BayesMallows: An R Package for the Bayesian Mallows Model
This paper presents the BayesMallows R package, for analysis of rank and preference data. Published in the R Journal.
BayesMallows
BayesMallows: Bayesian Preference Learning with the Mallows Rank Model
This R package contains functions for estimating the Bayesian Mallows model in a wide range of situation, using the Metropolis-Hastings algorithm.
Probabilistic preference learning with the Mallows rank model
This paper studies the analysis of rank and preference data. We consider both complete rankings, partial rankings, and pairwise preferences. We develop a complete Bayesian framework for estimating Mallows’ rank model in all this cases, including clustering of users with similar preferences and preference prediction. Published in Journal of Machine Learning Research. Joint first authorship with Valeria Vitelli.