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# CITATION file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
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cff-version: 1.2.0
message: 'To cite package "evoFE" in publications use:'
type: software
license: MIT
title: 'evoFE: Evolutionary Feature Engineering'
version: 0.1.0
abstract: 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) <https://doi.org/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) <https://doi.org/10.1145/2939672.2939785>, Gagolewski (2021)
  <https://doi.org/10.1016/j.softx.2021.100722>, Gagolewski (2026) <https://doi.org/10.32614/CRAN.package.lumbermark>,
  and Gagolewski (2026) <https://doi.org/10.32614/CRAN.package.deadwood>.
authors:
- family-names: Pereira
  given-names: Gustavo
  email: tanopereira@gmail.com
repository: https://cran.r-universe.dev
commit: 3657a95330439606d57f6c54a984b76a5115b78e
date-released: '2026-06-09'
contact:
- family-names: Pereira
  given-names: Gustavo
  email: tanopereira@gmail.com