BayesMallows 1.2.0

• Fixed a bug which caused assess_convergence() to fail with ‘parameter = “cluster_probs”’.
• Fixed a bug in smc_mallows_new_users_partial() and smc_mallows_new_users_partial_alpha_fixed().
• metropolis_hastings_aug_ranking_pseudo() has been deprecated. Please use metropolis_hastings_aug_ranking() instead, with pseudo=TRUE.
• smc_mallows_new_users_partial_alpha_fixed(), smc_mallows_new_users_complete(), and smc_mallows_new_users_partial() have been deprecated. Please use smc_mallows_new_users() instead, and set the type= argument to “complete”, “partial”, or “partial_alpha_fixed”.
• smc_mallows_new_item_rank_alpha_fixed() has been deprecated. Please use smc_mallows_new_item_rank() instead, with argument alpha_fixed=TRUE.
• Fixed unexpected behavior in leap-and-shift proposal distribution for SMC Mallows, causing the function to propose the current rank vector with nonzero probability.
• BayesMallows no longer depends on ‘dplyr’.
• Quite extensive internal refactoring of C++ code.
• Function lik_db_mix has been renamed to get_mallows_loglik. lik_db_mix still exists as deprecated.
• When no initial rankings are provided, compute_mallows() and compute_mallows_mixtures() no use independent initial rho in each cluster. Previously a single initial rho was used for all cluster. This should potentially improve convergence, but will lead to different results when n_clusters>=2 for a given random number seed.

BayesMallows 1.1.2

• Fixed an issue with stats::reshape causing an error on R-oldrel.
• Fixed an issue with checking the class of objects, where we now consistently use inherits().
• Internal C++ fixes to comply with CRAN checks.

BayesMallows 1.1.1

• Fixed C++ errors leading to CRAN issues.

BayesMallows 1.1.0

• Major update, introducing a whole new class of methods using sequential Monte Carlo. Also reducing the number of dependencies.

BayesMallows 1.0.4.9001

• This is a major update, with new functions for estimating the Bayesian Mallows model using sequential Monte Carlo. The methods are described in the vignette titled “SMC-Mallows Tutorial”.

BayesMallows 1.0.4.9000

• Removed a large number of dependencies by converting to base R code. This will make the package easier to install across a range of systems, and less vulnerable to changes in other packages.

BayesMallows 1.0.4

• Incorporates changes since 1.0.3, and also remove PLMIX from Imports.

BayesMallows 1.0.3.9001

• Fixed bug which caused plot_top_k to fail when plotting clusters.
• Improved the default value of rel_widths argument to plot_top_k.
• Wrote unit tests to check that the bugs don’t appear again.

BayesMallows 1.0.3.9000

• Fixed bug which caused importance sampling to fail when running in parallel.
• Fixed issue with error message when trying to plot error probability when compute_mallows has not been set up to compute error probability.
• Increased number of unit tests.

BayesMallows 1.0.3

• Fixed critical bug which caused results to be wrong with more than one mixture component in compute_mallows() and compute_mallows_mixtures(). Thanks to Anja Stein for discovering the bug.

BayesMallows 1.0.2

• Function generate_initial_ranking() now has two additional options for generating random initial rankings. This can help with convergence problems, by allowing the MCMC algorithm to run from a range of different starting points.

BayesMallows 1.0.1

• Fixes a bug in lik_db_mix and expected_dist, in which the scaling parameter used a different parametrization than the rest of the package. All functions in the package now use consistent parametrization of the Mallows model, as stated in the vignette.

Bayes Mallows 1.0.0

• Function for computing likelihood added.
• Options for dealing with missing values added, and documentation now states how missing values are dealt with.
• Function rank_freq_distr added, for computing the frequency distribution of ranking patterns.
• Function rank_distance added, for computing the distance between rankings.
• Function expected_dist added for computing expectation of several metrics under the Mallows model.

BayesMallows 0.5.0

• Function compute_consensus now includes an option for computing consensus of augmented ranks.

BayesMallows 0.4.4

• Fixed bug in predict_top_k and plot_top_k when using aug_thinning > 1.

BayesMallows 0.4.3

• Updated README and vignette.

BayesMallows 0.4.2

• Updating a unit test to make sure BayesMallows is compatible with dplyr version 1.0.0.

BayesMallows 0.4.1

• Improvement of plotting functions, as noted below.

BayesMallows 0.4.0.9002

• plot.BayesMallows and plot_elbow no longer print titles automatically.

BayesMallows 0.4.0.9001

• assess_convergence no longer prints legends for clusters, as the cluster number is essentially arbitrary.

BayesMallows 0.4.0.9000

• Added CITATION.
• Updated test of random number seed.

BayesMallows 0.4.0

• Implements all fixes since version 0.3.1 below.
• Fixed typo on y-axis label of elbow plot.
• Fixed an issue which caused the cluster probabilities to differ across platforms, despite using the same seed. https://stackoverflow.com/questions/54822702

BayesMallows 0.3.1.9005

• Fixed a bug which caused compute_mallows not to work (without giving any errors) when rankings contained missing values.
• Fixed a bug which caused compute_mallows to fail when preferences had integer columns.

BayesMallows 0.3.1.9004

• Changed the name of save_individual_cluster_probs to save_ind_clus, to save typing.

BayesMallows 0.3.1.9003

• Added a user prompt asking if the user really wants to save csv files, when save_individual_cluster_probs = TRUE in compute_mallows.
• Added alpha_max, the truncation of the exponential prior for alpha, as a user option in compute_mallows.

BayesMallows 0.3.1.9002

• Added functionality for checking label switching. See ?label_switching for more info.

BayesMallows 0.3.1.9001

• The internal function compute_importance_sampling_estimate has been updated to avoid numerical overflow. Previously, importance sampling failed at below 200 items. Now it works way above 10,000 items.

BayesMallows 0.3.1

• This is an update of some parts of the C++ code, to avoid failing the sanitizer checks clang-UBSAN and gcc-UBSAN.

BayesMallows 0.3.0

• See all bullet points below, since 0.2.0.

BayesMallows 0.2.0.9006

• generate_transitive_closure, generate_initial_ranking, and generate_constraints now are able to run in parallel.
• Large changes to the underlying code base which should make it more maintainable but not affect the user.

BayesMallows 0.2.0.9005

• estimate_partition_function now has an option to run in parallel, leading to significant speed-up.

BayesMallows 0.2.0.9004

• Implemented the Bernoulli error model. Set error_model = "bernoulli" in compute_mallows in order to use it. Examples will come later.

BayesMallows 0.2.0.9003

• Added parallelization option to compute_mallows_mixtures and added parallel to Suggests field.

BayesMallows 0.2.0.9002

• Deprecated functions compute_cp_consensus and compute_map_consensus have been removed. Use compute_consensus instead.

BayesMallows 0.2.0.9001

• Clusters are now 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.

BayesMallows 0.2.0.9000

• compute_mallows and sample_mallows now support Ulam distance, with argument metric = "ulam".
• Slimmed down the vignette significantly, in order to avoid clang-UBSAN error caused by running the vignette (which was then again caused by Rcpp, cf. this issue). The long vignette is no longer needed in any case, since all the functions are well documented with executable examples.

BayesMallows 0.2.0

• New release on CRAN, which contains all the updates in 0.1.1, described below.

BayesMallows 0.1.1.9009

• Rankcluster package has been removed from dependencies.

BayesMallows 0.1.1.9008

• Fixed bug with Cayley distance. For this distance, the computational shortcut on p. 8 of Vitelli et al. (2018), JMLR, does not work. However, it was still used. Now, Cayley distance is always computed with complete rank vectors.
• Fixed bug in the default argument 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)).
• Added Hamming distance (metric = "hamming") as an option to compute_mallows and sample_mallows.

BayesMallows 0.1.1.9007

• Updated 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 0.1.1.9006

• Objects of class 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.

BayesMallows 0.1.1.9005

• assess_convergence now automatically plots mixtures.
• compute_mallows_mixtures now returns an object of class BayesMallowsMixtures.

BayesMallows 0.1.1.9004

• assess_convergence now adds prefix Assessor to plots when parameter = "Rtilde".
• predict_top_k is now an exported function. Previously it was internal.

BayesMallows 0.1.1.9003

• compute_posterior_intervals now has default parameter = "alpha". Until now, this argument has had no default.
• Argument type to plot.BayesMallows and assess_convergence has been renamed to parameter, to be more consistent.

BayesMallows 0.1.1.9002

• Argument 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.

BayesMallows 0.1.1.9001

Argument cluster_assignment_thinning to compute_mallows has been renamed to clus_thin.

BayesMallows 0.1.1.9000

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".