# ------------------------------------------------ # CITATION.cff file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # ------------------------------------------------ cff-version: 1.2.0 message: 'To cite package "icarm" in publications use:' type: software license: MIT title: 'icarm: Interpretable Contextual-Accountable and Responsible Machine Learning' version: 0.1.0 doi: 10.32614/CRAN.package.icarm abstract: A general-purpose framework for Interpretable Contextual-Accountable and Responsible Machine Learning (ICARM) that works with any clean tabular data across any application domain including healthcare, finance, social science, business, and education. Automatically detects whether a prediction task is binary classification, multi-class classification, or regression from the target variable type. Provides a unified entry point icarm_fit() supporting both interpretable learners (Classification and Regression Trees (CART), logistic regression, linear regression, Generalized Additive Models (GAM)) and extended learners (random forest, 'XGBoost', Support Vector Machines (SVM)) with consistent interfaces for global and local model explanation, group-level fairness auditing across protected attributes, probability calibration, threshold analysis, multi-model comparison, reproducible JavaScript Object Notation (JSON) audit trails, and accountability scorecards. The contextual accountability framing emphasises that algorithmic fairness and interpretability requirements depend on the deployment domain and must be evaluated accordingly. Extends the 'civic.icarm' framework (Awe 2025) to general-purpose applications beyond civic and political education. authors: - family-names: Awe given-names: Olushina Olawale email: olawaleawe@gmail.com repository: https://cran.r-universe.dev commit: 3dbe08ce66055cd0516c95789d557617e45e471b date-released: '2026-06-30' contact: - family-names: Awe given-names: Olushina Olawale email: olawaleawe@gmail.com