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plsmmLasso  

Variable Selection and Inference for Partial Semiparametric Linear Mixed-Effects Model
View on CRAN: Click here


Download and install plsmmLasso package within the R console
Install from CRAN:
install.packages("plsmmLasso")

Install from Github:
library("remotes")
install_github("cran/plsmmLasso")

Install by package version:
library("remotes")
install_version("plsmmLasso", "1.1.0")



Attach the package and use:
library("plsmmLasso")
Maintained by
Sami Leon
[Scholar Profile | Author Map]
First Published: 2024-04-04
Latest Update: 2024-04-04
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.
How to cite:
Sami Leon (2024). plsmmLasso: Variable Selection and Inference for Partial Semiparametric Linear Mixed-Effects Model. R package version 1.1.0, https://cran.r-project.org/web/packages/plsmmLasso. Accessed 04 May. 2025.
Previous versions and publish date:
1.0.0 (2024-04-04 19:53)
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Complete documentation for plsmmLasso
Functions, R codes and Examples using the plsmmLasso R package
Full plsmmLasso package functions and examples
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