Description
Bayesian Estimation of Probit Unfolding Models for Binary Preference Data.
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) <doi:10.48550/arXiv.2504.00423>.
README.md
Bayesian Estimation of Probit Unfolding Models
Description
pumBayes is an R package designed for Bayesian estimation of probit unfolding models (PUM) for binary preference data. The package is publicly available and can be cited using the following DOI: 10.5281/zenodo.15069856.
Installation
You can install the latest version of pumBayes directly from GitHub using the following commands:
# Install devtools package if not already installed
install.packages("devtools")
# Install pumBayes from GitHub
library(devtools)
install_github("SkylarShiHub/pumBayes")