The hardware and bandwidth for this mirror is donated by dogado GmbH, the Webhosting and Full Service-Cloud Provider. Check out our Wordpress Tutorial.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]dogado.de.
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.
Version: | 0.1.2 |
Depends: | R (≥ 4.2.0) |
Imports: | Rcpp |
LinkingTo: | Rcpp, RcppArmadillo |
Published: | 2024-02-16 |
DOI: | 10.32614/CRAN.package.mixedBayes |
Author: | Kun Fan [aut, cre], Cen Wu [aut] |
Maintainer: | Kun Fan <kfan at ksu.edu> |
License: | GPL-2 |
URL: | https://github.com/kunfa/mixedBayes |
NeedsCompilation: | yes |
CRAN checks: | mixedBayes results |
Reference manual: | mixedBayes.pdf |
Package source: | mixedBayes_0.1.2.tar.gz |
Windows binaries: | r-devel: mixedBayes_0.1.2.zip, r-release: mixedBayes_0.1.2.zip, r-oldrel: mixedBayes_0.1.2.zip |
macOS binaries: | r-release (arm64): mixedBayes_0.1.2.tgz, r-oldrel (arm64): mixedBayes_0.1.2.tgz, r-release (x86_64): mixedBayes_0.1.2.tgz, r-oldrel (x86_64): mixedBayes_0.1.2.tgz |
Old sources: | mixedBayes archive |
Please use the canonical form https://CRAN.R-project.org/package=mixedBayes to link to this page.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.