Package: ml
Title: Supervised Learning with Mandatory Splits and Seeds
Version: 0.1.2
Authors@R: person("Simon", "Roth", email = "simon@epagogy.ai", role = c("aut", "cre"))
Description: 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'.
License: MIT + file LICENSE
SystemRequirements: Cargo ('Rust' package manager), rustc (>= 1.56.0,
        optional)
Encoding: UTF-8
RoxygenNote: 7.3.3
URL: https://github.com/epagogy/ml, https://epagogy.ai
BugReports: https://github.com/epagogy/ml/issues
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
Config/testthat/edition: 3
VignetteBuilder: knitr
NeedsCompilation: yes
Packaged: 2026-03-15 12:07:30 UTC; simon
Author: Simon Roth [aut, cre]
Maintainer: Simon Roth <simon@epagogy.ai>
Repository: CRAN
Date/Publication: 2026-03-19 14:30:02 UTC
Built: R 4.6.0; aarch64-apple-darwin23; 2026-04-25 20:28:45 UTC; unix
Archs: ml.so.dSYM
