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: 0.2-2
Depends: kernlab, Matrix
Published: 2018-05-26
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

Downloads:

Reference manual: CVST.pdf
Package source: CVST_0.2-2.tar.gz
Windows binaries: r-devel: CVST_0.2-2.zip, r-release: CVST_0.2-2.zip, r-oldrel: CVST_0.2-2.zip
OS X binaries: r-release: CVST_0.2-2.tgz, r-oldrel: CVST_0.2-2.tgz
Old sources: CVST archive

Reverse dependencies:

Reverse depends: DRR

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