Other packages > Find by keyword >

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", "2.2.7")



Attach the package and use:
library("BayesMallows")
Maintained by
Oystein Sorensen
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2018-10-08
Latest Update: 2025-06-27
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 2.2.7, https://cran.r-project.org/web/packages/BayesMallows. Accessed 16 Jul. 2026.
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), 2.2.3 (2025-01-14 12:30), 2.2.4 (2025-06-13 10:50), 2.2.5 (2025-06-27 14:00), 2.2.6 (2025-11-25 07:11), (2026-07-09 07:58)
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
Downloads during the last 30 days

Today's Hot Picks in Authors and Packages

nextGenShinyApps  
Craft Exceptional 'R Shiny' Applications and Dashboards with Novel Responsive Tools
Nove responsive tools for designing and developing 'Shiny' dashboards and applications. The scripts ...
Download / Learn more Package Citations See dependency  
schoolmath  
Functions and Datasets for Math Used in School
Contains functions and datasets for math taught in school. A main focus is set to prime-calculation. ...
Download / Learn more Package Citations See dependency  
Rnmr1D  
Perform the Complete Processing of a Set of Proton Nuclear Magnetic Resonance Spectra
Perform the complete processing of a set of proton nuclear magnetic resonance spectra from the free ...
Download / Learn more Package Citations See dependency  
bbdetection  
Identification of Bull and Bear States of the Market
Implements two algorithms of detecting Bull and Bear markets in stock prices: the algorithm of Pagan ...
Download / Learn more Package Citations See dependency  
tibble  
Simple Data Frames
Provides a 'tbl_df' class (the 'tibble') with stricter checking and better formatting than the tradi ...
Download / Learn more Package Citations See dependency  
stevedata  
Steve's Toy Data for Teaching About a Variety of Methodological, Social, and Political Topics
This is a collection of various kinds of data with broad uses for teaching. My students, and academ ...
Download / Learn more Package Citations See dependency  

27,806

R Packages

239,283

Dependencies

73,837

Author Associations

27,807

Publication Badges

© Copyright since 2022. All right reserved, rpkg.net.  Based in Cambridge, Massachusetts, USA