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'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 |
DOI: | 10.32614/CRAN.package.RcppDPR |
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 |
Reference manual: | RcppDPR.pdf |
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-devel (arm64): RcppDPR_0.1.10.tgz, r-release (arm64): RcppDPR_0.1.10.tgz, r-oldrel (arm64): RcppDPR_0.1.10.tgz, r-devel (x86_64): 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 |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.