plsmmLasso: Variable Selection and Inference for Partial Semiparametric Linear Mixed-Effects Model

Implements a partial linear semiparametric mixed-effects model (PLSMM) featuring a random intercept and applies a lasso penalty to both the fixed effects and the coefficients associated with the nonlinear function. The model also accommodates interactions between the nonlinear function and a grouping variable, allowing for the capture of group-specific nonlinearities. Nonlinear functions are modeled using a set of bases functions. Estimation is conducted using a penalized Expectation-Maximization algorithm, and the package offers flexibility in choosing between various information criteria for model selection. Post-selection inference is carried out using a debiasing method, while inference on the nonlinear functions employs a bootstrap approach.

Version: 1.1.0
Imports: dplyr, ggplot2, glmnet, hdi, MASS, mvtnorm, rlang, scalreg, stats
Published: 2024-06-04
DOI: 10.32614/CRAN.package.plsmmLasso
Author: Sami Leon ORCID iD [aut, cre, cph], Tong Tong Wu ORCID iD [ths]
Maintainer: Sami Leon <samileon at>
License: GPL (≥ 3)
NeedsCompilation: no
Materials: README NEWS
CRAN checks: plsmmLasso results


Reference manual: plsmmLasso.pdf


Package source: plsmmLasso_1.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
macOS binaries: r-release (arm64): plsmmLasso_1.1.0.tgz, r-oldrel (arm64): plsmmLasso_1.1.0.tgz, r-release (x86_64): plsmmLasso_1.1.0.tgz, r-oldrel (x86_64): plsmmLasso_1.1.0.tgz
Old sources: plsmmLasso archive


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