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.

Provides methods for inference using stacked multiple 
    imputations augmented with weights. The vignette provides example R code for 
    implementation in general multiple imputation settings. For additional details 
    about the estimation algorithm, we refer the reader to Beesley, Lauren J and 
    Taylor, Jeremy M G (2020) “A stacked approach for chained equations multiple 
    imputation incorporating the substantive model” <doi:10.1111/biom.13372>, 
    and Beesley, Lauren J and Taylor, Jeremy M G (2021) “Accounting for not-at-random 
    missingness through imputation stacking” <arXiv:2101.07954>.

Building predictive models with stacking which is a type of ensemble learning. Learners can be specified from those implemented in 'caret'. For more information of the package, see Nukui and Onogi (2023) <doi:10.1101/2023.06.06.543970>.

Model stacking is an ensemble technique that involves
    training a model to combine the outputs of many diverse statistical
    models, and has been shown to improve predictive performance in a
    variety of settings. 'stacks' implements a grammar for
    'tidymodels'-aligned model stacking.

Graphical outputs and treatment for a database of fish pass
    monitoring. It is a part of the 'STACOMI' open source project developed in
    France by the French Office for Biodiversity institute to centralize
    data obtained by fish pass monitoring. This version is available in French and
    English. See <http://stacomir.r-forge.r-project.org/> for more information on
    'STACOMI'.