rankrate: Joint Statistical Models for Preference Learning with Rankings and Ratings

In rankrate: Joint Statistical Models for Preference Learning with Rankings and Ratings

knitr::opts_chunk$set(
  collapse = TRUE,
  rmarkdown.html_vignette.check_title = FALSE
)

This package allows for joint modeling of ranking and rating preference data via the Mallows-Binomial model [@pearce2022unified]. Functions in the package may be used for density calculation, random data generation, and fitting the Mallows-Binomial model to data via multiple exact and approximate methods. Uncertainty quantification and estimation of confidence intervals is also possible via the nonparametric bootstrap, whose asymptotic validity was proven in @pearce2022validity. Additionally, the package includes 3 "toy" data sets and 1 real data from the American Institute of Biological Sciences, which were all studied in @gallo2022new.

For more details on how to use this package, see the tutorial.

A published version of the package may be installed from CRAN, or a development version from Github for the most up-to-date functionality:

## Published (CRAN) version
install.packages("rankrate")  

## Development (Github) version
# install.packages("devtools") # uncomment if you haven't installed 'devtools' before
devtools::install_github("pearce790/rankrate")

After installation, load the package with the following code:

library(rankrate)

Funding

This project was supported by the National Science Foundation under Grant No. 2019901.

References

rankrate documentation built on April 12, 2025, 1:46 a.m.