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README.md

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<img src="https://raw.githubusercontent.com/bambinos/bambi/main/docs/logos/RGB/Bambi_logo.png" width=200></img>
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[![PyPi version](https://badge.fury.io/py/bambi.svg)](https://badge.fury.io/py/bambi)
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[![codecov](https://codecov.io/gh/bambinos/bambi/branch/master/graph/badge.svg?token=ZqH0KCLKAE)](https://codecov.io/gh/bambinos/bambi)
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docs/index.qmd

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<center>
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[![PyPi version](https://badge.fury.io/py/bambi.svg)](https://badge.fury.io/py/bambi)
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[![codecov](https://codecov.io/gh/bambinos/bambi/branch/master/graph/badge.svg?token=ZqH0KCLKAE)](https://codecov.io/gh/bambinos/bambi)
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[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
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</center>
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Bambi is a high-level Bayesian model-building interface written in Python.
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Bambi is a high-level Bayesian model-building interface written in Python.
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It works with the [PyMC](https://www.pymc.io/) probabilistic programming framework and is
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designed to make it extremely easy to fit Bayesian mixed-effects models common in biology,
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social sciences and other disciplines.
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## Dependencies
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Bambi is tested on Python 3.11+ and depends on ArviZ, formulae, NumPy, pandas and PyMC
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(see [pyproject.toml](https://github.com/bambinos/bambi/blob/main/pyproject.toml)
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Bambi is tested on Python 3.11+ and depends on ArviZ, formulae, NumPy, pandas and PyMC
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(see [pyproject.toml](https://github.com/bambinos/bambi/blob/main/pyproject.toml)
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for version information).
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## Installation
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Bambi is available from the Python Package Index at
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[https://pypi.org/project/bambi](https://pypi.org/project/bambi), alternatively it can be
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Bambi is available from the Python Package Index at
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[https://pypi.org/project/bambi](https://pypi.org/project/bambi), alternatively it can be
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installed using Conda.
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### PyPI
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```
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Alternatively, if you want the bleeding edge version of the package, you can install from GitHub:
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```bash
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pip install git+https://github.com/bambinos/bambi.git
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```
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# See first rows
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data.head()
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# Initialize the fixed effects only model
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model = bmb.Model('Reaction ~ Days', data)
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# Use ArviZ to plot the results
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az.plot_trace(results)
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```
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```
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```
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Reaction Days Subject
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0 249.5600 0 308
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1 258.7047 1 308
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fitted = model.fit()
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```
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After this, we can evaluate the model as before.
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After this, we can evaluate the model as before.
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### More
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## Contributing
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We welcome contributions from interested individuals or groups!
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For information about contributing to Bambi, check out our instructions, policies,
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We welcome contributions from interested individuals or groups!
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For information about contributing to Bambi, check out our instructions, policies,
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and guidelines [here](https://github.com/bambinos/bambi/blob/main/CONTRIBUTING.md).
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## Contributors

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