Python implementation of Generalised Linear Model (GLM) using numpy, numba and scipy.

FIRLS is a package for solving sparse and dense penalised Generalised Linear Model. It is fully written in python. FIRLS includes these families with their natural link:

• Gaussian | identity
• Poisson | log
• Negative binomial | log
• Binomial | log
• Bernoulli | log

For each family norm 1 and norm 2 penalty can be added.

The library support solving large sparse problems. Currently the norm 1 is not supported. A sparse version of the cyclical coordinate descent algorithm will come later.

The package subclass BaseEstimator and LinearClassifierMixin and is usable with scikit-learn.

There is three main dependencies: numpy, scipy and numba. To use the scikit-learn API you will need to install it!

just do: pip install git+https://github.com/jcrichard/firls.git

Friedman, J., Hastie, T. and Tibshirani, R. (2010) Regularization Paths for Generalized Linear Models via Coordinate Descent, Journal of Statistics Software 33(1), pp. 1-22.

Hardin, J.W. (2018), Generalized Linear Models and Extensions: Fourth Edition, Stata Press.