Extends the mlr3 machine learning framework with spatio-temporal resampling methods to account for the presence of spatiotemporal autocorrelation (STAC) in predictor variables. STAC may cause highly biased performance estimates in cross-validation if ignored. A JSS article is available at <doi:10.18637/jss.v111.i07>.
Version: 2.3.3 Depends: mlr3 (≥ 0.12.0), R (≥ 3.5.0) Imports: checkmate, data.table, ggplot2 (≥ 3.4.0), mlr3misc (≥ 0.11.0), paradox, R6, utils Suggests: bbotk, blockCV (≥ 3.1.2), caret, CAST (≥ 0.8.0), ggsci, ggtext, here, knitr, lgr, mlr3filters (≥ 0.7.0.9000), mlr3pipelines, mlr3spatial, mlr3tuning, patchwork, plotly, rmarkdown, rpart, sf, sperrorest, terra, testthat (≥ 3.0.0), twosamples, vdiffr (≥ 1.0.0), withr Published: 2025-07-10 DOI: 10.32614/CRAN.package.mlr3spatiotempcv Author: Patrick Schratz [aut, cre], Marc Becker [aut], Jannes Muenchow [ctb], Michel Lang [ctb] Maintainer: Patrick Schratz <patrick.schratz at gmail.com> BugReports: https://github.com/mlr-org/mlr3spatiotempcv/issues License: LGPL-3 URL: https://mlr3spatiotempcv.mlr-org.com/, https://github.com/mlr-org/mlr3spatiotempcv, https://mlr3book.mlr-org.com NeedsCompilation: no Citation: mlr3spatiotempcv citation info Materials: README NEWS In views: Spatial, SpatioTemporal CRAN checks: mlr3spatiotempcv results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=mlr3spatiotempcv to link to this page.
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