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>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: sparsepca results


Reference manual: sparsepca.pdf


Package source: sparsepca_0.1.2.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


Please use the canonical form to link to this page.