CAST v0.4.1


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'caret' Applications for Spatial-Temporal Models

Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. This package includes functions to improve spatial-temporal modelling tasks using 'caret'. It prepares data for Leave-Location-Out and Leave-Time-Out cross-validation which are target-oriented validation strategies for spatial-temporal models. To decrease overfitting and improve model performances, the package implements a forward feature selection that selects suitable predictor variables in view to their contribution to the target-oriented performance.



Caret Applications for Spatio-Temporal models

This is the developer version of CAST. The CRAN Version can be found on

For a documentation of the methods see also:

Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., Nauss, T. (2018): Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software, 101, 1-9.

Meyer, H., Reudenbach, C., Wöllauer, S., Nauss, T. (2019): Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction. Ecological Modelling. 411.

and the talk from the OpenGeoHub summer school 2019:

Meyer, H., Pebesma, E. (2020): Predicting into unknown space? Estimating the area of applicability of spatial prediction models.

Functions in CAST

Name Description
CAST 'caret' Applications for Spatial-Temporal Models
CreateSpacetimeFolds Create Space-time Folds
ffs Forward feature selection
plot_ffs Plot results of a Forward feature selection or best subset selection
aoa Area of Applicability
bss Best subset feature selection
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Type Package
License GPL (>= 3) | file LICENSE
Encoding UTF-8
LazyData true
RoxygenNote 6.1.1
VignetteBuilder knitr
NeedsCompilation no
Packaged 2020-05-19 13:24:46 UTC; hanna
Repository CRAN
Date/Publication 2020-05-19 14:50:08 UTC

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