CAST
Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models
Science Score: 59.0%
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✓DOI references
Found 22 DOI reference(s) in README -
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4 of 14 committers (28.6%) from academic institutions -
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Low similarity (13.2%) to scientific vocabulary
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Repository
Developer Version of the R package CAST: Caret Applications for Spatio-Temporal models
Basic Info
- Host: GitHub
- Owner: HannaMeyer
- Language: R
- Default Branch: master
- Homepage: https://hannameyer.github.io/CAST/
- Size: 80.8 MB
Statistics
- Stars: 116
- Watchers: 18
- Forks: 29
- Open Issues: 8
- Releases: 1
Topics
Metadata Files
README.md
CAST: Caret Applications for Spatio-Temporal models 
Supporting functionality to run 'caret' with spatial or spatial-temporal data. 'caret' is a frequently used package for model training and prediction using machine learning. CAST includes functions to improve spatial or spatial-temporal modelling tasks using 'caret'. To decrease spatial overfitting and to improve model performances, the package implements a forward feature selection that selects suitable predictor variables in view to their contribution to spatial or spatio-temporal model performance. CAST further includes functionality to estimate the (spatial) area of applicability of prediction models.
Note: The developer version of CAST can be found on https://github.com/HannaMeyer/CAST. The CRAN Version can be found on https://CRAN.R-project.org/package=CAST
The figure shows a very simple workflow for a spatial prediction mapping workflow, indicating which function in CAST can be used in the different steps to support the spatial prediction.
Package Website
https://hannameyer.github.io/CAST/
Tutorials
The CAST package for training and assessment of spatial prediction models in R
The talk from the OpenGeoHub summer school 2019 on spatial validation and variable selection: https://www.youtube.com/watch?v=mkHlmYEzsVQ.
Tutorial (https://youtu.be/EyP04zLe9qo) and Lecture (https://youtu.be/OoNH6Nl-X2s) recording from OpenGeoHub summer school 2020 on the area of applicability. As well as talk at the OpenGeoHub summer school 2021: https://av.tib.eu/media/54879
Talk and tutorial from the OpenGeoHub 2022 summer school on Machine learning-based maps of the environment - challenges of extrapolation and overfitting, including discussions on the area of applicability and the nearest neighbor distance matching cross-validation (https://doi.org/10.5446/59412).
Scientific documentation of the methods
- Meyer, H., Ludwig, L., Milà, C., Linnenbrink, J., Schumacher, F. (2024): The CAST package for training and assessment of spatial prediction models in R. arXiv, https://doi.org/10.48550/arXiv.2404.06978.
Spatial cross-validation
Milà, C., Mateu, J., Pebesma, E., Meyer, H. (2022): Nearest Neighbour Distance Matching Leave-One-Out Cross-Validation for map validation. Methods in Ecology and Evolution 00, 1– 13. https://doi.org/10.1111/2041-210X.13851
Linnenbrink, J., Milà, C., Ludwig, M., and Meyer, H.: kNNDM (2023): k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation. EGUsphere [preprint]. https://doi.org/10.5194/egusphere-2023-1308
Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., Nauss, T. (2018): Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software, 101, 1-9. https://doi.org/10.1016/j.envsoft.2017.12.001
Spatial variable selection
Meyer, H., Reudenbach, C., Hengl, T., Katurji, M., Nauss, T. (2018): Improving performance of spatio-temporal machine learning models using forward feature selection and target-oriented validation. Environmental Modelling & Software, 101, 1-9. https://doi.org/10.1016/j.envsoft.2017.12.001
Meyer, H., Reudenbach, C., Wöllauer, S., Nauss, T. (2019): Importance of spatial predictor variable selection in machine learning applications - Moving from data reproduction to spatial prediction. Ecological Modelling. 411. https://doi.org/10.1016/j.ecolmodel.2019.108815
Area of applicability
Meyer, H., Pebesma, E. (2021). Predicting into unknown space? Estimating the area of applicability of spatial prediction models. Methods in Ecology and Evolution, 12, 1620– 1633. https://doi.org/10.1111/2041-210X.13650
Schumacher, F., Knoth, C., Ludwig, M., Meyer, H. (2024): Estimation of local training data point densities to support the assessment of spatial prediction uncertainty. EGUsphere. https://doi.org/10.5194/egusphere-2024-2730.
Applications and use cases
Meyer, H., Pebesma, E. (2022): Machine learning-based global maps of ecological variables and the challenge of assessing them. Nature Communications, 13. https://www.nature.com/articles/s41467-022-29838-9
Ludwig, M., Moreno-Martinez, A., Hoelzel, N., Pebesma, E., Meyer, H. (2023): Assessing and improving the transferability of current global spatial prediction models. Global Ecology and Biogeography. https://doi.org/10.1111/geb.13635.
Milà, C., Ludwig, M., Pebesma, E., Tonne, C., and Meyer, H.: Random forests with spatial proxies for environmental modelling: opportunities and pitfalls, EGUsphere [preprint]. https://doi.org/10.5194/egusphere-2024-138, 2024.
Owner
- Name: Hanna Meyer
- Login: HannaMeyer
- Kind: user
- Website: www.uni-muenster.de/RemoteSensing/
- Repositories: 2
- Profile: https://github.com/HannaMeyer
Researcher at the Institute of Landscape Ecology at Münster University, working on remote sensing of the environment, machine learning, spatial data analysis, R
GitHub Events
Total
- Create event: 4
- Release event: 1
- Issues event: 4
- Watch event: 13
- Delete event: 4
- Issue comment event: 1
- Push event: 28
- Pull request event: 11
- Fork event: 4
Last Year
- Create event: 4
- Release event: 1
- Issues event: 4
- Watch event: 13
- Delete event: 4
- Issue comment event: 1
- Push event: 28
- Pull request event: 11
- Fork event: 4
Committers
Last synced: over 2 years ago
Top Committers
| Name | Commits | |
|---|---|---|
| HannaMeyer | h****r@u****e | 279 |
| HannaMeyer | h****r@g****e | 29 |
| Hanna | h****1@g****m | 28 |
| Ludwigm6 | m****3@g****m | 19 |
| carlesmila | c****a@g****m | 16 |
| JanLinnenbrink | j****k@w****e | 5 |
| gisma | r****h@u****e | 4 |
| jonathom | j****n@w****e | 2 |
| Edzer Pebesma | e****a@u****e | 2 |
| dependabot[bot] | 4****] | 2 |
| Darius Görgen | d****2@w****e | 1 |
| abdelkrim-bsr | b****d@g****m | 1 |
| pat-s | p****z@g****m | 1 |
| Kendon Bell | k****B | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 6 months ago
All Time
- Total issues: 27
- Total pull requests: 49
- Average time to close issues: 18 days
- Average time to close pull requests: 4 days
- Total issue authors: 23
- Total pull request authors: 12
- Average comments per issue: 1.11
- Average comments per pull request: 0.1
- Merged pull requests: 36
- Bot issues: 0
- Bot pull requests: 6
Past Year
- Issues: 1
- Pull requests: 3
- Average time to close issues: N/A
- Average time to close pull requests: 43 minutes
- Issue authors: 1
- Pull request authors: 2
- Average comments per issue: 0.0
- Average comments per pull request: 0.0
- Merged pull requests: 1
- Bot issues: 0
- Bot pull requests: 2
Top Authors
Issue Authors
- tobwen (3)
- fmsabatini (3)
- ManuelSpinola (2)
- tnauss (1)
- barbmuhling (1)
- dylanbeaudette (1)
- joshualerickson (1)
- topepo (1)
- WalidGharianiEAGLE (1)
- pecto2020 (1)
- pgoodling-usgs (1)
- joheisig (1)
- Ludwigm6 (1)
- HannaMeyer (1)
- dschlaep (1)
Pull Request Authors
- carlesmila (24)
- Ludwigm6 (18)
- dependabot[bot] (10)
- JanLinnenbrink (9)
- fab-scm (6)
- Nowosad (4)
- jonathom (4)
- pat-s (2)
- gisma (2)
- edzer (2)
- goergen95 (1)
- kendonB (1)
- abdelkrim-bsr (1)
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Packages
- Total packages: 1
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Total downloads:
- cran 1,392 last-month
- Total docker downloads: 13
- Total dependent packages: 3
- Total dependent repositories: 6
- Total versions: 20
- Total maintainers: 1
cran.r-project.org: CAST
'caret' Applications for Spatial-Temporal Models
- Homepage: https://github.com/HannaMeyer/CAST
- Documentation: http://cran.r-project.org/web/packages/CAST/CAST.pdf
- License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
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Latest release: 1.0.3
published about 1 year ago
Rankings
Maintainers (1)
Dependencies
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- R >= 4.1.0 depends
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