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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.10 |
rolling linux/jammy R-4.5 | RcppDPR_0.1.10.tar.gz |
2.4 MiB |
0.1.10 |
rolling linux/noble R-4.5 | RcppDPR_0.1.10.tar.gz |
2.4 MiB |
0.1.10 |
rolling source/ R- | RcppDPR_0.1.10.tar.gz |
2.9 MiB |
0.1.10 |
latest linux/jammy R-4.5 | RcppDPR_0.1.10.tar.gz |
2.4 MiB |
0.1.10 |
latest linux/noble R-4.5 | RcppDPR_0.1.10.tar.gz |
2.4 MiB |
0.1.10 |
latest source/ R- | RcppDPR_0.1.10.tar.gz |
2.9 MiB |
0.1.10 |
2026-04-26 source/ R- | RcppDPR_0.1.10.tar.gz |
2.9 MiB |
0.1.10 |
2026-04-23 source/ R- | RcppDPR_0.1.10.tar.gz |
2.9 MiB |
0.1.10 |
2026-04-09 windows/windows R-4.5 | RcppDPR_0.1.10.zip |
2.8 MiB |
0.1.10 |
2025-04-20 source/ R- | RcppDPR_0.1.10.tar.gz |
2.9 MiB |