Causal Inference in Experiments with Mixed-Subjects Designs R package mixedsubjects version 1.0.0 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, PPI++, Doubly-Tuned, 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. Software Imports: stats Suggests: knitr, rmarkdown, testthat (>= 3.0.0) Klint Kanopka <klint.kanopka@nyu.edu> Comprehensive R Archive Network (CRAN) Austin van Loon [aut], Klint Kanopka [aut, cre], Yuan Huang [ctb] MIT + file LICENSE (https://CRAN.R-project.org/package=mixedsubjects/LICENSE) 2026-07-02 application/tgz https://CRAN.R-project.org/package=mixedsubjects doi:10.32614/CRAN.package.mixedsubjects