R/RcppExports.R

In bliss: Bayesian Functional Linear Regression with Sparse Step Functions

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

ginv_cpp <- function(x, tol) {
    .Call(`_bliss_ginv_cpp`, x, tol)
}

mvrnormArma <- function(mu, VarCovar, sigma_sq) {
    .Call(`_bliss_mvrnormArma`, mu, VarCovar, sigma_sq)
}

integrate_trapeze_cpp <- function(x, y) {
    .Call(`_bliss_integrate_trapeze_cpp`, x, y)
}

uniform_cpp <- function(m, l, grid) {
    .Call(`_bliss_uniform_cpp`, m, l, grid)
}

triangular_cpp <- function(m, l, grid) {
    .Call(`_bliss_triangular_cpp`, m, l, grid)
}

gaussian_cpp <- function(m, l, grid) {
    .Call(`_bliss_gaussian_cpp`, m, l, grid)
}

Epanechnikov_cpp <- function(m, l, grid) {
    .Call(`_bliss_Epanechnikov_cpp`, m, l, grid)
}

compute_beta_cpp <- function(b, m, l, grid, p, K, basis, normalization_values) {
    .Call(`_bliss_compute_beta_cpp`, b, m, l, grid, p, K, basis, normalization_values)
}

compute_beta_sample_cpp <- function(posterior_sample, K, grid, p, basis, normalization_values) {
    .Call(`_bliss_compute_beta_sample_cpp`, posterior_sample, K, grid, p, basis, normalization_values)
}

potential_intervals_List <- function(x_list, grids, p_l_vec, basis_vec, q) {
    .Call(`_bliss_potential_intervals_List`, x_list, grids, p_l_vec, basis_vec, q)
}

moving_average_cpp <- function(v, range) {
    .Call(`_bliss_moving_average_cpp`, v, range)
}

potential_intervals_extract <- function(potential_intervals, mk, lk, dims) {
    .Call(`_bliss_potential_intervals_extract`, potential_intervals, mk, lk, dims)
}

update_mqk <- function(count, k, y, b_tilde, sigma_sq, m_q, l_q, x_tilde, potential_intervals_q, potential_intervals_dims_q, m_possible_q, p_q, Q, K, g, sum_K, lambda_id0) {
    invisible(.Call(`_bliss_update_mqk`, count, k, y, b_tilde, sigma_sq, m_q, l_q, x_tilde, potential_intervals_q, potential_intervals_dims_q, m_possible_q, p_q, Q, K, g, sum_K, lambda_id0))
}

update_lqk <- function(count, k, y, b_tilde, sigma_sq, m_q, l_q, x_tilde, potential_intervals_q, potential_intervals_dims_q, l_possible_q, phi_l_q, l_values_length_q, Q, K, g, sum_K, lambda_id0) {
    invisible(.Call(`_bliss_update_lqk`, count, k, y, b_tilde, sigma_sq, m_q, l_q, x_tilde, potential_intervals_q, potential_intervals_dims_q, l_possible_q, phi_l_q, l_values_length_q, Q, K, g, sum_K, lambda_id0))
}

update_b_tilde <- function(y, sigma_sq, x_tilde, Sigma_b_tilde_inv, tol, b_tilde) {
    invisible(.Call(`_bliss_update_b_tilde`, y, sigma_sq, x_tilde, Sigma_b_tilde_inv, tol, b_tilde))
}

loss_cpp <- function(d, grid, posterior_expe) {
    .Call(`_bliss_loss_cpp`, d, grid, posterior_expe)
}

Bliss_Gibbs_Sampler_cpp <- function(Q, y, x, grids, iter, K, basis, g, lambda, V_tilde, l_values_length, probs_l, progress, tol) {
    .Call(`_bliss_Bliss_Gibbs_Sampler_cpp`, Q, y, x, grids, iter, K, basis, g, lambda, V_tilde, l_values_length, probs_l, progress, tol)
}

Bliss_Simulated_Annealing_cpp <- function(iter, beta_sample, grid, burnin, Temp, k_max, p_l, dm, dl, p, basis, normalization_values, progress, starting_point) {
    .Call(`_bliss_Bliss_Simulated_Annealing_cpp`, iter, beta_sample, grid, burnin, Temp, k_max, p_l, dm, dl, p, basis, normalization_values, progress, starting_point)
}

dposterior_cpp <- function(rposterior, y, N, K, potential_intervals, potential_intervals_dims, p_l, Q) {
    .Call(`_bliss_dposterior_cpp`, rposterior, y, N, K, potential_intervals, potential_intervals_dims, p_l, Q)
}