Statsmodels Docs Source Api Rst At Main Github

There was an error while loading. Please reload this page. There was an error while loading. Please reload this page. The main statsmodels API is split into models: statsmodels.api: Cross-sectional models and methods.

Canonically imported using import statsmodels.api as sm. statsmodels.tsa.api: Time-series models and methods. Canonically imported using import statsmodels.tsa.api as tsa. statsmodels.formula.api: A convenience interface for specifying models using formula strings and DataFrames. This API directly exposes the from_formula class method of models that support the formula API. Canonically imported using import statsmodels.formula.api as smf

The API focuses on models and the most frequently used statistical test, and tools. Import Paths and Structure explains the design of the two API modules and how importing from the API differs from directly importing from the module where the model is defined. See the detailed topic pages in the User Guide for a complete list of available models, statistics, and tools. The main statsmodels API is split into models: statsmodels.api: Cross-sectional models and methods. Canonically imported using import statsmodels.api as sm.

statsmodels.tsa.api: Time-series models and methods. Canonically imported using import statsmodels.tsa.api as tsa. statsmodels.formula.api: A convenience interface for specifying models using formula strings and DataFrames. This API directly exposes the from_formula class method of models that support the formula API. Canonically imported using import statsmodels.formula.api as smf The API focuses on models and the most frequently used statistical test, and tools.

Import Paths and Structure explains the design of the two API modules and how importing from the API differs from directly importing from the module where the model is defined. See the detailed topic pages in the User Guide for a complete list of available models, statistics, and tools. This document covers the documentation generation and publishing process for the statsmodels library. It explains how documentation is structured, built, and deployed to the web. For information about developing or contributing to documentation content, see the Documentation Contributing Guide. Statsmodels maintains comprehensive documentation including API reference, user guides, tutorials, and examples.

The documentation system uses Sphinx to generate HTML from reStructuredText files (.rst) and docstrings in the Python code. The documentation source is organized hierarchically with different levels of information for different audiences: The documentation uses reStructuredText (.rst) with specialized directives for mathematical notation, code examples, and API documentation: The statsmodels library provides the webdoc() function to access documentation directly from Python: There was an error while loading. Please reload this page.

statsmodels is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator. The results are tested against existing statistical packages to ensure that they are correct. The package is released under the open source Modified BSD (3-clause) license. The online documentation is hosted at statsmodels.org. Since version 0.5.0 of statsmodels, you can use R-style formulas together with pandas data frames to fit your models.

Here is a simple example using ordinary least squares: You can also use numpy arrays instead of formulas: Have a look at dir(results) to see available results. Attributes are described in results.__doc__ and results methods have their own docstrings. When using statsmodels in scientific publication, please consider using the following citation:

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