R/RcppExports.R

In smoothemplik: Smoothed Empirical Likelihood

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

ELCPP <- function(z, ct, mu, lambda_init, return_weights, lower, upper, order, weight_tolerance, thresh, itermax, verbose = FALSE, alpha = 0.3, beta = 0.8, backeps = 0.0) {
    .Call(`_smoothemplik_ELCPP`, z, ct, mu, lambda_init, return_weights, lower, upper, order, weight_tolerance, thresh, itermax, verbose, alpha, beta, backeps)
}

EuLCPP <- function(z, mu, ct, vt, shift, n_orig, weight_tolerance, trunc_to = 0.0, SEL = TRUE, return_weights = FALSE, verbose = FALSE, chull_diag = FALSE) {
    .Call(`_smoothemplik_EuLCPP`, z, mu, ct, vt, shift, n_orig, weight_tolerance, trunc_to, SEL, return_weights, verbose, chull_diag)
}

brentMinCPP <- function(f, interval = numeric(), lower = NA_real_, upper = NA_real_, tol = 1e-8, maxiter = 500L, trace = 0L) {
    .Call(`_smoothemplik_brentMinCPP`, f, interval, lower, upper, tol, maxiter, trace)
}

brentZeroCPP <- function(f, interval = numeric(), lower = NA_real_, upper = NA_real_, f_lower = NULL, f_upper = NULL, extendInt = "no", tol = 1e-8, maxiter = 500L, trace = 0L) {
    .Call(`_smoothemplik_brentZeroCPP`, f, interval, lower, upper, f_lower, f_upper, extendInt, tol, maxiter, trace)
}

dampedNewtonCPP <- function(fn, par, thresh = 1e-16, itermax = 100L, verbose = FALSE, alpha = 0.3, beta = 0.8, backeps = 0.0) {
    .Call(`_smoothemplik_dampedNewtonCPP`, fn, par, thresh, itermax, verbose, alpha, beta, backeps)
}

interpToHigherCPP <- function(x, f, mean, var, at, gap) {
    .Call(`_smoothemplik_interpToHigherCPP`, x, f, mean, var, at, gap)
}

interpToLowerCPP <- function(x, f, mean, var, at, gap) {
    .Call(`_smoothemplik_interpToLowerCPP`, x, f, mean, var, at, gap)
}

#' @importFrom RcppParallel RcppParallelLibs
NULL

kernelFunCPP <- function(x, kernel, order, convolution = FALSE) {
    .Call(`_smoothemplik_kernelFunCPP`, x, kernel, order, convolution)
}

kernelWeightsOneCPP <- function(x, xout, bw, kernel = "gaussian", order = 2L, convolution = FALSE) {
    .Call(`_smoothemplik_kernelWeightsOneCPP`, x, xout, bw, kernel, order, convolution)
}

sparseKernelWeightsOneCPP <- function(x, xout, bw, kernel = "gaussian", order = 2L, convolution = FALSE) {
    .Call(`_smoothemplik_sparseKernelWeightsOneCPP`, x, xout, bw, kernel, order, convolution)
}

kernelWeightsCPP <- function(x, xout, bw, kernel = "gaussian", order = 2L, convolution = FALSE) {
    .Call(`_smoothemplik_kernelWeightsCPP`, x, xout, bw, kernel, order, convolution)
}

sparseKernelWeightsCPP <- function(x, xout, bw, kernel = "gaussian", order = 2L, convolution = FALSE) {
    .Call(`_smoothemplik_sparseKernelWeightsCPP`, x, xout, bw, kernel, order, convolution)
}

kernelDensityCPP <- function(x, xout, weights, bw, kernel = "gaussian", order = 2L, convolution = FALSE, chunks = 0L) {
    .Call(`_smoothemplik_kernelDensityCPP`, x, xout, weights, bw, kernel, order, convolution, chunks)
}

kernelSmoothCPP <- function(x, y, xout, weights, bw, kernel = "gaussian", order = 2L, LOO = FALSE, convolution = FALSE, chunks = 0L) {
    .Call(`_smoothemplik_kernelSmoothCPP`, x, y, xout, weights, bw, kernel, order, LOO, convolution, chunks)
}

dlogCPP <- function(x, d) {
    .Call(`_smoothemplik_dlogCPP`, x, d)
}

tlogCPP <- function(x, a = as.numeric( c(1.0)), k = 4L, d = 0L) {
    .Call(`_smoothemplik_tlogCPP`, x, a, k, d)
}

logTaylorCPP <- function(x, lower, upper, der = as.integer( c(0)), order = 4L) {
    .Call(`_smoothemplik_logTaylorCPP`, x, lower, upper, der, order)
}

getParabola3CPP <- function(x, y) {
    .Call(`_smoothemplik_getParabola3CPP`, x, y)
}

getParabolaCPP <- function(x, f, fp, fpp) {
    .Call(`_smoothemplik_getParabolaCPP`, x, f, fp, fpp)
}

svdlmCPP <- function(x, y, rel_tol = 1e-9, abs_tol = 1e-100) {
    .Call(`_smoothemplik_svdlmCPP`, x, y, rel_tol, abs_tol)
}