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.

https://hannameyer.github.io/CAST/

• Book chapter: The CAST package for training and assessment of spatial prediction models in R. Preprint

• Introduction to CAST

• Visualization of nearest neighbor distance distributions

• Nearest neighbor distance matching Cross-validation in CAST

• Area of applicability of spatial prediction models

• Improve computation time of CAST methods

• 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).

• Meyer, H., Ludwig, M., Milà, C., Linnenbrink, J., Schumacher, F. (2026). The CAST Package for Training and Assessment of Spatial Prediction Models. In: Rocchini, D. (eds) R Coding for Ecology. Use R!. Springer, Cham. https://doi.org/10.1007/978-3-031-99665-8_11. Preprint: https://doi.org/10.48550/arXiv.2404.06978.

• 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 13, 1304– 1316. https://doi.org/10.1111/2041-210X.13851

• Linnenbrink, J., Milà, C., Ludwig, M., and Meyer, H.: kNNDM (2024): k-fold Nearest Neighbour Distance Matching Cross-Validation for map accuracy estimation. Geosci. Model Dev., 17, 5897–5912. https://doi.org/10.5194/gmd-17-5897-2024.

• 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., 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

• 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. (2025): Estimation of local training data point densities to support the assessment of spatial prediction uncertainty. Geosci. Model Dev., 18, 10185–10202. https://doi.org/10.5194/gmd-18-10185-2025.

• 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, 32, 356–368. https://doi.org/10.1111/geb.13635.

• Milà, C., Ludwig, M., Pebesma, E., Tonne, C., and Meyer, H. (2024): Random forests with spatial proxies for environmental modelling: opportunities and pitfalls. Geosci. Model Dev., 17, 6007–6033. https://doi.org/10.5194/gmd-17-6007-2024.