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nipals: Principal Components Analysis using NIPALS or Weighted EMPCA, with Gram-Schmidt Orthogonalization

Principal Components Analysis of a matrix using Non-linear Iterative Partial Least Squares or weighted Expectation Maximization PCA with Gram-Schmidt orthogonalization of the scores and loadings. Optimized for speed. See Andrecut (2009) <doi:10.1089/cmb.2008.0221>.

Version: 0.8
Depends: R (≥ 3.4.0)
Suggests: knitr, rmarkdown, testthat
Published: 2021-09-15
DOI: 10.32614/CRAN.package.nipals
Author: Kevin Wright ORCID iD [aut, cre]
Maintainer: Kevin Wright <kw.stat at gmail.com>
BugReports: https://github.com/kwstat/nipals/issues
License: GPL-3
URL: https://kwstat.github.io/nipals/
NeedsCompilation: no
Materials: NEWS
In views: MissingData
CRAN checks: nipals results

Documentation:

Reference manual: nipals.pdf
Vignettes: EMPCA notes
NIPALS algorithm
Comparing results and performance of NIPALS functions in R
NIPALS optimization notes

Downloads:

Package source: nipals_0.8.tar.gz
Windows binaries: r-devel: nipals_0.8.zip, r-release: nipals_0.8.zip, r-oldrel: nipals_0.8.zip
macOS binaries: r-release (arm64): nipals_0.8.tgz, r-oldrel (arm64): nipals_0.8.tgz, r-release (x86_64): nipals_0.8.tgz, r-oldrel (x86_64): nipals_0.8.tgz
Old sources: nipals archive

Reverse dependencies:

Reverse imports: areabiplot, gge, powerPLS, scp
Reverse suggests: pRoloc

Linking:

Please use the canonical form https://CRAN.R-project.org/package=nipals to link to this page.

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.