Clustering and classification inference for high dimension low sample size (HDLSS) data with U-statistics. The package contains implementations of nonparametric statistical tests for sample homogeneity, group separation, clustering, and classification of multivariate data. The methods have high statistical power and are tailored for data in which the dimension L is much larger than sample size n. See Gabriela B. Cybis, Marcio Valk and SÃlvia RC Lopes (2018) <doi:10.1080/00949655.2017.1374387> and Marcio Valk and Gabriela B. Cybis (2018) <arXiv:1805.12179>.
| Version: | 0.2.0 |
| Depends: | R (≥ 3.4.0), dendextend, robcor |
| Suggests: | testthat |
| Published: | 2020-01-20 |
| Author: | Gabriela Cybis [aut, cre], Marcio Valk [aut], Kazuki Yokoyama [ctb] |
| Maintainer: | Gabriela Cybis <gcybis at gmail.com> |
| License: | GPL-3 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | uclust results |
| Reference manual: | uclust.pdf |
| Package source: | uclust_0.2.0.tar.gz |
| Windows binaries: | r-devel: uclust_0.2.0.zip, r-release: uclust_0.2.0.zip, r-oldrel: uclust_0.2.0.zip |
| macOS binaries: | r-release: uclust_0.2.0.tgz, r-oldrel: uclust_0.2.0.tgz |
| Old sources: | uclust archive |
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