Package: Bayenet 0.2

Xi Lu

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:Xi Lu [aut, cre], Cen Wu [aut]

Bayenet_0.2.tar.gz
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://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • X - Simulated data for demonstrating the features of Bayenet.
  • Y - Simulated data for demonstrating the features of Bayenet.
  • clin - Simulated data for demonstrating the features of Bayenet.
  • coef - Simulated data for demonstrating the features of Bayenet.

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascppopenmp

1.00 score 223 downloads 2 exports 16 dependencies

Last updated 8 months agofrom:fff23dccd7. Checks:OK: 2. Indexed: no.

TargetResultDate
Doc / VignettesOKDec 02 2024
R-4.5-linux-x86_64OKDec 02 2024

Exports:BayenetSelection

Dependencies:codagslhbmemlatticeMASSMatrixMatrixModelsmcmcMCMCpackquantregRcppRcppArmadilloSparseMSuppDistssurvivalVGAM