funcml-package: funcml: Functional Machine Learning Framework

In funcml: Functional Machine Learning Framework

funcml: Functional Machine Learning Framework

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) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1214/aos/1013203451")}, Goldstein et al. (2015) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1080/10618600.2014.907095")}, Apley and Zhu (2020) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1111/rssb.12377")}, Lundberg and Lee (2017) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.1705.07874")}, and Ribeiro et al. (2016) \Sexpr[results=rd]{tools:::Rd_expr_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) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1093/ije/dyw323")}.

Author(s)

Maintainer: Imad El Badisy [e-mail]

See Also

Useful links:

• https://github.com/ielbadisy/funcml

• Report bugs at https://github.com/ielbadisy/funcml/issues

funcml documentation built on Aug. 22, 2026, 5:08 p.m.