gneiss 0.1.3

Compositional data analysis tools and visualizations

# gneiss

[](https://travis-ci.org/biocore/gneiss)
[](https://coveralls.io/r/biocore/gneiss)
[](https://gitter.im/biocore/gneiss?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)

Canonically pronouced *nice*


gneiss is a compositional statistics and visualization toolbox.

Note that gneiss is not compatible with python 2, and is compatible with Python 3.4 or later.
gneiss is currently in alpha. We are actively developing it, and __backward-incompatible interface changes may arise__.

# Installation

To install this package, it is recommended to use conda. An environment can installed as follows

```
conda create -n gneiss_env python=3
```

gneiss then can be installed as follows
```
source activate gneiss_env
conda install pyqt
pip install gneiss
```

gneiss can also be installed through conda
```
conda install -c biocore gneiss
```

To run through the tutorials, you'll need a few more packages, namely `seaborn`, `biom-format` and `h5py`.
These packages can be installed with conda as follows
```
conda install seaborn h5py
pip install biom-format
```

# Examples

IPython notebooks demonstrating some of the modules in gneiss can be found as follows

* [What are balances](https://github.com/biocore/gneiss/blob/master/ipynb/balance_trees.ipynb)
* [Linear regression on balances in the 88 soils](https://github.com/biocore/gneiss/blob/master/ipynb/88soils.ipynb)
* [Linear mixed effects models on balances in a CF study](https://github.com/biocore/gneiss/blob/master/ipynb/cfstudy.ipynb)
* [Linear mixed effects models on balances in a PTSD study](https://github.com/biocore/gneiss/blob/master/ipynb/ptsd_mice.ipynb)