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: 2026-07-01 07:32:40 UTC; root
Author: Olushina Olawale Awe [aut, cre], Ludwigsburg University of
        Education [fnd]
Maintainer: Olushina Olawale Awe <olawaleawe@gmail.com>
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