{ "_id": "6a44f5e46ff6f2357551faf0", "Package": "icarm", "Title": "Interpretable Contextual-Accountable and Responsible Machine\nLearning", "Version": "0.1.0", "Authors@R": "c(person(\"Olushina Olawale\", \"Awe\", email = \"olawaleawe@gmail.com\", role = c(\"aut\", \"cre\")), person(\"Ludwigsburg University of Education\", role = \"fnd\"))", "Description": "A general-purpose framework for Interpretable\nContextual-Accountable and Responsible Machine Learning (ICARM)\nthat works with any clean tabular data across any application\ndomain including healthcare, finance, social science, business,\nand education. Automatically detects whether a prediction task\nis binary classification, multi-class classification, or\nregression from the target variable type. Provides a unified\nentry point icarm_fit() supporting both interpretable learners\n(CART, logistic regression, linear regression, GAM) and\nextended learners (random forest, XGBoost, SVM) with consistent\ninterfaces for global and local model explanation, group-level\nfairness auditing across protected attributes, probability\ncalibration, threshold analysis, multi-model comparison,\nreproducible JSON audit trails, and accountability scorecards.\nThe contextual accountability framing emphasises that\nalgorithmic fairness and interpretability requirements depend\non the deployment domain and must be evaluated accordingly.\nExtends the civic.icarm framework (Awe 2025)\n to\ngeneral-purpose applications beyond civic and political\neducation.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "Language": "en-GB", "Config/testthat/edition": "3", "LazyData": "true", "RoxygenNote": "7.3.3", "Config/pak/sysreqs": "libicu-dev", "Repository": "https://olawaleawe.r-universe.dev", "Date/Publication": "2026-06-18 16:42:38 UTC", "RemoteUrl": "https://github.com/olawaleawe/icarm", "RemoteRef": "HEAD", "RemoteSha": "67f26ebf7a01663ff7b3620c9e76c4ac96738f9c", "NeedsCompilation": "no", "Packaged": { "Date": "2026-07-01 11:08:36 UTC", "User": "root" }, "Author": "Olushina Olawale Awe [aut, cre],\nLudwigsburg University of Education [fnd]", "Maintainer": "Olushina Olawale Awe ", "MD5sum": "47f3c8a7ede14dcb320dcfc39449849d", "_user": "olawaleawe", "_type": "src", "_file": "icarm_0.1.0.tar.gz", "_fileid": "4e758f7a0e6cee73861954900288ff2f716f9ec8b67eab9051f82ce9f9c1ba03", "_filesize": 183306, "_sha256": "4e758f7a0e6cee73861954900288ff2f716f9ec8b67eab9051f82ce9f9c1ba03", "_created": "2026-07-01T11:08:36.000Z", "_published": "2026-07-01T11:11:32.039Z", "_distro": "resolute", "_jobs": [ { "job": 84518186266, "time": 140, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "8008138893" }, { "job": 84518186259, "time": 143, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "8008139926" }, { "job": 84518186302, "time": 94, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "8008120765" }, { "job": 84518186290, "time": 107, "config": "macos-release-arm64", "r": "4.6.1", "check": "OK", "artifact": "8008126501" }, { "job": 84517598878, "time": 192, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "8008084378" }, { "job": 84518186232, "time": 146, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "8008141185" }, { "job": 84518186323, "time": 72, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "8008112793" }, { "job": 84518186301, "time": 72, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "8008111934" }, { "job": 84518186288, "time": 108, "config": "windows-release", "r": "4.6.1", "check": "OK", "artifact": "8008128361" } ], "_buildurl": "https://github.com/r-universe/olawaleawe/actions/runs/28512973101", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/olawaleawe/icarm", "_commit": { "id": "67f26ebf7a01663ff7b3620c9e76c4ac96738f9c", "author": "Olawaleawe ", "committer": "Olawaleawe ", "message": "feat: icarm v0.1.0 - Interpretable Contextual-Accountable and Responsible ML\n", "time": 1781800958 }, "_maintainer": { "name": "Olushina Olawale Awe", "email": "olawaleawe@gmail.com", "login": "olawaleawe", "description": "I am passionate about data science, research collaboration, statistical learning and business analytics. 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