# OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming language

arXiv: https://arxiv.org/abs/2211.04550 · HTML: https://mlsanity.com/paper/2211.04550/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 18 references that are in the map.
- First posted on arXiv 8 Nov 2022.

## Builds on (most cited first, 5 of 5)

- [PyOD: A Python Toolbox for Scalable Outlier Detection](https://mlsanity.com/paper/1901.01588/) (2019)
- [Deep Nearest Neighbor Anomaly Detection](https://mlsanity.com/paper/2002.10445/) (2020)
- [MLJ: A Julia package for composable machine learning](https://mlsanity.com/paper/2007.12285/) (2020)
- [Flexible model composition in machine learning and its implementation in MLJ](https://mlsanity.com/paper/2012.15505/) (2020)
- [Designing Machine Learning Toolboxes: Concepts, Principles and Patterns](https://mlsanity.com/paper/2101.04938/) (2021)

## Built on by (most cited first, 1 of 1)

- [A Probabilistic Transformation of Distance-Based Outliers](https://mlsanity.com/paper/2305.09446/) (2023)

## 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.