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.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
0.1.0 |
rolling linux/jammy R-4.5 | lmmprobe_0.1.0.tar.gz |
1.3 MiB |
0.1.0 |
rolling linux/noble R-4.5 | lmmprobe_0.1.0.tar.gz |
1.3 MiB |
0.1.0 |
rolling source/ R- | lmmprobe_0.1.0.tar.gz |
1.1 MiB |
0.1.0 |
latest linux/jammy R-4.5 | lmmprobe_0.1.0.tar.gz |
1.3 MiB |
0.1.0 |
latest linux/noble R-4.5 | lmmprobe_0.1.0.tar.gz |
1.3 MiB |
0.1.0 |
latest source/ R- | lmmprobe_0.1.0.tar.gz |
1.1 MiB |
0.1.0 |
2026-04-26 source/ R- | lmmprobe_0.1.0.tar.gz |
1.1 MiB |
0.1.0 |
2026-04-23 source/ R- | lmmprobe_0.1.0.tar.gz |
1.1 MiB |
0.1.0 |
2026-04-09 windows/windows R-4.5 | lmmprobe_0.1.0.zip |
1.7 MiB |