ppc64le/linux/: statsmodels-0.15.0 metadata and description

Statistical computations and models for Python

classifiers	Development Status :: 4 - Beta Environment :: Console Programming Language :: Cython Programming Language :: Python :: 3.10 Programming Language :: Python :: 3.11 Programming Language :: Python :: 3.12 Programming Language :: Python :: 3.13 Programming Language :: Python :: 3.14 Operating System :: OS Independent Intended Audience :: End Users/Desktop Intended Audience :: Developers Intended Audience :: Science/Research Natural Language :: English Topic :: Office/Business :: Financial Topic :: Scientific/Engineering Environment :: MetaData :: IBM Python Ecosystem
description_content_type	text/markdown
keywords	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
license_expression	BSD-3-Clause
license_file	LICENSE.txt
maintainer_email	statsmodels Developers <[email protected]>
metadata_version	2.4
project_urls	homepage, https://www.statsmodels.org documentation, https://www.statsmodels.org/stable/index.html repository, https://github.com/statsmodels/statsmodels changelog, https://bashtage.github.io/arch/changes.html bugtracker, https://github.com/statsmodels/statsmodels/issues
provides_extras	test doc dev polars
requires_dist	numpy<3,>=1.23.5 scipy!=1.9.2,>=1.8 pandas!=2.1.0,>=1.4 patsy>=0.5.6 packaging>=21.3 formulaic>=1.1.0 pytest<10,>=8.4.1; extra == "test" pytest-randomly; extra == "test" pytest-run-parallel; extra == "test" pytest-xdist; extra == "test" pytest-cov; extra == "test" coverage[toml]; extra == "test" meson; extra == "test" ninja; sys_platform != "emscripten" and extra == "test" sphinx; extra == "doc" pandas!=2.1.0,>=2.2.2; extra == "doc" pydata-sphinx-theme; extra == "doc" jupyter; extra == "doc" notebook; extra == "doc" nbsphinx; extra == "doc" nbconvert; extra == "doc" pyyaml; extra == "doc" pandas-datareader; extra == "doc" simplegeneric; extra == "doc" seaborn; extra == "doc" numpydoc; extra == "doc" pymc; os_name != "nt" and extra == "doc" arviz; os_name != "nt" and extra == "doc" jinja2; extra == "doc" pickleshare; extra == "doc" pytest<10,>=8.4.1; extra == "doc" cython<4,>=3.0.10; extra == "dev" setuptools_scm~=9.0; extra == "dev" meson-python>=0.18.0; extra == "dev" meson; extra == "dev" ninja; extra == "dev" matplotlib>=3; extra == "dev" colorama; extra == "dev" joblib; extra == "dev" pywinpty; os_name == "nt" and extra == "dev" ruff; extra == "dev" polars>=1.0.0; extra == "polars"
requires_python	>=3.10

File	Tox results	History
statsmodels-0.15.0-cp312-cp312-manylinux_2_34_ppc64le.manylinux_2_39_ppc64le.whl Size 15 MB Type Python Wheel Python 3.12		Uploaded to ppc64le/linux by ppc64le 2026-10-22 08:38:23
statsmodels-0.15.0-cp313-cp313-manylinux_2_34_ppc64le.manylinux_2_39_ppc64le.whl Size 15 MB Type Python Wheel Python 3.13		Uploaded to ppc64le/linux by ppc64le 2026-10-22 08:38:24
statsmodels-0.15.0-cp314-cp314-manylinux_2_34_ppc64le.manylinux_2_39_ppc64le.whl Size 16 MB Type Python Wheel Python 3.14		Uploaded to ppc64le/linux by ppc64le 2026-10-22 08:38:25

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

devpi