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deepgmm  

Deep Gaussian Mixture Models
View on CRAN: Click here


Download and install deepgmm package within the R console
Install from CRAN:
install.packages("deepgmm")

Install from Github:
library("remotes")
install_github("cran/deepgmm")

Install by package version:
library("remotes")
install_version("deepgmm", "0.2.1")



Attach the package and use:
library("deepgmm")
Maintained by
Suren Rathnayake
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2019-02-22
Latest Update: 2022-11-20
Description:
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.
How to cite:
Suren Rathnayake (2019). deepgmm: Deep Gaussian Mixture Models. R package version 0.2.1, https://cran.r-project.org/web/packages/deepgmm. Accessed 05 Jun. 2026.
Previous versions and publish date:
0.1.56 (2019-02-22 14:40), 0.1.59 (2019-12-16 06:20), 0.1.62 (2020-10-20 08:00)
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View deepgmm citation profile
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Complete documentation for deepgmm
Functions, R codes and Examples using the deepgmm R package
Some associated functions: deepgmm . model_selection . 
Some associated R codes: adjustedRandIndex.R . compute_est.R . compute_lik.R . deep.sem.alg.1.R . deep.sem.alg.2.R . deep.sem.alg.3.R . deepgmm.R . factanal_para.R . fix_para.R . ginv.R . initial_clustering.R . misc.R . model.selection.R . print.R . valid_args.R .  Full deepgmm package functions and examples
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