Nothing
#' 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
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.