{
    "info": {
        "author": "Michele Cappellari",
        "author_email": "michele.cappellari@physics.ox.ac.uk",
        "bugtrack_url": null,
        "classifiers": [
            "Development Status :: 5 - Production/Stable",
            "Intended Audience :: Developers",
            "Intended Audience :: Science/Research",
            "Operating System :: OS Independent",
            "Programming Language :: Python :: 3"
        ],
        "description": "The AdaMet Package\n==================\n\n**Adaptive Metropolis for Bayesian Analysis**\n\n.. image:: https://img.shields.io/pypi/v/adamet.svg\n        :target: https://pypi.org/project/adamet/\n.. image:: https://img.shields.io/badge/arXiv-1208.3522-orange.svg\n    :target: https://arxiv.org/abs/1208.3522\n.. image:: https://img.shields.io/badge/DOI-10.1093/mnras/stt562-blue.svg\n    :target: https://doi.org/10.1093/mnras/stt562\n\nAdaMet is a well-tested Python implementation of the Adaptive Metropolis algorithm by\n`Haario H., Saksman E., Tamminen J., (2001) <https://projecteuclid.org/euclid.bj/1080222083>`_.\nIt was used in a number of published papers in the astrophysics literature.\n\nAttribution\n-----------\n\nIf you use this software for your research, please cite \n`Cappellari et al. (2013a) <http://adsabs.harvard.edu/abs/2013MNRAS.432.1709C>`_ \nwhere the implementation was introduced. The BibTeX entry for the paper is::\n\n    @ARTICLE{Cappellari2013a,\n        author = {{Cappellari}, M. and {Scott}, N. and {Alatalo}, K. and\n            {Blitz}, L. and {Bois}, M. and {Bournaud}, F. and {Bureau}, M. and\n            {Crocker}, A.~F. and {Davies}, R.~L. and {Davis}, T.~A. and {de Zeeuw},\n            P.~T. and {Duc}, P.-A. and {Emsellem}, E. and {Khochfar}, S. and\n            {Krajnovi{\\'c}}, D. and {Kuntschner}, H. and {McDermid}, R.~M. and\n            {Morganti}, R. and {Naab}, T. and {Oosterloo}, T. and {Sarzi}, M. and\n            {Serra}, P. and {Weijmans}, A.-M. and {Young}, L.~M.},\n        title = \"{The ATLAS$^{3D}$ project - XV. Benchmark for early-type\n            galaxies scaling relations from 260 dynamical models: mass-to-light\n            ratio, dark matter, Fundamental Plane and Mass Plane}\",\n        journal = {MNRAS},\n        eprint = {1208.3522},\n        year = 2013,\n        volume = 432,\n        pages = {1709-1741},\n        doi = {10.1093/mnras/stt562}\n    }\n\nInstallation\n------------\n\ninstall with::\n\n    pip install adamet\n\nWithout writing access to the global ``site-packages`` directory, use::\n\n    pip install --user adamet\n\nDocumentation\n-------------\n\nSee ``adamet/examples``\n\nLicense\n-------\n\nCopyright (c) 2012-2018 Michele Cappellari\n\nThis software is provided as is without any warranty whatsoever.\nPermission to use, for non-commercial purposes is granted.\nPermission to modify for personal or internal use is granted,\nprovided this copyright and disclaimer are included in all\ncopies of the software. All other rights are reserved.\nIn particular, redistribution of the code is not allowed.",
        "description_content_type": "",
        "docs_url": null,
        "download_url": "",
        "downloads": {
            "last_day": -1,
            "last_month": -1,
            "last_week": -1
        },
        "home_page": "http://purl.org/cappellari/software",
        "keywords": "",
        "license": "Other/Proprietary License",
        "maintainer": "",
        "maintainer_email": "",
        "name": "adamet",
        "package_url": "https://pypi.org/project/adamet/",
        "platform": "",
        "project_url": "https://pypi.org/project/adamet/",
        "project_urls": {
            "Homepage": "http://purl.org/cappellari/software"
        },
        "release_url": "https://pypi.org/project/adamet/2.0.7/",
        "requires_dist": null,
        "requires_python": "",
        "summary": "AdaMet: Adaptive Metropolis for Bayesian Analysis",
        "version": "2.0.7"
    },
    "last_serial": 3882681,
    "releases": {
        "2.0.7": [
            {
                "comment_text": "",
                "digests": {
                    "md5": "523090c284c4ab0dd2f34ffb68609354",
                    "sha256": "e67cd3d080d7d1915716fab4b8accf8a0187842fb28a00753c51c4552ea5cd8b"
                },
                "downloads": -1,
                "filename": "adamet-2.0.7.tar.gz",
                "has_sig": false,
                "md5_digest": "523090c284c4ab0dd2f34ffb68609354",
                "packagetype": "sdist",
                "python_version": "source",
                "requires_python": null,
                "size": 12454,
                "upload_time": "2018-05-21T09:12:32",
                "url": "https://files.pythonhosted.org/packages/48/04/2ecabe60443e6833e58126aa3ce3a792f33d156a32f326c6e98b7796ae7f/adamet-2.0.7.tar.gz"
            }
        ]
    },
    "urls": [
        {
            "comment_text": "",
            "digests": {
                "md5": "523090c284c4ab0dd2f34ffb68609354",
                "sha256": "e67cd3d080d7d1915716fab4b8accf8a0187842fb28a00753c51c4552ea5cd8b"
            },
            "downloads": -1,
            "filename": "adamet-2.0.7.tar.gz",
            "has_sig": false,
            "md5_digest": "523090c284c4ab0dd2f34ffb68609354",
            "packagetype": "sdist",
            "python_version": "source",
            "requires_python": null,
            "size": 12454,
            "upload_time": "2018-05-21T09:12:32",
            "url": "https://files.pythonhosted.org/packages/48/04/2ecabe60443e6833e58126aa3ce3a792f33d156a32f326c6e98b7796ae7f/adamet-2.0.7.tar.gz"
        }
    ]
}