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Merge branch 'hotfix/v2.1.2'
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@ygeunkim
ygeunkim committed Oct 12, 2024
2 parents 7cfa8e8 + 64d554a commit 32ee857
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2 changes: 1 addition & 1 deletion DESCRIPTION
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Package: bvhar
Type: Package
Title: Bayesian Vector Heterogeneous Autoregressive Modeling
Version: 2.1.1
Version: 2.1.2
Authors@R:
c(person(given = "Young Geun",
family = "Kim",
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8 changes: 8 additions & 0 deletions NEWS.md
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# bvhar 2.1.2

* Fix MCMC algorithm for `include_mean = TRUE` case.

* Fix predictive distribution update codes (`predict()`, `forecast_roll()`, and `forecast_expand()` for `ldltmod` and `svmod` classes).

* Fix out-of-forecasting (`forecast_roll()` and `forecast_expand()`) result process codes.

# bvhar 2.1.1

* When using GIG generation in MCMC, it has maximum iteration numbers of while statement.
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40 changes: 16 additions & 24 deletions R/RcppExports.R
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Expand Up @@ -868,11 +868,10 @@ forecast_bvharldlt <- function(num_chains, month, step, response_mat, HARtrans,
#' @param step Integer, Step to forecast
#' @param y_test Evaluation time series data period after `y`
#' @param nthreads Number of threads
#' @param chunk_size Chunk size for OpenMP static scheduling
#'
#' @noRd
roll_bvarldlt <- function(y, lag, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size) {
.Call(`_bvhar_roll_bvarldlt`, y, lag, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size)
roll_bvarldlt <- function(y, lag, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads) {
.Call(`_bvhar_roll_bvarldlt`, y, lag, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads)
}

#' Out-of-Sample Forecasting of VAR-SV based on Rolling Window
Expand All @@ -899,11 +898,10 @@ roll_bvarldlt <- function(y, lag, num_chains, num_iter, num_burn, thinning, spar
#' @param step Integer, Step to forecast
#' @param y_test Evaluation time series data period after `y`
#' @param nthreads Number of threads
#' @param chunk_size Chunk size for OpenMP static scheduling
#'
#' @noRd
roll_bvharldlt <- function(y, week, month, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size) {
.Call(`_bvhar_roll_bvharldlt`, y, week, month, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size)
roll_bvharldlt <- function(y, week, month, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads) {
.Call(`_bvhar_roll_bvharldlt`, y, week, month, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads)
}

#' Out-of-Sample Forecasting of VAR-SV based on Rolling Window
Expand All @@ -930,11 +928,10 @@ roll_bvharldlt <- function(y, week, month, num_chains, num_iter, num_burn, thinn
#' @param step Integer, Step to forecast
#' @param y_test Evaluation time series data period after `y`
#' @param nthreads Number of threads
#' @param chunk_size Chunk size for OpenMP static scheduling
#'
#' @noRd
expand_bvarldlt <- function(y, lag, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size) {
.Call(`_bvhar_expand_bvarldlt`, y, lag, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size)
expand_bvarldlt <- function(y, lag, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads) {
.Call(`_bvhar_expand_bvarldlt`, y, lag, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads)
}

#' Out-of-Sample Forecasting of VAR-SV based on Rolling Window
Expand All @@ -961,11 +958,10 @@ expand_bvarldlt <- function(y, lag, num_chains, num_iter, num_burn, thinning, sp
#' @param step Integer, Step to forecast
#' @param y_test Evaluation time series data period after `y`
#' @param nthreads Number of threads
#' @param chunk_size Chunk size for OpenMP static scheduling
#'
#' @noRd
expand_bvharldlt <- function(y, week, month, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size) {
.Call(`_bvhar_expand_bvharldlt`, y, week, month, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size)
expand_bvharldlt <- function(y, week, month, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads) {
.Call(`_bvhar_expand_bvharldlt`, y, week, month, num_chains, num_iter, num_burn, thinning, sparse, level, fit_record, param_reg, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads)
}

#' Forecasting BVAR(p)
Expand Down Expand Up @@ -1278,11 +1274,10 @@ forecast_bvharsv <- function(num_chains, month, step, response_mat, HARtrans, sv
#' @param step Integer, Step to forecast
#' @param y_test Evaluation time series data period after `y`
#' @param nthreads Number of threads
#' @param chunk_size Chunk size for OpenMP static scheduling
#'
#' @noRd
roll_bvarsv <- function(y, lag, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size) {
.Call(`_bvhar_roll_bvarsv`, y, lag, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size)
roll_bvarsv <- function(y, lag, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads) {
.Call(`_bvhar_roll_bvarsv`, y, lag, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads)
}

#' Out-of-Sample Forecasting of VAR-SV based on Rolling Window
Expand All @@ -1309,11 +1304,10 @@ roll_bvarsv <- function(y, lag, num_chains, num_iter, num_burn, thinning, sv, sp
#' @param step Integer, Step to forecast
#' @param y_test Evaluation time series data period after `y`
#' @param nthreads Number of threads
#' @param chunk_size Chunk size for OpenMP static scheduling
#'
#' @noRd
roll_bvharsv <- function(y, week, month, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size) {
.Call(`_bvhar_roll_bvharsv`, y, week, month, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size)
roll_bvharsv <- function(y, week, month, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads) {
.Call(`_bvhar_roll_bvharsv`, y, week, month, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads)
}

#' Out-of-Sample Forecasting of VAR-SV based on Rolling Window
Expand All @@ -1340,11 +1334,10 @@ roll_bvharsv <- function(y, week, month, num_chains, num_iter, num_burn, thinnin
#' @param step Integer, Step to forecast
#' @param y_test Evaluation time series data period after `y`
#' @param nthreads Number of threads
#' @param chunk_size Chunk size for OpenMP static scheduling
#'
#' @noRd
expand_bvarsv <- function(y, lag, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size) {
.Call(`_bvhar_expand_bvarsv`, y, lag, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size)
expand_bvarsv <- function(y, lag, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads) {
.Call(`_bvhar_expand_bvarsv`, y, lag, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads)
}

#' Out-of-Sample Forecasting of VAR-SV based on Rolling Window
Expand All @@ -1371,11 +1364,10 @@ expand_bvarsv <- function(y, lag, num_chains, num_iter, num_burn, thinning, sv,
#' @param step Integer, Step to forecast
#' @param y_test Evaluation time series data period after `y`
#' @param nthreads Number of threads
#' @param chunk_size Chunk size for OpenMP static scheduling
#'
#' @noRd
expand_bvharsv <- function(y, week, month, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size) {
.Call(`_bvhar_expand_bvharsv`, y, week, month, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads, chunk_size)
expand_bvharsv <- function(y, week, month, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads) {
.Call(`_bvhar_expand_bvharsv`, y, week, month, num_chains, num_iter, num_burn, thinning, sv, sparse, level, fit_record, param_sv, param_prior, param_intercept, param_init, prior_type, grp_id, own_id, cross_id, grp_mat, include_mean, step, y_test, get_lpl, seed_chain, seed_forecast, nthreads)
}

#' VAR(1) Representation Given VAR Coefficient Matrix
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