GLMNet
GLMNet.jl is a Julia wrapper for fitting Lasso and Elastic Net generalized linear models using glmnet.
About GLMNet
GLMNet.jl is a Julia wrapper around the Fortran code from glmnet for fitting Lasso and ElasticNet regularization paths for linear, logistic, multinomial, and Cox models using cyclic coordinate descent.
The glmnet function returns fits across lambda values, with intercepts in path.a0 and coefficients in path.betas as a CompressedPredictorMatrix. It provides predict for outputs along the path and glmnetcv for choosing lambda by cross-validation.
Key features
- Fit Lasso and ElasticNet paths for linear, logistic, multinomial, and Cox models
- Wrap glmnet Fortran code for use from Julia
- Store intercepts per lambda in path.a0
- Store coefficients in compressed path.betas matrix
- Predict outputs along regularization path with predict
- Select lambda by cross-validation with glmnetcv
Best for: Julia users fitting Lasso or ElasticNet generalized linear models with glmnet.
Good for
- Supports linear, logistic, multinomial, and Cox models
- Includes glmnetcv for lambda selection by cross-validation
- Provides predict for outputs along the regularization path
GLMNet at a glance
- Category
- Development › Developer Tools
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Open source
- Not stated
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
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GLMNet FAQ
How do I access fitted intercepts and coefficients?
Intercepts are in path.a0 and coefficients are in path.betas, which can be indexed like an AbstractMatrix or converted with convert(Matrix, path.betas).
How do I choose lambda?
Use glmnetcv(X, y) to run cross-validation, then use argmin(cv.meanloss) and coef(cv) to get the selected model.
How do I predict for new data?
Use predict(path, new predictors) to get outputs for each model along the path.
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