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Implements methods for processing a sample of (hard) clusterings, e.g. the MCMC output of a Bayesian clustering model. Among them are methods that find a single best clustering to represent the sample, which are based on the posterior similarity matrix or a relabelling algorithm.
| Version: | 1.0.1 |
| Depends: | R (≥ 2.10), lpSolve |
| Published: | 2022-05-02 |
| DOI: | 10.32614/CRAN.package.mcclust |
| Author: | Arno Fritsch |
| Maintainer: | Arno Fritsch <arno.fritsch at tu-dortmund.de> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | yes |
| In views: | Cluster |
| CRAN checks: | mcclust results |
| Reference manual: | mcclust.pdf |
| Package source: | mcclust_1.0.1.tar.gz |
| Windows binaries: | r-devel: mcclust_1.0.1.zip, r-release: mcclust_1.0.1.zip, r-oldrel: mcclust_1.0.1.zip |
| macOS binaries: | r-release (arm64): mcclust_1.0.1.tgz, r-oldrel (arm64): mcclust_1.0.1.tgz, r-release (x86_64): mcclust_1.0.1.tgz, r-oldrel (x86_64): mcclust_1.0.1.tgz |
| Old sources: | mcclust archive |
| Reverse depends: | BClustLonG, BCSub, CSclone |
| Reverse imports: | AntMAN, clustAnalytics, MitoHEAR, multilink, semiArtificial |
| Reverse suggests: | IMIFA, mixdir, tip |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.