Package: icarm
Title: Interpretable Contextual-Accountable and Responsible Machine
        Learning
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 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.
License: MIT + file LICENSE
Encoding: UTF-8
Language: en-GB
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
Config/testthat/edition: 3
LazyData: true
RoxygenNote: 7.3.3
NeedsCompilation: no
Packaged: 2026-06-18 21:08:32 UTC; olawa
Author: Olushina Olawale Awe [aut, cre],
  Ludwigsburg University of Education [fnd]
Maintainer: Olushina Olawale Awe <olawaleawe@gmail.com>
Repository: CRAN
Date/Publication: 2026-06-30 20:40:10 UTC
Built: R 4.7.0; ; 2026-06-30 23:51:12 UTC; windows
