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  [aut,
cre, cph],
Tong Tong Wu
[ths] |
| Maintainer: |
Sami Leon <samileon at hotmail.fr> |
| BugReports: |
https://github.com/Sami-Leon/plsmmLasso/issues |
| License: |
GPL (≥ 3) |
| URL: |
https://github.com/Sami-Leon/plsmmLasso |
| NeedsCompilation: |
no |
| Materials: |
README NEWS |
| CRAN checks: |
plsmmLasso results |
Documentation:
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