Package: RcppDPR 0.1.9
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:
RcppDPR_0.1.9.tar.gz
RcppDPR_0.1.9.tar.gz(r-4.5-noble)RcppDPR_0.1.9.tar.gz(r-4.4-noble)
RcppDPR_0.1.9.tgz(r-4.4-emscripten)RcppDPR_0.1.9.tgz(r-4.3-emscripten)
RcppDPR.pdf |RcppDPR.html✨
RcppDPR/json (API)
NEWS
| # Install 'RcppDPR' in R: |
| install.packages('RcppDPR', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 4 hours agofrom:bf50405c3f. Checks:3 OK. Indexed: yes.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Mar 15 2025 |
| R-4.5-linux-x86_64 | OK | Mar 15 2025 |
| R-4.4-linux-x86_64 | OK | Mar 15 2025 |
Exports:fit_model
Dependencies:RcppRcppArmadilloRcppGSL
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Fit Dirichlet Process Regression model | fit_model |
| Use a DPR model to predict results from new data | predict.DPR_Model |