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Metadata-Version: 1.1
Name: statsmodels
Version: 0.8.0
Summary: Statistical computations and models for Python
Home-page: http://www.statsmodels.org/
Author: Skipper Seabold, Josef Perktold
Author-email: pystatsmodels@googlegroups.com
License: BSD License
Description-Content-Type: UNKNOWN
Description: |Travis Build Status| |Appveyor Build Status| |Coveralls Coverage|
        
        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
        
        .. |Travis Build Status| image:: https://travis-ci.org/statsmodels/statsmodels.svg?branch=master
           :target: https://travis-ci.org/statsmodels/statsmodels
        .. |Appveyor Build Status| image:: https://ci.appveyor.com/api/projects/status/gx18sd2wc63mfcuc/branch/master?svg=true
           :target: https://ci.appveyor.com/project/josef-pkt/statsmodels/branch/master
        .. |Coveralls Coverage| image:: https://coveralls.io/repos/github/statsmodels/statsmodels/badge.svg?branch=master
           :target: https://coveralls.io/github/statsmodels/statsmodels?branch=master
        
Platform: any
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Programming Language :: Cython
Classifier: Programming Language :: Python :: 2.6
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: End Users/Desktop
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Scientific/Engineering

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