mlr3spatiotempcv: Spatiotemporal Resampling Methods for 'mlr3'

Extends the mlr3 ML 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.

Version: 1.0.1
Depends: R (≥ 3.5.0)
Imports: checkmate, data.table, ggplot2, mlr3 (≥ 0.12.0), mlr3misc (≥ 0.9.2), paradox, R6, utils
Suggests: bbotk, blockCV (≥ 2.1.4), caret, CAST, ggsci, ggtext, knitr, lgr, mlr3filters, mlr3pipelines, mlr3tuning, patchwork, plotly, raster, rgdal, rmarkdown, rpart, sf, skmeans, sperrorest, testthat (≥ 3.0.0), vdiffr (≥ 1.0.0), withr
Published: 2022-03-03
Author: Patrick Schratz ORCID iD [aut, cre], Marc Becker ORCID iD [aut], Jannes Muenchow ORCID iD [ctb], Michel Lang ORCID iD [ctb]
Maintainer: Patrick Schratz <patrick.schratz at>
License: LGPL-3
NeedsCompilation: no
Materials: README NEWS
In views: Spatial, SpatioTemporal
CRAN checks: mlr3spatiotempcv results


Reference manual: mlr3spatiotempcv.pdf
Vignettes: Getting Started
Spatiotemporal Visualization


Package source: mlr3spatiotempcv_1.0.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): mlr3spatiotempcv_1.0.1.tgz, r-release (x86_64): mlr3spatiotempcv_1.0.1.tgz, r-oldrel: mlr3spatiotempcv_1.0.1.tgz
Old sources: mlr3spatiotempcv archive


Please use the canonical form to link to this page.