R CMD Check via {tic} CRAN lifecycle codecov

{sperrorest} is not actively developed. Alternatives for spatial cross-validation in R:


Spatial Error Estimation and Variable Importance

This package implements spatial error estimation and permutation-based spatial variable importance using different spatial cross-validation and spatial block bootstrap methods. 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 Science 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 Alexander Brenning. 2010a. “Data Mining in Precision Agriculture: Management of Spatial Information.” In Computational Intelligence for Knowledge-Based Systems Design: 13th International Conference on Information Processingand Management of Uncertainty, IPMU 2010, Dortmund, Germany, June 28 - July 2, 2010. Proceedings, edited by Eyke Hüllermeier, Rudolf Kruse, and Frank Hoffmann, 350–59. Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-14049-5_36.

Russ, Georg, and Alexander Brenning. 2010b. “Spatial Variable Importance Assessment for Yield Prediction in Precision Agriculture.” In Lecture Notes in Computer Science, 184–95. Springer Science + Business Media. https://doi.org/10.1007/978-3-642-13062-5_18.