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Plackett-Luce provides functions to prepare rankings data in order to fit the Plackett-Luce model or Plackett-Luce trees. The implementation can handle ties, sub-rankings and rankings that imply disconnected or weakly connected preference networks. Methods are provided for summary and inference.


The main function in the package is the model-fitting function PlackettLuce and the help file for that function provides details of the Plackett-Luce model, which is extended here to accommodate ties.

Rankings data must be passed to PlackettLuce in a specific form, see rankings for more details. Other functions for handling rankings include choices to express the rankings as choices from alternatives; adjacency to create an adjacency matrix of wins and losses implied by the rankings and connectivity to check the connectivity of the underlying preference network.

Several methods are available to inspect fitted Plackett-Luce models, help files are available for less common methods or where arguments may be specified: coef, deviance, fitted, itempar, logLik, print, qvcalc, summary, vcov.

PlackettLuce also provides the function pltree to fit a Plackett-Luce tree i.e. a tree that partitions the rankings by covariate values, identifying subgroups with different sets of worth parameters for the items. In this case group must be used to prepare the data.

Several data sets are provided in the package: beans, nascar, pudding. The help files for these give further illustration of preparing rankings data for modelling. The read.soc function enables further example data sets of "Strict Orders - Complete List" format (i.e. complete rankings with no ties) to be downloaded from PrefLib.

A full explanation of the methods with illustrations using the package data sets is given in the vignette, vignette("Overview", package = "PlackettLuce").