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", "2.1.1")



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: 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 2.1.1, https://cran.r-project.org/web/packages/BayesMallows
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)
Other packages that cited BayesMallows R package
View BayesMallows citation profile
Other R packages that BayesMallows depends, imports, suggests or enhances
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
Get rewarded with contribution points by helping add
Reviews / comments / questions /suggestions ↴↴↴

Today's Hot Picks in Authors and Packages

steepness  
Testing Steepness of Dominance Hierarchies
The steepness package computes steepness as a property of dominance hierarchies. Steepness is define ...
Download / Learn more Package Citations See dependency  
ftaproxim  
Fault Tree Analysis Based on Proxel Simulation
Calculation and plotting of instantaneous unavailabilities of basic events along with the top event ...
Download / Learn more Package Citations See dependency  
mistral  
Methods in Structural Reliability
Various reliability analysis methods for rare event inference (computing failure probability and qua ...
Download / Learn more Package Citations See dependency  
rdbnomics  
Download DBnomics Data
R access to hundreds of millions data series from DBnomics API (). ...
Download / Learn more Package Citations See dependency  
critpath  
Setting the Critical Path in Project Management
Solving the problem of project management using CPM (Critical Path Method), PERT (Program Evaluation ...
Download / Learn more Package Citations See dependency  
quickcode  
Quick and Essential 'R' Tricks for Better Scripts
The NOT functions, 'R' tricks and a compilation of some simple quick plus often used 'R' codes to im ...
Download / Learn more Package Citations See dependency  

22,114

R Packages

188,080

Dependencies

55,244

Author Associations

22,115

Publication Badges

© Copyright 2022 - present. All right reserved, rpkg.net. Contact Us / Suggestions / Concerns