rankrate: Statistical Tools for Preference Learning with Rankings and
An implementation of the statistical methodology proposed by Pearce and Erosheva,
"A Unified Statistical Learning Model for Rankings and Scores with Application to Grant Panel Review" (2022),
which at time of release has been accepted in the Journal of Machine Learning Research. The package provides tools
for estimating parameters of a Mallows-Binomial model, the first joint statistical preference
learning model for rankings and ratings. The package includes functions for simulating rankings and ratings from the model,
calculating the density of Mallows-Binomial data, estimating parameters using various exact and approximate algorithms,
and for obtaining approximate confidence intervals based on the nonparametric bootstrap.
||stats, nloptr, gtools, lpSolve
[aut, cre, cph]
||Michael Pearce <pearce790 at gmail.com>
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