Statsmodels Github

Leo Migdal

-Dec 4, 2025, 7:43 AM

statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. The documentation for the latest release is at The documentation for the development version is at Recent improvements are highlighted in the release notes https://www.statsmodels.org/stable/release/ 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: pip install statsmodels Copy PIP instructions Statistical computations and models for Python

statsmodels is a Python package that provides a complement to scipy for statistical computations including descriptive statistics and estimation and inference for statistical models. The documentation for the latest release is at The documentation for the development version is at You’ve probably seen data where a simple straight line just doesn’t cut it. Maybe you’re modeling bike rentals and temperature, where the relationship looks more like a mountain than a slope. Or perhaps you’re analyzing medical data where effects taper off at extreme values.

This is where Generalized Additive Models come in. Statsmodels provides GAM functionality that handles penalized estimation of smooth terms in generalized linear models, letting you model complex patterns without losing interpretability. Think of GAMs as the middle ground between rigid linear models and black-box machine learning. Linear regression assumes your features have a straight-line relationship with your outcome. Real data laughs at this assumption. Between 0 and 25 degrees Celsius, temperature might have a linear effect on bike rentals, but at higher temperatures the effect levels off or even reverses.

GAMs replace each linear term in your regression equation with a smooth function. Instead of forcing a straight line, they fit flexible curves that adapt to your data’s natural shape. The key difference from something like polynomial regression is that GAMs use splines, which are piecewise polynomials that connect smoothly at specific points called knots. Here’s what makes this useful. You can capture common nonlinear patterns that classic linear models miss, including hockey stick curves where you see sharp changes, or mountain-shaped curves that peak and decline. And unlike random forests or neural networks, you can still explain what your model is doing.

Statsmodels: statistical modeling and econometrics in Python documentation for statsmodels - currently temporary structure and location There was an error while loading. Please reload this page. Statsmodels: statistical modeling and econometrics in Python store for csv files used in examples that are not included in statsmodels.datasets

The easiest way to install statsmodels is to install it as part of the Anaconda distribution, a cross-platform distribution for data analysis and scientific computing. This is the recommended installation method for most users. Instructions for installing from PyPI, source or a development version are also provided. statsmodels supports Python 3.9, 3.10, 3.11, 3.12, and 3.13. statsmodels is available through conda provided by Anaconda. The latest release can be installed using:

To obtain the latest released version of statsmodels using pip: 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:

There was an error while loading. Please reload this page. The statsmodels code base is hosted on Github. To contribute you will need to sign up for a free Github account. We use the Git version control system for development. Git allows many people to work together on the same project.

In a nutshell, it allows you to make changes to the code independent of others who may also be working on the code and allows you to easily contribute your changes to the codebase. It also keeps a complete history of all changes to the code, so you can easily undo changes or see when a change was made, by whom, and why. To install and configure Git, and to setup SSH keys, see setting up git. To learn more about Git, you may want to visit: Below, we describe the bare minimum git commands you need to contribute to statsmodels. State space modeling: Local Linear Trends

Statsmodels Github

People Also Search

Statsmodels Is A Python Package That Provides A Complement To

An Extensive List Of Result Statistics Are Available For Each

You Can Also Use Numpy Arrays Instead Of Formulas: Have

Statsmodels Is A Python Package That Provides A Complement To

This Is Where Generalized Additive Models Come In. Statsmodels Provides