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license: MIT
title: 'mixedsubjects: Causal Inference in Experiments with Mixed-Subjects Designs'
version: 1.0.0
doi: 10.32614/CRAN.package.mixedsubjects
abstract: Implements seven estimators for average treatment effect (ATE) estimation
  in mixed-subjects designs (MSDs), where human subjects data is augmented with predictions
  from large language models (LLMs)., Includes Difference-in-Means, GREG, Prediction
  Powered Inference (PPI), Power Tuned PPI (PPI++), Doubly-Tuned PPI (D-T PPI++),
  Difference-in-Predictions (DiP), DiP++, and D-T DiP estimators. Provides point estimates,
  variance estimation via delta-method or bootstrap, and optimal design selection
  for budget allocation between human observations and LLM predictions.
authors:
- family-names: Loon
  given-names: Austin
  name-particle: van
  email: avanloon@mit.edu
- family-names: Kanopka
  given-names: Klint
  email: klint.kanopka@nyu.edu
repository: https://klintkanopka.r-universe.dev
repository-code: https://github.com/klintkanopka/mixedsubjects
commit: 97d8391df42c7f26350d15e747862ce58cd6b46e
url: https://klintkanopka.com/mixedsubjects/
date-released: '2026-07-03'
contact:
- family-names: Kanopka
  given-names: Klint
  email: klint.kanopka@nyu.edu