sperrorest: Perform Spatial Error Estimation and Variable Importance in Parallel

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

Version: 3.0.1
Depends: R (≥ 2.10)
Imports: future, future.apply, graphics, ROCR, stats, stringr
Suggests: knitr, MASS, nnet, parallel, ranger, rmarkdown, rpart, testthat
Published: 2020-05-26
Author: Alexander Brenning ORCID iD [aut, cre], Patrick Schratz ORCID iD [aut], Tobias Herrmann ORCID iD [aut]
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

Downloads:

Reference manual: sperrorest.pdf
Vignettes: Custom Predict and Model Functions
Spatial Modeling Using Statistical Learning Techniques
Package source: sperrorest_3.0.1.tar.gz
Windows binaries: r-devel: sperrorest_3.0.1.zip, r-release: sperrorest_3.0.0.zip, r-oldrel: sperrorest_3.0.1.zip
macOS binaries: r-release: sperrorest_3.0.0.tgz, r-oldrel: sperrorest_3.0.1.tgz
Old sources: sperrorest archive

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