mlr3spatiotempcv

Spatiotemporal resampling methods for mlr3

https://github.com/mlr-org/mlr3spatiotempcv

Science Score: 59.0%

This score indicates how likely this project is to be science-related based on various indicators:

○
CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
✓
.zenodo.json file
Found .zenodo.json file
✓
DOI references
Found 3 DOI reference(s) in README
✓
Academic publication links
Links to: joss.theoj.org
✓
Committers with academic emails
2 of 12 committers (16.7%) from academic institutions
○
Institutional organization owner
○
JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (12.6%) to scientific vocabulary

Keywords

cross-validation mlr3 r r-package resampling resampling-methods spatial temporal

Keywords from Contributors

reproducibility battery animal data-profilers datacleaner parallel pipeline-testing yolov5s meshing interpretability

Last synced: 4 months ago · JSON representation

Repository

Spatiotemporal resampling methods for mlr3

Basic Info

Host: GitHub
Owner: mlr-org
License: lgpl-3.0
Language: TeX
Default Branch: main
Homepage: https://mlr3spatiotempcv.mlr-org.com
Size: 453 MB

Statistics

Stars: 52
Watchers: 14
Forks: 9
Open Issues: 4
Releases: 20

Topics

cross-validation mlr3 r r-package resampling resampling-methods spatial temporal

Created about 7 years ago · Last pushed 6 months ago

Metadata Files

Readme Changelog License Codemeta

mlr3spatiotempcv

Package website: release | dev

Spatiotemporal resampling methods for mlr3.

This package extends the mlr3 package framework with spatiotemporal resampling and visualization methods.

If you prefer the tidymodels ecosystem, have a look at the {spatialsample} package for spatial sampling methods.

Installation

CRAN version

r install.packages("mlr3spatiotempcv")

Development version

```r remotes::install_github("mlr-org/mlr3spatiotempcv")

R Universe Repo

install.packages('mlr3spatiotempcv', mlrorg = 'https://mlr-org.r-universe.dev') ```

Get Started

See the "Get Started" vignette for a quick introduction.

For more detailed information including an usage example see the "Spatiotemporal Analysis" chapter in the mlr3book.

Article "Spatiotemporal Visualization" shows how 3D subplots grids can be created.

Citation

To cite the package in publications, use the output of citation("mlr3spatiotempcv").

Resources

Other spatiotemporal resampling packages

This list does not claim to be comprehensive.

(Disclaimer: Because CRAN does not like DOI URLs in their automated checks, direct linking to scientific articles is not possible...)

| Name | Language | Resources | | ------------- | -------- | ----------------------------------------------------------------------------------------------------------------------------- | | blockCV | R | CRAN | | CAST | R | Paper, CRAN | | ENMeval | R | CRAN | | spatialsample | R | CRAN | | sperrorest | R | CRAN | | Pyspatialml | Python | GitHub | | spacv | Python | GitHub | | Museo Toolbox | Python | Paper, GitHub | | spatial-kfold | Python | GitHub |

FAQ

Which resampling method should I use?

There is no single-best resampling method. It depends on your dataset characteristics and what your model should is about to predict on. The resampling scheme should reflect the final purpose of the model - this concept is called "target-oriented" resampling. For example, if the model was trained on multiple forest plots and its purpose is to predict something on unknown forest stands, the resampling structure should reflect this.

Are there more resampling methods than the one {mlr3spatiotempcv} offers?

{mlr3spatiotempcv} aims to offer all resampling methods that exist in R. Though this does not mean that it covers all resampling methods. If there are some that you are missing, feel free to open an issue.

How can I use the "blocking" concept of the old {mlr}?

This concept is now supported via the "column roles" concept available in {mlr3} [Task](https://mlr3.mlr-org.com/reference/Task.html) objects. See [this documentation](https://mlr3.mlr-org.com/reference/Resampling.html#grouping-blocking) for more information.

For the methods that offer buffering, how can an appropriate value be chosen?

There is no easy answer to this question. Buffering train and test sets reduces the similarity between both. The degree of this reduction depends on the dataset itself and there is no general approach how to choosen an appropriate buffer size. Some studies used the distance at which the autocorrelation levels off. This buffer distance often removes quite a lot of observations and needs to be calculated first.

Owner

Name: mlr-org
Login: mlr-org
Kind: organization
Location: Munich, Germany

Website: https://mlr-org.com
Repositories: 80
Profile: https://github.com/mlr-org

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GitHub Events

Total

Create event: 6
Issues event: 2
Release event: 2
Watch event: 2
Delete event: 2
Issue comment event: 8
Push event: 202
Pull request event: 7

Last Year

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Issues event: 2
Release event: 2
Watch event: 2
Delete event: 2
Issue comment event: 8
Push event: 202
Pull request event: 7

Committers

Last synced: 9 months ago

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Total Commits: 1,002
Total Committers: 12
Avg Commits per committer: 83.5
Development Distribution Score (DDS): 0.184

Past Year

Commits: 8
Committers: 2
Avg Commits per committer: 4.0
Development Distribution Score (DDS): 0.125

Top Committers

Name	Email	Commits
pat-s	p**z@g**m	818
Alexander Brenning	a**g@u**e	68
be-marc	m**r@p**e	52
jannes-m	m**a@g**e	21
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mb706	m****6	1
jawond	j**k@g**m	1
Lorenz Walthert	l**t@i**m	1
Alexander Brenning	y**w@a**e	1

Committer Domains (Top 20 + Academic)

ads.uni-jena.de: 1 gfk.com: 1 gmx.de: 1 posteo.de: 1 uni-jena.de: 1

Issues and Pull Requests

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Average comments per pull request: 1.56
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Pull requests: 5
Average time to close issues: N/A
Average time to close pull requests: 7 days
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Pull request authors: 2
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jue-d (1)
mafla (1)

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pat-s (48)
be-marc (13)
mllg (4)
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Issue Labels

Status: Accepted (3) Type: Enhancement (3) Priority: Medium (2) Type: Bug (2) Type: Question (1) Priority: High (1) Status: In Progress (1) Priority: Low (1)

Pull Request Labels

CRAN release :station: (5) Type: Bug (1)

Dependencies

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DESCRIPTION cran

R >= 3.5.0 depends
mlr3 >= 0.12.0 depends
R6 * imports
checkmate * imports
data.table * imports
ggplot2 * imports
mlr3misc >= 0.9.2 imports
paradox * imports
utils * imports
bbotk * suggests
blockCV >= 2.1.4 suggests
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ggsci * suggests
ggtext * suggests
here * suggests
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lgr * suggests
mlr3filters * suggests
mlr3pipelines * suggests
mlr3tuning * suggests
patchwork * suggests
plotly * suggests
raster * suggests
rgdal * suggests
rmarkdown * suggests
rpart * suggests
sf * suggests
skmeans * suggests
sperrorest * suggests
terra * suggests
testthat >= 3.0.0 suggests
vdiffr >= 1.0.0 suggests
withr * suggests

mlr3spatiotempcv

Science Score: 59.0%

Keywords

Keywords from Contributors

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Basic Info

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Metadata Files

README.md

mlr3spatiotempcv

Installation

R Universe Repo

Get Started

Citation

Resources

Other spatiotemporal resampling packages

FAQ

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CodeMeta (codemeta.json)

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