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civic.icarm  

Interpretable Civic-Accountable and Responsible Machine Learning
View on CRAN: Click here


Download and install civic.icarm package within the R console
Install from CRAN:
install.packages("civic.icarm")

Install from Github:
library("remotes")
install_github("cran/civic.icarm")

Install by package version:
library("remotes")
install_version("civic.icarm", "0.2.0")



Attach the package and use:
library("civic.icarm")
Maintained by
Olushina Olawale Awe
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-06-17
Latest Update: 2026-06-17
Description:
A general-purpose framework for Interpretable Civic-Accountable and Responsible Machine Learning (ICARM). Works with any clean tabular data and automatically detects whether a task is binary classification, multi-class classification, or regression from the target variable type. Provides a single unified entry point civic_fit() alongside tidy interfaces for global and local model explanations, group-level fairness auditing, probability calibration, multi-model comparison, threshold analysis, and reproducible audit trails. Designed to support the DataCitizen-Pro research agenda at Ludwigsburg University of Education: developing data literacy, statistical reasoning, and democratic judgment formation in civic and political teacher education. References: Biecek (2018) <doi:10.18637/jss.v085.i04>, Kuhn (2008) <doi:10.18637/jss.v028.i05>, Awe (2025) <https://github.com/Olawaleawe/civic.icarm>.
How to cite:
Olushina Olawale Awe (2026). civic.icarm: Interpretable Civic-Accountable and Responsible Machine Learning. R package version 0.2.0, https://cran.r-project.org/web/packages/civic.icarm. Accessed 14 Jul. 2026.
Previous versions and publish date:
(2026-07-09 07:26), 0.2.0 (2026-06-17 19:50)
Other packages that cited civic.icarm R package
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Complete documentation for civic.icarm
Functions, R codes and Examples using the civic.icarm R package
Full civic.icarm package functions and examples
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