shrinkTVP: Efficient Bayesian Inference for Time-Varying Parameter Models with Shrinkage

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors. Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and Cadonna et al. (2020) <doi:10.3390/econometrics8020020>.

Version: 2.0.2
Depends: R (≥ 3.3.0)
Imports: Rcpp, GIGrvg, stochvol (≥ 3.0.3), coda, methods, utils, zoo
LinkingTo: Rcpp, RcppArmadillo, GIGrvg, RcppProgress, stochvol
Suggests: testthat, knitr, rmarkdown, R.rsp
Published: 2021-05-13
Author: Peter Knaus ORCID iD [aut, cre], Angela Bitto-Nemling [aut], Annalisa Cadonna ORCID iD [aut], Sylvia Frühwirth-Schnatter ORCID iD [aut], Daniel Winkler [ctb], Kemal Dingic [ctb]
Maintainer: Peter Knaus <peter.knaus at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: yes
Materials: NEWS
CRAN checks: shrinkTVP results


Reference manual: shrinkTVP.pdf
Vignettes: Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP
Package source: shrinkTVP_2.0.2.tar.gz
Windows binaries: r-devel:, r-devel-UCRT:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): shrinkTVP_2.0.2.tgz, r-release (x86_64): shrinkTVP_2.0.2.tgz, r-oldrel: shrinkTVP_2.0.2.tgz
Old sources: shrinkTVP archive


Please use the canonical form to link to this page.