PLMIX: an R package for modelling and clustering partially ranked data. Issue 5 (23rd March 2020)

• Record Type: Journal Article
• Authors: Mollica, Cristina; Tardella, Luca
• Is Part Of: Journal of statistical computation and simulation. Volume 90:Issue 5(2020)
• Page Start: 925
• Page End: 959
• Publication Date: 2020-03-23
• Subjects: R package -- ranking data -- Plackett–Luce model -- mixture models -- Bayesian inference; 62F07 -- 62F15; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2020.1711909
• Languages: English
• ISSNs: 0094-9655

Abstract

ABSTRACT: The PLMIX package offers a comprehensive framework aimed at endowing the R statistical environment with some recent methodological advances in modelling and clustering partially ranked data. The usefulness of the 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 possibly 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 contexts; (iv) it provides effective tools for clustering heterogeneous partially ranked data. Specific S3 classes and methods are also supplied to enhance the usability and foster exchange with other packages. The functionality of the novel package is illustrated with several applications to simulated and real datasets.