nsprcomp: Non-Negative and Sparse PCA

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'. See <https://sigg-iten.ch/learningbits/2013/05/27/nsprcomp-is-on-cran/> and Sigg et al. (2008) <doi:10.1145/1390156.1390277> for more details.

Version: 0.5.1-2
Depends: R (≥ 3.4.0)
Imports: stats
Suggests: MASS, testthat (≥ 0.8), roxygen2
Published: 2018-06-05
Author: Christian Sigg ORCID iD [aut, cre], R Core team [ctb] (prcomp interface, formula implementation and documentation)
Maintainer: Christian Sigg <christian at sigg-iten.ch>
BugReports: https://github.com/chrsigg/nsprcomp/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://sigg-iten.ch/research/
NeedsCompilation: no
Citation: nsprcomp citation info
Materials: README
In views: Psychometrics
CRAN checks: nsprcomp results

Downloads:

Reference manual: nsprcomp.pdf
Package source: nsprcomp_0.5.1-2.tar.gz
Windows binaries: r-devel: nsprcomp_0.5.1-2.zip, r-release: nsprcomp_0.5.1-2.zip, r-oldrel: nsprcomp_0.5.1-2.zip
OS X binaries: r-release: nsprcomp_0.5.1-2.tgz, r-oldrel: nsprcomp_0.5.1-2.tgz
Old sources: nsprcomp archive

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