Package: emBayes
Type: Package
Title: Robust Bayesian Variable Selection via Expectation-Maximization
Version: 0.1.6
Date: 2024-08-26
Authors@R: c( person("Yuwen", "Liu", role = c("aut", "cre") , email = "yuwenliu9@gmail.com"),
              person("Cen", "Wu", role = "aut"))
Maintainer: Yuwen Liu <yuwenliu9@gmail.com>
Description: 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++'.
Depends: R (>= 4.2.0)
License: GPL-2
Encoding: UTF-8
LazyData: true
Imports: Rcpp, glmnet
LinkingTo: Rcpp, RcppArmadillo
RoxygenNote: 7.2.3
NeedsCompilation: yes
Packaged: 2024-09-13 23:02:10 UTC; 26057
Author: Yuwen Liu [aut, cre],
  Cen Wu [aut]
Repository: CRAN
Date/Publication: 2024-09-15 00:00:02 UTC
Built: R 4.6.0; x86_64-apple-darwin20; 2026-04-28 02:46:07 UTC; unix
Archs: emBayes.so.dSYM
