Package: deepgmm 0.1.12
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.
Authors:
deepgmm_0.1.12.tar.gz
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deepgmm.pdf |deepgmm.html✨
deepgmm/json (API)
# Install 'deepgmm' in R: |
install.packages('deepgmm', repos = c('https://suren-rathnayake.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/suren-rathnayake/deepgmm/issues
clusteringdeep-learningmixed-models
Last updated 2 years agofrom:cc9ec2436b. Checks:OK: 1 ERROR: 6. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Nov 02 2024 |
R-4.5-win | ERROR | Nov 02 2024 |
R-4.5-linux | ERROR | Nov 02 2024 |
R-4.4-win | ERROR | Nov 02 2024 |
R-4.4-mac | ERROR | Nov 02 2024 |
R-4.3-win | ERROR | Nov 02 2024 |
R-4.3-mac | ERROR | Nov 02 2024 |
Exports:deepgmmmodel_selection
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Fits Deep Gaussian Mixture Models Using Stochastic EM algorithm. | deepgmm |
Function to compare different models | model_selection |