plsmmLasso: Variable Selection and Inference for Partial Semiparametric Linear Mixed-Effects Model (original) (raw)
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 |
| 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 |
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