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https://github.com/jbytecode/LinRegOutliers
Direct and robust methods for outlier detection in linear regression
https://github.com/jbytecode/LinRegOutliers
linear-regression outliers-detection robust-statistics
Last synced: 2 months ago
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Direct and robust methods for outlier detection in linear regression
- Host: GitHub
- URL: https://github.com/jbytecode/LinRegOutliers
- Owner: jbytecode
- License: mit
- Created: 2020-08-22T19:54:14.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2024-11-04T19:26:28.000Z (3 months ago)
- Last Synced: 2024-11-04T20:29:37.771Z (3 months ago)
- Topics: linear-regression, outliers-detection, robust-statistics
- Language: Julia
- Homepage:
- Size: 1.4 MB
- Stars: 44
- Watchers: 4
- Forks: 6
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- Contributing: CONTRIBUTING.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
Awesome Lists containing this project
- awesome-sciml - jbytecode/LinRegOutliers: Direct and robust methods for outlier detection in linear regression
README
[](https://travis-ci.org/jbytecode/LinRegOutliers) [](https://doi.org/10.21105/joss.02892)
[](https://jbytecode.github.io/LinRegOutliers/dev/)
[](https://codecov.io/gh/jbytecode/LinRegOutliers)
# LinRegOutliers
A Julia package for outlier detection in linear regression.
## Implemented Methods
- Ordinary Least Squares and Weighted Least Squares regression
- Regression diagnostics (DFBETA, DFFIT, CovRatio, Cook's Distance, Mahalanobis, Hadi's measure, etc.)
- Hadi & Simonoff (1993)
- Kianifard & Swallow (1989)
- Sebert & Montgomery & Rollier (1998)
- Least Median of Squares
- Least Trimmed Squares
- Minimum Volume Ellipsoid (MVE)
- MVE & LTS Plot
- Billor & Chatterjee & Hadi (2006)
- Pena & Yohai (1995)
- Satman (2013)
- Satman (2015)
- Setan & Halim & Mohd (2000)
- Least Absolute Deviations (LAD)
- Quantile Regression Parameter Estimation (quantileregression)
- Least Trimmed Absolute Deviations (LTA)
- Hadi (1992)
- Marchette & Solka (2003) Data Images
- Satman's GA based LTS estimation (2012)
- Fischler & Bolles (1981) RANSAC Algorithm
- Minimum Covariance Determinant Estimator
- Imon (2005) Algorithm
- Barratt & Angeris & Boyd (2020) CCF algorithm
- Atkinson (1994) Forward Search Algorithm
- BACON Algorithm (Billor & Hadi & Velleman (2000))
- Hadi (1994) Algorithm
- Chatterjee & Mächler (1997)
- Theil-Sen estimator for multiple regression
- Deepest Regression Estimator
- Summary
## Unimplemented Methods
- Pena & Yohai (1999). See [#25](https://github.com/jbytecode/LinRegOutliers/issues/25) for the related issue.
## Installation
```LinRegOutliers``` can be installed using the ```Julia``` REPL.
```julia
julia> ]
(@v1.9) pkg> add LinRegOutliers
```
or
```julia
julia> using Pgk
julia> Pkg.add("LinRegOutliers")
```
then
```julia
julia> using LinRegOutliers
```
to make all the stuff be ready!
## Examples
We provide some examples [here](https://github.com/jbytecode/LinRegOutliers/blob/master/examples.md).
## Documentation
Please check out the reference manual [here](https://jbytecode.github.io/LinRegOutliers/).
## News
- We implemented ~25 outlier detection algorithms which covers a high percentage of the literature.
- Visit the [CHANGELOG.md](https://github.com/jbytecode/LinRegOutliers/blob/master/CHANGELOG.md) for the log of latest changes.
## Contributions
You are probably the right contributor
- If you have statistics background
- If you like Julia
However, 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!
## Citation
Please refer our original paper if you use the package in your research using
```
Satman 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
```
or the bibtex entry
```
@article{Satman2021,
doi = {10.21105/joss.02892},
url = {https://doi.org/10.21105/joss.02892},
year = {2021},
publisher = {The Open Journal},
volume = {6},
number = {57},
pages = {2892},
author = {Mehmet Hakan Satman and Shreesh Adiga and Guillermo Angeris and Emre Akadal},
title = {LinRegOutliers: A Julia package for detecting outliers in linear regression},
journal = {Journal of Open Source Software}
}
```
## Contact & Communication
- Please use issues for a new feature request or bug reports.
- We are in #linregoutliers channel on [Julia Slack](http://julialang.slack.com/) for any discussion requires online chatting.