{ "_id": "6ad3080b90c8bbdf41a50d27", "Package": "EFAtools", "Title": "Fast and Flexible Implementations of Exploratory Factor Analysis\nTools", "Version": "1.1.0.9000", "Authors@R": "c(person(given = \"Markus\",\nfamily = \"Steiner\",\nrole = c(\"aut\", \"cre\"),\nemail = \"markus.d.steiner@gmail.com\",\ncomment = c(ORCID = \"0000-0002-8126-0757\")),\nperson(given = \"Silvia\",\nfamily = \"Steiner\",\nrole = \"aut\",\nemail = \"silvia.steiner.grieder@gmail.com\",\ncomment = c(ORCID = \"0000-0002-0118-7722\")),\nperson(given = \"William\",\nfamily = \"Revelle\",\nrole = \"ctb\"),\nperson(given = \"Max\",\nfamily = \"Auerswald\",\nrole = \"ctb\"),\nperson(given = \"Morten\",\nfamily = \"Moshagen\",\nrole = \"ctb\"),\nperson(given = \"John\",\nfamily = \"Ruscio\",\nrole = \"ctb\"),\nperson(given = \"Brendan\",\nfamily = \"Roche\",\nrole = \"ctb\"),\nperson(given = \"Urbano\",\nfamily = \"Lorenzo-Seva\",\nrole = \"ctb\"),\nperson(given = \"David\",\nfamily = \"Navarro-Gonzalez\",\nrole = \"ctb\"),\nperson(given = \"Johan\",\nfamily = \"Braeken\",\nrole = \"ctb\"),\nperson(given = \"Andreas\",\nfamily = \"Soteriades\",\nrole = \"ctb\"))", "Description": "Provides a complete workflow for exploratory factor\nanalysis (EFA). It covers data screening and factorability\nchecks, a suite of factor retention criteria for choosing the\nnumber of factors, and factor extraction by principal axis\nfactoring, maximum likelihood, unweighted least squares, or\ndiagonally weighted least squares from Pearson, Spearman,\nKendall, polychoric, tetrachoric, or two-stage full-information\nmaximum likelihood correlations. A built-in rotation engine\noffers a range of orthogonal and oblique rotations, and\nstandard errors for loadings and related quantities can be\nobtained by analytic, robust, or bootstrap methods. Further\ntools support model averaging across analytic choices,\nmultigroup EFA with factor congruence, EFA on multiply imputed\ndata, Schmid-Leiman transformation, reliability coefficients\n(including McDonald's omegas), factor score estimation, data\nsimulation, and power analysis. Computationally intensive\nprocedures are implemented in 'C++' for speed.", "License": "GPL-3", "Encoding": "UTF-8", "LazyData": "true", "URL": "https://github.com/mdsteiner/EFAtools,\nhttps://mdsteiner.github.io/EFAtools/", "BugReports": "https://github.com/mdsteiner/EFAtools/issues", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Config/roxygen2/version": "8.0.0", "Roxygen": "list(markdown = TRUE)", "Repository": "https://mdsteiner.r-universe.dev", "Date/Publication": "2026-10-17 03:29:48 UTC", "RemoteUrl": "https://github.com/mdsteiner/efatools", "RemoteRef": "HEAD", "RemoteSha": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "NeedsCompilation": "yes", "Packaged": { "Date": "2026-10-17 05:17:40 UTC", "User": "root" }, "Author": "Markus Steiner [aut, cre] (ORCID:\n),\nSilvia Steiner [aut] (ORCID: ),\nWilliam Revelle [ctb],\nMax Auerswald [ctb],\nMorten Moshagen [ctb],\nJohn Ruscio [ctb],\nBrendan Roche [ctb],\nUrbano Lorenzo-Seva [ctb],\nDavid Navarro-Gonzalez [ctb],\nJohan Braeken [ctb],\nAndreas Soteriades [ctb]", "Maintainer": "Markus Steiner ", "_user": "mdsteiner", "_type": "src", "_file": "EFAtools_1.1.0.9000.tar.gz", "_fileid": "https://r2.ropensci.org/2231eb6e8b622e452e6c2ec4ed76c7935bf61ab014e36af8a658c2e4a59fb48c", "_filesize": 2729301, "_sha256": "2231eb6e8b622e452e6c2ec4ed76c7935bf61ab014e36af8a658c2e4a59fb48c", "_expires": "2027-01-25T05:30:49.000Z", "_created": "2026-10-17T05:17:40.000Z", "_published": "2026-10-17T05:30:51.868Z", "_buildurl": "https://github.com/r-universe/mdsteiner/actions/runs/39005449707", "_status": "success", "_host": "GitHub-Actions", "_jobs": [ { "job": 117317968727, "time": 370, "config": "linux-devel-arm64", "r": "4.7.0", "check": "ERROR", "artifact": "12091421092" }, { "job": 117317968595, "time": 389, "config": "linux-devel-x86_64", "r": "4.7.0", "check": "ERROR", "artifact": "12090863246" }, { "job": 117317968755, "time": 320, "config": "linux-release-arm64", "r": "4.6.1", "check": "ERROR", "artifact": "12091141399" }, { "job": 117317968618, "time": 302, "config": "linux-release-x86_64", "r": "4.6.1", "check": "ERROR", "artifact": "12090733678" }, { "job": 117317968903, "time": 350, "config": "macos-oldrel-arm64", "r": "4.5.3", "check": "ERROR", "artifact": "12090893097" }, { "job": 117317968798, "time": 685, "config": "macos-oldrel-x86_64", "r": "4.5.3", "check": "ERROR", "artifact": "12090828832" }, { "job": 117317968768, "time": 388, "config": "macos-release-arm64", "r": "4.6.1", "check": "ERROR", "artifact": "12091097609" }, { "job": 117317968780, "time": 683, "config": "macos-release-x86_64", "r": "4.6.1", "check": "ERROR", "artifact": "12090724417" }, { "job": 117316920782, "time": 341, "config": "source", "r": "4.6.1", "check": "OK", "artifact": "12091375544" }, { "job": 117317968708, "time": 121, "config": "wasm-release", "r": "4.6.0", "check": "OK", "artifact": "12091375798" }, { "job": 117317968703, "time": 584, "config": "windows-devel-arm64", "r": "4.7.0", "check": "ERROR", "artifact": "12091266389" }, { "job": 117317968693, "time": 305, "config": "windows-devel-x86_64", "r": "4.7.0", "check": "ERROR", "artifact": "12090629209" }, { "job": 117317968698, "time": 296, "config": "windows-oldrel-x86_64", "r": "4.5.3", "check": "ERROR", "artifact": "12090753438" }, { "job": 117317968841, "time": 567, "config": "windows-release-arm64", "r": "4.6.1", "check": "ERROR", "artifact": "12090897095" }, { "job": 117317968681, "time": 448, "config": "windows-release-x86_64", "r": "4.6.1", "check": "ERROR", "artifact": "12091396376" } ], "_upstream": "https://github.com/mdsteiner/efatools", "_commit": { "id": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "author": "Markus Steiner ", "committer": "Markus Steiner ", "message": "updates to efa_reliability, efa_schmid_leiman, and efa_power\n", "time": 1792207788 }, "_maintainer": { "name": "Markus Steiner", "email": "markus.d.steiner@gmail.com", "login": "mdsteiner", "description": "", "uuid": 26483702, "orcid": "0000-0002-8126-0757" }, "_distro": "resolute", "_registered": true, "_dependencies": [ { "package": "R", "version": ">= 4.1.0", "role": "Depends" }, { "package": "Rcpp", "role": "LinkingTo" }, { "package": "RcppArmadillo", "role": "LinkingTo" }, { "package": "roptim", "role": "LinkingTo" }, { "package": "psych", "role": "Imports" }, { "package": "stats", "role": "Imports" }, { "package": "ggplot2", "version": ">= 3.4.0", "role": "Imports" }, { "package": "cli", "role": "Imports" }, { "package": "Rcpp", "role": "Imports" }, { "package": "future.apply", "role": "Imports" }, { "package": "checkmate", "role": "Imports" }, { "package": "progressr", "role": "Imports" }, { "package": "rlang", "role": "Imports" }, { "package": "clue", "role": "Imports" }, { "package": "lifecycle", "role": "Imports" }, { "package": "testthat", "version": ">= 3.0.0", "role": "Suggests" }, { "package": "future", "role": "Suggests" }, { "package": "GPArotation", "version": ">= 2022.4-1", "role": "Suggests" }, { "package": "lavaan", "role": "Suggests" }, { "package": "lavaan.mi", "role": "Suggests" }, { "package": "MASS", "role": "Suggests" }, { "package": "mice", "role": "Suggests" }, { "package": "nFactors", "role": "Suggests" }, { "package": "knitr", "role": "Suggests" }, { "package": "rmarkdown", "role": "Suggests" }, { "package": "vdiffr", "role": "Suggests" }, { "package": "polycor", "role": "Suggests" }, { "package": "robustbase", "role": "Suggests" }, { "package": "mnormt", "role": "Suggests" }, { "package": "semTools", "role": "Suggests" }, { "package": "withr", "role": "Suggests" } ], "_owner": "mdsteiner", "_selfowned": true, "_usedby": 3, "_updates": [ { "week": "2026-03", "n": 3 }, { "week": "2026-10", "n": 1 }, { "week": "2026-15", "n": 4 }, { "week": "2026-16", "n": 3 }, { "week": "2026-17", "n": 9 }, { "week": "2026-18", "n": 4 }, { "week": "2026-19", "n": 6 }, { "week": "2026-23", "n": 9 }, { "week": "2026-24", "n": 47 }, { "week": "2026-25", "n": 42 }, { "week": "2026-26", "n": 10 }, { "week": "2026-27", "n": 8 }, { "week": "2026-28", "n": 5 }, { "week": "2026-29", "n": 7 }, { "week": "2026-30", "n": 4 }, { "week": "2026-31", "n": 21 }, { "week": "2026-32", "n": 9 }, { "week": "2026-33", "n": 14 }, { "week": "2026-34", "n": 11 }, { "week": "2026-38", "n": 1 }, { "week": "2026-39", "n": 2 }, { "week": "2026-41", "n": 1 }, { "week": "2026-42", "n": 3 } ], "_tags": [ { "name": "v.0.7.0", "date": "2026-04-30" }, { "name": "v.0.7.1", "date": "2026-05-08" }, { "name": "v.0.8.0", "date": "2026-07-07" }, { "name": "v.1.0.0", "date": "2026-07-27" }, { "name": "v.1.1.0", "date": "2026-09-15" } ], "_stars": 10, "_contributors": [ { "user": "mdsteiner", "count": 587, "uuid": 26483702 }, { "user": "sgrieder", "count": 225, "uuid": 44674512 }, { "user": "andreassoteriadesmoj", "count": 11, "uuid": 91131120 } ], "_userbio": { "uuid": 26483702, "type": "user", "name": "Markus Steiner", "followers": 7 }, "_downloads": { "count": 2442, "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/EFAtools" }, "_mentions": 1, "_devurl": "https://github.com/mdsteiner/efatools", "_pkgdown": "https://mdsteiner.github.io/EFAtools/", "_searchresults": 124, "_topics": [ "openblas", "cpp" ], "_rbuild": "4.6.1", "_assets": [ "extra/citation.cff", "extra/citation.html", "extra/citation.json", "extra/citation.txt", "extra/contents.json", "extra/EFAtools.html", "extra/manual.pdf", "extra/NEWS.html", "extra/NEWS.txt", "extra/readme.html", "extra/readme.md" ], "_homeurl": "https://github.com/mdsteiner/efatools", "_realowner": "mdsteiner", "_cranurl": true, "_releases": [ { "version": "0.1.0", "date": "2020-07-07" }, { "version": "0.1.1", "date": "2020-07-13" }, { "version": "0.2.0", "date": "2020-09-17" }, { "version": "0.3.0", "date": "2020-11-04" }, { "version": "0.3.1", "date": "2021-03-27" }, { "version": "0.4.0", "date": "2022-03-21" }, { "version": "0.4.1", "date": "2022-04-24" }, { "version": "0.4.2", "date": "2022-09-27" }, { "version": "0.4.3", "date": "2022-10-02" }, { "version": "0.4.4", "date": "2023-01-06" }, { "version": "0.4.5", "date": "2024-12-22" }, { "version": "0.4.6", "date": "2025-03-22" }, { "version": "0.5.0", "date": "2025-05-27" }, { "version": "0.6.0", "date": "2025-06-19" }, { "version": "0.6.1", "date": "2025-08-01" }, { "version": "0.7.0", "date": "2026-04-30" }, { "version": "0.7.1", "date": "2026-05-08" }, { "version": "0.8.0", "date": "2026-07-07" }, { "version": "1.0.0", "date": "2026-07-23" }, { "version": "1.1.0", "date": "2026-08-21" } ], "_exports": [ "BARTLETT", "CD", "COMPARE", "EFA", "efa_average", "EFA_AVERAGE", "efa_bartlett", "efa_cd", "efa_compare", "efa_ekc", "efa_fit", "efa_group", "efa_hull", "efa_kgc", "efa_kmo", "efa_map", "efa_mi", "efa_nest", "efa_parallel", "EFA_POOLED", "efa_power", "efa_procrustes", "efa_reliability", "efa_retain", "efa_schmid_leiman", "efa_scores", "efa_scree", "efa_screen", "efa_simulate", "efa_smt", "EKC", "estimate_control", "FACTOR_SCORES", "HULL", "KGC", "KMO", "MAP", "N_FACTORS", "NEST", "OMEGA", "PARALLEL", "PROCRUSTES", "rotate_control", "SCREE", "SL", "SMT" ], "_datasets": [ { "name": "DOSPERT", "title": "DOSPERT", "object": "DOSPERT", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "DOSPERT_raw", "title": "DOSPERT_raw", "object": "DOSPERT_raw", "class": [ "data.frame" ], "fields": [ "ethR_1", "ethR_2", "ethR_3", "ethR_4", "ethR_5", "ethR_6", "finR_1", "finR_2", "finR_3", "finR_4", "finR_5", "finR_6", "heaR_1", "heaR_2", "heaR_3", "heaR_4", "heaR_5", "heaR_6", "recR_1", "recR_2", "recR_3", "recR_4", "recR_5", "recR_6", "socR_1", "socR_2", "socR_3", "socR_4", "socR_5", "socR_6" ], "rows": 3123, "table": true, "tojson": true }, { "name": "GRiPS_raw", "title": "GRiPS_raw", "object": "GRiPS_raw", "class": [ "data.frame" ], "fields": [ "fun", "friends", "enjoy", "hurt", "part", "commonly", "chances", "attracted" ], "rows": 810, "table": true, "tojson": true }, { "name": "IDS2_R", "title": "Intelligence subtests from the Intelligence and Development Scales-2", "object": "IDS2_R", "class": [ "matrix", "array" ], "fields": [ "GS", "PL", "TC", "CB", "NL", "NLM", "GF", "RGF", "CM", "EP", "CA", "OP", "RS", "DP" ], "rows": 14, "table": true, "tojson": true }, { "name": "population_models", "title": "population_models", "object": "population_models", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "RiskDimensions", "title": "RiskDimensions", "object": "RiskDimensions", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "SPSS_23", "title": "Various outputs from SPSS (version 23) FACTOR", "object": "SPSS_23", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "SPSS_27", "title": "Various outputs from SPSS (version 27) FACTOR", "object": "SPSS_27", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "test_models", "title": "Four test models used in Grieder and Steiner (2022)", "object": "test_models", "class": [ "list" ], "fields": [], "table": true, "tojson": true }, { "name": "UPPS_raw", "title": "UPPS_raw", "object": "UPPS_raw", "class": [ "data.frame" ], "fields": [ "perseverance_1", "perseverance_2", "perseverance_3", "perseverance_4", "perseverance_5", "perseverance_6", "perseverance_7", "perseverance_8", "perseverance_9", "perseverance_10", "premeditation_1", "premeditation_2", "premeditation_3", "premeditation_4", "premeditation_5", "premeditation_6", "premeditation_7", "premeditation_8", "premeditation_9", "premeditation_10", "premeditation_11", "ss_1", "ss_2", "ss_3", "ss_4", "ss_5", "ss_6", "ss_7", "ss_8", "ss_9", "ss_10", "ss_11", "ss_12", "urgency_1", "urgency_2", "urgency_3", "urgency_4", "urgency_5", "urgency_6", "urgency_7", "urgency_8", "urgency_9", "urgency_10", "urgency_11", "urgency_12" ], "rows": 645, "table": true, "tojson": true }, { "name": "WJIV_ages_14_19", "title": "Woodcock Johnson IV: ages 14 to 19", "object": "WJIV_ages_14_19", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "WJIV_ages_20_39", "title": "Woodcock Johnson IV: ages 20 to 39", "object": "WJIV_ages_20_39", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "WJIV_ages_3_5", "title": "Woodcock Johnson IV: ages 3 to 5", "object": "WJIV_ages_3_5", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "WJIV_ages_40_90", "title": "Woodcock Johnson IV: ages 40 to 90 plus", "object": "WJIV_ages_40_90", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "WJIV_ages_6_8", "title": "Woodcock Johnson IV: ages 6 to 8", "object": "WJIV_ages_6_8", "class": [ "list" ], "fields": [], "table": false, "tojson": true }, { "name": "WJIV_ages_9_13", "title": "Woodcock Johnson IV: ages 9 to 13", "object": "WJIV_ages_9_13", "class": [ "list" ], "fields": [], "table": false, "tojson": true } ], "_help": [ { "page": "BARTLETT", "title": "Bartlett's test of sphericity", "topics": [ "BARTLETT" ] }, { "page": "CD", "title": "Comparison data", "topics": [ "CD" ] }, { "page": "COMPARE", "title": "Compare two vectors or matrices (communalities or loadings)", "topics": [ "COMPARE" ] }, { "page": "DOSPERT", "title": "DOSPERT", "topics": [ "DOSPERT" ] }, { "page": "DOSPERT_raw", "title": "DOSPERT_raw", "topics": [ "DOSPERT_raw" ] }, { "page": "EFA", "title": "Exploratory factor analysis (EFA)", "topics": [ "EFA" ] }, { "page": "efa_average", "title": "Model averaging across different EFA estimators and types", "concept": [ "factor analysis" ], "topics": [ "efa_average" ] }, { "page": "EFA_AVERAGE-superseded", "title": "Model averaging across different EFA methods and types", "topics": [ "EFA_AVERAGE" ] }, { "page": "efa_bartlett", "title": "Bartlett's test of sphericity", "concept": [ "factor analysis suitability" ], "topics": [ "efa_bartlett" ] }, { "page": "efa_cd", "title": "Comparison data", "concept": [ "factor retention criteria" ], "topics": [ "efa_cd" ] }, { "page": "efa_compare", "title": "Compare two vectors or matrices (communalities or loadings)", "concept": [ "factor comparison" ], "topics": [ "efa_compare" ] }, { "page": "efa_ekc", "title": "Empirical Kaiser criterion", "concept": [ "factor retention criteria" ], "topics": [ "efa_ekc" ] }, { "page": "efa_fit", "title": "Exploratory factor analysis (EFA)", "concept": [ "factor analysis" ], "topics": [ "efa_fit" ] }, { "page": "efa_group", "title": "Multigroup exploratory factor analysis", "concept": [ "factor analysis" ], "topics": [ "efa_group" ] }, { "page": "efa_hull", "title": "Hull method for determining the number of factors to retain", "concept": [ "factor retention criteria" ], "topics": [ "efa_hull" ] }, { "page": "efa_kgc", "title": "Kaiser-Guttman criterion", "concept": [ "factor retention criteria" ], "topics": [ "efa_kgc" ] }, { "page": "efa_kmo", "title": "Kaiser-Meyer-Olkin criterion", "concept": [ "factor analysis suitability" ], "topics": [ "efa_kmo" ] }, { "page": "efa_map", "title": "Velicer's minimum average partial (MAP) criterion", "concept": [ "factor retention criteria" ], "topics": [ "efa_map" ] }, { "page": "efa_mi", "title": "Exploratory factor analysis on multiple data imputations", "concept": [ "factor analysis" ], "topics": [ "efa_mi" ] }, { "page": "efa_nest", "title": "Next eigenvalue sufficiency test (NEST)", "concept": [ "factor retention criteria" ], "topics": [ "efa_nest" ] }, { "page": "efa_parallel", "title": "Parallel analysis", "concept": [ "factor retention criteria" ], "topics": [ "efa_parallel" ] }, { "page": "EFA_POOLED", "title": "Exploratory factor analysis on multiple data imputations", "topics": [ "EFA_POOLED" ] }, { "page": "efa_power", "title": "Power analysis for exploratory factor analysis", "concept": [ "power analysis" ], "topics": [ "efa_power" ] }, { "page": "efa_procrustes", "title": "Rotate a loading matrix to a target using Procrustes alignment", "concept": [ "factor rotation" ], "topics": [ "efa_procrustes" ] }, { "page": "efa_reliability", "title": "Reliability coefficients for a factor solution", "concept": [ "reliability coefficients" ], "topics": [ "efa_reliability" ] }, { "page": "efa_retain", "title": "Various factor retention criteria", "concept": [ "factor retention criteria" ], "topics": [ "efa_retain" ] }, { "page": "efa_schmid_leiman", "title": "Schmid-Leiman transformation", "concept": [ "factor rotation", "reliability coefficients" ], "topics": [ "efa_schmid_leiman" ] }, { "page": "efa_scores", "title": "Estimate factor scores and score-quality diagnostics for an EFA model", "concept": [ "factor scoring" ], "topics": [ "efa_scores" ] }, { "page": "efa_scree", "title": "Scree plot", "concept": [ "factor retention criteria" ], "topics": [ "efa_scree" ] }, { "page": "efa_screen", "title": "Screen data for exploratory factor analysis", "concept": [ "factor analysis suitability" ], "topics": [ "efa_screen" ] }, { "page": "efa_simulate", "title": "Simulate data from a common-factor population model", "concept": [ "data simulation" ], "topics": [ "efa_simulate" ] }, { "page": "efa_smt", "title": "Sequential chi square model tests, RMSEA lower bound, and AIC", "concept": [ "factor retention criteria" ], "topics": [ "efa_smt" ] }, { "page": "EKC", "title": "Empirical Kaiser criterion", "topics": [ "EKC" ] }, { "page": "estimate_control", "title": "Control objects for estimation and rotation settings", "concept": [ "Control functions" ], "topics": [ "estimate_control", "rotate_control" ] }, { "page": "FACTOR_SCORES", "title": "Estimate factor scores for an EFA model", "topics": [ "FACTOR_SCORES" ] }, { "page": "format.efa_retain", "title": "Format method for efa_retain objects", "topics": [ "format.efa_retain" ] }, { "page": "format.efa_retention", "title": "Format method for efa_retention objects", "topics": [ "format.efa_retention" ] }, { "page": "GRiPS_raw", "title": "GRiPS_raw", "topics": [ "GRiPS_raw" ] }, { "page": "HULL", "title": "Hull method", "topics": [ "HULL" ] }, { "page": "IDS2_R", "title": "Intelligence subtests from the Intelligence and Development Scales-2", "topics": [ "IDS2_R" ] }, { "page": "KGC", "title": "Kaiser-Guttman criterion", "topics": [ "KGC" ] }, { "page": "KMO", "title": "Kaiser-Meyer-Olkin criterion", "topics": [ "KMO" ] }, { "page": "MAP", "title": "Minimum average partial", "topics": [ "MAP" ] }, { "page": "N_FACTORS", "title": "Various factor retention criteria", "topics": [ "N_FACTORS" ] }, { "page": "NEST", "title": "Next eigenvalue sufficiency test", "topics": [ "NEST" ] }, { "page": "OMEGA", "title": "McDonald's omega", "topics": [ "OMEGA" ] }, { "page": "PARALLEL", "title": "Parallel analysis", "topics": [ "PARALLEL" ] }, { "page": "plot.efa_average", "title": "Plot efa_average object", "topics": [ "plot.efa_average" ] }, { "page": "plot.efa_compare", "title": "Plot efa_compare object", "topics": [ "plot.efa_compare" ] }, { "page": "plot.efa_group", "title": "Plot a multigroup factor analysis", "concept": [ "factor analysis" ], "topics": [ "plot.efa_group" ] }, { "page": "plot.efa_power", "title": "Plot the RMSEA power curve", "concept": [ "power analysis" ], "topics": [ "plot.efa_power" ] }, { "page": "plot.efa_retain", "title": "Plot method for efa_retain objects", "topics": [ "plot.efa_retain" ] }, { "page": "plot.efa_retention", "title": "Plot method for efa_retention objects", "topics": [ "plot.efa_retention" ] }, { "page": "population_models", "title": "population_models", "topics": [ "population_models" ] }, { "page": "print.efa", "title": "Print and summarise an efa object", "topics": [ "format.efa", "format.efa_mi", "format.summary.efa", "print.efa", "print.efa_mi", "print.summary.efa", "summary.efa", "summary.efa_mi" ] }, { "page": "print.efa_average", "title": "Print and format an efa_average object", "topics": [ "format.efa_average", "print.efa_average" ] }, { "page": "print.efa_bartlett", "title": "Print and format an efa_bartlett object", "topics": [ "format.efa_bartlett", "print.efa_bartlett" ] }, { "page": "print.efa_compare", "title": "Print and format an efa_compare object", "topics": [ "format.efa_compare", "print.efa_compare" ] }, { "page": "print.efa_control", "title": "Print and format a control object", "concept": [ "Control functions" ], "topics": [ "format.efa_estimate_control", "format.efa_rotate_control", "print.efa_control", "print.efa_estimate_control", "print.efa_rotate_control" ] }, { "page": "print.efa_group", "title": "Print and format a multigroup factor analysis", "concept": [ "factor analysis" ], "topics": [ "format.efa_group", "print.efa_group" ] }, { "page": "print.efa_kmo", "title": "Print and format an efa_kmo object", "topics": [ "format.efa_kmo", "print.efa_kmo" ] }, { "page": "print.efa_loadings", "title": "Print a loading matrix", "topics": [ "format.efa_loadings", "print.efa_loadings" ] }, { "page": "print.efa_power", "title": "Print and format an efa_power object", "concept": [ "power analysis" ], "topics": [ "format.efa_power", "print.efa_power" ] }, { "page": "print.efa_reliability", "title": "Print and format a reliability object", "concept": [ "reliability coefficients" ], "topics": [ "format.efa_reliability", "print.efa_reliability" ] }, { "page": "print.efa_retain", "title": "Print method for efa_retain objects", "topics": [ "print.efa_retain" ] }, { "page": "print.efa_retention", "title": "Print method for efa_retention objects", "topics": [ "print.efa_retention" ] }, { "page": "print.efa_schmid_leiman", "title": "Print and format an efa_schmid_leiman object", "topics": [ "format.efa_schmid_leiman", "print.efa_schmid_leiman" ] }, { "page": "print.efa_scores", "title": "Print and format an efa_scores object", "concept": [ "factor scoring" ], "topics": [ "format.efa_scores", "format.summary.efa_scores", "print.efa_scores", "print.summary.efa_scores", "summary.efa_scores" ] }, { "page": "print.efa_screen", "title": "Print and format an efa_screen object", "concept": [ "factor analysis suitability" ], "topics": [ "format.efa_screen", "print.efa_screen" ] }, { "page": "print.efa_simulated", "title": "Print and format an efa_simulated object", "concept": [ "data simulation" ], "topics": [ "format.efa_simulated", "print.efa_simulated" ] }, { "page": "print.efa_sl_loadings", "title": "Print an efa_sl_loadings object", "topics": [ "format.efa_sl_loadings", "print.efa_sl_loadings" ] }, { "page": "print.OMEGA", "title": "Print and format an OMEGA object", "topics": [ "format.OMEGA", "print.OMEGA" ] }, { "page": "PROCRUSTES", "title": "Rotate a loading matrix to a target using Procrustes alignment", "topics": [ "PROCRUSTES" ] }, { "page": "residuals.efa", "title": "Extract residuals from an efa object", "topics": [ "residuals.efa" ] }, { "page": "RiskDimensions", "title": "RiskDimensions", "topics": [ "RiskDimensions" ] }, { "page": "SCREE", "title": "Scree plot", "topics": [ "SCREE" ] }, { "page": "SL", "title": "Schmid-Leiman transformation", "topics": [ "SL" ] }, { "page": "SMT", "title": "Sequential model tests", "topics": [ "SMT" ] }, { "page": "SPSS_23", "title": "Various outputs from SPSS (version 23) FACTOR", "topics": [ "SPSS_23" ] }, { "page": "SPSS_27", "title": "Various outputs from SPSS (version 27) FACTOR", "topics": [ "SPSS_27" ] }, { "page": "test_models", "title": "Four test models used in Grieder and Steiner (2022)", "topics": [ "test_models" ] }, { "page": "UPPS_raw", "title": "UPPS_raw", "topics": [ "UPPS_raw" ] }, { "page": "WJIV_ages_14_19", "title": "Woodcock Johnson IV: ages 14 to 19", "topics": [ "WJIV_ages_14_19" ] }, { "page": "WJIV_ages_20_39", "title": "Woodcock Johnson IV: ages 20 to 39", "topics": [ "WJIV_ages_20_39" ] }, { "page": "WJIV_ages_3_5", "title": "Woodcock Johnson IV: ages 3 to 5", "topics": [ "WJIV_ages_3_5" ] }, { "page": "WJIV_ages_40_90", "title": "Woodcock Johnson IV: ages 40 to 90 plus", "topics": [ "WJIV_ages_40_90" ] }, { "page": "WJIV_ages_6_8", "title": "Woodcock Johnson IV: ages 6 to 8", "topics": [ "WJIV_ages_6_8" ] }, { "page": "WJIV_ages_9_13", "title": "Woodcock Johnson IV: ages 9 to 13", "topics": [ "WJIV_ages_9_13" ] } ], "_readme": "https://github.com/mdsteiner/efatools/raw/HEAD/README.md", "_rundeps": [ "backports", "checkmate", "cli", "clue", "cluster", "codetools", "cpp11", "digest", "farver", "future", "future.apply", "ggplot2", "globals", "glue", "GPArotation", "gtable", "isoband", "labeling", "lattice", "lifecycle", "listenv", "mnormt", "nlme", "parallelly", "progressr", "psych", "R6", "RColorBrewer", "Rcpp", "RcppArmadillo", "rlang", "roptim", "S7", "scales", "vctrs", "viridisLite", "withr" ], "_sysdeps": [ { "shlib": "liblapack", "package": "libopenblas0-pthread", "source": "openblas", "version": "0.3.32+ds-5", "name": "openblas", "homepage": "https://www.openblas.net/", "description": "Optimized BLAS (linear algebra) library (shared lib, pthread)" }, { "shlib": "libblas", "package": "libopenblas0-pthread", "source": "openblas", "version": "0.3.32+ds-5", "name": "openblas", "homepage": "https://www.openblas.net/", "description": "Optimized BLAS (linear algebra) library (shared lib, pthread)" }, { "shlib": "libstdc++", "package": "libstdc++6", "source": "gcc", "version": "16-20260322-1ubuntu1", "name": "c++", "homepage": "http://gcc.gnu.org/", "description": "GNU Standard C++ Library v3" } ], "_vignettes": [ { "source": "Ordinal_and_missing_data.Rmd", "filename": "Ordinal_and_missing_data.html", "title": "EFA with ordinal and missing data", "engine": "knitr::rmarkdown", "headings": [ "Ordinal Data", "Screening Ordinal Data", "Polychoric Correlations, DWLS, and Robust Standard Errors", "Why Not Just Treat the Items as Continuous?", "Missing Data", "Two-Stage Full-Information Maximum Likelihood", "Multiple Imputation with efa_mi()", "Ordinal and Missing Data", "The Effective Sample of a Polychoric DWLS Fit", "Keeping Both the Ordinal Treatment and the Cases", "Where to Next" ], "created": "2026-07-19 10:12:46", "modified": "2026-10-16 05:40:54", "commits": 10 }, { "source": "EFAtools.Rmd", "filename": "EFAtools.html", "title": "EFAtools", "engine": "knitr::rmarkdown", "headings": [ "Screening the Data", "Deciding How Many Factors to Retain", "Calling Individual Criteria", "Running Several Criteria at Once with efa_retain()", "Extracting Factors with efa_fit()", "Comparing Solutions with efa_compare()", "Averaging Across Analytic Choices with efa_average()", "Factor Scores with efa_scores()", "Schmid-Leiman Transformation and McDonald's Omegas", "Schmid-Leiman Transformation", "McDonald's Omegas", "Where to Next" ], "created": "2020-06-22 13:51:12", "modified": "2026-10-16 05:40:54", "commits": 34 }, { "source": "Migrating_to_efa.Rmd", "filename": "Migrating_to_efa.html", "title": "Migrating to the efa_* interface", "engine": "knitr::rmarkdown", "headings": [ "Why the efa_* Interface", "The Old-to-New Mapping", "Migrating from EFA() to efa_fit()", "What the type Presets Replicate", "Returned Objects Keep Their Legacy Classes", "Where to Next" ], "created": "2026-07-19 10:12:46", "modified": "2026-10-16 05:40:54", "commits": 8 } ], "_score": 9.274258944947679, "_indexed": true, "_nocasepkg": "efatools", "_universes": [ "mdsteiner" ], "_binaries": [ { "r": "4.7.0", "os": "linux", "version": "1.1.0.9000", "date": "2026-10-17T05:21:20.000Z", "distro": "resolute", "arch": "aarch64", "commit": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "fileid": "https://r2.ropensci.org/5721114dac06077fa4fd473c26acc6a73ac7614502b6874de6d6e51f53e6a540", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/mdsteiner/actions/runs/39005449707" }, { "r": "4.7.0", "os": "linux", "version": "1.1.0.9000", "date": "2026-10-17T05:21:55.000Z", "distro": "resolute", "arch": "x86_64", "commit": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "fileid": "https://r2.ropensci.org/f95c58b9a08a1ec73906c5a9e1233fb987ee012a0d5e2756ffca7c2acf4cb20d", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/mdsteiner/actions/runs/39005449707" }, { "r": "4.6.1", "os": "linux", "version": "1.1.0.9000", "date": "2026-10-17T05:20:37.000Z", "distro": "resolute", "arch": "aarch64", "commit": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "fileid": "https://r2.ropensci.org/f18ac2ffcb1f207e24e51b454c30d7e98117bac9337586a930d636757744dc6e", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/mdsteiner/actions/runs/39005449707" }, { "r": "4.6.1", "os": "linux", "version": "1.1.0.9000", "date": "2026-10-17T05:21:29.000Z", "distro": "resolute", "arch": "x86_64", "commit": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "fileid": "https://r2.ropensci.org/37a389d725de3eeeaa6512cab04096bbaec1c7612f83cd8e7ca49d5922a9060c", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/mdsteiner/actions/runs/39005449707" }, { "r": "4.5.3", "os": "mac", "version": "1.1.0.9000", "date": "2026-10-17T05:21:38.000Z", "arch": "aarch64", "commit": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "fileid": "https://r2.ropensci.org/ca7f1f04590b7d24d45d0bc8d6501141f93435bf0461286869fc97198131fb0c", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/mdsteiner/actions/runs/39005449707" }, { "r": "4.5.3", "os": "mac", "version": "1.1.0.9000", "date": "2026-10-17T05:23:37.000Z", "arch": "x86_64", "commit": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "fileid": "https://r2.ropensci.org/afd9b7dac6e1de7649e415598541ad852d5e7aa95122a2c3bf078ab10f75a374", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/mdsteiner/actions/runs/39005449707" }, { "r": "4.6.1", "os": "mac", "version": "1.1.0.9000", "date": "2026-10-17T05:21:44.000Z", "arch": "aarch64", "commit": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "fileid": "https://r2.ropensci.org/a8721ef9e53fecfef0b63e5d16b762d52e46ee12db7e47456b9fca34f250b9cf", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/mdsteiner/actions/runs/39005449707" }, { "r": "4.6.1", "os": "mac", "version": "1.1.0.9000", "date": "2026-10-17T05:23:09.000Z", "arch": "x86_64", "commit": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "fileid": "https://r2.ropensci.org/32c8e5a7db7f405cc28eacc0666a29629f68966b066b82b123d90543791d029f", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/mdsteiner/actions/runs/39005449707" }, { "r": "4.6.0", "os": "wasm", "version": "1.1.0.9000", "date": "2026-10-17T05:20:48.000Z", "arch": "emscripten", "commit": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "fileid": "https://r2.ropensci.org/5cfd34ac113e26dcbb2b9f1c679b8593416a4b09526ac4cdf80b1ba208f86a31", "status": "success", "buildurl": "https://github.com/r-universe/mdsteiner/actions/runs/39005449707" }, { "r": "4.7.0", "os": "win", "version": "1.1.0.9000", "date": "2026-10-17T05:23:50.000Z", "arch": "aarch64", "commit": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "fileid": "https://r2.ropensci.org/2cf2989cce3cab4a8bea8bdc9a4ce7678ef7649d5139c8031bd2c2cc5dff7a78", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/mdsteiner/actions/runs/39005449707" }, { "r": "4.7.0", "os": "win", "version": "1.1.0.9000", "date": "2026-10-17T05:19:56.000Z", "arch": "x86_64", "commit": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "fileid": "https://r2.ropensci.org/c90f1726087ad25d43db3593c14cbd3e7fc375c4fd9bf4974ad8a5b9d0f5d67e", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/mdsteiner/actions/runs/39005449707" }, { "r": "4.5.3", "os": "win", "version": "1.1.0.9000", "date": "2026-10-17T05:19:56.000Z", "arch": "x86_64", "commit": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "fileid": "https://r2.ropensci.org/3be9cf0adbe49c0249ad83be0533a34a19ada1ca7be5a594a43e5c3ce2adc204", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/mdsteiner/actions/runs/39005449707" }, { "r": "4.6.1", "os": "win", "version": "1.1.0.9000", "date": "2026-10-17T05:23:45.000Z", "arch": "aarch64", "commit": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "fileid": "https://r2.ropensci.org/a99e145c274ea56b1faf7d898e68dfff8a18f3fe9e83de4f5de152ce5e5e98d9", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/mdsteiner/actions/runs/39005449707" }, { "r": "4.6.1", "os": "win", "version": "1.1.0.9000", "date": "2026-10-17T05:19:59.000Z", "arch": "x86_64", "commit": "3bf2db05f4add4ede14867f2372fdd3670a86dba", "fileid": "https://r2.ropensci.org/d1b188e488402fbb78d25aabef2a7bb14dfc12e224949a259c704948b8fbb49a", "status": "failure", "check": "ERROR", "buildurl": "https://github.com/r-universe/mdsteiner/actions/runs/39005449707" } ] }