bvhar: Bayesian Vector Heterogeneous Autoregressive Modeling

Tools to research Bayesian Vector heterogeneous autoregressive (VHAR) model, referring to Kim & Baek (2023+) (<doi:10.1080/00949655.2023.2281644>). 'bvhar' can model Vector Autoregressive (VAR), VHAR, Bayesian VAR (BVAR), and Bayesian VHAR (BVHAR) models.

Version: 1.0.1
Depends: R (≥ 3.6.0)
Imports: lifecycle, magrittr, Rcpp, ggplot2, tidyr, tibble, dplyr, foreach, purrr, stats, optimParallel, posterior, bayesplot, doRNG, forcats
LinkingTo: Rcpp, RcppEigen, RcppProgress
Suggests: covr, knitr, parallel, rmarkdown, testthat (≥ 3.0.0)
Published: 2023-11-10
Author: Young Geun Kim ORCID iD [aut, cre, cph], Changryong Baek [ctb]
Maintainer: Young Geun Kim <dudrms33 at>
License: MIT + file LICENSE
NeedsCompilation: yes
Citation: bvhar citation info
Materials: README NEWS
CRAN checks: bvhar results


Reference manual: bvhar.pdf
Vignettes: Introduction to bvhar
Empirical Bayes
Minnesota Prior


Package source: bvhar_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): bvhar_1.0.1.tgz, r-oldrel (arm64): bvhar_1.0.1.tgz, r-release (x86_64): bvhar_1.0.1.tgz, r-oldrel (x86_64): not available
Old sources: bvhar archive


Please use the canonical form to link to this page.