Adds wrappers to add functionality for big.matrix objects (see the bigmemory project). This allows fast scalable principle components analysis (PCA), or singular value decomposition (SVD). There are also functions for transposing, using multicore 'apply' functionality, data importing and for compact display of big.matrix objects. Most functions also work for standard matrices if RAM is sufficient.
Version: | 1.1 |
Depends: | R (≥ 3.0), grDevices, graphics, stats, utils, reader (≥ 1.0.1), NCmisc (≥ 1.1), bigmemory (≥ 4.0.0), biganalytics |
Imports: | parallel, methods, bigmemory.sri, irlba |
Published: | 2017-11-21 |
Author: | Nicholas Cooper |
Maintainer: | Nicholas Cooper <njcooper at gmx.co.uk> |
License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
NeedsCompilation: | no |
CRAN checks: | bigpca results |
Reference manual: | bigpca.pdf |
Package source: | bigpca_1.1.tar.gz |
Windows binaries: | r-devel: not available, r-release: bigpca_1.1.zip, r-oldrel: bigpca_1.1.zip |
OS X El Capitan binaries: | r-release: bigpca_1.1.tgz |
OS X Mavericks binaries: | r-oldrel: bigpca_1.1.tgz |
Old sources: | bigpca archive |
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