{ "info": { "author": "", "author_email": "", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Environment :: Console", "Intended Audience :: Developers", "Intended Audience :: End Users/Desktop", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Natural Language :: English", "Operating System :: OS Independent", "Programming Language :: Cython", "Programming Language :: Python :: 2.7", "Programming Language :: Python :: 3.4", "Programming Language :: Python :: 3.5", "Programming Language :: Python :: 3.6", "Programming Language :: Python :: 3.7", "Topic :: Office/Business :: Financial", "Topic :: Scientific/Engineering" ], "description": "|Travis Build Status| |Azure CI Build Status| |Appveyor Build Status| |Coveralls Coverage|\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/version0.9.html\n\nBackups of documentation are available at https://statsmodels.github.io/stable/\nand https://statsmodels.github.io/dev/.\n\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 modle, 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 developement 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 master 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\nInstalling from sources\n=======================\n\nSee INSTALL.txt for requirements or see the documentation\n\nhttps://statsmodels.github.io/dev/install.html\n\nLicense\n=======\n\nModified BSD (3-clause)\n\nDiscussion and Development\n==========================\n\nDiscussions take place on our mailing list.\n\nhttp://groups.google.com/group/pystatsmodels\n\nWe are very interested in feedback about usability and suggestions for\nimprovements.\n\nBug Reports\n===========\n\nBug reports can be submitted to the issue tracker at\n\nhttps://github.com/statsmodels/statsmodels/issues\n\n.. |Travis Build Status| image:: https://travis-ci.org/statsmodels/statsmodels.svg?branch=master\n :target: https://travis-ci.org/statsmodels/statsmodels\n.. |Azure CI Build Status| image:: https://dev.azure.com/statsmodels/statsmodels-testing/_apis/build/status/statsmodels.statsmodels?branch=master\n :target: https://dev.azure.com/statsmodels/statsmodels-testing/_build/latest?definitionId=1&branch=master\n.. |Appveyor Build Status| image:: https://ci.appveyor.com/api/projects/status/gx18sd2wc63mfcuc/branch/master?svg=true\n :target: https://ci.appveyor.com/project/josef-pkt/statsmodels/branch/master\n.. |Coveralls Coverage| image:: https://coveralls.io/repos/github/statsmodels/statsmodels/badge.svg?branch=master\n :target: https://coveralls.io/github/statsmodels/statsmodels?branch=master\n\n\n", "description_content_type": "", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://www.statsmodels.org/", "keywords": "", "license": "BSD License", "maintainer": "Josef Perktold, Chad Fulton, Kerby Shedden", "maintainer_email": "prateek.3211@gmail.com", "name": "statsmodels-dq", "package_url": "https://pypi.org/project/statsmodels-dq/", "platform": "any", "project_url": "https://pypi.org/project/statsmodels-dq/", "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/prateek3211/statsmodels" }, "release_url": "https://pypi.org/project/statsmodels-dq/3.0/", "requires_dist": [ "numpy (>=1.11)", "scipy (>=0.18)", "pandas (>=0.19)", "patsy (>=0.4.0)", "cython (>=0.24) ; 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