BayesMallows

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>).

Downloads

Trend: -7.1% (30d vs prior 30d)

CRAN Check Status

  installed size is 13.9Mb
  sub-directories of 1Mb or more:
    doc    2.7Mb
    libs  10.4Mb

  installed size is 14.5Mb
  sub-directories of 1Mb or more:
    doc    2.7Mb
    libs  11.0Mb

  installed size is  5.2Mb
  sub-directories of 1Mb or more:
    doc    2.7Mb
    libs   1.7Mb

Check History

  installed size is 13.9Mb
  sub-directories of 1Mb or more:
    doc    2.7Mb
    libs  10.4Mb

  installed size is 14.5Mb
  sub-directories of 1Mb or more:
    doc    2.7Mb
    libs  11.0Mb

  installed size is  5.2Mb
  sub-directories of 1Mb or more:
    doc    2.7Mb
    libs   1.7Mb

Reverse Dependencies (1)

Dependency Network

Version History