Supervised Learning with Mandatory Splits and Seeds R package ml version 0.1.2 Implements the split-fit-evaluate-assess workflow from Hastie, Tibshirani, and Friedman (2009, ISBN:978-0-387-84857-0) "The Elements of Statistical Learning", Chapter 7. Provides three-way data splitting with automatic stratification, mandatory seeds for reproducibility, automatic data type handling, and 10 algorithms out of the box. Uses 'Rust' backend for cross-language deterministic splitting. Designed for tabular supervised learning with minimal ceremony. Polyglot parity with the 'Python' 'mlw' package on 'PyPI'. Software Depends: R (>= 4.1.0) Imports: cli, rlang, stats, utils, withr Suggests: testthat (>= 3.0.0), xgboost (>= 2.0.0), ranger, rpart, e1071, kknn, glmnet, naivebayes, lightgbm, tm, tibble, knitr, rmarkdown, caret, rsample Simon Roth <simon@epagogy.ai> Comprehensive R Archive Network (CRAN) Simon Roth [aut, cre] MIT + file LICENSE (https://CRAN.R-project.org/package=ml/LICENSE) 2026-03-19 application/tgz https://CRAN.R-project.org/package=ml doi:10.32614/CRAN.package.ml