Oystein Sorensen, Valeria Vitelli, Marta Crispino, Qinghua Liu
testthat, gtools (>= 3.8.1), knitr, PerMallows (>= 1.13),
rmarkdown, Rankcluster (>= 0.94), covr

testthat, gtools (>= 3.8.1), knitr, rmarkdown, PerMallows (>=
1.13), covr

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 <doi:10.1214/15-AOS1389>).
testthat, gtools (>= 3.8.1), knitr, rmarkdown, covr, parallel
(>= 3.5.1)

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., Annals of Applied Statistics, 2019 <doi:10.1214/18-AOAS1203>). 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 <doi:10.1214/15-AOS1389>).
R.rsp, testthat (>= 2.0), label.switching (>= 1.7), readr (>=
1.3.1), stringr (>= 1.4.0), gtools (>= 3.8.1), rmarkdown, covr,
parallel (>= 3.5.1)