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        "author": "Yunhui Ge, Robert M. Raddi, Vincent A. Voelz",
        "author_email": "vvoelz@gmail.com",
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            "License :: OSI Approved :: MIT License",
            "Operating System :: MacOS",
            "Operating System :: Unix",
            "Programming Language :: Python :: 2.7",
            "Topic :: Scientific/Engineering :: Bio-Informatics",
            "Topic :: Scientific/Engineering :: Chemistry"
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        "description": "BICePs - Bayesian Inference of Conformational Populations\n=========================================================\n\nThe BICePs algorithm (Bayesian Inference of Conformational Populations)\nis a statistically rigorous Bayesian inference method to reconcile\ntheoretical predictions of conformational state populations with sparse\nand/or noisy experimental measurements and objectively compare different\nmodels. Supported experimental observables include: \n\n- [NMR nuclear Overhauser effect (NOE)](https://en.wikipedia.org/wiki/Nuclear_Overhauser_effect). \n\n- [NMR chemical shifts](https://en.wikipedia.org/wiki/Chemical_shift) (`HA`,`NH`, `CA` and `N`). \n\n- [J couplings](https://en.wikipedia.org/wiki/J-coupling) (both small molecules and amino acids) \n\n- [Hydrogen--deuterium exchange (HDX)](https://en.wikipedia.org/wiki/Hydrogen\u2013deuterium_exchange).\n\nCitation [![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)\n\n### Check our [BICePs website](https://biceps.readthedocs.io/en/latest/) for more details!\n\n### Please check out the [theory of **BICePs**](./BICePs_2.0/markdown/Theory.ipynb) to learn more.\n\nInstallation (in progress)\n==========================\n\n<!--\nWe recommend that you install `BICePs` with `conda`. :\n\n```bash\n    $ conda install -c conda-forge BICePs\n```\n\nYou can install also `BICePs` with `pip`, if you prefer. :\n\n```bash\n    $ pip install BICePs\n```\n-->\n<!--\nConda is a cross-platform package manager built especially for\nscientific python. It will install `BICePs` along with all dependencies\nfrom a pre-compiled binary. If you don\\'t have Python or the `conda`\npackage manager, we recommend starting with the [Anaconda Scientific\nPython distribution \\<https://store.continuum.io/cshop/anaconda/\\>](),\nwhich comes pre-packaged with many of the core scientific python\npackages that BICePs uses (see below), or with the [Miniconda Python\ndistribution](http://conda.pydata.org/miniconda.html), which is a\nbare-bones Python installation.\n-->\n\nBICePs supports Python 2.7 or Python 3.4+ (coming soon) on Mac, Linux,\nand Windows.\n\n\nDependencies of BICePs\n----------------------\n\n> -   Pymbar \\--\\> 3.0 or later\n> -   MDTraj \\--\\> 1.9 or later\n> -   Matplotlib \\--\\> 1.5.1 or later\n> -   Numpy \\--\\> 1.14.0 or later\n\n-------------------------------------------\n\n\n### View [the workflow of BICePs](./BICePs_2.0/markdown/workflow.md).\n\n### BICePs is research software. If you make use of BICePs in scientific publications, please cite it.\n\n# To get started, see [BICePs_2.0](https://github.com/vvoelz/biceps/tree/master/BICePs_2.0) for the latest version of BICePs.\n\n\n\n\n\n",
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