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        "author": "Santiago Nunez-Corrales",
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        "description": "intros-MaxEnt\n=============\n\n**The Whole Tale Project**\n\nAuthor: Santiago Nunez-Corrales\n        (nunezco2@illinois.edu)\n\n\n**intros-MaxEnt** is a Python software package that provides an open\nimplementation of the MaxEnt algorithm for ecological niche modeling\nas described in\n\n    Phillips, S. J., Anderson, R. P., & Schapire, R. E. (2006). Maximum\n    entropy modeling of species geographic distributions.\n    Ecological modelling, 190(3-4), 231-259.\n\nOur goal with this package is, scientifically, to help improve the\nmodeling landscape in ecological niche modeling by making explicit the\nconsequences of varying various parameters in MaxEnt based on\nbiologically informed hypotheses.\n\nThe packaged provides facilities for the following tasks:\n\n1. Importing georeferenced data from taxa records for modeling species\n   distribution, as well as environmental variables data for locations\n   compliant with WGS84 latitude-longitude coordinates.\n2. Executing the MaxEnt algorithm with inspection and reparametrization\n   capabilities of models and outcomes.\n3. Analyzing and differentially comparing model outcomes including KML\n   and PNG image generation at various resolutions.\n4. Packaging of model configurations and experiments for extended\n   scientific replication.\n\nThis package is also intended to be used in Jupyter Notebooks at The\nWhole Tale environment as an example of scientific reproducibility. The\ncurrent software implementation was heavily informed by\n\n    Merow, C., Smith, M. J., & Silander, J. A. (2013). A practical guide\n    to MaxEnt for modeling species\u2019 distributions: what it does, and why\n    inputs and settings matter. Ecography, 36(10), 1058-1069.\n\nThis code is part of The Whole Tale Summer Internship 2018.\n\n\n",
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        "license": "LICENSE.txt",
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        "summary": "A MaxEnt package with introspection for ecological niche modeling.",
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