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DPP  

Inference of Parameters of Normal Distributions from a Mixture of Normals
View on CRAN: Click here


Download and install DPP package within the R console
Install from CRAN:
install.packages("DPP")

Install from Github:
library("remotes")
install_github("cran/DPP")

Install by package version:
library("remotes")
install_version("DPP", "0.1.2")



Attach the package and use:
library("DPP")
Maintained by
Luis M. Avila
[Scholar Profile | Author Map]
First Published: 2017-09-27
Latest Update: 2018-05-24
Description:
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) we use a Dirichlet Process Prior (DPP) to describe stochastically our prior assumptions about the dimensionality of the data.
How to cite:
Luis M. Avila (2017). DPP: Inference of Parameters of Normal Distributions from a Mixture of Normals. R package version 0.1.2, https://cran.r-project.org/web/packages/DPP. Accessed 06 May. 2025.
Previous versions and publish date:
0.1.0 (2017-09-27 20:19), 0.1.1 (2017-10-31 21:44)
Other packages that cited DPP R package
View DPP citation profile
Other R packages that DPP depends, imports, suggests or enhances
Complete documentation for DPP
Functions, R codes and Examples using the DPP R package
Some associated functions: DPP-package . GammaModel-class . NormalModel-class . dppMCMC_C . expectedNumberOfClusters . simulateChineseRestaurant . 
Some associated R codes: DPP_C.R . classes.R . miscFunctions.R .  Full DPP package functions and examples
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