A web-based graphical user interface to provide the basic steps of a machine learning workflow. It uses the functionalities of the 'mlr3' framework.

Extends the 'mlr3' ML framework with methods for spatial
    objects. Data storage and prediction are supported for packages
    'terra', 'raster' and 'stars'.

Extends the mlr3 machine learning framework with
    spatio-temporal resampling methods to account for the presence of
    spatiotemporal autocorrelation (STAC) in predictor variables. STAC may
    cause highly biased performance estimates in cross-validation if
    ignored. A JSS article is available at <doi:10.18637/jss.v111.i07>.

Concise and interpretable summaries for machine learning models 
    and learners of the 'mlr3' ecosystem.
    The package takes inspiration from the summary function for (generalized)
    linear models but extends it to non-parametric machine learning models, 
    based on generalization performance, model complexity, feature importances 
    and effects, and fairness metrics.

An implementation of the Super Learner prediction algorithm from
  van der Laan, Polley, and Hubbard (2007) <doi:10.2202/1544-6115.1309 
  using the 'mlr3' framework.

Deep Learning library that extends the mlr3 framework by building
  upon the 'torch' package. It allows to conveniently build, train,
  and evaluate deep learning models without having to worry about low level
  details. Custom architectures can be created using the graph language
  defined in 'mlr3pipelines'.

Hyperparameter optimization package of the 'mlr3' ecosystem.
    It features highly configurable search spaces via the 'paradox'
    package and finds optimal hyperparameter configurations for any 'mlr3'
    learner.  'mlr3tuning' works with several optimization algorithms e.g.
    Random Search, Iterated Racing, Bayesian Optimization (in 'mlr3mbo')
    and Hyperband (in 'mlr3hyperband'). Moreover, it can automatically
    optimize learners and estimate the performance of optimized models
    with nested resampling.