Package: Bayenet 0.4
Xi Lu
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.
Authors:
Bayenet_0.4.tar.gz
Bayenet_0.4.zip(r-4.7-x86_64)Bayenet_0.4.zip(r-4.7-arm64)Bayenet_0.4.zip(r-4.6-x86_64)Bayenet_0.4.zip(r-4.6-arm64)
Bayenet_0.4.tar.gz(r-4.7-arm64)Bayenet_0.4.tar.gz(r-4.7-x86_64)Bayenet_0.4.tar.gz(r-4.6-arm64)Bayenet_0.4.tar.gz(r-4.6-x86_64)
Bayenet_0.4.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
Bayenet/json (API)
| # Install 'Bayenet' in R: |
| install.packages('Bayenet', 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 from:5f29040d9d. Checks:10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 141 | ||
| linux-devel-x86_64 | OK | 127 | ||
| source / vignettes | OK | 203 | ||
| linux-release-arm64 | OK | 98 | ||
| linux-release-x86_64 | OK | 124 | ||
| windows-devel-arm64 | OK | 196 | ||
| windows-devel-x86_64 | OK | 172 | ||
| windows-release-arm64 | OK | 241 | ||
| windows-release-x86_64 | OK | 130 | ||
| wasm-release | OK | 123 |
Dependencies:codagslhbmemlatticeMASSMatrixMatrixModelsmcmcMCMCpackquantregRcppRcppArmadilloSparseMSuppDistssurvivalVGAM
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Robust Bayesian Elastic Net | Bayenet-package |
| fit a robust Bayesian elastic net variable selection model for genetic study. | Bayenet |
| simulated data for demonstrating the features of Bayenet. | clin coef dat X Y |
| make predictions from a Bayenet object | predict.Bayenet |
| print a Bayenet object | print.Bayenet |
| print a predict.Bayenet object | print.Bayenet.pred |
| print a Selection object | print.Selection |
| Variable selection for a Bayenet object | Selection |