Bayesian Estimation of Probit Unfolding Models for Binary Preference Data
Calculate a block version of Watanabe-Akaike Information Criterion (WA...
Density Function for Truncated Normal Distribution
Generate Data for Item Characteristic Curves
Generate Quantile Ranks for Legislators
Calculate Probabilities for the IDEAL Model
Calculate Probabilities for Dynamic Item Response Theory Model
Calculate Probabilities for Probit Unfolding Models
Preprocess Roll Call Data
Generate posterior samples from the dynamic probit unfolding model
Generate posterior samples from the static probit unfolding model
Generate Probability Samples for Voting "Yes"
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>.
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