R/RcppExports.R

Defines functions stratified_sample psd_chol fast_dmvnorm precompute_dmvnorm dmvnorm conditional_cov ukf_nlg gaussian_sim_smoother gaussian_fast_smoother gaussian_ccov_smoother gaussian_smoother sde_state_sampler_bsf_is2 sde_is_mcmc sde_da_mcmc sde_pm_mcmc bsf_smoother_sde bsf_sde loglik_sde psi_smoother_nlg psi_smoother gaussian_psi_smoother nonlinear_predict_past nongaussian_predict_past gaussian_predict_past nonlinear_predict nongaussian_predict gaussian_predict postcorrection_nonlinear postcorrection_nongaussian suggest_n_nonlinear suggest_n_nongaussian R_milstein nonlinear_is_mcmc nonlinear_ekf_mcmc nonlinear_da_mcmc nonlinear_pm_mcmc nongaussian_is_mcmc nongaussian_da_mcmc nongaussian_pm_mcmc gaussian_mcmc nonlinear_loglik nongaussian_loglik gaussian_loglik gaussian_kfilter importance_sample_ng ekpf_smoother ekpf ekf_fast_smoother_nlg ekf_smoother_nlg ekf_nlg bsf_smoother_nlg bsf_nlg bsf_smoother bsf gaussian_approx_model_nlg gaussian_approx_model

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

gaussian_approx_model <- function(model_, model_type) {
    .Call('_bssm_gaussian_approx_model', PACKAGE = 'bssm', model_, model_type)
}

gaussian_approx_model_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, max_iter, conv_tol, iekf_iter) {
    .Call('_bssm_gaussian_approx_model_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, max_iter, conv_tol, iekf_iter)
}

bsf <- function(model_, nsim, seed, gaussian, model_type) {
    .Call('_bssm_bsf', PACKAGE = 'bssm', model_, nsim, seed, gaussian, model_type)
}

bsf_smoother <- function(model_, nsim, seed, gaussian, model_type) {
    .Call('_bssm_bsf_smoother', PACKAGE = 'bssm', model_, nsim, seed, gaussian, model_type)
}

bsf_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) {
    .Call('_bssm_bsf_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed)
}

bsf_smoother_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) {
    .Call('_bssm_bsf_smoother_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed)
}

ekf_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter) {
    .Call('_bssm_ekf_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter)
}

ekf_smoother_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter) {
    .Call('_bssm_ekf_smoother_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter)
}

ekf_fast_smoother_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter) {
    .Call('_bssm_ekf_fast_smoother_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, iekf_iter)
}

ekpf <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) {
    .Call('_bssm_ekpf', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed)
}

ekpf_smoother <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed) {
    .Call('_bssm_ekpf_smoother', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed)
}

importance_sample_ng <- function(model_, nsim, use_antithetic, seed, model_type) {
    .Call('_bssm_importance_sample_ng', PACKAGE = 'bssm', model_, nsim, use_antithetic, seed, model_type)
}

gaussian_kfilter <- function(model_, model_type) {
    .Call('_bssm_gaussian_kfilter', PACKAGE = 'bssm', model_, model_type)
}

gaussian_loglik <- function(model_, model_type) {
    .Call('_bssm_gaussian_loglik', PACKAGE = 'bssm', model_, model_type)
}

nongaussian_loglik <- function(model_, nsim, sampling_method, seed, model_type) {
    .Call('_bssm_nongaussian_loglik', PACKAGE = 'bssm', model_, nsim, sampling_method, seed, model_type)
}

nonlinear_loglik <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter, method) {
    .Call('_bssm_nonlinear_loglik', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter, method)
}

gaussian_mcmc <- function(model_, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, model_type) {
    .Call('_bssm_gaussian_mcmc', PACKAGE = 'bssm', model_, output_type, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, model_type)
}

nongaussian_pm_mcmc <- function(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type) {
    .Call('_bssm_nongaussian_pm_mcmc', PACKAGE = 'bssm', model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type)
}

nongaussian_da_mcmc <- function(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type) {
    .Call('_bssm_nongaussian_da_mcmc', PACKAGE = 'bssm', model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, model_type)
}

nongaussian_is_mcmc <- function(model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, is_type, model_type, approx) {
    .Call('_bssm_nongaussian_is_mcmc', PACKAGE = 'bssm', model_, output_type, nsim, iter, burnin, thin, gamma, target_acceptance, S, seed, end_ram, n_threads, sampling_method, is_type, model_type, approx)
}

nonlinear_pm_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type) {
    .Call('_bssm_nonlinear_pm_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type)
}

nonlinear_da_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type) {
    .Call('_bssm_nonlinear_da_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, max_iter, conv_tol, sampling_method, iekf_iter, output_type)
}

nonlinear_ekf_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, iekf_iter, output_type) {
    .Call('_bssm_nonlinear_ekf_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, iekf_iter, output_type)
}

nonlinear_is_mcmc <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, is_type, sampling_method, max_iter, conv_tol, iekf_iter, output_type, approx) {
    .Call('_bssm_nonlinear_is_mcmc', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, seed, nsim, iter, burnin, thin, gamma, target_acceptance, S, end_ram, n_threads, is_type, sampling_method, max_iter, conv_tol, iekf_iter, output_type, approx)
}

R_milstein <- function(x0, L, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed) {
    .Call('_bssm_R_milstein', PACKAGE = 'bssm', x0, L, t, theta, drift_pntr, diffusion_pntr, ddiffusion_pntr, positive, seed)
}

suggest_n_nongaussian <- function(model_, theta, candidates, replications, seed, model_type) {
    .Call('_bssm_suggest_n_nongaussian', PACKAGE = 'bssm', model_, theta, candidates, replications, seed, model_type)
}

suggest_n_nonlinear <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, theta_map, candidates, replications, seed) {
    .Call('_bssm_suggest_n_nonlinear', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, theta_map, candidates, replications, seed)
}

postcorrection_nongaussian <- function(model_, model_type, output_type, nsim, seed, n_threads, is_type, counts, theta, modes) {
    .Call('_bssm_postcorrection_nongaussian', PACKAGE = 'bssm', model_, model_type, output_type, nsim, seed, n_threads, is_type, counts, theta, modes)
}

postcorrection_nonlinear <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta_init, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, output_type, nsim, seed, n_threads, is_type, counts, theta, modes) {
    .Call('_bssm_postcorrection_nonlinear', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta_init, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, output_type, nsim, seed, n_threads, is_type, counts, theta, modes)
}

gaussian_predict <- function(model_, theta, alpha, predict_type, seed, model_type) {
    .Call('_bssm_gaussian_predict', PACKAGE = 'bssm', model_, theta, alpha, predict_type, seed, model_type)
}

nongaussian_predict <- function(model_, theta, alpha, predict_type, seed, model_type) {
    .Call('_bssm_nongaussian_predict', PACKAGE = 'bssm', model_, theta, alpha, predict_type, seed, model_type)
}

nonlinear_predict <- function(y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed) {
    .Call('_bssm_nonlinear_predict', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed)
}

gaussian_predict_past <- function(model_, theta, alpha, predict_type, seed, model_type) {
    .Call('_bssm_gaussian_predict_past', PACKAGE = 'bssm', model_, theta, alpha, predict_type, seed, model_type)
}

nongaussian_predict_past <- function(model_, theta, alpha, predict_type, seed, model_type) {
    .Call('_bssm_nongaussian_predict_past', PACKAGE = 'bssm', model_, theta, alpha, predict_type, seed, model_type)
}

nonlinear_predict_past <- function(y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed) {
    .Call('_bssm_nonlinear_predict_past', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, log_prior_pdf, known_params, known_tv_params, time_varying, n_states, n_etas, theta, alpha, predict_type, seed)
}

gaussian_psi_smoother <- function(model_, nsim, seed, model_type) {
    .Call('_bssm_gaussian_psi_smoother', PACKAGE = 'bssm', model_, nsim, seed, model_type)
}

psi_smoother <- function(model_, nsim, seed, model_type) {
    .Call('_bssm_psi_smoother', PACKAGE = 'bssm', model_, nsim, seed, model_type)
}

psi_smoother_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter) {
    .Call('_bssm_psi_smoother_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, nsim, seed, max_iter, conv_tol, iekf_iter)
}

loglik_sde <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) {
    .Call('_bssm_loglik_sde', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed)
}

bsf_sde <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) {
    .Call('_bssm_bsf_sde', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed)
}

bsf_smoother_sde <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed) {
    .Call('_bssm_bsf_smoother_sde', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed)
}

sde_pm_mcmc <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type) {
    .Call('_bssm_sde_pm_mcmc', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type)
}

sde_da_mcmc <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type) {
    .Call('_bssm_sde_da_mcmc', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, type)
}

sde_is_mcmc <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, is_type, n_threads, type) {
    .Call('_bssm_sde_is_mcmc', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, theta, nsim, L_c, L_f, seed, iter, burnin, thin, gamma, target_acceptance, S, end_ram, is_type, n_threads, type)
}

sde_state_sampler_bsf_is2 <- function(y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, nsim, L_f, seed, approx_loglik_storage, theta) {
    .Call('_bssm_sde_state_sampler_bsf_is2', PACKAGE = 'bssm', y, x0, positive, drift_pntr, diffusion_pntr, ddiffusion_pntr, log_prior_pdf_pntr, log_obs_density_pntr, nsim, L_f, seed, approx_loglik_storage, theta)
}

gaussian_smoother <- function(model_, model_type) {
    .Call('_bssm_gaussian_smoother', PACKAGE = 'bssm', model_, model_type)
}

gaussian_ccov_smoother <- function(model_, model_type) {
    .Call('_bssm_gaussian_ccov_smoother', PACKAGE = 'bssm', model_, model_type)
}

gaussian_fast_smoother <- function(model_, model_type) {
    .Call('_bssm_gaussian_fast_smoother', PACKAGE = 'bssm', model_, model_type)
}

gaussian_sim_smoother <- function(model_, nsim, use_antithetic, seed, model_type) {
    .Call('_bssm_gaussian_sim_smoother', PACKAGE = 'bssm', model_, nsim, use_antithetic, seed, model_type)
}

ukf_nlg <- function(y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, alpha, beta, kappa) {
    .Call('_bssm_ukf_nlg', PACKAGE = 'bssm', y, Z, H, T, R, Zg, Tg, a1, P1, theta, log_prior_pdf, known_params, known_tv_params, n_states, n_etas, time_varying, alpha, beta, kappa)
}

conditional_cov <- function(Vt, Ct, use_svd) {
    invisible(.Call('_bssm_conditional_cov', PACKAGE = 'bssm', Vt, Ct, use_svd))
}

dmvnorm <- function(x, mean, sigma, lwr, logd) {
    .Call('_bssm_dmvnorm', PACKAGE = 'bssm', x, mean, sigma, lwr, logd)
}

precompute_dmvnorm <- function(sigma, Linv, nonzero) {
    .Call('_bssm_precompute_dmvnorm', PACKAGE = 'bssm', sigma, Linv, nonzero)
}

fast_dmvnorm <- function(x, mean, Linv, nonzero, constant) {
    .Call('_bssm_fast_dmvnorm', PACKAGE = 'bssm', x, mean, Linv, nonzero, constant)
}

psd_chol <- function(x) {
    .Call('_bssm_psd_chol', PACKAGE = 'bssm', x)
}

stratified_sample <- function(p, r, N) {
    .Call('_bssm_stratified_sample', PACKAGE = 'bssm', p, r, N)
}

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bssm documentation built on Sept. 6, 2021, 9:12 a.m.