sahirbhatnagar/ggmix: A General Framework for Variable Selection in Linear Mixed Models with Applications to Genetic Studies with Structured Populations

Fit penalized multi variable linear mixed models with a single random effect to control for population structure in genetic association studies. This is a flexible framework which allows for both lasso and group lasso penalties. A low rank estimation procedure is implemented when the matrix of SNPs used to estimate the kinship matrix is not full rank. Can also handle prior annotation information, for example, rare variants, in the form of weights.

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LicenseMIT + file LICENSE
Package repositoryView on GitHub
Installation Install the latest version of this package by entering the following in R:
sahirbhatnagar/ggmix documentation built on March 1, 2019, 11:35 a.m.