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pumBayes  

Bayesian Estimation of Probit Unfolding Models for Binary Preference Data
View on CRAN: Click here


Download and install pumBayes package within the R console
Install from CRAN:
install.packages("pumBayes")

Install from Github:
library("remotes")
install_github("cran/pumBayes")

Install by package version:
library("remotes")
install_version("pumBayes", "1.0.2")



Attach the package and use:
library("pumBayes")
Maintained by
Skylar Shi
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-05-30
Latest Update: 2025-05-30
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>.
How to cite:
Skylar Shi (2025). pumBayes: Bayesian Estimation of Probit Unfolding Models for Binary Preference Data. R package version 1.0.2, https://cran.r-project.org/web/packages/pumBayes. Accessed 24 Oct. 2026.
Previous versions and publish date:
(2026-07-09 06:44), 1.0.0 (2025-05-30 11:00), 1.0.1 (2025-10-23 04:50)
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Complete documentation for pumBayes
Functions, R codes and Examples using the pumBayes R package
Full pumBayes package functions and examples
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