# -------------------------------------------- # CITATION file created with {cffr} R package # See also: https://docs.ropensci.org/cffr/ # -------------------------------------------- cff-version: 1.2.0 message: 'To cite package "funcml" in publications use:' type: software license: GPL-3.0-only title: 'funcml: Functional Machine Learning Framework' version: 0.7.2 identifiers: - type: doi value: 10.32614/CRAN.package.funcml abstract: '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) , Goldstein et al. (2015) , Apley and Zhu (2020) , Lundberg and Lee (2017) , and Ribeiro et al. (2016) . 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.' authors: - family-names: El Badisy given-names: Imad email: elbadisyimad@gmail.com preferred-citation: type: manual title: 'funcml: Machine Learning Framework for R' authors: - family-names: El Badisy given-names: Imad email: elbadisyimad@gmail.com year: '2026' notes: R package version 0.7.1 repository: https://ielbadisy.r-universe.dev repository-code: https://github.com/ielbadisy/funcml commit: 76863107351af812f041a94b20ecbe0e42066a79 url: https://github.com/ielbadisy/funcml date-released: '2026-05-14' contact: - family-names: El Badisy given-names: Imad email: elbadisyimad@gmail.com