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SOPC: The Sparse Online Principal Component Estimation Algorithm

The sparse online principal component can not only process the online data set, but also obtain a sparse solution of the online data set. The philosophy of the package is described in Guo G. (2022) <doi:10.1007/s00180-022-01270-z>.

Version: 0.1.0
Depends: R (≥ 4.1.0)
Imports: elasticnet, magrittr, stats
Suggests: testthat (≥ 3.0.0)
Published: 2023-05-07
DOI: 10.32614/CRAN.package.SOPC
Author: Guangbao Guo [aut, cre], Chunjie Wei [aut], Guoqi Qian [aut]
Maintainer: Guangbao Guo <ggb11111111 at 163.com>
License: MIT + file LICENSE
NeedsCompilation: no
CRAN checks: SOPC results

Documentation:

Reference manual: SOPC.pdf

Downloads:

Package source: SOPC_0.1.0.tar.gz
Windows binaries: r-devel: SOPC_0.1.0.zip, r-release: SOPC_0.1.0.zip, r-oldrel: SOPC_0.1.0.zip
macOS binaries: r-release (arm64): SOPC_0.1.0.tgz, r-oldrel (arm64): SOPC_0.1.0.tgz, r-release (x86_64): SOPC_0.1.0.tgz, r-oldrel (x86_64): SOPC_0.1.0.tgz
Old sources: SOPC archive

Reverse dependencies:

Reverse imports: LFM, SFM, TFM

Linking:

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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.