R/RcppExports.R

In matthewjdenny/ContentStructure: A Statistical Model for Communication Network Data

# This file was generated by Rcpp::compileAttributes
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

Main_Sampler <- function(number_of_outer_itterations, number_of_Gibbs_itterations, number_of_MH_itterations, number_of_actors, number_of_topics, number_of_clusters, number_of_latent_dimensions, number_of_documents, proposal_variance, topic_cluster_assignments, clp, plp, current_intercepts, betas, number_of_betas, indicator_array, observed_edges, token_topic_assignment_list, token_word_type_list, document_authors, beta, alpha_m, token_type_topic_counts, topic_token_sums, number_of_word_types, itterations_before_cluster_assingment_update, metropolis_target_accpet_rate, step_size, slice_sample_alpha, MH_prior_standard_deviation, seed) {
    .Call('ContentStructure_Main_Sampler', PACKAGE = 'ContentStructure', number_of_outer_itterations, number_of_Gibbs_itterations, number_of_MH_itterations, number_of_actors, number_of_topics, number_of_clusters, number_of_latent_dimensions, number_of_documents, proposal_variance, topic_cluster_assignments, clp, plp, current_intercepts, betas, number_of_betas, indicator_array, observed_edges, token_topic_assignment_list, token_word_type_list, document_authors, beta, alpha_m, token_type_topic_counts, topic_token_sums, number_of_word_types, itterations_before_cluster_assingment_update, metropolis_target_accpet_rate, step_size, slice_sample_alpha, MH_prior_standard_deviation, seed)
}

MH_Sampler <- function(number_of_MH_itterations, number_of_actors, number_of_topics, number_of_latent_dimensions, proposal_variance, topic_cluster_assignments, tpec, taec, clp, plp, current_intercepts, betas, number_of_betas, indicator_array, burnin, number_of_clusters, store_every_x_rounds, adaptive_metropolis_update_every, use_adaptive_metropolis, MH_prior_standard_deviation, seed) {
    .Call('ContentStructure_MH_Sampler', PACKAGE = 'ContentStructure', number_of_MH_itterations, number_of_actors, number_of_topics, number_of_latent_dimensions, proposal_variance, topic_cluster_assignments, tpec, taec, clp, plp, current_intercepts, betas, number_of_betas, indicator_array, burnin, number_of_clusters, store_every_x_rounds, adaptive_metropolis_update_every, use_adaptive_metropolis, MH_prior_standard_deviation, seed)
}