R/RcppExports.R

In FLORAL: Fit Log-Ratio Lasso Regression for Compositional Data

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

softthreshold <- function(x, lambda) {
    .Call('_FLORAL_softthreshold', PACKAGE = 'FLORAL', x, lambda)
}

gd_cov <- function(xx, xy, n, l, beta) {
    .Call('_FLORAL_gd_cov', PACKAGE = 'FLORAL', xx, xy, n, l, beta)
}

gd_cov_al <- function(xx, xy, n, l, a, beta, mu, alpha, adjust = FALSE, ncov = 0L, wcov = 0) {
    .Call('_FLORAL_gd_cov_al', PACKAGE = 'FLORAL', xx, xy, n, l, a, beta, mu, alpha, adjust, ncov, wcov)
}

linear_enet_al <- function(x, y, len, mu, ub, lambda, wcov, a, adjust, ncov, display_progress = TRUE) {
    .Call('_FLORAL_linear_enet_al', PACKAGE = 'FLORAL', x, y, len, mu, ub, lambda, wcov, a, adjust, ncov, display_progress)
}

logistic_enet_al <- function(x, y, len, mu, ub, lambda, wcov, a, adjust, ncov, display_progress = TRUE, loop1 = FALSE, loop2 = FALSE) {
    .Call('_FLORAL_logistic_enet_al', PACKAGE = 'FLORAL', x, y, len, mu, ub, lambda, wcov, a, adjust, ncov, display_progress, loop1, loop2)
}

cox_enet_al <- function(x, t, d, tj, len, mu, ub, lambda, wcov, a, adjust, ncov, devnull, display_progress = TRUE, loop1 = FALSE, loop2 = FALSE, notcv = TRUE) {
    .Call('_FLORAL_cox_enet_al', PACKAGE = 'FLORAL', x, t, d, tj, len, mu, ub, lambda, wcov, a, adjust, ncov, devnull, display_progress, loop1, loop2, notcv)
}

cox_timedep_enet_al <- function(x, t0, t1, d, tj, len, mu, ub, lambda, wcov, a, adjust, ncov, devnull, display_progress = TRUE) {
    .Call('_FLORAL_cox_timedep_enet_al', PACKAGE = 'FLORAL', x, t0, t1, d, tj, len, mu, ub, lambda, wcov, a, adjust, ncov, devnull, display_progress)
}

fg_enet_al <- function(x, t0, t1, d, tj, w, len, mu, ub, lambda, wcov, a, adjust, ncov, devnull, display_progress = TRUE) {
    .Call('_FLORAL_fg_enet_al', PACKAGE = 'FLORAL', x, t0, t1, d, tj, w, len, mu, ub, lambda, wcov, a, adjust, ncov, devnull, display_progress)
}

gee_NR <- function(N, nt, y, X, nx, linkinv, mueta, variance, beta_new, Rhat, fihat, lambda, a, alpha, ncov, wcov, eps = 1e-6, muu = 1e6) {
    .Call('_FLORAL_gee_NR', PACKAGE = 'FLORAL', N, nt, y, X, nx, linkinv, mueta, variance, beta_new, Rhat, fihat, lambda, a, alpha, ncov, wcov, eps, muu)
}

gee_cor <- function(N, nt, y, X, linkinv, variance, beta_new, corstr, maxclsz, scalefix, scalevalue = 1) {
    .Call('_FLORAL_gee_cor', PACKAGE = 'FLORAL', N, nt, y, X, linkinv, variance, beta_new, corstr, maxclsz, scalefix, scalevalue)
}

gee_fit <- function(y, X, nt, linkinv, mueta, variance, corstr, lambda, a, ncov, wcov, tol = 1e-3, eps = 1e-6, muu = 1e6, maxiter1 = 100L, maxiter2 = 10L, scalefix = FALSE, scalevalue = 1, display_progress = TRUE) {
    .Call('_FLORAL_gee_fit', PACKAGE = 'FLORAL', y, X, nt, linkinv, mueta, variance, corstr, lambda, a, ncov, wcov, tol, eps, muu, maxiter1, maxiter2, scalefix, scalevalue, display_progress)
}