R/RcppExports.R

In statKim/robfpca: Functional principal component analysis for partially observed elliptical process

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

fitdistr_cpp <- function(x, densfun, df = 3L) {
    .Call(`_robfpca_fitdistr_cpp`, x, densfun, df)
}

locScaleM_cpp <- function(x, psi, na_rm = 1L) {
    .Call(`_robfpca_locScaleM_cpp`, x, psi, na_rm)
}

mad <- function(x) {
    .Call(`_robfpca_mad`, x)
}

mscale_cpp <- function(u, delta = 0.5, tuning_chi = 1.547645, family = "bisquare") {
    .Call(`_robfpca_mscale_cpp`, u, delta, tuning_chi, family)
}

wfun <- function(x, psi) {
    .Call(`_robfpca_wfun`, x, psi)
}

psif <- function(x, psi) {
    .Call(`_robfpca_psif`, x, psi)
}

psipri <- function(x, psi) {
    .Call(`_robfpca_psipri`, x, psi)
}

loc_scale_M <- function(x, psi = "huber", eff = 0.95, maxit = 50L, tol = 1.e-4) {
    .Call(`_robfpca_loc_scale_M`, x, psi, eff, maxit, tol)
}

quantile_cpp <- function(x, probs) {
    .Call(`_robfpca_quantile_cpp`, x, probs)
}

get_sigma2_rob_cpp <- function(v) {
    .Call(`_robfpca_get_sigma2_rob_cpp`, v)
}

cor_gk_cpp <- function(X, type = "huber", MM = 1L, df = 3L) {
    .Call(`_robfpca_cor_gk_cpp`, X, type, MM, df)
}