pip install

To install this package run one of the following:
pip install -i https://pypi.anaconda.org/ales-erjavec/simple statsmodels

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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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