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>.

Interface to a large number of classification and regression
    techniques, including machine-readable parameter descriptions. There
    is also an experimental extension for survival analysis, clustering
    and general, example-specific cost-sensitive learning. Generic
    resampling, including cross-validation, bootstrapping and subsampling.
    Hyperparameter tuning with modern optimization techniques, for single-
    and multi-objective problems.  Filter and wrapper methods for feature
    selection. Extension of basic learners with additional operations
    common in machine learning, also allowing for easy nested resampling.
    Most operations can be parallelized.

Efficient, object-oriented programming on the
    building blocks of machine learning. Provides 'R6' objects for tasks,
    learners, resamplings, and measures. The package is geared towards
    scalability and larger datasets by supporting parallelization and
    out-of-memory data-backends like databases. While 'mlr3' focuses on
    the core computational operations, add-on packages provide additional
    functionality.