ddalpha: 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 <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>).

Version:	1.3.16
Depends:	R (≥ 2.10), stats, utils, graphics, grDevices, MASS, class, robustbase, sfsmisc, geometry
Imports:	Rcpp (≥ 0.11.0)
LinkingTo:	BH, Rcpp
Published:	2024-09-30
DOI:	10.32614/CRAN.package.ddalpha
Author:	Oleksii Pokotylo [aut, cre], Pavlo Mozharovskyi [aut], Rainer Dyckerhoff [aut], Stanislav Nagy [aut]
Maintainer:	Oleksii Pokotylo <alexey.pokotylo at gmail.com>
License:	GPL-2
NeedsCompilation:	yes
Citation:	ddalpha citation info
In views:	AnomalyDetection, FunctionalData
CRAN checks:	ddalpha results

Documentation:

Reference manual:

ddalpha.html , ddalpha.pdf

Downloads:

Package source:	ddalpha_1.3.16.tar.gz
Windows binaries:	r-devel: ddalpha_1.3.16.zip, r-release: ddalpha_1.3.16.zip, r-oldrel: ddalpha_1.3.16.zip
macOS binaries:	r-release (arm64): ddalpha_1.3.16.tgz, r-oldrel (arm64): ddalpha_1.3.16.tgz, r-release (x86_64): ddalpha_1.3.16.tgz, r-oldrel (x86_64): ddalpha_1.3.16.tgz
Old sources:	ddalpha archive

Reverse dependencies:

Reverse depends:	curveDepth
Reverse imports:	Anthropometry, gggda, KWCChangepoint, pdSpecEst, RepeatedHighDim, SimBaRepro
Reverse suggests:	butcher, ordr, recipes

Linking:

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