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"Description": "Two methods for performing a constrained principal\ncomponent analysis (PCA), where non-negativity and/or sparsity\nconstraints are enforced on the principal axes (PAs). The\nfunction 'nsprcomp' computes one principal component (PC) after\nthe other. Each PA is optimized such that the corresponding PC\nhas maximum additional variance not explained by the previous\ncomponents. In contrast, the function 'nscumcomp' jointly\ncomputes all PCs such that the cumulative variance is maximal.\nBoth functions have the same interface as the 'prcomp' function\nfrom the 'stats' package (plus some extra parameters), and both\nreturn the result of the analysis as an object of class\n'nsprcomp', which inherits from 'prcomp'. See\n\nand Sigg et al. (2008) for more\ndetails.",
"URL": "https://sigg-iten.ch/research/",
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"License": "GPL (>=2)",
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"Date/Publication": "2018-06-05 06:44:02 UTC",
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