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: 2.1.5
Depends: R (≥ 2.10)
Imports: pbapply, pbmcapply, magrittr, future.apply, future, doFuture, foreach, ROCR, parallel, graphics, stats, rpart, purrr, stringr, gdata, glue
Suggests: ipred, nnet, RSAGA, knitr, testthat, pacman, rmarkdown
Published: 2018-03-27
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/pat-s/sperrorest/issues
License: GPL-3
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
Parallel Modes of 'sperrorest'
Spatial Modeling Using Statistical Learning Techniques
Package source: sperrorest_2.1.5.tar.gz
Windows binaries: r-devel: sperrorest_2.1.5.zip, r-release: sperrorest_2.1.5.zip, r-oldrel: sperrorest_2.1.5.zip
OS X binaries: r-release: sperrorest_2.1.5.tgz, r-oldrel: sperrorest_2.1.5.tgz
Old sources: sperrorest archive


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