Package: fairGNN 0.1.0
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>.
Authors:
fairGNN_0.1.0.tar.gz
fairGNN_0.1.0.zip(r-4.7-any)fairGNN_0.1.0.zip(r-4.6-any)fairGNN_0.1.0.zip(r-4.5-any)
fairGNN_0.1.0.tgz(r-4.6-any)fairGNN_0.1.0.tgz(r-4.5-any)
fairGNN_0.1.0.tar.gz(r-4.7-any)fairGNN_0.1.0.tar.gz(r-4.6-any)
fairGNN_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
fairGNN/json (API)
| # Install 'fairGNN' in R: |
| install.packages('fairGNN', repos = c('https://rhysholland.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/rhysholland/fairgnn/issues
Last updated from:c9ca1a118f. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel | OK | 149 | ||
| source / vignettes | OK | 257 | ||
| linux-release | OK | 141 | ||
| macos-release | OK | 122 | ||
| macos-oldrel | OK | 91 | ||
| windows-devel | OK | 95 | ||
| windows-release | OK | 88 | ||
| windows-oldrel | OK | 94 | ||
| wasm-release | OK | 95 |
Exports:analyse_expertsanalyse_gnn_resultsanalyse_gnn_results_plusplot_sankeyprepare_datatrain_gnn
Dependencies:bitbit64clicliprcpp11crayondplyrfarvergenericsggalluvialggplot2gluegtablehmsisobandlabelinglazyevallifecyclemagrittrpillarpkgconfigprettyunitspROCprogresspurrrR6RColorBrewerRcppreadrrlangS7scalesstringistringrtibbletidyrtidyselecttzdbutf8vctrsviridisLitevroomwithr
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Analyse and Visualise Expert Network Specialisation | analyse_experts |
| Analyse and Visualize GNN Results | analyse_gnn_results |
| Analyse GNN Results (+ Gate Summary Tables) | analyse_gnn_results_plus |
| Create a Sankey Plot to Visualise Patient Routing | plot_sankey |
| Prepare Data for GNN Training | prepare_data |
| Train and Evaluate the Gated Neural Network (robust splits + safe ROC) | train_gnn |
