Functional Machine Learning Framework R package funcml version 0.7.1 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. Software 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 Imad El Badisy <elbadisyimad@gmail.com> Comprehensive R Archive Network (CRAN) Imad El Badisy [aut, cre] GPL-3 2026-04-21 application/tgz https://CRAN.R-project.org/package=funcml doi:10.32614/CRAN.package.funcml