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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

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/

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 model, 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 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 main 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

Getting the latest code

Installing the most recent nightly wheel

The most recent nightly wheel can be installed using pip.

python -m pip install -i https://pypi.anaconda.org/scientific-python-nightly-wheels/simple statsmodels --upgrade --use-deprecated=legacy-resolver

Installing from sources

See INSTALL.txt for requirements or see the documentation

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

Contributing

Contributions in any form are welcome, including:

  • Documentation improvements
  • Additional tests
  • New features to existing models
  • New models

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

for instructions on installing statsmodels in editable mode.

License

Modified BSD (3-clause)

Discussion and Development

Discussions take place on the mailing list

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

and in the issue tracker. 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

Project links

Key dates

PyPI data
Data sourced directly from PyPI's database.
  • Released:
Latest release

5 maintainers

PyPI data
Data sourced directly from PyPI's database.
Avatar for bashtage from gravatar.com bashtage Avatar for josefpktd from gravatar.com josefpktd Avatar for matthew.brett from gravatar.com matthew.brett
+2 more Avatar for rgommers from gravatar.com rgommers Avatar for Skipper.Seabold from gravatar.com Skipper.Seabold

Credits

Maintainer: statsmodels Developers

License expression

BSD-3-Clause
View SPDX License List

Requires

Python >=3.10

Provides Extra

test doc dev polars

Tags

statistics econometrics time series regression anova statistical tests linear models generalized linear models discrete choice models robust linear models mixed linear models time series analysis state space models vector autoregression volatility modeling unit root tests cointegration tests nonparametric methods multivariate statistics

Classifiers

Report project as malware

Release files for statsmodels 0.15.0

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for statsmodels 0.15.0
File Size Uploaded
statsmodels-0.15.0.tar.gz 15.7 MB Details

Built distributions (wheels)

Table of built distributions (wheels) for statsmodels 0.15.0
File
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statsmodels-0.15.0-cp315-cp315-macosx_10_15_x86_64.whl 11.7 MB CPython 3.15 CPython 3.15 macOS 10.15+ x86-64 Details
statsmodels-0.15.0-cp314-cp314t-win_arm64.whl 11.0 MB CPython 3.14 CPython 3.14 free-threading Windows ARM64 Details
statsmodels-0.15.0-cp314-cp314t-win_amd64.whl 11.6 MB CPython 3.14 CPython 3.14 free-threading Windows x86-64 Details
statsmodels-0.15.0-cp314-cp314t-musllinux_1_2_x86_64.whl 11.9 MB CPython 3.14 CPython 3.14 free-threading Linux musl 1.2+ x86-64 Details
statsmodels-0.15.0-cp314-cp314t-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 11.9 MB CPython 3.14 CPython 3.14 free-threading Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 Details
statsmodels-0.15.0-cp314-cp314t-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl 11.6 MB CPython 3.14 CPython 3.14 free-threading Linux glibc 2.27+ ARM64, Linux glibc 2.28+ ARM64 Details
statsmodels-0.15.0-cp314-cp314t-macosx_11_0_arm64.whl 11.8 MB CPython 3.14 CPython 3.14 free-threading macOS 11.0+ ARM64 Details
statsmodels-0.15.0-cp314-cp314t-macosx_10_15_x86_64.whl 11.9 MB CPython 3.14 CPython 3.14 free-threading macOS 10.15+ x86-64 Details
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statsmodels-0.15.0-cp314-cp314-win_amd64.whl 11.3 MB CPython 3.14 CPython 3.14 Windows x86-64 Details
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statsmodels-0.15.0-cp314-cp314-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl 11.8 MB CPython 3.14 CPython 3.14 Linux glibc 2.27+ ARM64, Linux glibc 2.28+ ARM64 Details
statsmodels-0.15.0-cp314-cp314-macosx_11_0_arm64.whl 11.6 MB CPython 3.14 CPython 3.14 macOS 11.0+ ARM64 Details
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statsmodels-0.15.0-cp313-cp313-musllinux_1_2_x86_64.whl 12.1 MB CPython 3.13 CPython 3.13 Linux musl 1.2+ x86-64 Details
statsmodels-0.15.0-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 12.1 MB CPython 3.13 CPython 3.13 Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 Details
statsmodels-0.15.0-cp313-cp313-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl 11.8 MB CPython 3.13 CPython 3.13 Linux glibc 2.28+ ARM64, Linux glibc 2.27+ ARM64 Details
statsmodels-0.15.0-cp313-cp313-macosx_11_0_arm64.whl 11.6 MB CPython 3.13 CPython 3.13 macOS 11.0+ ARM64 Details
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statsmodels-0.15.0-cp312-cp312-musllinux_1_2_x86_64.whl 12.1 MB CPython 3.12 CPython 3.12 Linux musl 1.2+ x86-64 Details
statsmodels-0.15.0-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 12.0 MB CPython 3.12 CPython 3.12 Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 Details
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statsmodels-0.15.0-cp311-cp311-musllinux_1_2_x86_64.whl 12.1 MB CPython 3.11 CPython 3.11 Linux musl 1.2+ x86-64 Details
statsmodels-0.15.0-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 12.1 MB CPython 3.11 CPython 3.11 Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 Details
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statsmodels-0.15.0-cp311-cp311-macosx_11_0_arm64.whl 11.6 MB CPython 3.11 CPython 3.11 macOS 11.0+ ARM64 Details
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statsmodels-0.15.0-cp310-cp310-musllinux_1_2_x86_64.whl 12.1 MB CPython 3.10 CPython 3.10 Linux musl 1.2+ x86-64 Details
statsmodels-0.15.0-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl 12.1 MB CPython 3.10 CPython 3.10 Linux glibc 2.28+ x86-64, Linux glibc 2.27+ x86-64 Details
statsmodels-0.15.0-cp310-cp310-manylinux_2_27_aarch64.manylinux_2_28_aarch64.whl 11.9 MB CPython 3.10 CPython 3.10 Linux glibc 2.27+ ARM64, Linux glibc 2.28+ ARM64 Details
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Total release size: 644.8 MB

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