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>
License: GPL-2
NeedsCompilation: no
Materials: ChangeLog
CRAN checks: SOIL results


Reference manual: SOIL.pdf
Package source: SOIL_1.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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


Please use the canonical form to link to this page.