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            "Programming Language :: Python :: 3",
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        "description": "# gneiss\n\n[![Build Status](https://travis-ci.org/biocore/gneiss.png?branch=master)](https://travis-ci.org/biocore/gneiss)\n[![Coverage Status](https://coveralls.io/repos/biocore/gneiss/badge.svg)](https://coveralls.io/r/biocore/gneiss)\n[![Gitter](https://badges.gitter.im/biocore/gneiss.svg)](https://gitter.im/biocore/gneiss?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)\n\nCanonically pronouced *nice*\n\n\ngneiss is a compositional data analysis and visualization toolbox designed for analyzing high dimensional proportions.  See [here](https://biocore.github.io/gneiss/) for API documentation.\n \nNote that gneiss is not compatible with python 2, and is compatible with Python 3.4 or later.\ngneiss is currently in alpha.  We are actively developing it, and __backward-incompatible interface changes may arise__.\n\n# Installation\n\nTo install this package, it is recommended to use conda.  First make sure that the appropriate channels are configured.\n\n```\nconda config --add channels https://conda.anaconda.org/bioconda\nconda config --add channels https://conda.anaconda.org/biocore\nconda config --add channels https://conda.anaconda.org/qiime2\nconda config --add channels https://conda.anaconda.org/qiime2/label/r2017.6\n```\n\nThen gneiss can be installed in a conda environment as follows\n```\nconda create -n gneiss_env gneiss\n```\nTo install the most up to date version of gneiss, run the following command\n\n```\npip install git+https://github.com/biocore/gneiss.git\n```\n\n# Tutorials\n\n* [What are balances](https://github.com/biocore/gneiss/blob/master/ipynb/balance_trees.ipynb)\n\n# Qiime2 tutorials\n\n* [Linear regression on balances in the 88 soils](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/qiime2/88soils-qiime2-tutorial.html)\n* [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)\n* [Linear regression on balances in the Chronic Fatigue Syndrome](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/qiime2/cfs-qiime2-tutorial.html)\n\n# Python tutorials\n\n* [Linear regression on balances in the 88 soils](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/python/88soils-python-tutorial.html)\n* [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)\n* [Linear regression on balances in the Chronic Fatigue Syndrome](https://biocore.github.io/gneiss/docs/v0.4.0/tutorials/python/cfs-python-tutorial.html)\n\n\nIf you use this software package in your own publications, please cite it at\n```\nMorton JT, Sanders J, Quinn RA, McDonald D, Gonzalez A, V\u00e1zquez-Baeza Y, \nNavas-Molina JA, Song SJ, Metcalf JL, Hyde ER, Lladser M, Dorrestein PC, \nKnight R. 2017. Balance trees reveal microbial niche differentiation. \nmSystems 2:e00162-16. https://doi.org/10.1128/mSystems.00162-16.\n```",
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