R/RcppExports.R

In rchan26/BayesLogitFusion: Monte Carlo Fusion for Bayesian Logistic Regression

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

weighted_mean <- function(x, weights) {
    .Call(`_BayesLogitFusion_weighted_mean`, x, weights)
}

column_mean <- function(matrix) {
    .Call(`_BayesLogitFusion_column_mean`, matrix)
}

weighted_column_mean <- function(matrix, weights) {
    .Call(`_BayesLogitFusion_weighted_column_mean`, matrix, weights)
}

row_wise_subtraction <- function(X, vect) {
    .Call(`_BayesLogitFusion_row_wise_subtraction`, X, vect)
}

log_rho_standard <- function(x, x_mean, time) {
    .Call(`_BayesLogitFusion_log_rho_standard`, x, x_mean, time)
}

log_rho_time_adapting <- function(x, weighted_mean, sub_posterior_weights, time) {
    .Call(`_BayesLogitFusion_log_rho_time_adapting`, x, weighted_mean, sub_posterior_weights, time)
}

#' Simulate from a Multivariate Gaussian Distribution
#'
#' Produces samples from the specified multivariate Gaussian distribution
#'
#' @param N the number of samples required
#' @param mu a vector of mean values
#' @param Sigma positive-definite symmetric matrix specifying the covariance of variables
#'
#' @return samples from a multivariate Gaussian distribution
#'
#' @export
mvrnormArma <- function(N, mu, Sigma) {
    .Call(`_BayesLogitFusion_mvrnormArma`, N, mu, Sigma)
}

#' Simulate from a tempered Multivariate Gaussian Distribution
#'
#' Produces samples from the specified tempered multivariate Gaussian distribution
#'
#' @param N the number of samples required
#' @param mu a vector of mean values
#' @param Sigma positive-definite symmetric matrix specifying the covariance of variables
#' @param beta inverse temperature level
#'
#' @return samples from a tempered multivariate Gaussian distribution
#'
#' @export
mvrnormArma_tempered <- function(N, mu, Sigma, beta) {
    .Call(`_BayesLogitFusion_mvrnormArma_tempered`, N, mu, Sigma, beta)
}

rho_importance_sample <- function(N, dim, time, m, samples_to_fuse, sub_posterior_weights) {
    .Call(`_BayesLogitFusion_rho_importance_sample`, N, dim, time, m, samples_to_fuse, sub_posterior_weights)
}

log_BLR_gradient <- function(beta, y_labels, X, X_beta, prior_means, prior_variances, C, power, precondition_mat) {
    .Call(`_BayesLogitFusion_log_BLR_gradient`, beta, y_labels, X, X_beta, prior_means, prior_variances, C, power, precondition_mat)
}

div_log_BLR_gradient <- function(X, X_beta, prior_variances, C, power, precondition_mat) {
    .Call(`_BayesLogitFusion_div_log_BLR_gradient`, X, X_beta, prior_variances, C, power, precondition_mat)
}

vec_norm_squared <- function(vect) {
    .Call(`_BayesLogitFusion_vec_norm_squared`, vect)
}

phi_BLR <- function(beta, y_labels, X, prior_means, prior_variances, C, power, precondition_mat) {
    .Call(`_BayesLogitFusion_phi_BLR`, beta, y_labels, X, prior_means, prior_variances, C, power, precondition_mat)
}

phi_LB_BLR <- function(X, prior_variances, C, power, precondition_mat) {
    .Call(`_BayesLogitFusion_phi_LB_BLR`, X, prior_variances, C, power, precondition_mat)
}