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mlr3spatiotempcv: Spatiotemporal Resampling Methods for 'mlr3'

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: 2.3.2
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: 2024-11-29
DOI: 10.32614/CRAN.package.mlr3spatiotempcv
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 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:

Reference manual: mlr3spatiotempcv.pdf
Vignettes: Getting Started (source, R code)
Spatiotemporal Visualization (source, R code)

Downloads:

Package source: mlr3spatiotempcv_2.3.2.tar.gz
Windows binaries: r-devel: mlr3spatiotempcv_2.3.2.zip, r-release: mlr3spatiotempcv_2.3.2.zip, r-oldrel: mlr3spatiotempcv_2.3.2.zip
macOS binaries: r-release (arm64): mlr3spatiotempcv_2.3.2.tgz, r-oldrel (arm64): mlr3spatiotempcv_2.3.2.tgz, r-release (x86_64): mlr3spatiotempcv_2.3.2.tgz, r-oldrel (x86_64): mlr3spatiotempcv_2.3.2.tgz
Old sources: mlr3spatiotempcv archive

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.