Functional Machine Learning Framework
Overview
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. Plug-in g-computation follows Naimi, Cole, and Kennedy (2016) doi:10.1093/ije/dyw323.
- Prepared for CRAN resubmission: removed the unused
methodspackage fromImports, added explicitimportFromdeclarations forpredict(),model.frame(),model.response(),setNames(),median()(stats) andtail()(utils) via a new package-levelR/funcml-package.R, and excludedLICENSE.mdfrom the build (the package uses the standardGPL-3license text that R bundles automatically; the file remains in the GitHub repo only).R CMD check --as-crannow passes with 0 errors, 0 warnings, 0 notes.
From NEWS.md in the release
Install
Health
- OK13 OK2026-08-26
- ERROR7 OK, 1 ERROR2026-04-22
Documentation
- Help pages
- 55includes a package overview page
- Examples that run
- 100%52 run in a plain check (2 of them under a condition)
- Exported functions with examples
- 100%
- Documented parameters
- 99%
- Return-value docs
- 100%
- Help pages citing references
- 4%
- funcml PDF
Downloads
Repository
- PRs merged (all time)
- 96%
- Median days to close, 50 most recently updated closed issues
- not measured
- Median days to close, 50 most recently updated closed PRs
- 0
Stars over time
Forks over time
Releases over time
PRs over time
Commits over time
6 development-tooling and community-health practices detected across 5 families in the upstream repository
Checks run against github.com/ielbadisy/funcml on 2026-10-11.
The R CMD check workflow runs on Linux
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Dependencies
Installing funcml adds 71 packages (42 with compiled code, 72 MB of source tarballs). Heaviest: stringi 11 MB, ggplot2 6.0 MB, data.table 5.7 MB.
Code & Tests
Datasets
People & History
Pick two releases to compare their code metrics.
- 0.9.0Latest2026-08-22 · current release · diff ↗ ·
removed 1 export
compare_learnersadded 4 exports
auc_ci, compare, dca, roc_curve - RR 4.6.0 released · 2026-04-24
- 0.7.12026-04-21
- RR 4.5.0 released · 2025-04-11
Package metadata
- First published
- 2026-04-21
- Total releases
- 2 / 1 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.5.0
- Bundled data
- 372 KB / 1 file
- Download size
- 1.1 MB
Cite
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citation("funcml")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
The repository also keeps a CITATION.cff.
Cite the R Observatory
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From data release v2026-10-17, which the citation names so these numbers can be found later. More on citing and the projects behind them.