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grpnet
View on CRAN: Click
here
Download and install grpnet package within the R console
Install from CRAN:
install.packages("grpnet")
Install from Github:
library("remotes")
install_github("cran/grpnet") Install by package version:
library("remotes")
install_version("grpnet", "1.1") Attach the package and use:
library("grpnet")
Maintained by
Nathaniel E. Helwig
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[Scholar Profile | Author Map]
All associated links for this package
First Published: 2023-07-13
Latest Update: 2025-06-10
Description:
Efficient algorithms for fitting generalized linear and additive models with group elastic net penalties. Implements group lasso, group MCP, and group SCAD with an optional group ridge penalty. Computes the regularization path for linear regression (gaussian), logistic regression (binomial), multinomial logistic regression (multinomial), log-linear count regression (poisson and negative.binomial), and log-linear continuous regression (gamma and inverse gaussian). Supports default and formula methods for model specification, k-fold cross-validation for tuning the regularization parameters, and nonparametric regression via tensor product reproducing kernel (smoothing spline) basis function expansion.
How to cite:
Nathaniel E. Helwig (2023). grpnet: Group Elastic Net Regularized GLMs and GAMs. R package version 1.1, https://cran.r-project.org/web/packages/grpnet. Accessed 15 Jul. 2026.
Previous versions and publish date:
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imports, suggests or enhances
Complete documentation for grpnet
Functions, R codes and Examples using
the grpnet R package
Some associated functions: StartupMessage . auto . coef . cv.grpnet . grpnet . plot.cv.grpnet . plot.grpnet . predict.cv.grpnet . predict.grpnet . print . rk.model.matrix . rk . row.kronecker .
Some associated R codes: coef.cv.grpnet.R . coef.grpnet.R . cv.grpnet.R . cv.grpnet.default.R . cv.grpnet.formula.R . grpnet.R . grpnet.default.R . grpnet.formula.R . predict.cv.grpnet.R . predict.grpnet.R . rk.R . rk.model.matrix.R . row.kronecker.R . zzz.R . Full grpnet package functions and examples
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