{"id":13699263,"url":"https://github.com/jbytecode/LinRegOutliers","last_synced_at":"2025-05-04T16:32:50.125Z","repository":{"id":55988390,"uuid":"289557168","full_name":"jbytecode/LinRegOutliers","owner":"jbytecode","description":"Direct and robust methods for outlier detection in linear 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Status](https://travis-ci.org/jbytecode/LinRegOutliers.svg?branch=master)](https://travis-ci.org/jbytecode/LinRegOutliers) [![DOI](https://joss.theoj.org/papers/10.21105/joss.02892/status.svg)](https://doi.org/10.21105/joss.02892)\n[![Doc](https://img.shields.io/badge/docs-dev-blue.svg)](https://jbytecode.github.io/LinRegOutliers/dev/)\n[![codecov](https://codecov.io/gh/jbytecode/LinRegOutliers/branch/master/graph/badge.svg?token=DM4XXML78A)](https://codecov.io/gh/jbytecode/LinRegOutliers)\n\n# LinRegOutliers\n\nA Julia package for outlier detection in linear regression.\n\n![](https://raw.githubusercontent.com/jbytecode/LinRegOutliers/master/docs/src/assets/logo.png)\n\n## Implemented Methods\n\n- Ordinary Least Squares and Weighted Least Squares regression \n- Regression diagnostics (DFBETA, DFFIT, CovRatio, Cook's Distance, Mahalanobis, Hadi's measure, etc.)\n- Hadi \u0026 Simonoff (1993)\n- Kianifard \u0026 Swallow (1989)\n- Sebert \u0026 Montgomery \u0026 Rollier (1998)\n- Least Median of Squares \n- Least Trimmed Squares \n- Minimum Volume Ellipsoid (MVE)\n- MVE \u0026 LTS Plot \n- Billor \u0026 Chatterjee \u0026 Hadi (2006)\n- Pena \u0026 Yohai (1995)\n- Satman (2013)\n- Satman (2015)\n- Setan \u0026 Halim \u0026 Mohd (2000)\n- Least Absolute Deviations (LAD)\n- Quantile Regression Parameter Estimation (quantileregression)\n- Least Trimmed Absolute Deviations (LTA)\n- Hadi (1992)\n- Marchette \u0026 Solka (2003) Data Images\n- Satman's GA based LTS estimation (2012)\n- Fischler \u0026 Bolles (1981) RANSAC Algorithm\n- Minimum Covariance Determinant Estimator\n- Imon (2005) Algorithm\n- Barratt \u0026 Angeris \u0026 Boyd (2020) CCF algorithm\n- Atkinson (1994) Forward Search Algorithm\n- BACON Algorithm (Billor \u0026 Hadi \u0026 Velleman (2000))\n- Hadi (1994) Algorithm\n- Chatterjee \u0026 Mächler (1997)\n- Theil-Sen estimator for multiple regression\n- Deepest Regression Estimator\n- Robust Hat Matrix based Initial Subset Regressor\n- Summary\n\n\n## Unimplemented Methods\n\n- Pena \u0026 Yohai (1999). See [#25](https://github.com/jbytecode/LinRegOutliers/issues/25) for the related issue.\n\n\n\n## Installation\n\n```LinRegOutliers``` can be installed using the ```Julia``` REPL.  \n\n```julia\njulia\u003e ]\n(@v1.9) pkg\u003e add LinRegOutliers\n```\n\nor\n\n```julia\njulia\u003e using Pgk\njulia\u003e Pkg.add(\"LinRegOutliers\")\n```\n\nthen\n\n```julia\njulia\u003e using LinRegOutliers\n```\n\nto make all the stuff be ready!\n\n\n## Examples\nWe provide some examples [here](https://github.com/jbytecode/LinRegOutliers/blob/master/examples.md).\n \n## Documentation\nPlease check out the reference manual [here](https://jbytecode.github.io/LinRegOutliers/).\n\n## News\n- We implemented ~25 outlier detection algorithms which covers a high percentage of the literature.\n- Visit the [CHANGELOG.md](https://github.com/jbytecode/LinRegOutliers/blob/master/CHANGELOG.md) for the log of latest changes.\n\n## Contributions\nYou are probably the right contributor\n\n- If you have statistics background\n- If you like Julia\n\nHowever, the second condition is more important because an outlier detection algorithm is just an algorithm. Reading the implemented methods is enough to implement new ones. Please follow the issues. [Here is the a bunch of first shot introductions for new comers](https://github.com/jbytecode/LinRegOutliers/issues/3). Welcome and thank you in advance!\n\n\n## Citation\nPlease refer our original paper if you use the package in your research using\n\n```\nSatman et al., (2021). LinRegOutliers: A Julia package for detecting outliers in linear regression. Journal of Open Source Software, 6(57), 2892, https://doi.org/10.21105/joss.02892\n```\n\nor the bibtex entry\n\n```\n@article{Satman2021,\n  doi = {10.21105/joss.02892},\n  url = {https://doi.org/10.21105/joss.02892},\n  year = {2021},\n  publisher = {The Open Journal},\n  volume = {6},\n  number = {57},\n  pages = {2892},\n  author = {Mehmet Hakan Satman and Shreesh Adiga and Guillermo Angeris and Emre Akadal},\n  title = {LinRegOutliers: A Julia package for detecting outliers in linear regression},\n  journal = {Journal of Open Source Software}\n}\n```\n\n\n## Contact \u0026 Communication\n- Please use issues for a new feature request or bug reports.\n- We are in #linregoutliers channel on [Julia Slack](http://julialang.slack.com/) for any discussion requires online chatting. \n","funding_links":[],"categories":["\u003cspan id=\"head75\"\u003e Outlier Detection\u003c/span\u003e","Machine Learning for Security"],"sub_categories":["Anomaly and Outlier Detection"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjbytecode%2FLinRegOutliers","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fjbytecode%2FLinRegOutliers","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fjbytecode%2FLinRegOutliers/lists"}