mixedBayes - Bayesian Longitudinal Regularized Quantile Mixed Model
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. In this package, we developed a
Bayesian quantile mixed effects model with spike- and -slab
priors to dissect important gene - environment interactions
under longitudinal genomics studies. 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.
