ipd: An R Package for Conducting Inference on Predicted Data
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
Keywords
Cite
@article{arxiv.2410.09665,
title = {ipd: An R Package for Conducting Inference on Predicted Data},
author = {Stephen Salerno and Jiacheng Miao and Awan Afiaz and Kentaro Hoffman and Anna Neufeld and Qiongshi Lu and Tyler H. McCormick and Jeffrey T. Leek},
journal= {arXiv preprint arXiv:2410.09665},
year = {2025}
}
Comments
5 pages, 1 figure