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        "author": "Gabriela Tavares",
        "author_email": "gtavares@caltech.edu",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 3 - Alpha",
            "License :: OSI Approved :: GNU General Public License v3 (GPLv3)",
            "Programming Language :: Python :: 2.7",
            "Programming Language :: Python :: 3.6",
            "Topic :: Scientific/Engineering"
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        "description": "aDDM Toolbox\n============\n\nThis toolbox can be used to perform model fitting and to generate\nsimulations for the attentional drift-diffusion model (aDDM), as well as\nfor the classic version of the drift-diffusion model (DDM) without an\nattentional component.\n\nPrerequisites\n-------------\n\naDDM-Toolbox supports Python 2.7 (and Python 3.6 tentatively -- please\nreport any bugs). The following libraries are required:\n\n-  deap\n-  future\n-  matplotlib\n-  numpy\n-  pandas\n-  scipy\n\nInstalling\n----------\n\n::\n\n    $ pip install addm_toolbox\n\nRunning tests\n-------------\n\nTo make sure everything is working correctly after installation, try\n(from a UNIX shell, not the Python interpreter):\n\n::\n\n    $ addm_toolbox_tests\n\nThis should take a while to finish, so maybe go get a cup of tea :)\n\nGetting started\n---------------\n\nTo get a feel for how the algorithm works, try:\n\n::\n\n    $ addm_demo --display-figures\n\nYou can see all the arguments available for the demo using:\n\n::\n\n    $ addm_demo --help\n\nHere is a list of useful scripts which can be similarly run from a UNIX\nshell:\n\n-  addm\\_demo\n-  ddm\\_pta\\_test\n-  addm\\_pta\\_test\n-  addm\\_pta\\_mle\n-  addm\\_pta\\_map\n-  addm\\_simulate\\_true\\_distributions\n-  addm\\_basinhopping\n-  addm\\_genetic\\_algorithm\n-  ddm\\_mla\n-  addm\\_mla\n\nYou can also have a look directly at the code in the following modules:\n\n-  addm.py contains the aDDM implementation, with functions to generate\n   model simulations and obtain the likelihood for a given data trial.\n-  ddm.py is equivalent to addm.py but for the DDM.\n-  addm\\_pta\\_test.py generates an artificial data set for a given set\n   of aDDM parameters and attempts to recover these parameters through\n   maximum a posteriori estimation.\n-  ddm\\_pta\\_test.py is equivalent to addm\\_pta\\_test.py but for the\n   DDM.\n-  addm\\_pta\\_mle.py fits the aDDM to a data set by performing maximum\n   likelihood estimation.\n-  addm\\_pta\\_map.py performs model comparison for the aDDM by obtaining\n   a posterior distribution over a set of models.\n-  simulate\\_addm\\_true\\_distributions.py generates aDDM simulations\n   using empirical data for the fixations.\n\nCommon issues\n-------------\n\nIf you get errors while using the toolbox under Python 3, try it with\nPython 2.7.\n\nIf you get a Python RuntimeError with the message \"Python is not\ninstalled as a framework.\", try creating the\nfile\u00a0~/.matplotlib/matplotlibrc and adding the following code:\n\n::\n\n    backend: TkAgg\n\nAuthors\n-------\n\n-  **Gabriela Tavares** - gtavares@caltech.edu,\n   `goptavares <https://github.com/goptavares>`__\n\nLicense\n-------\n\nThis project is licensed under the GNU GENERAL PUBLIC LICENSE - see the\nCOPYING file for details.\n\nAcknowledgments\n---------------\n\nThis toolbox was developed as part of a research project in the `Rangel\nNeuroeconomics Lab <http://www.rnl.caltech.edu/>`__ at the California\nInstitute of Technology.\n\n\n",
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