R/RcppExports.R

In matthewjdenny/GERGM: Estimation and Fit Diagnostics for Generalized Exponential Random Graph Models

# Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393

Corr_to_Part <- function(d, correlations, partials) {
    .Call(`_GERGM_Corr_to_Part`, d, correlations, partials)
}

Extended_Metropolis_Hastings_Sampler <- function(number_of_iterations, shape_parameter, number_of_nodes, statistics_to_use, initial_network, take_sample_every, thetas, triples, pairs, alphas, together, seed, number_of_samples_to_store, using_correlation_network, undirect_network, parallel, use_selected_rows, save_statistics_selected_rows_matrix, rows_to_use, base_statistics_to_save, base_statistic_alphas, num_non_base_statistics, non_base_statistic_indicator, p_ratio_multaplicative_factor, random_triad_sample_list, random_dyad_sample_list, use_triad_sampling, num_unique_random_triad_samples, include_diagonal) {
    .Call(`_GERGM_Extended_Metropolis_Hastings_Sampler`, number_of_iterations, shape_parameter, number_of_nodes, statistics_to_use, initial_network, take_sample_every, thetas, triples, pairs, alphas, together, seed, number_of_samples_to_store, using_correlation_network, undirect_network, parallel, use_selected_rows, save_statistics_selected_rows_matrix, rows_to_use, base_statistics_to_save, base_statistic_alphas, num_non_base_statistics, non_base_statistic_indicator, p_ratio_multaplicative_factor, random_triad_sample_list, random_dyad_sample_list, use_triad_sampling, num_unique_random_triad_samples, include_diagonal)
}

h_statistics <- function(statistics_to_use, current_edge_weights, triples, pairs, alphas, together, save_statistics_selected_rows_matrix, rows_to_use, base_statistics_to_save, base_statistic_alphas, num_non_base_statistics, non_base_statistic_indicator) {
    .Call(`_GERGM_h_statistics`, statistics_to_use, current_edge_weights, triples, pairs, alphas, together, save_statistics_selected_rows_matrix, rows_to_use, base_statistics_to_save, base_statistic_alphas, num_non_base_statistics, non_base_statistic_indicator)
}

extended_weighted_mple_objective <- function(number_of_nodes, statistics_to_use, current_network, triples, pairs, save_statistics_selected_rows_matrix, rows_to_use, base_statistics_to_save, base_statistic_alphas, num_non_base_statistics, non_base_statistic_indicator, thetas, alphas, together, integration_interval, parallel) {
    .Call(`_GERGM_extended_weighted_mple_objective`, number_of_nodes, statistics_to_use, current_network, triples, pairs, save_statistics_selected_rows_matrix, rows_to_use, base_statistics_to_save, base_statistic_alphas, num_non_base_statistics, non_base_statistic_indicator, thetas, alphas, together, integration_interval, parallel)
}

mple_distribution_objective <- function(number_of_nodes, statistics_to_use, current_network, triples, pairs, save_statistics_selected_rows_matrix, rows_to_use, base_statistics_to_save, base_statistic_alphas, num_non_base_statistics, non_base_statistic_indicator, thetas, alphas, together, integration_interval, parallel) {
    .Call(`_GERGM_mple_distribution_objective`, number_of_nodes, statistics_to_use, current_network, triples, pairs, save_statistics_selected_rows_matrix, rows_to_use, base_statistics_to_save, base_statistic_alphas, num_non_base_statistics, non_base_statistic_indicator, thetas, alphas, together, integration_interval, parallel)
}

get_indiviual_triad_values <- function(net, triples, alpha, together) {
    .Call(`_GERGM_get_indiviual_triad_values`, net, triples, alpha, together)
}

get_triad_weights <- function(net, triples, alpha, together, smoothing_parameter) {
    .Call(`_GERGM_get_triad_weights`, net, triples, alpha, together, smoothing_parameter)
}

Individual_Edge_Conditional_Prediction <- function(number_of_iterations, shape_parameter, number_of_nodes, statistics_to_use, initial_network, take_sample_every, thetas, triples, pairs, alphas, together, seed, number_of_samples_to_store, using_correlation_network, undirect_network, parallel, use_selected_rows, save_statistics_selected_rows_matrix, rows_to_use, base_statistics_to_save, base_statistic_alphas, num_non_base_statistics, non_base_statistic_indicator, p_ratio_multaplicative_factor, random_triad_sample_list, random_dyad_sample_list, use_triad_sampling, num_unique_random_triad_samples, i, j) {
    .Call(`_GERGM_Individual_Edge_Conditional_Prediction`, number_of_iterations, shape_parameter, number_of_nodes, statistics_to_use, initial_network, take_sample_every, thetas, triples, pairs, alphas, together, seed, number_of_samples_to_store, using_correlation_network, undirect_network, parallel, use_selected_rows, save_statistics_selected_rows_matrix, rows_to_use, base_statistics_to_save, base_statistic_alphas, num_non_base_statistics, non_base_statistic_indicator, p_ratio_multaplicative_factor, random_triad_sample_list, random_dyad_sample_list, use_triad_sampling, num_unique_random_triad_samples, i, j)
}

Distribution_Metropolis_Hastings_Sampler <- function(number_of_iterations, variance, number_of_nodes, statistics_to_use, initial_network, take_sample_every, thetas, triples, pairs, alphas, together, seed, number_of_samples_to_store, parallel, use_selected_rows, save_statistics_selected_rows_matrix, rows_to_use, base_statistics_to_save, base_statistic_alphas, num_non_base_statistics, non_base_statistic_indicator, p_ratio_multaplicative_factor, random_triad_sample_list, random_dyad_sample_list, use_triad_sampling, num_unique_random_triad_samples, rowwise_distribution) {
    .Call(`_GERGM_Distribution_Metropolis_Hastings_Sampler`, number_of_iterations, variance, number_of_nodes, statistics_to_use, initial_network, take_sample_every, thetas, triples, pairs, alphas, together, seed, number_of_samples_to_store, parallel, use_selected_rows, save_statistics_selected_rows_matrix, rows_to_use, base_statistics_to_save, base_statistic_alphas, num_non_base_statistics, non_base_statistic_indicator, p_ratio_multaplicative_factor, random_triad_sample_list, random_dyad_sample_list, use_triad_sampling, num_unique_random_triad_samples, rowwise_distribution)
}

log_space_multinomial_sampler <- function(unnormalized_discrete_distribution, uniform_draw) {
    .Call(`_GERGM_log_space_multinomial_sampler`, unnormalized_discrete_distribution, uniform_draw)
}

Edge_Group_MH_Sampler <- function(number_of_iterations, shape_parameter, number_of_nodes, statistics_to_use, initial_network, take_sample_every, thetas, triples, pairs, alphas, together, seed, number_of_samples_to_store, undirect_network, parallel, use_selected_rows, save_statistics_selected_rows_matrix, rows_to_use, base_statistics_to_save, base_statistic_alphas, num_non_base_statistics, non_base_statistic_indicator, p_ratio_multaplicative_factor, use_triad_sampling, include_diagonal, sample_edges_at_a_time) {
    .Call(`_GERGM_Edge_Group_MH_Sampler`, number_of_iterations, shape_parameter, number_of_nodes, statistics_to_use, initial_network, take_sample_every, thetas, triples, pairs, alphas, together, seed, number_of_samples_to_store, undirect_network, parallel, use_selected_rows, save_statistics_selected_rows_matrix, rows_to_use, base_statistics_to_save, base_statistic_alphas, num_non_base_statistics, non_base_statistic_indicator, p_ratio_multaplicative_factor, use_triad_sampling, include_diagonal, sample_edges_at_a_time)
}

Metropolis_Hastings_Sampler <- function(number_of_iterations, shape_parameter, number_of_nodes, statistics_to_use, initial_network, take_sample_every, thetas, triples, pairs, alphas, together, seed, number_of_samples_to_store, using_correlation_network, undirect_network, parallel) {
    .Call(`_GERGM_Metropolis_Hastings_Sampler`, number_of_iterations, shape_parameter, number_of_nodes, statistics_to_use, initial_network, take_sample_every, thetas, triples, pairs, alphas, together, seed, number_of_samples_to_store, using_correlation_network, undirect_network, parallel)
}

frobenius_norm <- function(mat1, mat2) {
    .Call(`_GERGM_frobenius_norm`, mat1, mat2)
}

weighted_mple_objective <- function(number_of_nodes, statistics_to_use, current_network, thetas, triples, pairs, alphas, together, integration_interval, parallel) {
    .Call(`_GERGM_weighted_mple_objective`, number_of_nodes, statistics_to_use, current_network, thetas, triples, pairs, alphas, together, integration_interval, parallel)
}