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l0ara  

Sparse Generalized Linear Model with L0 Approximation for Feature Selection
View on CRAN: Click here


Download and install l0ara package within the R console
Install from CRAN:
install.packages("l0ara")

Install from Github:
library("remotes")
install_github("cran/l0ara")

Install by package version:
library("remotes")
install_version("l0ara", "0.1.6")



Attach the package and use:
library("l0ara")
Maintained by
Wenchuan Guo
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2016-07-27
Latest Update: 2020-02-06
Description:
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.
How to cite:
Wenchuan Guo (2016). l0ara: Sparse Generalized Linear Model with L0 Approximation for Feature Selection. R package version 0.1.6, https://cran.r-project.org/web/packages/l0ara. Accessed 04 Aug. 2026.
Previous versions and publish date:
(2026-07-09 07:51), 0.1.2 (2016-07-27 03:45), 0.1.3 (2016-08-05 11:53), 0.1.5 (2017-07-24 05:09), 0.1.6 (2020-02-06 06:40)
Other packages that cited l0ara R package
View l0ara citation profile
Other R packages that l0ara depends, imports, suggests or enhances
Complete documentation for l0ara
Functions, R codes and Examples using the l0ara R package
Some associated functions: coef.cv.l0ara . coef.l0ara . cv.l0ara . l0ara . plot.cv.l0ara . plot.l0ara . predict.l0ara . print.cv.l0ara . print.l0ara . 
Some associated R codes: RcppExports.R . cv.l0ara.R . l0ara.R . summary.R .  Full l0ara package functions and examples
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