R/RcppExports.R

In BGGM: Bayesian Gaussian Graphical Models

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

mean_array <- function(x) {
    .Call(`_BGGM_mean_array`, x)
}

quantile_type_1 <- function(x, prob) {
    .Call(`_BGGM_quantile_type_1`, x, prob)
}

Sigma_i_not_i <- function(x, index) {
    .Call(`_BGGM_Sigma_i_not_i`, x, index)
}

select_col <- function(x, index) {
    .Call(`_BGGM_select_col`, x, index)
}

select_row <- function(x, index) {
    .Call(`_BGGM_select_row`, x, index)
}

remove_row <- function(x, which) {
    .Call(`_BGGM_remove_row`, x, which)
}

remove_col <- function(x, index) {
    .Call(`_BGGM_remove_col`, x, index)
}

internal_missing_gaussian <- function(Y, Y_missing, Sigma, iter_missing) {
    .Call(`_BGGM_internal_missing_gaussian`, Y, Y_missing, Sigma, iter_missing)
}

missing_gaussian <- function(Y, Y_missing, Sigma, iter_missing, progress_impute, store_all, lambda) {
    .Call(`_BGGM_missing_gaussian`, Y, Y_missing, Sigma, iter_missing, progress_impute, store_all, lambda)
}

Theta_continuous <- function(Y, iter, delta, epsilon, prior_only, explore, start, progress, impute, Y_missing) {
    .Call(`_BGGM_Theta_continuous`, Y, iter, delta, epsilon, prior_only, explore, start, progress, impute, Y_missing)
}

sample_prior <- function(Y, iter, delta, epsilon, prior_only, explore, progress) {
    .Call(`_BGGM_sample_prior`, Y, iter, delta, epsilon, prior_only, explore, progress)
}

mv_continuous <- function(Y, X, delta, epsilon, iter, start, progress) {
    .Call(`_BGGM_mv_continuous`, Y, X, delta, epsilon, iter, start, progress)
}

trunc_mvn <- function(mu, rinv, z, y, cutpoints) {
    .Call(`_BGGM_trunc_mvn`, mu, rinv, z, y, cutpoints)
}

mv_binary <- function(Y, X, delta, epsilon, iter, beta_prior, cutpoints, start, progress) {
    .Call(`_BGGM_mv_binary`, Y, X, delta, epsilon, iter, beta_prior, cutpoints, start, progress)
}

mv_ordinal_cowles <- function(Y, X, delta, epsilon, iter, MH) {
    .Call(`_BGGM_mv_ordinal_cowles`, Y, X, delta, epsilon, iter, MH)
}

mv_ordinal_albert <- function(Y, X, iter, delta, epsilon, K, start, progress) {
    .Call(`_BGGM_mv_ordinal_albert`, Y, X, iter, delta, epsilon, K, start, progress)
}

copula <- function(z0_start, levels, K, Sigma_start, iter, delta, epsilon, idx, progress) {
    .Call(`_BGGM_copula`, z0_start, levels, K, Sigma_start, iter, delta, epsilon, idx, progress)
}

pcor_to_cor_internal <- function(x, p) {
    .Call(`_BGGM_pcor_to_cor_internal`, x, p)
}

predictability_helper <- function(Y, y, XX, Xy, n, iter) {
    .Call(`_BGGM_predictability_helper`, Y, y, XX, Xy, n, iter)
}

beta_helper_fast <- function(XX, Xy, p, iter) {
    .Call(`_BGGM_beta_helper_fast`, XX, Xy, p, iter)
}

pred_helper_latent <- function(Y, XX, Xy, quantiles, n, iter) {
    .Call(`_BGGM_pred_helper_latent`, Y, XX, Xy, quantiles, n, iter)
}

KL_univariate <- function(var_1, var_2) {
    .Call(`_BGGM_KL_univariate`, var_1, var_2)
}

ppc_helper_nodewise_fast <- function(Theta, n1, n2, p) {
    .Call(`_BGGM_ppc_helper_nodewise_fast`, Theta, n1, n2, p)
}

KL_divergnece_mvn <- function(Theta_1, Theta_2) {
    .Call(`_BGGM_KL_divergnece_mvn`, Theta_1, Theta_2)
}

sum_squares <- function(Rinv_1, Rinv_2) {
    .Call(`_BGGM_sum_squares`, Rinv_1, Rinv_2)
}

my_dnorm <- function(x, means, sds) {
    .Call(`_BGGM_my_dnorm`, x, means, sds)
}

hamming_distance <- function(Rinv_1, Rinv_2, df1, df2, dens, pcors, BF_cut) {
    .Call(`_BGGM_hamming_distance`, Rinv_1, Rinv_2, df1, df2, dens, pcors, BF_cut)
}

correlation <- function(Rinv_1, Rinv_2) {
    .Call(`_BGGM_correlation`, Rinv_1, Rinv_2)
}

ppc_helper_fast <- function(Theta, n1, n2, p, BF_cut, dens, ppc_ss, ppc_cors, ppc_hd) {
    .Call(`_BGGM_ppc_helper_fast`, Theta, n1, n2, p, BF_cut, dens, ppc_ss, ppc_cors, ppc_hd)
}

mvnrnd <- function(n, mu, Sigma) {
    .Call(`_BGGM_mvnrnd`, n, mu, Sigma)
}

var <- function(Y, X, delta, epsilon, beta_prior, iter, start, progress) {
    .Call(`_BGGM_var`, Y, X, delta, epsilon, beta_prior, iter, start, progress)
}

hft_algorithm <- function(Sigma, adj, tol, max_iter) {
    .Call(`_BGGM_hft_algorithm`, Sigma, adj, tol, max_iter)
}

bic_fast <- function(Theta, S, n, prior_prob) {
    .Call(`_BGGM_bic_fast`, Theta, S, n, prior_prob)
}

find_ids <- function(x) {
    .Call(`_BGGM_find_ids`, x)
}

search <- function(S, iter, old_bic, start_adj, n, gamma, stop_early, progress) {
    .Call(`_BGGM_search`, S, iter, old_bic, start_adj, n, gamma, stop_early, progress)
}

fast_g_matrix_F <- function(Y, adj, mu_samples, cov_samples, iter, p, N, prior_sd, kappa1, progress) {
    .Call(`_BGGM_fast_g_matrix_F`, Y, adj, mu_samples, cov_samples, iter, p, N, prior_sd, kappa1, progress)
}

contrained_helper <- function(cors, adj, iter, progress) {
    .Call(`_BGGM_contrained_helper`, cors, adj, iter, progress)
}

missing_copula <- function(Y, Y_missing, z0_start, Sigma_start, levels, iter_missing, progress_impute, K, idx, epsilon, delta) {
    .Call(`_BGGM_missing_copula`, Y, Y_missing, z0_start, Sigma_start, levels, iter_missing, progress_impute, K, idx, epsilon, delta)
}

missing_copula_data <- function(Y, Y_missing, z0_start, Sigma_start, levels, iter_missing, progress_impute, K, idx, lambda) {
    .Call(`_BGGM_missing_copula_data`, Y, Y_missing, z0_start, Sigma_start, levels, iter_missing, progress_impute, K, idx, lambda)
}