Probit unfolding models (PUMs) are a novel class of scaling models that allow for items with both monotonic and non-monotonic response functions and have shown great promise in the estimation of preferences from voting data in various deliberative bodies. This paper presents the R package pumBayes, which enables Bayesian inference for both static and dynamic PUMs using Markov chain Monte Carlo algorithms that require minimal or no tuning. In addition to functions that carry out the sampling from the posterior distribution of the models, the package also includes various support functions that can be used to pre-process data, select hyperparameters, summarize output, and compute metrics of model fit. We demonstrate the use of the package through an analysis of two datasets, one corresponding to roll-call voting data from the 116th U.S. House of Representatives, and a second one corresponding to voting records in the U.S. Supreme Court between 1937 and 2021.

翻译：概率展开模型（PUMs）是一类新颖的尺度模型，它允许项目同时包含单调和非单调响应函数，在从各种审议机构的投票数据中估计偏好方面显示出巨大潜力。本文介绍了R包pumBayes，该包支持使用几乎无需调优的马尔可夫链蒙特卡洛算法对静态和动态PUM进行贝叶斯推断。除了提供从模型后验分布中采样的函数外，该包还包含多种辅助函数，可用于数据预处理、超参数选择、输出汇总以及模型拟合度指标计算。我们通过分析两个数据集来演示该包的使用：一个对应于美国第116届众议院的唱名表决数据，另一个对应于1937年至2021年间美国最高法院的投票记录。