gamlr: Gamma Lasso Regression

The gamma lasso algorithm provides regularization paths corresponding to a range of non-convex cost functions between L0 and L1 norms. As much as possible, usage for this package is analogous to that for the glmnet package (which does the same thing for penalization between L1 and L2 norms). For details see: Taddy (2015), One-Step Estimator Paths for Concave Regularization, http://arxiv.org/abs/1308.5623.

Install the latest version of this package by entering the following in R:
install.packages("gamlr")
AuthorMatt Taddy <taddy@chicagobooth.edu>
Date of publication2015-08-26 08:39:48
MaintainerMatt Taddy <taddy@chicagobooth.edu>
LicenseGPL-3
Version1.13-3
http://github.com/TaddyLab/gamlr, http://faculty.chicagobooth.edu/matt.taddy/index.html

View on CRAN

Functions

AICc Man page
coef.cv.gamlr Man page
coef.gamlr Man page
cv.gamlr Man page
gamlr Man page
hockey Man page
logLik.gamlr Man page
plot.cv.gamlr Man page
plot.gamlr Man page
predict.cv.gamlr Man page
predict.gamlr Man page

Files

inst
inst/CITATION
src
src/Makevars
src/gui.h
src/gui.c
src/lhd.h
src/lhd.c
src/vec.c
src/vec.h
src/gamlr.c
NAMESPACE
data
data/hockey.rda
data/datalist
R
R/AICc.R R/cv.gamlr.R R/gamlr.R
README.md
MD5
DESCRIPTION
man
man/AICc.Rd man/gamlr.Rd man/hockey.Rd man/cv.gamlr.Rd

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

Please suggest features or report bugs with the GitHub issue tracker.

All documentation is copyright its authors; we didn't write any of that.