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Anchor is a python package to estimate modality of splicing, percent methylated, any data that is normalized between 0 and 1

Project description

![Anchor logo](


## 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:

## Installation

To install `anchor`, we recommend using the
[Anaconda Python Distribution]( and creating an
environment, so the `anchor` code and dependencies don't interfere with
anything else. Here is the command to create an environment:

conda create -n anchor-env pandas scipy numpy matplotlib seaborn

### 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
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]())

import anchor

bm = anchor.BayesianModalities()
modalities = bm.fit_transform(data)

If you want to see all the intermediate Bayes factors, then you can do:

import anchor

bm = anchor.BayesianModalities()
bayes_factors =

## History

### 1.1.1 (2017-06-29)

- In `infotheory.binify`, round the decimal numbers before they are written as strings

### 1.0.1 (2017-06-28)

- Documentation and build fixes

### 1.0.0 (2017-06-28)

* Updated to Python 3.5, 3.6

### 0.1.0 (2015-07-08)

* First release on PyPI.

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