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splmm (version 1.1.3)

Simultaneous Penalized Linear Mixed Effects Models

Description

Contains functions that fit linear mixed-effects models for high-dimensional data (p>>n) with penalty for both the fixed effects and random effects for variable selection. The details of the algorithm can be found in Luoying Yang PhD thesis (Yang and Wu 2020). The algorithm implementation is based on the R package 'lmmlasso'. Reference: Yang L, Wu TT (2020). Model-Based Clustering of Longitudinal Data in High-Dimensionality. Unpublished thesis.

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Version

Install

install.packages('splmm')

Monthly Downloads

203

Version

1.1.3

License

GPL-3

Maintainer

Luoying Yang

Last Published

September 8th, 2021

Functions in splmm (1.1.3)

splmmControl

Options for the 'splmm' Algorithm
plot.splmm

Plot the tuning results of a splmm.tuning object
splmmTuning

Tuning funtion of 'splmm' object
cognitive

Kenya School Lunch Intervention Cognitive Dataset
splmm

Function to fit linear mixed-effects model with double penalty for fixed effects and random effects
summary.splmm

Summarize an 'splmm' object
simulated_data

Dataset simulated for toy example
splmm-package

tools:::Rd_package_title("splmm")
print.splmm

Print a short summary of a splmm object.
plot3D.splmm

3D Plot the tuning results of a 'splmm.tuning' object when tuning over both lambda 1 and lambda 2 grids