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fairmetrics (version 1.0.7)

Fairness Evaluation Metrics with Confidence Intervals for Binary Protected Attributes

Description

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 (2024) .

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Version

Install

install.packages('fairmetrics')

Monthly Downloads

149

Version

1.0.7

License

MIT + file LICENSE

Maintainer

Benjamin Smith

Last Published

October 6th, 2025

Functions in fairmetrics (1.0.7)

get_fairness_metrics

Compute Fairness Metrics for Binary Classification
mimic_preprocessed

Preprocessed Clinical Data from the MIMIC-II Database
mimic

Clinical data from the MIMIC-II database for a case study on indwelling arterial catheters
eval_stats_parity

Examine Statistical Parity of a Model
eval_treatment_equality

Examine Treatment Equality of a Model
eval_pos_pred_parity

Examine Positive Predictive Parity of a Model
eval_pos_class_bal

Examine Balance for the Positive Class of a Model
eval_acc_parity

Examine Accuracy Parity of a Model
eval_pred_equality

Examine Predictive Equality of a Model
eval_bs_parity

Examine Brier Score Parity of a Model
eval_neg_pred_parity

Examine Negative Predictive Parity of a Model
eval_cond_acc_equality

Examine Conditional Use Accuracy Equality of a Model
eval_eq_odds

Examine Equalized Odds of a Predictive Model
eval_eq_opp

Evaluate Equal Opportunity Compliance of a Predictive Model
eval_neg_class_bal

Examine Balance for Negative Class of a Model