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_glmOR
conda install -c edeno regularized_glmWood, S. (2006). Generalized additive models: an introduction with R (CRC press).