DPP
Inference of Parameters of Normal Distributions from a Mixture of Normals
This MCMC method takes a data numeric vector (Y) and assigns the elements of Y to a (potentially infinite) number of normal distributions. The individual normal distributions from a mixture of normals can be inferred. Following the method described in Escobar (1994) <doi:10.2307/2291223> we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.2 |
rolling linux/jammy R-4.5 | DPP_0.1.2.tar.gz |
622.0 KiB |
0.1.2 |
rolling linux/noble R-4.5 | DPP_0.1.2.tar.gz |
626.3 KiB |
0.1.2 |
rolling source/ R- | DPP_0.1.2.tar.gz |
346.3 KiB |
0.1.2 |
latest linux/jammy R-4.5 | DPP_0.1.2.tar.gz |
622.0 KiB |
0.1.2 |
latest linux/noble R-4.5 | DPP_0.1.2.tar.gz |
626.3 KiB |
0.1.2 |
latest source/ R- | DPP_0.1.2.tar.gz |
346.3 KiB |
0.1.2 |
2026-04-26 source/ R- | DPP_0.1.2.tar.gz |
346.3 KiB |
0.1.2 |
2026-04-23 source/ R- | DPP_0.1.2.tar.gz |
346.3 KiB |
0.1.2 |
2026-04-09 windows/windows R-4.5 | DPP_0.1.2.zip |
939.7 KiB |
0.1.2 |
2025-04-20 source/ R- | DPP_0.1.2.tar.gz |
346.3 KiB |