{ "_id": "6a0ffeecacfb0bcc41c7a44d", "Package": "geeCRT", "Type": "Package", "Title": "Bias-Corrected GEE for Cluster Randomized Trials", "Version": "1.1.4", "Date": "2025-06-22", "Authors@R": "c(person(given = \"Hengshi\", family = \"Yu\", role = c(\"aut\", \"cre\"), \nemail=\"hengshi@umich.edu\"),\nperson(given = \"Fan\", family = \"Li\", role =\"aut\",\nemail = \"fan.f.li@yale.edu\"),\nperson(given = \"Paul\", family = \"Rathouz\", role =\"aut\",\nemail = \"paul.rathouz@austin.utexas.edu\"),\nperson(given = \"Elizabeth L.\", family = \"Turner\", role =\"aut\",\nemail = \"liz.turner@duke.edu\"),\nperson(given = \"John\", family = \"Preisser\", role =\"aut\",\nemail = \"jpreisse@bios.unc.edu\")\n)", "Maintainer": "Hengshi Yu ", "Description": "Population-averaged models have been increasingly used in\nthe design and analysis of cluster randomized trials (CRTs). To\nfacilitate the applications of population-averaged models in\nCRTs, the package implements the generalized estimating\nequations (GEE) and matrix-adjusted estimating equations (MAEE)\napproaches to jointly estimate the marginal mean models\ncorrelation models both for general CRTs and stepped wedge\nCRTs. Despite the general GEE/MAEE approach, the package also\nimplements a fast cluster-period GEE method by Li et al. (2022)\n specifically for stepped\nwedge CRTs with large and variable cluster-period sizes and\ngives a simple and efficient estimating equations approach\nbased on the cluster-period means to estimate the intervention\neffects as well as correlation parameters. In addition, the\npackage also provides functions for generating correlated\nbinary data with specific mean vector and correlation matrix\nbased on the multivariate probit method in Emrich and Piedmonte\n(1991) or the conditional\nlinear family method in Qaqish (2003)\n.", "License": "GPL (>= 2)", "LazyData": "TRUE", "VignetteBuilder": "knitr", "RoxygenNote": "7.2.3", "Encoding": "UTF-8", "NeedsCompilation": "no", "Packaged": { "Date": "2026-05-22 06:55:41 UTC", "User": "root" }, "Author": "Hengshi Yu [aut, cre], Fan Li [aut], Paul Rathouz [aut],\nElizabeth L. Turner [aut], John Preisser [aut]", "Repository": "https://hengshiyu.r-universe.dev", "Date/Publication": "2025-06-26 04:38:34 UTC", "RemoteUrl": "https://github.com/hengshiyu/geecrt", "RemoteRef": "HEAD", "RemoteSha": "e051eb0306e651a75d8fb625752d6482c921ac4a", "MD5sum": "a5b92d62b063e1df30c6a8ddb5835723", "_user": "hengshiyu", "_type": "src", "_file": "geeCRT_1.1.4.tar.gz", "_fileid": "8eeec1f8ee805d5176bd9229906c4df550a6f25c41d7f65f7b22acf254e2a650", "_filesize": 283490, "_sha256": "8eeec1f8ee805d5176bd9229906c4df550a6f25c41d7f65f7b22acf254e2a650", "_created": "2026-05-22T06:55:41.000Z", "_published": "2026-05-22T06:59:55.987Z", "_distro": "noble", "_jobs": [ { "job": 77331286562, "time": 130, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "NOTE", "artifact": "7154586149" }, { "job": 77331286542, "time": 140, "config": "linux-release-x86_64", "r": "4.6.0", "check": "NOTE", "artifact": "7154588183" }, { "job": 77331286566, "time": 172, "config": "macos-oldrel-arm64", "r": 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"simbinCLF", "simbinPROBIT" ], "_datasets": [ { "name": "sampleSWCRTLarge", "title": "simulated large SW-CRT data", "object": "sampleSWCRTLarge", "class": [ "data.frame" ], "fields": [ "period1", "period2", "period3", "period4", "period5", "treatment", "id", "period", "y_bin", "y_con" ], "rows": 1508, "table": true, "tojson": true }, { "name": "sampleSWCRTSmall", "title": "simulated small SW-CRT data", "object": "sampleSWCRTSmall", "class": [ "data.frame" ], "fields": [ "period1", "period2", "period3", "period4", "treatment", "id", "period", "y_bin", "y_con" ], "rows": 373, "table": true, "tojson": true } ], "_help": [ { "page": "cpgeeSWD", "title": "Cluster-Period GEE for Estimating the Mean and Correlation Parameters in Cross-Sectional SW-CRTs", "topics": [ "cpgeeSWD" ] }, { "page": "geemaee", "title": "GEE and Matrix-adjusted Estimating Equations (MAEE) for Estimating the Marginal Mean and Correlation Parameters in CRTs", "topics": [ "geemaee" ] }, { "page": "print.cpgeeSWD", "title": "The print format for cpgeeSWD output", "topics": [ "print.cpgeeSWD" ] }, { "page": "print.geemaee", "title": "The print format for geemaee output", "topics": [ "print.geemaee" ] }, { "page": "sampleSWCRTLarge", "title": "simulated large SW-CRT data", "topics": [ "sampleSWCRTLarge" ] }, { "page": "sampleSWCRTSmall", "title": "simulated small SW-CRT data", "topics": [ "sampleSWCRTSmall" ] }, { "page": "simbinCLF", "title": "Generating Correlated Binary Data using the Conditional Linear Family Method.", "topics": [ "simbinCLF" ] }, { "page": "simbinPROBIT", "title": "Generating Correlated Binary Data using the Multivariate Probit Method.", "topics": [ "simbinPROBIT" ] } ], "_readme": "https://github.com/hengshiyu/geecrt/raw/HEAD/README.md", "_rundeps": [ "MASS", "mvtnorm", "rootSolve" ], "_vignettes": [ { "source": "geeCRT.Rmd", "filename": "geeCRT.html", "title": "geeCRT package", "author": "Hengshi Yu, Fan Li, Paul Rathouz, Elizabeth L. Turner, John Preisser", "engine": "knitr::rmarkdown", "headings": [ "Overview", "geemaee() examples: matrix-adjusted GEE for estimating the mean and correlation parameters in SW-CRTs", "Simulated dataset with 12 clusters and 4 periods", "Simulated dataset with 12 clusters and 5 periods", "geemaee() examples: matrix-adjusted GEE for estimating the mean and correlation parameters in SW-CRTs when some clusters had only 1 observation", "Simulated dataset with 12 clusters and small cluster sizes", "Simulated dataset with 12 clusters and large cluster sizes", "geemaee() examples: matrix-adjusted GEE for estimating the mean and correlation parameters in general CRTs", "geemaee() examples: matrix-adjusted GEE for estimating the mean and correlation parameters in SW-CRTs with all cluster-period sizes being 1", "cpgeeSWD() examples: cluster-period GEE for estimating the marginal mean and correlation parameters in cross-sectional SW-CRTs", "simbinPROBIT() example: generating correlated binary data using the multivariate probit method", "simbinCLF() example: generating correlated binary data using the conditional linear family method", "Session Information", "References" ], "created": "2021-04-07 21:59:03", "modified": "2024-02-21 06:02:25", "commits": 7 } ], "_score": 3.6989700043360187, "_indexed": true, "_nocasepkg": "geecrt", "_universes": [ "hengshiyu" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.1.4", "date": "2026-05-22T06:57:38.000Z", "distro": "noble", "commit": "e051eb0306e651a75d8fb625752d6482c921ac4a", "fileid": "8e6b3934b89e18a71c05880821895d9eda331493bfe9263542a315d8a687de5c", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/hengshiyu/actions/runs/26273051810" }, { "r": "4.6.0", "os": "linux", "version": "1.1.4", "date": "2026-05-22T06:57:54.000Z", "distro": "noble", "commit": "e051eb0306e651a75d8fb625752d6482c921ac4a", "fileid": "da7ac3c08c4b74239722d208ee266b069da1fa149b03ffc51a21c9faca086844", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/hengshiyu/actions/runs/26273051810" }, { "r": "4.5.3", "os": "mac", "version": "1.1.4", "date": "2026-05-22T06:58:50.000Z", "commit": "e051eb0306e651a75d8fb625752d6482c921ac4a", "fileid": "795c93e5b2f5637bcfb45a7a127f27ffd783801a58913bcfedc470fbab2af8ab", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/hengshiyu/actions/runs/26273051810" }, { "r": "4.6.0", "os": "mac", "version": "1.1.4", "date": "2026-05-22T06:57:33.000Z", "commit": "e051eb0306e651a75d8fb625752d6482c921ac4a", "fileid": "3a73b9458da0eb4cfc22d42c29ad2ad6eb3f8f3ec98fa31b6e2e6d6a8db8c1f2", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/hengshiyu/actions/runs/26273051810" }, { "r": "4.6.0", "os": "wasm", "version": "1.1.4", "date": "2026-05-22T06:57:48.000Z", "commit": "e051eb0306e651a75d8fb625752d6482c921ac4a", "fileid": "e16210366343e8c8db6953d8561d54a2428ca5385e055ace6939c5b6e3371df9", "status": "success", "buildurl": "https://github.com/r-universe/hengshiyu/actions/runs/26273051810" }, { "r": "4.7.0", "os": "win", "version": "1.1.4", "date": "2026-05-22T06:56:47.000Z", "commit": "e051eb0306e651a75d8fb625752d6482c921ac4a", "fileid": "98a5b79fbc9095bf8822b2b82dfd7dcb25b871d992b7827a0337513d6be1e228", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/hengshiyu/actions/runs/26273051810" }, { "r": "4.5.3", "os": "win", "version": "1.1.4", "date": "2026-05-22T06:57:02.000Z", "commit": "e051eb0306e651a75d8fb625752d6482c921ac4a", "fileid": "f0981e43c445201cfe0edce7af786fc769bcd2606dbf02f86e1af05bdb733ece", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/hengshiyu/actions/runs/26273051810" }, { "r": "4.6.0", "os": "win", "version": "1.1.4", "date": "2026-05-22T06:56:52.000Z", "commit": "e051eb0306e651a75d8fb625752d6482c921ac4a", "fileid": "70c2591c0361edeafa6e4939354f98bf63643ba6d8c1b93aeb498fc417d5c311", "status": "success", "check": "NOTE", "buildurl": "https://github.com/r-universe/hengshiyu/actions/runs/26273051810" } ] }