Release 0 14 4 Statsmodels 0 14 4

Leo Migdal

-Dec 4, 2025, 5:49 AM

statsmodels is using github to store the updated documentation. Two version are available: Development, the latest build of the main branch API stability is not guaranteed for new features, although even in this case changes will be made in a backwards compatible way if possible. The stability of a new feature depends on how much time it was already in statsmodels main and how much usage it has already seen. If there are specific known problems or limitations, then they are mentioned in the docstrings.

This release bring official Pyodide support to a statsmodel release. It is otherwise identical to the previous release. Special thanks to Agriya Khetarpal for working through Pyodide-specific issues, and improving other areas of statsmodels while doing so. There was an error while loading. Please reload this page. There was an error while loading.

Please reload this page. This patch release fixes an issue with recent SciPy releases (1.16+) that prevented statsmodels from importing. It also addresses some small changes that improve future compatibility. There was an error while loading. Please reload this page. There was an error while loading.

Please reload this page. The statsmodels developers are pleased to announce the release of 0.14.4. This release contains one feature and no fixes. 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/ For an overview of changes that occurred previous to the 0.5.0 release see Pre 0.5.0 Release History. Statistical computations and models for Python The statsmodels/statsmodels repo was created 13 years ago and the last code push was Yesterday. The project is extremely popular with a mindblowing 10055 github stars!

Here are some statsmodels code examples and snippets. The statsmodels package has 2835 open issues on GitHub Probabilistic modeling and statistical inference in TensorFlow There was an error while loading. Please reload this page. I wrote a package (dcurves) that depends on statsmodels, and I wanted to include support for python 3.12.

However, that introduces some issues with the latest released statsmodels version (0.14.4). The dev version (0.15.0) works perfectly however. Do you know when you are thinking of making a new release to PyPI? statsmodels is using github to store the updated documentation. Two version are available: Development, the latest build of the main branch

API stability is not guaranteed for new features, although even in this case changes will be made in a backwards compatible way if possible. The stability of a new feature depends on how much time it was already in statsmodels main and how much usage it has already seen. If there are specific known problems or limitations, then they are mentioned in the docstrings. TreatmentEffect estimates treatment effect for a binary treatment and potential outcome for a continuous outcome variable using 5 different methods, ipw, ra, aipw, aipw-wls, ipw-ra. Standard errors and inference are based on the joint GMM representation of selection or treatment model, outcome model and effect functions. statsmodels.discrete.truncated_model.HurdleCountModel implements hurdle models for count data with either Poisson or NegativeBinomialP as submodels.

Three left truncated models used for zero truncation are available, statsmodels.discrete.truncated_model.TruncatedLFPoisson, statsmodels.discrete.truncated_model.TruncatedLFNegativeBinomialP and statsmodels.discrete.truncated_model.TruncatedLFGeneralizedPoisson. Models for right censoring at one are implemented but only as support for the hurdle models.

Release 0 14 4 Statsmodels 0 14 4

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