Bayesian Forecasting with Large Vector Autoregressions R package bvars version 1.0 CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries) 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. Software Depends: R (>= 4.1.0), RcppArmadillo, bsvars Imports: generics, Rcpp (>= 1.0.14), RcppProgress, RcppTN, R6 LinkingTo: Rcpp, RcppArmadillo, RcppProgress, RcppTN, bsvars Tomasz Woźniak <wozniak.tom@pm.me> Comprehensive R Archive Network (CRAN) Rui Liu [aut] (ORCID: <https://orcid.org/0009-0008-9348-8581>), Andrés Ramirez Hassan [aut] (ORCID: <https://orcid.org/0000-0002-0467-7903>), Tomasz Woźniak [aut, cre, cph] (ORCID: <https://orcid.org/0000-0003-2212-2378>) GPL (>= 3) 2026-06-08 application/tgz https://CRAN.R-project.org/package=bvars doi:10.32614/CRAN.package.bvars