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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 (CART,
  logistic regression, linear regression, GAM) and extended learners (random forest,
  XGBoost, 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 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) <https://cran.r-project.org/package=civic.icarm> to general-purpose applications
  beyond civic and political education.
authors:
- family-names: Awe
  given-names: Olushina Olawale
  email: olawaleawe@gmail.com
repository: https://olawaleawe.r-universe.dev
commit: 67f26ebf7a01663ff7b3620c9e76c4ac96738f9c
date-released: '2026-06-18'
contact:
- family-names: Awe
  given-names: Olushina Olawale
  email: olawaleawe@gmail.com