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: 0.1.62
Imports: mvtnorm, corpcor, mclust
Suggests: testthat
Published: 2020-10-20
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

Documentation:

Reference manual: deepgmm.pdf

Downloads:

Package source: deepgmm_0.1.62.tar.gz
Windows binaries: r-devel: deepgmm_0.1.62.zip, r-release: deepgmm_0.1.62.zip, r-oldrel: deepgmm_0.1.62.zip
macOS binaries: r-release (arm64): deepgmm_0.1.62.tgz, r-release (x86_64): deepgmm_0.1.62.tgz, r-oldrel: deepgmm_0.1.62.tgz
Old sources: deepgmm archive

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