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) . The underlying implementation is written in C++.


Reference manual

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0.2.2 by Gregor Zens, 16 days ago

Browse source code at https://github.com/cran/UPG

Authors: Gregor Zens [aut, cre] , Sylvia Frühwirth-Schnatter [aut] , Helga Wagner [aut] , Daniel F. Schmidt [ctb] , Enes Makalic [ctb]

Documentation:   PDF Manual  

GPL-3 license

Imports ggplot2, knitr, matrixStats, mnormt, pgdraw, reshape2, Rcpp, RcppProgress, coda

Linking to Rcpp, RcppArmadillo, RcppProgress

System requirements: C++11

See at CRAN