glmnetLRC

Lasso and Elastic-Net Logistic Regression Classification with an Arbitrary Loss Function

The package vignette includes examples to help you get started, as well as mathematical details of the algorithms used by the package.

To cite:

Sego LH, Venzin AM, Ramey JA. 2016. glmnetLRC: Lasso and Elastic-Net Logistic Regression Classification (LRC) with an Arbitrary Loss Function in R. Pacific Northwest National Laboratory. http://pnnl.github.io/glmnetLRC.

To install:

Begin by installing dependencies from CRAN:

install.packages(c("devtools", "glmnet", "plyr"))

The Smisc package (which is imported by glmnetLRC) contains C code and requires compilation:

• Mac: you'll need to install Xcode
• Windows: you'll need to install R tools
• Linux/Unix: compilation should take place automatically

With the compilation tools in place, you can now install the Smisc and glmnetLRC packages from the PNNL github site:

devtools::install_github("pnnl/Smisc")
devtools::install_github("pnnl/glmnetLRC")

To contribute:

We welcome contributions to this package. Please follow these steps when contributing.

Acknowledgements:

This package was developed with support from the Signature Discovery Initiative at Pacific Northwest National Laboratory, conducted under the Laboratory Directed Research and Development Program at PNNL, a multiprogram national laboratory operated by Battelle for the U.S. Department of Energy.