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arXiv · 2410.09665

ipd: An R Package for Conducting Inference on Predicted Data

Stephen Salerno·Jiacheng Miao·Awan Afiaz·Kentaro Hoffman·Anna Neufeld·Qiongshi Lu·Tyler H. McCormick·Jeffrey T. Leek
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Abstract

Summary: ipd is an open-source R software package for the downstream modeling of an outcome and its associated features where a potentially sizable portion of the outcome data has been imputed by an artificial intelligence or machine learning (AI/ML) prediction algorithm. The package implements several recent proposed methods for inference on predicted data (IPD) with a single, user-friendly wrapper function, ipd. The package also provides custom print, summary, tidy, glance, and augment methods to facilitate easy model inspection. This document introduces the ipd software package and provides a demonstration of its basic usage. Availability: ipd is freely available on CRAN or as a developer version at our GitHub page: github.com/ipd-tools/ipd. Full documentation, including detailed instructions and a usage `vignette' are available at github.com/ipd-tools/ipd. Contact: jtleek@fredhutch.org and tylermc@uw.edu

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Stephen Salerno, Jiacheng Miao, Awan Afiaz, Kentaro Hoffman, Anna Neufeld, Qiongshi Lu, Tyler H. McCormick, Jeffrey T. Leek. 2024-10-12. ipd: An R Package for Conducting Inference on Predicted Data. https://doi.org/10.1093/bioinformatics/btaf055

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