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 + fileLICENSE |
| URL: | https://github.com/rhysholland/fairGNN |
| NeedsCompilation: | no |
| SystemRequirements: | Optional 'LibTorch' backend; install viatorch::install_torch(). |
| CRAN checks: | fairGNN results |
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