Highly efficient Bayesian implementations of probit, logit, multinomial logit and binomial logit models. Functions for plotting and tabulating the estimation output are available as well. Estimation is based on Gibbs sampling where the Markov chain Monte Carlo algorithms are based on the latent variable representations and boosting algorithms outlined in Frühwirth-Schnatter S., Zens G., Wagner H. (2020) <arXiv:2011.06898>. The underlying implementation is written in C++.
| Version: | 0.2.2 |
| Depends: | R (≥ 3.5.0) |
| Imports: | ggplot2, knitr, matrixStats, mnormt, pgdraw, reshape2, Rcpp, RcppProgress, coda |
| LinkingTo: | Rcpp, RcppArmadillo, RcppProgress |
| Published: | 2021-01-07 |
| Author: | Gregor Zens [aut, cre], Sylvia Frühwirth-Schnatter [aut], Helga Wagner [aut], Daniel F. Schmidt [ctb], Enes Makalic [ctb] |
| Maintainer: | Gregor Zens <gzens at wu.ac.at> |
| License: | GPL-3 |
| NeedsCompilation: | yes |
| SystemRequirements: | C++11 |
| Language: | en-US |
| Citation: | UPG citation info |
| Materials: | README NEWS |
| CRAN checks: | UPG results |
| Reference manual: | UPG.pdf |
| Vignettes: |
author2019mypaper |
| Package source: | UPG_0.2.2.tar.gz |
| Windows binaries: | r-devel: UPG_0.2.2.zip, r-release: UPG_0.2.2.zip, r-oldrel: UPG_0.2.2.zip |
| macOS binaries: | r-release: UPG_0.2.2.tgz, r-oldrel: UPG_0.2.2.tgz |
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