Metadata-Version: 1.1
Name: anchor-bio
Version: 1.0.1
Summary: Anchor is a python package to estimate modality of splicing, percent methylated, any data that is normalized between 0 and 1
Home-page: https://github.com/YeoLab/anchor
Author: Olga Botvinnik
Author-email: olga.botvinnik@gmail.com
License: BSD
Description: ![Anchor logo](https://raw.githubusercontent.com/YeoLab/anchor/master/logo/v1/logo.png)
        
        [![](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)
        
        ## What is `anchor`?
        
        Anchor 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.
        
        * Free software: BSD license
        * Documentation: https://YeoLab.github.io/anchor
        
        ## Installation
        
        To install this code, clone this github repository and use `pip` to install
        
            git clone git@github.com:YeoLab/anchor
            cd anchor
            pip install .  # The "." means "install *this*, the folder where I am now"
        
        
        To install ``anchor``, we recommend using the `Anaconda Python
        Distribution <http://anaconda.org/>`__ and creating an environment.
        
        
        
        ### Stable (recommended)
        
        
        To install this code from the Python Package Index, you'll need to specify ``anchor-bio`` (``anchor`` was already taken - boo).
        
        ```
        pip install anchor-bio
        ```
        
        ### Bleeding-edge (for the brave)
        
        If you want the latest and greatest version, clone this github repository and use `pip` to install
        
        ```
        git clone git@github.com:YeoLab/anchor
        cd anchor
        pip install .  # The "." means "install *this*, the folder where I am now"
        ```
        
        
        ## Usage
        
        `anchor` was structured like `scikit-learn`, where if you want the "final
        answer" of your estimator, you use `fit_transform()`, but if you want to see the
        intermediates, you use `fit()`.
        
        If you want the modality assignments for your data, first make sure that you
        have a `pandas.DataFrame`, here it is called `data`, in the format (samples,
        features). This uses a log2 Bayes Factor cutoff of 5, and the default Beta
        distribution parameterizations (shown [here]())
        
        ```python
        import anchor
        
        bm = anchor.BayesianModalities()
        modalities = bm.fit_transform(data)
        ```
        
        If you want to see all the intermediate Bayes factors, then you can do:
        
        ```python
        import anchor
        
        bm = anchor.BayesianModalities()
        bayes_factors = bm.fit(data)
        ```
        
        
        ## History
        
        ### 1.0.0 (2017-06-28)
        
        * Updated to Python 3.5, 3.6
        
        ### 0.1.0 (2015-07-08)
        
        * First release on PyPI.
        
Keywords: anchor
Platform: UNKNOWN
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: BSD License
Classifier: Natural Language :: English
Classifier: Programming Language :: Python :: 2
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
