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:Anja Zgodic [aut, cre], Ray Bai [aut], Jiajia Zhang [aut], Alex McLain [aut], Peter Olejua [aut]

lmmprobe_0.1.0.tar.gz
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✨
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:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • real_data - Systemic Lupus Erythematosus (SLE) Gene Expression Data

On CRAN:

Conda:

openblascppopenmp

2.70 score 5 scripts 124 downloads 1 exports 22 dependencies

Last updated from:cbdfa860d2. Checks:6 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64OK178
linux-devel-x86_64OK196
source / vignettesOK257
linux-release-arm64OK179
linux-release-x86_64OK177
wasm-releaseOK148

Exports:lmmprobe

Dependencies:bootcodetoolsdigestfuturefuture.applyglobalslatticelistenvlme4MASSMatrixminqanlmenloptrparallellyrbibutilsRcppRcppArmadilloRcppEigenRdpackreformulasrlang

Introduction to lmmprobe

Rendered fromlmmprobe-intro.Rmdusingknitr::rmarkdownon Jun 10 2026.

Last update: 2026-03-12
Started: 2026-03-12