Metadata-Version: 2.1
Name: hawkeslib
Version: 0.2.2
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
Description: 
        # 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
        ```
        
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
Provides-Extra: test
Provides-Extra: docs
