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.
| Version: | 1.2.1 |
| Depends: | stats, utils, graphics, grDevices, MASS, class, robustbase |
| Imports: | Rcpp (≥ 0.11.0) |
| LinkingTo: | BH, Rcpp |
| Published: | 2016-10-10 |
| Author: | Oleksii Pokotylo [aut, cre], Pavlo Mozharovskyi [aut], Rainer Dyckerhoff [aut] |
| Maintainer: | Oleksii Pokotylo <alexey.pokotylo at gmail.com> |
| License: | GPL-2 |
| NeedsCompilation: | yes |
| SystemRequirements: | C++11 |
| Citation: | ddalpha citation info |
| CRAN checks: | ddalpha results |
| Reference manual: | ddalpha.pdf |
| Package source: | ddalpha_1.2.1.tar.gz |
| Windows binaries: | r-devel: ddalpha_1.2.1.zip, r-release: ddalpha_1.2.1.zip, r-oldrel: ddalpha_1.2.1.zip |
| OS X Mavericks binaries: | r-release: ddalpha_1.2.1.tgz, r-oldrel: ddalpha_1.2.1.tgz |
| Old sources: | ddalpha archive |
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