R/sperrorest-package.R

In sperrorest: Perform Spatial Error Estimation and Variable Importance Assessment

#' 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 Brenning (2012).
#'
#' @references 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.
#' @import future
#' @docType package
#' @name sperrorest-package
#' @aliases sperrorest-package
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