item_char

This function calculates the data needed to plot the item characteristic
curve for a specific issue based on posterior samples.

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

item_char function

<dl><dt>vote_num</dt>
<dd>The vote number of the issue to be reviewed. This refers to numbers
in the column names of the input vote matrix, not the clerk session vote number.</dd>
<dt>x</dt>
<dd>A vector showing the range of beta in the x axis.</dd>
<dt>post_samples</dt>
<dd>A list of posterior samples of parameters obtained from `sample_pum_static` in `pumBayes`.</dd></dl>

Arguments

Generate Data for Item Characteristic Curves — item_char

<dl>

<dt>vote_num</dt>
<dd>The vote number of the issue to be reviewed. This refers to numbers
in the column names of the input vote matrix, not the clerk session vote number.</dd>


<dt>x</dt>
<dd>A vector showing the range of beta in the x axis.</dd>


<dt>post_samples</dt>
<dd>A list of posterior samples of parameters obtained from `sample_pum_static` in `pumBayes`.</dd>

</dl>

item_char: Generate Data for Item Characteristic Curves

Description

Usage

Value

Arguments

Examples