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        "author": "Adam Mantz",
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        "description": ".. image:: https://img.shields.io/pypi/v/cashstatistic.svg\n   :alt: PyPi\n   :target: https://pypi.python.org/pypi/cashstatistic\n.. image:: https://img.shields.io/pypi/l/cashstatistic.svg\n   :alt: MIT\n   :target: https://mit-license.org/license.txt\n===============\ncashstatistic\n===============\n**Utility functions related to the Cash statistic**\n\n\nThe Poisson distribution is\n\nP(x|mu) = exp(-mu) mu^x / x!\n\nThe `Cash statistic <http://adsabs.harvard.edu/abs/1979ApJ...228..939C>`_ is defined to be the model (mu) dependent part of -2ln(P), analogous to the role that chi^2 plays for the Gaussian distribution,\n\nC = 2( mu - x\\*ln(mu) ).\n\nA modified version,\n\nC_m = 2( mu - x + x\\*ln(x/mu) ),\n\nis equivalent to C for parameter inference (i.e. has the same dependence on mu), and also has the nice property of becoming equivalent to chi^2 when x is large. `Kaastra (2017) <http://adsabs.harvard.edu/abs/2017A%26A...605A..51K>`_ was kind enough to provide approximate expressions for the mean and variance of C_m, which can be used to determine whether the actual C_m corresponding to a fitted model is indicative of a good fit (just as chi^2 does for the Gaussian distribution).\n\nThis package contains python code to calculate C, C_m, and the theoretical mean and variance of C_m. The `GitHub repo <https://github.com/abmantz/cstat>`_ contains implementations in other languages.\n\n\n",
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