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  "Title": "Bayesian Forecasting with Large Vector Autoregressions",
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  "Date": "2026-06-03",
  "Authors@R": "c(person(\"Rui\", \"Liu\", , \"rl3023@columbia.edu\", role = c(\"aut\"), comment = c(ORCID = \"0009-0008-9348-8581\")),\nperson(\"Andrés\", \"Ramirez Hassan\", , \"aramir21@gmail.com\", role = c(\"aut\"), comment = c(ORCID = \"0000-0002-0467-7903\")),\nperson(\"Tomasz\", \"Woźniak\", , \"wozniak.tom@pm.me\", role = c(\"aut\", \"cre\", \"cph\"), comment = c(ORCID = \"0000-0003-2212-2378\")))",
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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\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 (2024)\n<doi:10.32614/CRAN.package.bsvars>, 'bsvarSIGNs' by Wang &\nWoźniak (2025) <doi:10.32614/CRAN.package.bsvarSIGNs>, and\n'bpvars' by Woźniak (2025) <doi:10.32614/CRAN.package.bpvars>\nregarding objects, workflows, and code structure, and they\nconstitute an integrated toolset.",
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