complete_rankings

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

complete_rankings function


<div class="section" id="complete-rankings">
<h3><code>complete_rankings</code></h3><p></p><p>
</p><p>A data frame with 100 rows and 7 columns:</p><dl>
<dt>timepoint</dt>
<dd><p>Timepoint</p></dd><p></p><p><dt>user</dt>
<dd><p>User id</p></dd></p><p><dt>item1, item2, item3, item4, item5</dt>
<dd><p>Ranking given to the item.</p></dd></p><p>
</p></dl><p></p><p></p></div>


Format

Simulated Data — complete_rankings


<div class='section' id='complete-rankings'>
<h3><code>complete_rankings</code></h3>


<p>A data frame with 100 rows and 7 columns:</p><dl>
<dt>timepoint</dt>
<dd><p>Timepoint</p></dd>

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

<dt>item1, item2, item3, item4, item5</dt>
<dd><p>Ranking given to the item.</p></dd>


</dl>

</div>


complete_rankings: Simulated Data

Description

Usage

Arguments

Format

`complete_rankings`