MIIPW: GEE with Missing Data Using Mean Score, IPW, and Multiple Imputation

MIIPW is an R package for fitting Generalized Estimating Equations (GEE) when data is missing at random (MAR), using a combination of Mean Score (MS), Inverse Probability Weighting (IPW), and Multiple Imputation (MI) techniques.

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✨ Features

The MIIPW package supports robust marginal modeling with missing outcome and covariate data by integrating:

• 📌 Mean Score Approach for consistent estimation under missingness
• 🧮 IPW Estimation using modeled response or missingness probabilities
• ♻️ Multiple Imputation (MI) integration for general missing data structures
• 📈 Flexible model specification with various correlation structures
• 📦 Easy interface using gee() style model formulas

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🛠 Installation

# Install from CRAN (when available)
install.packages("MIIPW")

# Or install the development version from GitHub:
# install.packages("remotes")
remotes::install_github("kumarbhrigu/MIIPW")