Package: pumBayes Type: Package Title: Bayesian Estimation of Probit Unfolding Models for Binary Preference Data Version: 1.0.2 Authors@R: c(person("Skylar", "Shi", role = c("aut", "cre"), email = "dshi98@uw.edu", comment = c(ORCID = "0009-0001-2818-0299")), person("Abel", "Rodriguez", role = "aut", email = "abelr@uw.edu", comment = c(ORCID = "0000-0001-5503-7394")), person("Rayleigh", "Lei", role = "aut", email = "rayleigh@umich.edu", comment = c(ORCID = "0000-0002-0444-9708")), person("Jonathan", "Olmsted", role = "cph")) Maintainer: Skylar Shi Description: 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) . License: GPL-3 Encoding: UTF-8 RoxygenNote: 7.3.3 Depends: R (>= 3.6.0) Imports: Rcpp Suggests: knitr, rmarkdown, pscl, MCMCpack LinkingTo: Rcpp, RcppArmadillo, RcppDist, mvtnorm, RcppTN URL: https://github.com/SkylarShiHub/pumBayes BugReports: https://github.com/SkylarShiHub/pumBayes/issues Language: en NeedsCompilation: yes Repository: https://skylarshihub.r-universe.dev Date/Publication: 2026-02-09 06:19:43 UTC RemoteUrl: https://github.com/skylarshihub/pumbayes RemoteRef: HEAD RemoteSha: 9399409a5a250a4c7d5396545aa3b10fb49d1f55 Packaged: 2026-07-09 06:25:34 UTC; root Author: Skylar Shi [aut, cre] (ORCID: ), Abel Rodriguez [aut] (ORCID: ), Rayleigh Lei [aut] (ORCID: ), Jonathan Olmsted [cph]