{ "_id": "6ae06b1393f02b9aed979e68", "Package": "pumBayes", "Type": "Package", "Title": "Bayesian Estimation of Probit Unfolding Models for Binary\nPreference Data", "Version": "1.0.2", "Authors@R": "c(person(\"Skylar\", \"Shi\", role = c(\"aut\", \"cre\"), \nemail = \"dshi98@uw.edu\",\ncomment = c(ORCID = \"0009-0001-2818-0299\")),\nperson(\"Abel\", \"Rodriguez\", role = \"aut\",\nemail = \"abelr@uw.edu\",\ncomment = c(ORCID = \"0000-0001-5503-7394\")),\nperson(\"Rayleigh\", \"Lei\", role = \"aut\",\nemail = \"rayleigh@umich.edu\",\ncomment = c(ORCID = \"0000-0002-0444-9708\")),\nperson(\"Jonathan\", \"Olmsted\", role = \"cph\"))", "Maintainer": "Skylar Shi ", "Description": "Bayesian estimation and analysis methods for Probit\nUnfolding Models (PUMs), a novel class of scaling models\ndesigned for binary preference data. These models allow for\nboth monotonic and non-monotonic response functions. The\npackage supports Bayesian inference for both static and dynamic\nPUMs using Markov chain Monte Carlo (MCMC) algorithms with\nminimal or no tuning. Key functionalities include posterior\nsampling, hyperparameter selection, data preprocessing, model\nfit evaluation, and visualization. The methods are particularly\nsuited to analyzing voting data, such as from the U.S. Congress\nor Supreme Court, but can also be applied in other contexts\nwhere non-monotonic responses are expected. For methodological\ndetails, see Shi et al. 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