R/RcppExports.R

In USCbiostats/partition: Agglomerative Partitioning Framework for Dimension Reduction

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

rank_c <- function(x) {
    .Call(`_partition_rank_c`, x)
}

apply_rank <- function(x) {
    .Call(`_partition_apply_rank`, x)
}

corr_c_mat <- function(x) {
    .Call(`_partition_corr_c_mat`, x)
}

corr_c_2mat <- function(x, y) {
    .Call(`_partition_corr_c_2mat`, x, y)
}

corr_c_2vec <- function(x, y) {
    .Call(`_partition_corr_c_2vec`, x, y)
}

icc_c <- function(x) {
    .Call(`_partition_icc_c`, x)
}

scale_rowmeans <- function(x) {
    .Call(`_partition_scale_rowmeans`, x)
}

subset_matrix <- function(x, i, m) {
    .Call(`_partition_subset_matrix`, x, i, m)
}

min_icc_c <- function(columns, x, k, threshold) {
    .Call(`_partition_min_icc_c`, columns, x, k, threshold)
}

kmeans_c <- function(x, k, n_iter = 10L, verbose = FALSE, seed = 1L) {
    .Call(`_partition_kmeans_c`, x, k, n_iter, verbose, seed)
}

assign_cluster <- function(x, init_centroids) {
    .Call(`_partition_assign_cluster`, x, init_centroids)
}

kmean_assignment <- function(x, k, n_iter = 10L, verbose = FALSE, seed = 1L) {
    .Call(`_partition_kmean_assignment`, x, k, n_iter, verbose, seed)
}

minR2_c <- function(x) {
    .Call(`_partition_minR2_c`, x)
}

pca_c <- function(x) {
    .Call(`_partition_pca_c`, x)
}

reduce_partition_c <- function(x, df, assign_partition, partitioner, threshold, tolerance, var_prefix, niter) {
    .Call(`_partition_reduce_partition_c`, x, df, assign_partition, partitioner, threshold, tolerance, var_prefix, niter)
}