  One key task in environmental science is to map environmental variables
continuously in space or even in space and time. Machine learning algorithms
are frequently used to learn from local field observations to make spatial
predictions by estimating the value of the variable of interest in places where
it has not been measured. However, the application of machine learning
strategies for spatial mapping involves additional challenges compared to
"non-spatial" prediction tasks that often originate from spatial
autocorrelation and from training data that are not independent and identically
distributed.
  In the past few years, we developed a number of methods to support the
application of machine learning for spatial data which involves the development
of suitable cross-validation strategies for performance assessment and model
selection, spatial feature selection, and methods to assess the area of
applicability of the trained models. The intention of the CAST package is to
support the application of machine learning strategies for predictive mapping
by implementing such methods and making them available for easy integration
into modelling workflows.
  Here we introduce the CAST package and its core functionalities. At the case
study of mapping plant species richness, we will go through the different steps
of the modelling workflow and show how CAST can be used to support more
reliable spatial predictions.
