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: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=plsmmLasso to link to this page.
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