CVST: Fast Cross-Validation via Sequential Testing

The fast cross-validation via sequential testing (CVST) procedure is an improved cross-validation procedure which uses non-parametric testing coupled with sequential analysis to determine the best parameter set on linearly increasing subsets of the data. By eliminating under-performing candidates quickly and keeping promising candidates as long as possible, the method speeds up the computation while preserving the capability of a full cross-validation. Additionally to the CVST the package contains an implementation of the ordinary k-fold cross-validation with a flexible and powerful set of helper objects and methods to handle the overall model selection process. The implementations of the Cochran's Q test with permutations and the sequential testing framework of Wald are generic and can therefore also be used in other contexts.

Version:	0.2-3
Depends:	kernlab, Matrix
Published:	2022-02-21
DOI:	10.32614/CRAN.package.CVST
Author:	Tammo Krueger, Mikio Braun
Maintainer:	Tammo Krueger <tammokrueger at googlemail.com>
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2.0)]
NeedsCompilation:	no
Materials:	README
CRAN checks:	CVST results

Documentation:

Reference manual:

CVST.pdf

Downloads:

Package source:	CVST_0.2-3.tar.gz
Windows binaries:	r-devel: CVST_0.2-3.zip, r-release: CVST_0.2-3.zip, r-oldrel: CVST_0.2-3.zip
macOS binaries:	r-release (arm64): CVST_0.2-3.tgz, r-oldrel (arm64): CVST_0.2-3.tgz, r-release (x86_64): CVST_0.2-3.tgz, r-oldrel (x86_64): CVST_0.2-3.tgz
Old sources:	CVST archive

Reverse dependencies:

Reverse depends:	DRR
Reverse imports:	GeneralisedCovarianceMeasure

Linking:

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