{ "_id": "6a1fcc13b401979e73432953", "Package": "coefa", "Type": "Package", "Title": "Meta Analysis of Factor Analysis Based on CO-Occurrence Matrices", "Version": "1.0.3", "Authors@R": "c(person(given = \"Xijian\", \nfamily = \"Zheng\",\nrole = c(\"aut\",\"cre\"),\nemail = \"psydreammer@foxmail.com\"),\nperson(given = \"Huiyong\",\nfamily = \"Fan\",\nrole = \"aut\",\nemail = \"570950454@qq.com\"))", "Description": "Provide a series of functions to conduct a meta analysis\nof factor analysis based on co-occurrence matrices. The tool\ncan be used to solve the factor structure (i.e. inner structure\nof a construct, or scale) debate in several disciplines, such\nas psychology, psychiatry, management, education so on.\nReferences: Shafer (2005) ;\nShafer (2006) ; Loeber and Schmaling\n(1985) .", "License": "GPL (>= 3)", "Encoding": "UTF-8", "LazyData": "TRUE", "RoxygenNote": "7.2.1", "Date": "2023-02-5", "Config/testthat/edition": "3", "Author": "Xijian Zheng [aut, cre], Huiyong Fan [aut]", "Maintainer": "Xijian Zheng ", "VignetteBuilder": "knitr", "Language": "en-US", "NeedsCompilation": "no", "Packaged": { "Date": "2026-06-03 06:35:08 UTC", "User": "root" }, "Config/pak/sysreqs": "libicu-dev", "Repository": "https://xijianzheng.r-universe.dev", "Date/Publication": "2023-02-04 15:12:30 UTC", "RemoteUrl": "https://github.com/cran/coefa", "RemoteRef": "HEAD", "RemoteSha": "bf3d90c572972c1133ae0b9dc356a2f712f88d3f", "MD5sum": "03a6f20c095ccb730630c04411a205c7", "_user": "xijianzheng", "_type": "src", "_file": "coefa_1.0.3.tar.gz", "_fileid": "b00468878bb905df7dd829d4b9aa3308d61ddb4965a0ffa8d8854c177c52e3cf", "_filesize": 745128, "_sha256": "b00468878bb905df7dd829d4b9aa3308d61ddb4965a0ffa8d8854c177c52e3cf", "_created": "2026-06-03T06:35:08.000Z", "_published": "2026-06-03T06:39:15.216Z", "_distro": "noble", "_jobs": [ { "job": 79235995859, "time": 132, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "OK", "artifact": "7377410751" }, { "job": 79235995869, "time": 147, "config": "linux-release-x86_64", "r": "4.6.0", "check": "OK", "artifact": "7377413764" }, { "job": 79235995857, "time": 215, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "OK", "artifact": "7377422109" }, { "job": 79235995858, "time": 165, "config": "macos-release-arm64", "r": "4.6.0", "check": "OK", "artifact": "7377409511" }, { "job": 79235689446, "time": 146, "config": "source", "r": "4.6.0", "check": "OK", "artifact": "7377375007" }, { "job": 79235995868, "time": 116, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "7377405652" }, { "job": 79235995873, "time": 81, "config": "windows-devel", "r": "4.7.0", "check": "OK", "artifact": "7377396817" }, { "job": 79235995854, "time": 82, "config": "windows-oldrel", "r": "4.5.3", "check": "OK", "artifact": "7377398276" }, { "job": 79235995848, "time": 99, "config": "windows-release", "r": "4.6.0", "check": "OK", "artifact": "7377401071" } ], "_buildurl": "https://github.com/r-universe/xijianzheng/actions/runs/26867892203", "_status": "success", "_host": "GitHub-Actions", "_upstream": "https://github.com/cran/coefa", "_commit": { "id": "bf3d90c572972c1133ae0b9dc356a2f712f88d3f", "author": "Xijian Zheng ", "committer": "cran-robot ", "message": "version 1.0.3\n", "time": 1675523550 }, "_maintainer": { "name": "Xijian Zheng", "email": "psydreammer@foxmail.com", "login": "xijianzheng", "description": "", "uuid": 113590726 }, "_registered": true, "_dependencies": [ { "package": "openxlsx", "role": "Depends" }, { "package": "psych", "role": "Depends" }, { "package": "R", "version": ">= 2.10", "role": "Depends" }, { "package": "purrr", "role": "Imports" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "spelling", "role": "Suggests" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" } ], "_owner": "cran", "_selfowned": true, "_usedby": 0, "_updates": [], "_tags": [], "_stars": 0, "_contributors": [ { "user": "xijianzheng", "count": 3, "uuid": 113590726 } ], "_userbio": { "uuid": 113590726, "type": "user", "name": "XijianZheng" }, "_downloads": { "count": 200, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/coefa" }, "_searchresults": 4, "_rbuild": "4.6.0", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/coefa.html", "extra/contents.json", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md", "manual.pdf" ], "_realowner": "xijianzheng", "_cranurl": false, "_releases": [ { "version": "1.0.1", "date": "2022-09-26" }, { "version": "1.0.2", "date": "2022-11-18" }, { "version": "1.0.3", "date": "2023-02-04" } ], "_exports": [ "coefa_acm", "coefa_fa", "coefa_fixdia", "coefa_gcm", "coefa_read", "coefa_summary", "coefa_tflm", "coefa_tflm2" ], "_datasets": [ { "name": "matrices_acm", "title": "The aggregated co-occurrence matrix", "object": "matrices_acm", "class": [ "matrix", "array" ], "fields": [ "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "23", "24", "25", "26", "27", "28", "29", "30", "31", "32", "33", "34", "35", "36", "37", "38" ], "rows": 38, "table": true, "tojson": true }, { "name": "spence8", "title": "The factor loading matrices of 8 exploratory factor analysis for spence children anxiety scale(SCAS).", "object": "spence8", "class": [ "list" ], "fields": [], "table": false, "tojson": true } ], "_help": [ { "page": "colon-coefa", "title": "Meta Analysis of Factor Analysis Based on Co-occurrence Matrices", "topics": [ ":coefa" ] }, { "page": "coefa_acm", "title": "Aggregate co-occurrence matrices", "topics": [ "coefa_acm" ] }, { "page": "coefa_fa", "title": "PCA and EFA", "topics": [ "coefa_fa" ] }, { "page": "coefa_fixdia", "title": "Fix(Replace) the diagonal values in the matrix", "topics": [ "coefa_fixdia" ] }, { "page": "coefa_gcm", "title": "Generate co-occurrence matrices", "topics": [ "coefa_gcm" ] }, { "page": "coefa_read", "title": "Obtain the factor loading matrices in the original study and delete Na value", "topics": [ "coefa_read" ] }, { "page": "coefa_summary", "title": "Summary", "topics": [ "coefa_summary" ] }, { "page": "coefa_tflm", "title": "Trimming factor loading matrices", "topics": [ "coefa_tflm" ] }, { "page": "coefa_tflm2", "title": "Trimming factor loading matrices(The cutoff range can be selected(0~1))", "topics": [ "coefa_tflm2" ] }, { "page": "matrices_acm", "title": "The aggregated co-occurrence matrix", "topics": [ "matrices_acm" ] }, { "page": "spence8", "title": "The factor loading matrices of 8 exploratory factor analysis for spence children anxiety scale(SCAS).", "topics": [ "spence8" ] } ], "_readme": "https://github.com/cran/coefa/raw/HEAD/README.md", "_rundeps": [ "cli", "glue", "GPArotation", "lattice", "lifecycle", "magrittr", "mnormt", "nlme", "openxlsx", "psych", "purrr", "Rcpp", "rlang", "stringi", "vctrs", "zip" ], "_vignettes": [ { "source": "Introduction.Rmd", "filename": "Introduction.html", "title": "coefa: A R Package for Meta Analysis of Factor Analysis Based on Co-occurrence Matrices", "author": "Xijian Zheng & Huiyong Fan", "engine": "knitr::rmarkdown", "headings": [ "1 Introduction", "2 The coefa R package", "2.1 Five steps of COEFA in coefa package", "Step1: Obtain factor loading matrices for the EFA in the original study", "Step2: Assign (Trim) the original factor loading matrices. Significant loading in factor loading matrices (loading greater than cutoff ) are assigned a value of 1, and the others are assigned a value of 0.", "Step3: Generate co-occurrence matrices using each factor loading matrix multiply its transport.", "Step4: Aggregate co-occurrence matrix. The users have two options,weight by sample size or not.", "Step5: Exploratory factor analysis or principal component analysis using the Aggregated co-occurrence matrix.", "2.2 Environment of the coefa package runing.", "2.3 Usage of coefa package", "Step1:Obtain factor loading matrices for the EFA in the original study.", "Step2: Assign (Trim) the original factor loading matrices.Significant loading in factor loading matrices (loading greater than the cutoff value ) are assigned a value of 1, and the others are assigned a value of 0.", "Step3: Generate the co-occurrence matrices for each primary study.", "Step4: Generate the aggregated co-occurrence matrix.", "Step5: Exploratory factor analysis or principal component analysis Using the Aggregated co-occurrence matrix.", "References" ], "created": "2022-09-26 11:40:02", "modified": "2023-02-04 15:12:30", "commits": 3 } ], "_score": 2.6989700043360187, "_indexed": true, "_nocasepkg": "coefa", "_universes": [ "xijianzheng" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.0.3", "date": "2026-06-03T06:37:20.000Z", "distro": "noble", "commit": "bf3d90c572972c1133ae0b9dc356a2f712f88d3f", "fileid": "dae9bbb9339c2c61eae721ad9345d6095c683f397c3910dfee3bd5feea05be33", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/xijianzheng/actions/runs/26867892203" }, { "r": "4.6.0", "os": "linux", "version": "1.0.3", "date": "2026-06-03T06:37:28.000Z", "distro": "noble", "commit": "bf3d90c572972c1133ae0b9dc356a2f712f88d3f", "fileid": "54d9806b45e0647a90fdbd18fe581cd842898f3ba09ba8b688a7c55de2c932de", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/xijianzheng/actions/runs/26867892203" }, { "r": "4.5.3", "os": "mac", "version": "1.0.3", "date": "2026-06-03T06:38:04.000Z", "commit": "bf3d90c572972c1133ae0b9dc356a2f712f88d3f", "fileid": "c68adf77f08bd0f42ce7866e8e457ca542bf641ec7ac0c3d42d1e87e1251e09c", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/xijianzheng/actions/runs/26867892203" }, { "r": "4.6.0", "os": "mac", "version": "1.0.3", "date": "2026-06-03T06:37:15.000Z", "commit": "bf3d90c572972c1133ae0b9dc356a2f712f88d3f", "fileid": "571ae7d0789e38ff7f1ca5ae99ea2f3f75e5a0bee25d36e429933e3ac89f9795", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/xijianzheng/actions/runs/26867892203" }, { "r": "4.6.0", "os": "wasm", "version": "1.0.3", "date": "2026-06-03T06:37:20.000Z", "commit": "bf3d90c572972c1133ae0b9dc356a2f712f88d3f", "fileid": "381c48ed21e8e376bcdadeaefc01d0327806ca72e3bca8921d1e0fc92e18ae98", "status": "success", "buildurl": "https://github.com/r-universe/xijianzheng/actions/runs/26867892203" }, { "r": "4.7.0", "os": "win", "version": "1.0.3", "date": "2026-06-03T06:36:22.000Z", "commit": "bf3d90c572972c1133ae0b9dc356a2f712f88d3f", "fileid": "c00022e56a73891aa8fe28bfbd1470540015cdb46814e393d0d0795fe5ad0969", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/xijianzheng/actions/runs/26867892203" }, { "r": "4.5.3", "os": "win", "version": "1.0.3", "date": "2026-06-03T06:36:29.000Z", "commit": "bf3d90c572972c1133ae0b9dc356a2f712f88d3f", "fileid": "b7673a469183ca106d5b2af713f5b673a54e446f0dcf13a9854fadf0e9f0c045", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/xijianzheng/actions/runs/26867892203" }, { "r": "4.6.0", "os": "win", "version": "1.0.3", "date": "2026-06-03T06:36:39.000Z", "commit": "bf3d90c572972c1133ae0b9dc356a2f712f88d3f", "fileid": "d0cbf1abb433650e7eec7380d1e4b566f449532aa189705c9c0cb630a0cb2200", "status": "success", "check": "OK", "buildurl": "https://github.com/r-universe/xijianzheng/actions/runs/26867892203" } ] }