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‎.Rbuildignore‎

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^CRAN-SUBMISSION$
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^README\.Rmd$
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^README\.html$
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.vscode

‎.gitignore‎

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^README\.html$

‎README.Rmd‎

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---
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```{r, include=FALSE}
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library(mlr3)
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library(mlr3spatial)
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library(terra, exclude = "resample")
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library(sf)
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lgr::get_logger("mlr3")$set_threshold("warn")
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lgr::get_logger("mlr3spatial")$set_threshold("warn")
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set.seed(1)
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options(datatable.print.class = FALSE, datatable.print.keys = FALSE)
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options(
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datatable.print.nrows = 10,
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datatable.print.class = FALSE,
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datatable.print.keys = FALSE,
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width = 100)
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```
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# mlr3spatial
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Package website: [release](https://mlr3spatial.mlr-org.com/) | [dev](https://mlr3spatial.mlr-org.com/dev/)
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<!-- badges: start -->
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[![r-cmd-check](https://github.com/mlr-org/mlr3spatial/actions/workflows/r-cmd-check.yml/badge.svg)](https://github.com/mlr-org/mlr3spatial/actions/workflows/r-cmd-check.yml)
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[![Mattermost](https://img.shields.io/badge/chat-mattermost-orange.svg)](https://lmmisld-lmu-stats-slds.srv.mwn.de/mlr_invite/)
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<!-- badges: end -->
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Package website: [release](https://mlr3spatial.mlr-org.com/) | [dev](https://mlr3spatial.mlr-org.com/dev/)
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{mlr3spatial} is an extension package for spatial objects within the [mlr3](https://mlr3.mlr-org.com) ecosystem.
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## Feature Overview
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* Read training data from [sf](https://CRAN.R-project.org/package=sf) objects.
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* Predict on raster objects from the packages [{terra}](https://CRAN.R-project.org/package=terra), [{raster}](https://CRAN.R-project.org/package=raster) and [{stars}](https://CRAN.R-project.org/package=stars).
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* Write model predictions to raster files.
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* Predict large raster objects in parallel.
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* Read raster objects in chunks to avoid memory issues.
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* Built-in toy task [`leipzig`](https://mlr3spatial.mlr-org.com/dev/reference/leipzig.html).
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Check out [{mlr3spatiotempcv}](https://github.com/mlr-org/mlr3spatiotempcv) for spatiotemporal resampling within mlr3.
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*mlr3spatial* is the package for spatial objects within the [mlr3](https://mlr3.mlr-org.com) ecosystem.
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The package directly loads data from [sf](https://CRAN.R-project.org/package=sf) objects to train any mlr3 learner.
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The learner can predict on various raster formats ([{terra}](https://CRAN.R-project.org/package=terra), [{raster}](https://CRAN.R-project.org/package=raster) and [{stars}](https://CRAN.R-project.org/package=stars)) and writes the prediction raster to disk.
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mlr3spatial reads large raster objects in chunks to avoid memory issues and predicts the chunks in parallel.
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Check out [mlr3spatiotempcv](https://github.com/mlr-org/mlr3spatiotempcv) for spatiotemporal resampling within mlr3.
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## Installation
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## Example
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```{r, include = FALSE}
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# mute load messages
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library(mlr3)
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library(mlr3spatial)
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library(terra, exclude = "resample")
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library(sf)
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```
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```{r}
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library(mlr3)
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library(mlr3spatial)

‎README.md‎

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# mlr3spatial
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Package website: [release](https://mlr3spatial.mlr-org.com/) |
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[dev](https://mlr3spatial.mlr-org.com/dev/)
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<!-- badges: start -->
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[![r-cmd-check](https://github.com/mlr-org/mlr3spatial/actions/workflows/r-cmd-check.yml/badge.svg)](https://github.com/mlr-org/mlr3spatial/actions/workflows/r-cmd-check.yml)
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[![CRAN
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status](https://www.r-pkg.org/badges/version/mlr3spatial)](https://CRAN.R-project.org/package=mlr3spatial)
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[![StackOverflow](https://img.shields.io/badge/stackoverflow-mlr3-orange.svg)](https://stackoverflow.com/questions/tagged/mlr3)
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[![Mattermost](https://img.shields.io/badge/chat-mattermost-orange.svg)](https://lmmisld-lmu-stats-slds.srv.mwn.de/mlr_invite/)
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Package website: [release](https://mlr3spatial.mlr-org.com/) \|
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[dev](https://mlr3spatial.mlr-org.com/dev/)
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{mlr3spatial} is an extension package for spatial objects within the
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[mlr3](https://mlr3.mlr-org.com) ecosystem.
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## Feature Overview
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- Read training data from [sf](https://CRAN.R-project.org/package=sf)
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objects.
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- Predict on raster objects from the packages
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[{terra}](https://CRAN.R-project.org/package=terra),
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[{raster}](https://CRAN.R-project.org/package=raster) and
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[{stars}](https://CRAN.R-project.org/package=stars).
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- Write model predictions to raster files.
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- Predict large raster objects in parallel.
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- Read raster objects in chunks to avoid memory issues.
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- Built-in toy task
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[`leipzig`](https://mlr3spatial.mlr-org.com/dev/reference/leipzig.html).
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Check out
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[{mlr3spatiotempcv}](https://github.com/mlr-org/mlr3spatiotempcv) for
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*mlr3spatial* is the package for spatial objects within the
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[mlr3](https://mlr3.mlr-org.com) ecosystem. The package directly loads
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data from [sf](https://CRAN.R-project.org/package=sf) objects to train
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any mlr3 learner. The learner can predict on various raster formats
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([{terra}](https://CRAN.R-project.org/package=terra),
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[{raster}](https://CRAN.R-project.org/package=raster) and
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[{stars}](https://CRAN.R-project.org/package=stars)) and writes the
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prediction raster to disk. mlr3spatial reads large raster objects in
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chunks to avoid memory issues and predicts the chunks in parallel. Check
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out [mlr3spatiotempcv](https://github.com/mlr-org/mlr3spatiotempcv) for
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spatiotemporal resampling within mlr3.
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## Installation
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## FAQ
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<details>
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<summary>
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Will mlr3spatial support spatial learners?
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</summary>
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<br> Eventually. It is not yet clear whether these would live in
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<summary>Will mlr3spatial support spatial learners?</summary> <br>
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Eventually. It is not yet clear whether these would live in
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mlr3extralearners or in {mlr3spatial}. So far there are none yet.
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</details>
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<details>
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<summary>
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Why are there two packages, {mlr3spatial} and {mlr3spatiotempcv}?
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</summary>
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<br> mlr3spatiotempcv is solely devoted to resampling techniques. There
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are quite a few and keeping packages small is one of the development
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philosophies of the mlr3 framework. Also back in the days when
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mlr3spatiotempcv was developed it was not yet clear how we want to
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structure additional spatial components such as prediction support for
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spatial classes and so on.
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<summary>Why are there two packages, {mlr3spatial} and
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{mlr3spatiotempcv}?</summary> <br> mlr3spatiotempcv is solely devoted to
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resampling techniques. There are quite a few and keeping packages small
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is one of the development philosophies of the mlr3 framework. Also back
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in the days when mlr3spatiotempcv was developed it was not yet clear how
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we want to structure additional spatial components such as prediction
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support for spatial classes and so on.
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</details>

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