Metadata-Version: 2.1
Name: hawkeslib
Version: 0.2
Summary: parameter estimation for simple Hawkes (self-exciting) processes
Home-page: http://hawkeslib.rtfd.io
Author: Caner Turkmen
Author-email: caner.turkmen@boun.edu.tr
License: MIT
Platform: UNKNOWN
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Cython
Classifier: Development Status :: 3 - Alpha
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Requires-Python: >=2.7.5
Description-Content-Type: text/markdown
Requires-Dist: numpy (>=1.14)
Requires-Dist: Cython (>=0.28)
Requires-Dist: scipy (>=1.1)
Requires-Dist: numdifftools (>=0.9)
Provides-Extra: docs
Requires-Dist: Sphinx ; extra == 'docs'
Requires-Dist: sphinx-rtd-theme (>=0.4) ; extra == 'docs'
Provides-Extra: test
Requires-Dist: mock ; extra == 'test'
Requires-Dist: nose ; extra == 'test'


# Welcome to `hawkeslib`

[![Build Status](https://travis-ci.org/canerturkmen/hawkeslib.svg?branch=master)](https://travis-ci.org/canerturkmen/hawkeslib)
[![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT)
[![Documentation Status](https://readthedocs.org/projects/hawkeslib/badge/?version=latest)](https://hawkeslib.readthedocs.io/en/latest/?badge=latest)
[![Python 2.7](https://img.shields.io/badge/python-2.7-blue.svg)](https://www.python.org/downloads/release/python-2715/)
[![Python 3.6](https://img.shields.io/badge/python-3.6-blue.svg)](https://www.python.org/downloads/release/python-360/)

`hawkeslib` started with the ambition of having a simple Python implementation
of *plain-vanilla* Hawkes (or *self-exciting* processes), i.e. those
with factorized triggering kernels with exponential decay functions.

The [docs](http://hawkeslib.rtfd.io/) contain tutorials, examples and a detailed API reference.
For other examples, see the `examples/` folder.

The following models are available:

- Univariate Hawkes Process (with exponential delay)
- Bayesian Univariate Hawkes Process (with exponential delay)
- Poisson Process
- 'Bayesian' Poisson process

Bayesian variants implement methods for sampling from the posterior as well as calculating
marginal likelihood (e.g. for Bayesian model comparison).

## Installation

`Cython` (>=0.28) and `numpy` (>=1.14) and `scipy` must be installed prior to the installation as
they are required for the build.

```
$ pip install -U Cython numpy scipy
$ pip install hawkeslib
```


