bvars: Bayesian Forecasting with Large Vector Autoregressions

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.

Version:	1.0
Depends:	R (≥ 4.1.0), RcppArmadillo, bsvars
Imports:	generics, Rcpp (≥ 1.0.14), RcppProgress, RcppTN, R6
LinkingTo:	Rcpp, RcppArmadillo, RcppProgress, RcppTN, bsvars
Published:	2026-06-08
DOI:	10.32614/CRAN.package.bvars
Author:	Rui Liu [aut], Andrés Ramirez Hassan [aut], Tomasz Woźniak [aut, cre, cph]
Maintainer:	Tomasz Woźniak <wozniak.tom at pm.me>
BugReports:	https://github.com/bsvars/bvars/issues
License:	GPL (≥ 3)
URL:	https://bsvars.org/bvars/
NeedsCompilation:	yes
Materials:	README, NEWS
In views:	TimeSeries
CRAN checks:	bvars results

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