deepgmm: Deep Gaussian Mixture Models

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>
License: GPL (≥ 3)
NeedsCompilation: no
CRAN checks: deepgmm results


Reference manual: deepgmm.pdf


Package source: deepgmm_0.2.1.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
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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