{"id":13569496,"url":"https://github.com/cgevans/scikits-bootstrap","last_synced_at":"2025-04-05T07:02:20.289Z","repository":{"id":4215075,"uuid":"5336221","full_name":"cgevans/scikits-bootstrap","owner":"cgevans","description":"Python/numpy bootstrap confidence interval estimation.","archived":false,"fork":false,"pushed_at":"2024-02-02T18:57:35.000Z","size":176,"stargazers_count":176,"open_issues_count":4,"forks_count":36,"subscribers_count":16,"default_branch":"main","last_synced_at":"2025-03-29T06:03:47.527Z","etag":null,"topics":["numpy","python","statistics"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/cgevans.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2012-08-08T02:27:40.000Z","updated_at":"2025-02-01T17:12:04.000Z","dependencies_parsed_at":"2022-08-06T15:15:59.531Z","dependency_job_id":"4ddb0f4b-aa6b-4b9e-b764-e167b12eeb8b","html_url":"https://github.com/cgevans/scikits-bootstrap","commit_stats":{"total_commits":124,"total_committers":14,"mean_commits":8.857142857142858,"dds":0.564516129032258,"last_synced_commit":"6b4c0318ded10723b967199d3986b2e96f5873fa"},"previous_names":[],"tags_count":6,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cgevans%2Fscikits-bootstrap","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cgevans%2Fscikits-bootstrap/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cgevans%2Fscikits-bootstrap/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/cgevans%2Fscikits-bootstrap/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/cgevans","download_url":"https://codeload.github.com/cgevans/scikits-bootstrap/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":247299829,"owners_count":20916190,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["numpy","python","statistics"],"created_at":"2024-08-01T14:00:40.739Z","updated_at":"2025-04-05T07:02:20.271Z","avatar_url":"https://github.com/cgevans.png","language":"Python","funding_links":[],"categories":["Uncategorized","🎲 Statistics \u0026 Probability"],"sub_categories":["Uncategorized","Tools"],"readme":"[![DOI](https://zenodo.org/badge/5336221.svg)](https://zenodo.org/badge/latetdoi/5336221)\n[![Codecov](https://img.shields.io/codecov/c/github/cgevans/scikits-bootstrap)](https://codecov.io/gh/cgevans/scikits-bootstrap)\n[![PyPI](https://img.shields.io/pypi/v/scikits-bootstrap)](https://pypi.org/project/scikits.bootstrap/)\n[![GitHub release (latest SemVer)](https://img.shields.io/github/v/release/cgevans/scikits-bootstrap)](https://github.com/cgevans/scikits-bootstrap/releases)\n[![PyPI - Python Version](https://img.shields.io/pypi/pyversions/scikits-bootstrap)](https://pypi.org/project/scikits.bootstrap/)\n\n\nDocumentation: [Stable](https://scikits-bootstrap.readthedocs.io/en/stable/), [Latest](https://scikits-bootstrap.readthedocs.io/en/latest/).\n\nscikits-bootstrap\n=================\n\nScikits.bootstrap provides bootstrap statistics confidence interval algorithms\nfor Numpy/Scipy/Pandas. It originally required scipy, but no longer needs it.\n\nIt also provides an algorithm which estimates the probability that the\nstatistics lies satisfies some criteria, e.g., lies in some interval.\n\nMuch of the code has been written based off the descriptions from Efron\nand Tibshirani's Introduction to the Bootstrap, and results should match\nthe results obtained from following those explanations. However, the\ncurrent ABC code is based off of the modified-BSD-licensed R port of the\nEfron bootstrap code, as I do not believe I currently have a sufficient\nunderstanding of the ABC method to write the code independently.\n\nPlease contact me (Constantine Evans \u003ccevans@costinet.org\u003e, or Matrix\n\u003c@cge:matrix.org\u003e) with any questions, suggestions, vulnerabilities, or other\ncomments ([PGP key](https://costinet.org/new-cge-pgp.key)), or, preferably, use\nGithub's issue and pull requests.\n\nIf you'd like to add something, or make improvements, please keep the following\nin mind:\n\n- I'd like to keep the library as widely supported as possible, with as\n  few dependencies as possible, preferably small ones.  I'm also very\n  wary of anything that would break backwards compatibility, even in\n  unknown ways.\n\n- I am following semantic versioning.\n\n- Code should be black-formatted, and should have type annotations that work\n  in 3.7 through the latest stable Python, and PyPy3.\n\n- Docstrings should be in Numpy format.  They should preferably include\n  references for the implemented algorithms (see the current code for\n  examples).\n\n- All code should have working unit tests, preferably ones that do more\n  than just testing fixed output with a fixed random seed, though given\n  the non-deterministic nature of the bootstrap, good, deterministic\n  tests may be difficult.  Non-deterministic tests with extremely small\n  chances of failure are acceptable, but shouldn't use such large numbers\n  of samples that they overly slow down the tests.\n\n- I am a physicist, and am not very familiar with applications of the\n  bootstrap for business or development statistics.  So if you'd like\n  to add something for those, it would be useful to give some more\n  explanation than otherwise as to how they are generally useful.\n\nThe package is licensed under the BSD 3-Clause License. It is supported\nby the [Evans Foundation](https://evansfmm.org).\n\nI don't see a particular need to cite this package, but if you want to,\nplease use the Zenodo DOI above, or the one appropriate for the version\nyou used.\n\nVersion Information\n===================\n\n-   v1.1.0: Randomness is now generated via a numpy.random\n    Generator. Anything that relied on using numpy.random.seed to obtain\n    deterministic results will fail (mostly of relevance for testing).\n    Seeds (or Generators) can now be passed to relevant functions with\n    the `seed` argument, but note that changes in Numpy's random number\n    generation means this will not give the same results that would be\n    obtained using `numpy.random.seed` to set the seed in previous\n    versions.\n\n    There is a new pval function, and there are several bugfixes.\n\n    Numba is now supported in some instances (np.average or np.mean as\n    statfunction, 1-D data), using use\\_numba=True. Pypy3 is also\n    supported. Typing information has been added, with code passing\n    `mypy --strict --allow-untyped-calls --ignore-missing-imports`, and\n    tests cover 100% of the code (though many tests use fixed seeds).\n\n    Handling of multiple data sets (tuples/etc of arrays) now can be\n    specified as multi=\"paired\" (the previous handling), where the sets\n    must be of the same length, and samples are taken keeping\n    corresponding points connected, or multi=\"independent\", treating\n    data sets as independent and sampling them seperately (in which case\n    they may be different sizes).\n\n-   v1.0.1: Licensing information added.\n\n-   v1.0.0: scikits.bootstrap now uses pyerf, which means that it\n    doesn't actually need scipy at all. It should work with PyPy, has\n    some improved error and warning messages, and should be a bit faster\n    in many cases. The old ci\\_abc function has been removed: use\n    method='abc' instead.\n\n-   v0.3.3: Bug fixes. Warnings have been cleaned up, and are\n    implemented for BCa when all statistic values are equal (a common\n    confusion in prior versions). Related numpy warnings are now\n    suppressed. Some tests on Python 2 were fixed, and the PyPI website\n    link is now correct.\n\n-   v0.3.2: This version contains various fixes to allow compatibility\n    with Python 3.3. While I have not used the package extensively with\n    Python 3, all tests now pass, and importing works properly. The\n    compatibility changes slightly modify the output of\n    bootstrap\\_indexes, from a Python list to a Numpy array that can be\n    iterated over in the same manner. This should only be important in\n    extremely unusual situations.\n\nInstallation and Usage\n======================\n\nscikits.bootstrap is tested on Python 3.7 - 3.10, and PyPy 3. The package\ncan be installed using pip.\n\n`pip install scikits.bootstrap`\n\nUsage example for python 3.x:\n\n    import scikits.bootstrap as boot\n    import numpy as np\n    boot.ci(np.random.rand(100), np.average)\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcgevans%2Fscikits-bootstrap","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fcgevans%2Fscikits-bootstrap","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fcgevans%2Fscikits-bootstrap/lists"}