{ "info": { "author": "Georg M. Goerg", "author_email": "my_three_initials@stat.cmu.edu", "bugtrack_url": null, "classifiers": [ "Development Status :: 1 - Planning", "Environment :: Console", "Intended Audience :: Science/Research", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Operating System :: OS Independent", "Programming Language :: Python :: 2.7", "Topic :: Scientific/Engineering" ], "description": "pyLICORS: readme\n ================\n ``pylicors`` is a Python module for predictive state estimation from \n continuous valued spatio-temporal data.\n \n .. warning::\n This package is in a **Planning status**. That means it has only very limited\n functionality, and function names, arguments, output, etc. **WILL** change in \n the future. Do not use this yet for any more involved analysis or automated\n analysis of any kind. The code you write now **will break** with the next releases.\n \n ``pylicors`` has two *cousins* in `R`_: \n - `LICORS`_\n - `LSC`_\n \n Support and Documentation\n -------------------------\n \n See `pylicors website `_ \n on `PyPI `_ to view\n documentation, report bugs, and obtain support.\n \n License\n -------\n \n ``pylicors`` is offered under the `GPL3 license\n `_.\n \n Authors\n -------\n \n ``pylicors`` is developed and maintained by `Georg M. Goerg\n `_.\n \n Credits\n -------\n \n - `numpy`_\n - `pyopencv`_\n - `OpenCV`_\n \n .. _numpy: numpy.scipy.org/\n .. _pyopencv: http://code.google.com/p/pyopencv/\n .. _`OpenCV`: http://opencv.willowgarage.com/wiki/", "description_content_type": null, "docs_url": null, "download_url": null, "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "www.stat.cmu.edu/~gmg", "keywords": "forecasting spatio-temporal nonparametric statistics estimation", "license": "GPLv3", "maintainer": null, "maintainer_email": null, "name": "pyLICORS", "package_url": "https://pypi.org/project/pyLICORS/", "platform": "UNKNOWN", "project_url": "https://pypi.org/project/pyLICORS/", "project_urls": { "Homepage": "www.stat.cmu.edu/~gmg" }, "release_url": "https://pypi.org/project/pyLICORS/0.0.1dev/", "requires_dist": null, "requires_python": null, "summary": "A Python interface for predictive state estimation from spatio-temporal data", "version": "0.0.1dev" }, "last_serial": 796911, "releases": { "0.0.1dev": [ { "comment_text": "", "digests": { "md5": "f3551e21684882c98780307ebd9550ed", "sha256": "1245232c9933babc16ee2f7c9a0edc196109ed1138159245c7dfab2058b4794b" }, "downloads": -1, "filename": "pyLICORS-0.0.1dev.tar.gz", "has_sig": false, "md5_digest": "f3551e21684882c98780307ebd9550ed", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 792583, "upload_time": "2012-12-05T01:30:46", "url": "https://files.pythonhosted.org/packages/49/7b/287118149743754b253b443a08143584b1e3e16e163965efd74ff2d68dbf/pyLICORS-0.0.1dev.tar.gz" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "f3551e21684882c98780307ebd9550ed", "sha256": "1245232c9933babc16ee2f7c9a0edc196109ed1138159245c7dfab2058b4794b" }, "downloads": -1, "filename": "pyLICORS-0.0.1dev.tar.gz", "has_sig": false, "md5_digest": "f3551e21684882c98780307ebd9550ed", "packagetype": "sdist", "python_version": "source", "requires_python": null, "size": 792583, "upload_time": "2012-12-05T01:30:46", "url": "https://files.pythonhosted.org/packages/49/7b/287118149743754b253b443a08143584b1e3e16e163965efd74ff2d68dbf/pyLICORS-0.0.1dev.tar.gz" } ] }