CAST: '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.

Version: 0.4.2
Depends: R (≥ 3.1.0)
Imports: caret, stats, utils, ggplot2, graphics, reshape, FNN, plyr
Suggests: doParallel, GSIF, randomForest, lubridate, raster, sp, knitr, mapview, rmarkdown, sf, scales, parallel, latticeExtra, virtualspecies, gridExtra, viridis, rgeos
Published: 2020-07-17
Author: Hanna Meyer [cre, aut], Chris Reudenbach [ctb], Marvin Ludwig [ctb], Thomas Nauss [ctb], Edzer Pebesma [ctb]
Maintainer: Hanna Meyer <hanna.meyer at>
License: GPL (≥ 3) | file LICENSE
NeedsCompilation: no
Materials: README NEWS
CRAN checks: CAST results


Reference manual: CAST.pdf
Vignettes: Area of applicability of spatial prediction models
Introduction to CAST
Package source: CAST_0.4.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release: CAST_0.4.2.tgz, r-oldrel: CAST_0.4.2.tgz
Old sources: CAST archive

Reverse dependencies:

Reverse suggests: mlr3spatiotempcv


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