Package: BayesMallows
Type: Package
Title: Bayesian Preference Learning with the Mallows Rank Model
Version: 2.2.7.9000
Authors@R: c(person("Oystein", "Sorensen",
                    email = "oystein.sorensen.1985@gmail.com",
                    role = c("aut", "cre"),
                    comment = c(ORCID = "0000-0003-0724-3542")),
            person("Waldir", "Leoncio",
                    email = "w.l.netto@medisin.uio.no",
                    role = c("aut")),
            person("Valeria", "Vitelli",
                    role = c("aut"),
                    email = "valeria.vitelli@medisin.uio.no",
                    comment = c(ORCID = "0000-0002-6746-0453")),
            person("Marta", "Crispino",
                    email = "crispino.marta8@gmail.com",
                    role = c("aut")),
            person("Qinghua", "Liu",
                    email = "qinghual@math.uio.no",
                    role = c("aut")),
            person("Cristina", "Mollica",
                    email = "cristina.mollica@uniroma1.it",
                    role = c("aut")),
            person("Luca", "Tardella",
                    role = c("aut")),
            person("Anja", "Stein",
                    role = c("aut"))            
                    )
Maintainer: Oystein Sorensen <oystein.sorensen.1985@gmail.com>
Description: 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>).
URL: https://github.com/ocbe-uio/BayesMallows,
        https://ocbe-uio.github.io/BayesMallows/
BugReports: https://github.com/ocbe-uio/BayesMallows/issues
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.3
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)
VignetteBuilder: knitr, rmarkdown
RdMacros: Rdpack
Config/testthat/edition: 3
Roxygen: list(markdown = TRUE)
Config/pak/sysreqs: cmake make libuv1-dev
Repository: https://ocbe-uio.r-universe.dev
Date/Publication: 2026-02-06 11:59:32 UTC
RemoteUrl: https://github.com/ocbe-uio/BayesMallows
RemoteRef: HEAD
RemoteSha: a26cf89d3142ea3499489730e7c2b3ef9a26bfb2
NeedsCompilation: yes
Packaged: 2026-06-06 06:41:44 UTC; root
Author: Oystein Sorensen [aut, cre] (ORCID:
    <https://orcid.org/0000-0003-0724-3542>),
  Waldir Leoncio [aut],
  Valeria Vitelli [aut] (ORCID: <https://orcid.org/0000-0002-6746-0453>),
  Marta Crispino [aut],
  Qinghua Liu [aut],
  Cristina Mollica [aut],
  Luca Tardella [aut],
  Anja Stein [aut]