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RcppDPR  

'Rcpp' Implementation of Dirichlet Process Regression
View on CRAN: Click here


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

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

Install by package version:
library("remotes")
install_version("RcppDPR", "0.1.10")



Attach the package and use:
library("RcppDPR")
Maintained by
Mohammad Abu Gazala
[Scholar Profile | Author Map]
All associated links for this package
First Published: 2025-03-15
Latest Update: 2025-03-19
Description:
'Rcpp' reimplementation of the the Bayesian non-parametric Dirichlet Process Regression model for penalized regression first published in Zeng and Zhou (2017) <doi:10.1038/s41467-017-00470-2>. A full Bayesian version is implemented with Gibbs sampling, as well as a faster but less accurate variational Bayes approximation.
How to cite:
Mohammad Abu Gazala (2025). RcppDPR: 'Rcpp' Implementation of Dirichlet Process Regression. R package version 0.1.10, https://cran.r-project.org/web/packages/RcppDPR. Accessed 27 Oct. 2026.
Previous versions and publish date:
(2026-07-09 08:22), 0.1.9 (2025-03-15 18:20)
Other packages that cited RcppDPR R package
View RcppDPR citation profile
Other R packages that RcppDPR depends, imports, suggests or enhances
Complete documentation for RcppDPR
Functions, R codes and Examples using the RcppDPR R package
Full RcppDPR package functions and examples
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