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: 0.1.59
Imports: mvtnorm, corpcor
Suggests: testthat
Published: 2019-12-16
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

Downloads:

Reference manual: deepgmm.pdf
Package source: deepgmm_0.1.59.tar.gz
Windows binaries: r-devel: deepgmm_0.1.59.zip, r-devel-gcc8: deepgmm_0.1.59.zip, r-release: deepgmm_0.1.59.zip, r-oldrel: deepgmm_0.1.59.zip
OS X binaries: r-release: deepgmm_0.1.59.tgz, r-oldrel: deepgmm_0.1.59.tgz
Old sources: deepgmm archive

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