BayesMallows

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).

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