Sparse High-Dimensional Linear Mixed Modeling with a Partitioned Empirical Bayes ECM Algorithm

Implements a partitioned Empirical Bayes Expectation Conditional Maximization (ECM) algorithm for sparse high-dimensional linear mixed modeling as described in Zgodic, Bai, Zhang, and McLain (2025) . The package provides efficient estimation and inference for mixed models with high-dimensional fixed effects.

LMM-PROBE

This is a package to perform sparse high-dimensional linear mixed modeling based on a partitioned empirical Bayes ECM algorithm. Please refer to the package manual for more details on the lmmprobe function.

To install the package, please follow the code snippet below:

library(devtools)
install_github("anjazgodic/lmmprobe")

Here is an example for conducting analysis using LMM-PROBE:

library(lmmprobe)
data(SLE)
Y <- matrix(real_data[,"y"], ncol=1)
Z <- real_data[,4:ncol(real_data)]
V <- matrix(real_data[,"id"], ncol=1)
ID_data <- as.numeric(as.character(real_data$id))
full_res <- lmmprobe(Y = Y, Z = Z, V = V, ID_data = ID_data)