Extends the 'mlr3' package with a connector to the package
    'batchtools'. This allows to run large-scale benchmark experiments on
    scheduled high-performance computing clusters.

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 three 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. 'DataBackendPolars' operates on
    'Polars' data frames.

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'.