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Merge pull request #15 from sor16/Rcpp-to-speed-up-mcmc
Rcpp to speed up mcmc (and various other changes)
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| Original file line number | Diff line number | Diff line change |
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| @@ -1,12 +1,12 @@ | ||
| Package: bdrc | ||
| Title: Bayesian Discharge Rating Curves | ||
| Version: 1.1.0.9000 | ||
| Version: 2.0.0 | ||
| Authors@R: c(person("Birgir", "Hrafnkelsson", email = "[email protected]", role = c("aut","cph"),comment=c(ORCID="0000-0003-1864-9652")), | ||
| person("Solvi", "Rognvaldsson", email = "[email protected]", role = c("aut","cre"),comment=c(ORCID="0000-0002-4376-3361")), | ||
| person("Axel Orn", "Jansson", email = "axelorn94@gmail.com", role = c("aut")), | ||
| person("Rafael Daníel", "Vias", email = "raffidv@gmail.com", role = c("aut"),comment=c(ORCID="0009-0007-2601-6800")) | ||
| person("Solvi", "Rognvaldsson", email = "[email protected]", role = c("aut"),comment=c(ORCID="0000-0002-4376-3361")), | ||
| person(given = "Rafael Daníel", family = "Vias", email = "raffidv@gmail.com", role = c("aut", "cre"), comment=c(ORCID = "0009-0007-2601-6800")), | ||
| person("Axel Orn", "Jansson", email = "axelorn94@gmail.com", role = c("aut")) | ||
| ) | ||
| Maintainer: Solvi Rognvaldsson <solviro@gmail.com> | ||
| Maintainer: Rafael Daníel Vias <raffidv@gmail.com> | ||
| Description: Fits a discharge rating curve based on the power-law and the generalized power-law from data on paired stage and discharge measurements in a given river using a Bayesian hierarchical model as described in Hrafnkelsson et al. (2020) <arXiv:2010.04769>. | ||
| Depends: R (>= 3.5.0) | ||
| License: MIT + file LICENSE | ||
|
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@@ -16,6 +16,7 @@ Imports: | |
| ggplot2, | ||
| grid, | ||
| gridExtra, | ||
| Rcpp, | ||
| rlang, | ||
| scales | ||
| Suggests: | ||
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@@ -28,3 +29,6 @@ VignetteBuilder: knitr | |
| URL: https://sor16.github.io/bdrc | ||
| BugReports: https://github.com/sor16/bdrc/issues | ||
| Encoding: UTF-8 | ||
| LinkingTo: | ||
| Rcpp, | ||
| RcppArmadillo | ||
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| Original file line number | Diff line number | Diff line change |
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| @@ -0,0 +1,119 @@ | ||
| # Generated by using Rcpp::compileAttributes() -> do not edit by hand | ||
| # Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393 | ||
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| gplm_density_evaluation_unknown_c_cpp <- function(theta, P, h, B, dist, A, y, epsilon, h_min, nugget, n_unique, mu_x, Sig_ab, Z, lambda_c, lambda_sb, lambda_pb, lambda_eta_1, lambda_seta) { | ||
| .Call(`_bdrc_gplm_density_evaluation_unknown_c_cpp`, theta, P, h, B, dist, A, y, epsilon, h_min, nugget, n_unique, mu_x, Sig_ab, Z, lambda_c, lambda_sb, lambda_pb, lambda_eta_1, lambda_seta) | ||
| } | ||
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| gplm_density_evaluation_known_c_cpp <- function(theta, P, h, B, dist, A, y, epsilon, nugget, n_unique, mu_x, Sig_ab, Z, lambda_sb, lambda_pb, lambda_eta_1, lambda_seta, c) { | ||
| .Call(`_bdrc_gplm_density_evaluation_known_c_cpp`, theta, P, h, B, dist, A, y, epsilon, nugget, n_unique, mu_x, Sig_ab, Z, lambda_sb, lambda_pb, lambda_eta_1, lambda_seta, c) | ||
| } | ||
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| gplm_predict_u_unknown_c_cpp <- function(theta, x, P, B_u, h_unique, h_u, dist_all, h_min, nugget, n_unique, n_u) { | ||
| .Call(`_bdrc_gplm_predict_u_unknown_c_cpp`, theta, x, P, B_u, h_unique, h_u, dist_all, h_min, nugget, n_unique, n_u) | ||
| } | ||
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| gplm_predict_u_known_c_cpp <- function(theta, x, P, B_u, h_unique, h_u, dist_all, c, nugget, n_unique, n_u) { | ||
| .Call(`_bdrc_gplm_predict_u_known_c_cpp`, theta, x, P, B_u, h_unique, h_u, dist_all, c, nugget, n_unique, n_u) | ||
| } | ||
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| gplm0_density_evaluation_unknown_c_cpp <- function(theta, h, y, A, dist, epsilon, h_min, nugget, n_unique, mu_x, Sig_ab, Z, lambda_c, lambda_se, lambda_sb, lambda_pb) { | ||
| .Call(`_bdrc_gplm0_density_evaluation_unknown_c_cpp`, theta, h, y, A, dist, epsilon, h_min, nugget, n_unique, mu_x, Sig_ab, Z, lambda_c, lambda_se, lambda_sb, lambda_pb) | ||
| } | ||
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| gplm0_density_evaluation_known_c_cpp <- function(theta, h, y, A, dist, epsilon, c, nugget, n_unique, mu_x, Sig_ab, Z, lambda_se, lambda_sb, lambda_pb) { | ||
| .Call(`_bdrc_gplm0_density_evaluation_known_c_cpp`, theta, h, y, A, dist, epsilon, c, nugget, n_unique, mu_x, Sig_ab, Z, lambda_se, lambda_sb, lambda_pb) | ||
| } | ||
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| gplm0_predict_u_unknown_c_cpp <- function(theta, x, h_unique, h_u, dist_all, h_min, nugget, n_unique, n_u) { | ||
| .Call(`_bdrc_gplm0_predict_u_unknown_c_cpp`, theta, x, h_unique, h_u, dist_all, h_min, nugget, n_unique, n_u) | ||
| } | ||
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| gplm0_predict_u_known_c_cpp <- function(theta, x, h_unique, h_u, dist_all, c, nugget, n_unique, n_u) { | ||
| .Call(`_bdrc_gplm0_predict_u_known_c_cpp`, theta, x, h_unique, h_u, dist_all, c, nugget, n_unique, n_u) | ||
| } | ||
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| matMult <- function(A, B) { | ||
| .Call(`_bdrc_matMult`, A, B) | ||
| } | ||
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| choleskyDecomp <- function(X) { | ||
| .Call(`_bdrc_choleskyDecomp`, X) | ||
| } | ||
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| solveArma <- function(A, B) { | ||
| .Call(`_bdrc_solveArma`, A, B) | ||
| } | ||
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| matInverse <- function(A) { | ||
| .Call(`_bdrc_matInverse`, A) | ||
| } | ||
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| compute_L <- function(X, Sig_x, Sig_eps, nugget) { | ||
| .Call(`_bdrc_compute_L`, X, Sig_x, Sig_eps, nugget) | ||
| } | ||
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| compute_w <- function(L, y, X, mu_x) { | ||
| .Call(`_bdrc_compute_w`, L, y, X, mu_x) | ||
| } | ||
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| get_MCMC_summary_cpp <- function(X, h) { | ||
| .Call(`_bdrc_get_MCMC_summary_cpp`, X, h) | ||
| } | ||
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| calc_variogram_arma <- function(param_mat, i, burnin, nr_iter) { | ||
| .Call(`_bdrc_calc_variogram_arma`, param_mat, i, burnin, nr_iter) | ||
| } | ||
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| variogram_chain <- function(T_max, param_mat1, param_mat2, burnin, nr_iter) { | ||
| .Call(`_bdrc_variogram_chain`, T_max, param_mat1, param_mat2, burnin, nr_iter) | ||
| } | ||
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| distance_matrix <- function(x) { | ||
| .Call(`_bdrc_distance_matrix`, x) | ||
| } | ||
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| create_A_cpp <- function(h) { | ||
| .Call(`_bdrc_create_A_cpp`, h) | ||
| } | ||
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| pri <- function(type, args) { | ||
| .Call(`_bdrc_pri`, type, args) | ||
| } | ||
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| chain_statistics_cpp <- function(chains) { | ||
| .Call(`_bdrc_chain_statistics_cpp`, chains) | ||
| } | ||
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| plm_density_evaluation_unknown_c_cpp <- function(theta, P, h, B, y, epsilon, Sig_x, mu_x, h_min, nugget, lambda_c, lambda_eta_1, lambda_seta) { | ||
| .Call(`_bdrc_plm_density_evaluation_unknown_c_cpp`, theta, P, h, B, y, epsilon, Sig_x, mu_x, h_min, nugget, lambda_c, lambda_eta_1, lambda_seta) | ||
| } | ||
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| plm_density_evaluation_known_c_cpp <- function(theta, P, h, B, y, epsilon, Sig_x, mu_x, c, nugget, lambda_eta_1, lambda_seta) { | ||
| .Call(`_bdrc_plm_density_evaluation_known_c_cpp`, theta, P, h, B, y, epsilon, Sig_x, mu_x, c, nugget, lambda_eta_1, lambda_seta) | ||
| } | ||
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| plm_predict_u_unknown_c_cpp <- function(theta, x, P, B_u, h_u, h_min, n_u) { | ||
| .Call(`_bdrc_plm_predict_u_unknown_c_cpp`, theta, x, P, B_u, h_u, h_min, n_u) | ||
| } | ||
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| plm_predict_u_known_c_cpp <- function(theta, x, P, B_u, h_u, c) { | ||
| .Call(`_bdrc_plm_predict_u_known_c_cpp`, theta, x, P, B_u, h_u, c) | ||
| } | ||
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| plm0_density_evaluation_unknown_c_cpp <- function(theta, h, y, epsilon, Sig_ab, mu_x, h_min, nugget, lambda_c, lambda_se) { | ||
| .Call(`_bdrc_plm0_density_evaluation_unknown_c_cpp`, theta, h, y, epsilon, Sig_ab, mu_x, h_min, nugget, lambda_c, lambda_se) | ||
| } | ||
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| plm0_density_evaluation_known_c_cpp <- function(theta, h, y, epsilon, Sig_ab, mu_x, c, nugget, lambda_se) { | ||
| .Call(`_bdrc_plm0_density_evaluation_known_c_cpp`, theta, h, y, epsilon, Sig_ab, mu_x, c, nugget, lambda_se) | ||
| } | ||
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| plm0_predict_u_unknown_c_cpp <- function(theta, x, h_u, h_min) { | ||
| .Call(`_bdrc_plm0_predict_u_unknown_c_cpp`, theta, x, h_u, h_min) | ||
| } | ||
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| plm0_predict_u_known_c_cpp <- function(theta, x, h_u, c) { | ||
| .Call(`_bdrc_plm0_predict_u_known_c_cpp`, theta, x, h_u, c) | ||
| } | ||
|
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,5 @@ | ||
| ## usethis namespace: start | ||
| #' @importFrom Rcpp sourceCpp | ||
| #' @useDynLib bdrc, .registration = TRUE | ||
| ## usethis namespace: end | ||
| NULL |
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