Package: DPP 0.1.2
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.
Authors:
DPP_0.1.2.tar.gz
DPP_0.1.2.zip(r-4.5)DPP_0.1.2.zip(r-4.4)DPP_0.1.2.zip(r-4.3)
DPP_0.1.2.tgz(r-4.4-x86_64)DPP_0.1.2.tgz(r-4.4-arm64)DPP_0.1.2.tgz(r-4.3-x86_64)DPP_0.1.2.tgz(r-4.3-arm64)
DPP_0.1.2.tar.gz(r-4.5-noble)DPP_0.1.2.tar.gz(r-4.4-noble)
DPP_0.1.2.tgz(r-4.4-emscripten)DPP_0.1.2.tgz(r-4.3-emscripten)
DPP.pdf |DPP.html✨
DPP/json (API)
| # Install 'DPP' in R: |
| install.packages('DPP', repos = c('https://lmavila.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 7 years agofrom:31e67d35a2. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Oct 30 2024 |
| R-4.5-win-x86_64 | OK | Oct 30 2024 |
| R-4.5-linux-x86_64 | OK | Oct 30 2024 |
| R-4.4-win-x86_64 | OK | Oct 30 2024 |
| R-4.4-mac-x86_64 | OK | Oct 30 2024 |
| R-4.4-mac-aarch64 | OK | Oct 30 2024 |
| R-4.3-win-x86_64 | OK | Oct 30 2024 |
| R-4.3-mac-x86_64 | OK | Oct 30 2024 |
| R-4.3-mac-aarch64 | OK | Oct 30 2024 |
Exports:DPPmcmcdppMCMC_CexpectedNumberOfClustersGammaModelModelNormalModelsimulateChineseRestaurant
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Inference of Parameters of Normal Distributions from a Mixture of Normals | DPP-package DPP DPPmcmc Model Rcpp_DPPmcmc Rcpp_Model |
| A Reference Class that provides DPP functionality | dppMCMC_C |
| Calculate the expected number of clusters from the number of individuals and a concentration parameter | expectedNumberOfClusters |
| Class '"GammaModel"' | GammaModel |
| Class '"NormalModel"' | NormalModel |
| Simulate a discrete distribution as in the chinese restaurant problem | simulateChineseRestaurant |
