New on CRAN: bvars (1.0). View at https://CRAN.R-project.org/package=bvars
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) <<a href="https://doi.org/10.1080%2F07350015.2018.1451336" target="_top">doi:10.1080/07350015.2018.1451336</a>>, 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) <<a href="https://doi.org/10.32614%2FCRAN.package.bsvars" target="_top">doi:10.32614/CRAN.package.bsvars</a>>, 'bsvarSIGNs' by Wang & Woźniak (2025) <<a href="https://doi.org/10.32614%2FCRAN.package.bsvarSIGNs" target="_top">doi:10.32614/CRAN.package.bsvarSIGNs</a>>, and 'bpvars' by Woźniak (2025) <<a href="https://doi.org/10.32614%2FCRAN.package.bpvars" target="_top">doi:10.32614/CRAN.package.bpvars</a>> regarding objects, workflows, and code structure, and they constitute an integrated toolset.
