bigRR: Generalized Ridge Regression (with special advantage for p >> n cases)

The package fits large-scale (generalized) ridge regression for various distributions of response. The shrinkage parameters (lambdas) can be pre-specified or estimated using an internal update routine (fitting a heteroscedastic effects model, or HEM). It gives possibility to shrink any subset of parameters in the model. It has special computational advantage for the cases when the number of shrinkage parameters exceeds the number of observations. For example, the package is very useful for fitting large-scale omics data, such as high-throughput genotype data (genomics), gene expression data (transcriptomics), metabolomics data, etc.

Version: 1.3-8
Depends: R (≥ 2.10), utils, hglm, DatABEL
Published: 2013-08-12
Author: Xia Shen, Moudud Alam and Lars Ronnegard
Maintainer: Xia Shen <xia.shen at slu.se>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: bigRR citation info
Materials: ChangeLog
In views: MachineLearning
CRAN checks: bigRR results

Downloads:

Reference manual: bigRR.pdf
Package source: bigRR_1.3-8.tar.gz
Windows binaries: r-devel: bigRR_1.3-8.zip, r-release: bigRR_1.3-8.zip, r-oldrel: bigRR_1.3-8.zip
OS X Snow Leopard binaries: r-release: bigRR_1.3-8.tgz, r-oldrel: bigRR_1.3-8.tgz
OS X Mavericks binaries: r-release: bigRR_1.3-8.tgz
Old sources: bigRR archive

Reverse dependencies:

Reverse suggests: GenABEL