{ "info": { "author": "Tristan Millington", "author_email": "tristan.millington@gmail.com", "bugtrack_url": null, "classifiers": [ "Development Status :: 2 - Pre-Alpha", "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", "Operating System :: OS Independent", "Programming Language :: Python :: 3" ], "description": "Network Inference Toolkit\n\nA bunch of scripts written to infer correlation/partial correlation networks from data. The goal is to have them like sklearn models. Currently very much\na work in progress\n\nImplemented:\nSPACE - Partial Correlation Estimation by Joint Sparse Regression Models by Peng, Wang and Zhu - https://doi.org/10.1198/jasa.2009.0126\nSCIO - Fast and adaptive sparse precision matrix estimation in high dimensions - Liu and Luo - https://doi.org/10.1016/j.jmva.2014.11.005\nCLIME - A Constrained L1 Minimization Approach to Sparse Precision Matrix Estimation - Cai, Liu and Luo - https://doi.org/10.1198/jasa.2011.tm10155\nDTrace - Sparse precision matrix estimation via lasso penalized D-trace loss - Zou and Zhang - https://doi.org/10.1093/biomet/ast059\nCorrelation Permutation - Estimates a sparse correlation matrix by permuting the dataset repeatedly to get a p-value to see if\nthe correlation between two variables is just as likely to occur through noise \nScaled Lasso - \"Sparse Matrix Inversion with Scaled Lasso\" by Sun and Zhang - http://www.jmlr.org/papers/volume14/sun13a/sun13a.pdf\n\n\n\n", "description_content_type": "text/markdown", "docs_url": null, "download_url": "", "downloads": { "last_day": -1, "last_month": -1, "last_week": -1 }, "home_page": "https://github.com/shazzzm/nitk", "keywords": "", "license": "", "maintainer": "", "maintainer_email": "", "name": "nitk", "package_url": "https://pypi.org/project/nitk/", "platform": "", "project_url": "https://pypi.org/project/nitk/", "project_urls": { "Homepage": "https://github.com/shazzzm/nitk" }, "release_url": "https://pypi.org/project/nitk/0.1/", "requires_dist": null, "requires_python": "", "summary": "A package for inferring sparse partial correlation networks", "version": "0.1" }, "last_serial": 5862577, "releases": { "0.1": [ { "comment_text": "", "digests": { "md5": "b89a11de4a1d474c97873e17b52c7b0d", "sha256": "57f9ae2cecb3cea9838647ce8bd1519bbcf6ade9d89f6b638912ed0d27d1ef2d" }, "downloads": -1, "filename": "nitk-0.1-py3-none-any.whl", "has_sig": false, "md5_digest": "b89a11de4a1d474c97873e17b52c7b0d", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 15553, "upload_time": "2019-09-20T14:21:00", "url": "https://files.pythonhosted.org/packages/fc/63/a5ae9c3ba1af1426de1a2f2ff03730baeb212775b1cefba5577c5be620f6/nitk-0.1-py3-none-any.whl" } ] }, "urls": [ { "comment_text": "", "digests": { "md5": "b89a11de4a1d474c97873e17b52c7b0d", "sha256": "57f9ae2cecb3cea9838647ce8bd1519bbcf6ade9d89f6b638912ed0d27d1ef2d" }, "downloads": -1, "filename": "nitk-0.1-py3-none-any.whl", "has_sig": false, "md5_digest": "b89a11de4a1d474c97873e17b52c7b0d", "packagetype": "bdist_wheel", "python_version": "py3", "requires_python": null, "size": 15553, "upload_time": "2019-09-20T14:21:00", "url": "https://files.pythonhosted.org/packages/fc/63/a5ae9c3ba1af1426de1a2f2ff03730baeb212775b1cefba5577c5be620f6/nitk-0.1-py3-none-any.whl" } ] }