Package: fairGATE
Title: Fair Gated Algorithm for Targeted Equity
Version: 1.0.0
Authors@R: 
    c(
      person("Rhys", "Holland", email = "rhys.holland@icloud.com",
             role = c("aut", "cre")),
      person("Raquel", "Iniesta", 
             email = "raquel.iniesta@kcl.ac.uk",
             role = "aut")
    )
Description: Fairness-aware correction of a prediction model for binary and
    continuous outcomes with two or more protected groups and their
    intersections. A cross-fitted base learner (gradient-boosted trees or a
    neural network) is fitted without the protected attribute, and a gated
    mixture of neural experts learns a per-person correction to its score
    under a fairness penalty (a random-effects or variance penalty on
    outcome-stratified group means, or a thresholded worst-group penalty on
    error rates), with a cross-fitted disparity signal, an intervention cost,
    smoothed early stopping, a per-person export of adjustments with
    bootstrap stability intervals, and routing analyses of which expert
    corrects whom. Methods draw on mixture-of-experts neural networks by
    Jordan and Jacobs (1994) <doi:10.1007/978-1-4471-2097-1_113>,
    gradient boosting by Chen and Guestrin (2016)
    <doi:10.1145/2939672.2939785>, cross-fitting by Chernozhukov and co-authors (2018)
    <doi:10.1111/ectj.12097>, equalised odds by Hardt, Price and Srebro
    (2016) <doi:10.48550/arXiv.1610.02413>, the random-effects variance
    estimator of DerSimonian and Laird (1986)
    <doi:10.1016/0197-2456(86)90046-2>, and personalised treatment prediction
    for depression by Iniesta, Stahl and McGuffin (2016)
    <doi:10.1016/j.jpsychires.2016.03.016>.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-GB
Depends: R (>= 4.1.0)
Imports: dplyr, ggalluvial, ggplot2, magrittr, pROC, purrr, readr,
        rlang, stats, tibble, tidyr, tidyselect, utils
Suggests: knitr, rmarkdown, testthat (>= 3.0.0), torch, xgboost (>=
        3.1.0)
Config/testthat/edition: 3
VignetteBuilder: knitr
URL: https://github.com/rhysholland/FairGATE
BugReports: https://github.com/rhysholland/FairGATE/issues
SystemRequirements: Optional 'LibTorch' backend for the neural-network
        components; install via torch::install_torch().
LazyData: true
Config/roxygen2/version: 8.1.0
NeedsCompilation: no
Packaged: 2026-10-26 10:26:04 UTC; k2584926
Author: Rhys Holland [aut, cre],
  Raquel Iniesta [aut]
Maintainer: Rhys Holland <rhys.holland@icloud.com>
Repository: CRAN
Date/Publication: 2026-10-26 13:10:07 UTC
Built: R 4.5.2; ; 2026-10-26 17:39:25 UTC; unix
