{ "_id": "6a106c32acfb0bcc41caa7c0", "Package": "BayesMallows", "Type": "Package", "Title": "Bayesian Preference Learning with the Mallows Rank Model", "Version": "2.2.7.9000", "Authors@R": "c(person(\"Oystein\", \"Sorensen\",\nemail = \"oystein.sorensen.1985@gmail.com\",\nrole = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0003-0724-3542\")),\nperson(\"Waldir\", \"Leoncio\",\nemail = \"w.l.netto@medisin.uio.no\",\nrole = c(\"aut\")),\nperson(\"Valeria\", \"Vitelli\",\nrole = c(\"aut\"),\nemail = \"valeria.vitelli@medisin.uio.no\",\ncomment = c(ORCID = \"0000-0002-6746-0453\")),\nperson(\"Marta\", \"Crispino\",\nemail = \"crispino.marta8@gmail.com\",\nrole = c(\"aut\")),\nperson(\"Qinghua\", \"Liu\",\nemail = \"qinghual@math.uio.no\",\nrole = c(\"aut\")),\nperson(\"Cristina\", \"Mollica\",\nemail = \"cristina.mollica@uniroma1.it\",\nrole = c(\"aut\")),\nperson(\"Luca\", \"Tardella\",\nrole = c(\"aut\")),\nperson(\"Anja\", \"Stein\",\nrole = c(\"aut\"))\n)", "Maintainer": "Oystein Sorensen ", "Description": "An implementation of the Bayesian version of the Mallows\nrank model (Vitelli et al., Journal of Machine Learning\nResearch, 2018 ;\nCrispino et al., Annals of Applied Statistics, 2019\n; Sorensen et al., R Journal, 2020\n; Stein, PhD Thesis, 2023\n). Both\nMetropolis-Hastings and sequential Monte Carlo algorithms for\nestimating the models are available. Cayley, footrule, Hamming,\nKendall, Spearman, and Ulam distances are supported in the\nmodels. The rank data to be analyzed can be in the form of\ncomplete rankings, top-k rankings, partially missing rankings,\nas well as consistent and inconsistent pairwise preferences.\nSeveral functions for plotting and studying the posterior\ndistributions of parameters are provided. The package also\nprovides functions for estimating the partition function\n(normalizing constant) of the Mallows rank model, both with the\nimportance sampling algorithm of Vitelli et al. and asymptotic\napproximation with the IPFP algorithm (Mukherjee, Annals of\nStatistics, 2016 ).", "URL": "https://github.com/ocbe-uio/BayesMallows,\nhttps://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", "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": { "Date": "2026-05-07 09:04:06 UTC", "User": "root" }, "Author": "Oystein Sorensen [aut, cre] (ORCID:\n),\nWaldir Leoncio [aut],\nValeria Vitelli [aut] (ORCID: ),\nMarta Crispino [aut],\nQinghua Liu [aut],\nCristina Mollica [aut],\nLuca Tardella [aut],\nAnja Stein [aut]", "MD5sum": "e78a12077d17d9856cd311525c241b6b", "_user": "ocbe-uio", "_type": "src", "_file": "BayesMallows_2.2.7.9000.tar.gz", "_fileid": "9c1f4c182d0c07a97bbe7c684dfa8e6f3e3f23425abe5e8425c5ddac4b095658", "_filesize": 4791353, "_sha256": "9c1f4c182d0c07a97bbe7c684dfa8e6f3e3f23425abe5e8425c5ddac4b095658", "_created": "2026-05-07T09:04:06.000Z", "_published": "2026-05-22T14:46:10.953Z", "_distro": "noble", "_jobs": [ { "job": 77402835152, "time": 319, "config": "linux-devel-arm64", "r": "4.7.0", "check": "OK", "artifact": "6851207598" }, { "job": 77402835351, "time": 285, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "6851198498" }, { "job": 77402836137, "time": 278, "config": "linux-release-arm64", "r": "4.6.0", 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"compute_exact_partition_function", "title": "Compute exact partition function", "concept": [ "partition function" ], "topics": [ "compute_exact_partition_function" ] }, { "page": "compute_expected_distance", "title": "Expected value of metrics under a Mallows rank model", "concept": [ "rank functions" ], "topics": [ "compute_expected_distance" ] }, { "page": "compute_mallows", "title": "Preference Learning with the Mallows Rank Model", "concept": [ "modeling" ], "topics": [ "compute_mallows" ] }, { "page": "compute_mallows_mixtures", "title": "Compute Mixtures of Mallows Models", "concept": [ "modeling" ], "topics": [ "compute_mallows_mixtures" ] }, { "page": "compute_mallows_sequentially", "title": "Estimate the Bayesian Mallows Model Sequentially", "concept": [ "modeling" ], "topics": [ "compute_mallows_sequentially" ] }, { "page": "compute_observation_frequency", "title": "Frequency distribution of the ranking sequences", "concept": [ "rank functions" ], "topics": [ 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"get_acceptance_ratios.SMCMallows" ] }, { "page": "get_cardinalities", "title": "Get cardinalities for each distance", "concept": [ "partition function" ], "topics": [ "get_cardinalities" ] }, { "page": "get_mallows_loglik", "title": "Likelihood and log-likelihood evaluation for a Mallows mixture model", "concept": [ "rank functions" ], "topics": [ "get_mallows_loglik" ] }, { "page": "get_transitive_closure", "title": "Get transitive closure", "concept": [ "preprocessing" ], "topics": [ "get_transitive_closure" ] }, { "page": "heat_plot", "title": "Heat plot of posterior probabilities", "concept": [ "posterior quantities" ], "topics": [ "heat_plot" ] }, { "page": "plot_elbow", "title": "Plot Within-Cluster Sum of Distances", "concept": [ "posterior quantities" ], "topics": [ "plot_elbow" ] }, { "page": "plot_top_k", "title": "Plot Top-k Rankings with Pairwise Preferences", "concept": [ "posterior quantities" ], "topics": [ "plot_top_k" ] }, { "page": "plot.BayesMallows", "title": "Plot Posterior Distributions", "concept": [ "posterior quantities" ], "topics": [ "plot.BayesMallows" ] }, { "page": "plot.SMCMallows", "title": "Plot SMC Posterior Distributions", "concept": [ "posterior quantities" ], "topics": [ "plot.SMCMallows" ] }, { "page": "potato_true_ranking", "title": "True ranking of the weights of 20 potatoes.", "concept": [ "datasets" ], "topics": [ "potato_true_ranking" ] }, { "page": "potato_visual", "title": "Potato weights assessed visually", "concept": [ "datasets" ], "topics": [ "potato_visual" ] }, { "page": "potato_weighing", "title": "Potato weights assessed by hand", "concept": [ "datasets" ], "topics": [ "potato_weighing" ] }, { "page": "predict_top_k", "title": "Predict Top-k Rankings with Pairwise Preferences", "concept": [ "posterior quantities" ], "topics": [ "predict_top_k" ] }, { "page": "print.BayesMallows", "title": "Print Method for BayesMallows Objects", "concept": [ "posterior quantities" ], "topics": [ "print.BayesMallows", "print.BayesMallowsMixtures", "print.SMCMallows" ] }, { "page": "sample_mallows", "title": "Random Samples from the Mallows Rank Model", "concept": [ "rank functions" ], "topics": [ "sample_mallows" ] }, { "page": "sample_prior", "title": "Sample from prior distribution", "concept": [ "modeling" ], "topics": [ "sample_prior" ] }, { "page": "set_compute_options", "title": "Specify options for computation", "concept": [ "preprocessing" ], "topics": [ "set_compute_options" ] }, { "page": "set_initial_values", "title": "Set initial values of scale parameter and modal ranking", "concept": [ "preprocessing" ], "topics": [ "set_initial_values" ] }, { "page": "set_model_options", "title": "Set options for Bayesian Mallows model", "concept": [ "preprocessing" ], "topics": [ "set_model_options" ] }, { "page": "set_priors", "title": "Set prior parameters for Bayesian Mallows model", "concept": [ "preprocessing" ], "topics": [ "set_priors" ] }, { "page": "set_progress_report", "title": "Set 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