Package: CAST
Type: Package
Title: 'caret' Applications for Spatial-Temporal Models
Version: 1.1.0
Authors@R: c(person("Hanna", "Meyer", email = "hanna.meyer@uni-muenster.de", role = c("cre", "aut")),
             person("Carles", "Milà", role = c("aut")),
             person("Marvin", "Ludwig", role = c("aut")),
             person("Jan", "Linnenbrink", role = c("aut")),
             person("Fabian", "Schumacher", role = c("aut")),
             person("Philipp", "Otto", role = c("ctb")),
             person("Chris", "Reudenbach", role = c("ctb")),
             person("Thomas", "Nauss", role = c("ctb")),
             person("Edzer", "Pebesma", role = c("ctb")),
             person("Jakub", "Nowosad", role = c("ctb")),
             person("Darius", "Görgen", role = c("ctb")))
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'. It includes the newly suggested
        'Nearest neighbor distance matching' cross-validation to
        estimate the performance of spatial prediction models and
        allows for spatial variable selection to selects suitable
        predictor variables in view to their contribution to the
        spatial 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>; Milà et al. (2022)
        <doi:10.1111/2041-210X.13851>; Meyer and Pebesma (2022)
        <doi:10.1038/s41467-022-29838-9>; Linnenbrink et al. (2024)
        <doi:10.5194/gmd-17-5897-2024>; Schumacher et al. (2025)
        <doi:10.5194/gmd-18-10185-2025>. The package is described in
        detail in Meyer et al. (2026)
        <doi:10.1007/978-3-031-99665-8_11>.
License: GPL (>= 2)
URL: https://github.com/HannaMeyer/CAST,
        https://hannameyer.github.io/CAST/
Encoding: UTF-8
LazyData: false
Depends: R (>= 4.1.0)
BugReports: https://github.com/HannaMeyer/CAST/issues/
Imports: caret, data.table, FNN, ggplot2, graphics, grDevices, methods,
        parallel, sf, stats, twosamples, terra, utils, zoo
Suggests: clustMixType, doParallel, geodata, gower, gridExtra, knitr,
        lubridate, MASS, PCAmixdata, randomForest, rmarkdown,
        rnaturalearth, scales, scam, stars, testthat (>= 3.0.0), tmap
RoxygenNote: 7.3.3
VignetteBuilder: knitr
Config/testthat/edition: 3
Config/pak/sysreqs: libabsl-dev cmake libgdal-dev gdal-bin libgeos-dev
        libicu-dev libssl-dev libproj-dev libsqlite3-dev
        libudunits2-dev
Repository: https://hannameyer.r-universe.dev
Date/Publication: 2026-06-19 12:53:46 UTC
RemoteUrl: https://github.com/hannameyer/cast
RemoteRef: HEAD
RemoteSha: 139849ddbb48287ac21ffd651312eca88f8f1a88
NeedsCompilation: no
Packaged: 2026-06-19 14:14:18 UTC; root
Author: Hanna Meyer [cre, aut],
  Carles Milà [aut],
  Marvin Ludwig [aut],
  Jan Linnenbrink [aut],
  Fabian Schumacher [aut],
  Philipp Otto [ctb],
  Chris Reudenbach [ctb],
  Thomas Nauss [ctb],
  Edzer Pebesma [ctb],
  Jakub Nowosad [ctb],
  Darius Görgen [ctb]
Maintainer: Hanna Meyer <hanna.meyer@uni-muenster.de>