grpSLOPEMC: Monte Carlo methods for Group SLOPE

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

#' Monte Carlo based adjustment of the SLOPE tuning parameters
#'
#' \code{lambdaGaussianMC} adjusts the SLOPE regularizing sequence for correlations in 
#'    the data via a Monte Carlo approach which assumes normality of the error terms.
#'
#' @param lambda_BH The regualrizing sequence as used in Theorem 1.1 in Bogdan et. al. (2015)
#' @param X The model matrix
#' @param lambda_length The corrections of the entries of \code{lambda_BH} will be 
#'    computed up to the index given by \code{lambda_length} only.
#' @param number_of_drawings The number of iterations in the Monte Carlo procedure
#'
#' @references M. Bogdan, E. van den Berg, C. Sabatti, W. Su, E. Candes (2015), \emph{SLOPE -- Adaptive variable selection via convex optimization}, \url{http://arxiv.org/abs/1407.3824}
#'
lambdaGaussianMCRcpp <- function(lambda_BH, X, lambda_length, number_of_drawings = 5000L) {
    .Call('grpSLOPEMC_lambdaGaussianMCRcpp', PACKAGE = 'grpSLOPEMC', lambda_BH, X, lambda_length, number_of_drawings)
}

#' Monte Carlo based Group SLOPE tuning parameter correction
#'
#' \code{lambdaChiMCAdjustment} approximates the variance of (G.10) in Brzyski et. al. (2016)
#'    via Monte Carlo, in order to adjust the lambda sequence for correlations in the  data.
#'
#' The adjustment is computed for the (s+1)st coefficient of lambda, assuming 
#' that the first s coefficients are known. It is required that rank(X) is greater than the 
#' sum of the number of elements in any s groups. 
#'
#' @param y The response vector 
#' @param X The model matrix
#' @param group_id A list obtained from \code{grpSLOPE::getGroupID}
#' @param lambda A vector containing the first s entries of lambda
#' @param w A vector of weights per group
#' @param number_of_drawings The number of iterations in the Monte Carlo procedure
#'
#' @references D. Brzyski, A. Gossmann, W. Su, M. Bogdan (2016), \emph{Group SLOPE - adaptive selection of groups of predictors}, \url{https://arxiv.org/abs/1610.04960}
#'
lambdaChiMCAdjustmentRcpp <- function(y, X, group_id, lambda, w, number_of_drawings = 5000L) {
    .Call('grpSLOPEMC_lambdaChiMCAdjustmentRcpp', PACKAGE = 'grpSLOPEMC', y, X, group_id, lambda, w, number_of_drawings)
}