Spatial Error Estimation and Variable Importance

This package implements spatial error estimation and permutation-based spatial variable importance using different spatial cross-validation and bootstrap methods. Supported resampling methods include various types of block resampling, leave-one-out sampling with buffer, and resampling at the level of predefined groups; users can implement their own resampling functions. To cite {sperrorest} in publications, reference the paper by @Brenning2012. To see the package in action, please check the vignette “Spatial Modeling Use Case”.


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Development version



Brenning, A. 2005. Spatial Prediction Models for Landslide Hazards: Review, Comparison and Evaluation. Natural Hazards and Earth System Sciences 5 (6). Copernicus GmbH:853–62.

Brenning, A. 2012. Spatial Cross-Validation and Bootstrap for the Assessment of Prediction Rules in Remote Sensing: The R Package Sperrorest. In 2012 IEEE International Geoscience and Remote Sensing Symposium, 5372–5.

Russ, Georg, and A. Brenning. 2010a. Data Mining in Precision Agriculture: Management of Spatial Information. In Computational Intelligence for Knowledge-Based Systems Design: 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings, edited by Eyke Hüllermeier, Rudolf Kruse, and Frank Hoffmann, 350–59. Springer.

Russ, G., and A. Brenning. 2010b. Spatial Variable Importance Assessment for Yield Prediction in Precision Agriculture. In Lecture Notes in Computer Science, 184–95.

Schratz, P., Muenchow, J., Iturritxa, E., Richter, J., Brenning, A. (2019). Hyperparameter tuning and performance assessment of statistical and machine-learning algorithms using spatial data. Ecological Modelling, 406: 109-120.