ml: Supervised Learning with Mandatory Splits and Seeds

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'.

Version:	0.1.2
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
Published:	2026-03-19
DOI:	10.32614/CRAN.package.ml
Author:	Simon Roth [aut, cre]
Maintainer:	Simon Roth <simon at epagogy.ai>
BugReports:	https://github.com/epagogy/ml/issues
License:	MIT + file LICENSE
URL:	https://github.com/epagogy/ml, https://epagogy.ai
NeedsCompilation:	yes
SystemRequirements:	Cargo ('Rust' package manager), rustc (>= 1.56.0, optional)
Citation:	ml citation info
Materials:	README, NEWS
CRAN checks:	ml results

Documentation:

Reference manual:	ml.html , ml.pdf
Vignettes:	Getting Started with ml (source, R code)

Downloads:

Package source:	ml_0.1.2.tar.gz
Windows binaries:	r-devel: ml_0.1.2.zip, r-release: ml_0.1.2.zip, r-oldrel: ml_0.1.2.zip
macOS binaries:	r-release (arm64): ml_0.1.2.tgz, r-oldrel (arm64): ml_0.1.2.tgz, r-release (x86_64): ml_0.1.2.tgz, r-oldrel (x86_64): ml_0.1.2.tgz

Linking:

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