Package: RcppDPR 0.1.10

Mohammad Abu Gazala

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.

Authors:Mohammad Abu Gazala [cre, aut], Daniel Nachun [ctb], Ping Zeng [ctb]

RcppDPR_0.1.10.tar.gz
RcppDPR_0.1.10.zip(r-4.7-x86_64)RcppDPR_0.1.10.zip(r-4.7-arm64)RcppDPR_0.1.10.zip(r-4.6-x86_64)RcppDPR_0.1.10.zip(r-4.6-arm64)
RcppDPR_0.1.10.tar.gz(r-4.7-arm64)RcppDPR_0.1.10.tar.gz(r-4.7-x86_64)RcppDPR_0.1.10.tar.gz(r-4.6-arm64)RcppDPR_0.1.10.tar.gz(r-4.6-x86_64)
RcppDPR_0.1.10.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
RcppDPR/json (API)

# Install 'RcppDPR' in R:

install.packages('RcppDPR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Uses libs:

openblas– Optimized BLAS
gsl– GNU Scientific Library (GSL)
c++– GNU Standard C++ Library v3

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblas gsl cpp

1.00 score 237 downloads 1 exports 3 dependencies

Last updated from:99be1f5316. Checks:10 OK. Indexed: yes.

Target	Result	Time
linux-devel-arm64	OK	225
linux-devel-x86_64	OK	299
source / vignettes	OK	194
linux-release-arm64	OK	230
linux-release-x86_64	OK	286
windows-devel-arm64	OK	469
windows-devel-x86_64	OK	489
windows-release-arm64	OK	455
windows-release-x86_64	OK	443
wasm-release	OK	112