pairwise_preferences

Simulated Data with Pairwise Preferences

datasets

Provides nested sequential Monte Carlo algorithms for performing
sequential inference in the Bayesian Mallows model, which is a widely used
probability model for rank and preference data. The package implements the
SMC2 (Sequential Monte Carlo Squared) algorithm for handling sequentially
arriving rankings and pairwise preferences, including support for complete
rankings, partial rankings, and pairwise comparisons. The methods are based
on Sorensen (2025) <doi:10.1214/25-BA1564>.

Oystein Sorensen

BayesMallowsSMC2

Nested Sequential Monte Carlo for the Bayesian Mallows Model

pairwise_preferences function

<div class="section" id="pairwise-preferences">
<h3><code>pairwise_preferences</code></h3>
A data frame with 400 rows and 4 columns:<dl>
<dt>timepoint</dt>
<dd>Timepoint</dd><dt>user</dt>
<dd>User id</dd><dt>top_item</dt>
<dd>Item preferred in the comparison.</dd><dt>bottom_item</dt>
<dd>Item disfavored in the comparison.</dd>
</dl></div>

Format

Simulated Data with Pairwise Preferences — pairwise_preferences

<div class='section' id='pairwise-preferences'>
<h3><code>pairwise_preferences</code></h3>


A data frame with 400 rows and 4 columns:<dl>
<dt>timepoint</dt>
<dd>Timepoint</dd>

<dt>user</dt>
<dd>User id</dd>

<dt>top_item</dt>
<dd>Item preferred in the comparison.</dd>

<dt>bottom_item</dt>
<dd>Item disfavored in the comparison.</dd>


</dl>

</div>

pairwise_preferences: Simulated Data with Pairwise Preferences

Description

Usage

Arguments

Format

`pairwise_preferences`