Bayes Toolbox

Spec sheet

Purpose

Bayes Toolbox is a Python package for running Bayesian analyses through a simple interface.Read 12 Oct 2026 · hyosubkim.github.io

Intended users

The project aims to make Bayesian statistics accessible to less experienced Python users in research and education.Read 12 Oct 2026 · hyosubkim.github.io

Free plan

YesRead 23 Sep 2026 · hyosubkim.github.io

Integrations

The example workflow imports the package alongside PyMC, ArviZ, NumPy, pandas, seaborn, matplotlib, and Xarray.Read 12 Oct 2026 · hyosubkim.github.io

Support

The project directs users with installation problems or other issues to open a GitHub issue.Read 12 Oct 2026 · hyosubkim.github.io

License

The software is distributed for free under the MIT License.Read 12 Oct 2026 · github.com

Bayesian analysis

YesRead 23 Sep 2026 · hyosubkim.github.io

Convenience features

The package includes functions for standardizing variables, parsing categorical variables, and implementing sum-to-zero constraints in ANOVA-like models.Read 12 Oct 2026 · hyosubkim.github.io

Dependencies

The project lists Aesara, ArviZ, NumPy, pandas, and PyMC as dependencies.Read 12 Oct 2026 · hyosubkim.github.io

Education

The project says it can help people learning to implement Bayesian models in Python and PyMC while studying Doing Bayesian Data Analysis.Read 12 Oct 2026 · hyosubkim.github.io

Examples and tutorials

The project provides example notebooks and tutorials, including examples adapted from Doing Bayesian Data Analysis.Read 12 Oct 2026 · hyosubkim.github.io

Installation

The recommended installation method is pip, using the command `pip install bayes_toolbox`.Read 12 Oct 2026 · hyosubkim.github.io

Jupyter

The documentation explains how to add the package's virtual environment as a Jupyter kernel.Read 12 Oct 2026 · hyosubkim.github.io

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.Read 12 Oct 2026 · hyosubkim.github.io

Model utilities

The package includes functions for standardizing variables, parsing categorical variables, and applying sum-to-zero constraints in ANOVA-like models.Read 12 Oct 2026 · hyosubkim.github.io

Models

Included models cover group comparisons, regression, hierarchical ANOVA and ANCOVA, logistic regression, and binary-outcome meta-analysis.Read 12 Oct 2026 · hyosubkim.github.io

Notebook support

The documentation explains how to add the package's virtual environment as a Jupyter notebook kernel.Read 12 Oct 2026 · hyosubkim.github.io

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.Read 12 Oct 2026 · hyosubkim.github.io

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.Read 12 Oct 2026 · hyosubkim.github.io

Research use

It wraps Bayesian generalized linear models in functions analogous to common frequentist tests used in behavioral and neural sciences.Read 12 Oct 2026 · hyosubkim.github.io

Sampling workflow

Model functions return PyMC model and ArviZ InferenceData objects.Read 12 Oct 2026 · hyosubkim.github.io

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.Read 12 Oct 2026 · hyosubkim.github.io

Plans and prices

Every Bayes Toolbox plan and price →

Where Specifiction places it

• Statistical Analysis Software#52 of 1067.8