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: 0.7
Depends: R (≥ 3.4.0)
Suggests: knitr, rmarkdown, testthat
Published: 2020-01-24
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: http://kwstat.github.io/nipals/
NeedsCompilation: no
Materials: NEWS
In views: MissingData
CRAN checks: nipals results

Downloads:

Reference manual: nipals.pdf
Vignettes: EMPCA notes
NIPALS algorithm
Comparing results and performance of NIPALS functions in R
NIPALS optimization notes
Package source: nipals_0.7.tar.gz
Windows binaries: r-devel: nipals_0.7.zip, r-devel-gcc8: nipals_0.7.zip, r-release: nipals_0.7.zip, r-oldrel: nipals_0.7.zip
OS X binaries: r-release: nipals_0.7.tgz, r-oldrel: nipals_0.7.tgz
Old sources: nipals archive

Reverse dependencies:

Reverse imports: gge

Linking:

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