math/py-statsmodels: Complement to SciPy for statistical computations

Statsmodels is a Python package that provides a complement to scipy for
statistical computations including descriptive statistics and estimation and
inference for statistical models.

Main Features:
* linear regression models: GLS (including WLS and LS aith AR errors) and OLS.
* glm: Generalized linear models with support for all of the one-parameter
  exponential family distributions.
* discrete: regression with discrete dependent variables, including Logit,
  Probit, MNLogit, Poisson, based on maximum likelihood estimators
* rlm: Robust linear models with support for several M-estimators.
* tsa: models for time series analysis - univariate: AR, ARIMA; multivariate:
  VAR and structural VAR
* nonparametric: (Univariate) kernel density estimators
* datasets: Datasets to be distributed and used for examples and in testing.
* stats: a wide range of statistical tests, diagnostics and specification tests
* iolib: Tools for reading Stata .dta files into numpy arrays, printing table
  output to ascii, latex, and html
* miscellaneous models
* sandbox: statsmodels contains a sandbox folder with code in various stages of
* developement and testing which is not considered "production ready", including
  Mixed models, GARCH and GMM estimators, kernel regression, panel data models.

WWW: https://www.github.com/statsmodels/statsmodels

PR:		ports/183932
Submitted by:	Johannes Jost Meixner <xmj chaot.net>

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