","text":"","code":"analyse(imputations, fun = ancova, delta = NULL, ...)"},{"path":"/reference/analyse.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analyse Multiple Imputed Datasets — analyse","text":"imputations imputations object created impute(). fun analysis function applied imputed dataset. See details. delta data.frame containing delta transformation applied imputed datasets prior running fun. See details. ... Additional arguments passed onto fun.","code":""},{"path":"/reference/analyse.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Analyse Multiple Imputed Datasets — analyse","text":"function works performing following steps: Extract dataset imputations object. Apply delta adjustments specified delta argument. Run analysis function fun dataset. Repeat steps 1-3 across datasets inside imputations object. Collect return analysis results. analysis function fun must take data.frame first argument. options analyse() passed onto fun via .... fun must return named list element list containing single numeric element called est (additionally se df originally specified method_bayes() method_approxbayes()) .e.: Please note vars$subjid column (defined original call draws()) scrambled data.frames provided fun. say contain original subject values hard coding subject ids strictly avoided. default fun ancova() function. Please note function requires vars object, created set_vars(), provided via vars argument e.g. analyse(imputeObj, vars = set_vars(...)). Please see documentation ancova() full details. Please also note theoretical justification conditional mean imputation method (method = method_condmean() draws()) relies fact ANCOVA linear transformation outcomes. Thus care required applying alternative analysis functions setting. delta argument can used specify offsets applied outcome variable imputed datasets prior analysis. typically used sensitivity tipping point analyses. delta dataset must contain columns vars$subjid, vars$visit (specified original call draws()) delta. Essentially data.frame merged onto imputed dataset vars$subjid vars$visit outcome variable modified : Please note order provide maximum flexibility, delta argument can used modify /outcome values including imputed. Care must taken defining offsets. recommend use helper function delta_template() define delta datasets provides utility variables is_missing can used identify exactly visits imputed.","code":"myfun <- function(dat, ...) { mod_1 <- lm(data = dat, outcome ~ group) mod_2 <- lm(data = dat, outcome ~ group + covar) x <- list( trt_1 = list( est = coef(mod_1)[[group]], se = sqrt(vcov(mod_1)[group, group]), df = df.residual(mod_1) ), trt_2 = list( est = coef(mod_2)[[group]], se = sqrt(vcov(mod_2)[group, group]), df = df.residual(mod_2) ) ) return(x) } imputed_data[[vars$outcome]] <- imputed_data[[vars$outcome]] + imputed_data[[\"delta\"]]"},{"path":[]},{"path":"/reference/analyse.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Analyse Multiple Imputed Datasets — analyse","text":"","code":"if (FALSE) { # \\dontrun{ vars <- set_vars( subjid = \"subjid\", visit = \"visit\", outcome = \"outcome\", group = \"group\", covariates = c(\"sex\", \"age\", \"sex*age\") ) analyse( imputations = imputeObj, vars = vars ) deltadf <- data.frame( subjid = c(\"Pt1\", \"Pt1\", \"Pt2\"), visit = c(\"Visit_1\", \"Visit_2\", \"Visit_2\"), delta = c( 5, 9, -10) ) analyse( imputations = imputeObj, delta = deltadf, vars = vars ) } # }"},{"path":"/reference/ancova.html","id":null,"dir":"Reference","previous_headings":"","what":"Analysis of Covariance — ancova","title":"Analysis of Covariance — ancova","text":"Performs analysis covariance two groups returning estimated \"treatment effect\" (.e. contrast two treatment groups) least square means estimates group.","code":""},{"path":"/reference/ancova.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Analysis of Covariance — ancova","text":"","code":"ancova(data, vars, visits = NULL, weights = c(\"proportional\", \"equal\"))"},{"path":"/reference/ancova.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Analysis of Covariance — ancova","text":"data data.frame containing data used model. vars vars object generated set_vars(). group, visit, outcome covariates elements required. See details. visits optional character vector specifying visits fit ancova model . NULL, separate ancova model fit outcomes visit (determined unique(data[[vars$visit]])). See details. weights Character, either \"proportional\" (default) \"equal\". Specifies weighting strategy used categorical covariates calculating lsmeans. See details.","code":""},{"path":"/reference/ancova.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Analysis of Covariance — ancova","text":"function works follows: Select first value visits. Subset data observations occurred visit. Fit linear model vars$outcome ~ vars$group + vars$covariates. Extract \"treatment effect\" & least square means treatment group. Repeat points 2-3 values visits. value visits provided set unique(data[[vars$visit]]). order meet formatting standards set analyse() results collapsed single list suffixed visit name, e.g.: Please note \"ref\" refers first factor level vars$group necessarily coincide control arm. Analogously, \"alt\" refers second factor level vars$group. \"trt\" refers model contrast translating mean difference second level first level. want include interaction terms model can done providing covariates argument set_vars() e.g. set_vars(covariates = c(\"sex*age\")).","code":"list( trt_visit_1 = list(est = ...), lsm_ref_visit_1 = list(est = ...), lsm_alt_visit_1 = list(est = ...), trt_visit_2 = list(est = ...), lsm_ref_visit_2 = list(est = ...), lsm_alt_visit_2 = list(est = ...), ... )"},{"path":"/reference/ancova.html","id":"weighting","dir":"Reference","previous_headings":"","what":"Weighting","title":"Analysis of Covariance — ancova","text":"\"proportional\" default scheme used. equivalent standardization, .e. lsmeans group equal predicted mean outcome ancova model group based baseline characteristics subjects regardless assigned group. alternative weighting scheme, \"equal\", creates hypothetical patients expanding combinations models categorical covariates. lsmeans calculated average predicted mean outcome hypothetical patients assuming come group turn. short: \"proportional\" weights categorical covariates based upon frequency occurrence data. \"equal\" weights categorical covariates equally across theoretical combinations.","code":""},{"path":[]},{"path":"/reference/ancova_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","title":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","text":"Performance analysis covariance. See ancova() full details.","code":""},{"path":"/reference/ancova_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","text":"","code":"ancova_single( data, outcome, group, covariates, weights = c(\"proportional\", \"equal\") )"},{"path":"/reference/ancova_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","text":"data data.frame containing data required model. outcome Character, name outcome variable data. group Character, name group variable data. covariates Character vector containing name additional covariates included model well interaction terms. weights Character, specifies whether use \"proportional\" \"equal\" weighting categorical covariate combination calculating lsmeans.","code":""},{"path":"/reference/ancova_single.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","text":"group must factor variable 2 levels. outcome must continuous numeric variable.","code":""},{"path":[]},{"path":"/reference/ancova_single.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Implements an Analysis of Covariance (ANCOVA) — ancova_single","text":"","code":"if (FALSE) { # \\dontrun{ iris2 <- iris[ iris$Species %in% c(\"versicolor\", \"virginica\"), ] iris2$Species <- factor(iris2$Species) ancova_single(iris2, \"Sepal.Length\", \"Species\", c(\"Petal.Length * Petal.Width\")) } # }"},{"path":"/reference/antidepressant_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Antidepressant trial data — antidepressant_data","title":"Antidepressant trial data — antidepressant_data","text":"dataset containing data publicly available example data set antidepressant clinical trial. dataset available website Drug Information Association Scientific Working Group Estimands Missing Data. per website, original data antidepressant clinical trial four treatments; two doses experimental medication, positive control, placebo published Goldstein et al (2004). mask real data, week 8 observations removed two arms created: original placebo arm \"drug arm\" created randomly selecting patients three non-placebo arms.","code":""},{"path":"/reference/antidepressant_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Antidepressant trial data — antidepressant_data","text":"","code":"antidepressant_data"},{"path":"/reference/antidepressant_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Antidepressant trial data — antidepressant_data","text":"data.frame 608 rows 11 variables: PATIENT: patients IDs. HAMATOTL: total score Hamilton Anxiety Rating Scale. PGIIMP: patient's Global Impression Improvement Rating Scale. RELDAYS: number days visit baseline. VISIT: post-baseline visit. levels 4,5,6,7. THERAPY: treatment group variable. equal PLACEBO observations placebo arm, DRUG observations active arm. GENDER: patient's gender. POOLINV: pooled investigator. BASVAL: baseline outcome value. HAMDTL17: Hamilton 17-item rating scale value. CHANGE: change baseline Hamilton 17-item rating scale.","code":""},{"path":"/reference/antidepressant_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Antidepressant trial data — antidepressant_data","text":"relevant endpoint Hamilton 17-item rating scale depression (HAMD17) baseline weeks 1, 2, 4, 6 assessments included. Study drug discontinuation occurred 24% subjects active drug 26% placebo. data study drug discontinuation missing single additional intermittent missing observation.","code":""},{"path":"/reference/antidepressant_data.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Antidepressant trial data — antidepressant_data","text":"Goldstein, Lu, Detke, Wiltse, Mallinckrodt, Demitrack. Duloxetine treatment depression: double-blind placebo-controlled comparison paroxetine. J Clin Psychopharmacol 2004;24: 389-399.","code":""},{"path":"/reference/apply_delta.html","id":null,"dir":"Reference","previous_headings":"","what":"Applies delta adjustment — apply_delta","title":"Applies delta adjustment — apply_delta","text":"Takes delta dataset adjusts outcome variable adding corresponding delta.","code":""},{"path":"/reference/apply_delta.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Applies delta adjustment — apply_delta","text":"","code":"apply_delta(data, delta = NULL, group = NULL, outcome = NULL)"},{"path":"/reference/apply_delta.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Applies delta adjustment — apply_delta","text":"data data.frame outcome column adjusted. delta data.frame (must contain column called delta). group character vector variables data delta used merge 2 data.frames together . outcome character, name outcome variable data.","code":""},{"path":"/reference/as_analysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct an analysis object — as_analysis","title":"Construct an analysis object — as_analysis","text":"Creates analysis object ensuring components correctly defined.","code":""},{"path":"/reference/as_analysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct an analysis object — as_analysis","text":"","code":"as_analysis(results, method, delta = NULL, fun = NULL, fun_name = NULL)"},{"path":"/reference/as_analysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct an analysis object — as_analysis","text":"results list lists contain analysis results imputation See analyse() details object look like. method method object specified draws(). delta delta dataset used. See analyse() details specified. fun analysis function used. fun_name character name analysis function (used printing) purposes.","code":""},{"path":"/reference/as_ascii_table.html","id":null,"dir":"Reference","previous_headings":"","what":"as_ascii_table — as_ascii_table","title":"as_ascii_table — as_ascii_table","text":"function takes data.frame attempts convert simple ascii format suitable printing screen assumed variable values .character() method order cast character.","code":""},{"path":"/reference/as_ascii_table.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"as_ascii_table — as_ascii_table","text":"","code":"as_ascii_table(dat, line_prefix = \" \", pcol = NULL)"},{"path":"/reference/as_ascii_table.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"as_ascii_table — as_ascii_table","text":"dat Input dataset convert ascii table line_prefix Symbols prefix infront every line table pcol name column handled p-value. Sets value <0.001 value 0 rounding","code":""},{"path":"/reference/as_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Set Class — as_class","title":"Set Class — as_class","text":"Utility function set objects class.","code":""},{"path":"/reference/as_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set Class — as_class","text":"","code":"as_class(x, cls)"},{"path":"/reference/as_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set Class — as_class","text":"x object set class . cls class set.","code":""},{"path":"/reference/as_cropped_char.html","id":null,"dir":"Reference","previous_headings":"","what":"as_cropped_char — as_cropped_char","title":"as_cropped_char — as_cropped_char","text":"Makes character string x chars Reduce x char string ...","code":""},{"path":"/reference/as_cropped_char.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"as_cropped_char — as_cropped_char","text":"","code":"as_cropped_char(inval, crop_at = 30, ndp = 3)"},{"path":"/reference/as_cropped_char.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"as_cropped_char — as_cropped_char","text":"inval single element value crop_at character limit ndp Number decimal places display","code":""},{"path":"/reference/as_dataframe.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert object to dataframe — as_dataframe","title":"Convert object to dataframe — as_dataframe","text":"Convert object dataframe","code":""},{"path":"/reference/as_dataframe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert object to dataframe — as_dataframe","text":"","code":"as_dataframe(x)"},{"path":"/reference/as_dataframe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert object to dataframe — as_dataframe","text":"x data.frame like object Utility function convert \"data.frame-like\" object actual data.frame avoid issues inconsistency methods ( [() dplyr's grouped dataframes)","code":""},{"path":"/reference/as_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a draws object — as_draws","title":"Creates a draws object — as_draws","text":"Creates draws object final output call draws().","code":""},{"path":"/reference/as_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a draws object — as_draws","text":"","code":"as_draws(method, samples, data, formula, n_failures = NULL, fit = NULL)"},{"path":"/reference/as_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a draws object — as_draws","text":"method method object generated either method_bayes(), method_approxbayes(), method_condmean() method_bmlmi(). samples list sample_single objects. See sample_single(). data R6 longdata object containing relevant input data information. formula Fixed effects formula object used model specification. n_failures Absolute number failures model fit. fit method_bayes() chosen, returns MCMC Stan fit object. Otherwise NULL.","code":""},{"path":"/reference/as_draws.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a draws object — as_draws","text":"draws object named list containing following: data: R6 longdata object containing relevant input data information. method: method object generated either method_bayes(), method_approxbayes() method_condmean(). samples: list containing estimated parameters interest. element samples named list containing following: ids: vector characters containing ids subjects included original dataset. beta: numeric vector estimated regression coefficients. sigma: list estimated covariance matrices (one level vars$group). theta: numeric vector transformed covariances. failed: Logical. TRUE model fit failed. ids_samp: vector characters containing ids subjects included given sample. fit: method_bayes() chosen, returns MCMC Stan fit object. Otherwise NULL. n_failures: absolute number failures model fit. Relevant method_condmean(type = \"bootstrap\"), method_approxbayes() method_bmlmi(). formula: fixed effects formula object used model specification.","code":""},{"path":"/reference/as_imputation.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an imputation object — as_imputation","title":"Create an imputation object — as_imputation","text":"function creates object returned impute(). Essentially glorified wrapper around list() ensuring required elements set class added expected.","code":""},{"path":"/reference/as_imputation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an imputation object — as_imputation","text":"","code":"as_imputation(imputations, data, method, references)"},{"path":"/reference/as_imputation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an imputation object — as_imputation","text":"imputations list imputations_list's created imputation_df() data longdata object created longDataConstructor() method method object created method_condmean(), method_bayes() method_approxbayes() references named vector. Identifies references used generating imputed values. form c(\"Group\" = \"Reference\", \"Group\" = \"Reference\").","code":""},{"path":"/reference/as_indices.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert indicator to index — as_indices","title":"Convert indicator to index — as_indices","text":"Converts string 0's 1's index positions 1's padding results 0's length","code":""},{"path":"/reference/as_indices.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert indicator to index — as_indices","text":"","code":"as_indices(x)"},{"path":"/reference/as_indices.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert indicator to index — as_indices","text":"x character vector whose values either \"0\" \"1\". elements vector must length","code":""},{"path":"/reference/as_indices.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Convert indicator to index — as_indices","text":".e.","code":"patmap(c(\"1101\", \"0001\")) -> list(c(1,2,4,999), c(4,999, 999, 999))"},{"path":"/reference/as_mmrm_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a ","title":"Creates a ","text":"Converts design matrix + key variables common format particular function following: Renames covariates V1, V2, etc avoid issues special characters variable names Ensures key variables right type Inserts outcome, visit subjid variables data.frame naming outcome, visit subjid provided also insert group variable data.frame named group","code":""},{"path":"/reference/as_mmrm_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a ","text":"","code":"as_mmrm_df(designmat, outcome, visit, subjid, group = NULL)"},{"path":"/reference/as_mmrm_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a ","text":"designmat data.frame matrix containing covariates use MMRM model. Dummy variables must already expanded , .e. via stats::model.matrix(). contain missing values outcome numeric vector. outcome value regressed MMRM model. visit character / factor vector. Indicates visit outcome value occurred . subjid character / factor vector. subject identifier used link separate visits belong subject. group character / factor vector. Indicates treatment group patient belongs .","code":""},{"path":"/reference/as_mmrm_formula.html","id":null,"dir":"Reference","previous_headings":"","what":"Create MMRM formula — as_mmrm_formula","title":"Create MMRM formula — as_mmrm_formula","text":"Derives MMRM model formula structure mmrm_df. returns formula object form:","code":""},{"path":"/reference/as_mmrm_formula.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create MMRM formula — as_mmrm_formula","text":"","code":"as_mmrm_formula(mmrm_df, cov_struct)"},{"path":"/reference/as_mmrm_formula.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create MMRM formula — as_mmrm_formula","text":"mmrm_df mmrm data.frame created as_mmrm_df() cov_struct Character - covariance structure used, must one \"us\", \"toep\", \"cs\", \"ar1\"","code":""},{"path":"/reference/as_mmrm_formula.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create MMRM formula — as_mmrm_formula","text":"","code":"outcome ~ 0 + V1 + V2 + V4 + ... + us(visit | group / subjid)"},{"path":"/reference/as_model_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand data.frame into a design matrix — as_model_df","title":"Expand data.frame into a design matrix — as_model_df","text":"Expands data.frame using formula create design matrix. Key details always place outcome variable first column return object.","code":""},{"path":"/reference/as_model_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand data.frame into a design matrix — as_model_df","text":"","code":"as_model_df(dat, frm)"},{"path":"/reference/as_model_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand data.frame into a design matrix — as_model_df","text":"dat data.frame frm formula","code":""},{"path":"/reference/as_model_df.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Expand data.frame into a design matrix — as_model_df","text":"outcome column may contain NA's none variables listed formula contain missing values","code":""},{"path":"/reference/as_simple_formula.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a simple formula object from a string — as_simple_formula","title":"Creates a simple formula object from a string — as_simple_formula","text":"Converts string list variables formula object","code":""},{"path":"/reference/as_simple_formula.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a simple formula object from a string — as_simple_formula","text":"","code":"as_simple_formula(outcome, covars)"},{"path":"/reference/as_simple_formula.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a simple formula object from a string — as_simple_formula","text":"outcome character (length 1 vector). Name outcome variable covars character (vector). Name covariates","code":""},{"path":"/reference/as_simple_formula.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Creates a simple formula object from a string — as_simple_formula","text":"formula","code":""},{"path":"/reference/as_stan_array.html","id":null,"dir":"Reference","previous_headings":"","what":"As array — as_stan_array","title":"As array — as_stan_array","text":"Converts numeric value length 1 1 dimension array. avoid type errors thrown stan length 1 numeric vectors provided R stan::vector inputs","code":""},{"path":"/reference/as_stan_array.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"As array — as_stan_array","text":"","code":"as_stan_array(x)"},{"path":"/reference/as_stan_array.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"As array — as_stan_array","text":"x numeric vector","code":""},{"path":"/reference/as_strata.html","id":null,"dir":"Reference","previous_headings":"","what":"Create vector of Stratas — as_strata","title":"Create vector of Stratas — as_strata","text":"Collapse multiple categorical variables distinct unique categories. e.g. return","code":"as_strata(c(1,1,2,2,2,1), c(5,6,5,5,6,5)) c(1,2,3,3,4,1)"},{"path":"/reference/as_strata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create vector of Stratas — as_strata","text":"","code":"as_strata(...)"},{"path":"/reference/as_strata.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create vector of Stratas — as_strata","text":"... numeric/character/factor vectors length","code":""},{"path":"/reference/as_strata.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create vector of Stratas — as_strata","text":"","code":"if (FALSE) { # \\dontrun{ as_strata(c(1,1,2,2,2,1), c(5,6,5,5,6,5)) } # }"},{"path":"/reference/assert_variables_exist.html","id":null,"dir":"Reference","previous_headings":"","what":"Assert that all variables exist within a dataset — assert_variables_exist","title":"Assert that all variables exist within a dataset — assert_variables_exist","text":"Performs assertion check ensure vector variable exists within data.frame expected.","code":""},{"path":"/reference/assert_variables_exist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assert that all variables exist within a dataset — assert_variables_exist","text":"","code":"assert_variables_exist(data, vars)"},{"path":"/reference/assert_variables_exist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assert that all variables exist within a dataset — assert_variables_exist","text":"data data.frame vars character vector variable names","code":""},{"path":"/reference/char2fct.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert character variables to factor — char2fct","title":"Convert character variables to factor — char2fct","text":"Provided vector variable names function converts character variables factors. affect numeric existing factor variables","code":""},{"path":"/reference/char2fct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert character variables to factor — char2fct","text":"","code":"char2fct(data, vars = NULL)"},{"path":"/reference/char2fct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert character variables to factor — char2fct","text":"data data.frame vars character vector variables data","code":""},{"path":"/reference/check_ESS.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostics of the MCMC based on ESS — check_ESS","title":"Diagnostics of the MCMC based on ESS — check_ESS","text":"Check quality MCMC draws posterior distribution checking whether relative ESS sufficiently large.","code":""},{"path":"/reference/check_ESS.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostics of the MCMC based on ESS — check_ESS","text":"","code":"check_ESS(stan_fit, n_draws, threshold_lowESS = 0.4)"},{"path":"/reference/check_ESS.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostics of the MCMC based on ESS — check_ESS","text":"stan_fit stanfit object. n_draws Number MCMC draws. threshold_lowESS number [0,1] indicating minimum acceptable value relative ESS. See details.","code":""},{"path":"/reference/check_ESS.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostics of the MCMC based on ESS — check_ESS","text":"warning message case detected problems.","code":""},{"path":"/reference/check_ESS.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diagnostics of the MCMC based on ESS — check_ESS","text":"check_ESS() works follows: Extract ESS stan_fit parameter model. Compute relative ESS (.e. ESS divided number draws). Check whether parameter ESS lower threshold. least one parameter relative ESS threshold, warning thrown.","code":""},{"path":"/reference/check_hmc_diagn.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn","title":"Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn","text":"Check : divergent iterations. Bayesian Fraction Missing Information (BFMI) sufficiently low. number iterations saturated max treedepth zero. Please see rstan::check_hmc_diagnostics() details.","code":""},{"path":"/reference/check_hmc_diagn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn","text":"","code":"check_hmc_diagn(stan_fit)"},{"path":"/reference/check_hmc_diagn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn","text":"stan_fit stanfit object.","code":""},{"path":"/reference/check_hmc_diagn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostics of the MCMC based on HMC-related measures. — check_hmc_diagn","text":"warning message case detected problems.","code":""},{"path":"/reference/check_mcmc.html","id":null,"dir":"Reference","previous_headings":"","what":"Diagnostics of the MCMC — check_mcmc","title":"Diagnostics of the MCMC — check_mcmc","text":"Diagnostics MCMC","code":""},{"path":"/reference/check_mcmc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Diagnostics of the MCMC — check_mcmc","text":"","code":"check_mcmc(stan_fit, n_draws, threshold_lowESS = 0.4)"},{"path":"/reference/check_mcmc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Diagnostics of the MCMC — check_mcmc","text":"stan_fit stanfit object. n_draws Number MCMC draws. threshold_lowESS number [0,1] indicating minimum acceptable value relative ESS. See details.","code":""},{"path":"/reference/check_mcmc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Diagnostics of the MCMC — check_mcmc","text":"warning message case detected problems.","code":""},{"path":"/reference/check_mcmc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Diagnostics of the MCMC — check_mcmc","text":"Performs checks quality MCMC. See check_ESS() check_hmc_diagn() details.","code":""},{"path":"/reference/compute_sigma.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma","title":"Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma","text":"Adapt covariance matrix reference-based methods. Used Copy Increments Reference (CIR) Jump Reference (JTR) methods, adapt covariance matrix different pre-deviation post deviation covariance structures. See Carpenter et al. (2013)","code":""},{"path":"/reference/compute_sigma.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma","text":"","code":"compute_sigma(sigma_group, sigma_ref, index_mar)"},{"path":"/reference/compute_sigma.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma","text":"sigma_group covariance matrix dimensions equal index_mar subjects original group sigma_ref covariance matrix dimensions equal index_mar subjects reference group index_mar logical vector indicating visits meet MAR assumption subject. .e. identifies observations non-MAR intercurrent event (ICE).","code":""},{"path":"/reference/compute_sigma.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Compute covariance matrix for some reference-based methods (JR, CIR) — compute_sigma","text":"Carpenter, James R., James H. Roger, Michael G. Kenward. \"Analysis longitudinal trials protocol deviation: framework relevant, accessible assumptions, inference via multiple imputation.\" Journal Biopharmaceutical statistics 23.6 (2013): 1352-1371.","code":""},{"path":"/reference/convert_to_imputation_list_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's) — convert_to_imputation_list_df","title":"Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's) — convert_to_imputation_list_df","text":"Convert list imputation_list_single() objects imputation_list_df() object (.e. list imputation_df() objects's)","code":""},{"path":"/reference/convert_to_imputation_list_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's) — convert_to_imputation_list_df","text":"","code":"convert_to_imputation_list_df(imputes, sample_ids)"},{"path":"/reference/convert_to_imputation_list_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert list of imputation_list_single() objects to an imputation_list_df() object (i.e. a list of imputation_df() objects's) — convert_to_imputation_list_df","text":"imputes list imputation_list_single() objects sample_ids list 1 element per required imputation_df. element must contain vector \"ID\"'s correspond imputation_single() ID's required dataset. total number ID's must equal total number rows within imputes$imputations accommodate method_bmlmi() impute_data_individual() function returns list imputation_list_single() objects 1 object per subject. imputation_list_single() stores subjects imputations matrix columns matrix correspond D method_bmlmi(). Note methods (.e. methods_*()) special case D = 1. number rows matrix varies subject equal number times patient selected imputation (non-conditional mean methods 1 per subject per imputed dataset). function best illustrated example: convert_to_imputation_df(imputes, sample_ids) result : Note different repetitions (.e. value set D) grouped together sequentially.","code":"imputes = list( imputation_list_single( id = \"Tom\", imputations = matrix( imputation_single_t_1_1, imputation_single_t_1_2, imputation_single_t_2_1, imputation_single_t_2_2, imputation_single_t_3_1, imputation_single_t_3_2 ) ), imputation_list_single( id = \"Tom\", imputations = matrix( imputation_single_h_1_1, imputation_single_h_1_2, ) ) ) sample_ids <- list( c(\"Tom\", \"Harry\", \"Tom\"), c(\"Tom\") ) imputation_list_df( imputation_df( imputation_single_t_1_1, imputation_single_h_1_1, imputation_single_t_2_1 ), imputation_df( imputation_single_t_1_2, imputation_single_h_1_2, imputation_single_t_2_2 ), imputation_df( imputation_single_t_3_1 ), imputation_df( imputation_single_t_3_2 ) )"},{"path":"/reference/d_lagscale.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate delta from a lagged scale coefficient — d_lagscale","title":"Calculate delta from a lagged scale coefficient — d_lagscale","text":"Calculates delta value based upon baseline delta value post ICE scaling coefficient.","code":""},{"path":"/reference/d_lagscale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate delta from a lagged scale coefficient — d_lagscale","text":"","code":"d_lagscale(delta, dlag, is_post_ice)"},{"path":"/reference/d_lagscale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate delta from a lagged scale coefficient — d_lagscale","text":"delta numeric vector. Determines baseline amount delta applied visit. dlag numeric vector. Determines scaling applied delta based upon visit ICE occurred . Must length delta. is_post_ice logical vector. Indicates whether visit \"post-ICE\" .","code":""},{"path":"/reference/d_lagscale.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculate delta from a lagged scale coefficient — d_lagscale","text":"See delta_template() full details calculation performed.","code":""},{"path":"/reference/delta_template.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a delta data.frame template — delta_template","title":"Create a delta data.frame template — delta_template","text":"Creates data.frame format required analyse() use applying delta adjustment.","code":""},{"path":"/reference/delta_template.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a delta data.frame template — delta_template","text":"","code":"delta_template(imputations, delta = NULL, dlag = NULL, missing_only = TRUE)"},{"path":"/reference/delta_template.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a delta data.frame template — delta_template","text":"imputations imputation object created impute(). delta NULL numeric vector. Determines baseline amount delta applied visit. See details. numeric vector must length number unique visits original dataset. dlag NULL numeric vector. Determines scaling applied delta based upon visit ICE occurred . See details. numeric vector must length number unique visits original dataset. missing_only Logical, TRUE non-missing post-ICE data delta value 0 assigned. Note calculation (described details section) performed first overwritten 0's end (.e. delta values missing post-ICE visits stay regardless option).","code":""},{"path":"/reference/delta_template.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create a delta data.frame template — delta_template","text":"apply delta adjustment analyse() function expects delta data.frame 3 variables: vars$subjid, vars$visit delta (vars object supplied original call draws() created set_vars() function). function return data.frame aforementioned variables one row per subject per visit. delta argument function NULL delta column returned data.frame 0 observations. delta argument NULL delta calculated separately subject accumulative sum delta multiplied scaling coefficient dlag based upon many visits subject's intercurrent event (ICE) visit question . best illustrated example: Let delta = c(5,6,7,8) dlag=c(1,2,3,4) (.e. assuming 4 visits) lets say subject ICE visit 2. calculation follows: say subject delta offset 0 applied visit-1, 6 visit-2, 20 visit-3 44 visit-4. comparison, lets say subject instead ICE visit 3, calculation follows: terms practical usage, lets say wanted delta 5 used post ICE visits regardless proximity ICE visit. can achieved setting delta = c(5,5,5,5) dlag = c(1,0,0,0). example lets say subject ICE visit-1, calculation follows: Another way using arguments set delta difference time visits dlag amount delta per unit time. example lets say visit weeks 1, 5, 6 & 9 want delta 3 applied week ICE. can achieved setting delta = c(0,4,1,3) (difference weeks visit) dlag = c(3, 3, 3, 3). example lets say subject ICE week-5 (.e. visit-2) calculation : .e. week-6 (1 week ICE) delta 3 week-9 (4 weeks ICE) delta 12. Please note function also returns several utility variables user can create custom logic defining delta set . additional variables include: is_mar - observation missing regarded MAR? variable set FALSE observations occurred non-MAR ICE, otherwise set TRUE. is_missing - outcome variable observation missing. is_post_ice - observation occur patient's ICE defined data_ice dataset supplied draws(). strategy - imputation strategy assigned subject. design implementation function largely based upon functionality implemented called \"five marcos\" James Roger. See Roger (2021).","code":"v1 v2 v3 v4 -------------- 5 6 7 8 # delta assigned to each visit 0 1 2 3 # lagged scaling starting from the first visit after the subjects ICE -------------- 0 6 14 24 # delta * lagged scaling -------------- 0 6 20 44 # accumulative sum of delta to be applied to each visit v1 v2 v3 v4 -------------- 5 6 7 8 # delta assigned to each visit 0 0 1 2 # lagged scaling starting from the first visit after the subjects ICE -------------- 0 0 7 16 # delta * lagged scaling -------------- 0 0 7 23 # accumulative sum of delta to be applied to each visit v1 v2 v3 v4 -------------- 5 5 5 5 # delta assigned to each visit 1 0 0 0 # lagged scaling starting from the first visit after the subjects ICE -------------- 5 0 0 0 # delta * lagged scaling -------------- 5 5 5 5 # accumulative sum of delta to be applied to each visit v1 v2 v3 v4 -------------- 0 4 1 3 # delta assigned to each visit 0 0 3 3 # lagged scaling starting from the first visit after the subjects ICE -------------- 0 0 3 9 # delta * lagged scaling -------------- 0 0 3 12 # accumulative sum of delta to be applied to each visit"},{"path":"/reference/delta_template.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create a delta data.frame template — delta_template","text":"Roger, James. Reference-based mi via multivariate normal rm (“five macros” miwithd), 2021. URL https://www.lshtm.ac.uk/research/centres-projects-groups/missing-data#dia-missing-data.","code":""},{"path":[]},{"path":"/reference/delta_template.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a delta data.frame template — delta_template","text":"","code":"if (FALSE) { # \\dontrun{ delta_template(imputeObj) delta_template(imputeObj, delta = c(5,6,7,8), dlag = c(1,2,3,4)) } # }"},{"path":"/reference/do_not_run.html","id":null,"dir":"Reference","previous_headings":"","what":"Do not run this function — do_not_run","title":"Do not run this function — do_not_run","text":"function exists suppress false positive R CMD Check unused libraries","code":""},{"path":"/reference/do_not_run.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Do not run this function — do_not_run","text":"","code":"do_not_run()"},{"path":"/reference/do_not_run.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Do not run this function — do_not_run","text":"rstantools RcppParallel required used installation time. case RcppParallel used src/Makevars file created fly installation rstantools. rstantools used configure file.","code":""},{"path":"/reference/draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the base imputation model and get parameter estimates — draws","title":"Fit the base imputation model and get parameter estimates — draws","text":"draws fits base imputation model observed outcome data according given multiple imputation methodology. According user's method specification, returns either draws posterior distribution model parameters required Bayesian multiple imputation frequentist parameter estimates original data bootstrapped leave-one-datasets required conditional mean imputation. purpose imputation model estimate model parameters absence intercurrent events (ICEs) handled using reference-based imputation methods. reason, observed outcome data ICEs, reference-based imputation methods specified, removed considered missing purpose estimating imputation model, purpose . imputation model mixed model repeated measures (MMRM) valid missing--random (MAR) assumption. can fit using maximum likelihood (ML) restricted ML (REML) estimation, Bayesian approach, approximate Bayesian approach according user's method specification. ML/REML approaches approximate Bayesian approach support several possible covariance structures, Bayesian approach based MCMC sampling supports unstructured covariance structure. case covariance matrix can assumed different across group.","code":""},{"path":"/reference/draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the base imputation model and get parameter estimates — draws","text":"","code":"draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE) # S3 method for class 'approxbayes' draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE) # S3 method for class 'condmean' draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE) # S3 method for class 'bmlmi' draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE) # S3 method for class 'bayes' draws(data, data_ice = NULL, vars, method, ncores = 1, quiet = FALSE)"},{"path":"/reference/draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the base imputation model and get parameter estimates — draws","text":"data data.frame containing data used model. See details. data_ice data.frame specifies information related ICEs imputation strategies. See details. vars vars object generated set_vars(). See details. method method object generated either method_bayes(), method_approxbayes(), method_condmean() method_bmlmi(). specifies multiple imputation methodology used. See details. ncores single numeric specifying number cores use creating draws object. Note parameter ignored method_bayes() (Default = 1). quiet Logical, TRUE suppress printing progress information printed console.","code":""},{"path":"/reference/draws.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the base imputation model and get parameter estimates — draws","text":"draws object named list containing following: data: R6 longdata object containing relevant input data information. method: method object generated either method_bayes(), method_approxbayes() method_condmean(). samples: list containing estimated parameters interest. element samples named list containing following: ids: vector characters containing ids subjects included original dataset. beta: numeric vector estimated regression coefficients. sigma: list estimated covariance matrices (one level vars$group). theta: numeric vector transformed covariances. failed: Logical. TRUE model fit failed. ids_samp: vector characters containing ids subjects included given sample. fit: method_bayes() chosen, returns MCMC Stan fit object. Otherwise NULL. n_failures: absolute number failures model fit. Relevant method_condmean(type = \"bootstrap\"), method_approxbayes() method_bmlmi(). formula: fixed effects formula object used model specification.","code":""},{"path":"/reference/draws.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit the base imputation model and get parameter estimates — draws","text":"draws performs first step multiple imputation (MI) procedure: fitting base imputation model. goal estimate parameters interest needed imputation phase (.e. regression coefficients covariance matrices MMRM model). function distinguishes following methods: Bayesian MI based MCMC sampling: draws returns draws posterior distribution parameters using Bayesian approach based MCMC sampling. method can specified using method = method_bayes(). Approximate Bayesian MI based bootstrapping: draws returns draws posterior distribution parameters using approximate Bayesian approach, sampling posterior distribution simulated fitting MMRM model bootstrap samples original dataset. method can specified using method = method_approxbayes()]. Conditional mean imputation bootstrap re-sampling: draws returns MMRM parameter estimates original dataset n_samples bootstrap samples. method can specified using method = method_condmean() argument type = \"bootstrap\". Conditional mean imputation jackknife re-sampling: draws returns MMRM parameter estimates original dataset leave-one-subject-sample. method can specified using method = method_condmean() argument type = \"jackknife\". Bootstrapped Maximum Likelihood MI: draws returns MMRM parameter estimates given number bootstrap samples needed perform random imputations bootstrapped samples. method can specified using method = method_bmlmi(). Bayesian MI based MCMC sampling proposed Carpenter, Roger, Kenward (2013) first introduced reference-based imputation methods. Approximate Bayesian MI discussed Little Rubin (2002). Conditional mean imputation methods discussed Wolbers et al (2022). Bootstrapped Maximum Likelihood MI described Von Hippel & Bartlett (2021). argument data contains longitudinal data. must least following variables: subjid: factor vector containing subject ids. visit: factor vector containing visit outcome observed . group: factor vector containing group subject belongs . outcome: numeric vector containing outcome variable. might contain missing values. Additional baseline time-varying covariates must included data. data must one row per visit per subject. means incomplete outcome data must set NA instead related row missing. Missing values covariates allowed. data incomplete expand_locf() helper function can used insert missing rows using Last Observation Carried Forward (LOCF) imputation impute covariates values. Note LOCF generally principled imputation method used appropriate specific covariate. Please note special provisioning baseline outcome values. want baseline observations included model part response variable removed advance outcome variable data. time want include baseline outcome covariate model, included separate column data (covariate). Character covariates explicitly cast factors. use custom analysis function requires specific reference levels character covariates (example computation least square means computation) advised manually cast character covariates factor advance running draws(). argument data_ice contains information occurrence ICEs. data.frame 3 columns: Subject ID: character vector containing ids subjects experienced ICE. column must named specified vars$subjid. Visit: character vector containing first visit occurrence ICE (.e. first visit affected ICE). visits must equal one levels data[[vars$visit]]. multiple ICEs happen subject, first non-MAR visit used. column must named specified vars$visit. Strategy: character vector specifying imputation strategy address ICE subject. column must named specified vars$strategy. Possible imputation strategies : \"MAR\": Missing Random. \"CIR\": Copy Increments Reference. \"CR\": Copy Reference. \"JR\": Jump Reference. \"LMCF\": Last Mean Carried Forward. explanations imputation strategies, see Carpenter, Roger, Kenward (2013), Cro et al (2021), Wolbers et al (2022). Please note user-defined imputation strategies can also set. data_ice argument necessary stage since (explained Wolbers et al (2022)), model fitted removing observations incompatible imputation model, .e. observed data data_ice[[vars$visit]] addressed imputation strategy different MAR excluded model fit. However observations discarded data imputation phase (performed function (impute()). summarize, stage pre-ICE data post-ICE data ICEs MAR imputation specified used. data_ice argument omitted, subject record within data_ice, assumed relevant subject's data pre-ICE missing visits imputed MAR assumption observed data used fit base imputation model. Please note ICE visit updated via update_strategy argument impute(); means subjects record data_ice always missing data imputed MAR assumption even strategy updated. vars argument named list specifies names key variables within data data_ice. list created set_vars() contains following named elements: subjid: name column data data_ice contains subject ids variable. visit: name column data data_ice contains visit variable. group: name column data contains group variable. outcome: name column data contains outcome variable. covariates: vector characters contains covariates included model (including interactions specified \"covariateName1*covariateName2\"``). covariates provided default model specification outcome ~ 1 + visit + groupwill used. Please note thegroup*visit` interaction included model default. strata: covariates used stratification variables bootstrap sampling. default vars$group set stratification variable. Needed method_condmean(type = \"bootstrap\") method_approxbayes(). strategy: name column data_ice contains subject-specific imputation strategy.","code":""},{"path":"/reference/draws.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit the base imputation model and get parameter estimates — draws","text":"James R Carpenter, James H Roger, Michael G Kenward. Analysis longitudinal trials protocol deviation: framework relevant, accessible assumptions, inference via multiple imputation. Journal Biopharmaceutical Statistics, 23(6):1352–1371, 2013. Suzie Cro, Tim P Morris, Michael G Kenward, James R Carpenter. Sensitivity analysis clinical trials missing continuous outcome data using controlled multiple imputation: practical guide. Statistics Medicine, 39(21):2815–2842, 2020. Roderick J. . Little Donald B. Rubin. Statistical Analysis Missing Data, Second Edition. John Wiley & Sons, Hoboken, New Jersey, 2002. [Section 10.2.3] Marcel Wolbers, Alessandro Noci, Paul Delmar, Craig Gower-Page, Sean Yiu, Jonathan W. Bartlett. Standard reference-based conditional mean imputation. https://arxiv.org/abs/2109.11162, 2022. Von Hippel, Paul T Bartlett, Jonathan W. Maximum likelihood multiple imputation: Faster imputations consistent standard errors without posterior draws. 2021.","code":""},{"path":[]},{"path":"/reference/encap_get_mmrm_sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Encapsulate get_mmrm_sample — encap_get_mmrm_sample","title":"Encapsulate get_mmrm_sample — encap_get_mmrm_sample","text":"Function creates new wrapper function around get_mmrm_sample() arguments get_mmrm_sample() enclosed within new function. makes running parallel single process calls function smoother. particular function takes care exporting arguments required parallel process cluster","code":""},{"path":"/reference/encap_get_mmrm_sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Encapsulate get_mmrm_sample — encap_get_mmrm_sample","text":"","code":"encap_get_mmrm_sample(cl, longdata, method)"},{"path":"/reference/encap_get_mmrm_sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Encapsulate get_mmrm_sample — encap_get_mmrm_sample","text":"cl Either cluster get_cluster() NULL longdata longdata object longDataConstructor$new() method method object","code":""},{"path":[]},{"path":"/reference/eval_mmrm.html","id":null,"dir":"Reference","previous_headings":"","what":"Evaluate a call to mmrm — eval_mmrm","title":"Evaluate a call to mmrm — eval_mmrm","text":"utility function attempts evaluate call mmrm managing warnings errors thrown. particular function attempts catch warnings errors instead surfacing simply add additional element failed value TRUE. allows multiple calls made without program exiting.","code":""},{"path":"/reference/eval_mmrm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Evaluate a call to mmrm — eval_mmrm","text":"","code":"eval_mmrm(expr)"},{"path":"/reference/eval_mmrm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Evaluate a call to mmrm — eval_mmrm","text":"expr expression evaluated. call mmrm::mmrm().","code":""},{"path":"/reference/eval_mmrm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Evaluate a call to mmrm — eval_mmrm","text":"function originally developed use glmmTMB needed hand-holding dropping false-positive warnings. important now kept around encase need catch false-positive warnings future.","code":""},{"path":[]},{"path":"/reference/eval_mmrm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Evaluate a call to mmrm — eval_mmrm","text":"","code":"if (FALSE) { # \\dontrun{ eval_mmrm({ mmrm::mmrm(formula, data) }) } # }"},{"path":"/reference/expand.html","id":null,"dir":"Reference","previous_headings":"","what":"Expand and fill in missing data.frame rows — expand","title":"Expand and fill in missing data.frame rows — expand","text":"functions essentially wrappers around base::expand.grid() ensure missing combinations data inserted data.frame imputation/fill methods updating covariate values newly created rows.","code":""},{"path":"/reference/expand.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expand and fill in missing data.frame rows — expand","text":"","code":"expand(data, ...) fill_locf(data, vars, group = NULL, order = NULL) expand_locf(data, ..., vars, group, order)"},{"path":"/reference/expand.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expand and fill in missing data.frame rows — expand","text":"data dataset expand fill . ... variables levels expanded (note duplicate entries levels result multiple rows level). vars character vector containing names variables need filled . group character vector containing names variables group performing LOCF imputation var. order character vector containing names additional variables sort data.frame performing LOCF.","code":""},{"path":"/reference/expand.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Expand and fill in missing data.frame rows — expand","text":"draws() function makes assumption subjects visits present data.frame covariate values non missing; expand(), fill_locf() expand_locf() utility functions support users ensuring data.frame's conform assumptions. expand() takes vectors expected levels data.frame expands combinations inserting missing rows data.frame. Note \"expanded\" variables cast factors. fill_locf() applies LOCF imputation named covariates fill NAs created insertion new rows expand() (though note distinction made existing NAs newly created NAs). Note data.frame sorted c(group, order) performing LOCF imputation; data.frame returned original sort order however. expand_locf() simple composition function fill_locf() expand() .e. fill_locf(expand(...)).","code":""},{"path":"/reference/expand.html","id":"missing-first-values","dir":"Reference","previous_headings":"","what":"Missing First Values","title":"Expand and fill in missing data.frame rows — expand","text":"fill_locf() function performs last observation carried forward imputation. natural consequence unable impute missing observations observation first value given subject / grouping. values deliberately imputed risks silent errors case time varying covariates. One solution first use expand_locf() just visit variable time varying covariates merge baseline covariates afterwards .e.","code":"library(dplyr) dat_expanded <- expand( data = dat, subject = c(\"pt1\", \"pt2\", \"pt3\", \"pt4\"), visit = c(\"vis1\", \"vis2\", \"vis3\") ) dat_filled <- dat_expanded %>% left_join(baseline_covariates, by = \"subject\")"},{"path":"/reference/expand.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Expand and fill in missing data.frame rows — expand","text":"","code":"if (FALSE) { # \\dontrun{ dat_expanded <- expand( data = dat, subject = c(\"pt1\", \"pt2\", \"pt3\", \"pt4\"), visit = c(\"vis1\", \"vis2\", \"vis3\") ) dat_filled <- fill_loc( data = dat_expanded, vars = c(\"Sex\", \"Age\"), group = \"subject\", order = \"visit\" ) ## Or dat_filled <- expand_locf( data = dat, subject = c(\"pt1\", \"pt2\", \"pt3\", \"pt4\"), visit = c(\"vis1\", \"vis2\", \"vis3\"), vars = c(\"Sex\", \"Age\"), group = \"subject\", order = \"visit\" ) } # }"},{"path":"/reference/extract_covariates.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract Variables from string vector — extract_covariates","title":"Extract Variables from string vector — extract_covariates","text":"Takes string including potentially model terms like * : extracts individual variables","code":""},{"path":"/reference/extract_covariates.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract Variables from string vector — extract_covariates","text":"","code":"extract_covariates(x)"},{"path":"/reference/extract_covariates.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract Variables from string vector — extract_covariates","text":"x string variable names potentially including interaction terms","code":""},{"path":"/reference/extract_covariates.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract Variables from string vector — extract_covariates","text":".e. c(\"v1\", \"v2\", \"v2*v3\", \"v1:v2\") becomes c(\"v1\", \"v2\", \"v3\")","code":""},{"path":"/reference/extract_data_nmar_as_na.html","id":null,"dir":"Reference","previous_headings":"","what":"Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na","title":"Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na","text":"Set NA outcome values MNAR missing (.e. occur ICE handled using reference-based imputation strategy)","code":""},{"path":"/reference/extract_data_nmar_as_na.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na","text":"","code":"extract_data_nmar_as_na(longdata)"},{"path":"/reference/extract_data_nmar_as_na.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na","text":"longdata R6 longdata object containing relevant input data information.","code":""},{"path":"/reference/extract_data_nmar_as_na.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set to NA outcome values that would be MNAR if they were missing (i.e. which occur after an ICE handled using a reference-based imputation strategy) — extract_data_nmar_as_na","text":"data.frame containing longdata$get_data(longdata$ids), MNAR outcome values set NA.","code":""},{"path":"/reference/extract_draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract draws from a stanfit object — extract_draws","title":"Extract draws from a stanfit object — extract_draws","text":"Extract draws stanfit object convert lists. function rstan::extract() returns draws given parameter array. function calls rstan::extract() extract draws stanfit object convert arrays lists.","code":""},{"path":"/reference/extract_draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract draws from a stanfit object — extract_draws","text":"","code":"extract_draws(stan_fit)"},{"path":"/reference/extract_draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract draws from a stanfit object — extract_draws","text":"stan_fit stanfit object.","code":""},{"path":"/reference/extract_draws.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract draws from a stanfit object — extract_draws","text":"named list length 2 containing: beta: list length equal number draws containing draws posterior distribution regression coefficients. sigma: list length equal number draws containing draws posterior distribution covariance matrices. element list list length equal 1 same_cov = TRUE equal number groups same_cov = FALSE.","code":""},{"path":"/reference/extract_imputed_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract imputed dataset — extract_imputed_df","title":"Extract imputed dataset — extract_imputed_df","text":"Takes imputation object generated imputation_df() uses extract completed dataset longdata object created longDataConstructor(). Also applies delta transformation data.frame provided delta argument. See analyse() details structure data.frame. Subject IDs returned data.frame scrambled .e. original values.","code":""},{"path":"/reference/extract_imputed_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract imputed dataset — extract_imputed_df","text":"","code":"extract_imputed_df(imputation, ld, delta = NULL, idmap = FALSE)"},{"path":"/reference/extract_imputed_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract imputed dataset — extract_imputed_df","text":"imputation imputation object generated imputation_df(). ld longdata object generated longDataConstructor(). delta Either NULL data.frame. used offset outcome values imputed dataset. idmap Logical. TRUE attribute called \"idmap\" attached return object contains list maps old subject ids new subject ids.","code":""},{"path":"/reference/extract_imputed_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract imputed dataset — extract_imputed_df","text":"data.frame.","code":""},{"path":"/reference/extract_imputed_dfs.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract imputed datasets — extract_imputed_dfs","title":"Extract imputed datasets — extract_imputed_dfs","text":"Extracts imputed datasets contained within imputations object generated impute().","code":""},{"path":"/reference/extract_imputed_dfs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract imputed datasets — extract_imputed_dfs","text":"","code":"extract_imputed_dfs( imputations, index = seq_along(imputations$imputations), delta = NULL, idmap = FALSE )"},{"path":"/reference/extract_imputed_dfs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract imputed datasets — extract_imputed_dfs","text":"imputations imputations object created impute(). index indexes imputed datasets return. default, datasets within imputations object returned. delta data.frame containing delta transformation applied imputed dataset. See analyse() details format specification data.frame. idmap Logical. subject IDs imputed data.frame's replaced new IDs ensure unique. Setting argument TRUE attaches attribute, called idmap, returned data.frame's provide map new subject IDs old subject IDs.","code":""},{"path":"/reference/extract_imputed_dfs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract imputed datasets — extract_imputed_dfs","text":"list data.frames equal length index argument.","code":""},{"path":[]},{"path":"/reference/extract_imputed_dfs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract imputed datasets — extract_imputed_dfs","text":"","code":"if (FALSE) { # \\dontrun{ extract_imputed_dfs(imputeObj) extract_imputed_dfs(imputeObj, c(1:3)) } # }"},{"path":"/reference/extract_params.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract parameters from a MMRM model — extract_params","title":"Extract parameters from a MMRM model — extract_params","text":"Extracts beta sigma coefficients MMRM model created mmrm::mmrm().","code":""},{"path":"/reference/extract_params.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract parameters from a MMRM model — extract_params","text":"","code":"extract_params(fit)"},{"path":"/reference/extract_params.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract parameters from a MMRM model — extract_params","text":"fit object created mmrm::mmrm()","code":""},{"path":"/reference/fit_mcmc.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the base imputation model using a Bayesian approach — fit_mcmc","title":"Fit the base imputation model using a Bayesian approach — fit_mcmc","text":"fit_mcmc() fits base imputation model using Bayesian approach. done MCMC method implemented stan run using function rstan::sampling(). function returns draws posterior distribution model parameters stanfit object. Additionally performs multiple diagnostics checks chain returns warnings case detected issues.","code":""},{"path":"/reference/fit_mcmc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the base imputation model using a Bayesian approach — fit_mcmc","text":"","code":"fit_mcmc(designmat, outcome, group, subjid, visit, method, quiet = FALSE)"},{"path":"/reference/fit_mcmc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the base imputation model using a Bayesian approach — fit_mcmc","text":"designmat design matrix fixed effects. outcome response variable. Must numeric. group Character vector containing group variable. subjid Character vector containing subjects IDs. visit Character vector containing visit variable. method method object generated method_bayes(). quiet Specify whether stan sampling log printed console.","code":""},{"path":"/reference/fit_mcmc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the base imputation model using a Bayesian approach — fit_mcmc","text":"named list composed following: samples: named list containing draws parameter. corresponds output extract_draws(). fit: stanfit object.","code":""},{"path":"/reference/fit_mcmc.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit the base imputation model using a Bayesian approach — fit_mcmc","text":"Bayesian model assumes multivariate normal likelihood function weakly-informative priors model parameters: particular, uniform priors assumed regression coefficients inverse-Wishart priors covariance matrices. chain initialized using REML parameter estimates MMRM starting values. function performs following steps: Fit MMRM using REML approach. Prepare input data MCMC fit described data{} block Stan file. See prepare_stan_data() details. Run MCMC according input arguments using starting values REML parameter estimates estimated point 1. Performs diagnostics checks MCMC. See check_mcmc() details. Extract draws model fit. chains perform method$n_samples draws keeping one every method$burn_between iterations. Additionally first method$burn_in iterations discarded. total number iterations method$burn_in + method$burn_between*method$n_samples. purpose method$burn_in ensure samples drawn stationary distribution Markov Chain. method$burn_between aims keep draws uncorrelated .","code":""},{"path":"/reference/fit_mmrm.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a MMRM model — fit_mmrm","title":"Fit a MMRM model — fit_mmrm","text":"Fits MMRM model allowing different covariance structures using mmrm::mmrm(). Returns list key model parameters beta, sigma additional element failed indicating whether fit failed converge. fit fail converge beta sigma present.","code":""},{"path":"/reference/fit_mmrm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a MMRM model — fit_mmrm","text":"","code":"fit_mmrm( designmat, outcome, subjid, visit, group, cov_struct = c(\"us\", \"toep\", \"cs\", \"ar1\"), REML = TRUE, same_cov = TRUE )"},{"path":"/reference/fit_mmrm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a MMRM model — fit_mmrm","text":"designmat data.frame matrix containing covariates use MMRM model. Dummy variables must already expanded , .e. via stats::model.matrix(). contain missing values outcome numeric vector. outcome value regressed MMRM model. subjid character / factor vector. subject identifier used link separate visits belong subject. visit character / factor vector. Indicates visit outcome value occurred . group character / factor vector. Indicates treatment group patient belongs . cov_struct character value. Specifies covariance structure use. Must one \"us\", \"toep\", \"cs\" \"ar1\" REML logical. Specifies whether restricted maximum likelihood used same_cov logical. Used specify shared individual covariance matrix used per group","code":""},{"path":"/reference/generate_data_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate data for a single group — generate_data_single","title":"Generate data for a single group — generate_data_single","text":"Generate data single group","code":""},{"path":"/reference/generate_data_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate data for a single group — generate_data_single","text":"","code":"generate_data_single(pars_group, strategy_fun = NULL, distr_pars_ref = NULL)"},{"path":"/reference/generate_data_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate data for a single group — generate_data_single","text":"pars_group simul_pars object generated set_simul_pars(). specifies simulation parameters given group. strategy_fun Function implementing trajectories intercurrent event (ICE). Must one getStrategies(). See getStrategies() details. NULL post-ICE outcomes untouched. distr_pars_ref Optional. Named list containing simulation parameters reference arm. contains following elements: mu: Numeric vector indicating mean outcome trajectory assuming ICEs. include outcome baseline. sigma Covariance matrix outcome trajectory assuming ICEs. NULL, parameters inherited pars_group.","code":""},{"path":"/reference/generate_data_single.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate data for a single group — generate_data_single","text":"data.frame containing simulated data. includes following variables: id: Factor variable specifies id subject. visit: Factor variable specifies visit assessment. Visit 0 denotes baseline visit. group: Factor variable specifies treatment group subject belongs . outcome_bl: Numeric variable specifies baseline outcome. outcome_noICE: Numeric variable specifies longitudinal outcome assuming ICEs. ind_ice1: Binary variable takes value 1 corresponding visit affected ICE1 0 otherwise. dropout_ice1: Binary variable takes value 1 corresponding visit affected drop-following ICE1 0 otherwise. ind_ice2: Binary variable takes value 1 corresponding visit affected ICE2. outcome: Numeric variable specifies longitudinal outcome including ICE1, ICE2 intermittent missing values.","code":""},{"path":[]},{"path":"/reference/getStrategies.html","id":null,"dir":"Reference","previous_headings":"","what":"Get imputation strategies — getStrategies","title":"Get imputation strategies — getStrategies","text":"Returns list defining imputation strategies used create multivariate normal distribution parameters merging source group reference group per patient.","code":""},{"path":"/reference/getStrategies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get imputation strategies — getStrategies","text":"","code":"getStrategies(...)"},{"path":"/reference/getStrategies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get imputation strategies — getStrategies","text":"... User defined methods added return list. Input must function.","code":""},{"path":"/reference/getStrategies.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get imputation strategies — getStrategies","text":"default Jump Reference (JR), Copy Reference (CR), Copy Increments Reference (CIR), Last Mean Carried Forward (LMCF) Missing Random (MAR) defined. user can define strategy functions (overwrite pre-defined ones) specifying named input function .e. NEW = function(...) .... exception MAR overwritten. user defined functions must take 3 inputs: pars_group, pars_ref index_mar. pars_group pars_ref lists elements mu sigma representing multivariate normal distribution parameters subject's current group reference group respectively. index_mar logical vector specifying visits subject met MAR assumption . function must return list elements mu sigma. See implementation strategy_JR() example.","code":""},{"path":"/reference/getStrategies.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get imputation strategies — getStrategies","text":"","code":"if (FALSE) { # \\dontrun{ getStrategies() getStrategies( NEW = function(pars_group, pars_ref, index_mar) code , JR = function(pars_group, pars_ref, index_mar) more_code ) } # }"},{"path":"/reference/get_ESS.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS","title":"Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS","text":"Extract Effective Sample Size (ESS) stanfit object","code":""},{"path":"/reference/get_ESS.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS","text":"","code":"get_ESS(stan_fit)"},{"path":"/reference/get_ESS.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS","text":"stan_fit stanfit object.","code":""},{"path":"/reference/get_ESS.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the Effective Sample Size (ESS) from a stanfit object — get_ESS","text":"named vector containing ESS parameter model.","code":""},{"path":"/reference/get_bootstrap_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a stack object populated with bootstrapped samples — get_bootstrap_stack","title":"Creates a stack object populated with bootstrapped samples — get_bootstrap_stack","text":"Function creates Stack() object populated stack bootstrap samples based upon method$n_samples","code":""},{"path":"/reference/get_bootstrap_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a stack object populated with bootstrapped samples — get_bootstrap_stack","text":"","code":"get_bootstrap_stack(longdata, method, stack = Stack$new())"},{"path":"/reference/get_bootstrap_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a stack object populated with bootstrapped samples — get_bootstrap_stack","text":"longdata longDataConstructor() object method method object stack Stack() object (exposed unit testing purposes)","code":""},{"path":"/reference/get_cluster.html","id":null,"dir":"Reference","previous_headings":"","what":"Create cluster — get_cluster","title":"Create cluster — get_cluster","text":"Create cluster","code":""},{"path":"/reference/get_cluster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create cluster — get_cluster","text":"","code":"get_cluster(ncores = 1)"},{"path":"/reference/get_cluster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create cluster — get_cluster","text":"ncores Number parallel processes use ncores 1 function return NULL function spawns PSOCK cluster. Ensures rbmi assert_that loaded sub-processes","code":""},{"path":"/reference/get_conditional_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive conditional multivariate normal parameters — get_conditional_parameters","title":"Derive conditional multivariate normal parameters — get_conditional_parameters","text":"Takes parameters multivariate normal distribution observed values calculate conditional distribution unobserved values.","code":""},{"path":"/reference/get_conditional_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive conditional multivariate normal parameters — get_conditional_parameters","text":"","code":"get_conditional_parameters(pars, values)"},{"path":"/reference/get_conditional_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive conditional multivariate normal parameters — get_conditional_parameters","text":"pars list elements mu sigma defining mean vector covariance matrix respectively. values vector observed values condition , must length pars$mu. Missing values must represented NA.","code":""},{"path":"/reference/get_conditional_parameters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Derive conditional multivariate normal parameters — get_conditional_parameters","text":"list conditional distribution parameters: mu - conditional mean vector. sigma - conditional covariance matrix.","code":""},{"path":"/reference/get_delta_template.html","id":null,"dir":"Reference","previous_headings":"","what":"Get delta utility variables — get_delta_template","title":"Get delta utility variables — get_delta_template","text":"function creates default delta template (1 row per subject per visit) extracts utility information users need define logic defining delta. See delta_template() full details.","code":""},{"path":"/reference/get_delta_template.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get delta utility variables — get_delta_template","text":"","code":"get_delta_template(imputations)"},{"path":"/reference/get_delta_template.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get delta utility variables — get_delta_template","text":"imputations imputations object created impute().","code":""},{"path":"/reference/get_draws_mle.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit the base imputation model on bootstrap samples — get_draws_mle","title":"Fit the base imputation model on bootstrap samples — get_draws_mle","text":"Fit base imputation model using ML/REML approach given number bootstrap samples specified method$n_samples. Returns parameter estimates model fit.","code":""},{"path":"/reference/get_draws_mle.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit the base imputation model on bootstrap samples — get_draws_mle","text":"","code":"get_draws_mle( longdata, method, sample_stack, n_target_samples, first_sample_orig, use_samp_ids, failure_limit = 0, ncores = 1, quiet = FALSE )"},{"path":"/reference/get_draws_mle.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit the base imputation model on bootstrap samples — get_draws_mle","text":"longdata R6 longdata object containing relevant input data information. method method object generated either method_approxbayes() method_condmean() argument type = \"bootstrap\". sample_stack stack object containing subject ids used mmrm iteration. n_target_samples Number samples needed created first_sample_orig Logical. TRUE function returns method$n_samples + 1 samples first sample contains parameter estimates original dataset method$n_samples samples contain parameter estimates bootstrap samples. FALSE function returns method$n_samples samples containing parameter estimates bootstrap samples. use_samp_ids Logical. TRUE, sampled subject ids returned. Otherwise subject ids original dataset returned. values used tell impute() subjects used derive imputed dataset. failure_limit Number failed samples allowed throwing error ncores Number processes parallelise job quiet Logical, TRUE suppress printing progress information printed console.","code":""},{"path":"/reference/get_draws_mle.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit the base imputation model on bootstrap samples — get_draws_mle","text":"draws object named list containing following: data: R6 longdata object containing relevant input data information. method: method object generated either method_bayes(), method_approxbayes() method_condmean(). samples: list containing estimated parameters interest. element samples named list containing following: ids: vector characters containing ids subjects included original dataset. beta: numeric vector estimated regression coefficients. sigma: list estimated covariance matrices (one level vars$group). theta: numeric vector transformed covariances. failed: Logical. TRUE model fit failed. ids_samp: vector characters containing ids subjects included given sample. fit: method_bayes() chosen, returns MCMC Stan fit object. Otherwise NULL. n_failures: absolute number failures model fit. Relevant method_condmean(type = \"bootstrap\"), method_approxbayes() method_bmlmi(). formula: fixed effects formula object used model specification.","code":""},{"path":"/reference/get_draws_mle.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Fit the base imputation model on bootstrap samples — get_draws_mle","text":"function takes Stack object contains multiple lists patient ids. function takes Stack pulls set ids constructs dataset just consisting patients (.e. potentially bootstrap jackknife sample). function fits MMRM model dataset create sample object. function repeats process n_target_samples reached. failure_limit samples fail converge function throws error. reaching desired number samples function generates returns draws object.","code":""},{"path":"/reference/get_ests_bmlmi.html","id":null,"dir":"Reference","previous_headings":"","what":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"Compute pooled point estimates, standard error degrees freedom according Von Hippel Bartlett formula Bootstrapped Maximum Likelihood Multiple Imputation (BMLMI).","code":""},{"path":"/reference/get_ests_bmlmi.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"","code":"get_ests_bmlmi(ests, D)"},{"path":"/reference/get_ests_bmlmi.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"ests numeric vector containing estimates analysis imputed datasets. D numeric representing number imputations bootstrap sample BMLMI method.","code":""},{"path":"/reference/get_ests_bmlmi.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"list containing point estimate, standard error degrees freedom.","code":""},{"path":"/reference/get_ests_bmlmi.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"ests must provided following order: firsts D elements related analyses random imputation one bootstrap sample. second set D elements (.e. D+1 2*D) related second bootstrap sample .","code":""},{"path":"/reference/get_ests_bmlmi.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Von Hippel and Bartlett pooling of BMLMI method — get_ests_bmlmi","text":"Von Hippel, Paul T Bartlett, Jonathan W8. Maximum likelihood multiple imputation: Faster imputations consistent standard errors without posterior draws. 2021","code":""},{"path":"/reference/get_example_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate a realistic example dataset — get_example_data","title":"Simulate a realistic example dataset — get_example_data","text":"Simulate realistic example dataset using simulate_data() hard-coded values input arguments.","code":""},{"path":"/reference/get_example_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate a realistic example dataset — get_example_data","text":"","code":"get_example_data()"},{"path":"/reference/get_example_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate a realistic example dataset — get_example_data","text":"get_example_data() simulates 1:1 randomized trial active drug (intervention) versus placebo (control) 100 subjects per group 6 post-baseline assessments (bi-monthly visits 12 months). One intercurrent event corresponding treatment discontinuation also simulated. Specifically, data simulated following assumptions: mean outcome trajectory placebo group increases linearly 50 baseline (visit 0) 60 visit 6, .e. slope 10 points/year. mean outcome trajectory intervention group identical placebo group visit 2. visit 2 onward, slope decreases 50% 5 points/year. covariance structure baseline follow-values groups implied random intercept slope model standard deviation 5 intercept slope, correlation 0.25. addition, independent residual error standard deviation 2.5 added assessment. probability study drug discontinuation visit calculated according logistic model depends observed outcome visit. Specifically, visit-wise discontinuation probability 2% 3% control intervention group, respectively, specified case observed outcome equal 50 (mean value baseline). odds discontinuation simulated increase +10% +1 point increase observed outcome. Study drug discontinuation simulated effect mean trajectory placebo group. intervention group, subjects discontinue follow slope mean trajectory placebo group time point onward. compatible copy increments reference (CIR) assumption. Study drop-study drug discontinuation visit occurs probability 50% leading missing outcome data time point onward.","code":""},{"path":[]},{"path":"/reference/get_jackknife_stack.html","id":null,"dir":"Reference","previous_headings":"","what":"Creates a stack object populated with jackknife samples — get_jackknife_stack","title":"Creates a stack object populated with jackknife samples — get_jackknife_stack","text":"Function creates Stack() object populated stack jackknife samples based upon","code":""},{"path":"/reference/get_jackknife_stack.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Creates a stack object populated with jackknife samples — get_jackknife_stack","text":"","code":"get_jackknife_stack(longdata, method, stack = Stack$new())"},{"path":"/reference/get_jackknife_stack.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Creates a stack object populated with jackknife samples — get_jackknife_stack","text":"longdata longDataConstructor() object method method object stack Stack() object (exposed unit testing purposes)","code":""},{"path":"/reference/get_mmrm_sample.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit MMRM and returns parameter estimates — get_mmrm_sample","title":"Fit MMRM and returns parameter estimates — get_mmrm_sample","text":"get_mmrm_sample fits base imputation model using ML/REML approach. Returns parameter estimates fit.","code":""},{"path":"/reference/get_mmrm_sample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit MMRM and returns parameter estimates — get_mmrm_sample","text":"","code":"get_mmrm_sample(ids, longdata, method)"},{"path":"/reference/get_mmrm_sample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit MMRM and returns parameter estimates — get_mmrm_sample","text":"ids vector characters containing ids subjects. longdata R6 longdata object containing relevant input data information. method method object generated either method_approxbayes() method_condmean().","code":""},{"path":"/reference/get_mmrm_sample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit MMRM and returns parameter estimates — get_mmrm_sample","text":"named list class sample_single. contains following: ids vector characters containing ids subjects included original dataset. beta numeric vector estimated regression coefficients. sigma list estimated covariance matrices (one level vars$group). theta numeric vector transformed covariances. failed logical. TRUE model fit failed. ids_samp vector characters containing ids subjects included given sample.","code":""},{"path":"/reference/get_pattern_groups.html","id":null,"dir":"Reference","previous_headings":"","what":"Determine patients missingness group — get_pattern_groups","title":"Determine patients missingness group — get_pattern_groups","text":"Takes design matrix multiple rows per subject returns dataset 1 row per subject new column pgroup indicating group patient belongs (based upon missingness pattern treatment group)","code":""},{"path":"/reference/get_pattern_groups.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Determine patients missingness group — get_pattern_groups","text":"","code":"get_pattern_groups(ddat)"},{"path":"/reference/get_pattern_groups.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Determine patients missingness group — get_pattern_groups","text":"ddat data.frame columns subjid, visit, group, is_avail","code":""},{"path":"/reference/get_pattern_groups.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Determine patients missingness group — get_pattern_groups","text":"column is_avail must character numeric 0 1","code":""},{"path":"/reference/get_pattern_groups_unique.html","id":null,"dir":"Reference","previous_headings":"","what":"Get Pattern Summary — get_pattern_groups_unique","title":"Get Pattern Summary — get_pattern_groups_unique","text":"Takes dataset pattern information creates summary dataset just 1 row per pattern","code":""},{"path":"/reference/get_pattern_groups_unique.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get Pattern Summary — get_pattern_groups_unique","text":"","code":"get_pattern_groups_unique(patterns)"},{"path":"/reference/get_pattern_groups_unique.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get Pattern Summary — get_pattern_groups_unique","text":"patterns data.frame columns pgroup, pattern group","code":""},{"path":"/reference/get_pattern_groups_unique.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Get Pattern Summary — get_pattern_groups_unique","text":"column pgroup must numeric vector indicating pattern group patient belongs column pattern must character string 0's 1's. must identical rows within pgroup column group must character / numeric vector indicating covariance group observation belongs . must identical within pgroup","code":""},{"path":"/reference/get_pool_components.html","id":null,"dir":"Reference","previous_headings":"","what":"Expected Pool Components — get_pool_components","title":"Expected Pool Components — get_pool_components","text":"Returns elements expected contained analyse object depending analysis method specified.","code":""},{"path":"/reference/get_pool_components.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Expected Pool Components — get_pool_components","text":"","code":"get_pool_components(x)"},{"path":"/reference/get_pool_components.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Expected Pool Components — get_pool_components","text":"x Character name analysis method, must one either \"rubin\", \"jackknife\", \"bootstrap\" \"bmlmi\".","code":""},{"path":"/reference/get_visit_distribution_parameters.html","id":null,"dir":"Reference","previous_headings":"","what":"Derive visit distribution parameters — get_visit_distribution_parameters","title":"Derive visit distribution parameters — get_visit_distribution_parameters","text":"Takes patient level data beta coefficients expands get patient specific estimate visit distribution parameters mu sigma. Returns values specific format expected downstream functions imputation process (namely list(list(mu = ..., sigma = ...), list(mu = ..., sigma = ...))).","code":""},{"path":"/reference/get_visit_distribution_parameters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Derive visit distribution parameters — get_visit_distribution_parameters","text":"","code":"get_visit_distribution_parameters(dat, beta, sigma)"},{"path":"/reference/get_visit_distribution_parameters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Derive visit distribution parameters — get_visit_distribution_parameters","text":"dat Patient level dataset, must 1 row per visit. Column order must order beta. number columns must match length beta beta List model beta coefficients. 1 element sample e.g. 3 samples models 4 beta coefficients argument form list( c(1,2,3,4) , c(5,6,7,8), c(9,10,11,12)). elements beta must length must length order dat. sigma List sigma. Must number entries beta.","code":""},{"path":"/reference/has_class.html","id":null,"dir":"Reference","previous_headings":"","what":"Does object have a class ? — has_class","title":"Does object have a class ? — has_class","text":"Utility function see object particular class. Useful know many classes object may .","code":""},{"path":"/reference/has_class.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Does object have a class ? — has_class","text":"","code":"has_class(x, cls)"},{"path":"/reference/has_class.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Does object have a class ? — has_class","text":"x object want check class . cls class want know .","code":""},{"path":"/reference/has_class.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Does object have a class ? — has_class","text":"TRUE object class. FALSE object class.","code":""},{"path":"/reference/ife.html","id":null,"dir":"Reference","previous_headings":"","what":"if else — ife","title":"if else — ife","text":"wrapper around () else() prevent unexpected interactions ifelse() factor variables","code":""},{"path":"/reference/ife.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"if else — ife","text":"","code":"ife(x, a, b)"},{"path":"/reference/ife.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"if else — ife","text":"x True / False value return True b value return False","code":""},{"path":"/reference/ife.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"if else — ife","text":"default ifelse() convert factor variables numeric values often undesirable. connivance function avoids problem","code":""},{"path":"/reference/imputation_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a valid imputation_df object — imputation_df","title":"Create a valid imputation_df object — imputation_df","text":"Create valid imputation_df object","code":""},{"path":"/reference/imputation_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a valid imputation_df object — imputation_df","text":"","code":"imputation_df(...)"},{"path":"/reference/imputation_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a valid imputation_df object — imputation_df","text":"... list imputation_single.","code":""},{"path":"/reference/imputation_list_df.html","id":null,"dir":"Reference","previous_headings":"","what":"List of imputations_df — imputation_list_df","title":"List of imputations_df — imputation_list_df","text":"container multiple imputation_df's","code":""},{"path":"/reference/imputation_list_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"List of imputations_df — imputation_list_df","text":"","code":"imputation_list_df(...)"},{"path":"/reference/imputation_list_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"List of imputations_df — imputation_list_df","text":"... objects class imputation_df","code":""},{"path":"/reference/imputation_list_single.html","id":null,"dir":"Reference","previous_headings":"","what":"A collection of imputation_singles() grouped by a single subjid ID — imputation_list_single","title":"A collection of imputation_singles() grouped by a single subjid ID — imputation_list_single","text":"collection imputation_singles() grouped single subjid ID","code":""},{"path":"/reference/imputation_list_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A collection of imputation_singles() grouped by a single subjid ID — imputation_list_single","text":"","code":"imputation_list_single(imputations, D = 1)"},{"path":"/reference/imputation_list_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A collection of imputation_singles() grouped by a single subjid ID — imputation_list_single","text":"imputations list imputation_single() objects ordered repetitions grouped sequentially D number repetitions performed determines many columns imputation matrix constructor function create imputation_list_single object contains matrix imputation_single() objects grouped single id. matrix split D columns (.e. non-bmlmi methods always 1) id attribute determined extracting id attribute contributing imputation_single() objects. error throw multiple id detected","code":""},{"path":"/reference/imputation_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a valid imputation_single object — imputation_single","title":"Create a valid imputation_single object — imputation_single","text":"Create valid imputation_single object","code":""},{"path":"/reference/imputation_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a valid imputation_single object — imputation_single","text":"","code":"imputation_single(id, values)"},{"path":"/reference/imputation_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a valid imputation_single object — imputation_single","text":"id character string specifying subject id. values numeric vector indicating imputed values.","code":""},{"path":"/reference/impute.html","id":null,"dir":"Reference","previous_headings":"","what":"Create imputed datasets — impute","title":"Create imputed datasets — impute","text":"impute() creates imputed datasets based upon data options specified call draws(). One imputed dataset created per \"sample\" created draws().","code":""},{"path":"/reference/impute.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create imputed datasets — impute","text":"","code":"impute( draws, references = NULL, update_strategy = NULL, strategies = getStrategies() ) # S3 method for class 'random' impute( draws, references = NULL, update_strategy = NULL, strategies = getStrategies() ) # S3 method for class 'condmean' impute( draws, references = NULL, update_strategy = NULL, strategies = getStrategies() )"},{"path":"/reference/impute.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create imputed datasets — impute","text":"draws draws object created draws(). references named vector. Identifies references used reference-based imputation methods. form c(\"Group1\" = \"Reference1\", \"Group2\" = \"Reference2\"). NULL (default), references assumed form c(\"Group1\" = \"Group1\", \"Group2\" = \"Group2\"). argument NULL imputation strategy (defined data_ice[[vars$strategy]] call draws) MAR set. update_strategy optional data.frame. Updates imputation method originally set via data_ice option draws(). See details section information. strategies named list functions. Defines imputation functions used. names list mirror values specified strategy column data_ice. Default = getStrategies(). See getStrategies() details.","code":""},{"path":"/reference/impute.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create imputed datasets — impute","text":"impute() uses imputation model parameter estimates, generated draws(), first calculate marginal (multivariate normal) distribution subject's longitudinal outcome variable depending covariate values. subjects intercurrent events (ICEs) handled using non-MAR methods, marginal distribution updated depending time first visit affected ICE, chosen imputation strategy chosen reference group described Carpenter, Roger, Kenward (2013) . subject's imputation distribution used imputing missing values defined marginal distribution conditional observed outcome values. One dataset generated per set parameter estimates provided draws(). exact manner missing values imputed conditional imputation distribution depends method object provided draws(), particular: Bayes & Approximate Bayes: imputed dataset contains 1 row per subject & visit original dataset missing values imputed taking single random sample conditional imputation distribution. Conditional Mean: imputed dataset contains 1 row per subject & visit bootstrapped jackknife dataset used generate corresponding parameter estimates draws(). Missing values imputed using mean conditional imputation distribution. Please note first imputed dataset refers conditional mean imputation original dataset whereas subsequent imputed datasets refer conditional mean imputations bootstrap jackknife samples, respectively, original data. Bootstrapped Maximum Likelihood MI (BMLMI): performs D random imputations bootstrapped dataset used generate corresponding parameter estimates draws(). total number B*D imputed datasets provided, B number bootstrapped datasets. Missing values imputed taking random sample conditional imputation distribution. update_strategy argument can used update imputation strategy originally set via data_ice option draws(). avoids re-run draws() function changing imputation strategy certain circumstances (detailed ). data.frame provided update_strategy argument must contain two columns, one subject ID another imputation strategy, whose names defined vars argument specified call draws(). Please note argument allows update imputation strategy arguments time first visit affected ICE. key limitation functionality one can switch MAR non-MAR strategy (vice versa) subjects without observed post-ICE data. reason change affect whether post-ICE data included base imputation model (explained help draws()). example, subject ICE \"Visit 2\" observed/known values \"Visit 3\" function throw error one tries switch strategy MAR non-MAR strategy. contrast, switching non-MAR MAR strategy, whilst valid, raise warning usable data utilised imputation model.","code":""},{"path":"/reference/impute.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Create imputed datasets — impute","text":"James R Carpenter, James H Roger, Michael G Kenward. Analysis longitudinal trials protocol deviation: framework relevant, accessible assumptions, inference via multiple imputation. Journal Biopharmaceutical Statistics, 23(6):1352–1371, 2013. [Section 4.2 4.3]","code":""},{"path":"/reference/impute.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create imputed datasets — impute","text":"","code":"if (FALSE) { # \\dontrun{ impute( draws = drawobj, references = c(\"Trt\" = \"Placebo\", \"Placebo\" = \"Placebo\") ) new_strategy <- data.frame( subjid = c(\"Pt1\", \"Pt2\"), strategy = c(\"MAR\", \"JR\") ) impute( draws = drawobj, references = c(\"Trt\" = \"Placebo\", \"Placebo\" = \"Placebo\"), update_strategy = new_strategy ) } # }"},{"path":"/reference/impute_data_individual.html","id":null,"dir":"Reference","previous_headings":"","what":"Impute data for a single subject — impute_data_individual","title":"Impute data for a single subject — impute_data_individual","text":"function performs imputation single subject time implementing process detailed impute().","code":""},{"path":"/reference/impute_data_individual.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Impute data for a single subject — impute_data_individual","text":"","code":"impute_data_individual( id, index, beta, sigma, data, references, strategies, condmean, n_imputations = 1 )"},{"path":"/reference/impute_data_individual.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Impute data for a single subject — impute_data_individual","text":"id Character string identifying subject. index sample indexes subject belongs e.g c(1,1,1,2,2,4). beta list beta coefficients sample, .e. beta[[1]] set beta coefficients first sample. sigma list sigma coefficients sample split group .e. sigma[[1]][[\"\"]] give sigma coefficients group first sample. data longdata object created longDataConstructor() references named vector. Identifies references used generating imputed values. form c(\"Group\" = \"Reference\", \"Group\" = \"Reference\"). strategies named list functions. Defines imputation functions used. names list mirror values specified method column data_ice. Default = getStrategies(). See getStrategies() details. condmean Logical. TRUE impute using conditional mean values, FALSE impute taking random draw multivariate normal distribution. n_imputations condmean = FALSE numeric representing number random imputations performed sample. Default 1 (one random imputation per sample).","code":""},{"path":"/reference/impute_data_individual.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Impute data for a single subject — impute_data_individual","text":"Note function performs required imputations subject time. .e. subject included samples 1,3,5,9 imputations (using sample-dependent imputation model parameters) performed one step order avoid look subjects's covariates expanding design matrix multiple times (computationally expensive). function also supports subject belonging sample multiple times, .e. 1,1,2,3,5,5, typically occur bootstrapped datasets.","code":""},{"path":"/reference/impute_internal.html","id":null,"dir":"Reference","previous_headings":"","what":"Create imputed datasets — impute_internal","title":"Create imputed datasets — impute_internal","text":"work horse function implements functionality impute. See user level function impute() details.","code":""},{"path":"/reference/impute_internal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create imputed datasets — impute_internal","text":"","code":"impute_internal( draws, references = NULL, update_strategy, strategies, condmean )"},{"path":"/reference/impute_internal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create imputed datasets — impute_internal","text":"draws draws object created draws(). references named vector. Identifies references used reference-based imputation methods. form c(\"Group1\" = \"Reference1\", \"Group2\" = \"Reference2\"). NULL (default), references assumed form c(\"Group1\" = \"Group1\", \"Group2\" = \"Group2\"). argument NULL imputation strategy (defined data_ice[[vars$strategy]] call draws) MAR set. update_strategy optional data.frame. Updates imputation method originally set via data_ice option draws(). See details section information. strategies named list functions. Defines imputation functions used. names list mirror values specified strategy column data_ice. Default = getStrategies(). See getStrategies() details. condmean logical. TRUE impute using conditional mean values, values impute taking random draw multivariate normal distribution.","code":""},{"path":"/reference/impute_outcome.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample outcome value — impute_outcome","title":"Sample outcome value — impute_outcome","text":"Draws random sample multivariate normal distribution.","code":""},{"path":"/reference/impute_outcome.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample outcome value — impute_outcome","text":"","code":"impute_outcome(conditional_parameters, n_imputations = 1, condmean = FALSE)"},{"path":"/reference/impute_outcome.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample outcome value — impute_outcome","text":"conditional_parameters list elements mu sigma contain mean vector covariance matrix sample . n_imputations numeric representing number random samples multivariate normal distribution performed. Default 1. condmean conditional mean imputation performed (opposed random sampling)","code":""},{"path":"/reference/invert.html","id":null,"dir":"Reference","previous_headings":"","what":"invert — invert","title":"invert — invert","text":"Utility function used replicated purrr::transpose. Turns list inside .","code":""},{"path":"/reference/invert.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"invert — invert","text":"","code":"invert(x)"},{"path":"/reference/invert.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"invert — invert","text":"x list","code":""},{"path":"/reference/invert_indexes.html","id":null,"dir":"Reference","previous_headings":"","what":"Invert and derive indexes — invert_indexes","title":"Invert and derive indexes — invert_indexes","text":"Takes list elements creates new list containing 1 entry per unique element value containing indexes original elements occurred .","code":""},{"path":"/reference/invert_indexes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Invert and derive indexes — invert_indexes","text":"","code":"invert_indexes(x)"},{"path":"/reference/invert_indexes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Invert and derive indexes — invert_indexes","text":"x list elements invert calculate index (see details).","code":""},{"path":"/reference/invert_indexes.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Invert and derive indexes — invert_indexes","text":"functions purpose best illustrated example: input: becomes:","code":"list( c(\"A\", \"B\", \"C\"), c(\"A\", \"A\", \"B\"))} list( \"A\" = c(1,2,2), \"B\" = c(1,2), \"C\" = 1 )"},{"path":"/reference/is_absent.html","id":null,"dir":"Reference","previous_headings":"","what":"Is value absent — is_absent","title":"Is value absent — is_absent","text":"Returns true value either NULL, NA \"\". case vector values must NULL/NA/\"\" x regarded absent.","code":""},{"path":"/reference/is_absent.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is value absent — is_absent","text":"","code":"is_absent(x, na = TRUE, blank = TRUE)"},{"path":"/reference/is_absent.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is value absent — is_absent","text":"x value check absent na NAs count absent blank blanks .e. \"\" count absent","code":""},{"path":"/reference/is_char_fact.html","id":null,"dir":"Reference","previous_headings":"","what":"Is character or factor — is_char_fact","title":"Is character or factor — is_char_fact","text":"returns true x character factor vector","code":""},{"path":"/reference/is_char_fact.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is character or factor — is_char_fact","text":"","code":"is_char_fact(x)"},{"path":"/reference/is_char_fact.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is character or factor — is_char_fact","text":"x character factor vector","code":""},{"path":"/reference/is_char_one.html","id":null,"dir":"Reference","previous_headings":"","what":"Is single character — is_char_one","title":"Is single character — is_char_one","text":"returns true x length 1 character vector","code":""},{"path":"/reference/is_char_one.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is single character — is_char_one","text":"","code":"is_char_one(x)"},{"path":"/reference/is_char_one.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is single character — is_char_one","text":"x character vector","code":""},{"path":"/reference/is_in_rbmi_development.html","id":null,"dir":"Reference","previous_headings":"","what":"Is package in development mode? — is_in_rbmi_development","title":"Is package in development mode? — is_in_rbmi_development","text":"Returns TRUE package developed .e. local copy source code actively editing Returns FALSE otherwise","code":""},{"path":"/reference/is_in_rbmi_development.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is package in development mode? — is_in_rbmi_development","text":"","code":"is_in_rbmi_development()"},{"path":"/reference/is_in_rbmi_development.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Is package in development mode? — is_in_rbmi_development","text":"Main use function parallel processing indicate whether sub-processes need load current development version code whether load main installed package system","code":""},{"path":"/reference/is_num_char_fact.html","id":null,"dir":"Reference","previous_headings":"","what":"Is character, factor or numeric — is_num_char_fact","title":"Is character, factor or numeric — is_num_char_fact","text":"returns true x character, numeric factor vector","code":""},{"path":"/reference/is_num_char_fact.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Is character, factor or numeric — is_num_char_fact","text":"","code":"is_num_char_fact(x)"},{"path":"/reference/is_num_char_fact.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Is character, factor or numeric — is_num_char_fact","text":"x character, numeric factor vector","code":""},{"path":"/reference/locf.html","id":null,"dir":"Reference","previous_headings":"","what":"Last Observation Carried Forward — locf","title":"Last Observation Carried Forward — locf","text":"Returns vector applied last observation carried forward imputation.","code":""},{"path":"/reference/locf.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Last Observation Carried Forward — locf","text":"","code":"locf(x)"},{"path":"/reference/locf.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Last Observation Carried Forward — locf","text":"x vector.","code":""},{"path":"/reference/locf.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Last Observation Carried Forward — locf","text":"","code":"if (FALSE) { # \\dontrun{ locf(c(NA, 1, 2, 3, NA, 4)) # Returns c(NA, 1, 2, 3, 3, 4) } # }"},{"path":"/reference/longDataConstructor.html","id":null,"dir":"Reference","previous_headings":"","what":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"longdata object allows efficient storage recall longitudinal datasets use bootstrap sampling. object works de-constructing data lists based upon subject id thus enabling efficient lookup.","code":""},{"path":"/reference/longDataConstructor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"object also handles multiple operations specific rbmi defining whether outcome value MAR / Missing well tracking imputation strategy assigned subject. recognised objects functionality fairly overloaded hoped can split area specific objects / functions future. additions functionality object avoided possible.","code":""},{"path":"/reference/longDataConstructor.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"data original dataset passed constructor (sorted id visit) vars vars object (list key variables) passed constructor visits character vector containing distinct visit levels ids character vector containing unique ids subject self$data formula formula expressing design matrix data constructed strata numeric vector indicating strata corresponding value self$ids belongs . stratification variable defined default 1 subjects (.e. group). field used part self$sample_ids() function enable stratified bootstrap sampling ice_visit_index list indexed subject storing index number first visit affected ICE. ICE set equal number visits plus 1. values list indexed subject storing numeric vector original (unimputed) outcome values group list indexed subject storing single character indicating imputation group subject belongs defined self$data[id, self$ivars$group] used determine reference group used imputing subjects data. is_mar list indexed subject storing logical values indicating subjects outcome values MAR . list defaulted TRUE subjects & outcomes modified calls self$set_strategies(). Note indicate values missing, variable True outcome values either occurred ICE visit post ICE visit imputation strategy MAR strategies list indexed subject storing single character value indicating imputation strategy assigned subject. list defaulted \"MAR\" subjects modified calls either self$set_strategies() self$update_strategies() strategy_lock list indexed subject storing single logical value indicating whether patients imputation strategy locked . strategy locked means change MAR non-MAR. Strategies can changed non-MAR MAR though trigger warning. Strategies locked patient assigned MAR strategy non-missing ICE date. list populated call self$set_strategies(). indexes list indexed subject storing numeric vector indexes specify rows original dataset belong subject .e. recover full data subject \"pt3\" can use self$data[self$indexes[[\"pt3\"]],]. may seem redundant filtering data directly however enables efficient bootstrap sampling data .e. list populated object initialisation. is_missing list indexed subject storing logical vector indicating whether corresponding outcome subject missing. list populated object initialisation. is_post_ice list indexed subject storing logical vector indicating whether corresponding outcome subject post date ICE. ICE data provided defaults False observations. list populated call self$set_strategies().","code":"indexes <- unlist(self$indexes[c(\"pt3\", \"pt3\")]) self$data[indexes,]"},{"path":[]},{"path":"/reference/longDataConstructor.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"longDataConstructor$get_data() longDataConstructor$add_subject() longDataConstructor$validate_ids() longDataConstructor$sample_ids() longDataConstructor$extract_by_id() longDataConstructor$update_strategies() longDataConstructor$set_strategies() longDataConstructor$check_has_data_at_each_visit() longDataConstructor$set_strata() longDataConstructor$new() longDataConstructor$clone()","code":""},{"path":"/reference/longDataConstructor.html","id":"method-get-data-","dir":"Reference","previous_headings":"","what":"Method get_data()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Returns data.frame based upon required subject IDs. Replaces missing values new ones provided.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$get_data( obj = NULL, nmar.rm = FALSE, na.rm = FALSE, idmap = FALSE )"},{"path":"/reference/longDataConstructor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"obj Either NULL, character vector subjects IDs imputation list object. See details. nmar.rm Logical value. TRUE remove observations regarded MAR (determined self$is_mar). na.rm Logical value. TRUE remove outcome values missing (determined self$is_missing). idmap Logical value. TRUE add attribute idmap contains mapping new subject ids old subject ids. See details.","code":""},{"path":"/reference/longDataConstructor.html","id":"details-1","dir":"Reference","previous_headings":"","what":"Details","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"obj NULL full original dataset returned. obj character vector new dataset consisting just subjects returned; character vector contains duplicate entries subject returned multiple times. obj imputation_df object (created imputation_df()) subject ids specified object returned missing values filled specified imputation list object. .e. return data.frame consisting observations pt1 twice observations pt3 . first set observations pt1 missing values filled c(1,2,3) second set filled c(4,5,6). length values must equal sum(self$is_missing[[id]]). obj NULL subject IDs scrambled order ensure unique .e. pt2 requested twice process guarantees set observations unique subject ID number. idmap attribute (requested) can used map new ids back old ids.","code":"obj <- imputation_df( imputation_single( id = \"pt1\", values = c(1,2,3)), imputation_single( id = \"pt1\", values = c(4,5,6)), imputation_single( id = \"pt3\", values = c(7,8)) ) longdata$get_data(obj)"},{"path":"/reference/longDataConstructor.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"data.frame.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-add-subject-","dir":"Reference","previous_headings":"","what":"Method add_subject()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"function decomposes patient data self$data populates corresponding lists .e. self$is_missing, self$values, self$group, etc. function called upon objects initialization.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$add_subject(id)"},{"path":"/reference/longDataConstructor.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"id Character subject id exists within self$data.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-validate-ids-","dir":"Reference","previous_headings":"","what":"Method validate_ids()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Throws error element ids within source data self$data.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$validate_ids(ids)"},{"path":"/reference/longDataConstructor.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"ids character vector ids.","code":""},{"path":"/reference/longDataConstructor.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"TRUE","code":""},{"path":"/reference/longDataConstructor.html","id":"method-sample-ids-","dir":"Reference","previous_headings":"","what":"Method sample_ids()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Performs random stratified sampling patient ids (replacement) patient equal weight picked within strata (.e dependent many non-missing visits ).","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$sample_ids()"},{"path":"/reference/longDataConstructor.html","id":"returns-2","dir":"Reference","previous_headings":"","what":"Returns","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Character vector ids.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-extract-by-id-","dir":"Reference","previous_headings":"","what":"Method extract_by_id()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Returns list key information given subject. convenience wrapper save manually grab element.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$extract_by_id(id)"},{"path":"/reference/longDataConstructor.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"id Character subject id exists within self$data.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-update-strategies-","dir":"Reference","previous_headings":"","what":"Method update_strategies()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Convenience function run self$set_strategies(dat_ice, update=TRUE) kept legacy reasons.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-5","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$update_strategies(dat_ice)"},{"path":"/reference/longDataConstructor.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"dat_ice data.frame containing ICE information see impute() format dataframe.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-set-strategies-","dir":"Reference","previous_headings":"","what":"Method set_strategies()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Updates self$strategies, self$is_mar, self$is_post_ice variables based upon provided ICE information.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-6","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$set_strategies(dat_ice = NULL, update = FALSE)"},{"path":"/reference/longDataConstructor.html","id":"arguments-5","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"dat_ice data.frame containing ICE information. See details. update Logical, indicates ICE data used update. See details.","code":""},{"path":"/reference/longDataConstructor.html","id":"details-2","dir":"Reference","previous_headings":"","what":"Details","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"See draws() specification dat_ice update=FALSE. See impute() format dat_ice update=TRUE. update=TRUE function ensures MAR strategies changed non-MAR presence post-ICE observations.","code":""},{"path":"/reference/longDataConstructor.html","id":"method-check-has-data-at-each-visit-","dir":"Reference","previous_headings":"","what":"Method check_has_data_at_each_visit()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Ensures visits least 1 observed \"MAR\" observation. Throws error criteria met. ensure initial MMRM can resolved.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-7","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$check_has_data_at_each_visit()"},{"path":"/reference/longDataConstructor.html","id":"method-set-strata-","dir":"Reference","previous_headings":"","what":"Method set_strata()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Populates self$strata variable. user specified stratification variables first visit used determine value variables. stratification variables specified everyone defined strata 1.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-8","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$set_strata()"},{"path":"/reference/longDataConstructor.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"Constructor function.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-9","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$new(data, vars)"},{"path":"/reference/longDataConstructor.html","id":"arguments-6","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"data longitudinal dataset. vars ivars object created set_vars().","code":""},{"path":"/reference/longDataConstructor.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"objects class cloneable method.","code":""},{"path":"/reference/longDataConstructor.html","id":"usage-10","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"","code":"longDataConstructor$clone(deep = FALSE)"},{"path":"/reference/longDataConstructor.html","id":"arguments-7","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for Storing / Accessing & Sampling Longitudinal Data — longDataConstructor","text":"deep Whether make deep clone.","code":""},{"path":"/reference/ls_design.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate design vector for the lsmeans — ls_design","title":"Calculate design vector for the lsmeans — ls_design","text":"Calculates design vector required generate lsmean standard error. ls_design_equal calculates applying equal weight per covariate combination whilst ls_design_proportional applies weighting proportional frequency covariate combination occurred actual dataset.","code":""},{"path":"/reference/ls_design.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate design vector for the lsmeans — ls_design","text":"","code":"ls_design_equal(data, frm, covars, fix) ls_design_proportional(data, frm, covars, fix)"},{"path":"/reference/ls_design.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate design vector for the lsmeans — ls_design","text":"data data.frame frm Formula used fit original model covars character vector variables names exist data extracted (ls_design_equal ) fix named list variables fixed values","code":""},{"path":"/reference/lsmeans.html","id":null,"dir":"Reference","previous_headings":"","what":"Least Square Means — lsmeans","title":"Least Square Means — lsmeans","text":"Estimates least square means linear model. done generating prediction model using hypothetical observation constructed averaging data. See details information.","code":""},{"path":"/reference/lsmeans.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Least Square Means — lsmeans","text":"","code":"lsmeans(model, ..., .weights = c(\"proportional\", \"equal\"))"},{"path":"/reference/lsmeans.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Least Square Means — lsmeans","text":"model model created lm. ... Fixes specific variables specific values .e. trt = 1 age = 50. name argument must name variable within dataset. .weights Character, specifies whether use \"proportional\" \"equal\" weighting categorical covariate combination calculating lsmeans.","code":""},{"path":"/reference/lsmeans.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Least Square Means — lsmeans","text":"lsmeans obtained calculating hypothetical patients predicting expected values. hypothetical patients constructed expanding possible combinations categorical covariate setting numerical covariates equal mean. final lsmean value calculated averaging hypothetical patients. .weights equals \"proportional\" values weighted frequency occur full dataset. .weights equals \"equal\" hypothetical patient given equal weight regardless actually occurs dataset. Use ... argument fix specific variables specific values. See references identical implementations done SAS R via emmeans package. function attempts re-implement emmeans derivation standard linear models without include dependencies.","code":""},{"path":"/reference/lsmeans.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Least Square Means — lsmeans","text":"https://CRAN.R-project.org/package=emmeans https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.3/statug/statug_glm_details41.htm","code":""},{"path":"/reference/lsmeans.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Least Square Means — lsmeans","text":"","code":"if (FALSE) { # \\dontrun{ mod <- lm(Sepal.Length ~ Species + Petal.Length, data = iris) lsmeans(mod) lsmeans(mod, Species = \"virginica\") lsmeans(mod, Species = \"versicolor\") lsmeans(mod, Species = \"versicolor\", Petal.Length = 1) } # }"},{"path":"/reference/method.html","id":null,"dir":"Reference","previous_headings":"","what":"Set the multiple imputation methodology — method","title":"Set the multiple imputation methodology — method","text":"functions determine methods rbmi use creating imputation models, generating imputed values pooling results.","code":""},{"path":"/reference/method.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set the multiple imputation methodology — method","text":"","code":"method_bayes( burn_in = 200, burn_between = 50, same_cov = TRUE, n_samples = 20, seed = sample.int(.Machine$integer.max, 1) ) method_approxbayes( covariance = c(\"us\", \"toep\", \"cs\", \"ar1\"), threshold = 0.01, same_cov = TRUE, REML = TRUE, n_samples = 20 ) method_condmean( covariance = c(\"us\", \"toep\", \"cs\", \"ar1\"), threshold = 0.01, same_cov = TRUE, REML = TRUE, n_samples = NULL, type = c(\"bootstrap\", \"jackknife\") ) method_bmlmi( covariance = c(\"us\", \"toep\", \"cs\", \"ar1\"), threshold = 0.01, same_cov = TRUE, REML = TRUE, B = 20, D = 2 )"},{"path":"/reference/method.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set the multiple imputation methodology — method","text":"burn_in numeric specifies many observations discarded prior extracting actual samples. Note sampler initialized maximum likelihood estimates weakly informative prior used thus theory value need high. burn_between numeric specifies \"thinning\" rate .e. many observations discarded sample. used prevent issues associated autocorrelation samples. same_cov logical, TRUE imputation model fitted using single shared covariance matrix observations. FALSE separate covariance matrix fit group determined group argument set_vars(). n_samples numeric determines many imputed datasets generated. case method_condmean(type = \"jackknife\") argument must set NULL. See details. seed numeric specifies seed used call Stan. argument passed onto seed argument rstan::sampling(). Note required method_bayes(), methods can achieve reproducible results setting seed via set.seed(). See details. covariance character string specifies structure covariance matrix used imputation model. Must one \"us\" (default), \"toep\", \"cs\" \"ar1\". See details. threshold numeric 0 1, specifies proportion bootstrap datasets can fail produce valid samples error thrown. See details. REML logical indicating whether use REML estimation rather maximum likelihood. type character string specifies resampling method used perform inference conditional mean imputation approach (set via method_condmean()) used. Must one \"bootstrap\" \"jackknife\". B numeric determines number bootstrap samples method_bmlmi. D numeric determines number random imputations bootstrap sample. Needed method_bmlmi().","code":""},{"path":"/reference/method.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Set the multiple imputation methodology — method","text":"case method_condmean(type = \"bootstrap\") n_samples + 1 imputation models datasets generated first sample based original dataset whilst n_samples samples bootstrapped datasets. Likewise, method_condmean(type = \"jackknife\") length(unique(data$subjid)) + 1 imputation models datasets generated. cases represented n + 1 displayed print message. user able specify different covariance structures using covariance argument. Currently supported structures include: Unstructured (\"us\") Toeplitz (\"toep\") Compound Symmetry (\"cs\") Autoregression-1 (\"ar1\") Note present Bayesian methods support unstructured. case method_condmean(type = \"bootstrap\"), method_approxbayes() method_bmlmi() repeated bootstrap samples original dataset taken MMRM fitted sample. Due randomness sampled datasets, well limitations optimisers used fit models, uncommon estimates particular dataset generated. instances rbmi designed throw bootstrapped dataset try another. However ensure errors due chance due underlying misspecification data /model tolerance limit set many samples can discarded. tolerance limit reached error thrown process aborted. tolerance limit defined ceiling(threshold * n_samples). Note jackknife method estimates need generated leave-one-datasets error thrown fail fit. Please note time writing (September 2021) Stan unable produce reproducible samples across different operating systems even seed used. care must taken using Stan across different machines. information limitation please consult Stan documentation https://mc-stan.org/docs/2_27/reference-manual/reproducibility-chapter.html","code":""},{"path":"/reference/parametric_ci.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate parametric confidence intervals — parametric_ci","title":"Calculate parametric confidence intervals — parametric_ci","text":"Calculates confidence intervals based upon parametric distribution.","code":""},{"path":"/reference/parametric_ci.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate parametric confidence intervals — parametric_ci","text":"","code":"parametric_ci(point, se, alpha, alternative, qfun, pfun, ...)"},{"path":"/reference/parametric_ci.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate parametric confidence intervals — parametric_ci","text":"point point estimate. se standard error point estimate. using non-\"normal\" distribution set 1. alpha type 1 error rate, value 0 1. alternative character string specifying alternative hypothesis, must one \"two.sided\" (default), \"greater\" \"less\". qfun quantile function assumed distribution .e. qnorm. pfun CDF function assumed distribution .e. pnorm. ... additional arguments passed qfun pfun .e. df = 102.","code":""},{"path":"/reference/pool.html","id":null,"dir":"Reference","previous_headings":"","what":"Pool analysis results obtained from the imputed datasets — pool","title":"Pool analysis results obtained from the imputed datasets — pool","text":"Pool analysis results obtained imputed datasets","code":""},{"path":"/reference/pool.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Pool analysis results obtained from the imputed datasets — pool","text":"","code":"pool( results, conf.level = 0.95, alternative = c(\"two.sided\", \"less\", \"greater\"), type = c(\"percentile\", \"normal\") ) # S3 method for class 'pool' as.data.frame(x, ...) # S3 method for class 'pool' print(x, ...)"},{"path":"/reference/pool.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Pool analysis results obtained from the imputed datasets — pool","text":"results analysis object created analyse(). conf.level confidence level returned confidence interval. Must single number 0 1. Default 0.95. alternative character string specifying alternative hypothesis, must one \"two.sided\" (default), \"greater\" \"less\". type character string either \"percentile\" (default) \"normal\". Determines method used calculate bootstrap confidence intervals. See details. used method_condmean(type = \"bootstrap\") specified original call draws(). x pool object generated pool(). ... used.","code":""},{"path":"/reference/pool.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Pool analysis results obtained from the imputed datasets — pool","text":"calculation used generate point estimate, standard errors confidence interval depends upon method specified original call draws(); particular: method_approxbayes() & method_bayes() use Rubin's rules pool estimates variances across multiple imputed datasets, Barnard-Rubin rule pool degree's freedom; see Little & Rubin (2002). method_condmean(type = \"bootstrap\") uses percentile normal approximation; see Efron & Tibshirani (1994). Note percentile bootstrap, standard error calculated, .e. standard errors NA object / data.frame. method_condmean(type = \"jackknife\") uses standard jackknife variance formula; see Efron & Tibshirani (1994). method_bmlmi uses pooling procedure Bootstrapped Maximum Likelihood MI (BMLMI). See Von Hippel & Bartlett (2021).","code":""},{"path":"/reference/pool.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Pool analysis results obtained from the imputed datasets — pool","text":"Bradley Efron Robert J Tibshirani. introduction bootstrap. CRC press, 1994. [Section 11] Roderick J. . Little Donald B. Rubin. Statistical Analysis Missing Data, Second Edition. John Wiley & Sons, Hoboken, New Jersey, 2002. [Section 5.4] Von Hippel, Paul T Bartlett, Jonathan W. Maximum likelihood multiple imputation: Faster imputations consistent standard errors without posterior draws. 2021.","code":""},{"path":"/reference/pool_bootstrap_normal.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Pooling via normal approximation — pool_bootstrap_normal","title":"Bootstrap Pooling via normal approximation — pool_bootstrap_normal","text":"Get point estimate, confidence interval p-value using normal approximation.","code":""},{"path":"/reference/pool_bootstrap_normal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Pooling via normal approximation — pool_bootstrap_normal","text":"","code":"pool_bootstrap_normal(est, conf.level, alternative)"},{"path":"/reference/pool_bootstrap_normal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Pooling via normal approximation — pool_bootstrap_normal","text":"est numeric vector point estimates bootstrap sample. conf.level confidence level returned confidence interval. Must single number 0 1. Default 0.95. alternative character string specifying alternative hypothesis, must one \"two.sided\" (default), \"greater\" \"less\".","code":""},{"path":"/reference/pool_bootstrap_normal.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap Pooling via normal approximation — pool_bootstrap_normal","text":"point estimate taken first element est. remaining n-1 values est used generate confidence intervals.","code":""},{"path":"/reference/pool_bootstrap_percentile.html","id":null,"dir":"Reference","previous_headings":"","what":"Bootstrap Pooling via Percentiles — pool_bootstrap_percentile","title":"Bootstrap Pooling via Percentiles — pool_bootstrap_percentile","text":"Get point estimate, confidence interval p-value using percentiles. Note quantile \"type=6\" used, see stats::quantile() details.","code":""},{"path":"/reference/pool_bootstrap_percentile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Bootstrap Pooling via Percentiles — pool_bootstrap_percentile","text":"","code":"pool_bootstrap_percentile(est, conf.level, alternative)"},{"path":"/reference/pool_bootstrap_percentile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Bootstrap Pooling via Percentiles — pool_bootstrap_percentile","text":"est numeric vector point estimates bootstrap sample. conf.level confidence level returned confidence interval. Must single number 0 1. Default 0.95. alternative character string specifying alternative hypothesis, must one \"two.sided\" (default), \"greater\" \"less\".","code":""},{"path":"/reference/pool_bootstrap_percentile.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Bootstrap Pooling via Percentiles — pool_bootstrap_percentile","text":"point estimate taken first element est. remaining n-1 values est used generate confidence intervals.","code":""},{"path":"/reference/pool_internal.html","id":null,"dir":"Reference","previous_headings":"","what":"Internal Pool Methods — pool_internal","title":"Internal Pool Methods — pool_internal","text":"Dispatches pool methods based upon results object class. See pool() details.","code":""},{"path":"/reference/pool_internal.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Internal Pool Methods — pool_internal","text":"","code":"pool_internal(results, conf.level, alternative, type, D) # S3 method for class 'jackknife' pool_internal(results, conf.level, alternative, type, D) # S3 method for class 'bootstrap' pool_internal( results, conf.level, alternative, type = c(\"percentile\", \"normal\"), D ) # S3 method for class 'bmlmi' pool_internal(results, conf.level, alternative, type, D) # S3 method for class 'rubin' pool_internal(results, conf.level, alternative, type, D)"},{"path":"/reference/pool_internal.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Internal Pool Methods — pool_internal","text":"results list results .e. x$results element analyse object created analyse()). conf.level confidence level returned confidence interval. Must single number 0 1. Default 0.95. alternative character string specifying alternative hypothesis, must one \"two.sided\" (default), \"greater\" \"less\". type character string either \"percentile\" (default) \"normal\". Determines method used calculate bootstrap confidence intervals. See details. used method_condmean(type = \"bootstrap\") specified original call draws(). D numeric representing number imputations bootstrap sample BMLMI method.","code":""},{"path":"/reference/prepare_stan_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Prepare input data to run the Stan model — prepare_stan_data","title":"Prepare input data to run the Stan model — prepare_stan_data","text":"Prepare input data run Stan model. Creates / calculates required inputs required data{} block MMRM Stan program.","code":""},{"path":"/reference/prepare_stan_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Prepare input data to run the Stan model — prepare_stan_data","text":"","code":"prepare_stan_data(ddat, subjid, visit, outcome, group)"},{"path":"/reference/prepare_stan_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Prepare input data to run the Stan model — prepare_stan_data","text":"ddat design matrix subjid Character vector containing subjects IDs. visit Vector containing visits. outcome Numeric vector containing outcome variable. group Vector containing group variable.","code":""},{"path":"/reference/prepare_stan_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Prepare input data to run the Stan model — prepare_stan_data","text":"stan_data object. named list per data{} block related Stan file. particular returns: N - number rows design matrix P - number columns design matrix G - number distinct covariance matrix groups (.e. length(unique(group))) n_visit - number unique outcome visits n_pat - total number pattern groups (defined missingness patterns & covariance group) pat_G - Index Sigma pattern group use pat_n_pt - number patients within pattern group pat_n_visit - number non-missing visits pattern group pat_sigma_index - rows/cols Sigma subset pattern group (padded 0's) y - outcome variable Q - design matrix (QR decomposition) R - R matrix QR decomposition design matrix","code":""},{"path":"/reference/prepare_stan_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Prepare input data to run the Stan model — prepare_stan_data","text":"group argument determines covariance matrix group subject belongs . want subjects use shared covariance matrix set group \"1\" everyone.","code":""},{"path":"/reference/print.analysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Print analysis object — print.analysis","title":"Print analysis object — print.analysis","text":"Print analysis object","code":""},{"path":"/reference/print.analysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print analysis object — print.analysis","text":"","code":"# S3 method for class 'analysis' print(x, ...)"},{"path":"/reference/print.analysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print analysis object — print.analysis","text":"x analysis object generated analyse(). ... used.","code":""},{"path":"/reference/print.draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Print draws object — print.draws","title":"Print draws object — print.draws","text":"Print draws object","code":""},{"path":"/reference/print.draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print draws object — print.draws","text":"","code":"# S3 method for class 'draws' print(x, ...)"},{"path":"/reference/print.draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print draws object — print.draws","text":"x draws object generated draws(). ... used.","code":""},{"path":"/reference/print.imputation.html","id":null,"dir":"Reference","previous_headings":"","what":"Print imputation object — print.imputation","title":"Print imputation object — print.imputation","text":"Print imputation object","code":""},{"path":"/reference/print.imputation.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Print imputation object — print.imputation","text":"","code":"# S3 method for class 'imputation' print(x, ...)"},{"path":"/reference/print.imputation.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Print imputation object — print.imputation","text":"x imputation object generated impute(). ... used.","code":""},{"path":"/reference/progressLogger.html","id":null,"dir":"Reference","previous_headings":"","what":"R6 Class for printing current sampling progress — progressLogger","title":"R6 Class for printing current sampling progress — progressLogger","text":"Object initalised total number iterations expected occur. User can update object add method indicate many iterations just occurred. Every time step * 100 % iterations occurred message printed console. Use quiet argument prevent object printing anything ","code":""},{"path":"/reference/progressLogger.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"R6 Class for printing current sampling progress — progressLogger","text":"step real, percentage iterations allow printing progress console step_current integer, total number iterations completed since progress last printed console n integer, current number completed iterations n_max integer, total number expected iterations completed acts denominator calculating progress percentages quiet logical holds whether print anything","code":""},{"path":[]},{"path":"/reference/progressLogger.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"R6 Class for printing current sampling progress — progressLogger","text":"progressLogger$new() progressLogger$add() progressLogger$print_progress() progressLogger$clone()","code":""},{"path":"/reference/progressLogger.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"R6 Class for printing current sampling progress — progressLogger","text":"Create progressLogger object","code":""},{"path":"/reference/progressLogger.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for printing current sampling progress — progressLogger","text":"","code":"progressLogger$new(n_max, quiet = FALSE, step = 0.1)"},{"path":"/reference/progressLogger.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for printing current sampling progress — progressLogger","text":"n_max integer, sets field n_max quiet logical, sets field quiet step real, sets field step","code":""},{"path":"/reference/progressLogger.html","id":"method-add-","dir":"Reference","previous_headings":"","what":"Method add()","title":"R6 Class for printing current sampling progress — progressLogger","text":"Records n iterations completed add number current step count (step_current) print progress message log step limit (step) reached. function nothing quiet set TRUE","code":""},{"path":"/reference/progressLogger.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for printing current sampling progress — progressLogger","text":"","code":"progressLogger$add(n)"},{"path":"/reference/progressLogger.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for printing current sampling progress — progressLogger","text":"n number successfully complete iterations since add() last called","code":""},{"path":"/reference/progressLogger.html","id":"method-print-progress-","dir":"Reference","previous_headings":"","what":"Method print_progress()","title":"R6 Class for printing current sampling progress — progressLogger","text":"method print current state progress","code":""},{"path":"/reference/progressLogger.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for printing current sampling progress — progressLogger","text":"","code":"progressLogger$print_progress()"},{"path":"/reference/progressLogger.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"R6 Class for printing current sampling progress — progressLogger","text":"objects class cloneable method.","code":""},{"path":"/reference/progressLogger.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for printing current sampling progress — progressLogger","text":"","code":"progressLogger$clone(deep = FALSE)"},{"path":"/reference/progressLogger.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for printing current sampling progress — progressLogger","text":"deep Whether make deep clone.","code":""},{"path":"/reference/pval_percentile.html","id":null,"dir":"Reference","previous_headings":"","what":"P-value of percentile bootstrap — pval_percentile","title":"P-value of percentile bootstrap — pval_percentile","text":"Determines (necessarily unique) quantile (type=6) \"est\" gives value 0 , derive p-value corresponding percentile bootstrap via inversion.","code":""},{"path":"/reference/pval_percentile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"P-value of percentile bootstrap — pval_percentile","text":"","code":"pval_percentile(est)"},{"path":"/reference/pval_percentile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"P-value of percentile bootstrap — pval_percentile","text":"est numeric vector point estimates bootstrap sample.","code":""},{"path":"/reference/pval_percentile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"P-value of percentile bootstrap — pval_percentile","text":"named numeric vector length 2 containing p-value H_0: theta=0 vs H_A: theta>0 (\"pval_greater\") p-value H_0: theta=0 vs H_A: theta<0 (\"pval_less\").","code":""},{"path":"/reference/pval_percentile.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"P-value of percentile bootstrap — pval_percentile","text":"p-value H_0: theta=0 vs H_A: theta>0 value alpha q_alpha = 0. least one estimate equal zero returns largest alpha q_alpha = 0. bootstrap estimates > 0 returns 0; bootstrap estimates < 0 returns 1. Analogous reasoning applied p-value H_0: theta=0 vs H_A: theta<0.","code":""},{"path":"/reference/random_effects_expr.html","id":null,"dir":"Reference","previous_headings":"","what":"Construct random effects formula — random_effects_expr","title":"Construct random effects formula — random_effects_expr","text":"Constructs character representation random effects formula fitting MMRM subject visit format required mmrm::mmrm().","code":""},{"path":"/reference/random_effects_expr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Construct random effects formula — random_effects_expr","text":"","code":"random_effects_expr( cov_struct = c(\"us\", \"toep\", \"cs\", \"ar1\"), cov_by_group = FALSE )"},{"path":"/reference/random_effects_expr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Construct random effects formula — random_effects_expr","text":"cov_struct Character - covariance structure used, must one \"us\", \"toep\", \"cs\", \"ar1\" cov_by_group Boolean - Whenever use separate covariances per group level","code":""},{"path":"/reference/random_effects_expr.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Construct random effects formula — random_effects_expr","text":"example assuming user specified covariance structure \"us\" groups provided return cov_by_group set FALSE indicates separate covariance matrices required per group following returned:","code":"us(visit | subjid) us( visit | group / subjid )"},{"path":"/reference/rbmi-package.html","id":null,"dir":"Reference","previous_headings":"","what":"rbmi: Reference Based Multiple Imputation — rbmi-package","title":"rbmi: Reference Based Multiple Imputation — rbmi-package","text":"rbmi package used perform reference based multiple imputation. package provides implementations common, patient-specific imputation strategies whilst allowing user select various standard Bayesian frequentist approaches. package designed around 4 core functions: draws() - Fits multiple imputation models impute() - Imputes multiple datasets analyse() - Analyses multiple datasets pool() - Pools multiple results single statistic learn rbmi, please see quickstart vignette: vignette(topic= \"quickstart\", package = \"rbmi\")","code":""},{"path":[]},{"path":"/reference/rbmi-package.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"rbmi: Reference Based Multiple Imputation — rbmi-package","text":"Maintainer: Craig Gower-Page craig.gower-page@roche.com Authors: Alessandro Noci alessandro.noci@roche.com contributors: Marcel Wolbers marcel.wolbers@roche.com [contributor] Roche [copyright holder, funder]","code":""},{"path":"/reference/record.html","id":null,"dir":"Reference","previous_headings":"","what":"Capture all Output — record","title":"Capture all Output — record","text":"function silences warnings, errors & messages instead returns list containing results (error) + warning error messages character vectors.","code":""},{"path":"/reference/record.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Capture all Output — record","text":"","code":"record(expr)"},{"path":"/reference/record.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Capture all Output — record","text":"expr expression executed","code":""},{"path":"/reference/record.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Capture all Output — record","text":"list containing results - object returned expr list() error thrown warnings - NULL character vector warnings thrown errors - NULL string error thrown messages - NULL character vector messages produced","code":""},{"path":"/reference/record.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Capture all Output — record","text":"","code":"if (FALSE) { # \\dontrun{ record({ x <- 1 y <- 2 warning(\"something went wrong\") message(\"O nearly done\") x + y }) } # }"},{"path":"/reference/recursive_reduce.html","id":null,"dir":"Reference","previous_headings":"","what":"recursive_reduce — recursive_reduce","title":"recursive_reduce — recursive_reduce","text":"Utility function used replicated purrr::reduce. Recursively applies function list elements 1 element remains","code":""},{"path":"/reference/recursive_reduce.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"recursive_reduce — recursive_reduce","text":"","code":"recursive_reduce(.l, .f)"},{"path":"/reference/recursive_reduce.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"recursive_reduce — recursive_reduce","text":".l list values apply function .f function apply element list turn .e. .l[[1]] <- .f( .l[[1]] , .l[[2]]) ; .l[[1]] <- .f( .l[[1]] , .l[[3]])","code":""},{"path":"/reference/remove_if_all_missing.html","id":null,"dir":"Reference","previous_headings":"","what":"Remove subjects from dataset if they have no observed values — remove_if_all_missing","title":"Remove subjects from dataset if they have no observed values — remove_if_all_missing","text":"function takes data.frame variables visit, outcome & subjid. removes rows given subjid non-missing values outcome.","code":""},{"path":"/reference/remove_if_all_missing.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Remove subjects from dataset if they have no observed values — remove_if_all_missing","text":"","code":"remove_if_all_missing(dat)"},{"path":"/reference/remove_if_all_missing.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Remove subjects from dataset if they have no observed values — remove_if_all_missing","text":"dat data.frame","code":""},{"path":"/reference/rubin_df.html","id":null,"dir":"Reference","previous_headings":"","what":"Barnard and Rubin degrees of freedom adjustment — rubin_df","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"Compute degrees freedom according Barnard-Rubin formula.","code":""},{"path":"/reference/rubin_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"","code":"rubin_df(v_com, var_b, var_t, M)"},{"path":"/reference/rubin_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"v_com Positive number representing degrees freedom complete-data analysis. var_b -variance point estimate across multiply imputed datasets. var_t Total-variance point estimate according Rubin's rules. M Number imputations.","code":""},{"path":"/reference/rubin_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"Degrees freedom according Barnard-Rubin formula. See Barnard-Rubin (1999).","code":""},{"path":"/reference/rubin_df.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"computation takes account limit cases missing data (.e. -variance var_b zero) complete-data degrees freedom set Inf. Moreover, v_com given NA, function returns Inf.","code":""},{"path":"/reference/rubin_df.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Barnard and Rubin degrees of freedom adjustment — rubin_df","text":"Barnard, J. Rubin, D.B. (1999). Small sample degrees freedom multiple imputation. Biometrika, 86, 948-955.","code":""},{"path":"/reference/rubin_rules.html","id":null,"dir":"Reference","previous_headings":"","what":"Combine estimates using Rubin's rules — rubin_rules","title":"Combine estimates using Rubin's rules — rubin_rules","text":"Pool together results M complete-data analyses according Rubin's rules. See details.","code":""},{"path":"/reference/rubin_rules.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Combine estimates using Rubin's rules — rubin_rules","text":"","code":"rubin_rules(ests, ses, v_com)"},{"path":"/reference/rubin_rules.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Combine estimates using Rubin's rules — rubin_rules","text":"ests Numeric vector containing point estimates complete-data analyses. ses Numeric vector containing standard errors complete-data analyses. v_com Positive number representing degrees freedom complete-data analysis.","code":""},{"path":"/reference/rubin_rules.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Combine estimates using Rubin's rules — rubin_rules","text":"list containing: est_point: pooled point estimate according Little-Rubin (2002). var_t: total variance according Little-Rubin (2002). df: degrees freedom according Barnard-Rubin (1999).","code":""},{"path":"/reference/rubin_rules.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Combine estimates using Rubin's rules — rubin_rules","text":"rubin_rules applies Rubin's rules (Rubin, 1987) pooling together results multiple imputation procedure. pooled point estimate est_point average across point estimates complete-data analyses (given input argument ests). total variance var_t sum two terms representing within-variance -variance (see Little-Rubin (2002)). function also returns df, estimated pooled degrees freedom according Barnard-Rubin (1999) can used inference based t-distribution.","code":""},{"path":"/reference/rubin_rules.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Combine estimates using Rubin's rules — rubin_rules","text":"Barnard, J. Rubin, D.B. (1999). Small sample degrees freedom multiple imputation. Biometrika, 86, 948-955 Roderick J. . Little Donald B. Rubin. Statistical Analysis Missing Data, Second Edition. John Wiley & Sons, Hoboken, New Jersey, 2002. [Section 5.4]","code":""},{"path":[]},{"path":"/reference/sample_ids.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample Patient Ids — sample_ids","title":"Sample Patient Ids — sample_ids","text":"Performs stratified bootstrap sample IDS ensuring return vector length input vector","code":""},{"path":"/reference/sample_ids.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample Patient Ids — sample_ids","text":"","code":"sample_ids(ids, strata = rep(1, length(ids)))"},{"path":"/reference/sample_ids.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample Patient Ids — sample_ids","text":"ids vector sample strata strata indicator, ids sampled within strata ensuring numbers strata maintained","code":""},{"path":"/reference/sample_ids.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sample Patient Ids — sample_ids","text":"","code":"if (FALSE) { # \\dontrun{ sample_ids( c(\"a\", \"b\", \"c\", \"d\"), strata = c(1,1,2,2)) } # }"},{"path":"/reference/sample_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Create and validate a sample_list object — sample_list","title":"Create and validate a sample_list object — sample_list","text":"Given list sample_single objects generate sample_single(), creates sample_list objects validate .","code":""},{"path":"/reference/sample_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create and validate a sample_list object — sample_list","text":"","code":"sample_list(...)"},{"path":"/reference/sample_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create and validate a sample_list object — sample_list","text":"... list sample_single objects.","code":""},{"path":"/reference/sample_mvnorm.html","id":null,"dir":"Reference","previous_headings":"","what":"Sample random values from the multivariate normal distribution — sample_mvnorm","title":"Sample random values from the multivariate normal distribution — sample_mvnorm","text":"Sample random values multivariate normal distribution","code":""},{"path":"/reference/sample_mvnorm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sample random values from the multivariate normal distribution — sample_mvnorm","text":"","code":"sample_mvnorm(mu, sigma)"},{"path":"/reference/sample_mvnorm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sample random values from the multivariate normal distribution — sample_mvnorm","text":"mu mean vector sigma covariance matrix Samples multivariate normal variables multiplying univariate random normal variables cholesky decomposition covariance matrix. mu length 1 just uses rnorm instead.","code":""},{"path":"/reference/sample_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Create object of sample_single class — sample_single","title":"Create object of sample_single class — sample_single","text":"Creates object class sample_single named list containing input parameters validate .","code":""},{"path":"/reference/sample_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create object of sample_single class — sample_single","text":"","code":"sample_single( ids, beta = NA, sigma = NA, theta = NA, failed = any(is.na(beta)), ids_samp = ids )"},{"path":"/reference/sample_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create object of sample_single class — sample_single","text":"ids Vector characters containing ids subjects included original dataset. beta Numeric vector estimated regression coefficients. sigma List estimated covariance matrices (one level vars$group). theta Numeric vector transformed covariances. failed Logical. TRUE model fit failed. ids_samp Vector characters containing ids subjects included given sample.","code":""},{"path":"/reference/sample_single.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create object of sample_single class — sample_single","text":"named list class sample_single. contains following: ids vector characters containing ids subjects included original dataset. beta numeric vector estimated regression coefficients. sigma list estimated covariance matrices (one level vars$group). theta numeric vector transformed covariances. failed logical. TRUE model fit failed. ids_samp vector characters containing ids subjects included given sample.","code":""},{"path":"/reference/scalerConstructor.html","id":null,"dir":"Reference","previous_headings":"","what":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Scales design matrix non-categorical columns mean 0 standard deviation 1.","code":""},{"path":"/reference/scalerConstructor.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"object initialisation used determine relevant mean SD's scale scaling (un-scaling) performed relevant object methods. Un-scaling done linear model Beta Sigma coefficients. purpose first column dataset scaled assumed outcome variable variables assumed post-transformation predictor variables (.e. dummy variables already expanded).","code":""},{"path":"/reference/scalerConstructor.html","id":"public-fields","dir":"Reference","previous_headings":"","what":"Public fields","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"centre Vector column means. first value outcome variable, variables predictors. scales Vector column standard deviations. first value outcome variable, variables predictors.","code":""},{"path":[]},{"path":"/reference/scalerConstructor.html","id":"public-methods","dir":"Reference","previous_headings":"","what":"Public methods","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"scalerConstructor$new() scalerConstructor$scale() scalerConstructor$unscale_sigma() scalerConstructor$unscale_beta() scalerConstructor$clone()","code":""},{"path":"/reference/scalerConstructor.html","id":"method-new-","dir":"Reference","previous_headings":"","what":"Method new()","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Uses dat determine relevant column means standard deviations use scaling un-scaling future datasets. Implicitly assumes new datasets column order dat","code":""},{"path":"/reference/scalerConstructor.html","id":"usage","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"","code":"scalerConstructor$new(dat)"},{"path":"/reference/scalerConstructor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"dat data.frame matrix. columns must numeric (.e dummy variables, must already expanded ).","code":""},{"path":"/reference/scalerConstructor.html","id":"details-1","dir":"Reference","previous_headings":"","what":"Details","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Categorical columns (determined values entirely 1 0) scaled. achieved setting corresponding values centre 0 scale 1.","code":""},{"path":"/reference/scalerConstructor.html","id":"method-scale-","dir":"Reference","previous_headings":"","what":"Method scale()","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Scales dataset continuous variables mean 0 standard deviation 1.","code":""},{"path":"/reference/scalerConstructor.html","id":"usage-1","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"","code":"scalerConstructor$scale(dat)"},{"path":"/reference/scalerConstructor.html","id":"arguments-1","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"dat data.frame matrix whose columns numeric (.e. dummy variables expanded ) whose columns order dataset used initialization function.","code":""},{"path":"/reference/scalerConstructor.html","id":"method-unscale-sigma-","dir":"Reference","previous_headings":"","what":"Method unscale_sigma()","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Unscales sigma value (matrix) estimated linear model using design matrix scaled object. function works first column initialisation data.frame outcome variable.","code":""},{"path":"/reference/scalerConstructor.html","id":"usage-2","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"","code":"scalerConstructor$unscale_sigma(sigma)"},{"path":"/reference/scalerConstructor.html","id":"arguments-2","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"sigma numeric value matrix.","code":""},{"path":"/reference/scalerConstructor.html","id":"returns","dir":"Reference","previous_headings":"","what":"Returns","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"numeric value matrix","code":""},{"path":"/reference/scalerConstructor.html","id":"method-unscale-beta-","dir":"Reference","previous_headings":"","what":"Method unscale_beta()","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"Unscales beta value (vector) estimated linear model using design matrix scaled object. function works first column initialization data.frame outcome variable.","code":""},{"path":"/reference/scalerConstructor.html","id":"usage-3","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"","code":"scalerConstructor$unscale_beta(beta)"},{"path":"/reference/scalerConstructor.html","id":"arguments-3","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"beta numeric vector beta coefficients estimated linear model.","code":""},{"path":"/reference/scalerConstructor.html","id":"returns-1","dir":"Reference","previous_headings":"","what":"Returns","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"numeric vector.","code":""},{"path":"/reference/scalerConstructor.html","id":"method-clone-","dir":"Reference","previous_headings":"","what":"Method clone()","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"objects class cloneable method.","code":""},{"path":"/reference/scalerConstructor.html","id":"usage-4","dir":"Reference","previous_headings":"","what":"Usage","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"","code":"scalerConstructor$clone(deep = FALSE)"},{"path":"/reference/scalerConstructor.html","id":"arguments-4","dir":"Reference","previous_headings":"","what":"Arguments","title":"R6 Class for scaling (and un-scaling) design matrices — scalerConstructor","text":"deep Whether make deep clone.","code":""},{"path":"/reference/set_simul_pars.html","id":null,"dir":"Reference","previous_headings":"","what":"Set simulation parameters of a study group. — set_simul_pars","title":"Set simulation parameters of a study group. — set_simul_pars","text":"function provides input arguments study group needed simulate data simulate_data(). simulate_data() generates data two-arms clinical trial longitudinal continuous outcomes two intercurrent events (ICEs). ICE1 may thought discontinuation study treatment due study drug condition related (SDCR) reasons. ICE2 may thought discontinuation study treatment due uninformative study drop-, .e. due study drug condition related (NSDRC) reasons outcome data ICE2 always missing.","code":""},{"path":"/reference/set_simul_pars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set simulation parameters of a study group. — set_simul_pars","text":"","code":"set_simul_pars( mu, sigma, n, prob_ice1 = 0, or_outcome_ice1 = 1, prob_post_ice1_dropout = 0, prob_ice2 = 0, prob_miss = 0 )"},{"path":"/reference/set_simul_pars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set simulation parameters of a study group. — set_simul_pars","text":"mu Numeric vector describing mean outcome trajectory visit (including baseline) assuming ICEs. sigma Covariance matrix outcome trajectory assuming ICEs. n Number subjects belonging group. prob_ice1 Numeric vector specifies probability experiencing ICE1 (discontinuation study treatment due SDCR reasons) visit subject observed outcome visit equal mean baseline (mu[1]). single numeric provided, probability applied visit. or_outcome_ice1 Numeric value specifies odds ratio experiencing ICE1 visit corresponding +1 higher value observed outcome visit. prob_post_ice1_dropout Numeric value specifies probability study drop-following ICE1. subject simulated drop-ICE1, outcomes ICE1 set missing. prob_ice2 Numeric specifies additional probability post-baseline visit affected study drop-. Outcome data subject's first simulated visit affected study drop-subsequent visits set missing. generates second intercurrent event ICE2, may thought treatment discontinuation due NSDRC reasons subsequent drop-. subject, ICE1 ICE2 simulated occur, assumed earlier counts. case ICEs simulated occur time, assumed ICE1 counts. means single subject can experience either ICE1 ICE2, . prob_miss Numeric value specifies additional probability given post-baseline observation missing. can used produce \"intermittent\" missing values associated ICE.","code":""},{"path":"/reference/set_simul_pars.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set simulation parameters of a study group. — set_simul_pars","text":"simul_pars object named list containing simulation parameters.","code":""},{"path":"/reference/set_simul_pars.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Set simulation parameters of a study group. — set_simul_pars","text":"details, please see simulate_data().","code":""},{"path":[]},{"path":"/reference/set_vars.html","id":null,"dir":"Reference","previous_headings":"","what":"Set key variables — set_vars","title":"Set key variables — set_vars","text":"function used define names key variables within data.frame's provided input arguments draws() ancova().","code":""},{"path":"/reference/set_vars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set key variables — set_vars","text":"","code":"set_vars( subjid = \"subjid\", visit = \"visit\", outcome = \"outcome\", group = \"group\", covariates = character(0), strata = group, strategy = \"strategy\" )"},{"path":"/reference/set_vars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set key variables — set_vars","text":"subjid name \"Subject ID\" variable. length 1 character vector. visit name \"Visit\" variable. length 1 character vector. outcome name \"Outcome\" variable. length 1 character vector. group name \"Group\" variable. length 1 character vector. covariates name covariates used context modeling. See details. strata name stratification variable used context bootstrap sampling. See details. strategy name \"strategy\" variable. length 1 character vector.","code":""},{"path":"/reference/set_vars.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Set key variables — set_vars","text":"draws() ancova() covariates argument can specified indicate variables included imputation analysis models respectively. wish include interaction terms need manually specified .e. covariates = c(\"group*visit\", \"age*sex\"). Please note use () function inhibit interpretation/conversion objects supported. Currently strata used draws() combination method_condmean(type = \"bootstrap\") method_approxbayes() order allow specification stratified bootstrap sampling. default strata set equal value group assumed users want preserve group size samples. See draws() details. Likewise, currently strategy argument used draws() specify name strategy variable within data_ice data.frame. See draws() details.","code":""},{"path":[]},{"path":"/reference/set_vars.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set key variables — set_vars","text":"","code":"if (FALSE) { # \\dontrun{ # Using CDISC variable names as an example set_vars( subjid = \"usubjid\", visit = \"avisit\", outcome = \"aval\", group = \"arm\", covariates = c(\"bwt\", \"bht\", \"arm * avisit\"), strategy = \"strat\" ) } # }"},{"path":"/reference/simulate_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate data — simulate_data","title":"Generate data — simulate_data","text":"Generate data two-arms clinical trial longitudinal continuous outcome two intercurrent events (ICEs). ICE1 may thought discontinuation study treatment due study drug condition related (SDCR) reasons. ICE2 may thought discontinuation study treatment due uninformative study drop-, .e. due study drug condition related (NSDRC) reasons outcome data ICE2 always missing.","code":""},{"path":"/reference/simulate_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate data — simulate_data","text":"","code":"simulate_data(pars_c, pars_t, post_ice1_traj, strategies = getStrategies())"},{"path":"/reference/simulate_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate data — simulate_data","text":"pars_c simul_pars object generated set_simul_pars(). specifies simulation parameters control arm. pars_t simul_pars object generated set_simul_pars(). specifies simulation parameters treatment arm. post_ice1_traj string specifies observed outcomes occurring ICE1 simulated. Must target function included strategies. Possible choices : Missing Random \"MAR\", Jump Reference \"JR\", Copy Reference \"CR\", Copy Increments Reference \"CIR\", Last Mean Carried Forward \"LMCF\". User-defined strategies also added. See getStrategies() details. strategies named list functions. Default equal getStrategies(). See getStrategies() details.","code":""},{"path":"/reference/simulate_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate data — simulate_data","text":"data.frame containing simulated data. includes following variables: id: Factor variable specifies id subject. visit: Factor variable specifies visit assessment. Visit 0 denotes baseline visit. group: Factor variable specifies treatment group subject belongs . outcome_bl: Numeric variable specifies baseline outcome. outcome_noICE: Numeric variable specifies longitudinal outcome assuming ICEs. ind_ice1: Binary variable takes value 1 corresponding visit affected ICE1 0 otherwise. dropout_ice1: Binary variable takes value 1 corresponding visit affected drop-following ICE1 0 otherwise. ind_ice2: Binary variable takes value 1 corresponding visit affected ICE2. outcome: Numeric variable specifies longitudinal outcome including ICE1, ICE2 intermittent missing values.","code":""},{"path":"/reference/simulate_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Generate data — simulate_data","text":"data generation works follows: Generate outcome data visits (including baseline) multivariate normal distribution parameters pars_c$mu pars_c$sigma control arm parameters pars_t$mu pars_t$sigma treatment arm, respectively. Note randomized trial, outcomes distribution baseline treatment groups, .e. one set pars_c$mu[1]=pars_t$mu[1] pars_c$sigma[1,1]=pars_t$sigma[1,1]. Simulate whether ICE1 (study treatment discontinuation due SDCR reasons) occurs visit according parameters pars_c$prob_ice1 pars_c$or_outcome_ice1 control arm pars_t$prob_ice1 pars_t$or_outcome_ice1 treatment arm, respectively. Simulate drop-following ICE1 according pars_c$prob_post_ice1_dropout pars_t$prob_post_ice1_dropout. Simulate additional uninformative study drop-probabilities pars_c$prob_ice2 pars_t$prob_ice2 visit. generates second intercurrent event ICE2, may thought treatment discontinuation due NSDRC reasons subsequent drop-. simulated time drop-subject's first visit affected drop-data visit subsequent visits consequently set missing. subject, ICE1 ICE2 simulated occur, assumed earlier counts. case ICEs simulated occur time, assumed ICE1 counts. means single subject can experience either ICE1 ICE2, . Adjust trajectories ICE1 according given assumption expressed post_ice1_traj argument. Note post-ICE1 outcomes intervention arm can adjusted. Post-ICE1 outcomes control arm adjusted. Simulate additional intermittent missing outcome data per arguments pars_c$prob_miss pars_t$prob_miss. probability ICE visit modeled according following logistic regression model: ~ 1 + (visit == 0) + ... + (visit == n_visits-1) + ((x-alpha)) : n_visits number visits (including baseline). alpha baseline outcome mean. term ((x-alpha)) specifies dependency probability ICE current outcome value. corresponding regression coefficients logistic model defined follows: intercept set 0, coefficients corresponding discontinuation visit subject outcome equal mean baseline set according parameters pars_c$prob_ice1 (pars_t$prob_ice1), regression coefficient associated covariate ((x-alpha)) set log(pars_c$or_outcome_ice1) (log(pars_t$or_outcome_ice1)). Please note baseline outcome missing affected ICEs.","code":""},{"path":"/reference/simulate_dropout.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate drop-out — simulate_dropout","title":"Simulate drop-out — simulate_dropout","text":"Simulate drop-","code":""},{"path":"/reference/simulate_dropout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate drop-out — simulate_dropout","text":"","code":"simulate_dropout(prob_dropout, ids, subset = rep(1, length(ids)))"},{"path":"/reference/simulate_dropout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate drop-out — simulate_dropout","text":"prob_dropout Numeric specifies probability post-baseline visit affected study drop-. ids Factor variable specifies id subject. subset Binary variable specifies subset affected drop-. .e. subset binary vector length equal length ids takes value 1 corresponding visit affected drop-0 otherwise.","code":""},{"path":"/reference/simulate_dropout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate drop-out — simulate_dropout","text":"binary vector length equal length ids takes value 1 corresponding outcome affected study drop-.","code":""},{"path":"/reference/simulate_dropout.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate drop-out — simulate_dropout","text":"subset can used specify outcome values affected drop-. default subset set 1 values except values corresponding baseline outcome, since baseline supposed affected drop-. Even subset specified user, values corresponding baseline outcome still hard-coded 0.","code":""},{"path":"/reference/simulate_ice.html","id":null,"dir":"Reference","previous_headings":"","what":"Simulate intercurrent event — simulate_ice","title":"Simulate intercurrent event — simulate_ice","text":"Simulate intercurrent event","code":""},{"path":"/reference/simulate_ice.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Simulate intercurrent event — simulate_ice","text":"","code":"simulate_ice(outcome, visits, ids, prob_ice, or_outcome_ice, baseline_mean)"},{"path":"/reference/simulate_ice.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Simulate intercurrent event — simulate_ice","text":"outcome Numeric variable specifies longitudinal outcome single group. visits Factor variable specifies visit assessment. ids Factor variable specifies id subject. prob_ice Numeric vector specifies visit probability experiencing ICE current visit subject outcome equal mean baseline. single numeric provided, probability applied visit. or_outcome_ice Numeric value specifies odds ratio ICE corresponding +1 higher value outcome visit. baseline_mean Mean outcome value baseline.","code":""},{"path":"/reference/simulate_ice.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Simulate intercurrent event — simulate_ice","text":"binary variable takes value 1 corresponding outcome affected ICE 0 otherwise.","code":""},{"path":"/reference/simulate_ice.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Simulate intercurrent event — simulate_ice","text":"probability ICE visit modeled according following logistic regression model: ~ 1 + (visit == 0) + ... + (visit == n_visits-1) + ((x-alpha)) : n_visits number visits (including baseline). alpha baseline outcome mean set via argument baseline_mean. term ((x-alpha)) specifies dependency probability ICE current outcome value. corresponding regression coefficients logistic model defined follows: intercept set 0, coefficients corresponding discontinuation visit subject outcome equal mean baseline set according parameter or_outcome_ice, regression coefficient associated covariate ((x-alpha)) set log(or_outcome_ice).","code":""},{"path":"/reference/simulate_test_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Create simulated datasets — simulate_test_data","title":"Create simulated datasets — simulate_test_data","text":"Creates longitudinal dataset format rbmi designed analyse.","code":""},{"path":"/reference/simulate_test_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create simulated datasets — simulate_test_data","text":"","code":"simulate_test_data( n = 200, sd = c(3, 5, 7), cor = c(0.1, 0.7, 0.4), mu = list(int = 10, age = 3, sex = 2, trt = c(0, 4, 8), visit = c(0, 1, 2)) ) as_vcov(sd, cor)"},{"path":"/reference/simulate_test_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create simulated datasets — simulate_test_data","text":"n number subjects sample. Total number observations returned thus n * length(sd) sd standard deviations outcome visit. .e. square root diagonal covariance matrix outcome cor correlation coefficients outcome values visit. See details. mu coefficients use construct mean outcome value visit. Must named list elements int, age, sex, trt & visit. See details.","code":""},{"path":"/reference/simulate_test_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Create simulated datasets — simulate_test_data","text":"number visits determined size variance covariance matrix. .e. 3 standard deviation values provided 3 visits per patient created. covariates simulated dataset produced follows: Patients age sampled random N(0,1) distribution Patients sex sampled random 50/50 split Patients group sampled random fixed group n/2 patients outcome variable sampled multivariate normal distribution, see details mean outcome variable derived : coefficients intercept, age sex taken mu$int, mu$age mu$sex respectively, must length 1 numeric. Treatment visit coefficients taken mu$trt mu$visit respectively must either length 1 (.e. constant affect across visits) equal number visits (determined length sd). .e. wanted treatment slope 5 visit slope 1 specify: correlation matrix constructed cor follows. Let cor = c(, b, c, d, e, f) correlation matrix :","code":"outcome = Intercept + age + sex + visit + treatment mu = list(..., \"trt\" = c(0,5,10), \"visit\" = c(0,1,2)) 1 a b d a 1 c e b c 1 f d e f 1"},{"path":"/reference/sort_by.html","id":null,"dir":"Reference","previous_headings":"","what":"Sort data.frame — sort_by","title":"Sort data.frame — sort_by","text":"Sorts data.frame (ascending default) based upon variables within dataset","code":""},{"path":"/reference/sort_by.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Sort data.frame — sort_by","text":"","code":"sort_by(df, vars = NULL, decreasing = FALSE)"},{"path":"/reference/sort_by.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Sort data.frame — sort_by","text":"df data.frame vars character vector variables decreasing logical whether sort order descending ascending (default) order. Can either single logical value (case applied variables) vector length vars","code":""},{"path":"/reference/sort_by.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Sort data.frame — sort_by","text":"","code":"if (FALSE) { # \\dontrun{ sort_by(iris, c(\"Sepal.Length\", \"Sepal.Width\"), decreasing = c(TRUE, FALSE)) } # }"},{"path":"/reference/split_dim.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform array into list of arrays — split_dim","title":"Transform array into list of arrays — split_dim","text":"Transform array list arrays listing performed given dimension.","code":""},{"path":"/reference/split_dim.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform array into list of arrays — split_dim","text":"","code":"split_dim(a, n)"},{"path":"/reference/split_dim.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform array into list of arrays — split_dim","text":"Array number dimensions least 2. n Positive integer. Dimension listed.","code":""},{"path":"/reference/split_dim.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform array into list of arrays — split_dim","text":"list length n arrays number dimensions equal number dimensions minus 1.","code":""},{"path":"/reference/split_dim.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transform array into list of arrays — split_dim","text":"example, 3 dimensional array n = 1, split_dim(,n) returns list 2 dimensional arrays (.e. list matrices) element list [, , ], takes values 1 length first dimension array. Example: inputs: <- array( c(1,2,3,4,5,6,7,8,9,10,11,12), dim = c(3,2,2)), means : n <- 1 output res <- split_dim(,n) list 3 elements:","code":"a[1,,] a[2,,] a[3,,] [,1] [,2] [,1] [,2] [,1] [,2] --------- --------- --------- 1 7 2 8 3 9 4 10 5 11 6 12 res[[1]] res[[2]] res[[3]] [,1] [,2] [,1] [,2] [,1] [,2] --------- --------- --------- 1 7 2 8 3 9 4 10 5 11 6 12"},{"path":"/reference/split_imputations.html","id":null,"dir":"Reference","previous_headings":"","what":"Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations","title":"Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations","text":"Split flat list imputation_single() multiple imputation_df()'s ID","code":""},{"path":"/reference/split_imputations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations","text":"","code":"split_imputations(list_of_singles, split_ids)"},{"path":"/reference/split_imputations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations","text":"list_of_singles list imputation_single()'s split_ids list 1 element per required split. element must contain vector \"ID\"'s correspond imputation_single() ID's required within sample. total number ID's must equal length list_of_singles","code":""},{"path":"/reference/split_imputations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Split a flat list of imputation_single() into multiple imputation_df()'s by ID — split_imputations","text":"function converts list imputations structured per patient structured per sample .e. converts :","code":"obj <- list( imputation_single(\"Ben\", numeric(0)), imputation_single(\"Ben\", numeric(0)), imputation_single(\"Ben\", numeric(0)), imputation_single(\"Harry\", c(1, 2)), imputation_single(\"Phil\", c(3, 4)), imputation_single(\"Phil\", c(5, 6)), imputation_single(\"Tom\", c(7, 8, 9)) ) index <- list( c(\"Ben\", \"Harry\", \"Phil\", \"Tom\"), c(\"Ben\", \"Ben\", \"Phil\") ) output <- list( imputation_df( imputation_single(id = \"Ben\", values = numeric(0)), imputation_single(id = \"Harry\", values = c(1, 2)), imputation_single(id = \"Phil\", values = c(3, 4)), imputation_single(id = \"Tom\", values = c(7, 8, 9)) ), imputation_df( imputation_single(id = \"Ben\", values = numeric(0)), imputation_single(id = \"Ben\", values = numeric(0)), imputation_single(id = \"Phil\", values = c(5, 6)) ) )"},{"path":"/reference/str_contains.html","id":null,"dir":"Reference","previous_headings":"","what":"Does a string contain a substring — str_contains","title":"Does a string contain a substring — str_contains","text":"Returns vector TRUE/FALSE element x contains element subs .e.","code":"str_contains( c(\"ben\", \"tom\", \"harry\"), c(\"e\", \"y\")) [1] TRUE FALSE TRUE"},{"path":"/reference/str_contains.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Does a string contain a substring — str_contains","text":"","code":"str_contains(x, subs)"},{"path":"/reference/str_contains.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Does a string contain a substring — str_contains","text":"x character vector subs character vector substrings look ","code":""},{"path":"/reference/strategies.html","id":null,"dir":"Reference","previous_headings":"","what":"Strategies — strategies","title":"Strategies — strategies","text":"functions used implement various reference based imputation strategies combining subjects distribution reference distribution based upon visits failed meet Missing--Random (MAR) assumption.","code":""},{"path":"/reference/strategies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Strategies — strategies","text":"","code":"strategy_MAR(pars_group, pars_ref, index_mar) strategy_JR(pars_group, pars_ref, index_mar) strategy_CR(pars_group, pars_ref, index_mar) strategy_CIR(pars_group, pars_ref, index_mar) strategy_LMCF(pars_group, pars_ref, index_mar)"},{"path":"/reference/strategies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Strategies — strategies","text":"pars_group list parameters subject's group. See details. pars_ref list parameters subject's reference group. See details. index_mar logical vector indicating visits meet MAR assumption subject. .e. identifies observations non-MAR intercurrent event (ICE).","code":""},{"path":"/reference/strategies.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Strategies — strategies","text":"pars_group pars_ref must list containing elements mu sigma. mu must numeric vector sigma must square matrix symmetric covariance matrix dimensions equal length mu index_mar. e.g. Users can define strategy functions include via strategies argument impute() using getStrategies(). said following strategies available \"box\": Missing Random (MAR) Jump Reference (JR) Copy Reference (CR) Copy Increments Reference (CIR) Last Mean Carried Forward (LMCF)","code":"list( mu = c(1,2,3), sigma = matrix(c(4,3,2,3,5,4,2,4,6), nrow = 3, ncol = 3) )"},{"path":"/reference/string_pad.html","id":null,"dir":"Reference","previous_headings":"","what":"string_pad — string_pad","title":"string_pad — string_pad","text":"Utility function used replicate str_pad. Adds white space either end string get equal desired length","code":""},{"path":"/reference/string_pad.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"string_pad — string_pad","text":"","code":"string_pad(x, width)"},{"path":"/reference/string_pad.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"string_pad — string_pad","text":"x string width desired length","code":""},{"path":"/reference/transpose_imputations.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose imputations — transpose_imputations","title":"Transpose imputations — transpose_imputations","text":"Takes imputation_df object transposes e.g.","code":"list( list(id = \"a\", values = c(1,2,3)), list(id = \"b\", values = c(4,5,6) ) )"},{"path":"/reference/transpose_imputations.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose imputations — transpose_imputations","text":"","code":"transpose_imputations(imputations)"},{"path":"/reference/transpose_imputations.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transpose imputations — transpose_imputations","text":"imputations imputation_df object created imputation_df()","code":""},{"path":"/reference/transpose_imputations.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose imputations — transpose_imputations","text":"becomes","code":"list( ids = c(\"a\", \"b\"), values = c(1,2,3,4,5,6) )"},{"path":"/reference/transpose_results.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose results object — transpose_results","title":"Transpose results object — transpose_results","text":"Transposes Results object (created analyse()) order group estimates together vectors.","code":""},{"path":"/reference/transpose_results.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose results object — transpose_results","text":"","code":"transpose_results(results, components)"},{"path":"/reference/transpose_results.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transpose results object — transpose_results","text":"results list results. components character vector components extract (.e. \"est\", \"se\").","code":""},{"path":"/reference/transpose_results.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Transpose results object — transpose_results","text":"Essentially function takes object format: produces:","code":"x <- list( list( \"trt1\" = list( est = 1, se = 2 ), \"trt2\" = list( est = 3, se = 4 ) ), list( \"trt1\" = list( est = 5, se = 6 ), \"trt2\" = list( est = 7, se = 8 ) ) ) list( trt1 = list( est = c(1,5), se = c(2,6) ), trt2 = list( est = c(3,7), se = c(4,8) ) )"},{"path":"/reference/transpose_samples.html","id":null,"dir":"Reference","previous_headings":"","what":"Transpose samples — transpose_samples","title":"Transpose samples — transpose_samples","text":"Transposes samples generated draws() grouped subjid instead sample number.","code":""},{"path":"/reference/transpose_samples.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transpose samples — transpose_samples","text":"","code":"transpose_samples(samples)"},{"path":"/reference/transpose_samples.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transpose samples — transpose_samples","text":"samples list samples generated draws().","code":""},{"path":"/reference/validate.analysis.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate analysis objects — validate.analysis","title":"Validate analysis objects — validate.analysis","text":"Validates return object analyse() function.","code":""},{"path":"/reference/validate.analysis.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate analysis objects — validate.analysis","text":"","code":"# S3 method for class 'analysis' validate(x, ...)"},{"path":"/reference/validate.analysis.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate analysis objects — validate.analysis","text":"x analysis results object (class \"jackknife\", \"bootstrap\", \"rubin\"). ... used.","code":""},{"path":"/reference/validate.draws.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate draws object — validate.draws","title":"Validate draws object — validate.draws","text":"Validate draws object","code":""},{"path":"/reference/validate.draws.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate draws object — validate.draws","text":"","code":"# S3 method for class 'draws' validate(x, ...)"},{"path":"/reference/validate.draws.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate draws object — validate.draws","text":"x draws object generated as_draws(). ... used.","code":""},{"path":"/reference/validate.html","id":null,"dir":"Reference","previous_headings":"","what":"Generic validation method — validate","title":"Generic validation method — validate","text":"function used perform assertions object conforms expected structure basic assumptions violated. throw error checks pass.","code":""},{"path":"/reference/validate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generic validation method — validate","text":"","code":"validate(x, ...)"},{"path":"/reference/validate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generic validation method — validate","text":"x object validated. ... additional arguments pass specific validation method.","code":""},{"path":"/reference/validate.is_mar.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate is_mar for a given subject — validate.is_mar","title":"Validate is_mar for a given subject — validate.is_mar","text":"Checks longitudinal data patient divided MAR followed non-MAR data; non-MAR observation followed MAR observation allowed.","code":""},{"path":"/reference/validate.is_mar.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate is_mar for a given subject — validate.is_mar","text":"","code":"# S3 method for class 'is_mar' validate(x, ...)"},{"path":"/reference/validate.is_mar.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate is_mar for a given subject — validate.is_mar","text":"x Object class is_mar. Logical vector indicating whether observations MAR. ... used.","code":""},{"path":"/reference/validate.is_mar.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate is_mar for a given subject — validate.is_mar","text":"error issue otherwise return TRUE.","code":""},{"path":"/reference/validate.ivars.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate inputs for vars — validate.ivars","title":"Validate inputs for vars — validate.ivars","text":"Checks required variable names defined within vars appropriate datatypes","code":""},{"path":"/reference/validate.ivars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate inputs for vars — validate.ivars","text":"","code":"# S3 method for class 'ivars' validate(x, ...)"},{"path":"/reference/validate.ivars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate inputs for vars — validate.ivars","text":"x named list indicating names key variables source dataset ... used","code":""},{"path":"/reference/validate.references.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate user supplied references — validate.references","title":"Validate user supplied references — validate.references","text":"Checks ensure user specified references expect values (.e. found within source data).","code":""},{"path":"/reference/validate.references.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate user supplied references — validate.references","text":"","code":"# S3 method for class 'references' validate(x, control, ...)"},{"path":"/reference/validate.references.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate user supplied references — validate.references","text":"x named character vector. control factor variable (group variable source dataset). ... used.","code":""},{"path":"/reference/validate.references.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate user supplied references — validate.references","text":"error issue otherwise return TRUE.","code":""},{"path":"/reference/validate.sample_list.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate sample_list object — validate.sample_list","title":"Validate sample_list object — validate.sample_list","text":"Validate sample_list object","code":""},{"path":"/reference/validate.sample_list.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate sample_list object — validate.sample_list","text":"","code":"# S3 method for class 'sample_list' validate(x, ...)"},{"path":"/reference/validate.sample_list.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate sample_list object — validate.sample_list","text":"x sample_list object generated sample_list(). ... used.","code":""},{"path":"/reference/validate.sample_single.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate sample_single object — validate.sample_single","title":"Validate sample_single object — validate.sample_single","text":"Validate sample_single object","code":""},{"path":"/reference/validate.sample_single.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate sample_single object — validate.sample_single","text":"","code":"# S3 method for class 'sample_single' validate(x, ...)"},{"path":"/reference/validate.sample_single.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate sample_single object — validate.sample_single","text":"x sample_single object generated sample_single(). ... used.","code":""},{"path":"/reference/validate.simul_pars.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate a simul_pars object — validate.simul_pars","title":"Validate a simul_pars object — validate.simul_pars","text":"Validate simul_pars object","code":""},{"path":"/reference/validate.simul_pars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate a simul_pars object — validate.simul_pars","text":"","code":"# S3 method for class 'simul_pars' validate(x, ...)"},{"path":"/reference/validate.simul_pars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate a simul_pars object — validate.simul_pars","text":"x simul_pars object generated set_simul_pars(). ... used.","code":""},{"path":"/reference/validate.stan_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate a stan_data object — validate.stan_data","title":"Validate a stan_data object — validate.stan_data","text":"Validate stan_data object","code":""},{"path":"/reference/validate.stan_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate a stan_data object — validate.stan_data","text":"","code":"# S3 method for class 'stan_data' validate(x, ...)"},{"path":"/reference/validate.stan_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate a stan_data object — validate.stan_data","text":"x stan_data object. ... used.","code":""},{"path":"/reference/validate_analyse_pars.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate analysis results — validate_analyse_pars","title":"Validate analysis results — validate_analyse_pars","text":"Validates analysis results generated analyse().","code":""},{"path":"/reference/validate_analyse_pars.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate analysis results — validate_analyse_pars","text":"","code":"validate_analyse_pars(results, pars)"},{"path":"/reference/validate_analyse_pars.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate analysis results — validate_analyse_pars","text":"results list results generated analysis fun used analyse(). pars list expected parameters analysis. lists .e. c(\"est\", \"se\", \"df\").","code":""},{"path":"/reference/validate_datalong.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate a longdata object — validate_datalong","title":"Validate a longdata object — validate_datalong","text":"Validate longdata object","code":""},{"path":"/reference/validate_datalong.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate a longdata object — validate_datalong","text":"","code":"validate_datalong(data, vars) validate_datalong_varExists(data, vars) validate_datalong_types(data, vars) validate_datalong_notMissing(data, vars) validate_datalong_complete(data, vars) validate_datalong_unifromStrata(data, vars) validate_dataice(data, data_ice, vars, update = FALSE)"},{"path":"/reference/validate_datalong.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate a longdata object — validate_datalong","text":"data data.frame containing longitudinal outcome data + covariates multiple subjects vars vars object created set_vars() data_ice data.frame containing subjects ICE data. See draws() details. update logical, indicates ICE data set first time update applied","code":""},{"path":"/reference/validate_datalong.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Validate a longdata object — validate_datalong","text":"functions used validate various different parts longdata object used draws(), impute(), analyse() pool(). particular: validate_datalong_varExists - Checks variable listed vars actually exists data validate_datalong_types - Checks types key variable expected .e. visit factor variable validate_datalong_notMissing - Checks none key variables (except outcome variable) contain missing values validate_datalong_complete - Checks data complete .e. 1 row subject * visit combination. e.g. nrow(data) == length(unique(subjects)) * length(unique(visits)) validate_datalong_unifromStrata - Checks make sure variables listed stratification variables vary time. e.g. subjects switch stratification groups.","code":""},{"path":"/reference/validate_strategies.html","id":null,"dir":"Reference","previous_headings":"","what":"Validate user specified strategies — validate_strategies","title":"Validate user specified strategies — validate_strategies","text":"Compares user provided strategies required (reference). throw error values reference defined.","code":""},{"path":"/reference/validate_strategies.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Validate user specified strategies — validate_strategies","text":"","code":"validate_strategies(strategies, reference)"},{"path":"/reference/validate_strategies.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Validate user specified strategies — validate_strategies","text":"strategies named list strategies. reference list character vector strategies need defined.","code":""},{"path":"/reference/validate_strategies.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Validate user specified strategies — validate_strategies","text":"throw error issue otherwise return TRUE.","code":""},{"path":"/news/index.html","id":"rbmi-126","dir":"Changelog","previous_headings":"","what":"rbmi 1.2.6","title":"rbmi 1.2.6","text":"CRAN release: 2023-11-24 Updated unit tests fix false-positive error CRAN’s testing servers","code":""},{"path":"/news/index.html","id":"rbmi-125","dir":"Changelog","previous_headings":"","what":"rbmi 1.2.5","title":"rbmi 1.2.5","text":"CRAN release: 2023-09-20 Updated internal Stan code ensure future compatibility (@andrjohns, #390) Updated package description include relevant references (#393) Fixed documentation typos (#393)","code":""},{"path":"/news/index.html","id":"rbmi-123","dir":"Changelog","previous_headings":"","what":"rbmi 1.2.3","title":"rbmi 1.2.3","text":"CRAN release: 2022-11-14 Minor internal tweaks ensure compatibility packages rbmi depends ","code":""},{"path":"/news/index.html","id":"rbmi-121","dir":"Changelog","previous_headings":"","what":"rbmi 1.2.1","title":"rbmi 1.2.1","text":"CRAN release: 2022-10-25 Removed native pipes |> testing code package backwards compatible older servers Replaced glmmTMB dependency mmrm package. resulted package stable (less model fitting convergence issues) well speeding run times 3-fold.","code":""},{"path":"/news/index.html","id":"rbmi-114","dir":"Changelog","previous_headings":"","what":"rbmi 1.1.4","title":"rbmi 1.1.4","text":"CRAN release: 2022-05-18 Updated urls references vignettes Fixed bug visit factor levels re-constructed incorrectly delta_template() Fixed bug wrong visit displayed error message specific visit doesn’t data draws() Fixed bug wrong input parameter displayed error message simulate_data()","code":""},{"path":"/news/index.html","id":"rbmi-111--113","dir":"Changelog","previous_headings":"","what":"rbmi 1.1.1 & 1.1.3","title":"rbmi 1.1.1 & 1.1.3","text":"CRAN release: 2022-03-08 change functionality 1.1.0 Various minor tweaks address CRAN checks messages","code":""},{"path":"/news/index.html","id":"rbmi-110","dir":"Changelog","previous_headings":"","what":"rbmi 1.1.0","title":"rbmi 1.1.0","text":"CRAN release: 2022-03-02 Initial public release","code":""}]