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        "author": "Colin Smith, Travis Jefferies, Isaac J. Faber",
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        "description": "pymetalog\n================\nColin Smith, Travis Jefferies, Isaac J. Faber\n\n### The Python Metalog Distribution\n\nThis repo is a working project for a python package (**pymetalog**) that generates functions\nfor the metalog distribution. The metalog distribution is a highly\nflexible probability distribution that can be used to model data without\ntraditional parameters.\n\n### Metalog Background\n\nIn economics, business, engineering, science and other fields,\ncontinuous uncertainties frequently arise that are not easily- or\nwell-characterized by previously-named continuous probability\ndistributions. Frequently, there is data available from measurements,\nassessments, derivations, simulations or other sources that characterize\nthe range of an uncertainty. But the underlying process that generated\nthis data is either unknown or fails to lend itself to convenient\nderivation of equations that appropriately characterize the probability\ndensity (PDF), cumulative (CDF) or quantile distribution functions.\n\nThe metalog distributions are a family of continuous univariate\nprobability distributions that directly address this need. They can be\nused in most any situation in which CDF data is known and a flexible,\nsimple, and easy-to-use continuous probability distribution is needed to\nrepresent that data. Consider their [uses and\nbenefits](http://www.metalogdistributions.com/usesbenefits.html). Also\nconsider their\n[applications](http://www.metalogdistributions.com/applicationsdata.html)\nover a wide range of fields and data sources.\n\nThis repository is a complement and extension of the information found\nin the [paper\npublished](http://pubsonline.informs.org/doi/abs/10.1287/deca.2016.0338)\nin Decision Analysis and the\n[website](http://www.metalogdistributions.com/)\n\n\n",
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        "summary": "A python package that generates functions for the metalog distribution. The metalog distribution is a highly flexible probability distribution that can be used to model data without traditional parameters.",
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