This paper presents the R package PlackettLuce, which implements a generalization of the Plackett–Luce model for rankings data. The generalization accommodates both ties (of arbitrary order) and partial rankings (complete rankings of subsets of items). By default, the implementation adds a set of pseudo-comparisons with a hypothetical item, ensuring that the underlying network of wins and losses between items is always strongly connected. In this way, the worth of each item always has a finite maximum likelihood estimate, with finite standard error. The use of pseudo-comparisons also has a regularization effect, shrinking the estimated parameters towards equal item worth. In addition to standard methods for model summary, PlackettLuce provides a method to compute quasi standard errors for the item parameters. This provides the basis for comparison intervals that do not change with the choice of identifiability constraint placed on the item parameters. Finally, the package provides a method for model-based partitioning using covariates whose values vary between rankings, enabling the identification of subgroups of judges or settings with different item worths. The features of the package are demonstrated through application to classic and novel data sets.

中文翻译：

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R中的建模排名：PlackettLuce软件包

本文介绍了R包PlackettLuce，它实现了Plackett-Luce模型的一般性用于排名数据。泛化既包含联系（任意顺序），也包含部分排名（项的子集的完整排名）。默认情况下，该实现会添加一组与假设项目的伪比较，以确保项目之间的基础赢利和亏损网络始终紧密相连。这样，每个物品的价值始终具有有限的最大似然估计，并具有有限的标准误差。伪比较的使用还具有正则化效果，将估计的参数缩小到相等的项目价值。除了用于模型汇总的标准方法之外，PlackettLuce提供了一种计算项目参数的准标准误差的方法。这提供了比较间隔的基础，该间隔不会随项目参数上可识别性约束的选择而改变。最后，该程序包提供了一种使用协变量的基于模型的分区方法，协变量的值在排名之间有所不同，从而能够识别具有不同项目价值的法官或设置子集。通过将其应用于经典和新颖的数据集来演示该软件包的功能。