rmallows

Sample from the Mallows distribution with arbitrary distance metric using
a Metropolis-Hastings algorithm.

internal

An implementation of the Bayesian version of the Mallows rank model
(Vitelli et al., Journal of Machine Learning Research, 2018 <https://jmlr.org/papers/v18/15-481.html>;
Crispino et al., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>;
Sorensen et al., R Journal, 2020 <doi:10.32614/RJ-2020-026>;
Stein, PhD Thesis, 2023 <https://eprints.lancs.ac.uk/id/eprint/195759>). Both Metropolis-Hastings
and sequential Monte Carlo algorithms for estimating the models are available. Cayley, footrule,
Hamming, Kendall, Spearman, and Ulam distances are supported in the models. The rank data to be
analyzed can be in the form of complete rankings, top-k rankings, partially missing rankings, as well
as consistent and inconsistent pairwise preferences. Several functions for plotting and studying the
posterior distributions of parameters are provided. The package also provides functions for estimating
the partition function (normalizing constant) of the Mallows rank model, both with the importance
sampling algorithm of Vitelli et al. and asymptotic approximation with the IPFP algorithm
(Mukherjee, Annals of Statistics, 2016 <doi:10.1214/15-AOS1389>).

Oystein Sorensen

BayesMallows

Bayesian Preference Learning with the Mallows Rank Model

Waldir Leoncio

Valeria Vitelli

Marta Crispino

Qinghua Liu

Cristina Mollica

Luca Tardella

Anja Stein

rmallows function

<dl><dt>rho0</dt>
<dd>Vector specifying the latent consensus ranking.</dd>
<dt>alpha0</dt>
<dd>Scalar specifying the scale parameter.</dd>
<dt>n_samples</dt>
<dd>Integer specifying the number of random samples to generate.</dd>
<dt>burnin</dt>
<dd>Integer specifying the number of iterations to discard as burn-in.</dd>
<dt>thinning</dt>
<dd>Integer specifying the number of MCMC iterations to perform
between each time a random rank vector is sampled.</dd>
<dt>leap_size</dt>
<dd>Integer specifying the step size of the leap-and-shift proposal distribution.</dd>
<dt>metric</dt>
<dd>Character string specifying the distance measure to use. Available
options are <code>"footrule"</code> (default), <code>"spearman"</code>, <code>"cayley"</code>, <code>"hamming"</code>,
<code>"kendall"</code>, and <code>"ulam"</code>.</dd></dl>

Arguments

Sample from the Mallows distribution. — rmallows

<dl>

<dt>rho0</dt>
<dd>Vector specifying the latent consensus ranking.</dd>


<dt>alpha0</dt>
<dd>Scalar specifying the scale parameter.</dd>


<dt>n_samples</dt>
<dd>Integer specifying the number of random samples to generate.</dd>


<dt>burnin</dt>
<dd>Integer specifying the number of iterations to discard as burn-in.</dd>


<dt>thinning</dt>
<dd>Integer specifying the number of MCMC iterations to perform
between each time a random rank vector is sampled.</dd>


<dt>leap_size</dt>
<dd>Integer specifying the step size of the leap-and-shift proposal distribution.</dd>


<dt>metric</dt>
<dd>Character string specifying the distance measure to use. Available
options are <code>"footrule"</code> (default), <code>"spearman"</code>, <code>"cayley"</code>, <code>"hamming"</code>,
<code>"kendall"</code>, and <code>"ulam"</code>.</dd>

</dl>

Sample from the Mallows distribution.

rmallows: Sample from the Mallows distribution.

Description

Usage

Arguments

References