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        "author": "Olga Botvinnik",
        "author_email": "olga.botvinnik@gmail.com",
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        "classifiers": [
            "Intended Audience :: Developers",
            "License :: OSI Approved :: BSD License",
            "Natural Language :: English",
            "Programming Language :: Python :: 2",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3",
            "Programming Language :: Python :: 3.4",
            "Programming Language :: Python :: 3.5",
            "Programming Language :: Python :: 3.6"
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        "description": "![Anchor logo](https://raw.githubusercontent.com/YeoLab/anchor/master/logo/v1/logo.png)\n\n[![](https://img.shields.io/travis/YeoLab/anchor.svg)](https://travis-ci.org/YeoLab/anchor)[![](https://img.shields.io/pypi/v/anchor.svg)](https://pypi.python.org/pypi/anchor)[![codecov](https://codecov.io/gh/YeoLab/anchor/branch/master/graph/badge.svg)](https://codecov.io/gh/YeoLab/anchor)\n\n## What is `anchor`?\n\nAnchor is a python package to find unimodal, bimodal, and multimodal features in any data that is normalized between 0 and 1, for example alternative splicing or other percent-based units.\n\n* Free software: BSD license\n* Documentation: https://YeoLab.github.io/anchor\n\n## Installation\n\nTo install `anchor`, we recommend using the\n[Anaconda Python Distribution](http://anaconda.org/) and creating an\nenvironment, so the `anchor` code and dependencies don't interfere with\nanything else. Here is the command to create an environment:\n\n\n```\nconda create -n anchor-env pandas scipy numpy matplotlib seaborn\n```\n\n### Stable (recommended)\n\n\nTo install this code from the Python Package Index, you'll need to specify ``anchor-bio`` (``anchor`` was already taken - boo).\n\n```\npip install anchor-bio\n```\n\n### Bleeding-edge (for the brave)\n\nIf you want the latest and greatest version, clone this github repository and use `pip` to install\n\n```\ngit clone git@github.com:YeoLab/anchor\ncd anchor\npip install .  # The \".\" means \"install *this*, the folder where I am now\"\n```\n\n\n## Usage\n\n`anchor` was structured like `scikit-learn`, where if you want the \"final\nanswer\" of your estimator, you use `fit_transform()`, but if you want to see the\nintermediates, you use `fit()`.\n\nIf you want the modality assignments for your data, first make sure that you\nhave a `pandas.DataFrame`, here it is called `data`, in the format (samples,\nfeatures). This uses a log2 Bayes Factor cutoff of 5, and the default Beta\ndistribution parameterizations (shown [here]())\n\n```python\nimport anchor\n\nbm = anchor.BayesianModalities()\nmodalities = bm.fit_transform(data)\n```\n\nIf you want to see all the intermediate Bayes factors, then you can do:\n\n```python\nimport anchor\n\nbm = anchor.BayesianModalities()\nbayes_factors = bm.fit(data)\n```\n\n\n## History\n\n### 1.1.1 (2017-06-29)\n\n- In `infotheory.binify`, round the decimal numbers before they are written as strings\n\n### 1.0.1 (2017-06-28)\n\n- Documentation and build fixes\n\n### 1.0.0 (2017-06-28)\n\n* Updated to Python 3.5, 3.6\n\n### 0.1.0 (2015-07-08)\n\n* First release on PyPI.\n",
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