Package: shrinkTVPVAR
Type: Package
Title: Efficient Bayesian Inference for TVP-VAR-SV Models with
        Shrinkage
Version: 1.0.1
Authors@R: c(
  person("Peter", "Knaus", email = "peter.knaus@wu.ac.at",
    role = c("aut", "cre"), comment = c(ORCID = "0000-0001-6498-7084")))
Maintainer: Peter Knaus <peter.knaus@wu.ac.at>
Description: Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter 
  vector autoregressive models with stochastic volatility (TVP-VAR-SV) under shrinkage priors and dynamic shrinkage processes. 
  Details on the TVP-VAR-SV model and the shrinkage priors can be found in Cadonna et al. (2020) <doi:10.3390/econometrics8020020>, 
  details on the software can be found in Knaus et al. (2021) <doi:10.18637/jss.v100.i13>, while details on the dynamic shrinkage process
  can be found in Knaus and Frühwirth-Schnatter (2023) <doi:10.48550/arXiv.2312.10487>.
License: GPL (>= 2)
Encoding: UTF-8
Depends: R (>= 3.3.0)
RoxygenNote: 7.3.2
LinkingTo: Rcpp, RcppProgress, RcppArmadillo, shrinkTVP (>= 3.1.0),
        stochvol
Imports: Rcpp, shrinkTVP (>= 3.1.0), stochvol, coda, methods,
        grDevices, RColorBrewer, lattice, zoo, mvtnorm
Suggests: testthat (>= 3.0.0)
Config/testthat/edition: 3
NeedsCompilation: yes
Packaged: 2025-06-03 10:10:37 UTC; Peter
Author: Peter Knaus [aut, cre] (ORCID: <https://orcid.org/0000-0001-6498-7084>)
Repository: CRAN
Date/Publication: 2025-06-03 13:40:07 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-26 01:58:14 UTC; unix
Archs: shrinkTVPVAR.so.dSYM
