splitTools: Tools for Data Splitting

Fast, lightweight toolkit for data splitting. Data sets can be partitioned into disjoint groups (e.g. into training, validation, and test) or into (repeated) k-folds for subsequent cross-validation. Besides basic splits, the package supports stratified, grouped as well as blocked splitting. Furthermore, cross-validation folds for time series data can be created. See e.g. Hastie et al. (2001) <doi:10.1007/978-0-387-84858-7> for the basic background on data partitioning and cross-validation.

Version: 1.0.1
Imports: stats
Suggests: knitr, rmarkdown, testthat (≥ 3.0.0)
Published: 2023-06-06
DOI: 10.32614/CRAN.package.splitTools
Author: Michael Mayer [aut, cre]
Maintainer: Michael Mayer <mayermichael79 at gmail.com>
BugReports: https://github.com/mayer79/splitTools/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/mayer79/splitTools
NeedsCompilation: no
Materials: README NEWS
In views: MachineLearning
CRAN checks: splitTools results


Reference manual: splitTools.pdf
Vignettes: Using 'splitTools'


Package source: splitTools_1.0.1.tar.gz
Windows binaries: r-devel: splitTools_1.0.1.zip, r-release: splitTools_1.0.1.zip, r-oldrel: splitTools_1.0.1.zip
macOS binaries: r-release (arm64): splitTools_1.0.1.tgz, r-oldrel (arm64): splitTools_1.0.1.tgz, r-release (x86_64): splitTools_1.0.1.tgz, r-oldrel (x86_64): splitTools_1.0.1.tgz
Old sources: splitTools archive

Reverse dependencies:

Reverse imports: mlexperiments, RAFS
Reverse suggests: mllrnrs, mlsurvlrnrs


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