Function reference • BayesMallows

Model estimation

Functions for estimating the Bayesian Mallows model.

`burnin<-`(): Set the burnin

burnin(): See the burnin

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

sample_prior(): Sample from prior distribution

update_mallows(): Update a Bayesian Mallows model with new users

assess_convergence(): Trace Plots from Metropolis-Hastings Algorithm

Preparing model estimation

Functions to run prior to fitting a model.

get_transitive_closure(): Get transitive closure

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

setup_rank_data(): Setup rank data

Posterior quantities

Functions for studying posterior distributions of model parameters.

assign_cluster(): Assign Assessors to Clusters

compute_consensus(): Compute Consensus Ranking

compute_posterior_intervals(): Compute Posterior Intervals

get_acceptance_ratios(): Get Acceptance Ratios

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

predict_top_k(): Predict Top-k Rankings with Pairwise Preferences

print(<BayesMallows>) print(<BayesMallowsMixtures>) print(<SMCMallows>): Print Method for BayesMallows Objects

Rank functions

Various functions for sampling ranks and working with ranks.

compute_expected_distance(): Expected value of metrics under a Mallows rank model

compute_observation_frequency(): Frequency distribution of the ranking sequences

compute_rank_distance(): Distance between a set of rankings and a given rank sequence

create_ranking() create_ordering(): Convert between ranking and ordering.

get_mallows_loglik(): Likelihood and log-likelihood evaluation for a Mallows mixture model

sample_mallows(): Random Samples from the Mallows Rank Model

Partition functions

Tools related to computing or estimating the partition function of the Mallows model with various distances.

compute_exact_partition_function(): Compute exact partition function

estimate_partition_function(): Estimate Partition Function

get_cardinalities(): Get cardinalities for each distance

Datasets

Example datasets included in the package.

beach_preferences: Beach preferences

bernoulli_data: Simulated intransitive pairwise preferences

cluster_data: Simulated clustering data

potato_true_ranking: True ranking of the weights of 20 potatoes.

potato_visual: Potato weights assessed visually

potato_weighing: Potato weights assessed by hand

sushi_rankings: Sushi rankings