R/RcppExports.R

In FrancescoBarile/FJLPackage: OurPackage

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

#' Estimation Linear Regression model via Gradient Descend method
#' Output: [list] Beta paraameters estimated
#' @title betahat_GD_Cpp
#' @name betahat_GD_Cpp
#' @param beta [numeric] vector containing an initial guess for the beta vector
#' @param X [numeric] design matrix
#' @param Y [numeric] response variable vector
#' @param tolerance [numeric] tolerance level, stopping criteria of the algorithm 
#'(error<tolerance: stop)
#' @param maxit [numeric] maximum number of iterations, used if the stopping 
#'criteria is never matched 
#' @param stepsize [numeric] learning parameter, to update the parameters at 
#'each iteration 
#' @export
NULL

betahat_GD_Cpp <- function(beta, X, Y, tolerance, maxit, stepsize) {
    .Call(`_cpp_betahat_GD_Cpp`, beta, X, Y, tolerance, maxit, stepsize)
}

#' Estimation Linear Regression model via Steepest Descend method
#' Output: [list] Beta paraameters estimated
#' @title betahat_SD_Cpp
#' @name betahat_SD_Cpp
#' @param beta [numeric] vector containing an initial guess for the beta vector
#' @param X [numeric] design matrix
#' @param Y [numeric] response variable vector
#' @param tolerance [numeric] tolerance level, stopping criteria of the algorithm 
#'(error<tolerance: stop)
#' @param maxit [numeric] maximum number of iterations, used if the stopping 
#'criteria is never matched 
#' @export
#' 
betahat_SD_Cpp <- function(beta, X, Y, tolerance, maxit) {
    .Call(`_cpp_betahat_SD_Cpp`, beta, X, Y, tolerance, maxit)
}