R/RcppExports.R

In missoNet: Joint Sparse Regression & Network Learning with Missing Data

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

getEvalErr <- function(yty, ytx, xty, xtx, Beta, n) {
    .Call(`_missoNet_getEvalErr`, yty, ytx, xty, xtx, Beta, n)
}

Q_func <- function(S, Theta) {
    .Call(`_missoNet_Q_func`, S, Theta)
}

GoF_func <- function(GoF, gamma, n, p, q, Theta, S, Beta, sparsity_tol = 1e-8) {
    .Call(`_missoNet_GoF_func`, GoF, gamma, n, p, q, Theta, S, Beta, sparsity_tol)
}

soft_threshold_mat <- function(X, lambda) {
    .Call(`_missoNet_soft_threshold_mat`, X, lambda)
}

soft_threshold_scalar <- function(X, lambda) {
    .Call(`_missoNet_soft_threshold_scalar`, X, lambda)
}

matrix_norm_diff <- function(A, B, type = "fro") {
    .Call(`_missoNet_matrix_norm_diff`, A, B, type)
}

eigen_decomp_sym <- function(A, min_eigval = 0.0) {
    .Call(`_missoNet_eigen_decomp_sym`, A, min_eigval)
}

make_symmetric <- function(A) {
    .Call(`_missoNet_make_symmetric`, A)
}

is_positive_definite <- function(A, tol = 1e-12) {
    .Call(`_missoNet_is_positive_definite`, A, tol)
}

fast_trace <- function(A) {
    .Call(`_missoNet_fast_trace`, A)
}

maxproj_cov <- function(input_mat, epsilon = -1.0, mu = -1.0, nitr_max = -1L, etol = -1.0, verbose = FALSE) {
    .Call(`_missoNet_maxproj_cov`, input_mat, epsilon, mu, nitr_max, etol, verbose)
}

getResCov <- function(E_input, n, rho_mat, eps = -1.0, mu = -1.0, max_iter = -1L, tol = -1.0, use_pairwise = FALSE, verbose = FALSE) {
    .Call(`_missoNet_getResCov`, E_input, n, rho_mat, eps, mu, max_iter, tol, use_pairwise, verbose)
}

make_positive_definite <- function(A, epsilon = -1.0, verbose = FALSE) {
    .Call(`_missoNet_make_positive_definite`, A, epsilon, verbose)
}

diagnose_matrix <- function(A, verbose = FALSE) {
    .Call(`_missoNet_diagnose_matrix`, A, verbose)
}

process_covariance_batch <- function(E_list, n_vec, rho_list, eps = -1.0, verbose = FALSE) {
    .Call(`_missoNet_process_covariance_batch`, E_list, n_vec, rho_list, eps, verbose)
}

updateBeta <- function(Theta, B0, n, xtx, xty, lamB, eta = 0.8, tolin = 1e-4, maxitrin = 1000L, adaptive_restart = TRUE, restart_tol = 1e-6) {
    .Call(`_missoNet_updateBeta`, Theta, B0, n, xtx, xty, lamB, eta, tolin, maxitrin, adaptive_restart, restart_tol)
}