2024/06/20 by Marta Crispino, Crispino, Marta, Cristina Mollica +3
Computer Science · #Bayesian Modeling and Causal Inference #Computation (stat.CO) #Data Management and Algorithms #FOS: Computer and information sciences #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2406.14636
openalex publication_date 2024/06/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
MSmix is a recently developed R package implementing maximum likelihood estimation of finite mixtures of Mallows models with Spearman distance for full and partial rankings. The package is designed to implement computationally tractable estimation routines of the model parameters, with the ability to handle arbitrary forms of partial rankings and sequences of a large number of items. The frequentist estimation task is accomplished via EM algorithms, integrating data augmentation strategies to recover the unobserved heterogeneity and the missing ranks. The package also provides functionalities for uncertainty quantification of the estimated parameters, via diverse bootstrap methods and asymptotic confidence intervals. Generic methods for S3 class objects are constructed for more effectively managing the output of the main routines. The usefulness of the package and its computational performance compared with competing software is illustrated via applications to both simulated and original real ranking datasets.