BAyesian Model-Building Interface (Bambi) in Python¶ Bambi is a high-level Bayesian model-building interface written in Python. It works with the probabilistic programming frameworks PyMC3 and is designed to make it extremely easy to fit Bayesian mixed-effects models common in biology, social sciences and other disciplines.

Dependencies¶ Bambi is tested on Python 3.6+ and depends on NumPy, Pandas, PyMC3, PyStan, Patsy and ArviZ (see requirements.txt for version information).

Installation¶ The latest release of Bambi can be installed 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

Usage¶ A simple fixed effects model is shown below as example. from bambi import Model import pandas as pd # Read in a tab-delimited file containing our data data = pd . read_table ( 'my_data.txt' , sep = ' \t ' ) # Initialize the model model = Model ( data ) # Fixed effects only model results = model . fit ( 'DV ~ IV1 + IV2' , samples = 1000 , chains = 4 ) # Use ArviZ to plot the results az . plot_trace ( results ) # Key summary and diagnostic info on the model parameters az . summary ( results ) # Drop the first 100 samples (burn-in) results_bi = results . sel ( draw = slice ( 100 , None )) For a more in-depth introduction to Bambi see our Quickstart or our set of example notebooks.