Nothing
Builds a LASSO, Ridge, or Elastic Net model with 'glmnet' or 'cv.glmnet' with bootstrap inference statistics (SE, CI, and p-value) for selected coefficients with no shrinkage applied for them. Model performance can be evaluated on test data and an automated alpha selection is implemented for Elastic Net. Parallelized computation is used to speed up the process. The methods are described in Friedman et al. (2010) <doi:10.18637/jss.v033.i01> and Simon et al. (2011) <doi:10.18637/jss.v039.i05>.
Package details |
|
---|---|
Author | Sebastian Bahr [cre, aut] |
Maintainer | Sebastian Bahr <sebastian.bahr@unibe.ch> |
License | GPL-3 |
Version | 0.0.1 |
URL | https://github.com/sebastianbahr/glmnetSE |
Package repository | View on CRAN |
Installation |
Install the latest version of this package by entering the following in R:
|
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.