R/RcppExports.R

In rlibkriging: Kriging Models using the 'libKriging' Library

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

mlpKriging_new <- function(y, X, hidden_dims, d_out = 2L, activation = "selu", kernel = "gauss", regmodel = "constant", normalize = FALSE, optim = "BFGS+Adam", objective = "LL", parameters = NULL) {
    .Call(`_rlibkriging_mlpKriging_new`, y, X, hidden_dims, d_out, activation, kernel, regmodel, normalize, optim, objective, parameters)
}

mlpKriging_fit <- function(model_ptr, y, X, regmodel = "constant", normalize = FALSE, optim = "BFGS+Adam", objective = "LL", parameters = NULL) {
    invisible(.Call(`_rlibkriging_mlpKriging_fit`, model_ptr, y, X, regmodel, normalize, optim, objective, parameters))
}

mlpKriging_predict <- function(model_ptr, x_new, withStd = TRUE, withCov = FALSE, withDeriv = FALSE) {
    .Call(`_rlibkriging_mlpKriging_predict`, model_ptr, x_new, withStd, withCov, withDeriv)
}

mlpKriging_simulate <- function(model_ptr, nsim, seed, x_new, will_update = FALSE) {
    .Call(`_rlibkriging_mlpKriging_simulate`, model_ptr, nsim, seed, x_new, will_update)
}

mlpKriging_update_simulate <- function(model_ptr, y_u, X_u) {
    .Call(`_rlibkriging_mlpKriging_update_simulate`, model_ptr, y_u, X_u)
}

mlpKriging_update <- function(model_ptr, y_u, X_u, refit = TRUE) {
    invisible(.Call(`_rlibkriging_mlpKriging_update`, model_ptr, y_u, X_u, refit))
}

mlpKriging_logLikelihood <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_logLikelihood`, model_ptr)
}

mlpKriging_logLikelihoodFun <- function(model_ptr, theta_gp, withGrad = TRUE, withHess = FALSE) {
    .Call(`_rlibkriging_mlpKriging_logLikelihoodFun`, model_ptr, theta_gp, withGrad, withHess)
}

mlpKriging_summary <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_summary`, model_ptr)
}

mlpKriging_theta <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_theta`, model_ptr)
}

mlpKriging_sigma2 <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_sigma2`, model_ptr)
}

mlpKriging_kernel <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_kernel`, model_ptr)
}

mlpKriging_featureDim <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_featureDim`, model_ptr)
}

mlpKriging_hiddenDims <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_hiddenDims`, model_ptr)
}

mlpKriging_activation <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_activation`, model_ptr)
}

mlpKriging_isFitted <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_isFitted`, model_ptr)
}

mlpKriging_X <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_X`, model_ptr)
}

mlpKriging_centerX <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_centerX`, model_ptr)
}

mlpKriging_scaleX <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_scaleX`, model_ptr)
}

mlpKriging_y <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_y`, model_ptr)
}

mlpKriging_centerY <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_centerY`, model_ptr)
}

mlpKriging_scaleY <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_scaleY`, model_ptr)
}

mlpKriging_normalize <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_normalize`, model_ptr)
}

mlpKriging_regmodel <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_regmodel`, model_ptr)
}

mlpKriging_F <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_F`, model_ptr)
}

mlpKriging_T <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_T`, model_ptr)
}

mlpKriging_M <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_M`, model_ptr)
}

mlpKriging_z <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_z`, model_ptr)
}

mlpKriging_beta <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_beta`, model_ptr)
}

mlpKriging_copy <- function(model_ptr) {
    .Call(`_rlibkriging_mlpKriging_copy`, model_ptr)
}

mlpKriging_save <- function(model_ptr, filename) {
    invisible(.Call(`_rlibkriging_mlpKriging_save`, model_ptr, filename))
}

warpKriging_new <- function(y, X, warping, kernel, regmodel = "constant", normalize = FALSE, optim = "BFGS+Adam", objective = "LL", parameters = NULL, noise = NULL) {
    .Call(`_rlibkriging_warpKriging_new`, y, X, warping, kernel, regmodel, normalize, optim, objective, parameters, noise)
}

warpKriging_fit <- function(model_ptr, y, X, regmodel = "constant", normalize = FALSE, optim = "BFGS+Adam", objective = "LL", parameters = NULL, noise = NULL) {
    invisible(.Call(`_rlibkriging_warpKriging_fit`, model_ptr, y, X, regmodel, normalize, optim, objective, parameters, noise))
}

warpKriging_predict <- function(model_ptr, x_new, withStd = TRUE, withCov = FALSE, withDeriv = FALSE) {
    .Call(`_rlibkriging_warpKriging_predict`, model_ptr, x_new, withStd, withCov, withDeriv)
}

warpKriging_simulate <- function(model_ptr, nsim, seed, x_new, will_update = FALSE) {
    .Call(`_rlibkriging_warpKriging_simulate`, model_ptr, nsim, seed, x_new, will_update)
}

warpKriging_update_simulate <- function(model_ptr, y_u, X_u) {
    .Call(`_rlibkriging_warpKriging_update_simulate`, model_ptr, y_u, X_u)
}

warpKriging_update <- function(model_ptr, y_u, X_u, refit = TRUE) {
    invisible(.Call(`_rlibkriging_warpKriging_update`, model_ptr, y_u, X_u, refit))
}

warpKriging_logLikelihood <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_logLikelihood`, model_ptr)
}

warpKriging_logLikelihoodFun <- function(model_ptr, theta_gp, withGrad = TRUE, withHess = FALSE) {
    .Call(`_rlibkriging_warpKriging_logLikelihoodFun`, model_ptr, theta_gp, withGrad, withHess)
}

warpKriging_summary <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_summary`, model_ptr)
}

warpKriging_theta <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_theta`, model_ptr)
}

warpKriging_sigma2 <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_sigma2`, model_ptr)
}

warpKriging_kernel <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_kernel`, model_ptr)
}

warpKriging_featureDim <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_featureDim`, model_ptr)
}

warpKriging_warping <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_warping`, model_ptr)
}

warpKriging_isFitted <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_isFitted`, model_ptr)
}

warpKriging_X <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_X`, model_ptr)
}

warpKriging_centerX <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_centerX`, model_ptr)
}

warpKriging_scaleX <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_scaleX`, model_ptr)
}

warpKriging_y <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_y`, model_ptr)
}

warpKriging_centerY <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_centerY`, model_ptr)
}

warpKriging_scaleY <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_scaleY`, model_ptr)
}

warpKriging_normalize <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_normalize`, model_ptr)
}

warpKriging_regmodel <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_regmodel`, model_ptr)
}

warpKriging_F <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_F`, model_ptr)
}

warpKriging_T <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_T`, model_ptr)
}

warpKriging_M <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_M`, model_ptr)
}

warpKriging_z <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_z`, model_ptr)
}

warpKriging_beta <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_beta`, model_ptr)
}

warpKriging_copy <- function(model_ptr) {
    .Call(`_rlibkriging_warpKriging_copy`, model_ptr)
}

warpKriging_save <- function(model_ptr, filename) {
    invisible(.Call(`_rlibkriging_warpKriging_save`, model_ptr, filename))
}

new_Kriging <- function(kernel, noise_model = "none") {
    .Call(`_rlibkriging_new_Kriging`, kernel, noise_model)
}

new_KrigingFit <- function(y, X, kernel, noise_model = "none", noise = NULL, regmodel = "constant", normalize = FALSE, optim = "BFGS", objective = "LL", parameters = NULL) {
    .Call(`_rlibkriging_new_KrigingFit`, y, X, kernel, noise_model, noise, regmodel, normalize, optim, objective, parameters)
}

kriging_fit <- function(k, y, X, noise = NULL, regmodel = "constant", normalize = FALSE, optim = "BFGS", objective = "LL", parameters = NULL) {
    invisible(.Call(`_rlibkriging_kriging_fit`, k, y, X, noise, regmodel, normalize, optim, objective, parameters))
}

kriging_copy <- function(k) {
    .Call(`_rlibkriging_kriging_copy`, k)
}

kriging_model <- function(k) {
    .Call(`_rlibkriging_kriging_model`, k)
}

kriging_summary <- function(k) {
    .Call(`_rlibkriging_kriging_summary`, k)
}

kriging_predict <- function(k, X_n, return_stdev = TRUE, return_cov = FALSE, return_deriv = FALSE) {
    .Call(`_rlibkriging_kriging_predict`, k, X_n, return_stdev, return_cov, return_deriv)
}

kriging_simulate <- function(k, nsim, seed, X_n, with_noise = NULL, will_update = FALSE) {
    .Call(`_rlibkriging_kriging_simulate`, k, nsim, seed, X_n, with_noise, will_update)
}

kriging_update_simulate <- function(k, y_u, noise_u, X_u) {
    .Call(`_rlibkriging_kriging_update_simulate`, k, y_u, noise_u, X_u)
}

kriging_update <- function(k, y_u, X_u, noise_u = NULL, refit = TRUE) {
    invisible(.Call(`_rlibkriging_kriging_update`, k, y_u, X_u, noise_u, refit))
}

kriging_save <- function(k, filename) {
    invisible(.Call(`_rlibkriging_kriging_save`, k, filename))
}

kriging_covMat <- function(k, X1, X2) {
    .Call(`_rlibkriging_kriging_covMat`, k, X1, X2)
}

kriging_logLikelihoodFun <- function(k, theta, return_grad = FALSE, return_hess = FALSE, bench = FALSE) {
    .Call(`_rlibkriging_kriging_logLikelihoodFun`, k, theta, return_grad, return_hess, bench)
}

kriging_logLikelihood <- function(k) {
    .Call(`_rlibkriging_kriging_logLikelihood`, k)
}

kriging_leaveOneOutFun <- function(k, theta, return_grad = FALSE, bench = FALSE) {
    .Call(`_rlibkriging_kriging_leaveOneOutFun`, k, theta, return_grad, bench)
}

kriging_leaveOneOutVec <- function(k, theta) {
    .Call(`_rlibkriging_kriging_leaveOneOutVec`, k, theta)
}

kriging_leaveOneOut <- function(k) {
    .Call(`_rlibkriging_kriging_leaveOneOut`, k)
}

kriging_logMargPostFun <- function(k, theta, return_grad = FALSE, bench = FALSE) {
    .Call(`_rlibkriging_kriging_logMargPostFun`, k, theta, return_grad, bench)
}

kriging_logMargPost <- function(k) {
    .Call(`_rlibkriging_kriging_logMargPost`, k)
}

kriging_kernel <- function(k) {
    .Call(`_rlibkriging_kriging_kernel`, k)
}

kriging_optim <- function(k) {
    .Call(`_rlibkriging_kriging_optim`, k)
}

kriging_objective <- function(k) {
    .Call(`_rlibkriging_kriging_objective`, k)
}

kriging_X <- function(k) {
    .Call(`_rlibkriging_kriging_X`, k)
}

kriging_centerX <- function(k) {
    .Call(`_rlibkriging_kriging_centerX`, k)
}

kriging_scaleX <- function(k) {
    .Call(`_rlibkriging_kriging_scaleX`, k)
}

kriging_y <- function(k) {
    .Call(`_rlibkriging_kriging_y`, k)
}

kriging_centerY <- function(k) {
    .Call(`_rlibkriging_kriging_centerY`, k)
}

kriging_scaleY <- function(k) {
    .Call(`_rlibkriging_kriging_scaleY`, k)
}

kriging_normalize <- function(k) {
    .Call(`_rlibkriging_kriging_normalize`, k)
}

kriging_regmodel <- function(k) {
    .Call(`_rlibkriging_kriging_regmodel`, k)
}

kriging_F <- function(k) {
    .Call(`_rlibkriging_kriging_F`, k)
}

kriging_T <- function(k) {
    .Call(`_rlibkriging_kriging_T`, k)
}

kriging_M <- function(k) {
    .Call(`_rlibkriging_kriging_M`, k)
}

kriging_z <- function(k) {
    .Call(`_rlibkriging_kriging_z`, k)
}

kriging_beta <- function(k) {
    .Call(`_rlibkriging_kriging_beta`, k)
}

kriging_is_beta_estim <- function(k) {
    .Call(`_rlibkriging_kriging_is_beta_estim`, k)
}

kriging_theta <- function(k) {
    .Call(`_rlibkriging_kriging_theta`, k)
}

kriging_is_theta_estim <- function(k) {
    .Call(`_rlibkriging_kriging_is_theta_estim`, k)
}

kriging_sigma2 <- function(k) {
    .Call(`_rlibkriging_kriging_sigma2`, k)
}

kriging_is_sigma2_estim <- function(k) {
    .Call(`_rlibkriging_kriging_is_sigma2_estim`, k)
}

kriging_noise_model <- function(k) {
    .Call(`_rlibkriging_kriging_noise_model`, k)
}

kriging_nugget <- function(k) {
    .Call(`_rlibkriging_kriging_nugget`, k)
}

kriging_is_nugget_estim <- function(k) {
    .Call(`_rlibkriging_kriging_is_nugget_estim`, k)
}

kriging_noise <- function(k) {
    .Call(`_rlibkriging_kriging_noise`, k)
}

linalg_get_num_nugget <- function() {
    .Call(`_rlibkriging_linalg_get_num_nugget`)
}

linalg_set_num_nugget <- function(nugget) {
    invisible(.Call(`_rlibkriging_linalg_set_num_nugget`, nugget))
}

linalg_check_chol_rcond <- function(cr) {
    invisible(.Call(`_rlibkriging_linalg_check_chol_rcond`, cr))
}

linalg_chol_rcond_checked <- function() {
    .Call(`_rlibkriging_linalg_chol_rcond_checked`)
}

linalg_chol_safe <- function(X) {
    .Call(`_rlibkriging_linalg_chol_safe`, X)
}

linalg_set_chol_warning <- function(warn) {
    invisible(.Call(`_rlibkriging_linalg_set_chol_warning`, warn))
}

linalg_rcond_approx_chol <- function(X) {
    .Call(`_rlibkriging_linalg_rcond_approx_chol`, X)
}

linalg_rcond_chol <- function(X) {
    .Call(`_rlibkriging_linalg_rcond_chol`, X)
}

linalg_chol_block <- function(C, Loo) {
    .Call(`_rlibkriging_linalg_chol_block`, C, Loo)
}

kriging_load <- function(filename) {
    .Call(`_rlibkriging_kriging_load`, filename)
}

warpkriging_load <- function(filename) {
    .Call(`_rlibkriging_warpkriging_load`, filename)
}

mlpkriging_load <- function(filename) {
    .Call(`_rlibkriging_mlpkriging_load`, filename)
}

class_saved <- function(filename) {
    .Call(`_rlibkriging_class_saved`, filename)
}

new_NestedKrigingFit <- function(y, X, kernel, nb_groups, aggregation = "NK", partition = "kmeans", seed = 123L, regmodel = "constant", optim = "BFGS", objective = "LL", parameters = NULL, warping = NULL) {
    .Call(`_rlibkriging_new_NestedKrigingFit`, y, X, kernel, nb_groups, aggregation, partition, seed, regmodel, optim, objective, parameters, warping)
}

nestedkriging_predict <- function(k, X_n, return_stdev = TRUE) {
    .Call(`_rlibkriging_nestedkriging_predict`, k, X_n, return_stdev)
}

nestedkriging_summary <- function(k) {
    .Call(`_rlibkriging_nestedkriging_summary`, k)
}

nestedkriging_kernel <- function(k) {
    .Call(`_rlibkriging_nestedkriging_kernel`, k)
}

nestedkriging_aggregation <- function(k) {
    .Call(`_rlibkriging_nestedkriging_aggregation`, k)
}

nestedkriging_nb_groups <- function(k) {
    .Call(`_rlibkriging_nestedkriging_nb_groups`, k)
}

nestedkriging_groups <- function(k) {
    .Call(`_rlibkriging_nestedkriging_groups`, k)
}

nestedkriging_theta <- function(k) {
    .Call(`_rlibkriging_nestedkriging_theta`, k)
}

nestedkriging_sigma2 <- function(k) {
    .Call(`_rlibkriging_nestedkriging_sigma2`, k)
}

nestedkriging_beta0 <- function(k) {
    .Call(`_rlibkriging_nestedkriging_beta0`, k)
}

nestedkriging_warping <- function(k) {
    .Call(`_rlibkriging_nestedkriging_warping`, k)
}

nestedkriging_X <- function(k) {
    .Call(`_rlibkriging_nestedkriging_X`, k)
}

nestedkriging_y <- function(k) {
    .Call(`_rlibkriging_nestedkriging_y`, k)
}

optim_is_reparametrized <- function() {
    .Call(`_rlibkriging_optim_is_reparametrized`)
}

optim_use_reparametrize <- function(reparametrize) {
    invisible(.Call(`_rlibkriging_optim_use_reparametrize`, reparametrize))
}

optim_get_theta_lower_factor <- function() {
    .Call(`_rlibkriging_optim_get_theta_lower_factor`)
}

optim_set_theta_lower_factor <- function(theta_lower_factor) {
    invisible(.Call(`_rlibkriging_optim_set_theta_lower_factor`, theta_lower_factor))
}

optim_get_theta_upper_factor <- function() {
    .Call(`_rlibkriging_optim_get_theta_upper_factor`)
}

optim_set_theta_upper_factor <- function(theta_upper_factor) {
    invisible(.Call(`_rlibkriging_optim_set_theta_upper_factor`, theta_upper_factor))
}

optim_variogram_bounds_heuristic_used <- function() {
    .Call(`_rlibkriging_optim_variogram_bounds_heuristic_used`)
}

optim_use_variogram_bounds_heuristic <- function(variogram_bounds_heuristic) {
    invisible(.Call(`_rlibkriging_optim_use_variogram_bounds_heuristic`, variogram_bounds_heuristic))
}

optim_set_log_level <- function(l) {
    invisible(.Call(`_rlibkriging_optim_set_log_level`, l))
}

optim_get_log_level <- function() {
    .Call(`_rlibkriging_optim_get_log_level`)
}

optim_get_max_iteration <- function() {
    .Call(`_rlibkriging_optim_get_max_iteration`)
}

optim_set_max_iteration <- function(max_iteration) {
    invisible(.Call(`_rlibkriging_optim_set_max_iteration`, max_iteration))
}

optim_get_gradient_tolerance <- function() {
    .Call(`_rlibkriging_optim_get_gradient_tolerance`)
}

optim_set_gradient_tolerance <- function(gradient_tolerance) {
    invisible(.Call(`_rlibkriging_optim_set_gradient_tolerance`, gradient_tolerance))
}

optim_get_objective_rel_tolerance <- function() {
    .Call(`_rlibkriging_optim_get_objective_rel_tolerance`)
}

optim_set_objective_rel_tolerance <- function(objective_rel_tolerance) {
    invisible(.Call(`_rlibkriging_optim_set_objective_rel_tolerance`, objective_rel_tolerance))
}

optim_get_thread_start_delay_ms <- function() {
    .Call(`_rlibkriging_optim_get_thread_start_delay_ms`)
}

optim_set_thread_start_delay_ms <- function(delay_ms) {
    invisible(.Call(`_rlibkriging_optim_set_thread_start_delay_ms`, delay_ms))
}

optim_get_thread_pool_size <- function() {
    .Call(`_rlibkriging_optim_get_thread_pool_size`)
}

optim_set_thread_pool_size <- function(pool_size) {
    invisible(.Call(`_rlibkriging_optim_set_thread_pool_size`, pool_size))
}

random_reset_seed <- function(seed) {
    invisible(.Call(`_rlibkriging_random_reset_seed`, seed))
}

random_randu <- function() {
    .Call(`_rlibkriging_random_randu`)
}

random_randu_vec <- function(n) {
    .Call(`_rlibkriging_random_randu_vec`, n)
}

random_randu_mat <- function(n, d) {
    .Call(`_rlibkriging_random_randu_mat`, n, d)
}

random_randn_mat <- function(n, d) {
    .Call(`_rlibkriging_random_randn_mat`, n, d)
}