Documentation System Statsmodels Statsmodels Deepwiki

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: 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. A Go implementation of the Model Context Protocol (MCP), enabling seamless integration between LLM applications and external data sources and tools. Fully local web research and report writing assistant Utilities intended for use with Llama models. 🤗 Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX. 🦜🔗 Build context-aware reasoning applications

This wiki page assembles a collection "official" and user-contributed examples, tutorials and recipes for statsmodels. A set of notebook examples are provided as part of the official Statsmodels documentation. If you have an interesting example, or if you can write a quick tutorial describing one of statsmodels' features, please consider posting it here. We would be delighted! Feel free to post your example file in any of the common formats (e.g. .py, .rst, .html) and to use any hosting service you like.

One very slick, free, and convenient alternative is to: www.dropbox.com/scl/fo/mylhfjbpl2zlc5z5m4prq/h?dl=0&rlkey=li52chs6rcl6lejspde6n0oqf This document explains how to install, set up a development environment, and contribute to the Statsmodels project. It covers installation methods, development setup, building and testing procedures, and documentation generation. For information about using the installed package, see the corresponding user documentation. Statsmodels can be installed in several ways depending on your needs:

Sources: docs/source/install.rst1-97 INSTALL.txt69-107 Sources: docs/source/install.rst128-165 INSTALL.txt1-67 requirements.txt1-8 setup.py46-71 The build process is managed by setuptools with custom enhancements for Cython extensions. The build configuration is defined in: statsmodels is a Python package that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests and exploring statistical data. Compare nested models with F-tests or likelihood ratio tests:

Focus on effect sizes, not just p-values: Use robust standard errors when necessary: Using p-values alone for variable selection: 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. statsmodels supports specifying models using R-style formulas and pandas DataFrames. 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. Please use following citation to cite statsmodels in scientific publications: Statsmodels is a Python library for statistical models and quantitative analysis. It provides a comprehensive suite of tools for model estimation, statistical tests, and data exploration. The library emphasizes statistical computation, model inspection, and rigorous statistical methods rather than machine learning or predictive modeling. This overview introduces the core architecture, main model families, and common usage patterns in statsmodels.

For more specific information about individual model types, see the following wiki pages: Statsmodels is organized around several model families that share common base classes and interfaces. At the highest level, models inherit from the base Model class, with specialized models extending this foundation to implement specific statistical techniques. Sources: statsmodels/base/model.py65-188 statsmodels/regression/linear_model.py193-449), statsmodels/discrete/discrete_model.py173-931 statsmodels/genmod/generalized_linear_model.py82-292 statsmodels/tsa/base/tsa_model.py98-135 The core architecture follows several key principles:

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