# Depth and depth-based classification with R-package ddalpha

arXiv: https://arxiv.org/abs/1608.04109 · HTML: https://mlsanity.com/paper/1608.04109/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Aug 2016.

## Built on by (most cited first, 5 of 5)

- [A Pseudo-Metric between Probability Distributions based on Depth-Trimmed Regions](https://mlsanity.com/paper/2103.12711/) (2021)
- [Statistical Depth Functions for Ranking Distributions: Definitions, Statistical Learning and Applications](https://mlsanity.com/paper/2201.08105/) (2022)
- [Anomaly detection using data depth: multivariate case](https://mlsanity.com/paper/2210.02851/) (2022)
- [Statistical process monitoring of artificial neural networks](https://mlsanity.com/paper/2209.07436/) (2022)
- [Kernel Outlier Detection](https://mlsanity.com/paper/2506.22994/) (2025)

## Abstract (arXiv)

Following the seminal idea of Tukey, data depth is a function that measures how close an arbitrary point of the space is located to an implicitly defined center of a data cloud. Having undergone theoretical and computational developments, it is now employed in numerous applications with classification being the most popular one. The R-package ddalpha is a software directed to fuse experience of the applicant with recent achievements in the area of data depth and depth-based classification. ddalpha provides an implementation for exact and approximate computation of most reasonable and widely applied notions of data depth. These can be further used in the depth-based multivariate and functional classifiers implemented in the package, where the DDα-procedure is in the main focus. The package is expandable with user-defined custom depth methods and separators. The implemented functions for depth visualization and the built-in benchmark procedures may also serve to provide insights into the geometry of the data and the quality of pattern recognition.