Depth-Based Classification and Calculation of Data Depth
Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included.

c(person("Oleksii", "Pokotylo", role=c("aut", "cre"),
email = "alexey.pokotylo@gmail.com"),
person("Pavlo", "Mozharovskyi", role=c("aut"),
email = "mozharovskyi@statistik.uni-koeln.de"),
person("Rainer", "Dyckerhoff", role=c("aut"),
email = "rainer.dyckerhoff@statistik.uni-koeln.de"))
Oleksii Pokotylo [aut, cre],
Pavlo Mozharovskyi [aut],
Rainer Dyckerhoff [aut]

stats, utils, graphics, grDevices, MASS, class, robustbase

c(person("Oleksii", "Pokotylo", role=c("aut", "cre"),
email = "alexey.pokotylo@gmail.com"),
person("Pavlo", "Mozharovskyi", role=c("aut"),
email = "pavlo.mozharovskyi@univ-rennes1.fr"),
person("Rainer", "Dyckerhoff", role=c("aut"),
email = "rainer.dyckerhoff@statistik.uni-koeln.de"))

c(person("Oleksii", "Pokotylo", role=c("aut", "cre"),
email = "alexey.pokotylo@gmail.com"),
person("Pavlo", "Mozharovskyi", role=c("aut"),
email = "pavlo.mozharovskyi@ensai.fr"),
person("Rainer", "Dyckerhoff", role=c("aut"),
email = "rainer.dyckerhoff@statistik.uni-koeln.de"),
person("Stanislav", "Nagy", role=c("aut"),
email = "nagy@karlin.mff.cuni.cz"))
stats, utils, graphics, grDevices, MASS, class, robustbase,
sfsmisc
Oleksii Pokotylo [aut, cre],
Pavlo Mozharovskyi [aut],
Rainer Dyckerhoff [aut],
Stanislav Nagy [aut]

R (>= 2.10), stats, utils, graphics, grDevices, MASS, class,
robustbase, sfsmisc, geometry
Contains procedures for depth-based supervised learning, which are entirely non-parametric, in particular the DDalpha-procedure (Lange, Mosler and Mozharovskyi, 2014 <doi:10.1007/s00362-012-0488-4>). The training data sample is transformed by a statistical depth function to a compact low-dimensional space, where the final classification is done. It also offers an extension to functional data and routines for calculating certain notions of statistical depth functions. 50 multivariate and 5 functional classification problems are included. (Pokotylo, Mozharovskyi and Dyckerhoff, 2019 <doi:10.18637/jss.v091.i05>).