19:57 sunpoet 

• math/py-spglm 1.0.8 Sparse generalize linear models

Add py-spglm 1.0.8

This module is an adaptation of a portion of GLM functionality from the
Statsmodels package, this it has been simplified and customized for the purposes
of serving as the base for several other PySAL modules, namely SpInt and GWR.
Currently, it supports the estimation of Gaussian, Poisson, and Logistic
regression using only iteratively weighted least squares estimation (IWLS). One
of the large differences this module and the functions avaialble in the
Statsmodels package is that the custom IWLS routine is fully sparse compatible,
which was necesary for the very sparse design matrices that arise in constrained
spatial interaction models. The somewhat limited functionality and computation
of only a subset of GLM diagnostics also decreases the computational overhead.
Another difference is that this module also supports the estimation of
QuasiPoisson models. One caveat is that this custom IWLS routine currently
generates estimates by directly solves the least squares normal equations rather
than using a more robust method like the pseudo inverse. For more robust
estimation of ill conditioned data and a fuller GLM framework we suggest using
the original GLM functionality from Statsmodels.

WWW: https://github.com/pysal/spglm