Package: shrinkTVP 3.0.1
Peter Knaus
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, 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>.
Authors:
shrinkTVP_3.0.1.tar.gz
shrinkTVP_3.0.1.tar.gz(r-4.5-noble)shrinkTVP_3.0.1.tar.gz(r-4.4-noble)
shrinkTVP_3.0.1.tgz(r-4.4-emscripten)shrinkTVP_3.0.1.tgz(r-4.3-emscripten)
shrinkTVP.pdf |shrinkTVP.html✨
shrinkTVP/json (API)
NEWS
| # Install 'shrinkTVP' in R: |
| install.packages('shrinkTVP', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 9 months agofrom:9e254834d8. Checks:ERROR: 1 NOTE: 1. Indexed: no.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | FAIL | Nov 15 2024 |
| R-4.5-linux-x86_64 | NOTE | Nov 15 2024 |
Exports:eval_pred_densforecast_shrinkTVPLPDSshrinkDTVPshrinkTVPsimTVPupdateTVP
Dependencies:codaGIGrvglatticeRcppRcppArmadilloRcppGSLRcppProgressstochvolzoo
Shrinkage in the Time-Varying Parameter Model Framework Using the R Package shrinkTVP
Rendered fromshrinkTVP.ltxusingR.rsp::texon Nov 15 2024.Last update: 2024-02-19
Started: 2019-08-07
