Resolve merge conflicts

Author: MaartenMarsman

.Rbuildignore

@@ -9,3 +9,5 @@
 ^docs$
 ^pkgdown$
 ^vignettes/introduction_cache
+^doc$
+^Meta$

.gitignore

@@ -6,4 +6,4 @@ src/*.o
 src/*.so
 src/*.dll
 inst/doc
-.DS_Store
+.DS_Store

DESCRIPTION

@@ -2,7 +2,11 @@ Package: bgms
 Type: Package
 Title: Bayesian Analysis of Networks of Binary and/or Ordinal Variables
 Version: 0.1.5.0
+<<<<<<< HEAD
 Date: 2025-08-03
+=======
+Date: 2025-06-27
+>>>>>>> adaMala
 Authors@R: c(
     person("Maarten", "Marsman", , "m.marsman@uva.nl", role = c("aut", "cre"),
            comment = c(ORCID = "0000-0001-5309-7502")),
@@ -21,9 +25,9 @@ Copyright: Includes datasets 'ADHD' and 'Boredom', which are licensed under CC-B
 License: GPL (>= 2)
 URL: https://Bayesian-Graphical-Modelling-Lab.github.io/bgms/
 BugReports: https://github.com/Bayesian-Graphical-Modelling-Lab/bgms/issues
-Imports: Rcpp (>= 1.0.7), Rdpack, methods
+Imports: Rcpp (>= 1.0.7), RcppParallel, Rdpack, methods, coda, dqrng
 RdMacros: Rdpack
-LinkingTo: Rcpp, RcppProgress, RcppArmadillo
+LinkingTo: Rcpp, RcppProgress, RcppArmadillo, RcppParallel, dqrng, BH
 RoxygenNote: 7.3.2
 Depends: 
     R (>= 3.5)
@@ -32,9 +36,11 @@ Encoding: UTF-8
 Suggests: 
     ggplot2,
     knitr,
+    parallel,
     qgraph,
+    posterior,
     rmarkdown,
     testthat (>= 3.0.0)
 VignetteBuilder: knitr
 Config/testthat/edition: 3
-Config/Needs/website: tidyverse/tidytemplate
+Config/Needs/website: tidyverse/tidytemplate

Meta/vignette.rds

@@ -1,5 +1,7 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(as_draws,bgms)
+S3method(coef,bgms)
 S3method(extract_arguments,bgmCompare)
 S3method(extract_arguments,bgms)
 S3method(extract_indicator_priors,bgmCompare)
@@ -10,23 +12,32 @@ S3method(extract_posterior_inclusion_probabilities,bgmCompare)
 S3method(extract_posterior_inclusion_probabilities,bgms)
 S3method(print,bgmCompare)
 S3method(print,bgms)
+S3method(print,summary.bgms)
+S3method(summary,bgms)
 export(bgm)
 export(bgmCompare)
 export(extract_arguments)
-export(extract_category_thresholds)
+export(extract_category_thresholds.bgms)
 export(extract_edge_indicators)
 export(extract_indicator_priors)
 export(extract_indicators)
 export(extract_main_difference.bgmCompare)
 export(extract_pairwise_difference.bgmCompare)
-export(extract_pairwise_interactions)
+export(extract_pairwise_interactions.bgms)
 export(extract_pairwise_thresholds)
 export(extract_posterior_inclusion_probabilities)
 export(mrfSampler)
-export(summarySBM)
+import(RcppParallel)
 importFrom(Rcpp,evalCpp)
+importFrom(RcppParallel,defaultNumThreads)
+importFrom(RcppParallel,setThreadOptions)
 importFrom(Rdpack,reprompt)
+importFrom(coda,effectiveSize)
+importFrom(coda,gelman.diag)
+importFrom(coda,mcmc)
+importFrom(coda,mcmc.list)
 importFrom(methods,hasArg)
+importFrom(posterior,as_draws)
 importFrom(stats,dpois)
 importFrom(utils,packageVersion)
 useDynLib(bgms, .registration=TRUE)

NAMESPACE

@@ -13,23 +13,22 @@ rcpp_ieee754_log <- function(x) {
     .Call(`_bgms_rcpp_ieee754_log`, x)
 }
 
-sample_omrf_gibbs <- function(no_states, no_variables, no_categories, interactions, thresholds, iter) {
-    .Call(`_bgms_sample_omrf_gibbs`, no_states, no_variables, no_categories, interactions, thresholds, iter)
+run_bgmCompare_parallel <- function(observations, num_groups, counts_per_category, blume_capel_stats, pairwise_stats, num_categories, main_alpha, main_beta, pairwise_scale, difference_scale, difference_selection_alpha, difference_selection_beta, difference_prior, iter, burnin, na_impute, missing_data_indices, is_ordinal_variable, baseline_category, difference_selection, main_effect_indices, pairwise_effect_indices, target_accept, nuts_max_depth, learn_mass_matrix, projection, group_membership, group_indices, interaction_index_matrix, inclusion_probability, num_chains, nThreads, seed, update_method, hmc_num_leapfrogs) {
+    .Call(`_bgms_run_bgmCompare_parallel`, observations, num_groups, counts_per_category, blume_capel_stats, pairwise_stats, num_categories, main_alpha, main_beta, pairwise_scale, difference_scale, difference_selection_alpha, difference_selection_beta, difference_prior, iter, burnin, na_impute, missing_data_indices, is_ordinal_variable, baseline_category, difference_selection, main_effect_indices, pairwise_effect_indices, target_accept, nuts_max_depth, learn_mass_matrix, projection, group_membership, group_indices, interaction_index_matrix, inclusion_probability, num_chains, nThreads, seed, update_method, hmc_num_leapfrogs)
 }
 
-sample_bcomrf_gibbs <- function(no_states, no_variables, no_categories, interactions, thresholds, variable_type, reference_category, iter) {
-    .Call(`_bgms_sample_bcomrf_gibbs`, no_states, no_variables, no_categories, interactions, thresholds, variable_type, reference_category, iter)
+run_bgm_parallel <- function(observations, num_categories, pairwise_scale, edge_prior, inclusion_probability, beta_bernoulli_alpha, beta_bernoulli_beta, dirichlet_alpha, lambda, interaction_index_matrix, iter, burnin, counts_per_category, blume_capel_stats, main_alpha, main_beta, na_impute, missing_index, is_ordinal_variable, baseline_category, edge_selection, update_method, pairwise_effect_indices, target_accept, pairwise_stats, hmc_num_leapfrogs, nuts_max_depth, learn_mass_matrix, num_chains, nThreads, seed) {
+    .Call(`_bgms_run_bgm_parallel`, observations, num_categories, pairwise_scale, edge_prior, inclusion_probability, beta_bernoulli_alpha, beta_bernoulli_beta, dirichlet_alpha, lambda, interaction_index_matrix, iter, burnin, counts_per_category, blume_capel_stats, main_alpha, main_beta, na_impute, missing_index, is_ordinal_variable, baseline_category, edge_selection, update_method, pairwise_effect_indices, target_accept, pairwise_stats, hmc_num_leapfrogs, nuts_max_depth, learn_mass_matrix, num_chains, nThreads, seed)
 }
 
-gibbs_sampler <- function(observations, indicator, interactions, thresholds, no_categories, interaction_scale, proposal_sd, proposal_sd_blumecapel, edge_prior, theta, beta_bernoulli_alpha, beta_bernoulli_beta, dirichlet_alpha, lambda, Index, iter, burnin, n_cat_obs, sufficient_blume_capel, threshold_alpha, threshold_beta, na_impute, missing_index, variable_bool, reference_category, save = FALSE, display_progress = FALSE, edge_selection = TRUE) {
-    .Call(`_bgms_gibbs_sampler`, observations, indicator, interactions, thresholds, no_categories, interaction_scale, proposal_sd, proposal_sd_blumecapel, edge_prior, theta, beta_bernoulli_alpha, beta_bernoulli_beta, dirichlet_alpha, lambda, Index, iter, burnin, n_cat_obs, sufficient_blume_capel, threshold_alpha, threshold_beta, na_impute, missing_index, variable_bool, reference_category, save, display_progress, edge_selection)
+sample_omrf_gibbs <- function(no_states, no_variables, no_categories, interactions, thresholds, iter) {
+    .Call(`_bgms_sample_omrf_gibbs`, no_states, no_variables, no_categories, interactions, thresholds, iter)
 }
 
-compare_anova_gibbs_sampler <- function(observations, main_effect_indices, pairwise_effect_indices, projection, num_categories, num_groups, group_indices, interaction_scale, pairwise_difference_scale, main_difference_scale, pairwise_difference_prior, main_difference_prior, inclusion_probability_difference, pairwise_beta_bernoulli_alpha, pairwise_beta_bernoulli_beta, main_beta_bernoulli_alpha, main_beta_bernoulli_beta, Index, iter, burnin, num_obs_categories, sufficient_blume_capel, prior_threshold_alpha, prior_threshold_beta, na_impute, missing_data_indices, is_ordinal_variable, baseline_category, independent_thresholds, save_main = FALSE, save_pairwise = FALSE, save_indicator = FALSE, display_progress = FALSE, difference_selection = TRUE) {
-    .Call(`_bgms_compare_anova_gibbs_sampler`, observations, main_effect_indices, pairwise_effect_indices, projection, num_categories, num_groups, group_indices, interaction_scale, pairwise_difference_scale, main_difference_scale, pairwise_difference_prior, main_difference_prior, inclusion_probability_difference, pairwise_beta_bernoulli_alpha, pairwise_beta_bernoulli_beta, main_beta_bernoulli_alpha, main_beta_bernoulli_beta, Index, iter, burnin, num_obs_categories, sufficient_blume_capel, prior_threshold_alpha, prior_threshold_beta, na_impute, missing_data_indices, is_ordinal_variable, baseline_category, independent_thresholds, save_main, save_pairwise, save_indicator, display_progress, difference_selection)
+sample_bcomrf_gibbs <- function(no_states, no_variables, no_categories, interactions, thresholds, variable_type, reference_category, iter) {
+    .Call(`_bgms_sample_bcomrf_gibbs`, no_states, no_variables, no_categories, interactions, thresholds, variable_type, reference_category, iter)
 }
 
 compute_Vn_mfm_sbm <- function(no_variables, dirichlet_alpha, t_max, lambda) {
     .Call(`_bgms_compute_Vn_mfm_sbm`, no_variables, dirichlet_alpha, t_max, lambda)
-}
-
+}