A simple python package for fitting L2- and smoothing-penalized generalized linear models.

Built primarily because the statsmodels GLM fit_regularized method is built to do elastic net (combination of L1 and L2 penalities), but if you just want to do an L2 or a smoothing penalty (like in generalized additive models), using a penalized iteratively reweighted least squares (p-IRLS) is much faster.

pip install regularized_glm

OR

conda install -c edeno regularized_glm

• Statsmodels
• pyGAM

Wood, S. (2006). Generalized additive models: an introduction with R (CRC press).