lmmprobe: 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)
<doi:10.1007/s11222-025-10649-z>. The package provides efficient estimation
and inference for mixed models with high-dimensional fixed effects.
| Version: |
0.1.0 |
| 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 |
| Published: |
2026-03-12 |
| DOI: |
10.32614/CRAN.package.lmmprobe |
| Author: |
Anja Zgodic [aut, cre],
Ray Bai  [aut],
Jiajia Zhang
[aut],
Alex McLain [aut],
Peter Olejua
[aut] |
| Maintainer: |
Anja Zgodic <anja.zgodic at gmail.com> |
| BugReports: |
https://github.com/anjazgodic/lmmprobe/issues |
| License: |
GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: |
https://github.com/anjazgodic/lmmprobe |
| NeedsCompilation: |
yes |
| Citation: |
lmmprobe citation info |
| Materials: |
README |
| CRAN checks: |
lmmprobe results |
Documentation:
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