
CRAN . RSTUDIO . COM {
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Title:
CRAN: Package glmnet
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 <<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.
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Keywords {🔍}
aut, glmnet, regression, source, reverse, family, code, glmnettgz, lasso, doijssvi, hastie, glmnetzip, rrelease, roldrel, elasticnet, regularized, linear, models, path, multinomial, cox, argument, depends, imports, survival, rcpp, suggests, trevor, cre, citation, binaries, arm, generalized, extremely, efficient, procedures, fitting, entire, regularization, logistic, poisson, model, multipleresponse, gaussian, grouped, important, additions, glm, object, opens,
Topics {✒️}
elastic-net regularization path multinomial regression models regularized cox regression jerome friedman [aut] trevor hastie [aut rob tibshirani [aut] balasubramanian narasimhan [aut] kenneth tay [aut] noah simon [aut] multiple-response gaussian modest computational cost cyclical coordinate descent path-wise fashion junyang qian [ctb] gz windows binaries workflows reverse enhances grouped multinomial regression zvcv reverse suggests glm family object linear regression glmnet results documentation tsgsis reverse imports canonical form https zip macos binaries org/package=glmnet survival cran checks path unpenalized poisson regression rcpp suggests reverse depends family argument programmed family cox model important additions families suffice relax option active sets papers cited rcpp linkingto rmarkdown published glmnet author gpl-2 url readme news reference manual pdf vignettes r-devel r-release r-oldrel vip linking r-project
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