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 [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:

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