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cff-version: 1.2.0
message: 'To cite package "mixedBayes" in publications use:'
type: software
license: GPL-2.0-only
title: 'mixedBayes: Bayesian Longitudinal Regularized Quantile Mixed Model'
version: 0.1.3
doi: 10.32614/CRAN.package.mixedBayes
abstract: In longitudinal studies, the same subjects are measured repeatedly over
  time, leading to correlations among the repeated measurements. Properly accounting
  for the intra-cluster correlations in the presence of data heterogeneity and long
  tailed distributions of the disease phenotype is challenging, especially in the
  context of high dimensional regressions. Here, we aim at developing novel Bayesian
  regularized quantile mixed effect models to tackle these challenges. We have proposed
  a Bayesian variable selection in the mixed effect models for longitudinal genomics
  studies. To dissect important gene - environment interactions, our model can simultaneously
  identify important main and interaction effects on the individual and group level,
  which have been facilitated by imposing the spike- and -slab priors through Laplacian
  shrinkage in the Bayesian quantile hierarchical models. The within - subject dependence
  among data can be accommodated by incorporating the random effects. An efficient
  Gibbs sampler has been developed to facilitate fast computation. The Markov chain
  Monte Carlo algorithms of the proposed and alternative methods are efficiently implemented
  in 'C++'. The development of this software package and the associated statistical
  methods have been partially supported by an Innovative Research Award from Johnson
  Cancer Research Center, Kansas State University.
authors:
- family-names: Fan
  given-names: Kun
  email: kfan@ksu.edu
- family-names: Wu
  given-names: Cen
repository: https://CRAN.R-project.org/package=mixedBayes
repository-code: https://github.com/kunfa/mixedBayes
url: https://github.com/kunfa/mixedBayes
date-released: '2024-09-27'
contact:
- family-names: Fan
  given-names: Kun
  email: kfan@ksu.edu