Description
Sparse High-Dimensional Linear Mixed Modeling with a Partitioned Empirical Bayes ECM Algorithm.
Description
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) <doi:10.1007/s11222-025-10649-z>. The package provides efficient estimation and inference for mixed models with high-dimensional fixed effects.
README.md
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)