Work

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

Year: 2025

Type: article

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, with the ability to handle arbitrary forms of partial rankings and a large number of items. The frequentist estimation task is accomplished via Expectation-Maximization algorithms, integrating data augmentation strategies to recover the unobserved heterogeneity and the missing ranks. The package also provides functionalities for uncertainty quantification of the parameter estimates, 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.

Source: The R Journal

DOI: 10.32614/rj-2025-020

Authors: Marta Crispino, Cristina Mollica, Lucia Modugno

Institutions: Bank of Italy, Sapienza University of Rome

Language: English

Topic: Bayesian Methods and Mixture Models

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: diamond

Funder: Banca d'Italia

OpenAlex ID: W7093078813