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  "Title": "Bayesian Forecasting with Large Vector Autoregressions",
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  "Description": "Provides fast and efficient procedures for Bayesian\nestimation and forecasting using state-of-the-art Vector\nAutoregressions. This package includes the model proposed by\nShang, Wang, Woźniak (2026) <doi:10.48550/arXiv.2608.28087> and\nChan (2020) <doi:10.1080/07350015.2018.1451336>, that is, a\nBayesian Vector Autoregression with Minnesota priors and a\nflexible structure of the error term specification. The latter\nincludes: conditional multivariate normal or Student’s t\ndistributions, as well as homoskedastic or heteroskedastic\nspecifications with a common volatility modelled by centred or\nnon-centred Stochastic Volatility. Additionally, the package\nfacilitates predictive analyses using density forecasting and\nforecast-error variance decompositions. All this is\ncomplemented by simple workflows, useful plots and summary\nfunctions, and comprehensive documentation. The 'bvars' package\naligns with R packages 'bsvars' by Woźniak (2026)\n<doi:10.32614/CRAN.package.bsvars>, 'bsvarSIGNs' by Wang &\nWoźniak (2026) <doi:10.32614/CRAN.package.bsvarSIGNs>, and\n'bpvars' by Woźniak (2026) <doi:10.32614/CRAN.package.bpvars>\nregarding objects, workflows, and code structure, and they\nconstitute an integrated toolset.",
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