Two partially supervised mixture modeling methods: soft-label and belief-based modeling are implemented. For completeness, we equipped the package also with the functionality of unsupervised, semi- and fully supervised mixture modeling. The package can be applied also to selection of the best-fitting from a set of models with different component numbers or constraints on their structures. For detailed introduction see: Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy Tiuryn (2012), The R Package bgmm: Mixture Modeling with Uncertain Knowledge, Journal of Statistical Software <doi:10.18637/jss.v047.i03>.
| Version: | 1.8.3 |
| Depends: | R (≥ 2.0), mvtnorm, car, lattice, combinat |
| Suggests: | testthat |
| Published: | 2017-02-27 |
| Author: | Przemyslaw Biecek \& Ewa Szczurek |
| Maintainer: | Przemyslaw Biecek <Przemyslaw.Biecek at gmail.com> |
| License: | GPL-3 |
| URL: | http://bgmm.molgen.mpg.de/ |
| NeedsCompilation: | no |
| Citation: | bgmm citation info |
| In views: | Cluster |
| CRAN checks: | bgmm results |
| Reference manual: | bgmm.pdf |
| Package source: | bgmm_1.8.3.tar.gz |
| Windows binaries: | r-devel: bgmm_1.8.3.zip, r-release: bgmm_1.8.3.zip, r-oldrel: bgmm_1.8.3.zip |
| OS X El Capitan binaries: | r-release: bgmm_1.8.3.tgz |
| OS X Mavericks binaries: | r-oldrel: bgmm_1.8.3.tgz |
| Old sources: | bgmm archive |
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