Bayes Toolbox
The facts below are what the maker publishes about Bayes Toolbox, checked and dated. Our editors have not written about it yet.
Plans and prices
From Bayes Toolbox's own pricing page, checked 12 Oct 2026.
| Plan | Price | What it gives you |
|---|---|---|
| Free / MIT licensed | Free | Python package · install with pip or from source |
The facts
Each one read from Bayes Toolbox's own pages, with the date it was checked.
What it does
- Bayesian analysis
- Yeschecked 23 Sep 2026
- Convenience features
- The package includes functions for standardizing variables, parsing categorical variables, and implementing sum-to-zero constraints in ANOVA-like models.checked 12 Oct 2026
- Dependencies
- The project lists Aesara, ArviZ, NumPy, pandas, and PyMC as dependencies.checked 12 Oct 2026
- Education
- The project says it can help people learning to implement Bayesian models in Python and PyMC while studying Doing Bayesian Data Analysis.checked 12 Oct 2026
- Examples and tutorials
- The project provides example notebooks and tutorials, including examples adapted from Doing Bayesian Data Analysis.checked 12 Oct 2026
- Installation
- The recommended installation method is pip, using the command `pip install bayes_toolbox`.checked 12 Oct 2026
- Integrations
- The example workflow imports the package alongside PyMC, ArviZ, NumPy, pandas, seaborn, matplotlib, and Xarray.checked 12 Oct 2026
- Intended users
- The project aims to make Bayesian statistics accessible to less experienced Python users in research and education.checked 12 Oct 2026
- Jupyter
- The documentation explains how to add the package's virtual environment as a Jupyter kernel.checked 12 Oct 2026
- License
- The software is distributed for free under the MIT License.checked 12 Oct 2026
- Model assumptions
- The project says its included models use mostly diffuse, uninformative priors and advises checking examples to confirm that a model's assumptions suit a particular question.checked 12 Oct 2026
- Models
- Included models cover group comparisons, regression, hierarchical ANOVA and ANCOVA, logistic regression, and binary-outcome meta-analysis.checked 12 Oct 2026
- Model utilities
- The package includes functions for standardizing variables, parsing categorical variables, and applying sum-to-zero constraints in ANOVA-like models.checked 12 Oct 2026
- Notebook support
- The documentation explains how to add the package's virtual environment as a Jupyter notebook kernel.checked 12 Oct 2026
- Purpose
- Bayes Toolbox is a Python package for running Bayesian analyses through a simple interface.checked 12 Oct 2026
- Python compatibility
- The FAQ says that as of December 2023, pip installation does not work with Python 3.12 or later and suggests Python 3.11 or installing dependencies from environment.yml.checked 12 Oct 2026
- Python version limitation
- The FAQ states that, as of December 2023, pip installation does not work with Python 3.12 or above and suggests Python 3.11 or installing dependencies from environment.yml.checked 12 Oct 2026
- Research use
- It wraps Bayesian generalized linear models in functions analogous to common frequentist tests used in behavioral and neural sciences.checked 12 Oct 2026
- Sampling workflow
- Model functions return PyMC model and ArviZ InferenceData objects.checked 12 Oct 2026
- Support
- The project directs users with code issues or suggestions to open a GitHub issue.checked 12 Oct 2026
- Validation
- The project says its models were run on data from Doing Bayesian Data Analysis and compared with the textbook and another Python port's results.checked 12 Oct 2026
Money
- Free plan
- Yeschecked 23 Sep 2026
