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
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

Documentation:

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

Downloads:

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

Linking:

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