ipd: An R Package for Conducting Inference on Predicted Data
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Abstract (arXiv)
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: [email protected] and [email protected]
Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Oct 2024.
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Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025A Unified Framework for Inference with General Missingness Patterns and Machine Learning Imputationcited by 7
- 2026Efficient Inference for Noisy LLM-as-a-Judge Evaluationcited by 7
- 2025Do We Really Even Need Data? A Modern Look at Drawing Inference with Predicted Datacited by 3
- 2025Classification errors distort findings in automated speech processing: examples and solutions from child-development researchcited by 1
- 2026Efficient Inference for Noisy LLM-as-a-Judge Evaluation8 Jan 2026
- 2025Do We Really Even Need Data? A Modern Look at Drawing Inference with Predicted Data5 Dec 2025
- 2025Classification errors distort findings in automated speech processing: examples and solutions from child-development research21 Aug 2025
- 2025A Unified Framework for Inference with General Missingness Patterns and Machine Learning Imputation21 Aug 2025