Package: bayesianVARs
Title: MCMC Estimation of Bayesian Vectorautoregressions
Version: 0.1.0
Authors@R: 
    person("Luis", "Gruber", , "Luis.Gruber@aau.at", role = c("cph", "aut", "cre"),
           comment = c(ORCID = "0000-0002-2399-738X"))
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 (2023) <arXiv:2206.04902>.
    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
BugReports: https://github.com/luisgruber/bayesianVARs/issues
Depends: R (>= 3.3.0)
Imports: colorspace, factorstochvol, GIGrvg, graphics, MASS, mvtnorm,
        Rcpp, scales, stats, stochvol (>= 3.0.2), utils
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
LinkingTo: factorstochvol, Rcpp, RcppArmadillo, RcppProgress, stochvol
VignetteBuilder: knitr
Config/testthat/edition: 3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.2.3
NeedsCompilation: yes
Packaged: 2024-01-11 09:08:39 UTC; lugruber
Author: Luis Gruber [cph, aut, cre] (<https://orcid.org/0000-0002-2399-738X>)
Maintainer: Luis Gruber <Luis.Gruber@aau.at>
Repository: CRAN
Date/Publication: 2024-01-13 17:00:02 UTC
