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 high-dimensional models with random effects.
|Author||Daniel E Runcie|
|Maintainer||Daniel Runcie <[email protected]>|
|License||MIT + file LICENSE|
|Package repository||View on GitHub|
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