Sparse High-Dimensional Linear Mixed Modeling with a Partitioned Empirical Bayes ECM Algorithm R package lmmprobe version 0.1.0 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. Software Depends: R (>= 3.5.0) Imports: Rcpp (>= 1.0.8.3), lme4 (>= 1.1-29), future.apply (>= 1.10.0) LinkingTo: Rcpp, RcppArmadillo Suggests: testthat (>= 3.0.0), knitr, rmarkdown, MASS Anja Zgodic <anja.zgodic@gmail.com> Comprehensive R Archive Network (CRAN) Anja Zgodic [aut, cre], Ray Bai [aut] (ORCID: <https://orcid.org/0000-0001-7150-7229>), Jiajia Zhang [aut] (ORCID: <https://orcid.org/0000-0001-8281-8532>), Alex McLain [aut] (ORCID: <https://orcid.org/0000-0002-5475-0670>), Peter Olejua [aut] (ORCID: <https://orcid.org/0000-0002-2478-0908>) GPL (>= 2) 2026-03-12 application/tgz https://CRAN.R-project.org/package=lmmprobe doi:10.32614/CRAN.package.lmmprobe