Package: evoFE
Type: Package
Title: Evolutionary Feature Engineering
Version: 0.1.0
Authors@R: person("Gustavo", "Pereira", role = c("aut", "cre"), email = "tanopereira@gmail.com")
Description: Automates feature engineering using evolutionary algorithms
    inspired by genetic programming. Starting from raw input features, the
    package evolves candidate transformation recipes through selection,
    crossover, and mutation, evaluating fitness via cross-validation or
    train/validation splits with gradient-boosted tree models ('LightGBM' or
    'XGBoost'). Built-in transformers include arithmetic, logarithmic, and
    power operations, interaction terms, target encoding, quantile and
    log-based binning, principal component analysis, truncated singular value
    decomposition, Uniform Manifold Approximation and Projection (UMAP)
    dimensionality reduction, and minimum spanning tree (MST) graph-based
    clustering. The evolutionary search yields an optimised feature recipe
    that can be applied to new data for prediction. Methods are described in
    McInnes et al. (2018) <doi:10.21105/joss.00861>,
    Ke et al. (2017)
    <https://papers.nips.cc/paper/6907-lightgbm-a-highly-efficient-gradient-boosting-decision-framework>,
    Chen and Guestrin (2016) <doi:10.1145/2939672.2939785>,
    Gagolewski (2021) <doi:10.1016/j.softx.2021.100722>,
    Gagolewski (2026) <doi:10.32614/CRAN.package.lumbermark>, and
    Gagolewski (2026) <doi:10.32614/CRAN.package.deadwood>.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: data.table, lightgbm, xgboost, stats, digest, uwot,
        quitefastmst, genieclust
Suggests: RhpcBLASctl, testthat, knitr, rmarkdown, lumbermark, deadwood
VignetteBuilder: knitr
Config/roxygen2/version: 8.0.0
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2026-06-04 20:51:19 UTC; vero
Author: Gustavo Pereira [aut, cre]
Maintainer: Gustavo Pereira <tanopereira@gmail.com>
Repository: CRAN
Date/Publication: 2026-06-09 15:50:14 UTC
Built: R 4.6.0; ; 2026-06-09 16:28:57 UTC; unix
