Package: CAST
Type: Package
Title: 'caret' Applications for Spatial-Temporal Models
Version: 0.6.0
Authors@R: 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")))
Author: Hanna Meyer [cre, aut],
  Marvin Ludwig [aut],
  Chris Reudenbach [ctb],
  Thomas Nauss [ctb],
  Edzer Pebesma [ctb]
Maintainer: Hanna Meyer <hanna.meyer@uni-muenster.de>
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>.
License: GPL (>= 2)
URL: https://github.com/HannaMeyer/CAST,
        https://hannameyer.github.io/CAST/
Encoding: UTF-8
Depends: R (>= 4.1.0)
Imports: caret, stats, utils, ggplot2, graphics, reshape, FNN, plyr,
        zoo, methods, grDevices, data.table, lattice
Suggests: doParallel, randomForest, lubridate, raster, sp, knitr,
        mapview, rmarkdown, sf, scales, parallel, latticeExtra,
        virtualspecies, gridExtra, viridis, rgeos, stars, scam, terra,
        rnaturalearth
RoxygenNote: 7.1.2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2022-03-17 07:47:50 UTC; hanna
Repository: CRAN
Date/Publication: 2022-03-17 13:20:06 UTC
