First, you need to install a few dependencies:

1. You'll need PyPolyaGamma to be in your Python path. You can get it here: git clone --recursive git@github.com:slinderman/pypolyagamma.git
2. If you want to use the switching models, you'll need Matt Johnson's PyHSMM package on your Python path too: git clone --recursive git@github.com:mattjj/pyhsmm.git Both of these repos have Cython extensions that you will need to compile. See the repo READMEs for more instructions.

Once you've installed the dependencies, clone PyGLM by running: git clone --recursive git@github.com:slinderman/pyglm.git Then build the Cython extensions with cd pyglm and python setup.py build_ext --inplace

To generate some synthetic data, run ipython examples/generate_synthetic_data.py

The examples directory has demos of fitting data, given as a TxN numpy array of spike counts, where T is the number of time bins and N is the number of neurons, using a simple generalized linear model with network interactions.