bootSVD: Fast, Exact Bootstrap Principal Component Analysis for High Dimensional Data

Implements fast, exact bootstrap Principal Component Analysis and Singular Value Decompositions for high dimensional data, as described in <doi:10.1080/01621459.2015.1062383> (see also <doi:10.48550/arXiv.1405.0922> ). For data matrices that are too large to operate on in memory, users can input objects with class 'ff' (see the 'ff' package), where the actual data is stored on disk. In response, this package will implement a block matrix algebra procedure for calculating the principal components (PCs) and bootstrap PCs. Depending on options set by the user, the 'parallel' package can be used to parallelize the calculation of the bootstrap PCs.

Version:	1.1
Depends:	R (≥ 3.0.2)
Imports:	ff, parallel
Published:	2021-02-02
DOI:	10.32614/CRAN.package.bootSVD
Author:	Aaron Fisher
Maintainer:	Aaron Fisher <afishe27 at alumni.jh.edu>
License:	GPL-2
URL:	http://arxiv.org/abs/1405.0922
NeedsCompilation:	no
Materials:	README
CRAN checks:	bootSVD results

Documentation:

Reference manual:

bootSVD.pdf

Downloads:

Package source:	bootSVD_1.1.tar.gz
Windows binaries:	r-devel: bootSVD_1.1.zip, r-release: bootSVD_1.1.zip, r-oldrel: bootSVD_1.1.zip
macOS binaries:	r-release (arm64): bootSVD_1.1.tgz, r-oldrel (arm64): bootSVD_1.1.tgz, r-release (x86_64): bootSVD_1.1.tgz, r-oldrel (x86_64): bootSVD_1.1.tgz
Old sources:	bootSVD archive

Reverse dependencies:

Reverse imports:

smartsnp

Linking:

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