Clusters features under the assumption that each cluster has a random effect and there is an outcome variable that is related to the random effects by a linear regression. In this way the cluster analysis is “supervised” by the outcome variable. An alternate specification is that features in each cluster have the same compound symmetric normal distribution, and the conditional distribution of the outcome given the features has the same coefficient for each feature in a cluster.
| Version: | 1.0 |
| Imports: | mvtnorm, gtools |
| Published: | 2015-05-18 |
| Author: | David A. Schoenfeld, Jesse Hsu |
| Maintainer: | David A. Schoenfeld <dschoenfeld at mgh.harvard.edu> |
| License: | GPL-2 |
| NeedsCompilation: | no |
| CRAN checks: | supcluster results |
| Reference manual: | supcluster.pdf |
| Package source: | supcluster_1.0.tar.gz |
| Windows binaries: | r-devel: supcluster_1.0.zip, r-release: supcluster_1.0.zip, r-oldrel: supcluster_1.0.zip |
| macOS binaries: | r-release: supcluster_1.0.tgz, r-oldrel: supcluster_1.0.tgz |
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