Package: ml
Title: Supervised Learning with Mandatory Splits and Seeds
Version: 1.0.0
Authors@R: person("Simon", "Roth", email = "simon@epagogy.ai", role = c("aut", "cre"))
Author: Simon Roth [aut, cre]
Maintainer: Simon Roth <simon@epagogy.ai>
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
Roxygen: list(markdown = TRUE)
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
Config/pak/sysreqs: libclang-dev
Repository: https://epagogy.r-universe.dev
Date/Publication: 2026-04-03 17:52:15 UTC
RemoteUrl: https://github.com/epagogy/ml
RemoteRef: HEAD
RemoteSha: 733303866ecba86ab83fdbd7ef3e7b8d2a24bf1f
RemoteSubdir: r
NeedsCompilation: yes
Packaged: 2026-09-30 07:57:22 UTC; root