Introduction | Loading the Package | Simulating Data
This package is considered a duplicate. The official version of this package is found at:https://anjazgodic.r-universe.dev/lmmprobe
Package: lmmprobe 0.1.0
Anja Zgodic
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.
Authors:
lmmprobe_0.1.0.tar.gz
lmmprobe_0.1.0.zip(r-4.7-x86_64)lmmprobe_0.1.0.zip(r-4.7-arm64)lmmprobe_0.1.0.zip(r-4.6-x86_64)lmmprobe_0.1.0.zip(r-4.6-arm64)
lmmprobe_0.1.0.tar.gz(r-4.7-arm64)lmmprobe_0.1.0.tar.gz(r-4.7-x86_64)lmmprobe_0.1.0.tar.gz(r-4.6-arm64)lmmprobe_0.1.0.tar.gz(r-4.6-x86_64)
lmmprobe_0.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
lmmprobe/json (API)
| # Install 'lmmprobe' in R: |
| install.packages('lmmprobe', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/anjazgodic/lmmprobe/issues
Uses libs:
Datasets:
- real_data - Systemic Lupus Erythematosus (SLE) Gene Expression Data
Last updated from:cbdfa860d2. Checks:10 OK. Indexed: no.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-arm64 | OK | 178 | ||
| linux-devel-x86_64 | OK | 192 | ||
| source / vignettes | OK | 280 | ||
| linux-release-arm64 | OK | 171 | ||
| linux-release-x86_64 | OK | 207 | ||
| windows-devel-arm64 | OK | 307 | ||
| windows-devel-x86_64 | OK | 240 | ||
| windows-release-arm64 | OK | 269 | ||
| windows-release-x86_64 | OK | 185 | ||
| wasm-release | OK | 147 |
Exports:lmmprobe
Dependencies:bootcodetoolsdigestfuturefuture.applyglobalslatticelistenvlme4MASSMatrixminqanlmenloptrparallellyrbibutilsRcppRcppArmadilloRcppEigenRdpackreformulas
