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        "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",
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