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-4.7-x86_64)DPP_0.1.2.zip(r-4.7-arm64)DPP_0.1.2.zip(r-4.6-x86_64)DPP_0.1.2.zip(r-4.6-arm64)DPP_0.1.2.zip(r-4.5-x86_64)
DPP_0.1.2.tgz(r-4.6-x86_64)DPP_0.1.2.tgz(r-4.6-arm64)DPP_0.1.2.tgz(r-4.5-x86_64)DPP_0.1.2.tgz(r-4.5-arm64)
DPP_0.1.2.tar.gz(r-4.7-arm64)DPP_0.1.2.tar.gz(r-4.7-x86_64)DPP_0.1.2.tar.gz(r-4.6-arm64)DPP_0.1.2.tar.gz(r-4.6-x86_64)
DPP_0.1.2.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
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 from:31e67d35a2. Checks:15 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 102 | ||
| linux-devel-x86_64 | OK | 120 | ||
| source / vignettes | OK | 146 | ||
| linux-release-arm64 | OK | 99 | ||
| linux-release-x86_64 | OK | 109 | ||
| macos-release-arm64 | OK | 191 | ||
| macos-release-x86_64 | OK | 272 | ||
| macos-oldrel-arm64 | OK | 210 | ||
| macos-oldrel-x86_64 | OK | 249 | ||
| windows-devel-arm64 | OK | 366 | ||
| windows-devel-x86_64 | OK | 195 | ||
| windows-release-arm64 | OK | 363 | ||
| windows-release-x86_64 | OK | 164 | ||
| windows-oldrel-x86_64 | OK | 127 | ||
| wasm-release | OK | 102 |
Exports:DPPmcmcdppMCMC_CexpectedNumberOfClustersGammaModelModelNormalModelsimulateChineseRestaurant
DPP Reference Manual
Last update: 2018-05-24
Started: 2018-05-24
Getting started with DPP
Introduction | Simulating data | Setup | Creating a dppMCMC_C object and running the MCMC | Results | The inferred number of categories/clusters/distributions
Last update: 2018-05-24
Started: 2018-05-24
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 |