.zenodo.json

{
    "language": "eng",
    "license": "MIT",
    "title": "pyeee: Parameter screening using Efficient/Sequential Elementary Effects, an extension of Morris' method",
    "references": [
	{
            "reference": "Cuntz, Mai et al. (2015) Computationally inexpensive identification of noninformative model parameters by sequential screening, Water Resources Research 51, 6417-6441, doi:10.1002/2015WR016907"
	}
    ],
    "related_identifiers": [
        {
            "scheme": "url",
            "identifier": "https://github.com/mcuntz/pyeee/",
            "relation": "isDerivedFrom",
            "resource_type": "software"
        },
        {
            "scheme": "url",
            "identifier": "https://mcuntz.github.io/pyeee/",
            "relation": "isDocumentedBy",
            "resource_type": "publication-softwaredocumentation"
        },
        {
            "scheme": "url",
            "identifier": "https://pypi.org/project/pyeee/",
            "relation": "isIdenticalTo",
            "resource_type": "software"
        },
        {
            "scheme": "url",
            "identifier": "https://anaconda.org/conda-forge/pyeee/",
            "relation": "isIdenticalTo",
            "resource_type": "software"
	}
    ],
    "upload_type": "software",
    "keywords": [
        "Python utilities",
        "Optimization",
        "Screening",
        "Morris",
        "Elementary Effects",
        "Morris method",
        "Python"
    ],
    "creators": [
        {
            "orcid": "0000-0002-5966-1829",
            "affiliation": "Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement - INRAE, Nancy, France",
            "name": "Matthias Cuntz"
	},
        {
            "orcid": "0000-0002-1132-2342",
            "affiliation": "University of Waterloo, ON, Canada",
            "name": "Juliane Mai"
	}
    ],
    "access_right": "open",
    "description": "<p><strong>pyeee</strong> is a Python library for performing parameter screening of computational models. It uses Efficient or Sequential Elementary Effects, an extension of Morris&#39; method of Elementary Effects, published by:</p>\n\n<p>Cuntz M, Mai J, Zink M, Thober S, Kumar R, Sch&auml;fer D, Schr&ouml;n M, Craven J, Rakovec O, Spieler D, Prykhodko V, Dalmasso G, Musuuza J, Langenberg B, Attinger A, and Samaniego L (2015) Computationally inexpensive identification of noninformative model parameters by sequential screening, <em>Water Resources Research</em> 51, 6417-6441, doi:<a href=\"https://doi.org/10.1002/2015WR016907\">10.1002/2015WR016907</a></p>\n\n<p><strong>pyeee</strong> can be used with Python functions as well as external executables using libraries such as <a href=\"https://github.org/mcuntz/partialwrap/\">partialwrap</a>. Function evaluations can be distributed with Python&#39;s multiprocessing or via MPI.</p>\n\n<p>The complete documentation of pyeee is available at: <a href=\"https://mcuntz.github.io/pyeee/\">https://mcuntz.github.io/pyeee/</a></p>\n\n<p>A similar package (EEE) using a combination of bash and Python scripts is presented at: <a href=\"https://doi.org/10.5281/zenodo.3620894\">https://doi.org/10.5281/zenodo.3620894</a></p>\n\n<p>The version 4.0 modernised code structure and documentation, moving everything to Github. Version 4.1 added <strong>pyeee</strong> to conda-forge. Version 5.0 removed the dependency to the general purpose package <a href=\"https://github.com/mcuntz/pyjams\">pyjams</a> and added the original Morris' Method of Elementary Effects.</p>"
}