A fast, flexible machine learning library, written in C++, that
             aims to provide fast, extensible implementations of cutting-edge
             machine learning algorithms.  See also Curtin et al. (2023)
             <doi:10.21105/joss.05026>.

Provides functions for obtaining estimates of the parameter of the niche preemption model (also known as the geometric series), in particular a maximum likelihood estimator (Graffelman, 2021) <doi:10.1101/2021.01.27.428381>. The niche preemption model is a widely used model in ecology and biodiversity studies.

An implementation of classifier chains (CC's) for multi-label
    prediction. Users can employ an external package (e.g. 'randomForest',
    'C50'), or supply their own. The package can train a single set of CC's or
    train an ensemble of CC's -- in parallel if running in a multi-core
    environment. New observations are classified using a Gibbs sampler since
    each unobserved label is conditioned on the others. The package includes
    methods for evaluating the predictions for accuracy and aggregating across
    iterations and models to produce binary or probabilistic classifications.

We implement a surrogate modeling algorithm to guide simulation-based
    sample size planning. The method is described in detail in our paper (Zimmer &
    Debelak (2023) <doi:10.1037/met0000611>). It supports multiple study design
    parameters and optimization with respect to a cost function. It can find
    optimal designs that correspond to a desired statistical power or that fulfill
    a cost constraint. We also provide a tutorial paper (Zimmer et al. (2023)
    <doi:10.3758/s13428-023-02269-0>).

Quantification is a prominent machine learning task that has received an 
    increasing amount of attention in the last years. The objective is to predict the 
    class distribution of a data sample. This package is a collection of machine learning 
    algorithms for class distribution estimation. This package include algorithms from
    different paradigms of quantification. These methods are described in the paper: 
    A. Maletzke, W. Hassan, D. dos Reis, and G. Batista. The importance of the test set 
    size in quantification assessment. In Proceedings of the Twenty-Ninth International 
    Joint Conference on Artificial Intelligence, IJCAI20, pages 2640–2646, 2020.
    <doi:10.24963/ijcai.2020/366>.