Fairness-Aware Gated Neural Networks

Tools for training and analysing fairness-aware gated neural networks for subgroup-aware prediction and interpretation in clinical datasets. Methods draw on prior work in mixture-of-experts neural networks by Jordan and Jacobs (1994) , fairness-aware learning by Hardt, Price, and Srebro (2016) , and personalised treatment prediction for depression by Iniesta, Stahl, and McGuffin (2016) .


Reference manual

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install.packages("fairGNN")

0.1.0 by Rhys Holland, a year ago


https://github.com/rhysholland/fairGNN


Report a bug at https://github.com/rhysholland/fairGNN/issues


Browse source code at https://github.com/cran/fairGNN


Authors: Rhys Holland [aut, cre]


Documentation:   PDF Manual  


MIT + file LICENSE license


Imports dplyr, tibble, ggplot2, readr, pROC, magrittr, tidyr, purrr, utils, stats, ggalluvial, tidyselect

Suggests knitr, torch, testthat, readxl, rmarkdown

System requirements: Optional 'LibTorch' backend; install via torch::install_torch().


See at CRAN