{
  "_id": "6a46da376527f6f71f9f6d16",
  "Package": "mixedsubjects",
  "Title": "Causal Inference in Experiments with Mixed-Subjects Designs",
  "Version": "1.0.0",
  "Authors@R": "c(\nperson(\"Austin\", \"van Loon\", email = \"avanloon@mit.edu\", role = c(\"aut\")),\nperson(\"Klint\", \"Kanopka\", email = \"klint.kanopka@nyu.edu\", role = c(\"aut\", \"cre\")),\nperson(\"Yuan\", \"Huang\", email = \"yh2741@nyu.edu\", role = \"ctb\")\n)",
  "Description": "Implements seven estimators for average treatment effect\n(ATE) estimation in mixed-subjects designs (MSDs), where human\nsubjects data is augmented with predictions from large language\nmodels (LLMs). Includes Difference-in-Means, GREG, PPI++,\nDoubly-Tuned, Difference-in-Predictions (DiP), DiP++, and D-T\nDiP estimators. Provides point estimates, variance estimation\nvia delta-method or bootstrap, and optimal design selection for\nbudget allocation between human observations and LLM\npredictions.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "RoxygenNote": "7.3.3",
  "VignetteBuilder": "knitr",
  "Config/testthat/edition": "3",
  "URL": "https://klintkanopka.com/mixedsubjects/",
  "BugReports": "https://github.com/klintkanopka/mixedsubjects/issues",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-07-02 21:35:12 UTC",
    "User": "root"
  },
  "Author": "Austin van Loon [aut], Klint Kanopka [aut, cre], Yuan Huang\n[ctb]",
  "Maintainer": "Klint Kanopka <klint.kanopka@nyu.edu>",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-07-02 18:30:02 UTC",
  "RemoteUrl": "https://github.com/cran/mixedsubjects",
  "RemoteRef": "HEAD",
  "RemoteSha": "144fa387a327efc928a1bc8159fd7cd1c55dddc6",
  "_user": "cran",
  "_type": "src",
  "_file": "mixedsubjects_1.0.0.tar.gz",
  "_fileid": "https://r2.ropensci.org/6a49d02714d8d0ac71b8ac7264bfee1fc4d489dce9d2a9951327739fd546e6af",
  "_filesize": 320113,
  "_sha256": "6a49d02714d8d0ac71b8ac7264bfee1fc4d489dce9d2a9951327739fd546e6af",
  "_expires": "2026-10-10T21:37:57.000Z",
  "_created": "2026-07-02T21:35:12.000Z",
  "_published": "2026-07-02T21:37:59.046Z",
  "_jobs": [
    {
      "job": 84883291765,
      "time": 117,
      "config": "linux-devel-x86_64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "8052219699"
    },
    {
      "job": 84883291779,
      "time": 129,
      "config": "linux-release-x86_64",
      "r": "4.6.1",
      "check": "OK",
      "artifact": "8052223568"
    },
    {
      "job": 84882691333,
      "time": 219,
      "config": "source",
      "r": "4.6.1",
      "check": "OK",
      "artifact": "8052181144"
    },
    {
      "job": 84883291729,
      "time": 94,
      "config": "wasm-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "8052212302"
    }
  ],
  "_host": "GitHub-Actions",
  "_buildurl": "https://github.com/r-universe/cran/actions/runs/28622879072",
  "_status": "success",
  "_upstream": "https://github.com/cran/mixedsubjects",
  "_commit": {
    "id": "144fa387a327efc928a1bc8159fd7cd1c55dddc6",
    "author": "Klint Kanopka <klint.kanopka@nyu.edu>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 1.0.0\n",
    "time": 1783017002
  },
  "_maintainer": {
    "name": "Klint Kanopka",
    "email": "klint.kanopka@nyu.edu",
    "login": "klintkanopka",
    "bluesky": "@klint.bsky.social",
    "twitter": "@KlintKanopka",
    "description": "Assitant Professor of Applied Statistics at NYU ASH. Interested in measurement, psychometrics, networks, and machine learning.",
    "uuid": 28941934
  },
  "_distro": "resolute",
  "_registered": true,
  "_dependencies": [
    {
      "package": "stats",
      "role": "Imports"
    },
    {
      "package": "knitr",
      "role": "Suggests"
    },
    {
      "package": "rmarkdown",
      "role": "Suggests"
    },
    {
      "package": "testthat",
      "version": ">= 3.0.0",
      "role": "Suggests"
    }
  ],
  "_owner": "cran",
  "_selfowned": false,
  "_usedby": 0,
  "_updates": [
    {
      "week": "2026-27",
      "n": 1
    }
  ],
  "_tags": [
    {
      "name": "1.0.0",
      "date": "2026-07-02"
    }
  ],
  "_stars": 0,
  "_contributors": [
    {
      "user": "klintkanopka",
      "count": 1,
      "uuid": 28941934
    }
  ],
  "_userbio": {
    "uuid": 6899542,
    "type": "organization",
    "name": "cran",
    "followers": 615,
    "description": "Unofficial read-only mirror of all CRAN R packages"
  },
  "_downloads": {
    "count": 0,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/mixedsubjects"
  },
  "_devurl": "https://github.com/klintkanopka/mixedsubjects",
  "_pkgdown": "https://klintkanopka.com/mixedsubjects/",
  "_searchresults": 2,
  "_rbuild": "4.6.1",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/mixedsubjects.html",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "LICENSE",
    "manual.pdf"
  ],
  "_cranurl": false,
  "_releases": [
    {
      "version": "1.0.0",
      "date": "2026-07-02"
    }
  ],
  "_exports": [
    "bootstrap_variance",
    "compare_variance_methods",
    "estimate_all",
    "msd_data",
    "msd_dim",
    "msd_dip",
    "msd_dip_pp",
    "msd_dt",
    "msd_dt_dip",
    "msd_greg",
    "msd_ppi",
    "optimal_design"
  ],
  "_help": [
    {
      "page": "bootstrap_variance",
      "title": "Variance Estimation for Mixed-Subjects Design",
      "topics": [
        "bootstrap_variance"
      ]
    },
    {
      "page": "compare_variance_methods",
      "title": "Compare variance estimates across methods",
      "topics": [
        "compare_variance_methods"
      ]
    },
    {
      "page": "estimate_all",
      "title": "Estimate all available estimators",
      "topics": [
        "estimate_all"
      ]
    },
    {
      "page": "msd_data",
      "title": "Data Preparation for Mixed-Subjects Design",
      "topics": [
        "msd_data"
      ]
    },
    {
      "page": "msd_dim",
      "title": "Difference-in-Means Estimator",
      "topics": [
        "msd_dim"
      ]
    },
    {
      "page": "msd_dip",
      "title": "DiP (Difference-in-Predictions) Estimator",
      "topics": [
        "msd_dip"
      ]
    },
    {
      "page": "msd_dip_pp",
      "title": "DiP++ Estimator",
      "topics": [
        "msd_dip_pp"
      ]
    },
    {
      "page": "msd_dt",
      "title": "D-T (Doubly-Tuned) Estimator",
      "topics": [
        "msd_dt"
      ]
    },
    {
      "page": "msd_dt_dip",
      "title": "D-T DiP (Doubly-Tuned Difference-in-Predictions) Estimator",
      "topics": [
        "msd_dt_dip"
      ]
    },
    {
      "page": "msd_greg",
      "title": "GREG Estimator",
      "topics": [
        "msd_greg"
      ]
    },
    {
      "page": "msd_ppi",
      "title": "PPI++ Estimator",
      "topics": [
        "msd_ppi"
      ]
    },
    {
      "page": "optimal_design",
      "title": "Optimal Design for Mixed-Subjects Experiments",
      "topics": [
        "optimal_design"
      ]
    },
    {
      "page": "print.msd_data",
      "title": "Print method for msd_data",
      "topics": [
        "print.msd_data"
      ]
    },
    {
      "page": "print.msd_design",
      "title": "Print method for msd_design",
      "topics": [
        "print.msd_design"
      ]
    },
    {
      "page": "print.msd_result",
      "title": "Print method for msd_result",
      "topics": [
        "print.msd_result"
      ]
    },
    {
      "page": "print.msd_summary",
      "title": "Print method for msd_summary",
      "topics": [
        "print.msd_summary"
      ]
    },
    {
      "page": "print.summary.msd_result",
      "title": "Print method for summary.msd_result",
      "topics": [
        "print.summary.msd_result"
      ]
    },
    {
      "page": "summary.msd_design",
      "title": "Summary method for msd_design",
      "topics": [
        "summary.msd_design"
      ]
    },
    {
      "page": "summary.msd_result",
      "title": "Summary method for msd_result",
      "topics": [
        "summary.msd_result"
      ]
    }
  ],
  "_readme": "https://github.com/cran/mixedsubjects/raw/HEAD/README.md",
  "_rundeps": [],
  "_vignettes": [
    {
      "source": "compare_estimators.Rmd",
      "filename": "compare_estimators.html",
      "title": "Comparing Estimators Under Different Data-Generating Processes",
      "author": "mixedsubjects",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "Simulation Engine",
        "Scenario 1: Poor Predictions — DiM Wins",
        "Scenario 2: Negatively Correlated Predictions",
        "Scenario 3: Heterogeneous Prediction Quality — Double-Tuned Wins",
        "Scenario 4: High Common-Mode Error",
        "Scenario 5: Common-Mode Error + Heterogeneous Quality",
        "Scenario 6: Near-Perfect Predictions",
        "Summary Table"
      ],
      "created": "2026-07-02 18:30:02",
      "modified": "2026-07-02 18:30:02",
      "commits": 1
    },
    {
      "source": "introduction.Rmd",
      "filename": "introduction.html",
      "title": "Introduction to Mixed-Subjects Designs with the mixedsubjects Package",
      "author": "Austin van Loon and Klint Kanopka",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Overview",
        "Why Mixed-Subjects Designs?",
        "What This Package Does",
        "Installation",
        "A Quick Example",
        "Simulating Experimental Data",
        "Creating an MSD Data Object",
        "Estimating the Treatment Effect",
        "Understanding the Data Structure",
        "What Data Do You Need?",
        "Two Types of Predictions",
        "Creating Your Data Object",
        "Flexible Column Names",
        "Formula Interface",
        "The Seven Estimators",
        "1. Difference-in-Means (DiM)",
        "2. GREG (Calibration Estimator)",
        "3. PPI++ (Power-Tuned)",
        "4. D-T (Doubly-Tuned)",
        "5. DiP (Difference-in-Predictions)",
        "6. DiP++ (Power-Tuned DiP)",
        "7. D-T DiP (Doubly-Tuned DiP)",
        "Comparing All Estimators",
        "Choosing the Right Estimator",
        "Decision Tree",
        "When to Use DiP-Type Estimators",
        "Variance Estimation",
        "Delta-Method (Default)",
        "Bootstrap (Optional)",
        "Optimal Experimental Design",
        "The Design Problem",
        "Using optimal_design()",
        "Interpreting the Results",
        "Comparing Designs",
        "Practical Workflow",
        "Step 1: Pilot Study",
        "Step 2: Estimate Prediction Quality",
        "Step 3: Plan Your Main Study",
        "Step 4: Run the Main Study",
        "Step 5: Analyze and Report",
        "Common Questions",
        "Q: What if my LLM predictions are biased?",
        "Q: How much do predictions need to correlate with outcomes?",
        "Q: Should I use cross-fitting?",
        "Q: What if predictions are worse in one treatment arm?",
        "Q: How many folds should I use for cross-fitting?",
        "Q: Can I use predictions from multiple LLMs?",
        "Technical Details",
        "Assumptions",
        "Variance Formulas",
        "Citation",
        "Session Info"
      ],
      "created": "2026-07-02 18:30:02",
      "modified": "2026-07-02 18:30:02",
      "commits": 1
    }
  ],
  "_score": 3,
  "_indexed": true,
  "_nocasepkg": "mixedsubjects",
  "_universes": [
    "cran",
    "klintkanopka"
  ],
  "_binaries": [
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "1.0.0",
      "date": "2026-07-02T21:37:12.000Z",
      "distro": "resolute",
      "commit": "144fa387a327efc928a1bc8159fd7cd1c55dddc6",
      "fileid": "https://r2.ropensci.org/238dae9d61fd17a772e1453bf80d0515e6b58cfa814cf3dcbe5a37dbe3b73b23",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/28622879072"
    },
    {
      "r": "4.6.1",
      "os": "linux",
      "version": "1.0.0",
      "date": "2026-07-02T21:37:24.000Z",
      "distro": "resolute",
      "commit": "144fa387a327efc928a1bc8159fd7cd1c55dddc6",
      "fileid": "https://r2.ropensci.org/02c010605c0afa09630a47d7dae708a037c10fa4f600fe419c9cd0f398f5bbe8",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/28622879072"
    },
    {
      "r": "4.6.0",
      "os": "wasm",
      "version": "1.0.0",
      "date": "2026-07-02T21:37:05.000Z",
      "commit": "144fa387a327efc928a1bc8159fd7cd1c55dddc6",
      "fileid": "https://r2.ropensci.org/a35c41af346ab7ce44849a239dc615684b3e03967ea5bc85f73f5af5ce57764c",
      "status": "success",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/28622879072"
    }
  ]
}