{ "_id": "6a46c632c3efdb0cb7aa469c", "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(Classification and Regression Trees (CART), logistic\nregression, linear regression, Generalized Additive Models\n(GAM)) and extended learners (random forest, 'XGBoost', Support\nVector Machines (SVM)) with consistent interfaces for global\nand local model explanation, group-level fairness auditing\nacross protected attributes, probability calibration, threshold\nanalysis, multi-model comparison, reproducible JavaScript\nObject Notation (JSON) audit trails, and accountability\nscorecards. The contextual accountability framing emphasises\nthat algorithmic fairness and interpretability requirements\ndepend on the deployment domain and must be evaluated\naccordingly. Extends 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", "NeedsCompilation": "no", "Packaged": { "Date": "2026-07-01 07:32:40 UTC", "User": "root" }, "Author": "Olushina Olawale Awe [aut, cre], Ludwigsburg University of\nEducation [fnd]", "Maintainer": "Olushina Olawale Awe ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-06-30 20:40:10 UTC", "RemoteUrl": "https://github.com/cran/icarm", "RemoteRef": "HEAD", "RemoteSha": "3dbe08ce66055cd0516c95789d557617e45e471b", "_user": "cran", "_type": "src", "_file": "icarm_0.1.0.tar.gz", "_fileid": "https://r2.ropensci.org/7a427d5cf979a961768fdeba66d4977cd18e419fc75704322609db72beb7b927", "_filesize": 184128, "_sha256": "7a427d5cf979a961768fdeba66d4977cd18e419fc75704322609db72beb7b927", "_expires": "2026-10-10T20:12:33.000Z", "_created": "2026-07-01T07:32:40.000Z", "_published": "2026-07-02T20:12:34.595Z", "_jobs": [ { "job": 84868500815, "time": 133, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "8003141494" }, { "job": 84868500261, "time": 149, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "8003147355" }, { "job": 84868499786, "time": 197, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "8003093252" }, { "job": 84868500287, "time": 172, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "8003155315" } ], "_host": "GitHub-Actions", "_buildurl": "https://github.com/r-universe/cran/actions/runs/28501099407", "_status": "success", "_upstream": "https://github.com/cran/icarm", "_commit": { "id": "3dbe08ce66055cd0516c95789d557617e45e471b", "author": "Olushina Olawale Awe ", "committer": "cran-robot ", "message": "version 0.1.0\n", "time": 1782852010 }, "_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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