# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "pumBayes" in publications use:' type: software license: GPL-3.0-only title: 'pumBayes: Bayesian Estimation of Probit Unfolding Models for Binary Preference Data' version: 1.0.2 doi: 10.32614/CRAN.package.pumBayes abstract: 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) . authors: - family-names: Shi given-names: Skylar email: dshi98@uw.edu orcid: https://orcid.org/0009-0001-2818-0299 - family-names: Rodriguez given-names: Abel email: abelr@uw.edu orcid: https://orcid.org/0000-0001-5503-7394 - family-names: Lei given-names: Rayleigh email: rayleigh@umich.edu orcid: https://orcid.org/0000-0002-0444-9708 repository: https://skylarshihub.r-universe.dev repository-code: https://github.com/SkylarShiHub/pumBayes commit: 9399409a5a250a4c7d5396545aa3b10fb49d1f55 url: https://github.com/SkylarShiHub/pumBayes date-released: '2026-02-09' contact: - family-names: Shi given-names: Skylar email: dshi98@uw.edu orcid: https://orcid.org/0009-0001-2818-0299