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    About Statsmodels
    =================

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


    Documentation
    =============

    The documentation for the latest release is at

       http://www.statsmodels.org/stable/

    The documentation for the development version is at

       http://www.statsmodels.org/dev/

    Recent improvements are highlighted in the release notes

       http://www.statsmodels.org/stable/release/version0.8.html

    Backups of documentation are available at http://statsmodels.github.io/stable/
    and http://statsmodels.github.io/dev/.



    Main Features
    =============

    * Linear regression models:

      - Ordinary least squares
      - Generalized least squares
      - Weighted least squares
      - Least squares with autoregressive errors
      - Quantile regression

    * Mixed Linear Model with mixed effects and variance components
    * GLM: Generalized linear models with support for all of the one-parameter
      exponential family distributions
    * GEE: Generalized Estimating Equations for one-way clustered or longitudinal data
    * Discrete models:

      - Logit and Probit
      - Multinomial logit (MNLogit)
      - Poisson regression
      - Negative Binomial regression

    * RLM: Robust linear models with support for several M-estimators.
    * Time Series Analysis: models for time series analysis

      - Complete StateSpace modeling framework

        - Seasonal ARIMA and ARIMAX models
        - VARMA and VARMAX models
        - Dynamic Factor models

      - Markov switching models (MSAR), also known as Hidden Markov Models (HMM)
      - Univariate time series analysis: AR, ARIMA
      - Vector autoregressive models, VAR and structural VAR
      - Hypothesis tests for time series: unit root, cointegration and others
      - Descriptive statistics and process models for time series analysis

    * Survival analysis:

      - Proportional hazards regression (Cox models)
      - Survivor function estimation (Kaplan-Meier)
      - Cumulative incidence function estimation

    * Nonparametric statistics: (Univariate) kernel density estimators
    * Datasets: Datasets used for examples and in testing
    * Statistics: a wide range of statistical tests

      - diagnostics and specification tests
      - goodness-of-fit and normality tests
      - functions for multiple testing
      - various additional statistical tests

    * Imputation with MICE and regression on order statistic
    * Mediation analysis
    * Principal Component Analysis with missing data
    * I/O

      - Tools for reading Stata .dta files into numpy arrays.
      - Table output to ASCII, LaTeX, and HTML

    * Miscellaneous models
    * Sandbox: statsmodels contains a sandbox folder with code in various stages of
      development and testing which is not considered "production ready".   This covers
      among others

      - Generalized method of moments (GMM) estimators
      - Kernel regression
      - Various extensions to scipy.stats.distributions
      - Panel data models
      - Information theoretic measures

    How to get it
    =============
    The master branch on GitHub is the most up to date code

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

    Source download of release tags are available on GitHub

        https://github.com/statsmodels/statsmodels/tags

    Binaries and source distributions are available from PyPi

        http://pypi.python.org/pypi/statsmodels/

    Binaries can be installed in Anaconda

        conda install statsmodels

    Development snapshots are also available in Anaconda (infrequently updated)

        conda install -c https://conda.binstar.org/statsmodels statsmodels

    Installing from sources
    =======================

    See INSTALL.txt for requirements or see the documentation

        http://statsmodels.github.io/dev/install.html

    License
    =======

    Modified BSD (3-clause)

    Discussion and Development
    ==========================

    Discussions take place on our mailing list.

        http://groups.google.com/group/pystatsmodels

    We are very interested in feedback about usability and suggestions for
    improvements.

    Bug Reports
    ===========

    Bug reports can be submitted to the issue tracker at

        https://github.com/statsmodels/statsmodels/issues

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