Package: Bayenet 0.3

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

Bayenet_0.3.tar.gz
Bayenet_0.3.tar.gz(r-4.7-arm64)Bayenet_0.3.tar.gz(r-4.7-x86_64)Bayenet_0.3.tar.gz(r-4.6-arm64)Bayenet_0.3.tar.gz(r-4.6-x86_64)
Bayenet_0.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
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'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • clin - Simulated data for demonstrating the features of Bayenet.
  • coef - Simulated data for demonstrating the features of Bayenet.
  • X - Simulated data for demonstrating the features of Bayenet.
  • Y - Simulated data for demonstrating the features of Bayenet.

On CRAN:

Conda:

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

openblascppopenmp

1.00 score 2 scripts 224 downloads 2 exports 16 dependencies

Last updated from:a76e7fe956. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK136
linux-devel-x86_64OK139
source / vignettesOK204
linux-release-arm64OK158
linux-release-x86_64OK122
wasm-releaseOK126

Exports:BayenetSelection

Dependencies:codagslhbmemlatticeMASSMatrixMatrixModelsmcmcMCMCpackquantregRcppRcppArmadilloSparseMSuppDistssurvivalVGAM