{ "info": { "author": "Constantine Evans", "author_email": "cevans@evanslabs.org", "bugtrack_url": null, "classifiers": [ "Development Status :: 4 - Beta", "Environment :: Console", "Intended Audience :: Developers", "Intended Audience :: Science/Research", "License :: OSI Approved :: BSD License", "Operating System :: OS Independent", "Programming Language :: Python", "Programming Language :: Python :: 3", "Programming Language :: Python :: Implementation :: CPython", "Programming Language :: Python :: Implementation :: PyPy", "Topic :: Scientific/Engineering" ], "description": "[![Travis](https://travis-ci.org/cgevans/scikits-bootstrap.svg?branch=master)](https://travis-ci.org/cgevans/scikits-bootstrap)\n\nscikits-bootstrap\n=================\n\nScikits.bootstrap provides bootstrap confidence interval algorithms for scipy.\n\nAt present, it is rather feature-incomplete and in flux. However, the functions\nthat have been written should be relatively stable as far as results.\n\nMuch of the code has been written based off the descriptions from Efron and\nTibshirani's Introduction to the Bootstrap, and results should match the results\nobtained from following those explanations. However, the current ABC code is\nbased off of the modified-BSD-licensed R port of the Efron bootstrap code, as\nI do not believe I currently have a sufficient understanding of the ABC method\nto write the code independently.\n\nIn any case, please contact me (Constantine Evans ) with\nany questions or suggestions. I'm trying to add documentation, and will\nbe adding tests as well. I'm especially interested, however, in how the API\nshould actually look; please let me know if you think the package should be\norganized differently.\n\nThe package is licensed under the Modified BSD License. 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