nsprcomp: Non-Negative and Sparse PCA

This package implements two methods for performing a constrained principal component analysis (PCA), where non-negativity and/or sparsity constraints are enforced on the principal axes (PAs). The function 'nsprcomp' computes one principal component (PC) after the other. Each PA is optimized such that the corresponding PC has maximum additional variance not explained by the previous components. In contrast, the function 'nscumcomp' jointly computes all PCs such that the cumulative variance is maximal. Both functions have the same interface as the 'prcomp' function from the 'stats' package (plus some extra parameters), and both return the result of the analysis as an object of class 'nsprcomp', which inherits from 'prcomp'.

Version: 0.5
Imports: stats
Suggests: MASS, testthat (≥ 0.8), roxygen2
Published: 2014-07-17
Author: Christian Sigg [aut, cre], R Core team [aut]
Maintainer: Christian Sigg <christian at sigg-iten.ch>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: http://sigg-iten.ch/research/
NeedsCompilation: no
Citation: nsprcomp citation info
Materials: README
In views: Psychometrics
CRAN checks: nsprcomp results


Reference manual: nsprcomp.pdf
Package source: nsprcomp_0.5.tar.gz
Windows binaries: r-devel: nsprcomp_0.5.zip, r-release: nsprcomp_0.5.zip, r-oldrel: nsprcomp_0.5.zip
OS X El Capitan binaries: r-release: nsprcomp_0.5.tgz
OS X Mavericks binaries: r-oldrel: nsprcomp_0.5.tgz
Old sources: nsprcomp archive


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