Package: bayesianVARs
Title: MCMC Estimation of Bayesian Vectorautoregressions
Version: 0.1.8
Authors@R: c(
    person("Luis", "Gruber", , "Luis.Gruber@aau.at", role = c("cph", "aut", "cre"),
           comment = c(ORCID = "0000-0002-2399-738X")),
    person("Stefan", "Haan", , "sthaan@edu.aau.at", role = "aut"),
    person("Gregor", "Kastner", , "gregor.kastner@aau.at", role = c("aut", "ths"),
           comment = c(ORCID = "0000-0002-8237-8271"))
  )
Description: 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>.
License: GPL (>= 3)
URL: https://github.com/luisgruber/bayesianVARs,
        https://luisgruber.github.io/bayesianVARs/
BugReports: https://github.com/luisgruber/bayesianVARs/issues
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
Suggests: coda, knitr, quarto, rmarkdown, testthat (>= 3.0.0),
        lpSolveAPI, bsvarSIGNs
LinkingTo: factorstochvol, Rcpp, RcppArmadillo, RcppProgress, stochvol,
        lpSolveAPI
VignetteBuilder: knitr, quarto
Config/testthat/edition: 3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.3
NeedsCompilation: yes
Packaged: 2026-10-23 10:31:44 UTC; root
Author: 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>)
Maintainer: Luis Gruber <Luis.Gruber@aau.at>
Repository: https://cran.r-universe.dev
Date/Publication: 2026-02-19 14:20:08 UTC
RemoteUrl: https://github.com/cran/bayesianVARs
RemoteRef: HEAD
RemoteSha: 64d81868e89460c736e2194fe01857c619a3fefb