This package provides a general framework for analyzing rank and preference data based on the Bayesian Mallows model first described in Vitelli et al.(2018). In particular, we added a new dataset to analyse on preference destination.
The BayesMallows package currently implements the complete model described in Vitelli et al. (2018), which includes a large number of distance metrics, handling of missing ranks and pairwise comparisons, and clustering of users with similar preferences. The extension to non-transitive pairwise comparisons by Crispino et al. (2019) is also implemented. In addition, the partition function of the Mallows model can be estimated using the importance sampling algorithm of Vitelli et al. (2018) and the asymptotic approximation of Mukherjee (2016). For a review of ranking models in general, see Q. Liu et al. (2019b). Crispino and Antoniano-Villalobos (2019) outlines how informative priors can be used within the model.
Asfaw, D., V. Vitelli, Ø Sørensen, E. Arjas, and A. Frigessi. 2016. “Time-Varying Rankings with the Bayesian Mallows Model.” Stat 6 (1): 14–30. https://doi.org/10.1002/sta4.132.
Barrett, N., and M. Crispino. 2018. “The Impact of 3-d Sound Spatialisation on Listeners’ Understanding of Human Agency in Acousmatic Music.” Journal of New Music Research 47 (5): 399–415. https://doi.org/10.1080/09298215.2018.1437187.
Crispino, M., and I. Antoniano-Villalobos. 2019. “Informative Extended Mallows Priors in the Bayesian Mallows Model.” https://arxiv.org/abs/1901.10870.
Crispino, M., E. Arjas, V. Vitelli, N. Barrett, and A. Frigessi. 2019. “A Bayesian Mallows Approach to Nontransitive Pair Comparison Data: How Human Are Sounds?” The Annals of Applied Statistics 13 (1): 492–519. https://doi.org/10.1214/18-aoas1203.
Liu, Q., A. H. Reiner, A. Frigessi, and I. Scheel. 2019a. “Diverse Personalized Recommendations with Uncertainty from Implicit Preference Data with the Bayesian Mallows Model.” Knowledge-Based Systems 186 (December): 104960. https://doi.org/10.1016/j.knosys.2019.104960.
Liu, Q, M Crispino, I Scheel, V Vitelli, and A Frigessi. 2019b. “Model-Based Learning from Preference Data.” Annual Review of Statistics and Its Application 6 (1). https://doi.org/10.1146/annurev-statistics-031017-100213.
Mukherjee, S. 2016. “Estimation in Exponential Families on Permutations.” The Annals of Statistics 44 (2): 853–75. https://doi.org/10.1214/15-aos1389.
Sørensen, Øystein, Marta Crispino, Qinghua Liu, and Valeria Vitelli. 2020. “BayesMallows: An R Package for the Bayesian Mallows Model.” The R Journal 12 (1): 324–42. https://doi.org/10.32614/RJ-2020-026.
Vitelli, V., Ø. Sørensen, M. Crispino, E. Arjas, and A. Frigessi. 2018. “Probabilistic Preference Learning with the Mallows Rank Model.” Journal of Machine Learning Research 18 (1): 1–49. https://jmlr.org/papers/v18/15-481.html.