UPG: Efficient Bayesian Models for Binary and Categorical Data

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

Documentation:

Reference manual: UPG.pdf
Vignettes: author2019mypaper

Downloads:

Package source: UPG_0.2.2.tar.gz
Windows binaries: r-devel: UPG_0.2.2.zip, r-devel-UCRT: UPG_0.2.2.zip, r-release: UPG_0.2.2.zip, r-oldrel: UPG_0.2.2.zip
macOS binaries: r-release (arm64): UPG_0.2.2.tgz, r-release (x86_64): UPG_0.2.2.tgz, r-oldrel: UPG_0.2.2.tgz

Linking:

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