Package: CAST 1.0.2

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. 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. (2023) <doi:10.5194/egusphere-2023-1308>. The package is described in detail in Meyer et al. (2024) <doi:10.48550/arXiv.2404.06978>.

Authors: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]

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Bug tracker:https://github.com/hannameyer/cast/issues

On CRAN:

autocorrelationcaretfeature-selectionmachine-learningoverfittingpredictive-modelingspatialspatio-temporalvariable-selection

17 exports 107 stars 7.02 score 87 dependencies 1 dependents 219 mentions 290 scripts 906 downloads

Last updated 11 days agofrom:c4862be55e. Checks:ERROR: 1 WARNING: 6. Indexed: yes.

Exports:aoabssclustered_sampleCreateSpacetimeFoldserrorProfilesffsgeodistglobal_validationknndmnndmnormalize_DIshow.aoashow.ffsshow.knndmshow.nndmshow.trainDItrainDI

Dependencies:caretclassclassIntcliclockcodetoolscolorspacecpp11data.tableDBIdiagramdigestdplyre1071fansifarverFNNforcatsforeachfuturefuture.applygenericsggplot2globalsgluegowergtablehardhatipredisobanditeratorsKernSmoothlabelinglatticelavalifecyclelistenvlubridatemagrittrMASSMatrixmgcvModelMetricsmunsellnlmennetnumDerivparallellypillarpkgconfigplyrpROCprodlimprogressrproxypurrrR6RColorBrewerRcpprecipesreshape2rlangrparts2scalessfshapespSQUAREMstringistringrsurvivalterratibbletidyrtidyselecttimechangetimeDatetwosamplestzdbunitsutf8vctrsviridisLitewithrwkzoo