ipd: An R Package for Conducting Inference on Predicted Data

TL;DR

The ipd R package offers a user-friendly tool for conducting statistical inference on outcomes with imputed data generated by AI/ML algorithms, facilitating analysis in predictive modeling contexts.

Contribution

This paper introduces the ipd R package, implementing recent methods for inference on predicted data with easy-to-use functions and comprehensive model inspection tools.

Findings

Provides a unified wrapper for inference methods on predicted data

Includes functions for model inspection and summarization

Available on CRAN and GitHub with full documentation

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