OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming language
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Abstract (arXiv)
OutlierDetection.jl is an open-source ecosystem for outlier detection in Julia. It provides a range of high-performance outlier detection algorithms implemented directly in Julia. In contrast to previous packages, our ecosystem enables the development highly-scalable outlier detection algorithms using a high-level programming language. Additionally, it provides a standardized, yet flexible, interface for future outlier detection algorithms and allows for model composition unseen in previous packages. Best practices such as unit testing, continuous integration, and code coverage reporting are enforced across the ecosystem. The most recent version of OutlierDetection.jl is available at https://github.com/OutlierDetectionJL/OutlierDetection.jl.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 5 of its 18 references that are in the map. First posted on arXiv 8 Nov 2022.
Builds on
5 of 18 references.
References · 5
- 2019PyOD: A Python Toolbox for Scalable Outlier Detectioncited by 221
- 2020Deep Nearest Neighbor Anomaly Detectioncited by 103
- 2020MLJ: A Julia package for composable machine learningcited by 9
- 2020Flexible model composition in machine learning and its implementation in MLJcited by 3
- 2021Designing Machine Learning Toolboxes: Concepts, Principles and Patternscited by 2
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Citing papers per year: 2023: 1.
1 paper.
- 2023A Probabilistic Transformation of Distance-Based Outliers16 May 2023