Fits approximate Linear Mixed Models with multiple random effects. The fitting process is optimized for repeated evaluation of the random effect model with different sets of fixed effects, ex. for GWAS analyses. The approximation is due to the use of a discrete grid of possible values for the random effect variance component proportions. We include functions for both frequentist and Bayesian GWAS, (Restricted) Maximum Likelihood evaluation, Bayesian Posterior inference of variance components, and Lasso/Elastic Net fitting of highdimensional models with random effects.
Package details 


Author  Daniel E Runcie 
Maintainer  Daniel Runcie <[email protected]> 
License  MIT + file LICENSE 
Version  0.0.0.9000 
Package repository  View on GitHub 
Installation 
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