A Comprehensive Analysis of High Dimensional Longitudinal Data

To provide a comprehensive analysis of high dimensional longitudinal data,this package provides analysis for any combination of 1) simultaneous variable selection and estimation, 2) mean regression or quantile regression for heterogeneous data, 3) cross-sectional or longitudinal data, 4) balanced or imbalanced data, 5) moderate, high or even ultra-high dimensional data, via computationally efficient implementations of penalized generalized estimating equations.

geeVerse

geeVerse is an R package to provide computationally efficient implementations of penalized generalized estimating equations for any combination of 1) simultaneous variable selection and estimation for high and even ultra-high dimensional data, 2) conditional quantile or mean regression, and 3) longitudinal or cross-sectional data analysis.

Installation

You can install the latest version of geeVerse from GitHub with:

# install.packages("devtools")
devtools::install_github("zzz1990771/geeVerse")

Usage and Example:

After installation, you can load the package as usual:

library(geeVerse)

To get detailed documentation on the qpgee function, use:

?qpgee

This will show you the function's usage, arguments, and examples.

Running an Example:

#settings
sim_data <- generate_data(
  nsub = 50, nobs = rep(5, 50), p = 10,
  beta0 = c(rep(1, 5), rep(0, 5)), rho = 0.3
)

# 2. Fit the model using the formula interface
fit <- qpgee(
  y ~ . - id,
  data = sim_data,
  id = sim_data$id,
  tau = 0.5,
  method = "HBIC"
)

# 3. View the summary of the results
summary(fit)

Updates

This package was re-factored with major functions to make it more consistent with other R packages.