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plsmmLasso

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

v1.1.0 · Jun 4, 2024 · GPL (>= 3)

Description

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.

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r-devel-linux-x86_64-fedora-gcc OK
r-devel-macos-arm64 OK
r-devel-windows-x86_64 OK
r-oldrel-macos-arm64 OK
r-oldrel-macos-x86_64 OK
r-oldrel-windows-x86_64 OK
r-patched-linux-x86_64 OK
r-release-linux-x86_64 OK
r-release-macos-arm64 OK
r-release-macos-x86_64 OK
r-release-windows-x86_64 OK
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Check History

OK 14 OK · 0 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE Mar 9, 2026

Dependency Network

Dependencies Reverse dependencies dplyr ggplot2 glmnet hdi MASS mvtnorm rlang scalreg plsmmLasso

Version History

new 1.1.0 Mar 10, 2026