R CMD Check via {tic} CRAN codecov Lifecycle: stable


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”.


CRAN release version


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. https://doi.org/10.5194/nhess-5-853-2005

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. https://doi.org/10.1109/IGARSS.2012.6352393

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. https://doi.org/10.1007/978-3-642-14049-5_36

Russ, G., and A. Brenning. 2010b. Spatial Variable Importance Assessment for Yield Prediction in Precision Agriculture. In Lecture Notes in Computer Science, 184–95. https://doi.org/10.1007/978-3-642-13062-5_18

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. https://doi.org/10.1016/j.ecolmodel.2019.06.002