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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>.
Version: | 1.0.0 |
Depends: | R (≥ 3.6.0) |
Imports: | Rcpp |
LinkingTo: | Rcpp, RcppArmadillo, RcppDist, mvtnorm, RcppTN |
Suggests: | knitr, rmarkdown, pscl, MCMCpack |
Published: | 2025-05-30 |
DOI: | 10.32614/CRAN.package.pumBayes |
Author: | Skylar Shi |
Maintainer: | Skylar Shi <dshi98 at uw.edu> |
BugReports: | https://github.com/SkylarShiHub/pumBayes/issues |
License: | GPL-3 |
URL: | https://github.com/SkylarShiHub/pumBayes |
NeedsCompilation: | yes |
Language: | en |
Materials: | README |
CRAN checks: | pumBayes results |
Reference manual: | pumBayes.pdf |
Package source: | pumBayes_1.0.0.tar.gz |
Windows binaries: | r-devel: pumBayes_1.0.0.zip, r-release: pumBayes_1.0.0.zip, r-oldrel: pumBayes_1.0.0.zip |
macOS binaries: | r-release (arm64): pumBayes_1.0.0.tgz, r-oldrel (arm64): pumBayes_1.0.0.tgz, r-release (x86_64): pumBayes_1.0.0.tgz, r-oldrel (x86_64): pumBayes_1.0.0.tgz |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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