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        "author": "Caner Turkmen",
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            "License :: OSI Approved :: MIT License",
            "Programming Language :: Cython",
            "Programming Language :: Python",
            "Programming Language :: Python :: 2",
            "Topic :: Scientific/Engineering",
            "Topic :: Scientific/Engineering :: Information Analysis"
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        "description": "\n# Welcome to `hawkeslib`\n\n[![Build Status](https://travis-ci.org/canerturkmen/hawkeslib.svg?branch=master)](https://travis-ci.org/canerturkmen/hawkeslib)\n[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)\n[![Documentation Status](https://readthedocs.org/projects/hawkeslib/badge/?version=latest)](https://hawkeslib.readthedocs.io/en/latest/?badge=latest)\n[![Python 2.7](https://img.shields.io/badge/python-2.7-blue.svg)](https://www.python.org/downloads/release/python-2715/)\n[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)\n\n`hawkeslib` started with the ambition of having a simple Python implementation\nof *plain-vanilla* Hawkes (or *self-exciting* processes), i.e. those\nwith factorized triggering kernels with exponential decay functions.\n\nThe [docs](http://hawkeslib.rtfd.io/) contain tutorials, examples and a detailed API reference.\nFor other examples, see the `examples/` folder.\n\nThe following models are available:\n\n- Univariate Hawkes Process (with exponential delay)\n- Bayesian Univariate Hawkes Process (with exponential delay)\n- Poisson Process\n- 'Bayesian' Poisson process\n\nBayesian variants implement methods for sampling from the posterior as well as calculating\nmarginal likelihood (e.g. for Bayesian model comparison).\n\n## Installation\n\n`Cython` (>=0.28) and `numpy` (>=1.14) and `scipy` must be installed prior to the installation as\nthey are required for the build.\n\n```\n$ pip install -U Cython numpy scipy\n$ pip install hawkeslib\n```\n\n\n",
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