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funcml  

Functional Machine Learning Framework
View on CRAN: Click here


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

Install from Github:
library("remotes")
install_github("cran/funcml")

Install by package version:
library("remotes")
install_version("funcml", "0.7.1")



Attach the package and use:
library("funcml")
Maintained by
Imad El Badisy
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2026-04-21
Latest Update: 2026-04-21
Description:
A compact and explicit machine learning framework for supervised learning, resampling-based evaluation, hyperparameter tuning, learner comparison, interpretation, and plug-in g-computation. The package uses standard formulas for model specification and provides stable S3 interfaces for fitting, evaluation, tuning, interpretation, and causal estimation across a learner registry with multiple backend engines. Implemented interpretation methods build on established approaches such as permutation-based variable importance, partial dependence, individual conditional expectation, accumulated local effects, SHAP, and LIME; see Friedman (2001) <doi:10.1214/aos/1013203451>, Goldstein et al. (2015) <doi:10.1080/10618600.2014.907095>, Apley and Zhu (2020) <doi:10.1111/rssb.12377>, Lundberg and Lee (2017) <doi:10.48550/arXiv.1705.07874>, and Ribeiro et al. (2016) <doi:10.48550/arXiv.1602.04938>. The framework is intentionally opinionated: preprocessing is expected to occur outside the modeling step, and the API emphasizes explicit inputs, consistent object contracts, and compact interfaces rather than feature-by-feature competition with larger machine learning ecosystems.
How to cite:
Imad El Badisy (2026). funcml: Functional Machine Learning Framework. R package version 0.7.1, https://cran.r-project.org/web/packages/funcml. Accessed 07 Jun. 2026.
Previous versions and publish date:
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Complete documentation for funcml
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Full funcml package functions and examples
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