Package: splmm 1.2.0

Eli Sun

splmm: Simultaneous Penalized Linear Mixed Effects Models

Contains functions that fit linear mixed-effects models for high-dimensional data (p>>n) with penalty for both the fixed effects and random effects for variable selection. The details of the algorithm can be found in Luoying Yang PhD thesis (Yang and Wu 2020). The algorithm implementation is based on the R package 'lmmlasso'. Reference: Yang L, Wu TT (2020). Model-Based Clustering of Longitudinal Data in High-Dimensionality. Unpublished thesis.

Authors:Luoying Yang [aut], Eli Sun [aut, cre], Tong Tong Wu [aut]

splmm_1.2.0.tar.gz
splmm_1.2.0.tar.gz(r-4.5-noble)splmm_1.2.0.tar.gz(r-4.4-noble)
splmm_1.2.0.tgz(r-4.4-emscripten)splmm_1.2.0.tgz(r-4.3-emscripten)
splmm.pdf |splmm.html✨
splmm/json (API)

# Install 'splmm' in R:
install.packages('splmm', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))
Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:

On CRAN:

Conda:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

openblascppopenmp

1.00 score 270 downloads 8 exports 43 dependencies

Last updated 9 months agofrom:7d91b0f817. Checks:3 OK. Indexed: yes.

TargetResultLatest binary
Doc / VignettesOKMar 11 2025
R-4.5-linux-x86_64OKMar 11 2025
R-4.4-linux-x86_64OKMar 11 2025

Exports:plot.splmmplot3D.splmmprint.splmmsplmmsplmm.defaultsplmmControlsplmmTuningsummary.splmm

Dependencies:clicolorspacecrayondigestemulatorfansifarverggplot2gluegridExtragtablehmsisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmisc3dmiscToolsmunsellmvtnormnlmepenalizedpillarpkgconfigplot3DprettyunitsprogressR6RColorBrewerRcppRcppArmadillorlangscalessurvivaltibbleutf8vctrsviridisLitewithr