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 et al. (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
Roxygen: list(markdown = TRUE)
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
Config/pak/sysreqs: libicu-dev libx11-dev
Repository: https://rhysholland.r-universe.dev
Date/Publication: 2026-10-20 12:55:57 UTC
RemoteUrl: https://github.com/rhysholland/fairgate
RemoteRef: HEAD
RemoteSha: e9598a45e86b5472388c95aae54cc2c85c734b41
NeedsCompilation: no
Packaged: 2026-10-20 17:20:09 UTC; root
Author: Rhys Holland [aut, cre],
  Raquel Iniesta [aut]
Maintainer: Rhys Holland <rhys.holland@icloud.com>