Integrates fairness auditing and bias mitigation methods for
    the 'mlr3' ecosystem.  This includes fairness metrics, reporting
    tools, visualizations and bias mitigation techniques such as
    "Reweighing" described in 'Kamiran, Calders' (2012)
    <doi:10.1007/s10115-011-0463-8> and "Equalized Odds" described in
    'Hardt et al.' (2016)
    <https://papers.nips.cc/paper/2016/file/9d2682367c3935defcb1f9e247a97c0d-Paper.pdf>.
    Integration with 'mlr3' allows for auditing of ML models as well as
    convenient joint tuning of machine learning algorithms and debiasing
    methods.

Extends the 'mlr3' ecosystem to functional analysis by adding
    support for irregular and regular functional data as defined in the
    'tf' package.  The package provides 'PipeOps' for preprocessing
    functional columns and for extracting scalar features, thereby
    allowing standard machine learning algorithms to be applied
    afterwards. Available operations include simple functional features
    such as the mean or maximum, smoothing, interpolation, flattening, and
    functional 'PCA'.

Extends 'mlr3' with filter methods for feature selection.
    Besides standalone filter methods built-in methods of any
    machine-learning algorithm are supported.  Partial scoring of
    multivariate filter methods is supported.

Feature selection package of the 'mlr3' ecosystem. It selects
    the optimal feature set for any 'mlr3' learner. The package works with
    several optimization algorithms e.g. Random Search, Recursive Feature
    Elimination, and Genetic Search. Moreover, it can automatically
    optimize learners and estimate the performance of optimized feature
    sets with nested resampling.

Successive Halving (Jamieson and Talwalkar (2016)
    <doi:10.48550/arXiv.1502.07943>) and Hyperband (Li et al. 2018
    <doi:10.48550/arXiv.1603.06560>) optimization algorithm for the mlr3
    ecosystem. The implementation in mlr3hyperband features improved
    scheduling and parallelizes the evaluation of configurations. The
    package includes tuners for hyperparameter optimization in mlr3tuning
    and optimizers for black-box optimization in bbotk.