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>.

Runs an evolutionary algorithm using the 'AlphaSimR' machinery <doi:10.1093/g3journal/jkaa017> .

Dynamic and Interactive Maps with R, powered by 'leaflet' <https://leafletjs.com>. 'evolMap' generates a web page with interactive and dynamic maps to which you can add geometric entities (points, lines or colored geographic areas), and/or markers with optional links between them. The dynamic ability of these maps allows their components to evolve over a continuous period of time or by periods.

Provides functions for covariance matrix comparisons, estimation of repeatabilities in measurements and matrices, and general evolutionary quantitative genetics tools. Melo D, Garcia G, Hubbe A, Assis A P, Marroig G. (2016) <doi:10.12688/f1000research.7082.3>.