R/RcppExports.R

In s3alfisc/fwildclusterboot: Fast Wild Cluster Bootstrap Inference for Linear Models

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

boot_algo3_crv1_denom <- function(B, G, ssc, H, Cg, v, cores) {
    .Call('_fwildclusterboot_boot_algo3_crv1_denom', PACKAGE = 'fwildclusterboot', B, G, ssc, H, Cg, v, cores)
}

boot_algo3_crv3 <- function(B, G, k, v, scores_mat, scores_boot, inv_tXX_tXgXg, cores, R, delta_b_star) {
    .Call('_fwildclusterboot_boot_algo3_crv3', PACKAGE = 'fwildclusterboot', B, G, k, v, scores_mat, scores_boot, inv_tXX_tXgXg, cores, R, delta_b_star)
}

convertSparse <- function(mat) {
    .Call('_fwildclusterboot_convertSparse', PACKAGE = 'fwildclusterboot', mat)
}

compute_H <- function(G, R, tXXinv, tXgXg, scores_list, cores) {
    .Call('_fwildclusterboot_compute_H', PACKAGE = 'fwildclusterboot', G, R, tXXinv, tXgXg, scores_list, cores)
}

pinv <- function(X) {
    .Call('_fwildclusterboot_pinv', PACKAGE = 'fwildclusterboot', X)
}

#' Moore-Penrose Pseudo Inverses via Eigen
#' @param A a matrix
#' @return A matrix. Pseudo-Inverse of A.
#' @noRd
NULL

#' Matrix Multiplication via Eigen
#' @param A A matrix.
#' @param B A matrix.
#' @param nthreads Integer. Number of threads to use for matrix multiplication.
#' @return A matrix
#' @noRd
eigenMapMatMult <- function(A, B, nthreads) {
    .Call('_fwildclusterboot_eigenMapMatMult', PACKAGE = 'fwildclusterboot', A, B, nthreads)
}

eigen_pinv <- function(A) {
    .Call('_fwildclusterboot_eigen_pinv', PACKAGE = 'fwildclusterboot', A)
}

#' Get maximum number of threads on hardware for open mp support
#' @noRd
cpp_get_nb_threads <- function() {
    .Call('_fwildclusterboot_cpp_get_nb_threads', PACKAGE = 'fwildclusterboot')
}

#' Implementation of the heteroskedastic wild bootstrap. Computes
#' HC robust variance estimators. For use in fwildclusterboot when no
#' cluster variable is provided
#' @param y A vector - the dependent variable
#' @param X A matrix - the design matrix
#' @param R A matrix - the constraints matrix for a hypothesis test R'beta = r.
#' @param r A vector - r in hypothesis test R'beta = r.
#' @param B An integer - controls the number of bootstrap iterations.
#' @param N_G_bootcluster - The number of bootstrap clusters. For 
#' heteroskesdatic wild bootstrap, N_G_bootcluster = N, where N 
#' is the number of observations.
#' @param cores Integer: the number of cores to be used.
#' @param type : Integer. Should rademacher or webb weights be used? 
#' For rademacher weights, set 'type = 0'. For webb weights, set 'type = 1'.
#' @param small_sample_correction: double. Small sample correction to be 
#' applied.
#' @return A matrix of bootstrapped t-statistics, where the null is imposed 
#' on the bootstrap dgp.
#' @noRd
NULL

#' Implementation of the wild  cluster bootstrap. Computes
#' cluster robust variance estimators. For use in fwildclusterboot when
#' the memory demands of the fast and wild algorithm are infeasible
#' @param y A vector - the dependent variable
#' @param X A matrix - the design matrix
#' @param R A matrix - the constraints matrix for a hypothesis test R'beta = r.
#' @param r A vector - r in hypothesis test R'beta = r.
#' @param B An integer - controls the number of bootstrap iterations.
#' @param N_G_bootcluster - The number of bootstrap clusters.
#' @param cores Integer: the number of cores to be used.
#' @param type : Integer. Should rademacher or webb weights be used?
#'  For rademacher weights, set 'type = 0'. For webb weights, set 'type = 1'.
#' @param cluster: Integer Vector. Contains information on the clusters.
#' @return A matrix of bootstrapped t-statistics, where the null is 
#' imposed on the bootstrap dgp.
#' @noRd
NULL

#' Implementation of the wild  cluster bootstrap. Computes
#' cluster robust variance estimators. For use in fwildclusterboot when
#' the memory demands of the fast and wild algorithm are infeasible
#' @param y A vector - the dependent variable
#' @param X A matrix - the design matrix
#' @param R A matrix - the constraints matrix for a hypothesis test R'beta = r.
#' @param r A vector - r in hypothesis test R'beta = r.
#' @param B An integer - controls the number of bootstrap iterations.
#' @param N_G_bootcluster - The number of bootstrap clusters.
#' @param cores Integer: the number of cores to be used.
#' @param cluster: Integer Vector. Contains information on the clusters.
#' @param v: enumerated weights matrix 
#' @return A matrix of bootstrapped t-statistics, where the null is
#'  imposed on the bootstrap dgp.
#' @noRd
NULL

#'create bootstrap sample weights
#' @param G the number of clusters
#' @param type 0 for rademacher, 1 for webb
#' @noRd
sample_weights <- function(G, type) {
    .Call('_fwildclusterboot_sample_weights', PACKAGE = 'fwildclusterboot', G, type)
}

wildboottestHC <- function(y, X, R, r, B, N_G_bootcluster, cores, type, small_sample_correction, bootstrap_type) {
    .Call('_fwildclusterboot_wildboottestHC', PACKAGE = 'fwildclusterboot', y, X, R, r, B, N_G_bootcluster, cores, type, small_sample_correction, bootstrap_type)
}

wildboottestCL <- function(y, X, R, r, B, N_G_bootcluster, cores, type, cluster, small_sample_correction) {
    .Call('_fwildclusterboot_wildboottestCL', PACKAGE = 'fwildclusterboot', y, X, R, r, B, N_G_bootcluster, cores, type, cluster, small_sample_correction)
}

wildboottestCL_enum <- function(y, X, R, r, B, N_G_bootcluster, cores, cluster, small_sample_correction, v) {
    .Call('_fwildclusterboot_wildboottestCL_enum', PACKAGE = 'fwildclusterboot', y, X, R, r, B, N_G_bootcluster, cores, cluster, small_sample_correction, v)
}