rankrate: Statistical Tools for Preference Learning with Rankings and Ratings

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.

Version: 1.0.0
Imports: stats, nloptr, gtools, lpSolve
Published: 2022-06-09
Author: Michael Pearce ORCID iD [aut, cre, cph]
Maintainer: Michael Pearce <pearce790 at gmail.com>
License: GPL-3
NeedsCompilation: no
Materials: NEWS
CRAN checks: rankrate results


Reference manual: rankrate.pdf


Package source: rankrate_1.0.0.tar.gz
Windows binaries: r-devel: rankrate_1.0.0.zip, r-release: rankrate_1.0.0.zip, r-oldrel: rankrate_1.0.0.zip
macOS binaries: r-release (arm64): rankrate_1.0.0.tgz, r-oldrel (arm64): rankrate_1.0.0.tgz, r-release (x86_64): rankrate_1.0.0.tgz, r-oldrel (x86_64): rankrate_1.0.0.tgz


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