UPG: Efficient Bayesian Algorithms for Binary and Categorical Data Regression Models

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 marginal data augmentation algorithms outlined in Frühwirth-Schnatter S., Zens G., Wagner H. (2020) <arXiv:2011.06898>.

Version: 0.3.0
Depends: R (≥ 3.5.0)
Imports: ggplot2, knitr, matrixStats, mnormt, pgdraw, reshape2, coda, truncnorm
Published: 2022-06-21
Author: Gregor Zens [aut, cre], Sylvia Frühwirth-Schnatter [aut], Helga Wagner [aut]
Maintainer: Gregor Zens <gzens at wu.ac.at>
License: GPL-3
NeedsCompilation: no
Language: en-US
Citation: UPG citation info
Materials: README NEWS
CRAN checks: UPG results

Documentation:

Reference manual: UPG.pdf
Vignettes: UPG_Vignette

Downloads:

Package source: UPG_0.3.0.tar.gz
Windows binaries: r-devel: UPG_0.3.0.zip, r-release: UPG_0.3.0.zip, r-oldrel: UPG_0.3.0.zip
macOS binaries: r-release (arm64): UPG_0.3.0.tgz, r-oldrel (arm64): UPG_0.3.0.tgz, r-release (x86_64): UPG_0.3.0.tgz, r-oldrel (x86_64): UPG_0.3.0.tgz
Old sources: UPG archive

Linking:

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