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ipd: an R package for conducting inference on predicted data
Stephen Salerno1, Jiacheng Miao2, Awan Afiaz1,3
1Public Health Sciences, Biostatistics, Fred Hutchinson Cancer Center, Seattle, WA 98109, United States.
Bioinformatics (Oxford, England)
|February 3, 2025
Summary
Introducing ipd, an R package for downstream modeling with imputed data. It simplifies inference on predicted data using AI/ML, offering user-friendly functions for model inspection and analysis.
Area of Science:
- Statistical Software
- Machine Learning Applications
- Data Science
Background:
- The ipd package is an open-source R software for downstream modeling.
- It addresses challenges in handling outcome data imputed by AI/ML algorithms.
- The package is available on CRAN and GitHub with comprehensive documentation.
Purpose of the Study:
- To introduce the ipd R package for statistical modeling.
- To provide a user-friendly tool for inference on data with AI/ML-imputed outcomes.
- To demonstrate the basic usage and features of the ipd package.
Main Methods:
- The ipd package implements recent methods for inference on predicted data.
- It offers a single, user-friendly wrapper function named 'ipd'.
- Custom methods (print, summary, tidy, glance, augment) are included for model inspection.
Main Results:
- The ipd package facilitates downstream modeling with imputed outcome data.
- It enables straightforward inference on AI/ML-predicted data.
- The package simplifies model inspection through custom S3 methods.
Conclusions:
- ipd is a valuable open-source R package for researchers and data scientists.
- It enhances the ability to perform reliable statistical modeling with imputed data.
- The package promotes reproducible research and efficient data analysis.
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