Package: sparsepca
Type: Package
Title: Sparse Principal Component Analysis (SPCA)
Version: 0.1.2
Author: N. Benjamin Erichson, Peng Zheng, and Sasha Aravkin
Maintainer: N. Benjamin Erichson <erichson@uw.edu>
Description: 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) <arXiv:1804.00341>. 
License: GPL (>= 3)
Encoding: UTF-8
LazyData: true
URL: https://github.com/erichson/spca
BugReports: https://github.com/erichson/spca/issues
Imports: rsvd
RoxygenNote: 6.0.1
NeedsCompilation: no
Packaged: 2018-04-09 22:02:31 UTC; benli
Repository: CRAN
Date/Publication: 2018-04-11 08:17:42 UTC
Built: R 4.6.0; ; 2026-04-26 20:26:05 UTC; unix
