statsmodels


Namestatsmodels JSON
Version 0.14.4 PyPI version JSON
download
home_pagehttps://www.statsmodels.org/
SummaryStatistical computations and models for Python
upload_time2024-10-03 16:15:36
maintainerstatsmodels Developers
docs_urlNone
authorNone
requires_python>=3.9
licenseBSD License
keywords
VCS
bugtrack_url
requirements numpy scipy pandas patsy packaging
Travis-CI No Travis.
coveralls test coverage No coveralls.
            .. image:: docs/source/images/statsmodels-logo-v2-horizontal.svg
  :alt: Statsmodels logo

|PyPI Version| |Conda Version| |License| |Azure CI Build Status|
|Codecov Coverage| |Coveralls Coverage| |PyPI downloads| |Conda downloads|

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.

.. code:: bash

   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

.. |Azure CI Build Status| image:: https://dev.azure.com/statsmodels/statsmodels-testing/_apis/build/status/statsmodels.statsmodels?branchName=main
   :target: https://dev.azure.com/statsmodels/statsmodels-testing/_build/latest?definitionId=1&branchName=main
.. |Codecov Coverage| image:: https://codecov.io/gh/statsmodels/statsmodels/branch/main/graph/badge.svg
   :target: https://codecov.io/gh/statsmodels/statsmodels
.. |Coveralls Coverage| image:: https://coveralls.io/repos/github/statsmodels/statsmodels/badge.svg?branch=main
   :target: https://coveralls.io/github/statsmodels/statsmodels?branch=main
.. |PyPI downloads| image:: https://img.shields.io/pypi/dm/statsmodels?label=PyPI%20Downloads
   :alt: PyPI - Downloads
   :target: https://pypi.org/project/statsmodels/
.. |Conda downloads| image:: https://img.shields.io/conda/dn/conda-forge/statsmodels.svg?label=Conda%20downloads
   :target: https://anaconda.org/conda-forge/statsmodels/
.. |PyPI Version| image:: https://img.shields.io/pypi/v/statsmodels.svg
   :target: https://pypi.org/project/statsmodels/
.. |Conda Version| image:: https://anaconda.org/conda-forge/statsmodels/badges/version.svg
   :target: https://anaconda.org/conda-forge/statsmodels/
.. |License| image:: https://img.shields.io/pypi/l/statsmodels.svg
   :target: https://github.com/statsmodels/statsmodels/blob/main/LICENSE.txt

            

Raw data

            {
    "_id": null,
    "home_page": "https://www.statsmodels.org/",
    "name": "statsmodels",
    "maintainer": "statsmodels Developers",
    "docs_url": null,
    "requires_python": ">=3.9",
    "maintainer_email": "pystatsmodels@googlegroups.com",
    "keywords": null,
    "author": null,
    "author_email": null,
    "download_url": "https://files.pythonhosted.org/packages/1f/3b/963a015dd8ea17e10c7b0e2f14d7c4daec903baf60a017e756b57953a4bf/statsmodels-0.14.4.tar.gz",
    "platform": "any",
    "description": ".. image:: docs/source/images/statsmodels-logo-v2-horizontal.svg\n  :alt: Statsmodels logo\n\n|PyPI Version| |Conda Version| |License| |Azure CI Build Status|\n|Codecov Coverage| |Coveralls Coverage| |PyPI downloads| |Conda downloads|\n\nAbout statsmodels\n=================\n\nstatsmodels is a Python package that provides a complement to scipy for\nstatistical computations including descriptive statistics and estimation\nand inference for statistical models.\n\n\nDocumentation\n=============\n\nThe documentation for the latest release is at\n\nhttps://www.statsmodels.org/stable/\n\nThe documentation for the development version is at\n\nhttps://www.statsmodels.org/dev/\n\nRecent improvements are highlighted in the release notes\n\nhttps://www.statsmodels.org/stable/release/\n\nBackups of documentation are available at https://statsmodels.github.io/stable/\nand https://statsmodels.github.io/dev/.\n\n\nMain Features\n=============\n\n* Linear regression models:\n\n  - Ordinary least squares\n  - Generalized least squares\n  - Weighted least squares\n  - Least squares with autoregressive errors\n  - Quantile regression\n  - Recursive least squares\n\n* Mixed Linear Model with mixed effects and variance components\n* GLM: Generalized linear models with support for all of the one-parameter\n  exponential family distributions\n* Bayesian Mixed GLM for Binomial and Poisson\n* GEE: Generalized Estimating Equations for one-way clustered or longitudinal data\n* Discrete models:\n\n  - Logit and Probit\n  - Multinomial logit (MNLogit)\n  - Poisson and Generalized Poisson regression\n  - Negative Binomial regression\n  - Zero-Inflated Count models\n\n* RLM: Robust linear models with support for several M-estimators.\n* Time Series Analysis: models for time series analysis\n\n  - Complete StateSpace modeling framework\n\n    - Seasonal ARIMA and ARIMAX models\n    - VARMA and VARMAX models\n    - Dynamic Factor models\n    - Unobserved Component models\n\n  - Markov switching models (MSAR), also known as Hidden Markov Models (HMM)\n  - Univariate time series analysis: AR, ARIMA\n  - Vector autoregressive models, VAR and structural VAR\n  - Vector error correction model, VECM\n  - exponential smoothing, Holt-Winters\n  - Hypothesis tests for time series: unit root, cointegration and others\n  - Descriptive statistics and process models for time series analysis\n\n* Survival analysis:\n\n  - Proportional hazards regression (Cox models)\n  - Survivor function estimation (Kaplan-Meier)\n  - Cumulative incidence function estimation\n\n* Multivariate:\n\n  - Principal Component Analysis with missing data\n  - Factor Analysis with rotation\n  - MANOVA\n  - Canonical Correlation\n\n* Nonparametric statistics: Univariate and multivariate kernel density estimators\n* Datasets: Datasets used for examples and in testing\n* Statistics: a wide range of statistical tests\n\n  - diagnostics and specification tests\n  - goodness-of-fit and normality tests\n  - functions for multiple testing\n  - various additional statistical tests\n\n* Imputation with MICE, regression on order statistic and Gaussian imputation\n* Mediation analysis\n* Graphics includes plot functions for visual analysis of data and model results\n\n* I/O\n\n  - Tools for reading Stata .dta files, but pandas has a more recent version\n  - Table output to ascii, latex, and html\n\n* Miscellaneous models\n* Sandbox: statsmodels contains a sandbox folder with code in various stages of\n  development and testing which is not considered \"production ready\".  This covers\n  among others\n\n  - Generalized method of moments (GMM) estimators\n  - Kernel regression\n  - Various extensions to scipy.stats.distributions\n  - Panel data models\n  - Information theoretic measures\n\nHow to get it\n=============\nThe main branch on GitHub is the most up to date code\n\nhttps://www.github.com/statsmodels/statsmodels\n\nSource download of release tags are available on GitHub\n\nhttps://github.com/statsmodels/statsmodels/tags\n\nBinaries and source distributions are available from PyPi\n\nhttps://pypi.org/project/statsmodels/\n\nBinaries can be installed in Anaconda\n\nconda install statsmodels\n\n\nGetting the latest code\n=======================\n\nInstalling the most recent nightly wheel\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nThe most recent nightly wheel can be installed using pip.\n\n.. code:: bash\n\n   python -m pip install -i https://pypi.anaconda.org/scientific-python-nightly-wheels/simple statsmodels --upgrade --use-deprecated=legacy-resolver\n\nInstalling from sources\n~~~~~~~~~~~~~~~~~~~~~~~\n\nSee INSTALL.txt for requirements or see the documentation\n\nhttps://statsmodels.github.io/dev/install.html\n\nContributing\n============\nContributions in any form are welcome, including:\n\n* Documentation improvements\n* Additional tests\n* New features to existing models\n* New models\n\nhttps://www.statsmodels.org/stable/dev/test_notes\n\nfor instructions on installing statsmodels in *editable* mode.\n\nLicense\n=======\n\nModified BSD (3-clause)\n\nDiscussion and Development\n==========================\n\nDiscussions take place on the mailing list\n\nhttps://groups.google.com/group/pystatsmodels\n\nand in the issue tracker. We are very interested in feedback\nabout usability and suggestions for improvements.\n\nBug Reports\n===========\n\nBug reports can be submitted to the issue tracker at\n\nhttps://github.com/statsmodels/statsmodels/issues\n\n.. |Azure CI Build Status| image:: https://dev.azure.com/statsmodels/statsmodels-testing/_apis/build/status/statsmodels.statsmodels?branchName=main\n   :target: https://dev.azure.com/statsmodels/statsmodels-testing/_build/latest?definitionId=1&branchName=main\n.. |Codecov Coverage| image:: https://codecov.io/gh/statsmodels/statsmodels/branch/main/graph/badge.svg\n   :target: https://codecov.io/gh/statsmodels/statsmodels\n.. |Coveralls Coverage| image:: https://coveralls.io/repos/github/statsmodels/statsmodels/badge.svg?branch=main\n   :target: https://coveralls.io/github/statsmodels/statsmodels?branch=main\n.. |PyPI downloads| image:: https://img.shields.io/pypi/dm/statsmodels?label=PyPI%20Downloads\n   :alt: PyPI - Downloads\n   :target: https://pypi.org/project/statsmodels/\n.. |Conda downloads| image:: https://img.shields.io/conda/dn/conda-forge/statsmodels.svg?label=Conda%20downloads\n   :target: https://anaconda.org/conda-forge/statsmodels/\n.. |PyPI Version| image:: https://img.shields.io/pypi/v/statsmodels.svg\n   :target: https://pypi.org/project/statsmodels/\n.. |Conda Version| image:: https://anaconda.org/conda-forge/statsmodels/badges/version.svg\n   :target: https://anaconda.org/conda-forge/statsmodels/\n.. |License| image:: https://img.shields.io/pypi/l/statsmodels.svg\n   :target: https://github.com/statsmodels/statsmodels/blob/main/LICENSE.txt\n",
    "bugtrack_url": null,
    "license": "BSD License",
    "summary": "Statistical computations and models for Python",
    "version": "0.14.4",
    "project_urls": {
        "Bug Tracker": "https://github.com/statsmodels/statsmodels/issues",
        "Documentation": "https://www.statsmodels.org/stable/index.html",
        "Homepage": "https://www.statsmodels.org/",
        "Source Code": "https://github.com/statsmodels/statsmodels"
    },
    "split_keywords": [],
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "af2c23bf5ad9e8a77c0c8d9750512bff89e32154dea91998114118e0e147ae67",
                "md5": "63639a8f9956ff5f0100bffb1cf65b5d",
                "sha256": "7a62f1fc9086e4b7ee789a6f66b3c0fc82dd8de1edda1522d30901a0aa45e42b"
            },
            "downloads": -1,
            "filename": "statsmodels-0.14.4-cp310-cp310-macosx_10_9_x86_64.whl",
            "has_sig": false,
            "md5_digest": "63639a8f9956ff5f0100bffb1cf65b5d",
            "packagetype": "bdist_wheel",
            "python_version": "cp310",
            "requires_python": ">=3.9",
            "size": 10216574,
            "upload_time": "2024-10-03T16:13:31",
            "upload_time_iso_8601": "2024-10-03T16:13:31.472276Z",
            "url": "https://files.pythonhosted.org/packages/af/2c/23bf5ad9e8a77c0c8d9750512bff89e32154dea91998114118e0e147ae67/statsmodels-0.14.4-cp310-cp310-macosx_10_9_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "baa52f09ab918296e534ea5d132e90efac51ae12ff15992d77539bbfca1158fa",
                "md5": "c9a4b976901f97b79edf406da986e4b0",
                "sha256": "46ac7ddefac0c9b7b607eed1d47d11e26fe92a1bc1f4d9af48aeed4e21e87981"
            },
            "downloads": -1,
            "filename": "statsmodels-0.14.4-cp310-cp310-macosx_11_0_arm64.whl",
            "has_sig": false,
            "md5_digest": "c9a4b976901f97b79edf406da986e4b0",
            "packagetype": "bdist_wheel",
            "python_version": "cp310",
            "requires_python": ">=3.9",
            "size": 9912430,
            "upload_time": "2024-10-03T16:13:44",
            "upload_time_iso_8601": "2024-10-03T16:13:44.683095Z",
            "url": "https://files.pythonhosted.org/packages/ba/a5/2f09ab918296e534ea5d132e90efac51ae12ff15992d77539bbfca1158fa/statsmodels-0.14.4-cp310-cp310-macosx_11_0_arm64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "5cf9205130cceeda0eebd5a1a58c04e060c2f87a1d63cbbe37a9caa0fcb50c68",
                "md5": "448e2ff29327e96b6a82f2a271602f4c",
                "sha256": "9729642884147ee9db67b5a06a355890663d21f76ed608a56ac2ad98b94d201a"
            },
            "downloads": -1,
            "filename": "statsmodels-0.14.4-cp310-cp310-win_amd64.whl",
            "has_sig": false,
            "md5_digest": "448e2ff29327e96b6a82f2a271602f4c",
            "packagetype": "bdist_wheel",
            "python_version": "cp310",
            "requires_python": ">=3.9",
            "size": 9845796,
            "upload_time": "2024-10-03T16:13:58",
            "upload_time_iso_8601": "2024-10-03T16:13:58.307169Z",
            "url": "https://files.pythonhosted.org/packages/5c/f9/205130cceeda0eebd5a1a58c04e060c2f87a1d63cbbe37a9caa0fcb50c68/statsmodels-0.14.4-cp310-cp310-win_amd64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "4888326f5f689e69d9c47a68a22ffdd20a6ea6410b53918f9a8e63380dfc181c",
                "md5": "2e610d649ff1a76a6d9b9ae1dbe28dae",
                "sha256": "5ed7e118e6e3e02d6723a079b8c97eaadeed943fa1f7f619f7148dfc7862670f"
            },
            "downloads": -1,
            "filename": "statsmodels-0.14.4-cp311-cp311-macosx_10_9_x86_64.whl",
            "has_sig": false,
            "md5_digest": "2e610d649ff1a76a6d9b9ae1dbe28dae",
            "packagetype": "bdist_wheel",
            "python_version": "cp311",
            "requires_python": ">=3.9",
            "size": 10221032,
            "upload_time": "2024-10-03T16:22:48",
            "upload_time_iso_8601": "2024-10-03T16:22:48.191622Z",
            "url": "https://files.pythonhosted.org/packages/48/88/326f5f689e69d9c47a68a22ffdd20a6ea6410b53918f9a8e63380dfc181c/statsmodels-0.14.4-cp311-cp311-macosx_10_9_x86_64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "4be4f9e96896278308e17dfd4f60a84826c48117674c980234ee38f59ab28a12",
                "md5": "cf829481e44d96990c57f00783ac7a24",
                "sha256": "a6087ecb0714f7c59eb24c22781491e6f1cfffb660b4740e167625ca4f052056"
            },
            "downloads": -1,
            "filename": "statsmodels-0.14.4-cp311-cp311-win_amd64.whl",
            "has_sig": false,
            "md5_digest": "cf829481e44d96990c57f00783ac7a24",
            "packagetype": "bdist_wheel",
            "python_version": "cp311",
            "requires_python": ">=3.9",
            "size": 9853281,
            "upload_time": "2024-10-03T16:14:11",
            "upload_time_iso_8601": "2024-10-03T16:14:11.019602Z",
            "url": "https://files.pythonhosted.org/packages/4b/e4/f9e96896278308e17dfd4f60a84826c48117674c980234ee38f59ab28a12/statsmodels-0.14.4-cp311-cp311-win_amd64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "67d8ac30cf4cf97adaa48548be57e7cf02e894f31b45fd55bf9213358d9781c9",
                "md5": "8e78a10632552a72c8c609c367c30cf3",
                "sha256": "17672b30c6b98afe2b095591e32d1d66d4372f2651428e433f16a3667f19eabb"
            },
            "downloads": -1,
            "filename": "statsmodels-0.14.4-cp312-cp312-macosx_11_0_arm64.whl",
            "has_sig": false,
            "md5_digest": "8e78a10632552a72c8c609c367c30cf3",
            "packagetype": "bdist_wheel",
            "python_version": "cp312",
            "requires_python": ">=3.9",
            "size": 9912317,
            "upload_time": "2024-10-03T16:22:29",
            "upload_time_iso_8601": "2024-10-03T16:22:29.504038Z",
            "url": "https://files.pythonhosted.org/packages/67/d8/ac30cf4cf97adaa48548be57e7cf02e894f31b45fd55bf9213358d9781c9/statsmodels-0.14.4-cp312-cp312-macosx_11_0_arm64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "599ae466a1b887a1441141e52dbcc98152f013d85076576da6eed2357f2016ae",
                "md5": "f696e6226e34ec25d45443262291c150",
                "sha256": "7f7917a51766b4e074da283c507a25048ad29a18e527207883d73535e0dc6184"
            },
            "downloads": -1,
            "filename": "statsmodels-0.14.4-cp312-cp312-win_amd64.whl",
            "has_sig": false,
            "md5_digest": "f696e6226e34ec25d45443262291c150",
            "packagetype": "bdist_wheel",
            "python_version": "cp312",
            "requires_python": ">=3.9",
            "size": 9823866,
            "upload_time": "2024-10-03T16:14:23",
            "upload_time_iso_8601": "2024-10-03T16:14:23.828107Z",
            "url": "https://files.pythonhosted.org/packages/59/9a/e466a1b887a1441141e52dbcc98152f013d85076576da6eed2357f2016ae/statsmodels-0.14.4-cp312-cp312-win_amd64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "1debcb8b01f5edf8f135eb3d0553d159db113a35b2948d0e51eeb735e7ae09ea",
                "md5": "fcbb3bf62f28401cfa4cc33c00b94e56",
                "sha256": "81030108d27aecc7995cac05aa280cf8c6025f6a6119894eef648997936c2dd0"
            },
            "downloads": -1,
            "filename": "statsmodels-0.14.4-cp313-cp313-win_amd64.whl",
            "has_sig": false,
            "md5_digest": "fcbb3bf62f28401cfa4cc33c00b94e56",
            "packagetype": "bdist_wheel",
            "python_version": "cp313",
            "requires_python": ">=3.9",
            "size": 9817574,
            "upload_time": "2024-10-03T16:14:37",
            "upload_time_iso_8601": "2024-10-03T16:14:37.461606Z",
            "url": "https://files.pythonhosted.org/packages/1d/eb/cb8b01f5edf8f135eb3d0553d159db113a35b2948d0e51eeb735e7ae09ea/statsmodels-0.14.4-cp313-cp313-win_amd64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "f91bf7c77e5a8c4aba97bca8c730cf4087b102f1cc796d9b71e3430dc31f9e57",
                "md5": "2892c423f838d79a67202e338f360f46",
                "sha256": "8286f69a5e1d0e0b366ffed5691140c83d3efc75da6dbf34a3d06e88abfaaab6"
            },
            "downloads": -1,
            "filename": "statsmodels-0.14.4-cp39-cp39-win_amd64.whl",
            "has_sig": false,
            "md5_digest": "2892c423f838d79a67202e338f360f46",
            "packagetype": "bdist_wheel",
            "python_version": "cp39",
            "requires_python": ">=3.9",
            "size": 9858334,
            "upload_time": "2024-10-03T16:14:50",
            "upload_time_iso_8601": "2024-10-03T16:14:50.387835Z",
            "url": "https://files.pythonhosted.org/packages/f9/1b/f7c77e5a8c4aba97bca8c730cf4087b102f1cc796d9b71e3430dc31f9e57/statsmodels-0.14.4-cp39-cp39-win_amd64.whl",
            "yanked": false,
            "yanked_reason": null
        },
        {
            "comment_text": "",
            "digests": {
                "blake2b_256": "1f3b963a015dd8ea17e10c7b0e2f14d7c4daec903baf60a017e756b57953a4bf",
                "md5": "38acb6af5decb5abea4721b6481ddb1b",
                "sha256": "5d69e0f39060dc72c067f9bb6e8033b6dccdb0bae101d76a7ef0bcc94e898b67"
            },
            "downloads": -1,
            "filename": "statsmodels-0.14.4.tar.gz",
            "has_sig": false,
            "md5_digest": "38acb6af5decb5abea4721b6481ddb1b",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": ">=3.9",
            "size": 20354802,
            "upload_time": "2024-10-03T16:15:36",
            "upload_time_iso_8601": "2024-10-03T16:15:36.273075Z",
            "url": "https://files.pythonhosted.org/packages/1f/3b/963a015dd8ea17e10c7b0e2f14d7c4daec903baf60a017e756b57953a4bf/statsmodels-0.14.4.tar.gz",
            "yanked": false,
            "yanked_reason": null
        }
    ],
    "upload_time": "2024-10-03 16:15:36",
    "github": true,
    "gitlab": false,
    "bitbucket": false,
    "codeberg": false,
    "github_user": "statsmodels",
    "github_project": "statsmodels",
    "travis_ci": false,
    "coveralls": false,
    "github_actions": true,
    "requirements": [
        {
            "name": "numpy",
            "specs": [
                [
                    ">=",
                    "1.22.3"
                ],
                [
                    "<",
                    "3"
                ]
            ]
        },
        {
            "name": "scipy",
            "specs": [
                [
                    "!=",
                    "1.9.2"
                ],
                [
                    ">=",
                    "1.8"
                ]
            ]
        },
        {
            "name": "pandas",
            "specs": [
                [
                    ">=",
                    "1.4"
                ],
                [
                    "!=",
                    "2.1.0"
                ]
            ]
        },
        {
            "name": "patsy",
            "specs": [
                [
                    ">=",
                    "0.5.6"
                ]
            ]
        },
        {
            "name": "packaging",
            "specs": [
                [
                    ">=",
                    "21.3"
                ]
            ]
        }
    ],
    "lcname": "statsmodels"
}
        
Elapsed time: 0.40601s