Package: BayesMallows 2.2.7.9000

Oystein Sorensen

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>; 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>).

Authors: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]

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manual.pdf |manual.html✨
card.svg |card.png
BayesMallows/json (API)
NEWS

# Install 'BayesMallows' in R:
install.packages('BayesMallows', repos = c('https://ocbe-uio.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ocbe-uio/bayesmallows/issues

Pkgdown/docs site:https://ocbe-uio.github.io

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

mallows-modelopenblascppopenmp

7.75 score 22 stars 47 scripts 556 downloads 34 exports 42 dependencies

Last updated from:a26cf89d31. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK319
linux-devel-x86_64OK285
source / vignettesOK373
linux-release-arm64OK278
linux-release-x86_64OK292
macos-release-arm64OK233
macos-release-x86_64OK716
macos-oldrel-arm64OK221
macos-oldrel-x86_64OK394
windows-develOK376
windows-releaseOK358
windows-oldrelOK349
wasm-releaseOK215

Exports:assess_convergenceassign_clusterburninburnin<-compute_consensuscompute_exact_partition_functioncompute_expected_distancecompute_mallowscompute_mallows_mixturescompute_mallows_sequentiallycompute_observation_frequencycompute_posterior_intervalscompute_rank_distancecreate_orderingcreate_rankingestimate_partition_functionget_acceptance_ratiosget_cardinalitiesget_mallows_loglikget_transitive_closureheat_plotplot_elbowplot_top_kpredict_top_ksample_mallowssample_priorset_compute_optionsset_initial_valuesset_model_optionsset_priorsset_progress_reportset_smc_optionssetup_rank_dataupdate_mallows

Dependencies:briocallrcliclustercpp11crayondescdiffobjevaluatefarverfsggplot2gluegtableisobandjsonlitelabelinglifecyclemagrittrpkgbuildpkgloadpraiseprocessxpsR6rbibutilsRColorBrewerRcppRcppArmadilloRdpackrelationsrlangrprojrootS7scalessetsslamtestthatvctrsviridisLitewaldowithr

Introduction

Rendered fromBayesMallows.Rmdusingknitr::rmarkdownon May 07 2026.

Last update: 2024-03-07
Started: 2023-10-04

MCMC with Parallel Chains

Rendered fromparallel_chains.Rmdusingknitr::rmarkdownon May 07 2026.

Last update: 2024-02-28
Started: 2023-10-12

Sequential Monte Carlo for the Bayesian Mallows model

Rendered fromSMC-Mallows.Rmdusingknitr::rmarkdownon May 07 2026.

Last update: 2024-03-07
Started: 2021-11-24

Readme and manuals

Help Manual

Help pageTopics
Trace Plots from Metropolis-Hastings Algorithmassess_convergence assess_convergence.BayesMallows assess_convergence.BayesMallowsMixtures
Assign Assessors to Clustersassign_cluster
Beach preferencesbeach_preferences
Simulated intransitive pairwise preferencesbernoulli_data
See the burninburnin burnin.BayesMallows burnin.BayesMallowsMixtures burnin.SMCMallows
Set the burninburnin<- burnin<-.BayesMallows burnin<-.BayesMallowsMixtures
Simulated clustering datacluster_data
Compute Consensus Rankingcompute_consensus compute_consensus.BayesMallows compute_consensus.SMCMallows
Compute exact partition functioncompute_exact_partition_function
Expected value of metrics under a Mallows rank modelcompute_expected_distance
Preference Learning with the Mallows Rank Modelcompute_mallows
Compute Mixtures of Mallows Modelscompute_mallows_mixtures
Estimate the Bayesian Mallows Model Sequentiallycompute_mallows_sequentially
Frequency distribution of the ranking sequencescompute_observation_frequency
Compute Posterior Intervalscompute_posterior_intervals compute_posterior_intervals.BayesMallows compute_posterior_intervals.SMCMallows
Distance between a set of rankings and a given rank sequencecompute_rank_distance
Convert between ranking and ordering.create_ordering create_ranking
Estimate Partition Functionestimate_partition_function
Get Acceptance Ratiosget_acceptance_ratios get_acceptance_ratios.BayesMallows get_acceptance_ratios.SMCMallows
Get cardinalities for each distanceget_cardinalities
Likelihood and log-likelihood evaluation for a Mallows mixture modelget_mallows_loglik
Get transitive closureget_transitive_closure
Heat plot of posterior probabilitiesheat_plot
Plot Within-Cluster Sum of Distancesplot_elbow
Plot Top-k Rankings with Pairwise Preferencesplot_top_k
Plot Posterior Distributionsplot.BayesMallows
Plot SMC Posterior Distributionsplot.SMCMallows
True ranking of the weights of 20 potatoes.potato_true_ranking
Potato weights assessed visuallypotato_visual
Potato weights assessed by handpotato_weighing
Predict Top-k Rankings with Pairwise Preferencespredict_top_k
Print Method for BayesMallows Objectsprint.BayesMallows print.BayesMallowsMixtures print.SMCMallows
Random Samples from the Mallows Rank Modelsample_mallows
Sample from prior distributionsample_prior
Specify options for computationset_compute_options
Set initial values of scale parameter and modal rankingset_initial_values
Set options for Bayesian Mallows modelset_model_options
Set prior parameters for Bayesian Mallows modelset_priors
Set progress report options for MCMC algorithmset_progress_report
Set SMC compute optionsset_smc_options
Setup rank datasetup_rank_data
Sounds datasounds
Sushi rankingssushi_rankings
Update a Bayesian Mallows model with new usersupdate_mallows update_mallows.BayesMallows update_mallows.BayesMallowsPriorSamples update_mallows.SMCMallows