BayesMallows            BayesMallows: Bayesian Preference Learning with
                        the Mallows Rank Model.
assess_convergence      Trace Plots from Metropolis-Hastings Algorithm
assign_cluster          Assign Assessors to Clusters
beach_preferences       Beach Preferences
calculate_backward_probability
                        Calculate Backward Probability
calculate_forward_probability
                        Calculate Forward Probability
compute_consensus       Compute Consensus Ranking
compute_consensus.BayesMallows
                        Compute Consensus Ranking
compute_consensus.consensus_SMCMallows
                        Compute Consensus Ranking
compute_mallows         Preference Learning with the Mallows Rank Model
compute_mallows_mixtures
                        Compute Mixtures of Mallows Models
compute_posterior_intervals
                        Compute Posterior Intervals
compute_posterior_intervals.BayesMallows
                        Compute posterior intervals
compute_posterior_intervals.SMCMallows
                        Compute posterior intervals
compute_posterior_intervals_alpha
                        Compute Posterior Intervals Alpha
compute_posterior_intervals_rho
                        Compute Posterior Intervals Rho
compute_rho_consensus   Compute rho consensus
correction_kernel       Correction Kernel
correction_kernel_pseudo
                        Correction Kernel (pseudolikelihood)
estimate_partition_function
                        Estimate Partition Function
expected_dist           Expected value of metrics under a Mallows rank
                        model
generate_constraints    Generate Constraint Set from Pairwise
                        Comparisons
generate_initial_ranking
                        Generate Initial Ranking
generate_transitive_closure
                        Generate Transitive Closure
get_mallows_loglik      Get Mallows log-likelihood
get_sample_probabilities
                        Get Sample Probabilities
label_switching         Checking for Label Switching in the Mallows
                        Mixture Model
leap_and_shift_probs    Leap and Shift Probabilities
lik_db_mix              Likelihood and log-likelihood evaluation for a
                        Mallows mixture model
metropolis_hastings_alpha
                        Metropolis-Hastings Alpha
metropolis_hastings_aug_ranking
                        Metropolis-Hastings Augmented Ranking
metropolis_hastings_aug_ranking_pseudo
                        Metropolis-Hastings Augmented Ranking
                        (pseudolikelihood)
metropolis_hastings_rho
                        Metropolis-Hastings Rho
obs_freq                Observation frequencies in the Bayesian Mallows
                        model
plot.BayesMallows       Plot Posterior Distributions
plot_alpha_posterior    Plot Alpha Posterior
plot_elbow              Plot Within-Cluster Sum of Distances
plot_rho_posterior      Plot the posterior for rho for each item
plot_top_k              Plot Top-k Rankings with Pairwise Preferences
potato_true_ranking     True ranking of the weights of 20 potatoes.
potato_visual           Result of ranking potatoes by weight, where the
                        assessors were only allowed to inspected the
                        potatoes visually. 12 assessors ranked 20
                        potatoes.
potato_weighing         Result of ranking potatoes by weight, where the
                        assessors were allowed to lift the potatoes. 12
                        assessors ranked 20 potatoes.
predict_top_k           Predict Top-k Rankings with Pairwise
                        Preferences
print.BayesMallows      Print Method for BayesMallows Objects
print.BayesMallowsMixtures
                        Print Method for BayesMallowsMixtures Objects
rank_conversion         Convert between ranking and ordering.
rank_distance           Distance between a set of rankings and a given
                        rank sequence
rank_freq_distr         Frequency distribution of the ranking sequences
sample_dataset          A synthetic 3D matrix ('n_users', 'n_items',
                        'Time') generated using the sample_mallows
                        function. These are test datasets used to run
                        the SMC-Mallows framework for the cases where
                        we know all of the users in our system and
                        their original ranking information are partial
                        rankings. However at some point in time, we
                        observe extra information about an existing
                        user in the form of a rank for an item that was
                        previously not known ('NA'). These datasets are
                        very contrived as the first time step
                        ('sample_dataset[, , 1]') we observed the top
                        'm / 2' items from each user, where 'm' is the
                        number of items in a ranking. Then, as we
                        increase the time, we observe the next top
                        ranked item from one user at a time, then the
                        next top ranked item, and so on until we have a
                        complete dataset at 'sample_dataset[, , Time]'.
sample_mallows          Random Samples from the Mallows Rank Model
smc_mallows_new_item_rank
                        SMC-Mallows new users rank
smc_mallows_new_item_rank_alpha_fixed
                        SMC-Mallows new item rank (alpha fixed)
smc_mallows_new_users_complete
                        SMC-Mallows New Users Complete
smc_mallows_new_users_partial
                        SMC-Mallows new users partial
smc_mallows_new_users_partial_alpha_fixed
                        SMC-mallows new users partial (alpha fixed)
smc_processing          SMC Processing
sushi_rankings          Sushi Rankings
