Metadata-Version: 2.1
Name: dynesty
Version: 1.0.0
Summary: A dynamic nested sampling package for computing Bayesian posteriors and evidences.
Home-page: https://github.com/joshspeagle/dynesty
Author: Joshua S Speagle
Author-email: jspeagle@cfa.harvard.edu
License: MIT
Keywords: nested sampling,dynamic,monte carlo,bayesian,inference,modeling
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: License :: OSI Approved :: MIT License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.6
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering
Classifier: Intended Audience :: Science/Research
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: matplotlib
Requires-Dist: six

dynesty
=======

![dynesty in action](https://github.com/joshspeagle/dynesty/blob/master/docs/images/title.gif)

A Dynamic Nested Sampling package for computing Bayesian posteriors and
evidences. Pure Python. MIT license.

### Documentation
Documentation can be found [here](https://dynesty.readthedocs.io).

### Installation
The most stable release of `dynesty` can be installed
through [pip](https://pip.pypa.io/en/stable) via
```
pip install dynesty
```
The current (less stable) development version can be installed by running
```
python setup.py install
```
from inside the repository.

### Demos
Several Jupyter notebooks that demonstrate most of the available features
of the code can be found 
[here](https://github.com/joshspeagle/dynesty/tree/master/demos).

### Attribution

Please cite [Speagle (2019)](https://arxiv.org/abs/1904.02180) if you find the 
package useful in your research, along with any relevant papers on the
[citations page](https://dynesty.readthedocs.io/en/latest/index.html#citations).


