SOIL: Sparsity Oriented Importance Learning

Sparsity Oriented Importance Learning (SOIL) provides a new variable importance measure for high dimensional linear regression and logistic regression from a sparse penalization perspective, by taking into account the variable selection uncertainty via the use of a sensible model weighting. The package is an implementation of Ye, C., Yang, Y., and Yang, Y. (2017+).

Version: 1.1
Imports: stats, glmnet, ncvreg, MASS, parallel, brglm2
Published: 2017-09-20
Author: Chenglong Ye, Yi Yang, Yuhong Yang
Maintainer: Yi Yang <yi.yang6 at mcgill.ca>
License: GPL-2
URL: https://github.com/emeryyi/SOIL
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: SOIL results

Downloads:

Reference manual: SOIL.pdf
Package source: SOIL_1.1.tar.gz
Windows binaries: r-devel: SOIL_1.1.zip, r-release: SOIL_1.1.zip, r-oldrel: SOIL_1.1.zip
OS X El Capitan binaries: r-release: SOIL_1.1.tgz
OS X Mavericks binaries: r-oldrel: SOIL_1.1.tgz
Old sources: SOIL archive

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