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: 2026-09-23 08:41:19 UTC; root
Author: Yuwen Liu [aut, cre], Cen Wu [aut]
Repository: https://yuwen-l.r-universe.dev
Date/Publication: 2024-09-15 03:01:11 UTC
RemoteUrl: https://github.com/cran/emBayes
RemoteRef: HEAD
RemoteSha: 98ef0812da2bef0674f171fb26de36714b6e36d0