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bvars
View on CRAN: Click
here
Download and install bvars package within the R console
Install from CRAN:
install.packages("bvars")
Install from Github:
library("remotes")
install_github("cran/bvars") Install by package version:
library("remotes")
install_version("bvars", "1.0") Attach the package and use:
library("bvars")
Maintained by
Tomasz Woźniak
[Scholar Profile | Author Map]
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-08
Latest Update: 2026-06-08
Description:
Provides fast and efficient procedures for Bayesian estimation and forecasting using state-of-the-art Vector Autoregressions. This package includes the model proposed by Chan (2020) <doi:10.1080/07350015.2018.1451336>, that is, a Bayesian Vector Autoregression with Minnesota priors and a flexible structure of the error term specification. The latter includes: conditional multivariate normal or Student’s t distributions, as well as homoskedastic or heteroskedastic specifications with a common volatility modelled by centred or non-centred Stochastic Volatility. Additionally, the package facilitates predictive analyses using density forecasting and forecast-error variance decompositions. All this is complemented by simple workflows, useful plots and summary functions, and comprehensive documentation. The 'bvars' package aligns with R packages 'bsvars' by Woźniak (2024) <doi:10.32614/CRAN.package.bsvars>, 'bsvarSIGNs' by Wang & Woźniak (2025) <doi:10.32614/CRAN.package.bsvarSIGNs>, and 'bpvars' by Woźniak (2025) <doi:10.32614/CRAN.package.bpvars> regarding objects, workflows, and code structure, and they constitute an integrated toolset.
How to cite:
Tomasz Woźniak (2026). bvars: Bayesian Forecasting with Large Vector Autoregressions. R package version 1.0, https://cran.r-project.org/web/packages/bvars. Accessed 25 Jun. 2026.
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