PACLasso: Penalized and Constrained Lasso Optimization

An implementation of both the equality and inequality constrained lasso functions for the algorithm described in "Penalized and Constrained Optimization" by James, Paulson, and Rusmevichientong (Journal of the American Statistical Association, 2019; see <http://www-bcf.usc.edu/~gareth/research/PAC.pdf> for a full-text version of the paper). The algorithm here is designed to allow users to define linear constraints (either equality or inequality constraints) and use a penalized regression approach to solve the constrained problem. The functions here are used specifically for constraints with the lasso formulation, but the method described in the PaC paper can be used for a variety of scenarios. In addition to the simple examples included here with the corresponding functions, complete code to entirely reproduce the results of the paper is available online through the Journal of the American Statistical Association.

Getting started

Package details

AuthorCourtney Paulson [aut, cre], Gareth James [ctb], Paat Rusmevichientong [ctb]
MaintainerCourtney Paulson <[email protected]>
LicenseGPL-3
Version1.0.0
URL http://www-bcf.usc.edu/~gareth/research/PAC.pdf
Package repositoryView on CRAN
Installation Install the latest version of this package by entering the following in R:
install.packages("PACLasso")

Try the PACLasso package in your browser

Any scripts or data that you put into this service are public.

PACLasso documentation built on May 2, 2019, 2:29 p.m.