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.

## Details

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")`

.