number of cores again for CRAN

NEWS.md

@@ -1,14 +1,15 @@
 # dynamite 1.4.5
-  
-  * `get_data` method for `dynamitefit` object now correctly uses the previously defined priors instead of the default ones.
+
+  * The `get_data()` method for `dynamitefit` objects now correctly uses the previously defined priors instead of the default ones.
   * Fixed a bug in indexing of random effect terms.
+  * Limited the number of parallel threads used by the `data.table` package to 1 in examples, tests, and vignettes for CRAN.
 
 # dynamite 1.4.4
 
   * Example of the `lfo()` method now uses a single chain and core to avoid a compatibility issue with CRAN.
-  * Fixed `plot_nus` for categorical responses.
-  * Fixed an issue which caused an error in error message of `predict` and `fitted` methods when `newdata` contained duplicate time points within group.
-  * Fixed an issue (#72) which caused NA ELPD value in `lfo` in case of missing data.
+  * Fixed `plot_nus()` for categorical responses.
+  * Fixed an issue which caused an error in error message of `predict()` and `fitted()` methods when `newdata` contained duplicate time points within group.
+  * Fixed an issue (#72) which caused NA ELPD value in `lfo()` in case of missing data.
 
 # dynamite 1.4.3
 

R/as_data_frame.R

@@ -72,6 +72,7 @@
 #'   model parameters in a long format. For a wide format, see
 #'   [dynamite::as_draws()].
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' as.data.frame(
 #'   gaussian_example_fit,
 #'   responses = "y",

R/as_data_table.R

@@ -13,6 +13,7 @@
 #' @return A `data.table` containing either samples or summary statistics of
 #'   the model parameters.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' as.data.table(
 #'   gaussian_example_fit,
 #'   responses = "y",

R/as_draws.R

@@ -23,6 +23,7 @@
 #' @inheritParams as.data.frame.dynamitefit
 #' @return A `draws_df` object.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' as_draws(gaussian_example_fit, types = c("sigma", "beta"))
 #'
 #' # Compute MCMC diagnostics using the posterior package

R/ci.R

@@ -18,6 +18,7 @@
 #'   to `NA`.
 #' @srrstats {RE4.3} Provides credible intervals.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' confint(gaussian_example_fit, level = 0.9)
 #'
 confint.dynamitefit <- function(object, parm, level = 0.95, ...) {

R/coef.R

@@ -23,6 +23,7 @@
 #'   model parameters in a long format.
 #' @srrstats {G2.3a, RE4.2} Provides model coefficients.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' betas <- coef(gaussian_example_fit, type = "beta")
 #' deltas <- coef(gaussian_example_fit, type = "delta")
 #'

R/dynamite.R

@@ -131,6 +131,7 @@
 #' from Panel Data with Dynamic Multivariate Panel Models. SocArxiv preprint,
 #' <https://osf.io/preprints/socarxiv/mdwu5/>.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' \donttest{
 #' # Please update your rstan and StanHeaders installation before running
 #' # on Windows
@@ -624,6 +625,7 @@ remove_redundant_parameters <- function(stan_input, backend,
 #' @family formulas
 #' @export
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' formula(gaussian_example_fit)
 #'
 formula.dynamitefit <- function(x, ...) {

R/dynamiteformula.R

@@ -120,6 +120,7 @@
 #' @srrstats {G2.3b} Uses tolower.
 #' @srrstats {RE1.0} Uses a formula interface.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' # A single gaussian response channel with a time-varying effect of 'x',
 #' # and a time-varying effect of the lag of 'y' using B-splines with
 #' # 20 degrees of freedom for the coefficients of the time-varying terms.
@@ -351,6 +352,7 @@ parse_name <- function(x) {
 #' @param e2 \[`dynamiteformula`]\cr A model formula specification.
 #' @export
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' obs(y ~ x, family = "gaussian") + obs(z ~ w, family = "exponential")
 #'
 `+.dynamiteformula` <- function(e1, e2) {
@@ -367,6 +369,7 @@ parse_name <- function(x) {
 #' @param ... Ignored.
 #' @export
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' x <- obs(y ~ x + random(~ 1 + lag(d)), family = "gaussian") +
 #'   obs(z ~ varying(~w), family = "exponential") +
 #'   aux(numeric(d) ~ log(y) | init(c(0, 1))) +

R/fitted.R

@@ -16,6 +16,7 @@
 #' @return A `data.frame` containing the fitted values.
 #' @srrstats {RE4.9} Returns the fitted values.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' fitted(gaussian_example_fit, n_draws = 2L)
 #' \donttest{
 #' set.seed(1)

R/getters.R

@@ -18,6 +18,7 @@
 #' @return A `data.frame` containing the prior definitions.
 #' @srrstats {BS5.2} Provides access to the prior definitions of the model.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' d <- data.frame(y = rnorm(10), x = 1:10, time = 1:10, id = 1)
 #' get_priors(obs(y ~ x, family = "gaussian"),
 #'   data = d, time = "time", group = "id"
@@ -60,6 +61,7 @@ get_priors.dynamitefit <- function(x, ...) {
 #'   extracts the full model code.
 #' @return The Stan model blocks as a `character` string.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' d <- data.frame(y = rnorm(10), x = 1:10, time = 1:10, id = 1)
 #' cat(get_code(obs(y ~ x, family = "gaussian"),
 #'   data = d, time = "time", group = "id"
@@ -164,6 +166,7 @@ get_code_ <- function(x, blocks = NULL) {
 #' @inheritParams get_priors.dynamiteformula
 #' @return A `list` containing the input data to Stan.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' d <- data.frame(y = rnorm(10), x = 1:10, time = 1:10, id = 1)
 #' str(get_data(obs(y ~ x, family = "gaussian"),
 #'   data = d, time = "time", group = "id"
@@ -218,6 +221,7 @@ get_data.dynamitefit <- function(x, ...) {
 #' @export
 #' @family output
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' get_parameter_dims(multichannel_example_fit)
 #'
 get_parameter_dims <- function(x, ...) {
@@ -305,6 +309,7 @@ get_parameters <- function(x) {
 #' @export
 #' @family output
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' get_parameter_types(multichannel_example_fit)
 #'
 get_parameter_types <- function(x, ...) {
@@ -343,6 +348,7 @@ get_parameter_types.dynamitefit <- function(x, ...) {
 #' @export
 #' @family output
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' get_parameter_names(multichannel_example_fit)
 #'
 get_parameter_names <- function(x, types = NULL, ...) {

R/lags.R

@@ -16,6 +16,7 @@
 #' @return An object of class `lags`.
 #' @srrstats {G2.3a} Uses match.arg
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' obs(y ~ -1 + varying(~x), family = "gaussian") +
 #'   lags(type = "varying") + splines(df = 20)
 #'

R/latent_factor.R

@@ -27,6 +27,7 @@
 #'   The number of knots is based `splines()` call.
 #' @return An object of class `latent_factor`.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' # three channel model with common factor affecting for responses x and y
 #' obs(y ~ 1, family = "gaussian") +
 #'   obs(x ~ 1, family = "poisson") +

R/lfo.R

@@ -36,6 +36,7 @@
 #' Approximate leave-future-out cross-validation for Bayesian time series
 #' models, Journal of Statistical Computation and Simulation, 90:14, 2499-2523.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' \donttest{
 #' # Please update your rstan and StanHeaders installation before running
 #' # on Windows
@@ -259,6 +260,7 @@ lfo <- function(x, L, verbose = TRUE, k_threshold = 0.7, ...) {
 #' @return Returns `x` invisibly.
 #' @export
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' \donttest{
 #' # Please update your rstan and StanHeaders installation before running
 #' # on Windows
@@ -291,6 +293,7 @@ print.lfo <- function(x, ...) {
 #' @return A ggplot object.
 #' @export
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' \donttest{
 #' # Please update your rstan and StanHeaders installation before running
 #' # on Windows

R/loo.R

@@ -19,6 +19,7 @@
 #' Practical Bayesian model evaluation using leave-one-out cross-validation and
 #' WAIC. Statistics and Computing. 27(5), 1413–1432.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' \donttest{
 #' # Please update your rstan and StanHeaders installation before running
 #' # on Windows

R/mcmc_diagnostics.R

@@ -12,6 +12,7 @@
 #'   positive (unrestricted) integer.
 #' @return Returns `x` (invisibly).
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' mcmc_diagnostics(gaussian_example_fit)
 #'
 mcmc_diagnostics <- function(x, n) {

R/ndraws.R

@@ -7,6 +7,7 @@
 #' @param x \[`dynamitefit`]\cr The model fit object.
 #' @return Number of posterior draws as a single `integer` value.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' ndraws(gaussian_example_fit)
 #'
 ndraws.dynamitefit <- function(x) {

R/nobs.R

@@ -7,6 +7,7 @@
 #' @return Total number of non-missing observations as an `integer`.
 #' @srrstats {RE4.5} Provides the number of observations.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' nobs(gaussian_example_fit)
 #'
 nobs.dynamitefit <- function(object, ...) {

R/plot.R

@@ -22,6 +22,7 @@
 #' method. Further plots can be easily constructed with the help of `as_draws`
 #' combined with `ggplot2`, for example.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' plot(gaussian_example_fit, type = "beta")
 #'
 plot.dynamitefit <- function(x, parameters = NULL, type = NULL,
@@ -97,6 +98,7 @@ plot.dynamitefit <- function(x, parameters = NULL, type = NULL,
 #' @srrstats {G2.3a} Uses match.arg.
 #' @srrstats {BS6.1, RE6.0, RE6.1, BS6.3} Implements the `plot` method.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' plot_deltas(gaussian_example_fit, level = 0.025, scales = "free") +
 #'   ggplot2::theme_minimal()
 #'
@@ -210,6 +212,7 @@ plot_deltas <- function(x, parameters = NULL, responses = NULL, level = 0.05,
 #' @return A `ggplot` object.
 #' @srrstats {BS6.1, RE6.0, RE6.1, BS6.3} Implements the `plot` method.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' plot_betas(gaussian_example_fit, level = 0.1)
 #'
 plot_betas <- function(x, parameters = NULL, responses = NULL, level = 0.05,
@@ -301,6 +304,7 @@ plot_betas <- function(x, parameters = NULL, responses = NULL, level = 0.05,
 #' @return A `ggplot` object.
 #' @srrstats {BS6.1, RE6.0, RE6.1, BS6.3} Implements the `plot` method.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' plot_nus(gaussian_example_fit)
 #'
 plot_nus <- function(x, parameters = NULL, responses = NULL, level = 0.05,

R/predict.R

@@ -92,6 +92,7 @@
 #' @srrstats {RE4.16} New group levels are supported via
 #'   `new_levels` parameter.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' out <- predict(gaussian_example_fit, type = "response", n_draws = 2L)
 #' head(out)
 #'

R/print.R

@@ -11,6 +11,7 @@
 #' @srrstats {BS6.0, RE4.17} Implements the `print` method for the model fit.
 #' @srrstats {BS5.3, BS5.5} Contains convergence statistics in the output.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' print(gaussian_example_fit)
 #'
 print.dynamitefit <- function(x, ...) {

R/random.R

@@ -21,6 +21,7 @@
 #'   you encounter divergences or other problems in sampling.
 #' @return An object of class `random_spec`.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' # two channel model with correlated random effects for responses x and y
 #' obs(y ~ 1 + random(~1), family = "gaussian") +
 #'   obs(x ~ 1 + random(~1 + z), family = "poisson") +

R/splines.R

@@ -32,6 +32,7 @@
 #'    `splines()`. If `TRUE`, any existing definitions will be replaced.
 #' @return An object of class `splines`.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' # Two channel model with varying effects, with explicit lower bounds for the
 #' # random walk prior standard deviations, with noncentered parameterization
 #' # for the first channel and centered for the second channel.

R/summary.R

@@ -11,6 +11,7 @@
 #' @return `summary` returns a `data.frame`.
 #' @srrstats {BS6.4, RE4.18} Implements `summary` method.
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' summary(gaussian_example_fit,
 #'   types = "beta",
 #'   probs = c(0.05, 0.1, 0.9, 0.95)

R/update.R

@@ -23,6 +23,7 @@
 #' @export
 #' @family fitting
 #' @examples
+#' data.table::setDTthreads(1) # For CRAN
 #' \dontrun{
 #' # re-estimate the example fit without thinning:
 #' # As the model is compiled on Windows, this will fail on other platforms