icarm: Interpretable Contextual-Accountable and Responsible Machine
Learning
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)
<https://cran.r-project.org/package=civic.icarm> to general-purpose
applications beyond civic and political education.
| Version: |
0.1.0 |
| Depends: |
R (≥ 4.1.0) |
| Imports: |
stats, utils, rpart, ggplot2, dplyr, tidyr, tibble, purrr, rlang, jsonlite, digest |
| Suggests: |
randomForest, xgboost, e1071, mgcv, glmnet, nnet, DALEX, pROC, vip, testthat, covr |
| Published: |
2026-06-30 |
| DOI: |
10.32614/CRAN.package.icarm (may not be active yet) |
| Author: |
Olushina Olawale Awe [aut, cre],
Ludwigsburg University of Education [fnd] |
| Maintainer: |
Olushina Olawale Awe <olawaleawe at gmail.com> |
| License: |
MIT + file LICENSE |
| NeedsCompilation: |
no |
| Language: |
en-GB |
| Materials: |
README |
| CRAN checks: |
icarm results |
Documentation:
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