R/RcppExports.R

In abn: Modelling Multivariate Data with Additive Bayesian Networks

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

#' @title logit functions
#'
#' @description transform \code{x} either via the logit, or expit.
#'
#'
#' @param x a numeric vector
#' @returns a numeric vector
#' @export
#' @rdname logit_cpp
logit_cpp <- function(x) {
    .Call(`_abn_logit_cpp`, x)
}

#' @title expit function
#'
#' @description transform \code{x} either via the logit, or expit.
#'
#'
#' @param x a numeric vector
#' @returns a numeric vector
#' @export
#' @rdname expit_cpp
expit_cpp <- function(x) {
    .Call(`_abn_expit_cpp`, x)
}

#' @title Iterative Reweighed Least Square algorithm for Binomials
#' @description IRLS to estimate network score of Binomial nodes.
#' @keywords internal
#' @returns a list
#' @export
irls_binomial_cpp <- function(A, b, maxit, tol) {
    .Call(`_abn_irls_binomial_cpp`, A, b, maxit, tol)
}

#' @title BR Iterative Reweighed Least Square algorithm for Binomials
#' @description IRLS to estimate network score of Binomial nodes.
#' @keywords internal
#' @returns a list
#' @export
irls_binomial_cpp_br <- function(A, b, maxit, tol) {
    .Call(`_abn_irls_binomial_cpp_br`, A, b, maxit, tol)
}

#' @title Fast Iterative Reweighed Least Square algorithm for Binomials
#' @description IRLS to estimate network score of Binomial nodes.
#' @keywords internal
#' @returns a list
#' @export
irls_binomial_cpp_fast <- function(A, b, maxit, tol) {
    .Call(`_abn_irls_binomial_cpp_fast`, A, b, maxit, tol)
}

#' @title Fast Br Iterative Reweighed Least Square algorithm for Binomials
#' @description IRLS to estimate network score of Binomial nodes.
#' @keywords internal
#' @returns a list
#' @export
irls_binomial_cpp_fast_br <- function(A, b, maxit, tol) {
    .Call(`_abn_irls_binomial_cpp_fast_br`, A, b, maxit, tol)
}

#' @title Iterative Reweighed Least Square algorithm for Gaussians
#' @description IRLS to estimate network score of Gaussian nodes.
#' @returns a list
#' @keywords internal
#' @export
irls_gaussian_cpp <- function(A, b, maxit, tol) {
    .Call(`_abn_irls_gaussian_cpp`, A, b, maxit, tol)
}

#' @title Fast Iterative Reweighed Least Square algorithm for Gaussians
#' @description IRLS to estimate network score of Gaussian nodes.
#' @keywords internal
#' @export
irls_gaussian_cpp_fast <- function(A, b, maxit, tol) {
    .Call(`_abn_irls_gaussian_cpp_fast`, A, b, maxit, tol)
}

#' @title Factorial
#' @description Calculate the factorial in C##
#' @keywords internal
#' @returns a double
#' @export
factorial <- function(n) {
    .Call(`_abn_factorial`, n)
}

#' @title Iterative Reweighed Least Square algorithm for Poissons
#' @description IRLS to estimate network score of Poisson nodes.
#' @keywords internal
#' @returns a list
#' @export
irls_poisson_cpp <- function(A, b, maxit, tol) {
    .Call(`_abn_irls_poisson_cpp`, A, b, maxit, tol)
}

#' @title Fast Factorial
#' @description Calculate the factorial in C##
#' @keywords internal
#' @returns a double
#' @export
factorial_fast <- function(n) {
    .Call(`_abn_factorial_fast`, n)
}

#' @title Fast Iterative Reweighed Least Square algorithm for Poissons
#' @description IRLS to estimate network score of Poisson nodes.
#' @keywords internal
#' @returns a list
#' @export
irls_poisson_cpp_fast <- function(A, b, maxit, tol) {
    .Call(`_abn_irls_poisson_cpp_fast`, A, b, maxit, tol)
}

#' @title Mutual Information
#' @description Calculates the mutual information.
#' @keywords internal
#' @returns a double
#' @export
mi_cpp <- function(joint_dist) {
    .Call(`_abn_mi_cpp`, joint_dist)
}

#' @title Rank of a matrix
#' @description similar to \code{base::rank}
#' @keywords internal
#' @returns an integer
#' @export
rank_cpp <- function(A) {
    .Call(`_abn_rank_cpp`, A)
}