Efficient Bayesian Inference for TVP-VAR-SV Models with Shrinkage R package shrinkTVPVAR version 1.0.1 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>. Software Depends: R (>= 3.3.0) Imports: Rcpp, shrinkTVP (>= 3.1.0), stochvol, coda, methods, grDevices, RColorBrewer, lattice, zoo, mvtnorm LinkingTo: Rcpp, RcppProgress, RcppArmadillo, shrinkTVP (>= 3.1.0), stochvol Suggests: testthat (>= 3.0.0) Peter Knaus <peter.knaus@wu.ac.at> Comprehensive R Archive Network (CRAN) Peter Knaus [aut, cre] (ORCID: <https://orcid.org/0000-0001-6498-7084>) GPL (>= 2) 2025-06-03 application/tgz https://CRAN.R-project.org/package=shrinkTVPVAR doi:10.32614/CRAN.package.shrinkTVPVAR