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