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.
copied from cf-staging / r-ddalpha| Label | Latest Version |
|---|---|
| main | 1.3.16 |
| cf201901 | 1.3.4 |
| cf202003 | 1.3.11 |
| gcc7 | 1.3.4 |
