R/RcppExports.R

In bigSurvSGD: Big Survival Analysis Using Stochastic Gradient Descent

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

#' @title Calculates the maximum penalty coefficient lambda for which all 
#' coefficients become zero
#' @name lambdaMaxC
NULL

lambdaMaxC <- function(subDataa, strata_size, norm_method, features_mean, features_sd) {
    .Call(`_bigSurvSGD_lambdaMaxC`, subDataa, strata_size, norm_method, features_mean, features_sd)
}

#' @title Updates the coefficients based on one pass of data
#' @name oneChunkC
NULL

oneChunkC <- function(subData, Beta, beta_type, strata_size, batch_size, t, m, v, vHat, lr_const, lr_tau, opt_method, norm_method, b1, b2, eps, lambda, alpha, features_mean, features_sd) {
    .Call(`_bigSurvSGD_oneChunkC`, subData, Beta, beta_type, strata_size, batch_size, t, m, v, vHat, lr_const, lr_tau, opt_method, norm_method, b1, b2, eps, lambda, alpha, features_mean, features_sd)
}

#' @title Calculates the gradient and Hessian corresponding to one individual
#' @name oneObsPlugingC
NULL

oneObsPlugingC <- function(subDataa, Beta, strata_size, norm_method, features_mean, features_sd) {
    .Call(`_bigSurvSGD_oneObsPlugingC`, subDataa, Beta, strata_size, norm_method, features_mean, features_sd)
}