R/RcppExports.R

In andrewthomasjones/EMMIXgene_no_f: A Mixture Model-Based Approach to the Clustering of Microarray Expression Data

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

#'@importFrom Rcpp sourceCpp
#'@useDynLib EMMIXgene
NULL

mahalanobis_c <- function(y, mu, sigma) {
    .Call('_EMMIXgene_mahalanobis_c', PACKAGE = 'EMMIXgene', y, mu, sigma)
}

t_dist <- function(y, mu, sigma, nu, p = 1L) {
    .Call('_EMMIXgene_t_dist', PACKAGE = 'EMMIXgene', y, mu, sigma, nu, p)
}

estep <- function(dat, params) {
    .Call('_EMMIXgene_estep', PACKAGE = 'EMMIXgene', dat, params)
}

mstep <- function(dat, tau, you, params) {
    .Call('_EMMIXgene_mstep', PACKAGE = 'EMMIXgene', dat, tau, you, params)
}

emmix_t <- function(dat, g = 1L, random_starts = 4L, max_it = 100L, tol = 0.0001, start_method = "both") {
    .Call('_EMMIXgene_emmix_t', PACKAGE = 'EMMIXgene', dat, g, random_starts, max_it, tol, start_method)
}

each_gene <- function(dat, random_starts = 4L, max_it = 100L, ll_thresh = 8, min_clust_size = 8L, tol = 0.0001, start_method = "both", three = FALSE) {
    .Call('_EMMIXgene_each_gene', PACKAGE = 'EMMIXgene', dat, random_starts, max_it, ll_thresh, min_clust_size, tol, start_method, three)
}

emmix_gene <- function(bigdat, random_starts = 4L, max_it = 100L, ll_thresh = 8, min_clust_size = 8L, tol = 0.0001, start_method = "both", three = FALSE) {
    .Call('_EMMIXgene_emmix_gene', PACKAGE = 'EMMIXgene', bigdat, random_starts, max_it, ll_thresh, min_clust_size, tol, start_method, three)
}

tkmeans <- function(M, k, alpha, weights, nstart, iter, tol, verbose) {
    .Call('_EMMIXgene_tkmeans', PACKAGE = 'EMMIXgene', M, k, alpha, weights, nstart, iter, tol, verbose)
}

nearest_cluster <- function(data, centres) {
    .Call('_EMMIXgene_nearest_cluster', PACKAGE = 'EMMIXgene', data, centres)
}