{"id":28142229,"url":"https://github.com/layerdynamics/jknife","last_synced_at":"2025-09-23T03:55:01.312Z","repository":{"id":291553944,"uuid":"977984268","full_name":"LayerDynamics/jknife","owner":"LayerDynamics","description":"Framework-agnostic Jackknife estimation utilities for statistical modeling.","archived":false,"fork":false,"pushed_at":"2025-05-05T09:43:14.000Z","size":11,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-08-19T10:37:59.869Z","etag":null,"topics":["jackknife","jackknife-variance-estimates"],"latest_commit_sha":null,"homepage":"","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"unlicense","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/LayerDynamics.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,"zenodo":null}},"created_at":"2025-05-05T09:37:05.000Z","updated_at":"2025-05-05T09:44:37.000Z","dependencies_parsed_at":"2025-05-05T10:40:54.572Z","dependency_job_id":"1b5e6218-32c8-4631-a1db-c82394b68d3c","html_url":"https://github.com/LayerDynamics/jknife","commit_stats":null,"previous_names":["layerdynamics/jknife"],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/LayerDynamics/jknife","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LayerDynamics%2Fjknife","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LayerDynamics%2Fjknife/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LayerDynamics%2Fjknife/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LayerDynamics%2Fjknife/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/LayerDynamics","download_url":"https://codeload.github.com/LayerDynamics/jknife/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/LayerDynamics%2Fjknife/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":276512732,"owners_count":25655450,"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","status":"online","status_checked_at":"2025-09-23T02:00:09.130Z","response_time":73,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":["jackknife","jackknife-variance-estimates"],"created_at":"2025-05-14T19:18:22.119Z","updated_at":"2025-09-23T03:55:01.250Z","avatar_url":"https://github.com/LayerDynamics.png","language":"Python","readme":"# jknife\n\nFramework-agnostic Jackknife estimation utilities for statistical modeling\n\n## Overview\n\njknife provides a generic implementation of the [jackknife resampling method](https://en.wikipedia.org/wiki/Jackknife_resampling) for statistical computing. It is designed to be framework-agnostic, making it compatible with scikit-learn, statsmodels, and custom models.\n\n## Features\n\n- Leave-one-out jackknife estimation with optional parallelization\n- Support for scikit-learn estimators via adapter functions\n- Intuitive API designed for both novice and expert users\n- Minimal dependencies (only numpy and scipy for core functionality)\n\n## Installation\n\n```bash\n# Basic installation\npip install jknife\n\n# With scikit-learn support\npip install jknife[sklearn]\n\n# With parallel processing support\npip install jknife[parallel]\n\n# With all optional dependencies\npip install jknife[all]\n\n# For development\npip install jknife[dev]\n```\n\n## Quick Example\n\n```python\nimport numpy as np\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.datasets import make_regression\nfrom jknife.core import jackknife\nfrom jknife.contrib.sklearn_adapters import sklearn_fit_fn, sklearn_coef_fn\n\n# Generate some data\nX, y = make_regression(n_samples=200, n_features=5, noise=10, random_state=42)\n\n# Perform jackknife estimation\nresult = jackknife(\n    X,\n    y,\n    fit_fn=sklearn_fit_fn(LinearRegression, fit_intercept=True),\n    coef_fn=sklearn_coef_fn,\n    n_jobs=-1,  # Use all available cores\n)\n\n# Print summary table\nprint(result.summary())\n```\n\n## License\n\nUNLICENSE","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flayerdynamics%2Fjknife","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Flayerdynamics%2Fjknife","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Flayerdynamics%2Fjknife/lists"}