astroabc 1.0.0

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<a href="https://github.com/EliseJ/astroABC"><img src="https://github.com/EliseJ/astroABC/blob/master/abc_logo.001.jpeg" align="left" hspace="10" vspace="6"></a>

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Version: 1.0.0


Author: Elise Jennings


**astroABC** is a Python implementation of an Approximate Bayesian Computation Sequential Monte Carlo (ABC SMC) sampler for parameter estimation.


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## Key features ##

- Parallel sampling using MPI or multiprocessing
- A Sequential Monte Carlo sampler following [Beaumont et al. 2009]
[Beaumont et al. 2009]:https://arxiv.org/abs/0805.2256
- A method for iterative adapting tolerance levels using the qth quantile of the distance for t iterations ([Turner & Van Zandt (2012)])
- Scikit-learn covariance matrix estimation using [Ledoit-Wolf shrinkage] for singular matrices
[Ledoit-Wolf shrinkage]:http://scikit-learn.org/stable/modules/covariance.html
- A module for specifying particle covariance using method proposed by [Turner & Van Zandt (2012)], optimal covariance matrix for a multivariate normal perturbation kernel ([Filippi et al 2013]) and a weighted covariance metric (Beaumont et al 2009)
[Turner & Van Zandt (2012)]:http://link.springer.com/article/10.1007/s11336-013-9381-x
[Filippi et al 2013]:https://www.degruyter.com/abstract/j/sagmb.2013.12.issue-1/sagmb-2012-0069/sagmb-2012-0069.xml
- Restart files output frequently so an interrupted sampling run can be resumed at any iteration
- Output and restart files are backed up every iteration before new output is written
- User defined distance metric and simulation methods
- A module for specifying heterogeneous parameter priors
- A module for specifying a constant, linear, log or exponential tolerance level
- Well-documented examples and sample scripts


### Wiki ###

For more information please read the [wiki](https://github.com/EliseJ/astroABC/wiki).

### Installing ###

Install astroABC using pip

```
$ sudo pip install astroabc
```

or git clone the repository using the url above.
Check the dependencies listed in the next section are installed.

### Dependencies ###

* numpy
* scipy
* mpi4py
* multiprocessing
* sklearn

Python distributions like [Anaconda] have most of what is needed.
You can then conda install or pip install all of the required dependencies.

```
$ conda install numpy scipy scikit-learn mpi4py
$ pip install numpy scipy scikit-learn mpi4py
```

[Anaconda]:https://www.continuum.io/downloads

### License ###

Copyright 2016 Elise Jennings

astroABC is free software made available under the MIT License. For details see the LICENSE.txt file.