The organizers of the European R User Meeting 2020 have put together a really impressive event, with lots of opportunities for interaction and stimulating discussions while being fully online. I have particularly enjoyed the good mix of academic presentations focusing on methodology and more business and industry related presentations focusing on use of R in production.
Today I presented the BayesMallows package in a five-minute lightning talk, and the slides (with links) are available here.
R package available from CRAN.
An implementation of the Bayesian version of the Mallows rank model. 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.