rqPen

rqPen-package

The package estimates a quantile regression model using LASSO, Adaptive LASSO, SCAD, MCP, elastic net, 
and their group counterparts, with the exception of elastic net for which there is no group penalty implementation.

Performs penalized quantile regression with LASSO, elastic net, SCAD and MCP penalty functions including group penalties. In addition, offers a group penalty that provides consistent variable selection across quantiles. Provides a function that automatically generates lambdas and evaluates different models with cross validation or BIC, including a large p version of BIC. Below URL provides a link to a work in progress vignette.

Ben Sherwood

Penalized Quantile Regression

Shaobo Li

Adam Maidman

rqPen function

The most important functions are rq.pen(), rq.group.pen(), rq.pen.cv() and rq.group.pen.cv(). These functions 
fit quantile regression models with individual or group penalties. The cv functions automate the cross-validation process for selection of tuning parameters.

rqPen functions

Maintainer: Ben Sherwood <a href="/link/ben.sherwood%40ku.edu?package=rqPen&version=4.1.3" data-mini-rdoc="rqPen::ben.sherwood@ku.edu">ben.sherwood@ku.edu</a>
Authors:<ul>
<li>Shaobo Li</li>
<li>Adam Maidman</li>
</ul>

Author

rqPen: A package for estimating quantile regression models using penalized objective functions. — rqPen

The most important functions are rq.pen(), rq.group.pen(), rq.pen.cv() and rq.group.pen.cv(). These functions 
fit quantile regression models with individual or group penalties. The cv functions automate the cross-validation process for selection of tuning parameters.

Maintainer: Ben Sherwood <a href='mailto:ben.sherwood@ku.edu'>ben.sherwood@ku.edu</a>
Authors:<ul>
<li>Shaobo Li</li>
<li>Adam Maidman</li>
</ul>

rqPen: rqPen: A package for estimating quantile regression models using penalized objective functions.

Description

Arguments

rqPen functions

Author

See Also