assess_convergence |
Trace Plots from Metropolis-Hastings Algorithm |
assign_cluster |
Assign Assessors to Clusters |
BayesMallows |
BayesMallows: Bayesian Preference Learning with the Mallows Rank Model. |
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) |
create_ordering |
Convert between ranking and ordering. |
create_ranking |
Convert between ranking and ordering. |
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 |
Likelihood and log-likelihood evaluation for a Mallows mixture model |
get_sample_probabilities |
Get Sample Probabilities |
label_switching |
Checking for Label Switching in the Mallows Mixture Model |
lik_db_mix |
Likelihood and log-likelihood evaluation for a Mallows mixture model |
metropolis_hastings_alpha |
Metropolis-Hastings Alpha |
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_users |
SMC-Mallows New Users |
smc_mallows_new_users_complete |
SMC-Mallows New Users |
smc_mallows_new_users_partial |
SMC-Mallows New Users |
smc_mallows_new_users_partial_alpha_fixed |
SMC-Mallows New Users |
smc_processing |
SMC Processing |
sushi_rankings |
Sushi Rankings |