CRAN/E | UPG

UPG

Efficient Bayesian Algorithms for Binary and Categorical Data Regression Models

Installation

About

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) .

Citation UPG citation info

Key Metrics

Version 0.3.1
R ≥ 3.5.0
Published 2022-08-05 238 days ago
Needs compilation? no
License GPL-3
CRAN checks UPG results
Language en-US

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Maintainer

Maintainer

Gregor Zens

gzens@wu.ac.at

Authors

Gregor Zens

aut / cre

Sylvia Frühwirth-Schnatter

aut

Helga Wagner

aut

Material

README
NEWS
Reference manual
Package source

Vignettes

UPG_Vignette

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Old Sources

UPG archive

Depends

R ≥ 3.5.0

Imports

ggplot2
knitr
matrixStats
mnormt
pgdraw
reshape2
coda
truncnorm