MSmix: An R Package for clustering partial rankings via mixtures of Mallows Models with Spearman distance

TL;DR

MSmix is an R package that efficiently estimates mixtures of Mallows models for full and partial rankings, enabling analysis of complex ranking data with uncertainty quantification.

Contribution

The package introduces computationally tractable EM algorithms for mixtures of Mallows models with Spearman distance, handling arbitrary partial rankings and large item sets.

Findings

Effective estimation of mixture models for partial rankings.

Demonstrated computational efficiency and accuracy on real and simulated data.

Provides uncertainty quantification tools for ranking analysis.

Abstract

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.