LargeRegression: Large Regressions

Uses gradient descent to minimize the sum of squared residuals for the regression problem. Can include an L2 penalty on the coefficient matrix. This function is very useful when there is an initial guess of what B should be in Y = XB. In general, this function performs faster than R's lm function. GPU acceleration can be used to make this function extremely fast. This package suggests cudaMatrixOps, which is not on CRAN but can be downloaded at stanford.edu/~jeffwong.

Version: 1.0
Depends: Matrix
Suggests: cudaMatrixOps
Published: 2011-08-06
Author: Jeffrey Wong
Maintainer: <jeff.ct.wong at stanford.edu>
License: GPL-2 | GPL-3 [expanded from: GPL]
NeedsCompilation: no
CRAN checks: LargeRegression results

Downloads:

Reference manual: LargeRegression.pdf
Package source: LargeRegression_1.0.tar.gz
Windows binaries: r-devel: LargeRegression_1.0.zip, r-release: LargeRegression_1.0.zip, r-oldrel: LargeRegression_1.0.zip
OS X Snow Leopard binaries: r-release: LargeRegression_1.0.tgz, r-oldrel: LargeRegression_1.0.tgz
OS X Mavericks binaries: r-release: LargeRegression_1.0.tgz