pumBayes: Bayesian Estimation of Probit Unfolding Models for Binary Preference Data

Bayesian estimation and analysis methods for Probit Unfolding Models (PUMs), a novel class of scaling models designed for binary preference data. These models allow for both monotonic and non-monotonic response functions. The package supports Bayesian inference for both static and dynamic PUMs using Markov chain Monte Carlo (MCMC) algorithms with minimal or no tuning. Key functionalities include posterior sampling, hyperparameter selection, data preprocessing, model fit evaluation, and visualization. The methods are particularly suited to analyzing voting data, such as from the U.S. Congress or Supreme Court, but can also be applied in other contexts where non-monotonic responses are expected. For methodological details, see Shi et al. (2025) <doi:10.48550/arXiv.2504.00423>.

Version:	1.0.2
Depends:	R (≥ 3.6.0)
Imports:	Rcpp
LinkingTo:	Rcpp, RcppArmadillo, RcppDist, mvtnorm, RcppTN
Suggests:	knitr, rmarkdown, pscl, MCMCpack
Published:	2026-02-09
DOI:	10.32614/CRAN.package.pumBayes
Author:	Skylar Shi [aut, cre], Abel Rodriguez [aut], Rayleigh Lei [aut], Jonathan Olmsted [cph]
Maintainer:	Skylar Shi <dshi98 at uw.edu>
BugReports:	https://github.com/SkylarShiHub/pumBayes/issues
License:	GPL-3
URL:	https://github.com/SkylarShiHub/pumBayes
NeedsCompilation:	yes
Language:	en
Materials:	README
CRAN checks:	pumBayes results

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