This subsample winner algorithm (SWA) for regression with a largep data (X, Y) selects the important variables (or features) among the p features X in explaining the response Y. The SWA first uses a base procedure, here a linear regression, on each of subsamples randomly drawn from the p variables, and then computes the scores of all features, i.e., the p variables, according to the performance of these features collected in each of the subsample analyses. It then obtains the 'semifinalist' of the features based on the resulting scores and determines the 'finalists', i.e., the important features, from the 'semifinalist'. Fan, Sun and Qiao (2017) <http://sr2c.case.edu/swareg/>.
Package details 


Author  Yiying Fan [aut, cre], Jiayang Sun [aut], Xingye Qiao [aut] 
Maintainer  Yiying Fan <[email protected]> 
License  GPL2  GPL3 
Version  0.1.0 
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.