This package implements the Bayesian Mallows Model described in Vitelli et al. (2018). The user can choose between footrule, Spearman, Cayley, Hamming, Kendall, or Ulam distance.
The following features are currently implemented:
Complete data (Vitelli et al. (2018)).
Clustering users with similar preferences (Vitelli et al. (2018)).
Handling missing ranks by imputation (Vitelli et al. (2018)).
Handling transitive pairwise preferences by imputation (Vitelli et al. (2018)).
Estimating the partition function of the Mallows model using importance sampling (Vitelli et al. (2018)) or an asymptotic approximation (Mukherjee (2016)).
Non-transitive pairwise comparisons (Crispino et al. (2018)).
This includes any combination thereof, e.g., clustering assessors based on pairwise preferences.
Future releases will include:
Time-varying ranks (Asfaw et al. (2016)).
Parallelization of Markov Chains.
All feedback and suggestions are very welcome.
To install the current release, use
To install the current development version, use
Asfaw, D., V. Vitelli, O. Sorensen, E. Arjas, and A. Frigessi. 2016. “Time‐varying Rankings with the Bayesian Mallows Model.” Stat 6 (1): 14–30. https://onlinelibrary.wiley.com/doi/abs/10.1002/sta4.132.
Crispino, M., E. Arjas, V. Vitelli, N. Barrett, and A. Frigessi. 2018. “A Bayesian Mallows approach to non-transitive pair comparison data: how human are sounds?” Accepted for Publication in Annals of Applied Statistics.
Mukherjee, S. 2016. “Estimation in Exponential Families on Permutations.” The Annals of Statistics 44 (2): 853–75.
Vitelli, V., O. Sorensen, 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. http://jmlr.org/papers/v18/15-481.html.