Depth and Depth-Based Classification with R Package ddalpha

• Main Authors: Oleksii Pokotylo, Pavlo Mozharovskyi, Rainer Dyckerhoff
• Format: Article
• Language: English
• Published: Foundation for Open Access Statistics 2019-10-01
• Series: Journal of Statistical Software
• Subjects: data depth; supervised classification; dd-plot; outsiders; visualization; functional classification; ddalpha
• Online Access: https://www.jstatsoft.org/index.php/jss/article/view/2796

Following the seminal idea of Tukey (1975), 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.