Package: bsvars
Type: Package
Title: Bayesian Estimation of Structural Vector Autoregressive Models
Description: Provides fast and efficient procedures for Bayesian
        analysis of Structural Vector Autoregressions. This package
        estimates a wide range of models, including homo-,
        heteroskedastic, and non-normal specifications. Structural
        models can be identified by adjustable exclusion restrictions,
        time-varying volatility, or non-normality, and include
        exclusion restrictions on autoregressive parameters. They all
        include a flexible three-level equation-specific local-global
        hierarchical prior distribution for the estimated level of
        shrinkage for autoregressive and structural parameters.
        Additionally, the package facilitates predictive and structural
        analyses such as impulse responses, forecast error variance and
        historical decompositions, forecasting, verification of
        heteroskedasticity, non-normality, and hypotheses on
        autoregressive parameters, as well as analyses of structural
        shocks, volatilities, and fitted values. Beautiful plots,
        informative summary functions, and extensive documentation
        including the vignette by Woźniak (2025)
        <doi:10.48550/arXiv.2410.15090> complement all this. The
        implemented techniques align closely with those presented in
        Lütkepohl, Shang, Uzeda, & Woźniak (2025)
        <doi:10.1016/j.jeconom.2025.106107>, Lütkepohl & Woźniak (2020)
        <doi:10.1016/j.jedc.2020.103862>, and Song & Woźniak (2021)
        <doi:10.1093/acrefore/9780190625979.013.174> and they embed
        many popular models proposed by other authors. The 'bsvars'
        package is aligned regarding objects, workflows, and code
        structure with the R packages 'bsvarSIGNs' by Wang & Woźniak
        (2025) <doi:10.32614/CRAN.package.bsvarSIGNs>, 'bvars' by Liu,
        Ramirez Hassan, Woźniak (2026)
        <doi:10.32614/CRAN.package.bvars>, and 'bpvars' by Woźniak
        (2026) <doi:10.32614/CRAN.package.bpvars>, and they constitute
        an integrated toolset.
Version: 4.0.0.9000
Date: 2026-08-24
Authors@R: c(person(
              given = "Tomasz", 
              family = "Woźniak",
              email = "wozniak.tom@pm.me", 
              role = c("aut", "cre"),
              comment = c(ORCID = "0000-0003-2212-2378", ROR = "01ej9dk98")
            ),
           person(
              given = "Fei", 
              family ="Shang",
              email = "sfmax2010@gmail.com", 
              role = c("ctb"),
              comment = c(ORCID = "0000-0003-1908-3275", "co-authored the Journal of Econometrics methodological paper by Lütkepohl, Shang, Uzeda, Woźniak (2026), introduced major code corrections and improvements")
            ),
            person(
              given = "Helmut", 
              family ="Lütkepohl",
              role = c("ctb"),
              comment = c("co-authored the Journal of Econometrics methodological paper by Lütkepohl, Shang, Uzeda, Woźniak (2026)")
            ),
            person(
              given = "Luis", 
              family ="Uzeda",
              role = c("ctb"),
              comment = c(ORCID = "0000-0002-2177-676X", "co-authored the Journal of Econometrics methodological paper by Lütkepohl, Shang, Uzeda, Woźniak (2026)")
            ),
           person(
              given = "Xiaolei", 
              family = "Wang",
              email = "adamwang15@gmail.com", 
              role = c("ctb"), 
              comment = c(ORCID = "0009-0005-6192-9061", "corrected C++ code for historical decompositions")
            ),
           person(
              given = "Bruno", 
              family ="Cavalcante",
              email = "bruno.cavalcante@fgv.edu.br", 
              role = c("ctb"),
              comment = c(ORCID = "0009-0005-7252-7658", "corrected R code for HMSH forecasting")
            )
          )
Maintainer: Tomasz Woźniak <wozniak.tom@pm.me>
Depends: R (>= 4.1.0)
Imports: Rcpp (>= 1.0.7), RcppProgress (>= 0.1), RcppTN, GIGrvg, R6,
        stochvol, generics
Suggests: knitr, tinytest
LinkingTo: Rcpp, RcppProgress, RcppArmadillo, RcppTN
License: GPL (>= 3)
URL: https://bsvars.org/bsvars/
BugReports: https://github.com/bsvars/bsvars/issues
Encoding: UTF-8
LazyData: true
VignetteBuilder: knitr
Config/roxygen2/version: 8.0.0
Repository: https://bsvars.r-universe.dev
Date/Publication: 2026-09-29 06:52:48 UTC
RemoteUrl: https://github.com/bsvars/bsvars
RemoteRef: HEAD
RemoteSha: 0606b87499a6bc8077dc5a07b7c026fef7aa65e9
NeedsCompilation: yes
Packaged: 2026-09-29 09:23:35 UTC; root
Author: Tomasz Woźniak [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-2212-2378>, ROR:
    <https://ror.org/01ej9dk98>),
  Fei Shang [ctb] (ORCID: <https://orcid.org/0000-0003-1908-3275>,
    co-authored the Journal of Econometrics methodological paper by
    Lütkepohl, Shang, Uzeda, Woźniak (2026), introduced major code
    corrections and improvements),
  Helmut Lütkepohl [ctb] (co-authored the Journal of Econometrics
    methodological paper by Lütkepohl, Shang, Uzeda, Woźniak (2026)),
  Luis Uzeda [ctb] (ORCID: <https://orcid.org/0000-0002-2177-676X>,
    co-authored the Journal of Econometrics methodological paper by
    Lütkepohl, Shang, Uzeda, Woźniak (2026)),
  Xiaolei Wang [ctb] (ORCID: <https://orcid.org/0009-0005-6192-9061>,
    corrected C++ code for historical decompositions),
  Bruno Cavalcante [ctb] (ORCID: <https://orcid.org/0009-0005-7252-7658>,
    corrected R code for HMSH forecasting)