fairGNN: 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) <doi:10.1007/978-1-4471-2097-1_113>,
fairness-aware learning by Hardt, Price, and Srebro (2016) <doi:10.48550/arXiv.1610.02413>,
and personalised treatment prediction for depression by Iniesta, Stahl, and McGuffin (2016)
<doi:10.1016/j.jpsychires.2016.03.016>.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
dplyr, tibble, ggplot2, readr, pROC, magrittr, tidyr, purrr, utils, stats, ggalluvial, tidyselect |
| Suggests: |
knitr, torch, testthat, readxl, rmarkdown |
| Published: |
2025-10-26 |
| DOI: |
10.32614/CRAN.package.fairGNN |
| Author: |
Rhys Holland [aut, cre] |
| Maintainer: |
Rhys Holland <rhys.holland at icloud.com> |
| BugReports: |
https://github.com/rhysholland/fairGNN/issues |
| License: |
MIT + file LICENSE |
| URL: |
https://github.com/rhysholland/fairGNN |
| NeedsCompilation: |
no |
| SystemRequirements: |
Optional 'LibTorch' backend; install via
torch::install_torch(). |
| CRAN checks: |
fairGNN results |
Documentation:
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