Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors, both dynamic and static. 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> and Knaus and Frühwirth-Schnatter (2023) <doi:10.48550/arXiv.2312.10487>. For details on the package, please see Knaus et al. (2021) <doi:10.18637/jss.v100.i13>.
| Version: |
3.0.1 |
| Depends: |
R (≥ 3.3.0) |
| Imports: |
Rcpp, GIGrvg, stochvol (≥ 3.0.3), coda, methods, utils, zoo |
| LinkingTo: |
Rcpp, RcppArmadillo, GIGrvg, RcppProgress, stochvol, RcppGSL |
| Suggests: |
testthat, knitr, rmarkdown, R.rsp |
| Published: |
2024-02-18 |
| Author: |
Peter Knaus  [aut,
cre],
Angela Bitto-Nemling [aut],
Annalisa Cadonna
[aut],
Sylvia Frühwirth-Schnatter
[aut],
Daniel Winkler [ctb],
Kemal Dingic [ctb] |
| Maintainer: |
Peter Knaus <peter.knaus at wu.ac.at> |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: |
yes |
| Citation: |
shrinkTVP citation info |
| Materials: |
NEWS |
| In views: |
Bayesian |
| CRAN checks: |
shrinkTVP results |