Package: Bayenet 0.2
Bayenet: Bayesian Quantile Elastic Net for Genetic Study
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 for quantile regression in genetic analysis. 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_0.2.tar.gz
Bayenet_0.2.zip(r-4.5)Bayenet_0.2.zip(r-4.4)Bayenet_0.2.zip(r-4.3)
Bayenet_0.2.tgz(r-4.4-x86_64)Bayenet_0.2.tgz(r-4.4-arm64)Bayenet_0.2.tgz(r-4.3-x86_64)Bayenet_0.2.tgz(r-4.3-arm64)
Bayenet_0.2.tar.gz(r-4.5-noble)Bayenet_0.2.tar.gz(r-4.4-noble)
Bayenet_0.2.tgz(r-4.4-emscripten)Bayenet_0.2.tgz(r-4.3-emscripten)
Bayenet.pdf |Bayenet.html✨
Bayenet/json (API)
| # Install 'Bayenet' in R: |
| install.packages('Bayenet', repos = c('https://xilustat.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/xilustat/bayenet/issues
Last updated 5 months agofrom:78fc0026e7. Checks:OK: 9. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Sep 10 2024 |
| R-4.5-win-x86_64 | OK | Sep 10 2024 |
| R-4.5-linux-x86_64 | OK | Sep 10 2024 |
| R-4.4-win-x86_64 | OK | Sep 10 2024 |
| R-4.4-mac-x86_64 | OK | Sep 10 2024 |
| R-4.4-mac-aarch64 | OK | Sep 10 2024 |
| R-4.3-win-x86_64 | OK | Sep 10 2024 |
| R-4.3-mac-x86_64 | OK | Sep 10 2024 |
| R-4.3-mac-aarch64 | OK | Sep 10 2024 |
Dependencies:codagslhbmemlatticeMASSMatrixMatrixModelsmcmcMCMCpackquantregRcppRcppArmadilloSparseMSuppDistssurvivalVGAM
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Bayesian Quantile Elastic Net for Genetic Study | 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 |
