Package: UPG
Type: Package
Title: Efficient Bayesian Models for Binary and Categorical Data
Version: 0.2.2
Authors@R: c(person("Gregor","Zens",role=c("aut","cre"),email="gzens@wu.ac.at"),
             person("Sylvia","Frühwirth-Schnatter", role="aut"),
             person("Helga","Wagner", role="aut"),
             person("Daniel F.","Schmidt", role="ctb"),
             person("Enes", "Makalic", role="ctb"))
Author: Gregor Zens [aut, cre],
  Sylvia Frühwirth-Schnatter [aut],
  Helga Wagner [aut],
  Daniel F. Schmidt [ctb],
  Enes Makalic [ctb]
Maintainer: Gregor Zens <gzens@wu.ac.at>
Description: 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++.
Encoding: latin1
License: GPL-3
Language: en-US
Depends: R (>= 3.5.0)
SystemRequirements: C++11
Imports: ggplot2, knitr, matrixStats, mnormt, pgdraw, reshape2, Rcpp,
        RcppProgress, coda
LazyData: true
LinkingTo: Rcpp, RcppArmadillo, RcppProgress
RoxygenNote: 7.1.1
NeedsCompilation: yes
Packaged: 2021-01-02 10:06:10 UTC; g.zens
Repository: CRAN
Date/Publication: 2021-01-07 09:00:05 UTC
Built: R 4.0.5; x86_64-w64-mingw32; 2022-04-21 10:12:15 UTC; windows
Archs: i386, x64
