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 Brenning (2012).
Brenning, A. 2012. Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: the R package 'sperrorest'. 2012 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 23-27 July 2012, p. 5372-5375.
Brenning, A. 2005. Spatial prediction models for landslide hazards: review, comparison and evaluation. Natural Hazards and Earth System Sciences, 5(6): 853-862.
Russ, G. & A. Brenning. 2010a. Data mining in precision agriculture: Management of spatial information. In 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010; Dortmund; 28 June - 2 July 2010. Lecture Notes in Computer Science, 6178 LNAI: 350-359.
Russ, G. & A. Brenning. 2010b. Spatial variable importance assessment for yield prediction in Precision Agriculture. In Advances in Intelligent Data Analysis IX, Proceedings, 9th International Symposium, IDA 2010, Tucson, AZ, USA, 19-21 May 2010. Lecture Notes in Computer Science, 6065 LNCS: 184-195.
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