Metadata-Version: 2.1
Name: gneiss
Version: 0.4.6
Summary: Compositional data analysis tools and visualizations
Home-page: UNKNOWN
Author: gneiss development team
Author-email: jamietmorton@gmail.com
Maintainer: gneiss development team
Maintainer-email: jamietmorton@gmail.com
License: BSD
Description: # gneiss
        
        [![Build Status](https://travis-ci.org/biocore/gneiss.png?branch=master)](https://travis-ci.org/biocore/gneiss)
        [![Coverage Status](https://coveralls.io/repos/biocore/gneiss/badge.svg)](https://coveralls.io/r/biocore/gneiss)
        [![Gitter](https://badges.gitter.im/biocore/gneiss.svg)](https://gitter.im/biocore/gneiss?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
        
        Canonically pronouced *nice*
        
        
        gneiss is a compositional data analysis and visualization toolbox designed for analyzing high dimensional proportions.  See [here](https://biocore.github.io/gneiss/) for API documentation.
         
        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.  First make sure that the appropriate channels are configured.
        
        ```
        conda config --add channels https://conda.anaconda.org/bioconda
        conda config --add channels https://conda.anaconda.org/biocore
        conda config --add channels https://conda.anaconda.org/qiime2
        conda config --add channels https://conda.anaconda.org/qiime2/label/r2017.6
        ```
        
        Then gneiss can be installed in a conda environment as follows
        ```
        conda create -n gneiss_env gneiss
        ```
        To install the most up to date version of gneiss, run the following command
        
        ```
        pip install git+https://github.com/biocore/gneiss.git
        ```
        
        # Tutorials
        
        * [What are balances](https://github.com/biocore/gneiss/blob/master/ipynb/balance_trees.ipynb)
        
        # Qiime2 tutorials
        
        * [Linear regression on balances in the 88 soils](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/qiime2/88soils-qiime2-tutorial.html)
        * [Linear mixed effects models on balances in a CF study](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/qiime2/cfstudy-qiime2-tutorial.html)
        * [Linear regression on balances in the Chronic Fatigue Syndrome](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/qiime2/cfs-qiime2-tutorial.html)
        
        # Python tutorials
        
        * [Linear regression on balances in the 88 soils](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/python/88soils-python-tutorial.html)
        * [Linear mixed effects models on balances in a CF study](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/python/cfstudy-python-tutorial.html)
        * [Linear regression on balances in the Chronic Fatigue Syndrome](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/python/cfs-python-tutorial.html)
        
        
        If you use this software package in your own publications, please cite it at
        ```
        Morton JT, Sanders J, Quinn RA, McDonald D, Gonzalez A, Vázquez-Baeza Y, 
        Navas-Molina JA, Song SJ, Metcalf JL, Hyde ER, Lladser M, Dorrestein PC, 
        Knight R. 2017. Balance trees reveal microbial niche differentiation. 
        mSystems 2:e00162-16. https://doi.org/10.1128/mSystems.00162-16.
        ```
        
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Software Development :: Libraries
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX
Classifier: Operating System :: MacOS :: MacOS X
Description-Content-Type: text/markdown