R: Bayesian Preference Learning with the Mallows Rank Model

# Bayesian Preference Learning with the Mallows Rank Model

## Documentation for package ‘BayesMallows’ version 2.2.5

- DESCRIPTION file.
- User guides, package vignettes and other documentation.
- Package NEWS.

## Help Pages

assess_convergence | Trace Plots from Metropolis-Hastings Algorithm
assess_convergence.BayesMallows | Trace Plots from Metropolis-Hastings Algorithm
assess_convergence.BayesMallowsMixtures | Trace Plots from Metropolis-Hastings Algorithm
assign_cluster | Assign Assessors to Clusters
beach_preferences | Beach preferences
bernoulli_data | Simulated intransitive pairwise preferences
burnin | See the burnin
burnin.BayesMallows | See the burnin
burnin.BayesMallowsMixtures | See the burnin
burnin.SMCMallows | See the burnin
burnin<- | Set the burnin
burnin<-.BayesMallows | Set the burnin
burnin<-.BayesMallowsMixtures | Set the burnin
cluster_data | Simulated clustering data
compute_consensus | Compute Consensus Ranking
compute_consensus.BayesMallows | Compute Consensus Ranking
compute_consensus.SMCMallows | Compute Consensus Ranking
compute_exact_partition_function | Compute exact partition function
compute_expected_distance | Expected value of metrics under a Mallows rank model
compute_mallows | Preference Learning with the Mallows Rank Model
compute_mallows_mixtures | Compute Mixtures of Mallows Models
compute_mallows_sequentially | Estimate the Bayesian Mallows Model Sequentially
compute_observation_frequency | Frequency distribution of the ranking sequences
compute_posterior_intervals | Compute Posterior Intervals
compute_posterior_intervals.BayesMallows | Compute Posterior Intervals
compute_posterior_intervals.SMCMallows | Compute Posterior Intervals
compute_rank_distance | Distance between a set of rankings and a given rank sequence
create_ordering | Convert between ranking and ordering.
create_ranking | Convert between ranking and ordering.
estimate_partition_function | Estimate Partition Function
get_acceptance_ratios | Get Acceptance Ratios
get_acceptance_ratios.BayesMallows | Get Acceptance Ratios
get_acceptance_ratios.SMCMallows | Get Acceptance Ratios
get_cardinalities | Get cardinalities for each distance
get_mallows_loglik | Likelihood and log-likelihood evaluation for a Mallows mixture model
get_transitive_closure | Get transitive closure
heat_plot | Heat plot of posterior probabilities
plot.BayesMallows | Plot Posterior Distributions
plot.SMCMallows | Plot SMC Posterior Distributions
plot_elbow | Plot Within-Cluster Sum of Distances
plot_top_k | Plot Top-k Rankings with Pairwise Preferences
potato_true_ranking | True ranking of the weights of 20 potatoes.
potato_visual | Potato weights assessed visually
potato_weighing | Potato weights assessed by hand
predict_top_k | Predict Top-k Rankings with Pairwise Preferences
print.BayesMallows | Print Method for BayesMallows Objects
print.BayesMallowsMixtures | Print Method for BayesMallows Objects
print.SMCMallows | Print Method for BayesMallows Objects
sample_mallows | Random Samples from the Mallows Rank Model
sample_prior | Sample from prior distribution
setup_rank_data | Setup rank data
set_compute_options | Specify options for computation
set_initial_values | Set initial values of scale parameter and modal ranking
set_model_options | Set options for Bayesian Mallows model
set_priors | Set prior parameters for Bayesian Mallows model
set_progress_report | Set progress report options for MCMC algorithm
set_smc_options | Set SMC compute options
sounds | Sounds data
sushi_rankings | Sushi rankings
update_mallows | Update a Bayesian Mallows model with new users
update_mallows.BayesMallows | Update a Bayesian Mallows model with new users
update_mallows.BayesMallowsPriorSamples | Update a Bayesian Mallows model with new users
update_mallows.SMCMallows | Update a Bayesian Mallows model with new users