{ "_id": "6adc9710ad718eea59ecd3e8", "Package": "RCTCovAdj", "Type": "Package", "Title": "Covariate Adjustment for Randomized Controlled Trials", "Version": "0.1.0", "Authors@R": "person(\"Se Yoon\", \"Lee\",\nemail = \"seyoonlee.stat.math@gmail.com\",\nrole = c(\"aut\", \"cre\", \"cph\"))", "Description": "Implements covariate-adjusted estimation and inference for\nmarginal mean differences in two-arm randomized controlled\ntrials with continuous outcomes. Methods include the unadjusted\ndifference in means, common-slope analysis of covariance,\ninteracted standardization with joint influence-function\ninference, and cross-fitted augmentation. The package also\nprovides analytic power and sample-size calculations,\nreproducible simulation designs, and article replication\nworkflows, including tools for verifying and analyzing an\nauthorized local trial-data file. The adjustment methods build\non Tsiatis et al. (2008) and Lin (2013)\n.", "License": "MIT + file LICENSE", "Encoding": "UTF-8", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "LazyData": "true", "LazyDataCompression": "xz", "RoxygenNote": "7.3.3", "NeedsCompilation": "no", "Packaged": { "Date": "2026-10-24 11:28:48 UTC", "User": "root" }, "Author": "Se Yoon Lee [aut, cre, cph]", "Maintainer": "Se Yoon Lee ", "Repository": "https://cran.r-universe.dev", "Date/Publication": "2026-09-24 14:50:11 UTC", "RemoteUrl": "https://github.com/cran/RCTCovAdj", "RemoteRef": "HEAD", "RemoteSha": "996c8b4eacb3fc4504d483c17c3908260ee35bca", "_user": "cran", "_type": "src", "_file": "RCTCovAdj_0.1.0.tar.gz", "_fileid": "https://r2.ropensci.org/9fa1b2e28fa046f0b5a9150bc88ebf85ad18cf66d14fc0e6986e66081a690b4f", "_filesize": 669279, "_sha256": "9fa1b2e28fa046f0b5a9150bc88ebf85ad18cf66d14fc0e6986e66081a690b4f", "_expires": "2027-02-01T11:31:26.000Z", "_created": "2026-10-24T11:28:48.000Z", "_published": "2026-10-24T11:31:28.353Z", "_upstream": "https://github.com/cran/RCTCovAdj", "_status": "success", "_maintainer": { "name": "Se Yoon Lee", "email": "seyoonlee.stat.math@gmail.com", "login": "yain22", "description": "", "uuid": 25113063 }, "_jobs": [ { "job": 120860815869, "time": 115, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12558756938" }, { "job": 120860815843, "time": 107, "config": "linux-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12559155396" }, { "job": 120860109272, "time": 221, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "12558741949" }, { "job": 120860815821, "time": 96, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "12558771925" }, { "job": 120860815816, "time": 75, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "12558257802" }, { "job": 120860815920, "time": 77, "config": "windows-release-x86_64", "r": "4.6.1", "check": "OK", "artifact": "12559170252" } ], "_buildurl": "https://github.com/r-universe/cran/actions/runs/40089811214", "_commit": { "id": "996c8b4eacb3fc4504d483c17c3908260ee35bca", "author": "Se Yoon Lee ", "committer": "cran-robot ", "message": "version 0.1.0\n", "time": 1790261411 }, "_distro": "resolute", "_host": "GitHub-Actions", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "digest", "role": "Imports" }, { "package": "graphics", "role": "Imports" }, { "package": "grDevices", "role": "Imports" }, { "package": "parallel", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "tools", "role": "Imports" }, { "package": "utils", "role": "Imports" }, { "package": "knitr", "role": "Suggests" }, { "package": "medicaldata", "version": ">= 0.2.0", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "withr", "role": "Suggests" } ], "_owner": "cran", "_selfowned": false, "_usedby": 0, "_updates": [ { "week": "2026-39", "n": 1 } ], "_tags": [ { "name": "0.1.0", "date": "2026-09-24" } ], "_stars": 0, "_contributors": [ { "user": "yain22", "count": 1, "uuid": 25113063 } ], "_userbio": { "uuid": 6899542, "type": "organization", "name": "cran", "followers": 634, "description": "Unofficial read-only mirror of all CRAN R packages" }, "_downloads": { "count": 377, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/RCTCovAdj" }, "_searchresults": 0, "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/manual.pdf", "extra/NEWS.html", "extra/NEWS.txt", "extra/RCTCovAdj.html", "extra/readme.html", "extra/readme.md", "LICENSE" ], "_realowner": "yain22", "_cranurl": false, "_releases": [ { "version": "0.1.0", "date": "2026-09-24" } ], "_exports": [ "analyze_licorice", "paper_power_design", "plot_licorice_results", "plot_power_design", "plot_simulation_results", "prepare_licorice_data", "rct_adjust", "rct_crossfit", "rct_power", "rct_sample_size", "rct_variance_factors", "read_licorice_data", "reproduce_paper_simulations", "run_paper_simulations", "simulate_rct_case" ], "_datasets": [ { "name": "paper_population_benchmarks", "title": "Population Benchmarks for the Simulation Cases", "object": "paper_population_benchmarks", "class": [ "data.frame" ], "fields": [ "case", "label", "pi", "b0", "b1", "quad", "VU", "Vstar", "Vlinear", "Vancova", "Vhc2_limit", "beta_opt", "beta_pool" ], "rows": 4, "table": true, "tojson": true }, { "name": "paper_power_design_results", "title": "Power and Sample-Size Results from the Article", "object": "paper_power_design_results", "class": [ "data.frame" ], "fields": [ "case", "analysis", "curve", "variance_factor", "relative_variance", "required_n_80", "achieved_power_80", "required_n_90", "achieved_power_90", "power_n_300", "power_n_500", "power_n_800" ], "rows": 6, "table": true, "tojson": true }, { "name": "paper_simulation_results", "title": "Monte Carlo Results from the Article", "object": "paper_simulation_results", "class": [ "data.frame" ], "fields": [ "case", "n", "pi", "method", "reps", "bias", "bias_mcse", "emp_sd", "emp_sd_mcse", "mean_se", "mean_se_mcse", "coverage", "coverage_mcse", "empirical_nvar", "nvar_mcse", "point_estimator_nvar_limit", "reported_se_nvar_limit", "relative_to_bound", "training_fallbacks", "full_data_fallbacks", "empty_arm_fallbacks", "hc2_leverage_fallbacks", "hc2_leverage_floor_candidates" ], "rows": 56, "table": true, "tojson": true }, { "name": "simulation_cases", "title": "Simulation Cases Used in the Article", "object": "simulation_cases", "class": [ "data.frame" ], "fields": [ "case", "label", "pi", "b0", "b1", "quad" ], "rows": 4, "table": true, "tojson": true } ], "_help": [ { "page": "RCTCovAdj-package", "title": "RCTCovAdj: Covariate Adjustment for Randomized Controlled Trials", "topics": [ "RCTCovAdj-package", "RCTCovAdj" ] }, { "page": "analyze_licorice", "title": "Analyze the Licorice-Gargle Trial Application", "topics": [ "analyze_licorice" ] }, { "page": "paper_population_benchmarks", "title": "Population Benchmarks for the Simulation Cases", "topics": [ "paper_population_benchmarks" ] }, { "page": "paper_power_design", "title": "Reproduce the Paper's Power and Sample-Size Application", "topics": [ "paper_power_design" ] }, { "page": "paper_power_design_results", "title": "Power and Sample-Size Results from the Article", "topics": [ "paper_power_design_results" ] }, { "page": "paper_simulation_results", "title": "Monte Carlo Results from the Article", "topics": [ "paper_simulation_results" ] }, { "page": "plot_licorice_results", "title": "Plot Aggregate Results from the Licorice Trial Application", "topics": [ "plot_licorice_results" ] }, { "page": "plot_power_design", "title": "Plot the Paper's Power-Design Application", "topics": [ "plot_power_design" ] }, { "page": "plot_simulation_results", "title": "Plot the Paper's Simulation Results", "topics": [ "plot_simulation_results" ] }, { "page": "prepare_licorice_data", "title": "Prepare the Licorice-Gargle Trial Records", "topics": [ "prepare_licorice_data" ] }, { "page": "print.licorice_analysis", "title": "Print a Licorice-Application Result", "topics": [ "print.licorice_analysis" ] }, { "page": "print.rct_adjustment", "title": "Print a Covariate-Adjustment Result", "topics": [ "print.rct_adjustment" ] }, { "page": "print.rct_power_design", "title": "Print a Power-Design Result", "topics": [ "print.rct_power_design" ] }, { "page": "print.rct_simulation", "title": "Print a Paper Simulation Result", "topics": [ "print.rct_simulation" ] }, { "page": "rct_adjust", "title": "Estimate a Marginal Mean Difference with Covariate Adjustment", "topics": [ "rct_adjust" ] }, { "page": "rct_adjustment_methods", "title": "Methods for Covariate-Adjustment Results", "topics": [ "as.data.frame.rct_adjustment", "coef.rct_adjustment", "confint.rct_adjustment", "rct_adjustment_methods", "summary.rct_adjustment", "vcov.rct_adjustment" ] }, { "page": "rct_crossfit", "title": "Cross-Fitted Covariate Adjustment", "topics": [ "rct_crossfit" ] }, { "page": "rct_power", "title": "Normal-Approximation Power for a Marginal Mean Difference", "topics": [ "rct_power" ] }, { "page": "rct_sample_size", "title": "Required Sample Size for a Marginal Mean Difference", "topics": [ "rct_sample_size" ] }, { "page": "rct_variance_factors", "title": "Analytic Variance Factors for the Paper's Gaussian Designs", "topics": [ "rct_variance_factors" ] }, { "page": "read_licorice_data", "title": "Read an Authorized Licorice-Gargle Data File", "topics": [ "read_licorice_data" ] }, { "page": "reproduce_paper_simulations", "title": "Write a Reproducible Simulation Run", "topics": [ "reproduce_paper_simulations" ] }, { "page": "run_paper_simulations", "title": "Run the Paper's Monte Carlo Experiment", "topics": [ "run_paper_simulations" ] }, { "page": "simulate_rct_case", "title": "Simulate One Continuous-Outcome Randomized Controlled Trial", "topics": [ "simulate_rct_case" ] }, { "page": "simulation_cases", "title": "Simulation Cases Used in the Article", "topics": [ "simulation_cases" ] } ], "_readme": "https://github.com/cran/RCTCovAdj/raw/HEAD/README.md", "_rundeps": [ "digest" ], "_vignettes": [ { "source": "covariate-adjustment.Rmd", "filename": "covariate-adjustment.html", "title": "Covariate Adjustment in Randomized Controlled Trials", "author": "Se Yoon Lee", "engine": "knitr::rmarkdown", "headings": [ "Aim and statistical setting", "Where the precision gain comes from", "Four analysis strategies", "A small simulated RCT", "Standard errors after empirical standardization", "Application 1: postoperative throat-pain scores", "Application 2: power and sample size", "Practical analysis checklist" ], "created": "2026-09-24 14:50:11", "modified": "2026-09-24 14:50:11", "commits": 1 }, { "source": "reproducing-paper.Rmd", "filename": "reproducing-paper.html", "title": "Reproducing the Paper's Numerical Results", "author": "Se Yoon Lee", "engine": "knitr::rmarkdown", "headings": [ "Reproducibility map", "Simulation laws", "A short executable check", "Full Monte Carlo experiment", "Postoperative throat-pain analysis", "Power and sample-size application", "Standalone replication scripts", "Recommended audit sequence", "References" ], "created": "2026-09-24 14:50:11", "modified": "2026-09-24 14:50:11", "commits": 1 } ], "_score": 3, "_indexed": false, "_nocasepkg": "rctcovadj", "_universes": [ "cran" ], "_indexurl": "https://yain22.r-universe.dev/RCTCovAdj", "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "0.1.0", "date": "2026-10-24T11:30:46.000Z", "distro": "resolute", "commit": "996c8b4eacb3fc4504d483c17c3908260ee35bca", "fileid": "https://r2.ropensci.org/911407a015b54aab6cbd66320c002f5d172f954c30c4c9f233c1831a4c2db24f", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40089811214" }, { "r": "4.6.1", "os": "linux", "version": "0.1.0", "date": "2026-10-24T11:30:36.000Z", "distro": "resolute", "commit": "996c8b4eacb3fc4504d483c17c3908260ee35bca", "fileid": "https://r2.ropensci.org/7d2b3d1d3cc6dd026d79235a035da29124c22eea7bf93fc04039e73d8dbff318", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40089811214" }, { "r": "4.6.0", "os": "wasm", "version": "0.1.0", "date": "2026-10-24T11:30:43.000Z", "commit": "996c8b4eacb3fc4504d483c17c3908260ee35bca", "fileid": "https://r2.ropensci.org/98da95635f02a9d9d8ef6ff4a305b8a579191dc7de073636a2a5734af2a089bf", "status": "success", "buildurl": "https://github.com/r-universe/cran/actions/runs/40089811214" }, { "r": "4.7.0", "os": "win", "version": "0.1.0", "date": "2026-10-24T11:29:55.000Z", "commit": "996c8b4eacb3fc4504d483c17c3908260ee35bca", "fileid": "https://r2.ropensci.org/70ceb8680d01d3bdefe8b8926d0093d0ea5d36c1a9074335826ec437febcd027", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40089811214" }, { "r": "4.6.1", "os": "win", "version": "0.1.0", "date": "2026-10-24T11:29:58.000Z", "commit": "996c8b4eacb3fc4504d483c17c3908260ee35bca", "fileid": "https://r2.ropensci.org/ea0118afe7b73c62d2cb2f713326491acff9594a24222fa1fc5b8bfc1cef08ae", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/cran/actions/runs/40089811214" } ] }