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>. The 'bsvars'
        package is aligned regarding objects, workflows, and code
        structure with the R package 'bsvarSIGNs' by Wang & Woźniak
        (2024) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they
        constitute an integrated toolset.
Version: 4.0
Date: 2025-04-16
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 = "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")
            ),
           person(
              given = "Fei", 
              family ="Shang",
              email = "sfmax2010@gmail.com", 
              role = c("ctb"),
              comment = c(ORCID = "0000-0003-1908-3275", "corrected C++ code for Student's t shocks")
            )
          )
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: 2026-07-02 12:33:45 UTC
RemoteUrl: https://github.com/bsvars/bsvars
RemoteRef: HEAD
RemoteSha: b687f45be6557f635ceef4ca06ac8cc43f40bb89
NeedsCompilation: yes
Packaged: 2026-07-02 15:22:22 UTC; root
Author: Tomasz Woźniak [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-2212-2378>, ROR:
    <https://ror.org/01ej9dk98>),
  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),
  Fei Shang [ctb] (ORCID: <https://orcid.org/0000-0003-1908-3275>,
    corrected C++ code for Student's t shocks)