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 described in "Gregor Zens, Sylvia Frühwirth-Schnatter & Helga Wagner (2023). Ultimate Pólya Gamma Samplers – Efficient MCMC for possibly imbalanced binary and categorical data, Journal of the American Statistical Association <doi:10.1080/01621459.2023.2259030>".

Version: 0.3.4
Depends: R (≥ 3.5.0)
Imports: ggplot2, knitr, matrixStats, mnormt, pgdraw, reshape2, coda, truncnorm
Published: 2023-11-04
DOI: 10.32614/CRAN.package.UPG
Author: Gregor Zens [aut, cre], Sylvia Frühwirth-Schnatter [aut], Helga Wagner [aut]
Maintainer: Gregor Zens <zens at iiasa.ac.at>
License: GPL-3
NeedsCompilation: no
Language: en-US
Citation: UPG citation info
Materials: README NEWS
CRAN checks: UPG results


Reference manual: UPG.pdf
Vignettes: UPG_Vignette


Package source: UPG_0.3.4.tar.gz
Windows binaries: r-devel: UPG_0.3.4.zip, r-release: UPG_0.3.4.zip, r-oldrel: UPG_0.3.4.zip
macOS binaries: r-release (arm64): UPG_0.3.4.tgz, r-oldrel (arm64): UPG_0.3.4.tgz, r-release (x86_64): UPG_0.3.4.tgz, r-oldrel (x86_64): UPG_0.3.4.tgz
Old sources: UPG archive


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