BayesPowerlaw

Written by Kristina Grigaityte.

BayesPowerlaw is a Python package that fits a single or a mixture of power law distributions to data using a Bayesian inference approach. Posterior distributions of parameters are numerically determined by Markov chain Monte Carlo sampling. In addition, the package provides capability for power law simulations, maximum likelihood estimation, and data plotting.

Installation

BayesPowerlaw can be installed from PyPI using the pip package manager (version 9.0.0 or higher). At the command line:

pip install BayesPowerlaw

The code for BayesPowerlaw is open source and available on GitHub.

Quick Start

To make the figures shown above, type this from within Python:

import BayesPowerlaw as bp
bp.demo()

Resources

• Tutorial
• Documentation
  • bp.bayes
  • bp.maxLikelihood
  • bp.power_law

Contact

For technical assistance or to report bugs, please contact Kristina Grigaityte.

For general correspondence, please contact Gurinder (Mickey) Atwal.

Indices and tables

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• Module Index
• Search Page