Implements methods for post-hoc analysis and
    visualisation of benchmark experiments, for 'mlr3' and beyond.

Extends the 'mlr3' package with cluster analysis.

A small collection of interesting and educational machine
    learning data sets which are used as examples in the 'mlr3' book
    (<https://mlr3book.mlr-org.com>), the use case gallery
    (<https://mlr3gallery.mlr-org.com>), or in other examples. All data
    sets are properly preprocessed and ready to be analyzed by most
    machine learning algorithms.  Data sets are automatically added to the
    dictionary of tasks if 'mlr3' is loaded.

Extends the 'mlr3' package with a backend to
    transparently work with databases such as 'SQLite', 'DuckDB', 'MySQL',
    'MariaDB', or 'PostgreSQL'. The package provides two additional backends:
    'DataBackendDplyr' relies on the abstraction of package 'dbplyr' to
    interact with most DBMS. 'DataBackendDuckDB' operates on 'DuckDB' data bases
    and also on Apache Parquet files.

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'.