Package: BayesMallows 2.2.2
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:
BayesMallows_2.2.2.tar.gz
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BayesMallows.pdf |BayesMallows.html✨
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
- beach_preferences - Beach preferences
- bernoulli_data - Simulated intransitive pairwise preferences
- cluster_data - Simulated clustering data
- potato_true_ranking - True ranking of the weights of 20 potatoes.
- potato_visual - Potato weights assessed visually
- potato_weighing - Potato weights assessed by hand
- sushi_rankings - Sushi rankings
Last updated 3 months agofrom:771fa30c97. Checks:OK: 9. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Oct 14 2024 |
R-4.5-win-x86_64 | OK | Oct 14 2024 |
R-4.5-linux-x86_64 | OK | Oct 14 2024 |
R-4.4-win-x86_64 | OK | Oct 14 2024 |
R-4.4-mac-x86_64 | OK | Oct 14 2024 |
R-4.4-mac-aarch64 | OK | Oct 14 2024 |
R-4.3-win-x86_64 | OK | Oct 14 2024 |
R-4.3-mac-x86_64 | OK | Oct 14 2024 |
R-4.3-mac-aarch64 | OK | Oct 14 2024 |
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:briocallrcliclustercolorspacecrayondescdiffobjdigestevaluatefansifarverfsggplot2gluegtableisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgbuildpkgconfigpkgloadpraiseprocessxpsR6rbibutilsRColorBrewerRcppRcppArmadilloRdpackrelationsrematch2rlangrprojrootscalessetsslamtestthattibbleutf8vctrsviridisLitewaldowithr
Introduction
Rendered fromBayesMallows.Rmd
usingknitr::rmarkdown
on Oct 14 2024.Last update: 2024-03-07
Started: 2023-10-04
MCMC with Parallel Chains
Rendered fromparallel_chains.Rmd
usingknitr::rmarkdown
on Oct 14 2024.Last update: 2024-02-28
Started: 2023-10-12
Sequential Monte Carlo for the Bayesian Mallows model
Rendered fromSMC-Mallows.Rmd
usingknitr::rmarkdown
on Oct 14 2024.Last update: 2024-03-07
Started: 2021-11-24