This package implements two methods for performing a
principal component analysis (PCA) with constraints, 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 variance under the
given constraints. Then the data matrix is deflated using one
of several deflation methods, and the process is repeated with
the deflated data matrix to compute the next PC.  In contrast,
the function 'nscumcomp' jointly computes all PCs such that the
cumulative variance is maximal, subject to an upper bound on
the divergence from orthonormality of the rotation matrix.
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'.
Christian Sigg [aut, cre], R Core team [aut]

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'.
'asdev.R' 'cardinality.R' 'normv.R' 'nscumcomp.R'
'nsprcomp-package.R' 'nsprcomp.R' 'peav.R'

c( person("Christian", "Sigg", email =
"christian@sigg-iten.ch", role = c("aut", "cre"), comment =
c(ORCID = "0000-0003-1067-9224")), person("R Core team", role =
"ctb", comment = "prcomp interface, formula implementation and
documentation"))
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.
Christian Sigg [aut, cre] (<https://orcid.org/0000-0003-1067-9224>),
R Core team [ctb] (prcomp interface, formula implementation and
documentation)