Bayenet
Robust Bayesian Elastic Net
As heavy-tailed error distribution and outliers in the response variable widely exist, models which are robust to data contamination are highly demanded. Here, we develop a novel robust Bayesian variable selection method with elastic net penalty. In particular, the spike-and-slab priors have been incorporated to impose sparsity. An efficient Gibbs sampler has been developed to facilitate computation.The core modules of the package have been developed in 'C++' and R.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.3 |
rolling linux/jammy R-4.5 | Bayenet_0.3.tar.gz |
163.7 KiB |
0.3 |
rolling linux/noble R-4.5 | Bayenet_0.3.tar.gz |
168.1 KiB |
0.3 |
rolling source/ R- | Bayenet_0.3.tar.gz |
23.1 KiB |
0.3 |
latest linux/jammy R-4.5 | Bayenet_0.3.tar.gz |
163.7 KiB |
0.3 |
latest linux/noble R-4.5 | Bayenet_0.3.tar.gz |
168.1 KiB |
0.3 |
latest source/ R- | Bayenet_0.3.tar.gz |
23.1 KiB |
0.3 |
2026-04-26 source/ R- | Bayenet_0.3.tar.gz |
23.1 KiB |
0.3 |
2026-04-23 source/ R- | Bayenet_0.3.tar.gz |
23.1 KiB |
0.3 |
2026-04-09 windows/windows R-4.5 | Bayenet_0.3.zip |
576.0 KiB |
0.3 |
2025-04-20 source/ R- | Bayenet_0.3.tar.gz |
23.1 KiB |