person("Marvin", "Ludwig", role = c("ctb")),

Suggests: doParallel, GSIF, randomForest, lubridate, raster, sp, knitr,

Imports: caret, stats, utils, ggplot2, graphics

Authors@R: c(person("Hanna", "Meyer", email = "hanna.meyer@uni-muenster.de", role = c("cre", "aut")),
mapview, rmarkdown, sf, scales, parallel, latticeExtra

person("Thomas", "Nauss", role = c("ctb")),
person("Edzer", "Pebesma", role = c("ctb")))
mapview, rmarkdown, sf, scales, parallel, latticeExtra,
virtualspecies, gridExtra, viridis, rgeos, reshape, FNN

Imports: caret, stats, utils, ggplot2, graphics, reshape, FNN, plyr

Description: 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.
Imports: caret, stats, utils, ggplot2, graphics, reshape, FNN, plyr,
zoo, methods, grDevices, data.table, lattice
virtualspecies, gridExtra, viridis, rgeos, stars, scam

Suggests: doParallel, randomForest, lubridate, raster, sp, knitr,

person("Marvin", "Ludwig", role = c("aut")),
Description: 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>.
virtualspecies, gridExtra, viridis, rgeos, stars, scam, terra,