post_rank

This function calculates quantile ranks for each legislator based on posterior samples of beta parameters from MCMC.
The function can handle any specified quantiles, such as median (0.5), and is flexible to support other quantiles provided as input.

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>.

Skylar Shi

pumBayes

Bayesian Estimation of Probit Unfolding Models for Binary
Preference Data

Abel Rodriguez

Rayleigh Lei

Jonathan Olmsted

post_rank function

<dl><dt>beta</dt>
<dd>A matrix of posterior samples of beta obtained from MCMC, with columns representing legislators.</dd>
<dt>quantiles</dt>
<dd>A numeric vector specifying the quantiles to be calculated for the ranks (default is `c(0.5)` for median rank).</dd></dl>

Arguments

Generate Quantile Ranks for Legislators — post_rank

<dl>

<dt>beta</dt>
<dd>A matrix of posterior samples of beta obtained from MCMC, with columns representing legislators.</dd>


<dt>quantiles</dt>
<dd>A numeric vector specifying the quantiles to be calculated for the ranks (default is `c(0.5)` for median rank).</dd>

</dl>

post_rank: Generate Quantile Ranks for Legislators

Description

Usage

Value

Arguments

Examples