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BayesMallows  

Bayesian Preference Learning with the Mallows Rank Model
View on CRAN: Click here


Download and install BayesMallows package within the R console
Install from CRAN:
install.packages("BayesMallows")

Install from Github:
library("remotes")
install_github("cran/BayesMallows")

Install by package version:
library("remotes")
install_version("BayesMallows", "2.2.3")



Attach the package and use:
library("BayesMallows")
Maintained by
Oystein Sorensen
[Scholar Profile | Author Map]
First Published: 2018-10-08
Latest Update: 2023-08-24
Description:
An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 ; Crispino et al., Annals of Applied Statistics, 2019 ; Sorensen et al., R Journal, 2020 ; Stein, PhD Thesis, 2023 ). 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 ).
How to cite:
Oystein Sorensen (2018). BayesMallows: Bayesian Preference Learning with the Mallows Rank Model. R package version 2.2.3, https://cran.r-project.org/web/packages/BayesMallows. Accessed 10 Apr. 2025.
Previous versions and publish date:
0.1.0 (2018-10-08 12:50), 0.1.1 (2018-10-15 20:20), 0.2.0 (2018-11-30 17:40), 0.3.0 (2019-01-30 19:23), 0.3.1 (2019-02-01 16:13), 0.4.0 (2019-02-22 15:30), 0.4.1 (2019-09-05 12:20), 0.4.2 (2020-03-23 14:40), 0.4.3 (2020-06-20 23:10), 0.4.4 (2020-08-07 10:32), 0.5.0 (2020-08-28 13:10), 1.0.0 (2021-01-08 10:30), 1.0.1 (2021-02-23 10:50), 1.0.2 (2021-06-04 16:50), 1.0.3 (2021-10-14 15:00), 1.0.4 (2021-11-17 12:40), 1.1.0 (2021-12-03 23:50), 1.1.1 (2022-04-02 01:40), 1.1.2 (2022-04-11 16:32), 1.2.0 (2022-05-25 01:50), 1.2.1 (2022-11-04 16:10), 1.2.2 (2023-02-03 14:52), 1.3.0 (2023-03-10 17:20), 1.3.1 (2023-08-22 00:40), 1.3.2 (2023-08-24 16:40), 1.4.0 (2023-10-04 19:10), 1.5.0 (2023-11-25 14:00), 2.0.0 (2024-01-15 11:10), 2.0.1 (2024-01-25 15:40), 2.1.0 (2024-03-13 13:20), 2.1.1 (2024-03-15 13:30), 2.2.0 (2024-04-19 09:12), 2.2.1 (2024-04-22 22:20), 2.2.2 (2024-08-17 15:00)
Other packages that cited BayesMallows R package
View BayesMallows citation profile
Other R packages that BayesMallows depends, imports, suggests or enhances
Complete documentation for BayesMallows
Functions, R codes and Examples using the BayesMallows R package
Some associated functions: BayesMallows-package . BayesMallows . assess_convergence . assign_cluster . asymptotic_partition_function . beach_preferences . bernoulli_data . calculate_backward_probability . calculate_forward_probability . cluster_data . compute_consensus.BayesMallows . compute_consensus.consensus_SMCMallows . compute_consensus . compute_expected_distance . compute_importance_sampling_estimate . compute_mallows . compute_mallows_mixtures . compute_observation_frequency . compute_posterior_intervals.BayesMallows . compute_posterior_intervals.SMCMallows . compute_posterior_intervals . compute_posterior_intervals_alpha . compute_posterior_intervals_rho . compute_rank_distance . compute_rho_consensus . correction_kernel . correction_kernel_pseudo . create_ranking . dot-generate_transitive_closure . estimate_partition_function . expected_dist . generate_constraints . generate_initial_ranking . generate_transitive_closure . get_cardinalities . get_exponent_sum . get_mallows_loglik . get_partition_function . get_rank_distance . get_sample_probabilities . get_transitive_closure . heat_plot . label_switching . leap_and_shift_probs . log_expected_dist . metropolis_hastings_alpha . metropolis_hastings_aug_ranking . metropolis_hastings_rho . obs_freq . plot.BayesMallows . plot.SMCMallows . plot_alpha_posterior . plot_elbow . plot_rho_posterior . plot_top_k . potato_true_ranking . potato_visual . potato_weighing . predict_top_k . print.BayesMallows . print.BayesMallowsMixtures . rank_conversion . rank_distance . rank_freq_distr . rmallows . run_mcmc . sample_dataset . sample_mallows . set_compute_options . set_initial_values . set_model_options . set_priors . set_smc_options . setup_rank_data . smc_mallows_new_item_rank . smc_mallows_new_users . smc_processing . sushi_rankings . update_mallows . validate_permutation . 
Some associated R codes: BayesMallows.R . RcppExports.R . all_topological_sorts.R . assess_convergence.R . assign_cluster.R . catch-routine-registration.R . compute_consensus.R . compute_mallows.R . compute_mallows_mixtures.R . compute_observation_frequency.R . compute_posterior_intervals.R . compute_rank_distance.R . data.R . estimate_partition_function.R . expected_dist.R . generate_constraints.R . generate_initial_ranking.R . generate_transitive_closure.R . get_cardinalities.R . get_mallows_loglik.R . get_transitive_closure.R . heat_plot.R . label_switching.R . misc.R . misc_expected_dist.R . obs_freq.R . plot.BayesMallows.R . plot.R . plot_elbow.R . plot_top_k.R . predict_top_k.R . print.BayesMallows.R . print.BayesMallowsMixtures.R . print.R . rank_conversion.R . rank_distance.R . rank_freq_distr.R . sample_mallows.R . set_compute_options.R . set_initial_values.R . set_model_options.R . set_priors.R . set_smc_options.R . setup_rank_data.R . smc_mallows_deprecated.R . smc_post_processing_functions.R . tidy_mcmc.R . update_mallows.R . validation_functions.R .  Full BayesMallows package functions and examples
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