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 Students 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 Woniak (2024) doi:10.32614/CRAN.package.bsvars, 'bsvarSIGNs' by Wang Woniak (2025) doi:10.32614/CRAN.package.bsvarSIGNs, and 'bpvars' by Woniak (2025) doi:10.32614/CRAN.package.bpvars regarding objects, workflows, and code structure, and they constitute an integrated toolset.