bigpca: PCA, transpose and multicore functionality for big.matrix objects

This package 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: 1.0
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, BH, irlba
OS_type: unix
Published: 2014-03-04
Author: Nicholas Cooper
Maintainer: Nicholas Cooper <nick.cooper at cimr.cam.ac.uk>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
CRAN checks: bigpca results

Downloads:

Reference manual: bigpca.pdf
Package source: bigpca_1.0.tar.gz
Windows binaries: r-devel: not available, r-release: not available, r-oldrel: not available
OS X Snow Leopard binaries: r-release: bigpca_1.0.tgz, r-oldrel: bigpca_1.0.tgz
OS X Mavericks binaries: r-release: bigpca_1.0.tgz