sparsepca: Sparse Principal Component Analysis (SPCA)

Sparse principal component analysis (SPCA) attempts to find sparse weight vectors (loadings), i.e., a weight vector with only a few 'active' (nonzero) values. This approach provides better interpretability for the principal components in high-dimensional data settings. This is, because the principal components are formed as a linear combination of only a few of the original variables. This package provides efficient routines to compute SPCA. Specifically, a variable projection solver is used to compute the sparse solution. In addition, a fast randomized accelerated SPCA routine and a robust SPCA routine is provided. Robust SPCA allows to capture grossly corrupted entries in the data. The methods are discussed in detail by N. Benjamin Erichson et al. (2018) <doi:10.48550/arXiv.1804.00341>.

Version:	0.1.2
Imports:	rsvd
Published:	2018-04-11
DOI:	10.32614/CRAN.package.sparsepca
Author:	N. Benjamin Erichson, Peng Zheng, and Sasha Aravkin
Maintainer:	N. Benjamin Erichson <erichson at uw.edu>
BugReports:	https://github.com/erichson/spca/issues
License:	GPL (≥ 3)
URL:	https://github.com/erichson/spca
NeedsCompilation:	no
CRAN checks:	sparsepca results

Documentation:

Reference manual:

sparsepca.pdf

Downloads:

Package source:	sparsepca_0.1.2.tar.gz
Windows binaries:	r-devel: sparsepca_0.1.2.zip, r-release: sparsepca_0.1.2.zip, r-oldrel: sparsepca_0.1.2.zip
macOS binaries:	r-release (arm64): sparsepca_0.1.2.tgz, r-oldrel (arm64): sparsepca_0.1.2.tgz, r-release (x86_64): sparsepca_0.1.2.tgz, r-oldrel (x86_64): sparsepca_0.1.2.tgz

Reverse dependencies:

Reverse imports:	categoryEncodings, scPCA, SparseBiplots
Reverse suggests:	parameters

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