_pkgdown.yml

home:
  title: BGGM
  links:
  - text: Ask a question about BGGM
    href: https://groups.google.com/forum/#!forum/bggm-users

url: https://donaldrwilliams.github.io/BGGM/
template:
  bootstrap: 5
  bootswatch: flatly
  opengraph:
    image:
      src: man/figures/hex.jpg
      creator: "@wdonald_1985"
      card: summary
navbar:
  title: "BGGM"
  left:
    - text: "Vignettes"
      href: articles/index.html
    - text: "Functions"
      href: reference/index.html
    - text: "News"
      href: news/index.html

articles:
  - title: "Getting Started"
    desc: >
      These vignettes provide an introduction to the various methods in **BGGM**.
    contents:
      - test_sum
      - netplot
      - mcmc_diagnostics
      - var_model
      - control
      - netstat_custom
      - ppc_custom
      - hyp_3_ways
      - predictability
      - in_tandem

reference:
  - title: "Missing Data"
    desc: >
     Handle Missing Values.
    contents:
    - bggm_missing
    - impute_data
  - title: "Estimation Based Methods"
    desc: >
      'Estimation' indicates that the methods to not employ Bayes factor testing. Rather,
      the graph is determined with the posterior distribution. The prior distribtuion has a
      minimal influence.
    contents:
    - estimate
    - coef.estimate
    - predict.estimate
    - plot.summary.estimate
    - select.estimate
    - summary.estimate
  - title: "Exploratory Hypothesis Testing"
    desc: >
      Bayes factor testing to determine the graph. 'Exploratory' reflects that there
      is not a specific hypothesis being test.
    contents:
    - explore
    - coef.explore
    - predict.explore
    - plot.summary.explore
    - plot.summary.select.explore
    - select.explore
    - summary.explore
    - summary.select.explore
  - title: "Confirmatory Hypothesis Testing"
    desc: >
     Test (in)equality constrained hypotheses with the Bayes factor.
    contents:
    - confirm
    - plot.confirm
  - title: "Compare Gaussian Graphical Models"
    desc: >
      A variety of methods for comparing GGMs.
  - subtitle: "Posterior Predictive Check"
    desc: >
     Compare groups with a posterior predictive check, where the null model
     is that the groups are equal. This works with any number of groups. There
     is also an option to compare the groups with a user defined test-statistic.
    contents:
    - ggm_compare_ppc
    - plot.ggm_compare_ppc
  - subtitle: "Partial Correlation Differences"
    desc: >
     Pairwise comparisons for each partial correlation in the respective models.
     This can be used for any number of groups. There is also an analytical solution.
    contents:
    - ggm_compare_estimate
    - plot.summary.ggm_compare_estimate
    - select.ggm_compare_estimate
    - summary.ggm_compare_estimate
  - subtitle: "Exploratory Hypothesis Testing"
    desc: >
      Pairwise comparisions with exploratory hypothesis testing. This method can be used to
      compare several groups simultaneously.
    contents:
    - ggm_compare_explore
    - plot.summary.ggm_compare_explore
    - select.ggm_compare_explore
    - summary.ggm_compare_explore
  - subtitle: "Confirmatory Hypothesis Testing"
    desc: >
       Test (in)equality constrained hypotheses with the Bayes factor.
    contents:
    - ggm_compare_confirm
    - plot.confirm
  - title: "Predictability"
    desc: >
      Bayesian variance explained for each node in the model.
    contents:
    - predictability
    - plot.predictability
    - summary.predictability
  - title: "Network Statistics"
    desc: >
     Compute network statistics from a partial correlation matrix or a
     weighted adjacency matrix.
    contents:
    - roll_your_own
    - plot.roll_your_own
  - title: "Partial Correlation Sums"
    desc: >
      Compute the sum of partial correlations within (one group) or between (two groups) GGMs. This
      can be used to compare sums.
    contents:
    - pcor_sum
    - plot.pcor_sum
  - title: "Network Plot"
    desc: >
     Network plot for the selected graphs. This works with all method for which there is
     a selected graph.
    contents:
    - plot.select

  - title: "Graphical VAR (vector autoregression)"
    desc: >
      A variety of methods for time series data. These particular models are VAR(1) models which
      are also known as time series chain graphical models.
  - subtitle: "Estimation"
    desc: >
      'Estimation' indicates that the methods to not employ Bayes factor testing. Rather,
      the graph is determined with the posterior distribution. The prior distribtuion has a
      minimal influence.
    contents:
    - var_estimate
    - select.var_estimate
    - summary.var_estimate
    - plot.summary.var_estimate
    - predict.var_estimate
  - title: Miscellaneous
    contents:
    - convergence
    - fisher_z_to_r
    - fisher_r_to_z
    - gen_ordinal
    - pcor_to_cor
    - pcor_mat
    - plot_prior
    - posterior_samples
    - map
    - regression_summary
    - summary.coef
    - weighted_adj_mat
    - zero_order_cors
  - title: "Data"
    desc: >
     Example datasets and correlation matrices.
    contents:
    - asd_ocd
    - bfi
    - csws
    - depression_anxiety_t1
    - depression_anxiety_t2
    - gss
    - ifit
    - iri
    - ptsd
    - ptsd_cor1
    - ptsd_cor2
    - ptsd_cor3
    - ptsd_cor4
    - rsa
    - Sachs
    - tas
    - women_math
# These are functions that could be added to some of the topics above
# Currently parked here to allow pkgdown::build_site() to compile
  - title: internal
    contents:
      - constrained_posterior
      - gen_net
      - impute_data
      - posterior_predict
      - precision
      - predicted_probability
      - print.BGGM
      - prior_belief_ggm
      - prior_belief_var
      - select