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>).
Version: 2.2.5 Depends: R (≥ 3.5.0) Imports: Rcpp (≥ 1.0.0), ggplot2 (≥ 3.1.0), Rdpack (≥ 1.0), sets (≥ 1.0-18), relations (≥ 0.6-8), rlang (≥ 0.3.1) LinkingTo: Rcpp, RcppArmadillo, testthat Suggests: knitr, testthat (≥ 3.0.0), label.switching (≥ 1.7), rmarkdown, covr, parallel (≥ 3.5.1) Published: 2025-06-27 DOI: 10.32614/CRAN.package.BayesMallows Author: Oystein Sorensen [aut, cre], Waldir Leoncio [aut], Valeria Vitelli [aut], Marta Crispino [aut], Qinghua Liu [aut], Cristina Mollica [aut], Luca Tardella [aut], Anja Stein [aut] Maintainer: Oystein Sorensen <oystein.sorensen.1985 at gmail.com> BugReports: https://github.com/ocbe-uio/BayesMallows/issues License: GPL-3 URL: https://github.com/ocbe-uio/BayesMallows, https://ocbe-uio.github.io/BayesMallows/ NeedsCompilation: yes Citation: BayesMallows citation info Materials: NEWS In views: Bayesian, MissingData CRAN checks: BayesMallows results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=BayesMallows to link to this page.
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