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Overview

geer fits marginal models for independent, repeated, or clustered responses using Generalized Estimating Equations (GEE). Supported estimation methods include the traditional GEE, bias-reducing GEE, bias-corrected GEE, and Jeffreys-prior penalized GEE. Continuous, binary, and count responses are handled by geewa, while binary responses can also be handled by geewa_binary through an odds-ratio parameterization.

Installation

You can install the development version of geer from GitHub:

# install.packages("devtools")
devtools::install_github("AnestisTouloumis/geer")

Usage

Load the package:

library("geer")

Quick example

Fit a bias-reducing GEE with an exchangeable working correlation to the epilepsy seizure count data:

data("epilepsy", package = "geer")

fit <- geewa(
  formula = seizures ~ treatment + lnbaseline + lnage,
  family = poisson(link = "log"),
  data = epilepsy,
  id = id,
  corstr = "exchangeable",
  method = "brgee-robust"
)
summary(fit, cov_type = "bias-corrected")

For binary responses, use geewa_binary() with an odds-ratio parameterization:

data("cerebrovascular", package = "geer")

fit_bin <- geewa_binary(
  formula = ecg ~ treatment + factor(period),
  link = "logit",
  data = cerebrovascular,
  id = id,
  orstr = "exchangeable",
  method = "brgee-robust"
)
summary(fit_bin, cov_type = "bias-corrected")

Fitting models

There are two core fitting functions:

• geewa() for continuous, binary, and count responses (Gaussian, Poisson, binomial, Gamma, inverse Gaussian, quasi, quasibinomial, and quasipoisson families).
• geewa_binary() for binary responses via a marginalized odds-ratio parameterization.

Both functions support the following estimation methods via the method argument:

The working correlation structure for geewa() is controlled by corstr: "independence", "exchangeable", "ar1", "m-dependent", "unstructured", "toeplitz", and "fixed". The working odds-ratio structure for geewa_binary() is controlled by orstr: "independence", "exchangeable", "unstructured", and "fixed".

Convergence and fitting options are set via geer_control().

Inference

Standard S3 methods are available for fitted geer objects:

• summary(), print() — coefficient table and model summary.
• coef(), vcov(), confint() — estimates, covariance matrices, and confidence intervals.
• fitted(), residuals(), predict() — fitted values and predictions.
• model.matrix() — design matrix.
• tidy(), glance() — tidy summaries following broom conventions.

The cov_type argument controls the covariance estimator used for inference: "bias-corrected" (default), "robust" (sandwich), "df-adjusted", or "naive" (model-based).

Model building and selection

• anova() — sequential or multi-model hypothesis test tables.
• add1(), drop1() — single-term additions and deletions with hypothesis tests and CIC.
• step_p() — stepwise model selection by hypothesis testing.
• geecriteria() — QIC, CIC, RJC, QICu, GESSC, and GPC model selection criteria.

emmeans support

Fitted geer objects are compatible with the emmeans package for estimated marginal means.

Datasets

The package includes seven example datasets: cerebrovascular, cholecystectomy, depression, epilepsy, leprosy, respiratory, and rinse.

References

Liang, K.Y. and Zeger, S.L. (1986) Longitudinal data analysis using generalized linear models. Biometrika, 73, 13–22.

Touloumis, A. (2026) Bias-Reduced GEE via Adjusted Estimating Equations, with Odds-Ratio Extensions. Preprint.

Touloumis, A. (2026) Jeffreys-Type Penalized GEE for Correlated Binary Data with an Odds-Ratio Parameterization. Preprint.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.