An implementation of the Bayesian version of the Mallows rank model (Vitelli et al., Journal of Machine Learning Research, 2018 < http://jmlr.org/papers/v18/15-481.html>; Crispino et al., to appear in Annals of Applied Statistics). Both 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
compute_mallows not to work (without giving any errors) when rankings contained missing values.compute_mallows to fail when preferences had integer columns.save_individual_cluster_probs to save_ind_clus, to save typing.save_individual_cluster_probs = TRUE in compute_mallows.alpha_max, the truncation of the exponential prior for alpha, as a user option in compute_mallows.?label_switching for more info.compute_importance_sampling_estimate has been updated to avoid numerical overflow. Previusly, importance sampling failed at below 200 items. Now it works way above 10,000 items.generate_transitive_closure, generate_initial_ranking, and generate_constraints now are able to run in parallel.estimate_partition_function now has an option to run in parallel, leading to significant speed-up.error_model = "bernoulli" in compute_mallows in order to use it. Examples will come later.compute_mallows_mixtures and added parallel to Suggests field.compute_cp_consensus and compute_map_consensus have been removed. Use compute_consensus instead.factor variables sorted according to the cluster number. Hence, in plot legends, "Cluster 10" comes after "Cluster 9", rather than after "Cluster 1" which it used to do until now, because it was a character.plot.BayesMallows no longer contains print statements which forces display of plots. Instead plots are returned from the function. Using p <- plot(fit) hence does no longer display a plot, whereas using plot(fit) without assigning it to an object, displays a plot. Until now the plot was always shown for rho and alpha.compute_mallows and sample_mallows now support Ulam distance, with argument metric = "ulam".Rcpp, cf. this issue). The long vignette is no longer needed in any case, since all the functions are well documented with executable examples.Rankcluster package has been removed from dependencies.leap_size to compute_mallows. It used to be floor(n_items / 5), which evaluates to zero when n_items <= 4. Updated it to max(1L, floor(n_items / 5)).metric = "hamming") as an option to compute_mallows and sample_mallows.generate_initial_ranking, generate_transitive_closure, and sample_mallows to avoid errors when package tibble version 2.0.0 is released. This update is purely internal.BayesMallows and BayesMallowsMixtures now have default print functions, hence avoiding excessive amounts of informations printed to the console if the user happens to write the name of such an object and press Return.compute_mallows_mixtures no longer sets include_wcd = TRUE by default. The user can choose this argument.compute_mallows has a new argument save_clus, which can be set to FALSE for not saving cluster assignments.assess_convergence now automatically plots mixtures.compute_mallows_mixtures now returns an object of class BayesMallowsMixtures.assess_convergence now adds prefix Assessor to plots when parameter = "Rtilde".predict_top_k is now an exported function. Previously it was internal.compute_posterior_intervals now has default parameter = "alpha". Until now, this argument has had no default.type to plot.BayesMallows and assess_convergence has been renamed to parameter, to be more consistent.save_augment_data to compute_mallows has been renamed to save_aug.compute_mallows fills in implied ranks when an assessor has only one missing rank. This avoids unnecessary augmentation in MCMC.generate_ranking and generate_ordering now work with missing ranks.Argument cluster_assignment_thinning to compute_mallows has been renamed to clus_thin.
Change the interface for computing consensus ranking. Now, CP and MAP consensus are both computed with the compute_consensus function, with argument type equal to either "CP" or "MAP".