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 ranking data modeling, the application of more sophisticated models is limited by the related computational issues. The EPLMIX package offers a comprehensive framework aiming at adjusting the R environment to the recent methodological advancements. The usefulness of the novel EPLMIX package can be motivated from several perspectives: (i) it contributes to fill the gap concerning the Bayesian estimation of ranking models in R, by focusing on the Plackett-Luce model as generative distribution and its extension within the finite mixture approach; (ii) it combines 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. The functionality of the novel package is illustrated with some real data examples.
EPLMIX: Extended Plackett-Luce models for modeling and clustering ranking data in R / Mollica, Cristina; Tardella, Luca. - STAMPA. - (2016), pp. 203-203. (Intervento presentato al convegno CFE-CMStatistics 2016 tenutosi a Siviglia).
EPLMIX: Extended Plackett-Luce models for modeling and clustering ranking data in R
Mollica, Cristina
Membro del Collaboration Group
;Tardella LucaMembro del Collaboration Group
2016
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 ranking data modeling, the application of more sophisticated models is limited by the related computational issues. The EPLMIX package offers a comprehensive framework aiming at adjusting the R environment to the recent methodological advancements. The usefulness of the novel EPLMIX package can be motivated from several perspectives: (i) it contributes to fill the gap concerning the Bayesian estimation of ranking models in R, by focusing on the Plackett-Luce model as generative distribution and its extension within the finite mixture approach; (ii) it combines 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. The functionality of the novel package is illustrated with some real data examples.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.