@article{JSSv091i05, title={Depth and Depth-Based Classification with R Package ddalpha}, volume={91}, url={https://www.jstatsoft.org/index.php/jss/article/view/v091i05}, doi={10.18637/jss.v091.i05}, 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.}, number={5}, journal={Journal of Statistical Software}, author={Pokotylo, Oleksii and Mozharovskyi, Pavlo and Dyckerhoff, Rainer}, year={2019}, pages={1–46} }