MCMC Estimation of Bayesian Vectorautoregressions R package bayesianVARs version 0.1.8 CRAN Task View: Bayesian (https://CRAN.R-project.org/view=Bayesian) CRAN Task View: TimeSeries (https://CRAN.R-project.org/view=TimeSeries) Efficient Markov Chain Monte Carlo (MCMC) algorithms for the fully Bayesian estimation of vectorautoregressions (VARs) featuring stochastic volatility (SV). Implements state-of-the-art shrinkage priors following Gruber & Kastner (2025) <doi:10.1016/j.ijforecast.2025.02.001>. Efficient equation-per-equation estimation following Kastner & Huber (2020) <doi:10.1002/for.2680> and Carrerio et al. (2021) <doi:10.1016/j.jeconom.2021.11.010>. Software Depends: R (>= 3.5.0) Imports: colorspace, factorstochvol (>= 1.1.0), GIGrvg (>= 0.7), graphics, MASS, mvtnorm, Rcpp (>= 1.0.0), scales, stats, stochvol (>= 3.0.3), utils LinkingTo: factorstochvol, Rcpp, RcppArmadillo, RcppProgress, stochvol, lpSolveAPI Suggests: coda, knitr, quarto, rmarkdown, testthat (>= 3.0.0), lpSolveAPI, bsvarSIGNs Luis Gruber <Luis.Gruber@aau.at> Comprehensive R Archive Network (CRAN) Luis Gruber [cph, aut, cre] (ORCID: <https://orcid.org/0000-0002-2399-738X>), Stefan Haan [aut], Gregor Kastner [aut, ths] (ORCID: <https://orcid.org/0000-0002-8237-8271>) GPL (>= 3) 2026-02-19 application/tgz https://CRAN.R-project.org/package=bayesianVARs doi:10.32614/CRAN.package.bayesianVARs