BayesMallows: An R Package for the Bayesian Mallows Model

TL;DR

BayesMallows is an R package that implements a Bayesian Mallows model for ranking data, supporting multiple distances and partial preferences, with efficient algorithms for large item sets and uncertainty quantification.

Contribution

This is the first Bayesian implementation of the Mallows model supporting various distances and large item sets, with algorithms for partition function approximation.

Findings

Supports multiple distance metrics including footrule, Spearman, Kendall, Cayley, Hamming, and Ulam.

Handles partial rankings and non-transitive preferences.

Provides posterior uncertainty quantification and visualization tools.

Abstract

BayesMallows is an R package for analyzing data in the form of rankings or preferences with the Mallows rank model, and its finite mixture extension, in a Bayesian probabilistic framework. The Mallows model is a well-known model, grounded on the idea that the probability density of an observed ranking decreases exponentially fast as its distance to the location parameter increases. Despite the model being quite popular, this is the first Bayesian implementation that allows a wide choice of distances, and that works well with a large amount of items to be ranked. BayesMallows supports footrule, Spearman, Kendall, Cayley, Hamming and Ulam distances, allowing full use of the rich expressiveness of the Mallows model. This is possible thanks to the implementation of fast algorithms for approximating the partition function of the model under various distances. Although developed for being used in computing the posterior distribution of the model, these algorithms may be of interest in their own right. BayesMallows handles non-standard data: partial rankings and pairwise comparisons, even in cases including non-transitive preference patterns. The advantage of the Bayesian paradigm in this context comes from its ability to coherently quantify posterior uncertainties of estimates of any quantity of interest. These posteriors are fully available to the user, and the package comes with convienient tools for summarizing and visualizing the posterior distributions.