{ "_id": "6ad7a3cf4b18b9008999a0b1", "Package": "fairGATE", "Title": "Fair Gated Algorithm for Targeted Equity", "Version": "1.0.0", "Authors@R": "c(\nperson(\"Rhys\", \"Holland\", email = \"rhys.holland@icloud.com\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Raquel\", \"Iniesta\",\nemail = \"raquel.iniesta@kcl.ac.uk\",\nrole = \"aut\")\n)", "Description": "Fairness-aware correction of a prediction model for binary\nand continuous outcomes with two or more protected groups and\ntheir intersections. A cross-fitted base learner\n(gradient-boosted trees or a neural network) is fitted without\nthe protected attribute, and a gated mixture of neural experts\nlearns a per-person correction to its score under a fairness\npenalty (a random-effects or variance penalty on\noutcome-stratified group means, or a thresholded worst-group\npenalty on error rates), with a cross-fitted disparity signal,\nan intervention cost, smoothed early stopping, a per-person\nexport of adjustments with bootstrap stability intervals, and\nrouting analyses of which expert corrects whom. Methods draw on\nmixture-of-experts neural networks by Jordan and Jacobs (1994)\n, gradient boosting by Chen\nand Guestrin (2016) ,\ncross-fitting by Chernozhukov et al. (2018)\n, equalised odds by Hardt, Price and\nSrebro (2016) , the\nrandom-effects variance estimator of DerSimonian and Laird\n(1986) , and personalised\ntreatment prediction for depression by Iniesta, Stahl and\nMcGuffin (2016) .", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Language": "en-GB", "Roxygen": "list(markdown = TRUE)", "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\ncomponents; 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": { "Date": "2026-10-20 17:20:09 UTC", "User": "root" }, "Author": "Rhys Holland [aut, cre],\nRaquel Iniesta [aut]", "Maintainer": "Rhys Holland ", "_user": "rhysholland", "_type": "src", "_file": "fairGATE_1.0.0.tar.gz", "_fileid": "https://r2.ropensci.org/c0ae15b0dfcd8c142f6bae70c9802e68552d475b67237bf7fefd51973386901d", "_filesize": 453029, "_sha256": "c0ae15b0dfcd8c142f6bae70c9802e68552d475b67237bf7fefd51973386901d", "_expires": "2027-01-28T17:24:28.000Z", "_created": "2026-10-20T17:20:09.000Z", "_published": "2026-10-20T17:24:31.231Z", "_buildurl": "https://github.com/r-universe/rhysholland/actions/runs/39464174177", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 118774801045, "time": 208, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12285206210" }, { "job": 118774800899, "time": 210, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12285355715" }, { "job": 118774801049, "time": 204, 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