Metadata-Version: 2.1
Name: biceps
Version: 2.0b0.post0
Summary: BICePs
Home-page: https://biceps.readthedocs.io/en/latest/index.html
Author: Robert M. Raddi,Yunhui Ge, Vincent A. Voelz
Author-email: vvoelz@gmail.com
License: MIT
Project-URL: Github, https://github.com/vvoelz/biceps
Project-URL: Documentation, https://biceps.readthedocs.io/en/latest/index.html
Description: 
        BICePs - Bayesian Inference of Conformational Populations
        =========================================================
        
        <!-- List badges here: -->
        [![Documentation Status](https://readthedocs.org/projects/biceps/badge/?version=latest)](https://biceps.readthedocs.io/en/latest/?badge=latest)
              
        
        <!--                   -->
        
        The BICePs algorithm (Bayesian Inference of Conformational Populations)
        is a statistically rigorous Bayesian inference method to reconcile
        theoretical predictions of conformational state populations with sparse
        and/or noisy experimental measurements and objectively compare different
        models. Supported experimental observables include: 
        
        - [NMR nuclear Overhauser effect](https://en.wikipedia.org/wiki/Nuclear_Overhauser_effect)  (`NOE`).
        
        - [NMR chemical shifts](https://en.wikipedia.org/wiki/Chemical_shift) (`HA`,`NH`, `CA` and `N`). 
        
        - [J couplings](https://en.wikipedia.org/wiki/J-coupling) (both small molecules and amino acids) (`J`).
        
        - [Hydrogen--deuterium exchange](https://en.wikipedia.org/wiki/Hydrogen–deuterium_exchange) (`HDX`).
        
        Citation [![DOI for Citing BICePs](https://img.shields.io/badge/DOI-10.1021.acs.jpcb.7b11871-green.svg)](http://doi.org/10.1021/acs.jpcb.7b11871)
        
        ### Check our [BICePs website](https://biceps.readthedocs.io/en/latest/) for more details!
        
        ### Please check out the [theory of **BICePs**](https://biceps.readthedocs.io/en/latest/theory.html) to learn more.
        
        Installation (in progress)
        ==========================
        
        <!--
        We recommend that you install `BICePs` with `conda`. :
        
        ```bash
            $ conda install -c conda-forge BICePs
        ```
        
        You can install also `BICePs` with `pip`, if you prefer. :
        
        ```bash
            $ pip install BICePs
        ```
        -->
        <!--
        Conda is a cross-platform package manager built especially for
        scientific python. It will install `BICePs` along with all dependencies
        from a pre-compiled binary. If you don\'t have Python or the `conda`
        package manager, we recommend starting with the [Anaconda Scientific
        Python distribution \<https://store.continuum.io/cshop/anaconda/\>](),
        which comes pre-packaged with many of the core scientific python
        packages that BICePs uses (see below), or with the [Miniconda Python
        distribution](http://conda.pydata.org/miniconda.html), which is a
        bare-bones Python installation.
        -->
        
        BICePs supports Python 2.7 (see [tag v1.0](https://github.com/vvoelz/biceps/releases/tag/v1.0)) or Python 3.4+ (v2.0 or greater) on Mac, Linux, and Windows.
        
        
        Dependencies of BICePs
        ----------------------
        
        > -   [pymbar](https://pymbar.readthedocs.io) == 3.0.2
        > -   [mdtraj](https://mdtraj.org) >= 1.5.0
        > -   matplotlib >= 2.1.2
        > -   numpy >= 1.14.0
        > -   multiprocessing (works with Python versions 3.0-3.7)
        
        NOTE: for pymbar, try: `$ pip install git+https://github.com/choderalab/pymbar.git@3.0.2`
        
        -------------------------------------------
        
        
        ### View [the workflow of BICePs](https://biceps.readthedocs.io/en/latest/workflow.html).
        
        ### BICePs is research software. If you make use of BICePs in scientific publications, please cite it.
        
        # To get started, see [biceps/releases](https://github.com/vvoelz/biceps/releases) for the latest version of BICePs.
        
        
        
        
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: MacOS
Classifier: Operating System :: Unix
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Topic :: Scientific/Engineering :: Chemistry
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Requires-Python: <3.8
Description-Content-Type: text/markdown
