An implementation of many measures for the
    assessment of the stability of feature selection. Both simple measures
    and measures which take into account the similarities between features
    are available, see Bommert (2020) <doi:10.17877/DE290R-21906>.

Resampling procedures to assess the stability of selected variables
    with additional finite sample error control for high-dimensional variable
    selection procedures such as Lasso or boosting. Both, standard stability
    selection (Meinshausen & Buhlmann, 2010, <doi:10.1111/j.1467-9868.2010.00740.x>) 
    and complementary pairs stability selection with improved error bounds 
    (Shah & Samworth, 2013, <doi:10.1111/j.1467-9868.2011.01034.x>) are
    implemented. The package can be combined with arbitrary user specified
    variable selection approaches.

Standardized accuracy (staccuracy) is a framework for expressing accuracy scores such that 50% represents a reference level of performance and 100% is a perfect prediction. The 'staccuracy' package provides tools for creating staccuracy functions as well as some recommended staccuracy measures. It also provides functions for some classic performance metrics such as mean absolute error (MAE), root mean squared error (RMSE), and area under the receiver operating characteristic curve (AUCROC), as well as their winsorized versions when applicable.

A minimalist implementation of model stacking by
    Wolpert (1992) <doi:10.1016/S0893-6080(05)80023-1> for boosted tree models.
    A classic, two-layer stacking model is implemented, where the first layer
    generates features using gradient boosting trees, and the second layer
    employs a logistic regression model that uses these features as inputs.
    Utilities for training the base models and parameters tuning are provided,
    allowing users to experiment with different ensemble configurations easily.
    It aims to provide a simple and efficient way to combine multiple
    gradient boosting models to improve predictive model performance
    and robustness.