Learn R Programming

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.18533211.

Before installing

The package requires a working installation of R (version ≥ 3.6.0).

If you do not yet have R installed, download it from: https://cran.r-project.org/

Most users run R through RStudio, a graphical interface for R.
RStudio can be downloaded from: https://posit.co/download/rstudio-desktop/

In RStudio, packages can be installed either by running commands in the Console or by using the Packages → Install button in the top-right panel.

Installation

You can install the stable version of pumBayes from CRAN:

install.packages("pumBayes")
library(pumBayes)

Or install the development version from GitHub:

install.packages("devtools")
library(devtools)
install_github("SkylarShiHub/pumBayes")
library(pumBayes)

Documentation

CRAN page: https://cran.r-project.org/package=pumBayes

Support and Contact

For bug reports and feature requests, please open an issue on GitHub:

https://github.com/SkylarShiHub/pumBayes/issues

For general questions about the methodology or the software, you may contact the authors:

Copy Link

Version

Install

install.packages('pumBayes')

Monthly Downloads

121

Version

1.0.2

License

GPL-3

Issues

Pull Requests

Stars

Forks

Maintainer

Skylar Shi

Last Published

February 9th, 2026

Functions in pumBayes (1.0.2)

preprocess_rollcall

Preprocess Roll Call Data
post_rank

Generate Quantile Ranks for Legislators
predict_irt

Calculate Probabilities for Dynamic Item Response Theory Model
h116

116th U.S. House of Representatives Roll Call Votes
dtnorm

Density Function for Truncated Normal Distribution
sample_pum_dynamic

Generate posterior samples from the dynamic probit unfolding model
item_char

Generate Data for Item Characteristic Curves
predict_ideal

Calculate Probabilities for the IDEAL Model
predict_pum

Calculate Probabilities for Probit Unfolding Models
calc_waic

Calculate a block version of Watanabe-Akaike Information Criterion (WAIC)
sample_pum_static

Generate posterior samples from the static probit unfolding model
tune_hyper

Generate Probability Samples for Voting "Yes"
scotus.1937.2021

U.S. Supreme Court Voting Data (1937-2021)