update citation

R/RcppExports.R

@@ -1016,7 +1016,7 @@ forecast_bvar <- function(object, step, num_sim) {
 #'     - Predictive distribution: Again generate \eqn{\tilde{Y}_{n + j}^{(b)} \sim \Phi^{(b)}, \Sigma_e^{(b)} \sim MN}
 #'     - tilde notation indicates simulated ones
 #' 
-#' @references Kim, Y. G., and Baek, C. (n.d.). *Bayesian vector heterogeneous autoregressive modeling*. submitted.
+#' @references Kim, Y. G., and Baek, C. (2024). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
 #' @noRd
 forecast_bvharmn <- function(object, step, num_sim) {
     .Call(`_bvhar_forecast_bvharmn`, object, step, num_sim)

R/bvhar-horseshoe.R

@@ -47,8 +47,6 @@
 #'   \item{y}{Raw input}
 #' }
 #' @references 
-#' Kim, Y. G., and Baek, C. (2023). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
-#'
 #' Kim, Y. G., and Baek, C. (n.d.). Working paper.
 #' @importFrom posterior as_draws_df bind_draws
 #' @importFrom stats cov

R/bvhar-minnesota.R

@@ -121,7 +121,7 @@ logml_weight_bvharhm <- function(param, daily, weekly, monthly, eps = 1e-04, y,
 #'   \item{spec}{Model specification (`bvharspec`)}
 #' }
 #' It is also `normaliw` and `bvharmod` class.
-#' @references Kim, Y. G., and Baek, C. (2023). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
+#' @references Kim, Y. G., and Baek, C. (2024). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
 #' @seealso 
 #' * [set_bvhar()] to specify the hyperparameters of BVHAR-S
 #' * [set_weight_bvhar()] to specify the hyperparameters of BVHAR-L

R/bvhar-package.R

@@ -8,7 +8,7 @@
 #' * Bayesian VAR (BVAR) model: [bvar_minnesota()], [bvar_flat()]
 #' * Bayesian VHAR (BVHAR) model: [bvhar_minnesota()]
 #' @references 
-#' Kim, Y. G., and Baek, C. (2023). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
+#' Kim, Y. G., and Baek, C. (2024). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
 "_PACKAGE"
 
 # The following block is used by usethis to automatically manage

R/bvhar-ssvs.R

@@ -62,8 +62,6 @@
 #'   \item{y}{Raw input}
 #' }
 #' @references 
-#' Kim, Y. G., and Baek, C. (2023). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
-#'
 #' Kim, Y. G., and Baek, C. (n.d.). Working paper.
 #' @importFrom posterior as_draws_df bind_draws
 #' @order 1

R/bvhar-sv.R

@@ -62,7 +62,7 @@
 #'   \item{pip}{Posterior inclusion probabilities.}
 #' }
 #' @references 
-#' Kim, Y. G., and Baek, C. (2023+). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
+#' Kim, Y. G., and Baek, C. (2024). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
 #' 
 #' Kim, Y. G., and Baek, C. (n.d.). Working paper.
 #' @importFrom posterior as_draws_df bind_draws summarise_draws

R/hyperparam.R

@@ -237,7 +237,7 @@ set_bvar_flat <- function(U) {
 #' )
 #' class(bvhar_var_spec)
 #' str(bvhar_var_spec)
-#' @references Kim, Y. G., and Baek, C. (2023+). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
+#' @references Kim, Y. G., and Baek, C. (2024). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
 #' @order 1
 #' @export
 set_bvhar <- function(sigma, lambda = .1, delta, eps = 1e-04) {
@@ -292,7 +292,7 @@ set_bvhar <- function(sigma, lambda = .1, delta, eps = 1e-04) {
 #'   \item{weekly}{Vector value assigned for weekly weight}
 #'   \item{monthly}{Vector value assigned for monthly weight}
 #' }
-#' @references Kim, Y. G., and Baek, C. (2023+). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
+#' @references Kim, Y. G., and Baek, C. (2024). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
 #' @examples 
 #' # BVHAR-L specification---------------------------
 #' bvhar_vhar_spec <- set_weight_bvhar(

R/simulate-param.R

@@ -119,7 +119,7 @@ sim_mncoef <- function(p, bayes_spec = set_bvar(), full = TRUE) {
 #'   \item{coefficients}{BVHAR coefficient (MN)}
 #'   \item{covmat}{BVHAR variance (IW or diagonal matrix of `sigma` of `bayes_spec`)}
 #' }
-#' @references Kim, Y. G., and Baek, C. (n.d.). *Bayesian vector heterogeneous autoregressive modeling*. submitted.
+#' @references Kim, Y. G., and Baek, C. (2024). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
 #' @examples 
 #' # Generate (Phi, Sigma)
 #' # BVHAR-S

R/tuning.R

@@ -280,7 +280,7 @@ logml_bvhar_vhar <- function(param, eps = 1e-04, y, har = c(5, 22), include_mean
 #' @param har Numeric vector for weekly and monthly order. By default, `c(5, 22)`.
 #' @param include_mean Add constant term (Default: `TRUE`) or not (`FALSE`)
 #' @param parallel List the same argument of [optimParallel::optimParallel()]. By default, this is empty, and the function does not execute parallel computation.
-#' @references Kim, Y. G., and Baek, C. (2023+). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
+#' @references Kim, Y. G., and Baek, C. (2024). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
 #' @importFrom stats optim
 #' @importFrom optimParallel optimParallel
 #' @order 1
@@ -524,7 +524,7 @@ bound_bvhar <- function(init_spec = set_bvhar(),
 #' @references 
 #' Giannone, D., Lenza, M., & Primiceri, G. E. (2015). *Prior Selection for Vector Autoregressions*. Review of Economics and Statistics, 97(2).
 #' 
-#' Kim, Y. G., and Baek, C. (n.d.). *Bayesian vector heterogeneous autoregressive modeling*. submitted.
+#' Kim, Y. G., and Baek, C. (2024). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
 #' @order 1
 #' @export
 choose_bayes <- function(bayes_bound = bound_bvhar(),

R/vhar-bayes.R

@@ -62,7 +62,7 @@
 #'   \item{pip}{Posterior inclusion probabilities.}
 #' }
 #' @references
-#' Kim, Y. G., and Baek, C. (2023+). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
+#' Kim, Y. G., and Baek, C. (2024). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
 #'
 #' Kim, Y. G., and Baek, C. (n.d.). Working paper.
 #' @importFrom posterior as_draws_df bind_draws summarise_draws

inst/CITATION

@@ -13,7 +13,7 @@ bibentry(
   #volume   = {},
   #number   = {},
   #pages    = {},
-  #doi = {},
+  doi = "10.32614/CRAN.package.bvhar",
   note = note,
   url = "https://cran.r-project.org/package=bvhar"
 )

src/forecast-minn.cpp

@@ -84,7 +84,7 @@ Rcpp::List forecast_bvar(Rcpp::List object, int step, int num_sim) {
 //'     - Predictive distribution: Again generate \eqn{\tilde{Y}_{n + j}^{(b)} \sim \Phi^{(b)}, \Sigma_e^{(b)} \sim MN}
 //'     - tilde notation indicates simulated ones
 //' 
-//' @references Kim, Y. G., and Baek, C. (n.d.). *Bayesian vector heterogeneous autoregressive modeling*. submitted.
+//' @references Kim, Y. G., and Baek, C. (2024). *Bayesian vector heterogeneous autoregressive modeling*. Journal of Statistical Computation and Simulation.
 //' @noRd
 // [[Rcpp::export]]
 Rcpp::List forecast_bvharmn(Rcpp::List object, int step, int num_sim) {