{ "_id": "6a102670acfb0bcc41c8f6fa", "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\")),\nperson(\"Carles\", \"Milà\", role = c(\"aut\")),\nperson(\"Marvin\", \"Ludwig\", role = c(\"aut\")),\nperson(\"Jan\", \"Linnenbrink\", role = c(\"aut\")),\nperson(\"Fabian\", \"Schumacher\", role = c(\"aut\")),\nperson(\"Philipp\", \"Otto\", role = c(\"ctb\")),\nperson(\"Chris\", \"Reudenbach\", role = c(\"ctb\")),\nperson(\"Thomas\", \"Nauss\", role = c(\"ctb\")),\nperson(\"Edzer\", \"Pebesma\", role = c(\"ctb\")),\nperson(\"Jakub\", \"Nowosad\", role = c(\"ctb\")),\nperson(\"Darius\", \"Görgen\", role = c(\"ctb\")))", "Description": "Supporting functionality to run 'caret' with spatial or\nspatial-temporal data. 'caret' is a frequently used package for\nmodel training and prediction using machine learning. CAST\nincludes functions to improve spatial or spatial-temporal\nmodelling tasks using 'caret'. It includes the newly suggested\n'Nearest neighbor distance matching' cross-validation to\nestimate the performance of spatial prediction models and\nallows for spatial variable selection to selects suitable\npredictor variables in view to their contribution to the\nspatial model performance. CAST further includes functionality\nto estimate the (spatial) area of applicability of prediction\nmodels. Methods are described in Meyer et al. (2018)\n; Meyer et al. (2019)\n; Meyer and Pebesma (2021)\n; Milà et al. (2022)\n; Meyer and Pebesma (2022)\n; Linnenbrink et al. (2024)\n; Schumacher et al. (2025)\n. The package is described in\ndetail in Meyer et al. 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