Statistical computations and models for Python

Navigation

Verified details

These details have been verified by PyPI
Maintainers
Avatar for bashtage from gravatar.com bashtage Avatar for josefpktd from gravatar.com josefpktd Avatar for matthew.brett from gravatar.com matthew.brett Avatar for rgommers from gravatar.com rgommers Avatar for Skipper.Seabold from gravatar.com Skipper.Seabold

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Project description

Travis Build Status Azure CI Build Status Appveyor Build Status Coveralls Coverage

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 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

https://pypi-hypernode.com/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

Contributing

Contributions in any form are welcome, including:

  • Documentation improvements

  • Additional tests

  • New features to existing models

  • New models

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

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 details

Verified details

These details have been verified by PyPI
Maintainers
Avatar for bashtage from gravatar.com bashtage Avatar for josefpktd from gravatar.com josefpktd Avatar for matthew.brett from gravatar.com matthew.brett Avatar for rgommers from gravatar.com rgommers Avatar for Skipper.Seabold from gravatar.com Skipper.Seabold

Unverified details

These details have not been verified by PyPI
Project links
Meta
Classifiers

Release history Release notifications | RSS feed

0.14.4

0.14.3

0.14.2

0.14.1

0.14.0

0.14.0rc0 pre-release

0.13.5

0.13.4

0.13.3

0.13.2

0.13.1

0.13.0

0.13.0rc0 pre-release

0.12.2

0.12.1

0.12.0

0.12.0rc0 pre-release

0.11.1

0.11.0

0.11.0rc2 pre-release

This version

0.11.0rc1 pre-release

0.10.2

0.10.1

0.10.0

0.10.0rc2 pre-release

0.9.0

0.9.0rc1 pre-release

0.8.0

0.8.0rc1 pre-release

0.6.1

0.6.0

0.6.0-rc2 pre-release

0.6.0-rc1 pre-release

0.5.0

0.5.0rc1 pre-release

0.4.3

0.4.1

0.4.0

0.4.0rc2 pre-release

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

statsmodels-0.11.0rc1.tar.gz (15.4 MB view details)

Uploaded Source

Built Distributions

statsmodels-0.11.0rc1-cp38-none-win_amd64.whl (8.3 MB view details)

Uploaded CPython 3.8 Windows x86-64

statsmodels-0.11.0rc1-cp38-none-win32.whl (7.8 MB view details)

Uploaded CPython 3.8 Windows x86

statsmodels-0.11.0rc1-cp38-cp38-manylinux1_x86_64.whl (8.7 MB view details)

Uploaded CPython 3.8

statsmodels-0.11.0rc1-cp37-none-win_amd64.whl (8.2 MB view details)

Uploaded CPython 3.7 Windows x86-64

statsmodels-0.11.0rc1-cp37-none-win32.whl (7.7 MB view details)

Uploaded CPython 3.7 Windows x86

statsmodels-0.11.0rc1-cp37-cp37m-manylinux1_x86_64.whl (8.6 MB view details)

Uploaded CPython 3.7m

statsmodels-0.11.0rc1-cp37-cp37m-macosx_10_9_x86_64.whl (8.4 MB view details)

Uploaded CPython 3.7m macOS 10.9+ x86-64

statsmodels-0.11.0rc1-cp36-none-win_amd64.whl (8.2 MB view details)

Uploaded CPython 3.6 Windows x86-64

statsmodels-0.11.0rc1-cp36-none-win32.whl (7.7 MB view details)

Uploaded CPython 3.6 Windows x86

statsmodels-0.11.0rc1-cp36-cp36m-manylinux1_x86_64.whl (8.6 MB view details)

Uploaded CPython 3.6m

statsmodels-0.11.0rc1-cp36-cp36m-manylinux1_i686.whl (8.2 MB view details)

Uploaded CPython 3.6m

statsmodels-0.11.0rc1-cp36-cp36m-macosx_10_9_x86_64.whl (8.4 MB view details)

Uploaded CPython 3.6m macOS 10.9+ x86-64

statsmodels-0.11.0rc1-cp35-none-win_amd64.whl (8.1 MB view details)

Uploaded CPython 3.5 Windows x86-64

statsmodels-0.11.0rc1-cp35-none-win32.whl (7.6 MB view details)

Uploaded CPython 3.5 Windows x86

statsmodels-0.11.0rc1-cp35-cp35m-manylinux1_x86_64.whl (8.6 MB view details)

Uploaded CPython 3.5m

statsmodels-0.11.0rc1-cp35-cp35m-manylinux1_i686.whl (8.1 MB view details)

Uploaded CPython 3.5m

statsmodels-0.11.0rc1-cp35-cp35m-macosx_10_6_intel.whl (8.3 MB view details)

Uploaded CPython 3.5m macOS 10.6+ intel

File details

Details for the file statsmodels-0.11.0rc1.tar.gz.

File metadata

  • Download URL: statsmodels-0.11.0rc1.tar.gz
  • Upload date:
  • Size: 15.4 MB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1.tar.gz
Algorithm Hash digest
SHA256 c791e5a0ef3e321b087cc6971878579375269d29f91475bbbbea391ce47ebf17
MD5 da288d34816b09a30feb8444fb9a07d4
BLAKE2b-256 ebbbb3de2a79ef664ca879f46383e8fd9915d394cb2c38e2ea5bbb3f1187663d

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp38-none-win_amd64.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp38-none-win_amd64.whl
  • Upload date:
  • Size: 8.3 MB
  • Tags: CPython 3.8, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp38-none-win_amd64.whl
Algorithm Hash digest
SHA256 892cd89de060a42032a9eafe32b40a128dc0386eaa6cdc9bc177f34425dcd563
MD5 4239fc4b7a2b800635efac6bf541491f
BLAKE2b-256 e366dd957c8bb97560ee7572a205e71927b131ecc61ea27d549a8a0324917d69

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp38-none-win32.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp38-none-win32.whl
  • Upload date:
  • Size: 7.8 MB
  • Tags: CPython 3.8, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp38-none-win32.whl
Algorithm Hash digest
SHA256 5d964437b8b1a75b2867b634f342b0af17e37d566a4af280b34f592cdb99cac9
MD5 b2d4ca3c36dfa0291f2a451f6ef61a9c
BLAKE2b-256 42617eae02aeea3d6a23c2fd67c85409618ef4434527de34965183f30bbc7d59

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp38-cp38-manylinux1_x86_64.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp38-cp38-manylinux1_x86_64.whl
  • Upload date:
  • Size: 8.7 MB
  • Tags: CPython 3.8
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp38-cp38-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 495be0ef9fed694b1aba0cea544facdb79f389ad17eb68f031deb3c87547a0f6
MD5 f118fe7aa1a547bdefebfc1505ec08c9
BLAKE2b-256 a0d8e05f999d46e4b6bcca1afdcf345e77d5db118d033fc752f203cc4c95ec5c

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp37-none-win_amd64.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp37-none-win_amd64.whl
  • Upload date:
  • Size: 8.2 MB
  • Tags: CPython 3.7, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp37-none-win_amd64.whl
Algorithm Hash digest
SHA256 74437f95301ebad6fe9355152103d051ad4dcdf4bbf9dc868a2f825338cf1314
MD5 f2c1908fb3818b2ed5a9f5536e749e93
BLAKE2b-256 833d6fe99cb7351a60ed119fda6f8395ddaebdf8e69c02367f77e695db04a06d

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp37-none-win32.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp37-none-win32.whl
  • Upload date:
  • Size: 7.7 MB
  • Tags: CPython 3.7, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp37-none-win32.whl
Algorithm Hash digest
SHA256 f834f7a529e521bd522522f763137f810913a6540d7a7947a95bba5515da2974
MD5 5d65b916ca9d4c6273635da31884c635
BLAKE2b-256 f75a8e493c5b04b7904636eca71e6c3da6675394481c7bdf0cb68ccb8219af88

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp37-cp37m-manylinux1_x86_64.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp37-cp37m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 8.6 MB
  • Tags: CPython 3.7m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp37-cp37m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 b3c4f479219b006c5c951b14b1bfd72473511c9fdcde01606e733d6f33bb902f
MD5 01f09e83a64a5bc98872ccc3ed7ee9c7
BLAKE2b-256 403bcbda7c208ef3c73896d6d80b0cfaf86b5ceb4f00e747d6870e69d2bc2f4d

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp37-cp37m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp37-cp37m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 8.4 MB
  • Tags: CPython 3.7m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 451e452da0c284906913866c809f436f876c14d073c5dda2bf9db122baed3358
MD5 ec76bd24dda52a365a808b3383044528
BLAKE2b-256 be264c7ec6ae23421c7be51e429b75f56ad149944a24fa03b3169e6584e2235e

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp36-none-win_amd64.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp36-none-win_amd64.whl
  • Upload date:
  • Size: 8.2 MB
  • Tags: CPython 3.6, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp36-none-win_amd64.whl
Algorithm Hash digest
SHA256 26a53525ddd8c53f16434306d9b80397ac433e5208f8a60e5a165304d795969f
MD5 573fabdb8405b4e38c1dea10797de099
BLAKE2b-256 b1d038dbcbf10831624d1a50482823626726f638cd7b33f85c44471077aef624

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp36-none-win32.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp36-none-win32.whl
  • Upload date:
  • Size: 7.7 MB
  • Tags: CPython 3.6, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp36-none-win32.whl
Algorithm Hash digest
SHA256 050485ba95f0a09e176b14a1032b14d4f906cc437933634dcdd9a25ec49ad777
MD5 8b5b685d81e415463b9bb60d37394678
BLAKE2b-256 3a74cc163b46038afcb8824a1b9d2f4e33fdd25baec826034058f6587fc436c2

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp36-cp36m-manylinux1_x86_64.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp36-cp36m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 8.6 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp36-cp36m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 8154f837094f0867fe5fd0558dd75e9b4dd6e88eeeba7fd10f6b4bc1a94f4f62
MD5 50e7df2d47ca2271cdfd4dc05b33a05a
BLAKE2b-256 5b10fbd9e05689441aa3afe66fe61effd14eeb3c24577ea2efd9ffb896882d6b

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp36-cp36m-manylinux1_i686.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp36-cp36m-manylinux1_i686.whl
  • Upload date:
  • Size: 8.2 MB
  • Tags: CPython 3.6m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp36-cp36m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 958740ec79717b7c26f7f654744335ce373ab2ce7662b80d1231101d835db141
MD5 45679294058f1a0ae12bd59b5fef79ae
BLAKE2b-256 e90811135cb322771b00af8123d8508cc566adc543b8d28b0e83ebb9796aac06

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp36-cp36m-macosx_10_9_x86_64.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp36-cp36m-macosx_10_9_x86_64.whl
  • Upload date:
  • Size: 8.4 MB
  • Tags: CPython 3.6m, macOS 10.9+ x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp36-cp36m-macosx_10_9_x86_64.whl
Algorithm Hash digest
SHA256 c407cc2a7faee7ec1c6b9a2e41541ff0d5081641dcdb29dccccf1f55131f2421
MD5 880430002455b200008b8e22fab325a9
BLAKE2b-256 9b627601c53aba20a116c82d451e4008d001ac30370255f01abac994c444da59

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp35-none-win_amd64.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp35-none-win_amd64.whl
  • Upload date:
  • Size: 8.1 MB
  • Tags: CPython 3.5, Windows x86-64
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp35-none-win_amd64.whl
Algorithm Hash digest
SHA256 53639829cb1453202298a1b98ddb145249f07dec08a0ebfb2dadd370f8913574
MD5 c6e9bafc308ee9a484ba3df26d9ce687
BLAKE2b-256 52c2edfb8d54add7a05d523e3834f5ca3e0211e90ac4239432f91438be04882b

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp35-none-win32.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp35-none-win32.whl
  • Upload date:
  • Size: 7.6 MB
  • Tags: CPython 3.5, Windows x86
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp35-none-win32.whl
Algorithm Hash digest
SHA256 903231076d77f9df6c8cb9e2c8abc31f97a764f477cd0909bc7f57d70a3c280c
MD5 940b44cee618b2cbe18e8e8e926fcf88
BLAKE2b-256 330c8febf4fc14295746ce960a845d09c964d309e21c585518a0cc73627f8a1e

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp35-cp35m-manylinux1_x86_64.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp35-cp35m-manylinux1_x86_64.whl
  • Upload date:
  • Size: 8.6 MB
  • Tags: CPython 3.5m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp35-cp35m-manylinux1_x86_64.whl
Algorithm Hash digest
SHA256 37cc519c5174a8c3dbcfd03977bf60a7906eca72a4be7918efd95709b9a9b729
MD5 62262954bdf9ee03394fd1c9966665b6
BLAKE2b-256 6ccdab41213b1a6086f708e73d4f61330eb4daabe8426cd43bb434c5efc9c6a7

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp35-cp35m-manylinux1_i686.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp35-cp35m-manylinux1_i686.whl
  • Upload date:
  • Size: 8.1 MB
  • Tags: CPython 3.5m
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp35-cp35m-manylinux1_i686.whl
Algorithm Hash digest
SHA256 74b13718fff0d54cb11984f4a3ea5e4f928f48551e6b6b8da8a109825d36799e
MD5 a5148f5cb55b039216b4856a1d10dde9
BLAKE2b-256 3bfa3c25bf2d7b473121faaf6d499e898fc48220638cd45d84817cd502d1e59d

See more details on using hashes here.

File details

Details for the file statsmodels-0.11.0rc1-cp35-cp35m-macosx_10_6_intel.whl.

File metadata

  • Download URL: statsmodels-0.11.0rc1-cp35-cp35m-macosx_10_6_intel.whl
  • Upload date:
  • Size: 8.3 MB
  • Tags: CPython 3.5m, macOS 10.6+ intel
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.13.0 pkginfo/1.5.0.1 requests/2.22.0 setuptools/42.0.2.post20191203 requests-toolbelt/0.9.1 tqdm/4.40.0 CPython/3.7.5

File hashes

Hashes for statsmodels-0.11.0rc1-cp35-cp35m-macosx_10_6_intel.whl
Algorithm Hash digest
SHA256 67c4fe022395c0f9211fa2247c3ba8c3c84a5b3693ea4841bf0abd3c76d24330
MD5 0ecdac36818dd53005fb259640065972
BLAKE2b-256 4f512dd99abf13e1a5136256be7bef063b3b5c88eb31af4bce8b878bdeef2959

See more details on using hashes here.

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page