Deep Gaussian Mixture Models

Deep Gaussian mixture models as proposed by Viroli and McLachlan (2019) 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.


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install.packages("deepgmm")

0.1.62 by Suren Rathnayake, a day ago


https://github.com/suren-rathnayake/deepgmm


Browse source code at https://github.com/cran/deepgmm


Authors: Cinzia Viroli , Geoffrey J. McLachlan


Documentation:   PDF Manual  


GPL (>= 3) license


Imports mvtnorm, corpcor, mclust

Suggests testthat


See at CRAN