R/RcppExports.R

In Polkas/miceFast: Fast Imputations Using 'Rcpp' and 'Armadillo'

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

VIF_ <- function(x, posit_y, posit_x, posit_x_var, correct) {
    .Call(`_miceFast_VIF_`, x, posit_y, posit_x, posit_x_var, correct)
}

fill_NA_N_ <- function(x, model, posit_y, posit_x, w, k = 10L, ridge = 1e-6) {
    .Call(`_miceFast_fill_NA_N_`, x, model, posit_y, posit_x, w, k, ridge)
}

fill_NA_ <- function(x, model, posit_y, posit_x, w, ridge = 1e-6) {
    .Call(`_miceFast_fill_NA_`, x, model, posit_y, posit_x, w, ridge)
}

#' Finding in random manner one of the k closets points in a certain vector for each value in a second vector
#'
#' @description this function using pre-sorting of a y and the binary search the one of the k closest value for each miss is returned.
#'
#' @param y numeric vector values to be look up
#' @param miss numeric vector a values to be look for
#' @param k integer a number of values which should be taken into account during sampling one of the k closest point
#'
#' @return a numeric vector
#'
#' @name neibo
#'
#' @export
neibo <- function(y, miss, k) {
    .Call(`_miceFast_neibo`, y, miss, k)
}

pmm_weighted_neibo <- function(y, X, w, X1, k, ridge) {
    .Call(`_miceFast_pmm_weighted_neibo`, y, X, w, X1, k, ridge)
}

pmm_neibo <- function(y, X, X1, k, ridge) {
    .Call(`_miceFast_pmm_neibo`, y, X, X1, k, ridge)
}