caret, stats, utils, ggplot2, graphics, reshape, FNN, plyr
doParallel, GSIF, randomForest, lubridate, raster, sp, knitr,
mapview, rmarkdown, sf, scales, parallel, latticeExtra,
virtualspecies, gridExtra, viridis, rgeos

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. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models by analysing the similarity between new data and training data.
caret, stats, utils, ggplot2, graphics, reshape, FNN, plyr,
zoo, methods, grDevices, data.table, lattice
doParallel, GSIF, randomForest, lubridate, raster, sp, knitr,
mapview, rmarkdown, sf, scales, parallel, latticeExtra,
virtualspecies, gridExtra, viridis, rgeos, stars, scam

doParallel, randomForest, lubridate, raster, sp, knitr,
mapview, rmarkdown, sf, scales, parallel, latticeExtra,
virtualspecies, gridExtra, viridis, rgeos, stars, scam

c(person("Hanna", "Meyer", email = "hanna.meyer@uni-muenster.de", role = c("cre", "aut")),
person("Marvin", "Ludwig", role = c("aut")),
person("Chris", "Reudenbach", role = c("ctb")),
person("Thomas", "Nauss", role = c("ctb")),
person("Edzer", "Pebesma", role = c("ctb")))
Hanna Meyer [cre, aut],
Marvin Ludwig [aut],
Chris Reudenbach [ctb],
Thomas Nauss [ctb],
Edzer Pebesma [ctb]
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. CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. To decrease spatial overfitting and to improve model performances, the package implements a forward feature selection that selects suitable predictor variables in view to their contribution to spatial or spatial-temporal model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models. Methods are described in Meyer et al. (2018) <doi:10.1016/j.envsoft.2017.12.001>; Meyer et al. (2019) <doi:10.1016/j.ecolmodel.2019.108815>; Meyer and Pebesma (2021) <doi:10.1111/2041-210X.13650>.
doParallel, randomForest, lubridate, raster, sp, knitr,
mapview, rmarkdown, sf, scales, parallel, latticeExtra,
virtualspecies, gridExtra, viridis, rgeos, stars, scam, terra,
rnaturalearth