l0ara: Sparse Generalized Linear Model with L0 Approximation for Feature Selection

An efficient procedure for feature selection for generalized linear models with L0 penalty, including linear, logistic, Poisson, gamma, inverse Gaussian regression. Adaptive ridge algorithms are used to fit the models.

Author
Wenchuan Guo, Zhenqiu Liu, Fengzhu Sun, Dermot P McGovern, Sandra Orsulic
Date of publication
2016-08-05 11:53:20
Maintainer
Wenchuan Guo <wguo007@ucr.edu>
License
GPL-2
Version
0.1.3

View on CRAN

Man pages

coef.cv.l0ara
print coefficients from a "cv.l0ara" object.
coef.l0ara
print coefficients from a "l0ara" object.
cv.l0ara
Cross validation for l0ara
l0ara
fit a generalized linear model with l0 penalty
plot.cv.l0ara
plot for an "cv.l0ara" object
plot.l0ara
plot for an "l0ara" object
predict.l0ara
make predictions from a "l0ara" object.
print.cv.l0ara
summarizing the fits from a "cv.l0ara" object.
print.l0ara
summarizing the fits from a "l0ara" object.

Files in this package

l0ara
l0ara/src
l0ara/src/Makevars
l0ara/src/Makevars.win
l0ara/src/RcppExports.cpp
l0ara/src/l0araC.cpp
l0ara/NAMESPACE
l0ara/NEWS
l0ara/R
l0ara/R/cv.l0ara.R
l0ara/R/l0ara.R
l0ara/R/RcppExports.R
l0ara/R/summary.R
l0ara/README.md
l0ara/MD5
l0ara/DESCRIPTION
l0ara/man
l0ara/man/print.cv.l0ara.Rd
l0ara/man/coef.l0ara.Rd
l0ara/man/predict.l0ara.Rd
l0ara/man/plot.cv.l0ara.Rd
l0ara/man/print.l0ara.Rd
l0ara/man/l0ara.Rd
l0ara/man/cv.l0ara.Rd
l0ara/man/plot.l0ara.Rd
l0ara/man/coef.cv.l0ara.Rd