PLMIX: An R package for modeling and clustering partially ranked data

TL;DR

PLMIX is an R package that facilitates modeling and clustering of partially ranked data, addressing computational challenges and enabling advanced Bayesian estimation for ranking models.

Contribution

It introduces a comprehensive R package that implements recent methodological advances in modeling and clustering partially ranked data, including Bayesian estimation and efficient computation.

Findings

Provides tools for Bayesian estimation of ranking models in R

Combines R routines with C++ for speed and efficiency

Demonstrates applications on simulated and real datasets

Abstract

Ranking data represent a peculiar form of multivariate ordinal data taking values in the set of permutations. Despite the numerous methodological contributions to increase the flexibility of ranked data modeling, the application of more sophisticated models is limited by the related computational issues. The PLMIX package offers a comprehensive framework aimed at endowing the R statistical environment with some recent methodological advances in modeling and clustering partially ranked data. The usefulness of the novel PLMIX package can be motivated from several perspectives: (i) it contributes to fill the gap concerning Bayesian estimation of ranking models in R, by focusing on the Plackett-Luce model and its extension within the finite mixture approach as the generative sampling distribution; (ii) it addresses computational complexity by combining the flexibility of R routines and the speed of compiled C++ code, with possible parallel execution; (iii) it covers the fundamental phases of ranking data analysis allowing for a more careful and critical application of ranking models in real experiments; (iv) it provides effective tools for clustering heterogeneous partially ranked data. The functionality of the novel package is illustrated with several applications to simulated and real datasets.