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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.

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Version

Install

install.packages('geeVerse')

Monthly Downloads

480

Version

0.3.0

License

GPL-3

Maintainer

Tianhai Zu

Last Published

August 20th, 2025

Functions in geeVerse (0.3.0)

PGEE

PGEE accelerated with RCpp
compile_result

Compile Results from qpgee()
compile_result.default

Compile Results from list of qpgee()
.update_beta

Update Beta Coefficients for One Iteration
geeVerse

GeeVerse: Wrapper for Quantile Penalized Generalized Estimating Equations
CVfit

Cross-Validation for Generalized Estimating Equations (GEE)
qpgeeControl

Control Parameters for qpgee
qpgee

Quantile Penalized Generalized Estimating Equations (QPGEE)
generate_data

Generate Data for Simulation
print.summary.qpgee

Print summary method for qpgee model objects
Siga_cov

Generate Covariance Matrix
compile_result.qpgee

Compile Results from qpgee()
pp_scad_lin

SCAD Penalty Derivative (Linearized Approximation)
simuGene

A Simulated Genetic Data from HapGen2
yeastG1

A Subset of Yeast Cell Cycle Gene Expression Data (G1 Phase)
.update_correlation

Update the Working Correlation Matrix