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glmnet  

Lasso and Elastic-Net Regularized Generalized Linear Models
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("glmnet", "4.1-8")



Attach the package and use:
library("glmnet")
Maintained by
Trevor Hastie
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2008-06-02
Latest Update: 2023-08-22
Description:
Extremely efficient procedures for fitting the entire lasso or elastic-net regularization path for linear regression, logistic and multinomial regression models, Poisson regression, Cox model, multiple-response Gaussian, and the grouped multinomial regression; see and . There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (). This comes with a modest computational cost, so when the built-in families suffice, they should be used instead. The other novelty is the relax option, which refits each of the active sets in the path unpenalized. The algorithm uses cyclical coordinate descent in a path-wise fashion, as described in the papers cited.
How to cite:
Trevor Hastie (2008). glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models. R package version 4.1-8, https://cran.r-project.org/web/packages/glmnet. Accessed 21 Nov. 2024.
Previous versions and publish date:
1.1-1 (2008-06-27 08:35), 1.1-2 (2008-12-23 09:02), 1.1-3 (2009-01-24 11:12), 1.1-4 (2009-12-18 17:36), 1.1-5 (2010-01-31 11:11), 1.1 (2008-06-02 09:00), 1.2 (2010-04-04 17:43), 1.3 (2010-04-25 09:26), 1.4 (2010-06-16 17:35), 1.5.1 (2010-11-19 08:33), 1.5.2 (2011-02-07 10:50), 1.5.3 (2011-03-01 01:04), 1.5 (2010-11-04 21:16), 1.6 (2011-04-24 08:08), 1.7.1 (2011-09-23 13:45), 1.7.3 (2012-02-19 10:12), 1.7.4 (2012-04-27 08:02), 1.7 (2011-06-15 20:50), 1.8-2 (2012-10-02 08:20), 1.8-4 (2012-12-27 23:11), 1.8-5 (2013-01-04 09:21), 1.8 (2012-07-03 19:50), 1.9-1 (2013-02-10 20:17), 1.9-3 (2013-03-02 08:14), 1.9-5 (2013-08-04 02:09), 1.9-8 (2014-05-24 22:49), 2.0-1 (2015-04-08 11:12), 2.0-2 (2015-04-12 00:56), 2.0-3 (2016-02-23 07:18), 2.0-4 (2016-03-13 11:29), 2.0-5 (2016-03-17 14:00), 2.0-8 (2017-04-30 09:02), 2.0-9 (2017-05-02 22:39), 2.0-10 (2017-05-06 08:23), 2.0-12 (2017-09-13 19:35), 2.0-13 (2017-09-22 07:43), 2.0-16 (2018-04-02 14:06), 2.0-18 (2019-05-20 07:10), 3.0-1 (2019-11-15 07:50), 3.0-2 (2019-12-11 18:00), 3.0 (2019-11-09 11:20), 4.0-2 (2020-06-16 02:00), 4.0 (2020-05-14 19:30), 4.1-1 (2021-02-21 18:40), 4.1-2 (2021-06-24 08:30), 4.1-3 (2021-11-02 19:50), 4.1-4 (2022-04-15 11:22), 4.1-6 (2022-11-27 23:10), 4.1-7 (2023-03-23 02:40), 4.1 (2021-01-11 09:00)
Other packages that cited glmnet R package
View glmnet citation profile
Other R packages that glmnet depends, imports, suggests or enhances
Complete documentation for glmnet
Functions, R codes and Examples using the glmnet R package
Some associated functions: BinomialExample . Cindex . CoxExample . MultiGaussianExample . MultinomialExample . PoissonExample . QuickStartExample . SparseExample . assess.glmnet . beta_CVX . bigGlm . cox.fit . cox.path . cox_obj_function . coxgrad . coxnet.deviance . cv.glmnet . dev_function . deviance.glmnet . elnet.fit . fid . get_cox_lambda_max . get_eta . get_start . glmnet-internal . glmnet-package . glmnet.control . glmnet.fit . glmnet.measures . glmnet . glmnet.path . makeX . mycoxph . mycoxpred . na.replace . obj_function . pen_function . plot.cv.glmnet . plot.glmnet . predict.cv.glmnet . predict.glmnet . predict.glmnetfit . print.cv.glmnet . print.glmnet . response.coxnet . rmult . stratifySurv . survfit.coxnet . survfit.cv.glmnet . use.cox.path . weighted_mean_sd . 
Some associated R codes: Cindex.R . RcppExports.R . assess.coxnet.R . assess.glmnet.R . auc.R . auc.mat.R . bigGlm.R . blend.relaxed.R . buildPredmat.array.R . buildPredmat.coxnetlist.R . buildPredmat.default.R . check.dots.R . check.exclude.R . coef.cv.glmnet.R . coef.cv.relaxed.R . coef.glmnet.R . coef.relaxed.R . coefnorm.R . confusion.glmnet.R . coxgrad.R . coxnet.R . coxnet.deviance.R . coxpath.R . cv.coxnet.R . cv.elnet.R . cv.fishnet.R . cv.glmnet.R . cv.glmnet.raw.R . cv.glmnetfit.R . cv.lognet.R . cv.mrelnet.R . cv.multnet.R . cv.relaxed.raw.R . cvcompute.R . cvstats.R . cvtype.R . data.R . deviance.glmnet.R . elnet.R . error.bars.R . family.glmnet.R . fishnet.R . fix.lam.R . getOptcv.glmnet.R . getOptcv.relaxed.R . getcoef.R . getcoef.multinomial.R . glmnet-package.R . glmnet.R . glmnet.control.R . glmnet.measures.R . glmnetFlex.R . glmnet_softmax.R . jerr.R . jerr.coxnet.R . jerr.elnet.R . jerr.fishnet.R . jerr.lognet.R . jerr.mrelnet.R . lambda.interp.R . lognet.R . makeX.R . mrelnet.R . na.mean.R . nonzeroCoef.R . onAttach.R . pb.R . plot.cv.glmnet.R . plot.cv.relaxed.R . plot.glmnet.R . plot.mrelnet.R . plot.multnet.R . plot.relaxed.R . plotCoef.R . predict.coxnet.R . predict.cv.glmnet.R . predict.cv.relaxed.R . predict.elnet.R . predict.fishnet.R . predict.glmnet.R . predict.lognet.R . predict.mrelnet.R . predict.multnet.R . predict.relaxed.R . print.bigGlm.R . print.confusion.table.R . print.cv.glmnet.R . print.cv.relaxed.R . print.glmnet.R . relax.glmnet.R . response.coxnet.R . rmult.R . roc.glmnet.R . stratifySurv.R . survfit.coxnet.R . survfit.cv.glmnet.R . zeromat.R .  Full glmnet package functions and examples
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