RcppDPR: 'Rcpp' Implementation of Dirichlet Process Regression

'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.

Version:	0.1.10
Imports:	Rcpp (≥ 1.0.13)
LinkingTo:	Rcpp, RcppArmadillo, RcppGSL
Suggests:	testthat (≥ 3.0.0), snpStats
Published:	2025-03-19
Author:	Mohammad Abu Gazala [cre, aut], Daniel Nachun [ctb], Ping Zeng [ctb]
Maintainer:	Mohammad Abu Gazala <abugazalamohammad at gmail.com>
License:	GPL-3
NeedsCompilation:	yes
Materials:	NEWS
CRAN checks:	RcppDPR results

Documentation:

Reference manual:

RcppDPR.html , RcppDPR.pdf

Downloads:

Package source:	RcppDPR_0.1.10.tar.gz
Windows binaries:	r-devel: RcppDPR_0.1.10.zip, r-release: RcppDPR_0.1.10.zip, r-oldrel: RcppDPR_0.1.10.zip
macOS binaries:	r-release (arm64): RcppDPR_0.1.10.tgz, r-oldrel (arm64): RcppDPR_0.1.10.tgz, r-release (x86_64): RcppDPR_0.1.10.tgz, r-oldrel (x86_64): RcppDPR_0.1.10.tgz
Old sources:	RcppDPR archive

Linking:

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