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Generating, evaluating, and selecting initialization strategies for Gaussian Mixture Models (GMMs), along with functions to run the Expectation-Maximization (EM) algorithm. Initialization methods are compared using log-likelihood, and the best-fitting model can be selected using BIC. Methods build on initialization strategies for finite mixture models described in Michael and Melnykov (2016) <doi:10.1007/s11634-016-0264-8> and Biernacki et al. (2003) <doi:10.1016/S0167-9473(02)00163-9>, and on the EM algorithm of Dempster et al. (1977) <doi:10.1111/j.2517-6161.1977.tb01600.x>. Background on model-based clustering includes Fraley and Raftery (2002) <doi:10.1198/016214502760047131> and McLachlan and Peel (2000, ISBN:9780471006268).
| Version: | 1.0.0 |
| Imports: | mvtnorm, mclust, mvnfast, stats |
| Published: | 2026-01-24 |
| DOI: | 10.32614/CRAN.package.GMMinit |
| Author: | Jing Li [aut, cre], Yana Melnykov [aut] |
| Maintainer: | Jing Li <jli178 at crimson.ua.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| CRAN checks: | GMMinit results |
| Reference manual: | GMMinit.html , GMMinit.pdf |
| Package source: | GMMinit_1.0.0.tar.gz |
| Windows binaries: | r-devel: GMMinit_1.0.0.zip, r-release: GMMinit_1.0.0.zip, r-oldrel: GMMinit_1.0.0.zip |
| macOS binaries: | r-release (arm64): GMMinit_1.0.0.tgz, r-oldrel (arm64): GMMinit_1.0.0.tgz, r-release (x86_64): GMMinit_1.0.0.tgz, r-oldrel (x86_64): GMMinit_1.0.0.tgz |
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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.