
cran.rstudio.com/web/packages/glmnet/index.html
Preview meta tags from the cran.rstudio.com website.
Linked Hostnames
4- 57 links towww.bioconductor.org
- 4 links todoi.org
- 2 links tocran.r-project.org
- 1 link toglmnet.stanford.edu
Thumbnail

Search Engine Appearance
glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models
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 <<a href="https://doi.org/10.18637%2Fjss.v033.i01" target="_top">doi:10.18637/jss.v033.i01</a>> and <<a href="https://doi.org/10.18637%2Fjss.v039.i05" target="_top">doi:10.18637/jss.v039.i05</a>>. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (<<a href="https://doi.org/10.18637%2Fjss.v106.i01" target="_top">doi:10.18637/jss.v106.i01</a>>). 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.
Bing
glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models
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 <<a href="https://doi.org/10.18637%2Fjss.v033.i01" target="_top">doi:10.18637/jss.v033.i01</a>> and <<a href="https://doi.org/10.18637%2Fjss.v039.i05" target="_top">doi:10.18637/jss.v039.i05</a>>. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (<<a href="https://doi.org/10.18637%2Fjss.v106.i01" target="_top">doi:10.18637/jss.v106.i01</a>>). 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.
DuckDuckGo
glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models
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 <<a href="https://doi.org/10.18637%2Fjss.v033.i01" target="_top">doi:10.18637/jss.v033.i01</a>> and <<a href="https://doi.org/10.18637%2Fjss.v039.i05" target="_top">doi:10.18637/jss.v039.i05</a>>. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (<<a href="https://doi.org/10.18637%2Fjss.v106.i01" target="_top">doi:10.18637/jss.v106.i01</a>>). 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.
General Meta Tags
15- titleCRAN: Package glmnet
- Content-Typetext/html; charset=utf-8
- viewportwidth=device-width, initial-scale=1.0, user-scalable=yes
- citation_titleLasso and Elastic-Net Regularized Generalized Linear Models [R package glmnet version 4.1-10]
- citation_author1Jerome Friedman
Open Graph Meta Tags
5- og:titleglmnet: Lasso and Elastic-Net Regularized Generalized Linear Models
- og:descriptionExtremely 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 <<a href="https://doi.org/10.18637%2Fjss.v033.i01" target="_top">doi:10.18637/jss.v033.i01</a>> and <<a href="https://doi.org/10.18637%2Fjss.v039.i05" target="_top">doi:10.18637/jss.v039.i05</a>>. There are two new and important additions. The family argument can be a GLM family object, which opens the door to any programmed family (<<a href="https://doi.org/10.18637%2Fjss.v106.i01" target="_top">doi:10.18637/jss.v106.i01</a>>). 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.
- og:imagehttps://CRAN.R-project.org/CRANlogo.png
- og:typewebsite
- og:urlhttps://CRAN.R-project.org/package=glmnet
Twitter Meta Tags
1- twitter:cardsummary
Link Tags
2- canonicalhttps://CRAN.R-project.org/package=glmnet
- stylesheet../../CRAN_web.css
Links
64- https://CRAN.R-project.org/package=glmnet
- https://CRAN.R-project.org/src/contrib/Archive/glmnet
- https://doi.org/10.18637%2Fjss.v033.i01
- https://doi.org/10.18637%2Fjss.v039.i05
- https://doi.org/10.18637%2Fjss.v106.i01