statsmodels-dq 3.0

Statistical computations and models for Python

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

https://www.statsmodels.org/stable/

The documentation for the development version is at

https://www.statsmodels.org/dev/

Recent improvements are highlighted in the release notes

https://www.statsmodels.org/stable/release/version0.9.html

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

  • Recursive least squares

• Mixed Linear Model with mixed effects and variance components

• GLM: Generalized linear models with support for all of the one-parameter exponential family distributions

• Bayesian Mixed GLM for Binomial and Poisson

• GEE: Generalized Estimating Equations for one-way clustered or longitudinal data

• Discrete models:

  • Logit and Probit

  • Multinomial logit (MNLogit)

  • Poisson and Generalized Poisson regression

  • Negative Binomial regression

  • Zero-Inflated Count models

• 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

    • Unobserved Component 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

  • Vector error correction modle, VECM

  • exponential smoothing, Holt-Winters

  • 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

• Multivariate:

  • Principal Component Analysis with missing data

  • Factor Analysis with rotation

  • MANOVA

  • Canonical Correlation

• Nonparametric statistics: Univariate and multivariate 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, regression on order statistic and Gaussian imputation

• Mediation analysis

• Graphics includes plot functions for visual analysis of data and model results

• I/O

  • Tools for reading Stata .dta files, but pandas has a more recent version

  • 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”. 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

https://pypi.org/project/statsmodels/

Binaries can be installed in Anaconda

conda install statsmodels

Installing from sources

See INSTALL.txt for requirements or see the documentation

https://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