Ricerc@Sapienza

Choice behavior and preferences typically involve numerous and subjective aspects that are difficult to be identified and quantified. For this reason, their exploration is frequently conducted through the collection of ordinal evidence in the form of ranking data. Multistage ranking models, including the popular Plackett-Luce distribution (PL), rely on the assumption that the ranking process is performed sequentially, by assigning the positions from the top to the bottom one (forward order).

The Plackett-Luce distribution (PL) is one of the most successful parametric options within the class of multistage ranking models to learn the preferences on a given set of items from a sample of ordered sequences. It postulates that the ranking process is carried out by sequentially assigning the positions according to the forward order, that is, from the top (most-liked) to the bottom (least-liked) alternative. This assumption has been relaxed with the Extended Plackett-Luce model (EPL), thanks to the introduction of the reference order parameter describing the rank attribution path.

The forward order assumption postulates that the ranking process of the
items is carried out by assigning the positions from the top (most-liked) to the bottom
(least-liked) alternative. This assumption has been recently relaxed in the Extended
Plackett-Luce model (EPL) through the introduction of the discrete reference
order parameter, describing the rank attribution path. By starting from two formal
properties of the EPL, we derive novel diagnostic tools for testing appropriateness of

Multistage ranking models, including the popular Plackett-Luce distribution (PL), rely on the assumption that the ranking process is performed sequentially, by assigning the positions from the top to the bottom one (forward order). A recent contribution to the ranking literature relaxed this assumption with the addition of the discrete-valued reference order parameter, yielding the novel Extended Plackett-Luce model (EPL). Inference on the EPL and its generalization into a finite mixture framework was originally addressed from the frequentist perspective.

The PLMIX package offers a comprehensive framework aimed at endowing the R sta- tistical environment with some recent methodological advances in modeling and clustering partially ranked data.