Package: emBayes 0.1.6

emBayes: Robust Bayesian Variable Selection via Expectation-Maximization

Variable selection methods have been extensively developed for analyzing highdimensional omics data within both the frequentist and Bayesian frameworks. This package provides implementations of the spike-and-slab quantile (group) LASSO which have been developed along the line of Bayesian hierarchical models but deeply rooted in frequentist regularization methods by utilizing Expectation–Maximization (EM) algorithm. The spike-and-slab quantile LASSO can handle data irregularity in terms of skewness and outliers in response variables, compared to its non-robust alternative, the spike-and-slab LASSO, which has also been implemented in the package. In addition, procedures for fitting the spike-and-slab quantile group LASSO and its non-robust counterpart have been implemented in the form of quantile/least-square varying coefficient mixed effect models for high-dimensional longitudinal data. The core module of this package is developed in 'C++'.

Authors:Yuwen Liu [aut, cre], Cen Wu [aut]

emBayes_0.1.6.tar.gz
emBayes_0.1.6.zip(r-4.5)emBayes_0.1.6.zip(r-4.4)emBayes_0.1.6.zip(r-4.3)
emBayes_0.1.6.tgz(r-4.4-x86_64)emBayes_0.1.6.tgz(r-4.4-arm64)emBayes_0.1.6.tgz(r-4.3-x86_64)emBayes_0.1.6.tgz(r-4.3-arm64)
emBayes_0.1.6.tar.gz(r-4.5-noble)emBayes_0.1.6.tar.gz(r-4.4-noble)
emBayes_0.1.6.tgz(r-4.4-emscripten)emBayes_0.1.6.tgz(r-4.3-emscripten)
emBayes.pdf |emBayes.html✨
emBayes/json (API)

Peer review:

On CRAN:

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

2 exports 0.36 score 11 dependencies 190 downloads

Last updated 4 days agofrom:98ef0812da. Checks:OK: 9. Indexed: yes.

Exports:cv.emBayesemBayes

Dependencies:codetoolsforeachglmnetiteratorslatticeMatrixRcppRcppArmadilloRcppEigenshapesurvival