SGL-package: Fit a GLM (or Cox Model) with a Combination of Lasso and...
In SGL: Fit a GLM (or Cox Model) with a Combination of Lasso and Group Lasso Regularization
Description
Details
Author(s)
References
Description
Fit a regularized generalized linear model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. Fits linear, logistic and Cox models.
Details
Package: SGL
Type: Package
Version: 1.0
Date: 2012-3-12
License: GPL
LazyLoad: yes
Only 4 functions:
SGL
cvSGL
predictSGL
plot.cvSGL
Author(s)
Noah Simon, Jerome Friedman, Trevor Hastie, and Rob Tibshirani
Maintainer: Noah Simon <nrsimon@uw.edu>
References
Simon, N., Friedman, J., Hastie T., and Tibshirani, R. (2011)
A Sparse-Group Lasso,
http://faculty.washington.edu/nrsimon/SGLpaper.pdf
SGL documentation built on Sept. 28, 2019, 1:03 a.m.
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Description Details Author(s) References
Description
Fit a regularized generalized linear model via penalized maximum likelihood. The model is fit for a path of values of the penalty parameter. Fits linear, logistic and Cox models.
Details
| Package: | SGL |
| Type: | Package |
| Version: | 1.0 |
| Date: | 2012-3-12 |
| License: | GPL |
| LazyLoad: | yes |
Only 4 functions:
SGL
cvSGL
predictSGL
plot.cvSGL
Author(s)
Noah Simon, Jerome Friedman, Trevor Hastie, and Rob Tibshirani
Maintainer: Noah Simon <nrsimon@uw.edu>
References
Simon, N., Friedman, J., Hastie T., and Tibshirani, R. (2011)
A Sparse-Group Lasso,
http://faculty.washington.edu/nrsimon/SGLpaper.pdf
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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