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.
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[aut, cre],
R Core team [ctb] (prcomp interface, formula implementation and
documentation)