Metadata-Version: 2.1
Name: bambi
Version: 0.6.2
Summary: BAyesian Model Building Interface in Python
Home-page: http://github.com/bambinos/bambi
Maintainer: Tal Yarkoni
Maintainer-email: tyarkoni@gmail.com
License: MIT
Download-URL: https://github.com/bambinos/bambi/archive/0.6.2.tar.gz
Description: # Bambi
        
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        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
        
        BAyesian Model-Building Interface in Python
        
        ## Overview
        
        Bambi is a high-level Bayesian model-building interface written in Python. It's built on top of the [PyMC3](https://github.com/pymc-devs/pymc3) probabilistic programming framework, and is designed to make it extremely easy to fit mixed-effects models common in social sciences settings using a Bayesian approach.
        
        ## Installation
        
        Bambi requires a working Python interpreter (3.7+). We recommend installing Python and key numerical libraries using the [Anaconda Distribution](https://www.anaconda.com/products/individual#Downloads), which has one-click installers available on all major platforms.
        
        Assuming a standard Python environment is installed on your machine (including pip), Bambi itself can be installed in one line using pip:
        
            pip install bambi
        
        Alternatively, if you want the bleeding edge version of the package you can install from GitHub:
        
            pip install git+https://github.com/bambinos/bambi.git
        
        ### Dependencies
        
        Bambi requires working versions of ArviZ, formulae, NumPy, pandas, PyMC3 and statsmodels. Dependencies are listed in `requirements.txt`, and should all be installed by the Bambi installer; no further action should be required.
        
        ## Example
        
        In the following two examples we assume the following basic setup
        
        ```python
        import bambi as bmb
        import numpy as np
        import pandas as pd
        
        data = pd.DataFrame({
            "y": np.random.normal(size=50),
            "g": np.random.choice(["Yes", "No"], size=50),
            "x1": np.random.normal(size=50),
            "x2": np.random.normal(size=50)
        })
        ```
        
        ### Linear regression
        
        ```python
        model = bmb.Model("y ~ x1 + x2", data)
        fitted = model.fit()
        ```
        
        In the first line we create and build a Bambi `Model`. The second line tells the sampler to start
        running and it returns an `InferenceData` object, which can be passed to several ArviZ functions
        such as `az.summary()` to get a summary of the parameters distribution and sample diagnostics or
         `az.plot_traces()` to visualize them.
        
        
        ### Logistic regression
        
        Here we just add the `family` argument set to `"bernoulli"` to tell Bambi we are modelling a binary
        response. By default, it uses a logit link. We can also use some syntax sugar to specify which event
        we want to model. We just say `g['Yes']` and Bambi will understand we want to model the probability
        of a `"Yes"` response. But this notation is not mandatory. If we use `"g ~ x1 + x2"`, Bambi will
        pick one of the events to model and will inform us which one it picked.
        
        
        ```python
        model = bmb.Model("g['Yes'] ~ x1 + x2", data, family="bernoulli")
        fitted = model.fit()
        ```
        
        ## Documentation
        
        The Bambi documentation can be found in the [official docs](https://bambinos.github.io/bambi/index.html)
        
        ## Citation
        
        If you use Bambi and want to cite it please use [![arXiv](https://img.shields.io/badge/arXiv-2012.10754-b31b1b.svg)](https://arxiv.org/abs/2012.10754)
        
        Here is the citation in BibTeX format
        
        ```
        @misc{capretto2020,
              title={Bambi: A simple interface for fitting Bayesian linear models in Python},
              author={Tomás Capretto and Camen Piho and Ravin Kumar and Jacob Westfall and Tal Yarkoni and Osvaldo A. Martin},
              year={2020},
              eprint={2012.10754},
              archivePrefix={arXiv},
              primaryClass={stat.CO}
        }
        ```
        
        ## Contributions
        
        Bambi is a community project and welcomes contributions. Additional information can be found in the [Contributing](https://github.com/bambinos/bambi/blob/master/CONTRIBUTING.md) Readme.
        
        For a list of contributors see the [GitHub contributor](https://github.com/bambinos/bambi/graphs/contributors) page
        
        ## Donations
        
        If you want to support Bambi financially, you can [make a donation](https://numfocus.org/donate-to-pymc3) to our sister project PyMC3.
        
        ## Code of Conduct
        
        Bambi wishes to maintain a positive community. Additional details can be found in the [Code of Conduct](https://github.com/bambinos/bambi/blob/master/CODE_OF_CONDUCT.md)
        
        ## License
        
        [MIT License](https://github.com/bambinos/bambi/blob/master/LICENSE)
        
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