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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.2.1 |
rolling linux/jammy R-4.5 | deepgmm_0.2.1.tar.gz |
83.8 KiB |
0.2.1 |
rolling linux/noble R-4.5 | deepgmm_0.2.1.tar.gz |
83.7 KiB |
0.2.1 |
rolling source/ R- | deepgmm_0.2.1.tar.gz |
13.1 KiB |
0.2.1 |
latest linux/jammy R-4.5 | deepgmm_0.2.1.tar.gz |
83.8 KiB |
0.2.1 |
latest linux/noble R-4.5 | deepgmm_0.2.1.tar.gz |
83.7 KiB |
0.2.1 |
latest source/ R- | deepgmm_0.2.1.tar.gz |
13.1 KiB |
0.2.1 |
2026-04-26 source/ R- | deepgmm_0.2.1.tar.gz |
13.1 KiB |
0.2.1 |
2026-04-23 source/ R- | deepgmm_0.2.1.tar.gz |
13.1 KiB |
0.2.1 |
2026-04-09 windows/windows R-4.5 | deepgmm_0.2.1.zip |
86.1 KiB |
0.2.1 |
2025-04-20 source/ R- | deepgmm_0.2.1.tar.gz |
13.1 KiB |