funcml: Functional Machine Learning Framework
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.
| Version: |
0.7.1 |
| Depends: |
R (≥ 3.5.0) |
| Imports: |
stats, utils, methods, ggplot2, functionals, grDevices, tools, MASS, mgcv, nnet, rpart, glmnet, ranger, e1071, randomForest, gbm, C50, kknn, earth, naivebayes, mda, ada, pls, partykit, dbarts, xgboost, lightgbm, shapviz |
| Suggests: |
testthat (≥ 3.1.0), knitr, rmarkdown, roxygen2, gggenes, ggfittext |
| Published: |
2026-04-21 |
| DOI: |
10.32614/CRAN.package.funcml (may not be active yet) |
| Author: |
Imad El Badisy [aut, cre] |
| Maintainer: |
Imad El Badisy <elbadisyimad at gmail.com> |
| BugReports: |
https://github.com/ielbadisy/funcml/issues |
| License: |
GPL-3 |
| URL: |
https://github.com/ielbadisy/funcml |
| NeedsCompilation: |
no |
| Citation: |
funcml citation info |
| Materials: |
README, NEWS |
| CRAN checks: |
funcml results |
Documentation:
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