BayesMallows: Bayesian Preference Learning with the Mallows Rank Model

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>). 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:	1.2.0
Depends:	R (≥ 3.5.0)
Imports:	Rcpp (≥ 1.0.0), ggplot2 (≥ 3.1.0), Rdpack (≥ 1.0), igraph (≥ 1.2.5), sets (≥ 1.0-18), relations (≥ 0.6-8), rlang (≥ 0.3.1), PerMallows (≥ 1.13), HDInterval (≥ 0.2.0), cowplot (≥ 1.0.0)
LinkingTo:	Rcpp, RcppArmadillo, testthat
Suggests:	R.rsp, xml2, knitr, testthat (≥ 3.0.0), label.switching (≥ 1.7), rmarkdown, covr, parallel (≥ 3.5.1)
Published:	2022-05-24
Author:	Oystein Sorensen [aut, cre], Valeria Vitelli [aut], Marta Crispino [aut], Qinghua Liu [aut], Cristina Mollica [aut], Luca Tardella [aut], Anja Stein [aut], Waldir Leoncio [ctr]
Maintainer:	Oystein Sorensen <oystein.sorensen.1985 at gmail.com>
License:	GPL-3
URL:	https://github.com/ocbe-uio/BayesMallows
NeedsCompilation:	yes
Citation:	BayesMallows citation info
Materials:	NEWS
In views:	Bayesian, MissingData
CRAN checks:	BayesMallows results

Documentation:

Reference manual:	BayesMallows.pdf
Vignettes:	BayesMallows: An R Package for the Bayesian Mallows Model SMC-Mallows Tutorial

Downloads:

Package source:	BayesMallows_1.2.0.tar.gz
Windows binaries:	r-devel: BayesMallows_1.2.0.zip, r-release: BayesMallows_1.2.0.zip, r-oldrel: BayesMallows_1.2.0.zip
macOS binaries:	r-release (arm64): BayesMallows_1.2.0.tgz, r-oldrel (arm64): BayesMallows_1.2.0.tgz, r-release (x86_64): BayesMallows_1.2.0.tgz, r-oldrel (x86_64): BayesMallows_1.2.0.tgz
Old sources:	BayesMallows archive

Reverse dependencies:

Reverse suggests:

PlackettLuce

Linking:

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