Package: mixedsubjects
Title: Causal Inference in Experiments with Mixed-Subjects Designs
Version: 1.0.0
Authors@R: c(
    person("Austin", "van Loon", email = "avanloon@mit.edu", role = c("aut")),
    person("Klint", "Kanopka", email = "klint.kanopka@nyu.edu", role = c("aut", "cre")),
    person("Yuan", "Huang", email = "yh2741@nyu.edu", role = "ctb")
  )
Description: 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.
License: MIT + file LICENSE
Encoding: UTF-8
RoxygenNote: 7.3.3
Imports: stats
Suggests: knitr, rmarkdown, testthat (>= 3.0.0)
VignetteBuilder: knitr
Config/testthat/edition: 3
URL: https://klintkanopka.com/mixedsubjects/
BugReports: https://github.com/klintkanopka/mixedsubjects/issues
NeedsCompilation: no
Packaged: 2026-06-26 16:15:06 UTC; klintkanopka
Author: Austin van Loon [aut],
  Klint Kanopka [aut, cre],
  Yuan Huang [ctb]
Maintainer: Klint Kanopka <klint.kanopka@nyu.edu>
Repository: CRAN
Date/Publication: 2026-07-02 18:30:02 UTC
Built: R 4.6.0; ; 2026-07-02 19:42:30 UTC; unix
