Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Oleksii Pokotylo, Pavlo Mozharovskyi, Rainer Dyckerhoff
Title: Depth and Depth-Based Classification with R Package ddalpha
Abstract: 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.

Page views:: 405. Submitted: 2016-08-11. Published: 2019-10-31.
Paper: Depth and Depth-Based Classification with R Package ddalpha     Download PDF (Downloads: 102)
Supplements:
ddalpha_1.3.10.tar.gz: R source package Download (Downloads: 13; 480KB)
v91i05-replication.zip: Replication materials Download (Downloads: 14; 71KB)

DOI: 10.18637/jss.v091.i05

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.