  We introduce the UPG package for highly efficient Bayesian inference in
probit, logit, multinomial logit and binomial logit models. UPG offers a
convenient estimation framework for balanced and imbalanced data settings where
sampling efficiency is ensured through Markov chain Monte Carlo boosting
methods. All sampling algorithms are implemented in C++, allowing for rapid
parameter estimation. In addition, UPG provides several methods for fast
production of output tables and summary plots that are easily accessible to a
broad range of users.
