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Test for cluster tendency (clusterability) of a data set. The methods implemented - reducing the data set to a single dimension using principal component analysis or computing pairwise distances, and performing a multimodality test like the Dip Test or Silverman's Critical Bandwidth Test - are described in Adolfsson, Ackerman, and Brownstein (2019) <doi:10.1016/j.patcog.2018.10.026>. Such methods can inform whether clustering algorithms are appropriate for a data set.
| Version: | 0.1.1.0 |
| Depends: | R (≥ 3.4.0) |
| Imports: | diptest, splines |
| Suggests: | testthat |
| Published: | 2020-03-04 |
| Author: | Zachariah Neville [aut, cre], Naomi Brownstein [aut], Maya Ackerman [aut], Andreas Adolfsson [aut] |
| Maintainer: | Zachariah Neville <z.neville at stat.fsu.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| Materials: | README NEWS |
| CRAN checks: | clusterability results |
| Reference manual: | clusterability.pdf |
| Package source: | clusterability_0.1.1.0.tar.gz |
| Windows binaries: | r-devel: clusterability_0.1.1.0.zip, r-release: clusterability_0.1.1.0.zip, r-oldrel: clusterability_0.1.1.0.zip |
| macOS binaries: | r-release (arm64): clusterability_0.1.1.0.tgz, r-oldrel (arm64): clusterability_0.1.1.0.tgz, r-release (x86_64): clusterability_0.1.1.0.tgz, r-oldrel (x86_64): clusterability_0.1.1.0.tgz |
| Old sources: | clusterability archive |
| Reverse suggests: | FCPS |
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