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            "License :: OSI Approved :: MIT License",
            "Natural Language :: English",
            "Operating System :: OS Independent",
            "Programming Language :: Python",
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        "description": "============================================\n``causalinfo``: Information on Causal Graphs \n============================================\n\n.. image:: https://badge.fury.io/py/causalinfo.png\n    :target: http://badge.fury.io/py/causalinfo\n\n`causalinfo` is a Python library to aid in experimenting with different\n*information measures on causal graphs*---a combination of information\ntheory with recent work on causal graphs [Pearl2000]_. These information\nmeasures can used to ascertain the degree to which one variable controls or\nexplains other variables in the graph. The use of these measures has important\nconnections to work on causal explanation in philosophy of science, and to\nunderstanding information processing in biological networks. \n\nThe library is a work in progress, and will be extended as research continues.\n\nWhat does it do?\n----------------\n\n`causalinfo` has been written primarily for interactive use within `IPython\nNotebook`_. You can create variables and assign probability distributions to\nthem, or relate them to other variables using conditional probabilities.\nSeveral related variables can be combined into a directed acyclic graph, which\ncan generate a joint distribution for all variables under observation, or\nunder controlled interventions on certain variables. You can also calculate\nvarious information measures between variables in the graph whilst controlling\nother variables. These include correlative measures, such as Mutual\nInformation, but also causal measures, such as Information Flow\n[AyPolani2008]_, and Causal Specificity [GriffithsEtAl2015]_.\n\nFor some brief examples of how to use the library, please see the IPython Notebooks\nthat are included:\n\n* Introduction_. A short introduction to some of the things you can do with\n  the library.\n\n* Rain_. Performing some interventions on a causal graph; an example from\n  Judea Pearl's book.\n\n* Signaling_. Measuring Causation in Signaling Networks. Some examples from\n  [CalcottEtAl2016]_.\n\n* `Information Flow`_. Measuring the flow of information in Causal networks\n  from [AyPolani2008]_.\n\n.. _Introduction: https://github.com/brettc/causalinfo/blob/master/notebooks/introduction.ipynb\n\n.. _Rain: https://github.com/brettc/causalinfo/blob/master/notebooks/rain.ipynb\n\n.. _Signaling: https://github.com/brettc/causalinfo/blob/master/notebooks/signaling.ipynb\n\n.. _`Information Flow`: https://github.com/brettc/causalinfo/blob/master/notebooks/ay_polani.ipynb\n\n\n.. TODO: Add a getting started guide\n.. Getting Started\n    ---------------\n    .. code:: bash \n    pip install causalinfo\n    curl https://raw.githubusercontent.com/brettc/causalinfo/master/notebooks/introduction.ipynb \n\nSome Caveats\n------------\n\nThe library is not meant for large scale analysis. The code has been written\nto offload as much as possible on to other libraries (such as Pandas_ and\nNetworkx_), and to allow easy inspection of what is going on within `IPython\nNotebook`_, thus it is not optimized for speed. Calculating the joint\ndistribution for a causal graph with many variables can become very *slow*\n(especially if the variables have many states). \n\n\nAuthorship\n----------\n\nAll code was written by `Brett Calcott`_.\n\n\nAcknowledgments\n---------------\n\nThis work is part of the research project on the `Causal Foundations of\nBiological Information`_ at the `University of Sydney`_, Australia. The work\nwas made possible through the support of a grant from the Templeton World\nCharity Foundation. The opinions expressed are those of the author and do not\nnecessarily reflect the views of the Templeton World Charity Foundation. \n\nLicense\n-------\n\nMIT licensed. See the bundled LICENSE_ file for more details.\n\n\n.. Miscellaneous Links------------\n\n.. _LICENSE: https://github.com/brettc/causalinfo/blob/master/LICENSE\n\n.. _`Brett Calcott`: http://brettcalcott.com\n\n.. _`University of Sydney`: http://sydney.edu.au/ \n\n.. _`IPython Notebook`: http://ipython.org/notebook.html \n\n.. _Pandas: http://pandas.pydata.org/\n\n.. _Networkx: https://networkx.github.io/ \n\n.. _`Causal Foundations of Biological Information`: http://sydney.edu.au/foundations_of_science/research/causal_foundations_biological_information.shtml \n\n\nReferences\n----------\n\n.. [AyPolani2008] Ay, N., & Polani, D. (2008). Information flows in causal\n    networks. Advances in Complex Systems, 11(01), 17\u201341.\n\n.. [GriffithsEtAl2015] Griffiths, P. E., Pocheville, A., Calcott, B., Stotz, K., \n    Kim, H., & Knight, R. (2015). Measuring Causal Specificity. Philosophy of Science, 82(October), 529\u2013555.\n\n.. [CalcottEtAl2016] Calcott, B., Griffiths, P. E., Pocheville, A.\n    (Forthcoming). Signals that Make a Difference. British Journal for Philosophy of Science.\n\n.. [Pearl2000] Pearl, J. (2000). Causality. Cambridge University Press. \n\n\n.. vim: fo=tcroqn tw=78",
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