vscc: Variable Selection for Clustering and Classification

Performs variable selection/feature reduction under a clustering or classification framework. In particular, it can be used in an automated fashion using mixture model-based methods ('teigen' and 'mclust' are currently supported). Can account for mixtures of non-Gaussian distributions via Manly transform (via 'ManlyMix'). See Andrews and McNicholas (2014) <doi:10.1007/s00357-013-9139-2> and Neal and McNicholas (2023) <doi:10.48550/arXiv.2305.16464>.

Version: 0.7
Depends: ManlyMix
Imports: teigen, mclust, MixGHD
Published: 2023-10-17
DOI: 10.32614/CRAN.package.vscc
Author: Jeffrey L. Andrews [aut], Mackenzie R. Neal [aut], Paul D. McNicholas ORCID iD [aut, cre]
Maintainer: Paul D. McNicholas <mcnicholas at math.mcmaster.ca>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: vscc citation info
Materials: ChangeLog
CRAN checks: vscc results


Reference manual: vscc.pdf


Package source: vscc_0.7.tar.gz
Windows binaries: r-devel: vscc_0.7.zip, r-release: vscc_0.7.zip, r-oldrel: vscc_0.7.zip
macOS binaries: r-release (arm64): vscc_0.7.tgz, r-oldrel (arm64): vscc_0.7.tgz, r-release (x86_64): vscc_0.7.tgz, r-oldrel (x86_64): vscc_0.7.tgz
Old sources: vscc archive


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