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Deep Gaussian mixture models as proposed by Viroli and McLachlan (2019) <doi:10.1007/s11222-017-9793-z> provide a generalization of classical Gaussian mixtures to multiple layers. Each layer contains a set of latent variables that follow a mixture of Gaussian distributions. To avoid overparameterized solutions, dimension reduction is applied at each layer by way of factor models.
| Version: | 0.2.1 |
| Imports: | mvtnorm, corpcor, mclust |
| Published: | 2022-11-20 |
| DOI: | 10.32614/CRAN.package.deepgmm |
| Author: | Cinzia Viroli, Geoffrey J. McLachlan |
| Maintainer: | Suren Rathnayake <surenr at gmail.com> |
| License: | GPL (≥ 3) |
| URL: | https://github.com/suren-rathnayake/deepgmm |
| NeedsCompilation: | no |
| CRAN checks: | deepgmm results |
| Reference manual: | deepgmm.pdf |
| Package source: | deepgmm_0.2.1.tar.gz |
| Windows binaries: | r-devel: deepgmm_0.2.1.zip, r-release: deepgmm_0.2.1.zip, r-oldrel: deepgmm_0.2.1.zip |
| macOS binaries: | r-release (arm64): deepgmm_0.2.1.tgz, r-oldrel (arm64): deepgmm_0.2.1.tgz, r-release (x86_64): deepgmm_0.2.1.tgz, r-oldrel (x86_64): deepgmm_0.2.1.tgz |
| Old sources: | deepgmm archive |
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