Plackett-Luce Models for Rankings
Create an Adjacency Matrix for a set of Rankings
Aggregate Rankings
Choices Object
Complete Orderings with the Missing Redundant Rank
Check Connectivity of Rankings
Decode Orderings using a Key to Item Names
Fitted Probabilities for PlackettLuce Objects
Group Rankings
Extract Item Parameters of Plackett-Luce Models
Deprecated functions in package PlackettLuce
Plackett-Luce Models for Rankings
Fit a Plackett-Luce Model
Fit a Plackett-Luce Model with Linear Predictor for Log-worth
PlackettLuce Wrapper for Model-based Recursive Partitioning
Plackett-Luce Tree Summaries
Plackett-Luce Trees
Read Preflib Election Data Files
Quasi Variances for Model Coefficients
Rankings Object
Objects exported from other packages
Simulate from PlackettLuce fitted objects
Plackett-Luce Model Summaries
Functions to prepare rankings data and fit the Plackett-Luce model jointly attributed to Plackett (1975) <doi:10.2307/2346567> and Luce (1959, ISBN:0486441369). The standard Plackett-Luce model is generalized to accommodate ties of any order in the ranking. Partial rankings, in which only a subset of items are ranked in each ranking, are also accommodated in the implementation. Disconnected/weakly connected networks implied by the rankings may be handled by adding pseudo-rankings with a hypothetical item. Optionally, a multivariate normal prior may be set on the log-worth parameters and ranker reliabilities may be incorporated as proposed by Raman and Joachims (2014) <doi:10.1145/2623330.2623654>. Maximum a posteriori estimation is used when priors are set. Methods are provided to estimate standard errors or quasi-standard errors for inference as well as to fit Plackett-Luce trees. See the package website or vignette for further details.
Useful links