sperrorest: Perform Spatial Error Estimation and Variable Importance Assessment

Implements spatial error estimation and permutation-based variable importance measures for predictive models using spatial cross-validation and spatial block bootstrap.

Version: 3.0.5
Depends: R (≥ 2.10)
Imports: dplyr, future, future.apply, graphics, ROCR, stats, stringr
Suggests: knitr, MASS, nnet, parallel, ranger, rmarkdown, rpart, sp, testthat
Published: 2022-10-16
DOI: 10.32614/CRAN.package.sperrorest
Author: Alexander Brenning ORCID iD [aut, cre], Patrick Schratz ORCID iD [aut], Tobias Herrmann ORCID iD [ctb]
Maintainer: Alexander Brenning <alexander.brenning at uni-jena.de>
BugReports: https://github.com/giscience-fsu/sperrorest/issues
License: GPL-3
URL: https://giscience-fsu.github.io/sperrorest/, https://github.com/giscience-fsu/sperrorest
NeedsCompilation: no
Citation: sperrorest citation info
Materials: README NEWS
In views: Spatial
CRAN checks: sperrorest results


Reference manual: sperrorest.pdf
Vignettes: Custom Predict and Model Functions
Spatial Modeling Using Statistical Learning Techniques


Package source: sperrorest_3.0.5.tar.gz
Windows binaries: r-devel: sperrorest_3.0.5.zip, r-release: sperrorest_3.0.5.zip, r-oldrel: sperrorest_3.0.5.zip
macOS binaries: r-release (arm64): sperrorest_3.0.5.tgz, r-oldrel (arm64): sperrorest_3.0.5.tgz, r-release (x86_64): sperrorest_3.0.5.tgz, r-oldrel (x86_64): sperrorest_3.0.5.tgz
Old sources: sperrorest archive

Reverse dependencies:

Reverse suggests: mlr3spatiotempcv


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