splmm v1.1.2

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Simultaneous Penalized Linear Mixed Effects Models

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 Unpublished thesis.

Functions in splmm

Name Description
summary.splmm Summarize an 'splmm' object
splmmControl Options for the 'splmm' Algorithm
splmmTuning Tuning funtion of 'splmm' object
splmm Function to fit linear mixed-effects model with double penalty for fixed effects and random effects
splmm-package 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
plot.splmm Plot the tuning results of a splmm.tuning object
cognitive Kenya School Lunch Intervention Cognitive Dataset
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Details

Type Package
Date 2020-11-20
License GPL-3
LinkingTo Rcpp, RcppArmadillo
NeedsCompilation yes
Packaged 2020-11-21 00:53:19 UTC; lyang19
Repository CRAN
Date/Publication 2020-11-22 00:00:02 UTC

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