Introduction | Data Split and Model Construction | Fairness Evaluation | Conditional Fairness Evaluation | Appendix: Confidence Interval Construction | References
Package: fairmetrics 1.0.8
Benjamin Smith
fairmetrics: Fairness Evaluation Metrics with Confidence Intervals for Binary Protected Attributes
A collection of functions for computing fairness metrics for machine learning and statistical models, including confidence intervals for each metric. The package supports the evaluation of group-level fairness criterion commonly used in fairness research, particularly in healthcare for binary protected attributes. It is based on the overview of fairness in machine learning written by Gao et al (2025) <doi:10.1002/sim.70234>.
Authors:
fairmetrics_1.0.8.tar.gz
fairmetrics_1.0.8.zip(r-4.7)fairmetrics_1.0.8.zip(r-4.6)fairmetrics_1.0.8.zip(r-4.5)
fairmetrics_1.0.8.tgz(r-4.6-any)fairmetrics_1.0.8.tgz(r-4.5-any)
fairmetrics_1.0.8.tar.gz(r-4.7-any)fairmetrics_1.0.8.tar.gz(r-4.6-any)
fairmetrics_1.0.8.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
fairmetrics/json (API)
| # Install 'fairmetrics' in R: |
| install.packages('fairmetrics', repos = c('https://jianhuig.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/jianhuig/fairmetrics/issues
Pkgdown/docs site:https://jianhuig.github.io
Datasets:
- mimic - Clinical data from the MIMIC-II database for a case study on indwelling arterial catheters
- mimic_preprocessed - Preprocessed Clinical Data from the MIMIC-II Database
Last updated from:e32f070bd6. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 141 | ||
| source / vignettes | OK | 239 | ||
| linux-release-x86_64 | OK | 137 | ||
| macos-release-arm64 | OK | 115 | ||
| macos-oldrel-arm64 | OK | 205 | ||
| windows-devel | OK | 75 | ||
| windows-release | OK | 80 | ||
| windows-oldrel | OK | 108 | ||
| wasm-release | OK | 109 |
Exports:eval_acc_parityeval_bs_parityeval_cond_acc_equalityeval_eq_oddseval_eq_oppeval_neg_class_baleval_neg_pred_parityeval_pos_class_baleval_pos_pred_parityeval_pred_equalityeval_stats_parityeval_treatment_equalityget_fairness_metrics
Dependencies:
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Examine Accuracy Parity of a Model | eval_acc_parity |
| Examine Brier Score Parity of a Model | eval_bs_parity |
| Examine Conditional Use Accuracy Equality of a Model | eval_cond_acc_equality |
| Examine Equalized Odds of a Predictive Model | eval_eq_odds |
| Evaluate Equal Opportunity Compliance of a Predictive Model | eval_eq_opp |
| Examine Balance for Negative Class of a Model | eval_neg_class_bal |
| Examine Negative Predictive Parity of a Model | eval_neg_pred_parity |
| Examine Balance for the Positive Class of a Model | eval_pos_class_bal |
| Examine Positive Predictive Parity of a Model | eval_pos_pred_parity |
| Examine Predictive Equality of a Model | eval_pred_equality |
| Examine Statistical Parity of a Model | eval_stats_parity |
| Examine Treatment Equality of a Model | eval_treatment_equality |
| Compute Fairness Metrics for Binary Classification | get_fairness_metrics |
| Clinical data from the MIMIC-II database for a case study on indwelling arterial catheters | mimic |
| Preprocessed Clinical Data from the MIMIC-II Database | mimic_preprocessed |