R/RcppExports.R

In bvegetabile/paperOptBalGPPS: “Optimally Balanced Gaussian Process Propensity Scores for Estimating Treatment Effects”

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

construct_sqexp <- function(X, M, sig, noise) {
    .Call(`_paperOptBalGPPS_construct_sqexp`, X, M, sig, noise)
}

sqexp_ard <- function(X, hyperparams, scale = 1.0, noise = 1e-6) {
    .Call(`_paperOptBalGPPS_sqexp_ard`, X, hyperparams, scale, noise)
}

sqexp_common <- function(X, lengthscale, scale = 1.0, noise = 1e-6) {
    .Call(`_paperOptBalGPPS_sqexp_common`, X, lengthscale, scale, noise)
}

polykernel <- function(X, sig_zero, pwr = 1L, scale = 1.0, noise = 1e-6) {
    .Call(`_paperOptBalGPPS_polykernel`, X, sig_zero, pwr, scale, noise)
}

par_ep <- function(y, cov_matrix, tol, max_iters, verbose) {
    .Call(`_paperOptBalGPPS_par_ep`, y, cov_matrix, tol, max_iters, verbose)
}

seq_ep <- function(y, cov_matrix, tol, max_iters, verbose) {
    .Call(`_paperOptBalGPPS_seq_ep`, y, cov_matrix, tol, max_iters, verbose)
}

par_sqexp <- function(design_x, vec_theta, sig_noise = 1e-6) {
    .Call(`_paperOptBalGPPS_par_sqexp`, design_x, vec_theta, sig_noise)
}