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.
- Version1.1.0
- R versionunknown
- LicenseGPL (≥ 3)
- Needs compilation?No
- Last release06/04/2024
Documentation
Team
Sami Leon
Tong Tong Wu
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- Imports8 packages