{
  "_id": "6a1f25a9b401979e734219b9",
  "Package": "psych",
  "Version": "2.6.5",
  "Date": "2026-05-11",
  "Title": "Procedures for Psychological, Psychometric, and Personality\nResearch",
  "Authors@R": "person(\"William\", \"Revelle\", role =c(\"aut\",\"cre\"), email=\"revelle@northwestern.edu\", comment=c(ORCID  = \"0000-0003-4880-9610\") )",
  "Description": "A general purpose toolbox developed originally for\npersonality, psychometric theory and experimental psychology.\nFunctions are primarily for multivariate analysis and scale\nconstruction using factor analysis, principal component\nanalysis, cluster analysis and reliability analysis, although\nothers provide basic descriptive statistics. Item Response\nTheory is done using factor analysis of tetrachoric and\npolychoric correlations. Functions for analyzing data at\nmultiple levels include within and between group statistics,\nincluding correlations and factor analysis.  Validation and\ncross validation of scales developed using basic machine\nlearning algorithms are provided, as are functions for\nsimulating and testing particular item and test structures.\nSeveral functions serve as a useful front end for structural\nequation modeling.  Graphical displays of path diagrams,\nincluding mediation models, factor analysis and structural\nequation models are created using basic graphics. Some of the\nfunctions are written to support a book on psychometric theory\nas well as publications in personality research. For more\ninformation, see the <https://personality-project.org/r/> web\npage.",
  "License": "GPL (>= 2)",
  "LazyData": "yes",
  "ByteCompile": "true",
  "VignetteBuilder": "knitr",
  "URL": "https://personality-project.org/r/psych/\nhttps://personality-project.org/r/psych-manual.pdf",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-05-16 06:04:35 UTC",
    "User": "root"
  },
  "Author": "William Revelle [aut, cre] (ORCID:\n<https://orcid.org/0000-0003-4880-9610>)",
  "Maintainer": "William Revelle <revelle@northwestern.edu>",
  "Repository": "https://revelle.r-universe.dev",
  "Date/Publication": "2026-05-16 04:06:58 UTC",
  "RemoteUrl": "https://github.com/cran/psych",
  "RemoteRef": "HEAD",
  "RemoteSha": "2b17d1e78b536c0e4cc807d8a83f546e0a0da85d",
  "MD5sum": "ad174683ed300b2239d384204a6c35c0",
  "_user": "revelle",
  "_type": "src",
  "_file": "psych_2.6.5.tar.gz",
  "_fileid": "06aad377936f33f2b750fad910cfd764b33e604a5835aacb0657c3c4c0db67b0",
  "_filesize": 3333757,
  "_sha256": "06aad377936f33f2b750fad910cfd764b33e604a5835aacb0657c3c4c0db67b0",
  "_created": "2026-05-16T06:04:35.000Z",
  "_published": "2026-06-02T18:49:13.798Z",
  "_distro": "noble",
  "_jobs": [
    {
      "job": 79147271859,
      "time": 244,
      "config": "linux-devel-x86_64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "7031073747"
    },
    {
      "job": 79147271838,
      "time": 251,
      "config": "linux-release-x86_64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7031074467"
    },
    {
      "job": 79147271773,
      "time": 264,
      "config": "macos-oldrel-arm64",
      "r": "4.5.3",
      "check": "OK",
      "artifact": "7031129496"
    },
    {
      "job": 79147271798,
      "time": 225,
      "config": "macos-release-arm64",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7031126098"
    },
    {
      "job": 79147271250,
      "time": 342,
      "config": "source",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7031044682"
    },
    {
      "job": 79147271256,
      "time": 106,
      "config": "wasm-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7366910841"
    },
    {
      "job": 79147271588,
      "time": 234,
      "config": "windows-devel",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "7031072819"
    },
    {
      "job": 79147272070,
      "time": 230,
      "config": "windows-oldrel",
      "r": "4.5.3",
      "check": "OK",
      "artifact": "7031072376"
    },
    {
      "job": 79147272073,
      "time": 219,
      "config": "windows-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "7031070883"
    }
  ],
  "_buildurl": "https://github.com/r-universe/revelle/actions/runs/25954446202",
  "_status": "success",
  "_host": "GitHub-Actions",
  "_upstream": "https://github.com/cran/psych",
  "_commit": {
    "id": "2b17d1e78b536c0e4cc807d8a83f546e0a0da85d",
    "author": "William Revelle <revelle@northwestern.edu>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 2.6.5\n",
    "time": 1778904418
  },
  "_maintainer": {
    "name": "William Revelle",
    "email": "revelle@northwestern.edu",
    "login": "revelle",
    "uuid": 11491332,
    "orcid": "0000-0003-4880-9610"
  },
  "_registered": true,
  "_dependencies": [
    {
      "package": "mnormt",
      "role": "Imports"
    },
    {
      "package": "parallel",
      "role": "Imports"
    },
    {
      "package": "stats",
      "role": "Imports"
    },
    {
      "package": "graphics",
      "role": "Imports"
    },
    {
      "package": "grDevices",
      "role": "Imports"
    },
    {
      "package": "methods",
      "role": "Imports"
    },
    {
      "package": "lattice",
      "role": "Imports"
    },
    {
      "package": "nlme",
      "role": "Imports"
    },
    {
      "package": "GPArotation",
      "role": "Imports"
    },
    {
      "package": "psychTools",
      "role": "Suggests"
    },
    {
      "package": "lavaan",
      "role": "Suggests"
    },
    {
      "package": "lme4",
      "role": "Suggests"
    },
    {
      "package": "Rcsdp",
      "role": "Suggests"
    },
    {
      "package": "graph",
      "role": "Suggests"
    },
    {
      "package": "knitr",
      "role": "Suggests"
    },
    {
      "package": "Rgraphviz",
      "role": "Suggests"
    }
  ],
  "_owner": "cran",
  "_selfowned": true,
  "_usedby": 348,
  "_updates": [
    {
      "week": "2025-26",
      "n": 1
    },
    {
      "week": "2026-06",
      "n": 1
    },
    {
      "week": "2026-13",
      "n": 1
    },
    {
      "week": "2026-20",
      "n": 1
    }
  ],
  "_tags": [
    {
      "name": "2.5.6",
      "date": "2025-06-23"
    },
    {
      "name": "2.6.1",
      "date": "2026-02-03"
    },
    {
      "name": "2.6.3",
      "date": "2026-03-25"
    },
    {
      "name": "2.6.5",
      "date": "2026-05-16"
    }
  ],
  "_stars": 57,
  "_contributors": [
    {
      "user": "revelle",
      "count": 97,
      "uuid": 11491332
    }
  ],
  "_userbio": {
    "uuid": 11491332,
    "type": "user",
    "name": "revelle"
  },
  "_downloads": {
    "count": 283252,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/psych"
  },
  "_mentions": 1085,
  "_searchresults": 38528,
  "_rbuild": "4.6.0",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/psych.html",
    "manual.pdf"
  ],
  "_realowner": "revelle",
  "_cranurl": false,
  "_releases": [
    {
      "version": "1.0-17",
      "date": "2007-05-06"
    },
    {
      "version": "1.0-18",
      "date": "2007-05-10"
    },
    {
      "version": "1.0-23",
      "date": "2007-05-29"
    },
    {
      "version": "1.0-25",
      "date": "2007-06-04"
    },
    {
      "version": "1.0-27",
      "date": "2007-08-03"
    },
    {
      "version": "1.0-33",
      "date": "2007-10-11"
    },
    {
      "version": "1.0-42",
      "date": "2008-03-24"
    },
    {
      "version": "1.0-51",
      "date": "2008-07-12"
    },
    {
      "version": "1.0-58",
      "date": "2008-09-10"
    },
    {
      "version": "1.0-61",
      "date": "2009-01-05"
    },
    {
      "version": "1.0-62",
      "date": "2009-01-05"
    },
    {
      "version": "1.0-63",
      "date": "2009-01-25"
    },
    {
      "version": "1.0-65",
      "date": "2009-02-08"
    },
    {
      "version": "1.0-67",
      "date": "2009-03-27"
    },
    {
      "version": "1.0-70",
      "date": "2009-05-25"
    },
    {
      "version": "1.0-72",
      "date": "2009-06-02"
    },
    {
      "version": "1.0-74",
      "date": "2009-06-26"
    },
    {
      "version": "1.0-75",
      "date": "2009-07-01"
    },
    {
      "version": "1.0-76",
      "date": "2009-07-18"
    },
    {
      "version": "1.0-77",
      "date": "2009-07-27"
    },
    {
      "version": "1.0-78",
      "date": "2009-07-30"
    },
    {
      "version": "1.0-80",
      "date": "2009-09-30"
    },
    {
      "version": "1.0-81",
      "date": "2009-10-05"
    },
    {
      "version": "1.0-83",
      "date": "2009-10-26"
    },
    {
      "version": "1.0-85",
      "date": "2009-12-20"
    },
    {
      "version": "1.0-87",
      "date": "2010-04-06"
    },
    {
      "version": "1.0-88",
      "date": "2010-04-27"
    },
    {
      "version": "1.0-89",
      "date": "2010-06-28"
    },
    {
      "version": "1.0-90",
      "date": "2010-07-24"
    },
    {
      "version": "1.0-91",
      "date": "2010-09-15"
    },
    {
      "version": "1.0-92",
      "date": "2010-09-22"
    },
    {
      "version": "1.0-93",
      "date": "2010-12-23"
    },
    {
      "version": "1.0-94",
      "date": "2011-01-09"
    },
    {
      "version": "1.0-95",
      "date": "2011-03-31"
    },
    {
      "version": "1.0-96",
      "date": "2011-04-06"
    },
    {
      "version": "1.0-97",
      "date": "2011-05-15"
    },
    {
      "version": "1.0-98",
      "date": "2011-06-09"
    },
    {
      "version": "1.1.10",
      "date": "2011-10-17"
    },
    {
      "version": "1.1.11",
      "date": "2011-11-02"
    },
    {
      "version": "1.1.1111",
      "date": "2011-11-15"
    },
    {
      "version": "1.1.12",
      "date": "2012-01-02"
    },
    {
      "version": "1.2.1",
      "date": "2012-02-16"
    },
    {
      "version": "1.2.4",
      "date": "2012-09-17"
    },
    {
      "version": "1.2.8",
      "date": "2012-09-21"
    },
    {
      "version": "1.2.12",
      "date": "2013-01-20"
    },
    {
      "version": "1.3.2",
      "date": "2013-02-26"
    },
    {
      "version": "1.3.10",
      "date": "2013-10-07"
    },
    {
      "version": "1.3.10.12",
      "date": "2013-10-14"
    },
    {
      "version": "1.3.12",
      "date": "2013-12-12"
    },
    {
      "version": "1.4.1",
      "date": "2014-01-20"
    },
    {
      "version": "1.4.2",
      "date": "2014-02-03"
    },
    {
      "version": "1.4.2.3",
      "date": "2014-02-04"
    },
    {
      "version": "1.4.3",
      "date": "2014-03-25"
    },
    {
      "version": "1.4.4",
      "date": "2014-04-14"
    },
    {
      "version": "1.4.5",
      "date": "2014-05-14"
    },
    {
      "version": "1.4.8",
      "date": "2014-08-10"
    },
    {
      "version": "1.4.8.11",
      "date": "2014-08-12"
    },
    {
      "version": "1.5.1",
      "date": "2015-01-21"
    },
    {
      "version": "1.5.4",
      "date": "2015-04-27"
    },
    {
      "version": "1.5.6",
      "date": "2015-07-08"
    },
    {
      "version": "1.5.8",
      "date": "2015-08-30"
    },
    {
      "version": "1.6.4",
      "date": "2016-05-12"
    },
    {
      "version": "1.6.6",
      "date": "2016-06-28"
    },
    {
      "version": "1.6.9",
      "date": "2016-09-17"
    },
    {
      "version": "1.6.12",
      "date": "2017-01-08"
    },
    {
      "version": "1.7.3.21",
      "date": "2017-03-22"
    },
    {
      "version": "1.7.5",
      "date": "2017-05-03"
    },
    {
      "version": "1.7.8",
      "date": "2017-09-09"
    },
    {
      "version": "1.8.3.3",
      "date": "2018-03-30"
    },
    {
      "version": "1.8.4",
      "date": "2018-05-06"
    },
    {
      "version": "1.8.10",
      "date": "2018-10-31"
    },
    {
      "version": "1.8.12",
      "date": "2019-01-12"
    },
    {
      "version": "1.9.12",
      "date": "2019-12-20"
    },
    {
      "version": "1.9.12.31",
      "date": "2020-01-09"
    },
    {
      "version": "2.0.7",
      "date": "2020-07-26"
    },
    {
      "version": "2.0.8",
      "date": "2020-09-04"
    },
    {
      "version": "2.0.9",
      "date": "2020-10-05"
    },
    {
      "version": "2.0.12",
      "date": "2020-12-16"
    },
    {
      "version": "2.1.3",
      "date": "2021-03-27"
    },
    {
      "version": "2.1.6",
      "date": "2021-06-18"
    },
    {
      "version": "2.1.9",
      "date": "2021-09-22"
    },
    {
      "version": "2.2.3",
      "date": "2022-03-19"
    },
    {
      "version": "2.2.5",
      "date": "2022-05-10"
    },
    {
      "version": "2.2.9",
      "date": "2022-09-29"
    },
    {
      "version": "2.3.3",
      "date": "2023-03-18"
    },
    {
      "version": "2.3.6",
      "date": "2023-06-21"
    },
    {
      "version": "2.3.9",
      "date": "2023-09-26"
    },
    {
      "version": "2.3.12",
      "date": "2023-12-20"
    },
    {
      "version": "2.4.1",
      "date": "2024-01-18"
    },
    {
      "version": "2.4.3",
      "date": "2024-03-19"
    },
    {
      "version": "2.4.6.26",
      "date": "2024-06-27"
    },
    {
      "version": "2.4.12",
      "date": "2024-12-23"
    },
    {
      "version": "2.5.3",
      "date": "2025-03-22"
    },
    {
      "version": "2.5.6",
      "date": "2025-06-23"
    },
    {
      "version": "2.6.1",
      "date": "2026-02-03"
    },
    {
      "version": "2.6.3",
      "date": "2026-03-25"
    },
    {
      "version": "2.6.5",
      "date": "2026-05-16"
    }
  ],
  "_exports": [
    "%+%",
    "acs",
    "alpha",
    "alpha.ci",
    "alpha2r",
    "anova.psych",
    "AUC",
    "autoR",
    "bassAckward",
    "bassAckward.diagram",
    "bestItems",
    "bestScales",
    "bi.bars",
    "bifactor",
    "bigCor",
    "biPlot",
    "biplot.psych",
    "biquartimin",
    "biserial",
    "block.random",
    "cancorDiagram",
    "cd.validity",
    "CFA",
    "CFA.bifactor",
    "char2numeric",
    "chi2r",
    "circ.sim",
    "circ.sim.plot",
    "circ.simulation",
    "circ.tests",
    "circadian.cor",
    "circadian.F",
    "circadian.linear.cor",
    "circadian.mean",
    "circadian.phase",
    "circadian.reliability",
    "circadian.sd",
    "circadian.stats",
    "circular.cor",
    "circular.mean",
    "cluster.cor",
    "cluster.fit",
    "cluster.loadings",
    "cluster.plot",
    "cluster2keys",
    "cohen.d",
    "cohen.d.by",
    "cohen.d.ci",
    "cohen.kappa",
    "cohen.profile",
    "cohenPooled",
    "comorbidity",
    "con2cat",
    "congeneric.sim",
    "congruence",
    "cor.ci",
    "cor.plot",
    "cor.plot.upperLowerCi",
    "cor.smooth",
    "cor.smoother",
    "cor.wt",
    "cor2",
    "cor2cov",
    "cor2dist",
    "corCi",
    "corFiml",
    "corInfo",
    "corPlot",
    "corPlotUpperLowerCi",
    "corr.p",
    "corr.test",
    "correct.cor",
    "cortest",
    "corTest",
    "cortest.bartlett",
    "cortest.jennrich",
    "cortest.mat",
    "cortest.normal",
    "cosinor",
    "cosinor.period",
    "cosinor.plot",
    "count.pairwise",
    "crossValidation",
    "crossValidationBoot",
    "cs",
    "cta",
    "cta.15",
    "d.ci",
    "d.robust",
    "d2CL",
    "d2OVL",
    "d2OVL2",
    "d2r",
    "d2t",
    "d2U3",
    "densityBy",
    "describe",
    "describe.by",
    "describeBy",
    "describeData",
    "describeFast",
    "dia.arrow",
    "dia.cone",
    "dia.curve",
    "dia.curved.arrow",
    "dia.ellipse",
    "dia.ellipse1",
    "dia.rect",
    "dia.self",
    "dia.shape",
    "dia.triangle",
    "diagram",
    "directSl",
    "distance",
    "draw.cor",
    "draw.tetra",
    "dummy.code",
    "eigen.loadings",
    "eigenCi",
    "ellipses",
    "equamax",
    "error.bars",
    "error.bars.by",
    "error.bars.tab",
    "error.crosses",
    "error.dots",
    "errorCircles",
    "esem",
    "esem.diagram",
    "esemDiagram",
    "extension.diagram",
    "fa",
    "fa.congruence",
    "fa.diagram",
    "fa.extend",
    "fa.extension",
    "fa.graph",
    "fa.lookup",
    "fa.multi",
    "fa.multi.diagram",
    "fa.organize",
    "fa.parallel",
    "fa.parallel.poly",
    "fa.plot",
    "fa.poly",
    "fa.pooled",
    "fa.random",
    "fa.rgraph",
    "fa.sapa",
    "fa.sort",
    "fa.stats",
    "fa2irt",
    "faBy",
    "fac",
    "faCor",
    "factor.congruence",
    "factor.fit",
    "factor.minres",
    "factor.model",
    "factor.pa",
    "factor.plot",
    "factor.residuals",
    "factor.rotate",
    "factor.scores",
    "factor.stats",
    "factor.wls",
    "factor2cluster",
    "faReg",
    "faRegression",
    "faRotate",
    "faRotations",
    "fisherz",
    "fisherz2r",
    "fparse",
    "fromTo",
    "fsi",
    "g2r",
    "geometric.mean",
    "glb",
    "glb.algebraic",
    "glb.fa",
    "guttman",
    "harmonic.mean",
    "headtail",
    "headTail",
    "het.diagram",
    "histBy",
    "ICC",
    "iclust",
    "ICLUST",
    "ICLUST.cluster",
    "iclust.diagram",
    "ICLUST.graph",
    "ICLUST.rgraph",
    "iclust.sort",
    "ICLUST.sort",
    "interbattery",
    "interp.boxplot",
    "interp.median",
    "interp.q",
    "interp.qplot.by",
    "interp.quantiles",
    "interp.quart",
    "interp.quartiles",
    "interp.values",
    "irt.0p",
    "irt.1p",
    "irt.2p",
    "irt.CFA",
    "irt.discrim",
    "irt.fa",
    "irt.item.diff.rasch",
    "irt.person.rasch",
    "irt.responses",
    "irt.se",
    "irt.select",
    "irt.stats.like",
    "irt.tau",
    "isCorrelation",
    "isCovariance",
    "item.dichot",
    "item.lookup",
    "item.sim",
    "item.validity",
    "itemSort",
    "kaiser",
    "keys.lookup",
    "keys2list",
    "keysort",
    "KMO",
    "kurtosi",
    "lavaan.diagram",
    "lavParse",
    "levels2numeric",
    "lmCor",
    "lmCor.diagram",
    "lmCorLookup",
    "lmDiagram",
    "logistic",
    "logistic.grm",
    "logit",
    "lookup",
    "lookupFromKeys",
    "lookupItems",
    "lowerCor",
    "lowerMat",
    "lowerUpper",
    "m2d",
    "m2t",
    "make.congeneric",
    "make.hierarchical",
    "make.irt.stats",
    "make.keys",
    "makePositiveKeys",
    "manhattan",
    "mardia",
    "mat.regress",
    "mat.sort",
    "matMult",
    "matPlot",
    "matReg",
    "matSort",
    "mediate",
    "mediate.diagram",
    "minkowski",
    "mixed.cor",
    "mixedCor",
    "mlArrange",
    "mlPlot",
    "mlr",
    "moderate.diagram",
    "mssd",
    "multi.arrow",
    "multi.curved.arrow",
    "multi.hist",
    "multi.rect",
    "multi.self",
    "multilevel.reliability",
    "nchar2numeric",
    "nfactors",
    "omega",
    "omega.diagram",
    "omega.graph",
    "omegaDirect",
    "omegaFromSem",
    "omegah",
    "omegaSem",
    "omegaStats",
    "outlier",
    "p.rep",
    "p.rep.f",
    "p.rep.r",
    "p.rep.t",
    "paired.r",
    "pairs.panels",
    "pairwiseCount",
    "pairwiseCountBig",
    "pairwiseDescribe",
    "pairwiseImpute",
    "pairwisePlot",
    "pairwiseReport",
    "pairwiseSample",
    "pairwiseZero",
    "parcels",
    "partial.r",
    "paSelect",
    "pca",
    "phi",
    "phi.demo",
    "phi.list",
    "phi2poly",
    "phi2poly.matrix",
    "phi2tetra",
    "Pinv",
    "plot.irt",
    "plot.poly",
    "plot.poly.parallel",
    "plot.psych",
    "plot.reliability",
    "plot.residuals",
    "polar",
    "poly.mat",
    "polychoric",
    "polydi",
    "polyserial",
    "predict.psych",
    "predicted.validity",
    "principal",
    "print.psych",
    "Procrustes",
    "progressBar",
    "Promax",
    "psych",
    "psych.misc",
    "quickView",
    "r.con",
    "r.test",
    "r2alpha",
    "r2c",
    "r2chi",
    "r2d",
    "r2p",
    "r2t",
    "radar",
    "rangeCorrection",
    "reflect",
    "reliability",
    "removeMissing",
    "rescale",
    "resid.psych",
    "residuals.psych",
    "response.frequencies",
    "responseFrequency",
    "reverse.code",
    "RMSEA",
    "rmssd",
    "RV",
    "SAPAfy",
    "scaling.fits",
    "scatter.hist",
    "scatterHist",
    "schmid",
    "score.alpha",
    "score.irt",
    "score.irt.2",
    "score.irt.poly",
    "score.items",
    "score.multiple.choice",
    "scoreBy",
    "scoreFast",
    "scoreIrt",
    "scoreIrt.1pl",
    "scoreIrt.2pl",
    "scoreItems",
    "scoreOverlap",
    "scoreVeryFast",
    "scoreWtd",
    "scree",
    "scrub",
    "SD",
    "selectFromKeys",
    "sem.diagram",
    "sem.graph",
    "set.cor",
    "setCor",
    "shannon",
    "sim",
    "sim.anova",
    "sim.bonds",
    "sim.circ",
    "sim.congeneric",
    "sim.correlation",
    "sim.dichot",
    "sim.general",
    "sim.hierarchical",
    "sim.irt",
    "sim.item",
    "sim.minor",
    "sim.multi",
    "sim.multilevel",
    "sim.npl",
    "sim.npn",
    "sim.omega",
    "sim.parallel",
    "sim.poly",
    "sim.poly.ideal",
    "sim.poly.ideal.npl",
    "sim.poly.ideal.npn",
    "sim.poly.mat",
    "sim.poly.npl",
    "sim.poly.npn",
    "sim.rasch",
    "sim.simplex",
    "sim.spherical",
    "sim.structural",
    "sim.structure",
    "sim.VSS",
    "simCor",
    "simulation.circ",
    "skew",
    "smc",
    "spider",
    "splitHalf",
    "statsBy",
    "statsBy.boot",
    "statsBy.boot.summary",
    "structure.diagram",
    "structure.graph",
    "structure.list",
    "structure.sem",
    "summary.psych",
    "super.matrix",
    "superCor",
    "superMatrix",
    "t2d",
    "t2r",
    "table2df",
    "table2matrix",
    "tableF",
    "target.rot",
    "TargetQ",
    "TargetT",
    "tenberge",
    "test.all",
    "test.irt",
    "test.psych",
    "testRetest",
    "tetrachoric",
    "thurstone",
    "topBottom",
    "tr",
    "unidim",
    "validityItem",
    "varimin",
    "vech",
    "vgQ.bimin",
    "vgQ.targetQ",
    "vgQ.varimin",
    "violin",
    "violinBy",
    "vss",
    "VSS",
    "VSS.parallel",
    "VSS.plot",
    "VSS.scree",
    "VSS.sim",
    "VSS.simulate",
    "vssSelect",
    "winsor",
    "winsor.mean",
    "winsor.means",
    "winsor.sd",
    "winsor.var",
    "wkappa",
    "Yule",
    "Yule.inv",
    "Yule2phi",
    "Yule2phi.matrix",
    "Yule2poly",
    "Yule2poly.matrix",
    "Yule2tetra",
    "YuleBonett",
    "YuleCor"
  ],
  "_datasets": [
    {
      "name": "Bechtoldt",
      "title": "Seven data sets showing a bifactor solution.",
      "object": "Bechtoldt",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "First_Names",
        "Word_Number",
        "Sentences",
        "Vocabulary",
        "Completion",
        "First_Letters",
        "Four_letter_words",
        "Suffixes",
        "Flags",
        "Figures",
        "Cards",
        "Addition",
        "Multiplication",
        "Three_Higher",
        "Letter_Series",
        "Pedigrees",
        "Letter_Grouping"
      ],
      "rows": 17,
      "table": true,
      "tojson": true
    },
    {
      "name": "Bechtoldt.1",
      "title": "Seven data sets showing a bifactor solution.",
      "object": "Bechtoldt.1",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "First_Names",
        "Word_Number",
        "Sentences",
        "Vocabulary",
        "Completion",
        "First_Letters",
        "Four_letter_words",
        "Suffixes",
        "Flags",
        "Figures",
        "Cards",
        "Addition",
        "Multiplication",
        "Three_Higher",
        "Letter_Series",
        "Pedigrees",
        "Letter_Grouping"
      ],
      "rows": 17,
      "table": true,
      "tojson": true
    },
    {
      "name": "Bechtoldt.2",
      "title": "Seven data sets showing a bifactor solution.",
      "object": "Bechtoldt.2",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "First_Names",
        "Word_Number",
        "Sentences",
        "Vocabulary",
        "Completion",
        "First_Letters",
        "Four_letter_words",
        "Suffixes",
        "Flags",
        "Figures",
        "Cards",
        "Addition",
        "Multiplication",
        "Three_Higher",
        "Letter_Series",
        "Pedigrees",
        "Letter_Grouping"
      ],
      "rows": 17,
      "table": true,
      "tojson": true
    },
    {
      "name": "bfi",
      "title": "25 Personality items representing 5 factors",
      "object": "bfi",
      "class": [
        "data.frame"
      ],
      "fields": [
        "A1",
        "A2",
        "A3",
        "A4",
        "A5",
        "C1",
        "C2",
        "C3",
        "C4",
        "C5",
        "E1",
        "E2",
        "E3",
        "E4",
        "E5",
        "N1",
        "N2",
        "N3",
        "N4",
        "N5",
        "O1",
        "O2",
        "O3",
        "O4",
        "O5",
        "gender",
        "education",
        "age"
      ],
      "rows": 2800,
      "table": true,
      "tojson": true
    },
    {
      "name": "bfi.dictionary",
      "title": "25 Personality items representing 5 factors",
      "object": "bfi.dictionary",
      "class": [
        "data.frame"
      ],
      "fields": [
        "ItemLabel",
        "Item",
        "Giant3",
        "Big6",
        "Little12",
        "Keying",
        "IPIP100"
      ],
      "rows": 28,
      "table": true,
      "tojson": true
    },
    {
      "name": "bfi.keys",
      "title": "25 Personality items representing 5 factors",
      "object": "bfi",
      "class": [
        "list"
      ],
      "fields": [],
      "table": true,
      "tojson": true
    },
    {
      "name": "bock.table",
      "title": "Bock and Liberman (1970) data set of 1000 observations of the LSAT",
      "object": "bock",
      "class": [
        "data.frame"
      ],
      "fields": [
        "index",
        "Q1",
        "Q2",
        "Q3",
        "Q4",
        "Q5",
        "Ob6",
        "Ob7"
      ],
      "rows": 32,
      "table": true,
      "tojson": true
    },
    {
      "name": "cattell",
      "title": "12 cognitive variables from Cattell (1963)",
      "object": "cattell",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "Verbal",
        "Verbal2",
        "Space1",
        "Space2",
        "Reason1",
        "Reason2",
        "Number1",
        "Number2",
        "IPATSer",
        "IPATCLAS",
        "IPATMatr",
        "IPATTOP"
      ],
      "rows": 12,
      "table": true,
      "tojson": true
    },
    {
      "name": "Chen",
      "title": "12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation",
      "object": "Schmid",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "soc",
        "diff",
        "slo",
        "con",
        "for",
        "dcon",
        "tired",
        "ener",
        "worn",
        "pep",
        "calm",
        "blue",
        "hap",
        "nerv",
        "down",
        "afr",
        "frust",
        "wor"
      ],
      "rows": 18,
      "table": true,
      "tojson": true
    },
    {
      "name": "Dwyer",
      "title": "8 cognitive variables used by Dwyer for an example.",
      "object": "Dwyer",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "V1",
        "V2",
        "V3",
        "V4",
        "V5",
        "V6",
        "V7",
        "V8"
      ],
      "rows": 8,
      "table": true,
      "tojson": true
    },
    {
      "name": "Garcia",
      "title": "Data from the sexism (protest) study of Garcia, Schmitt, Branscome, and Ellemers (2010)",
      "object": "GSBE",
      "class": [
        "data.frame"
      ],
      "fields": [
        "protest",
        "sexism",
        "anger",
        "liking",
        "respappr",
        "prot2"
      ],
      "rows": 129,
      "table": true,
      "tojson": true
    },
    {
      "name": "Gleser",
      "title": "Example data from Gleser, Cronbach and Rajaratnam (1965) to show basic principles of generalizability theory.",
      "object": "Gleser",
      "class": [
        "data.frame"
      ],
      "fields": [
        "J11",
        "J12",
        "J21",
        "J22",
        "J31",
        "J32",
        "J41",
        "J42",
        "J51",
        "J52",
        "J61",
        "J62"
      ],
      "rows": 12,
      "table": true,
      "tojson": true
    },
    {
      "name": "Gorsuch",
      "title": "Example data set from Gorsuch (1997) for an example factor extension.",
      "object": "Gorsuch",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "info",
        "verbal",
        "analogies",
        "ego",
        "guilt",
        "tension",
        "info2",
        "tension2",
        "v123",
        "v564"
      ],
      "rows": 10,
      "table": true,
      "tojson": true
    },
    {
      "name": "Harman_5",
      "title": "Six data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt",
      "object": "Harman_5",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "V1",
        "V2",
        "V3",
        "V4",
        "V5"
      ],
      "rows": 5,
      "table": true,
      "tojson": true
    },
    {
      "name": "Harman.5",
      "title": "Six data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt",
      "object": "Harman.5",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "population",
        "schooling",
        "employment",
        "professional",
        "housevalue"
      ],
      "rows": 12,
      "table": true,
      "tojson": true
    },
    {
      "name": "Harman.8",
      "title": "Six data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt",
      "object": "Harman.8",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "Height",
        "Arm span",
        "Forearm",
        "Leg length",
        "Weight",
        "Hips",
        "Chest girth",
        "Chest width"
      ],
      "rows": 8,
      "table": true,
      "tojson": true
    },
    {
      "name": "Harman.Burt",
      "title": "Six data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt",
      "object": "Harman",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "Sociability",
        "Sorrow",
        "Tenderness",
        "Joy",
        "Wonder",
        "Disgust",
        "Anger",
        "Fear"
      ],
      "rows": 8,
      "table": true,
      "tojson": true
    },
    {
      "name": "Harman.Holzinger",
      "title": "Six data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt",
      "object": "Harman",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "Word_meaning",
        "Sentence_completion",
        "Odd_words",
        "Mixed_Arithmetic",
        "Remainders",
        "Missing_Numbers",
        "Gloves",
        "Boots",
        "Hatchets"
      ],
      "rows": 9,
      "table": true,
      "tojson": true
    },
    {
      "name": "Harman.political",
      "title": "Six data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt",
      "object": "Harman.political",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "Lewis",
        "Roosevelt",
        "Party Voting",
        "Median Rental",
        "Homeownership",
        "Unemployment",
        "Mobility",
        "Education"
      ],
      "rows": 8,
      "table": true,
      "tojson": true
    },
    {
      "name": "Holzinger",
      "title": "Seven data sets showing a bifactor solution.",
      "object": "Holzinger",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "T1",
        "T2",
        "T3.4",
        "T6",
        "T28",
        "T29",
        "T32",
        "T34",
        "T35",
        "T36a",
        "T13",
        "T18",
        "T25b",
        "T77"
      ],
      "rows": 14,
      "table": true,
      "tojson": true
    },
    {
      "name": "Holzinger.9",
      "title": "Seven data sets showing a bifactor solution.",
      "object": "Holzinger.9",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "vis_perc",
        "cubes",
        "lozenges",
        "par_comp",
        "sen_comp",
        "wordmean",
        "addition",
        "count_dot",
        "s_c_caps"
      ],
      "rows": 9,
      "table": true,
      "tojson": true
    },
    {
      "name": "lsat6",
      "title": "Bock and Liberman (1970) data set of 1000 observations of the LSAT",
      "object": "bock",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "Q1",
        "Q2",
        "Q3",
        "Q4",
        "Q5"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
    {
      "name": "lsat7",
      "title": "Bock and Liberman (1970) data set of 1000 observations of the LSAT",
      "object": "bock",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "Q1",
        "Q2",
        "Q3",
        "Q4",
        "Q5"
      ],
      "rows": 1000,
      "table": true,
      "tojson": true
    },
    {
      "name": "Reise",
      "title": "Seven data sets showing a bifactor solution.",
      "object": "Reise",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "phone",
        "routine",
        "illness",
        "listen",
        "explain",
        "respect",
        "time",
        "courtesy",
        "helpful",
        "happy",
        "referral",
        "necessary",
        "delay",
        "problem",
        "help",
        "paperwork"
      ],
      "rows": 16,
      "table": true,
      "tojson": true
    },
    {
      "name": "sat.act",
      "title": "3 Measures of ability: SATV, SATQ, ACT",
      "object": "sat.act",
      "class": [
        "data.frame"
      ],
      "fields": [
        "gender",
        "education",
        "age",
        "ACT",
        "SATV",
        "SATQ"
      ],
      "rows": 700,
      "table": true,
      "tojson": true
    },
    {
      "name": "Schmid",
      "title": "12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation",
      "object": "Schmid",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "V1",
        "V2",
        "V3",
        "V4",
        "V5",
        "V6",
        "V7",
        "V8",
        "V9",
        "V10",
        "V11",
        "V12"
      ],
      "rows": 12,
      "table": true,
      "tojson": true
    },
    {
      "name": "schmid.leiman",
      "title": "12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation",
      "object": "Schmid",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "V1",
        "V2",
        "V3",
        "V4",
        "V5",
        "V6",
        "V7",
        "V8",
        "V9",
        "V10",
        "V11",
        "V12"
      ],
      "rows": 12,
      "table": true,
      "tojson": true
    },
    {
      "name": "small.msq",
      "title": "A small example data set taken from a larger data set",
      "object": "small.msq",
      "class": [
        "data.frame"
      ],
      "fields": [
        "active",
        "alert",
        "aroused",
        "sleepy",
        "tired",
        "drowsy",
        "anxious",
        "jittery",
        "nervous",
        "calm",
        "relaxed",
        "at.ease",
        "gender",
        "drug"
      ],
      "rows": 200,
      "table": true,
      "tojson": true
    },
    {
      "name": "Tal_Or",
      "title": "Data set testing causal direction in presumed media influence",
      "object": "tal_or",
      "class": [
        "data.frame"
      ],
      "fields": [
        "cond",
        "pmi",
        "import",
        "reaction",
        "gender",
        "age"
      ],
      "rows": 123,
      "table": true,
      "tojson": true
    },
    {
      "name": "Tal.Or",
      "title": "Data set testing causal direction in presumed media influence",
      "object": "Tal.Or",
      "class": [
        "data.frame"
      ],
      "fields": [
        "cond",
        "pmi",
        "import",
        "reaction",
        "gender",
        "age"
      ],
      "rows": 123,
      "table": true,
      "tojson": true
    },
    {
      "name": "Thurstone",
      "title": "Seven data sets showing a bifactor solution.",
      "object": "Thurstone",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "Sentences",
        "Vocabulary",
        "Sent.Completion",
        "First.Letters",
        "Four.Letter.Words",
        "Suffixes",
        "Letter.Series",
        "Pedigrees",
        "Letter.Group"
      ],
      "rows": 9,
      "table": true,
      "tojson": true
    },
    {
      "name": "Thurstone.33",
      "title": "Seven data sets showing a bifactor solution.",
      "object": "Thurstone.33",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "Definitions",
        "Arithmetical_Problems",
        "Classification",
        "Artificial_Languange",
        "Antonyms",
        "Number_Series_Completion",
        "Analogies",
        "Logical_Inference",
        "Paragraph_Reading"
      ],
      "rows": 9,
      "table": true,
      "tojson": true
    },
    {
      "name": "Thurstone.33G",
      "title": "Seven data sets showing a bifactor solution.",
      "object": "Thurstone.33G",
      "class": [
        "matrix",
        "array"
      ],
      "fields": {},
      "rows": 9,
      "table": true,
      "tojson": true
    },
    {
      "name": "Thurstone.9",
      "title": "Seven data sets showing a bifactor solution.",
      "object": "Thurstone.9",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "Prefixes",
        "Suffixes",
        "Vocabulary",
        "Sentences",
        "First.Last",
        "FirstLetters",
        "FourLetters",
        "Completion",
        "SameorOpposite"
      ],
      "rows": 9,
      "table": true,
      "tojson": true
    },
    {
      "name": "Tucker",
      "title": "9 Cognitive variables discussed by Tucker and Lewis (1973)",
      "object": "Tucker",
      "class": [
        "data.frame"
      ],
      "fields": [
        "t42",
        "t54",
        "t45",
        "t46",
        "t23",
        "t24",
        "t27",
        "t10",
        "t51"
      ],
      "rows": 9,
      "table": true,
      "tojson": true
    },
    {
      "name": "West",
      "title": "12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation",
      "object": "Schmid",
      "class": [
        "matrix",
        "array"
      ],
      "fields": [
        "V1",
        "V2",
        "V3",
        "V4",
        "V5",
        "V6",
        "V7",
        "V8",
        "V9",
        "V10",
        "V11",
        "V12",
        "V13",
        "V14",
        "V15",
        "V16"
      ],
      "rows": 16,
      "table": true,
      "tojson": true
    },
    {
      "name": "withinBetween",
      "title": "An example of the distinction between within group and between group correlations",
      "object": "withinBetween",
      "class": [
        "data.frame"
      ],
      "fields": [
        "Group",
        "V1",
        "V2",
        "V3",
        "V4",
        "V5",
        "V6",
        "V7",
        "V8",
        "V9"
      ],
      "rows": 16,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "00.psych-package",
      "title": "A package for personality, psychometric, and psychological research",
      "topics": [
        "psych-package",
        "psych"
      ]
    },
    {
      "page": "alpha",
      "title": "Find two estimates of reliability: Cronbach's alpha and Guttman's Lambda 6.",
      "topics": [
        "alpha",
        "alpha.ci",
        "alpha.scale",
        "alpha2r",
        "r2alpha"
      ]
    },
    {
      "page": "anova.psych",
      "title": "Model comparison for regression, mediation, cluster and factor analysis",
      "topics": [
        "anova.psych"
      ]
    },
    {
      "page": "AUC",
      "title": "Decision Theory measures of specificity, sensitivity, and d prime",
      "topics": [
        "AUC",
        "auc",
        "Sensitivity",
        "Specificity"
      ]
    },
    {
      "page": "bassAckward",
      "title": "The Bass-Ackward factoring algorithm discussed by Goldberg",
      "topics": [
        "bassAckward",
        "bassAckward.diagram"
      ]
    },
    {
      "page": "bifactor",
      "title": "Seven data sets showing a bifactor solution.",
      "topics": [
        "Bechtoldt",
        "Bechtoldt.1",
        "Bechtoldt.2",
        "Holzinger",
        "Holzinger.9",
        "Reise",
        "Thurstone",
        "Thurstone.33",
        "Thurstone.33G",
        "Thurstone.9"
      ]
    },
    {
      "page": "best.scales",
      "title": "A bootstrap aggregation function for choosing most predictive unit weighted items",
      "topics": [
        "bestItems",
        "bestScales",
        "BISCUIT",
        "biscuit",
        "BISCWIT",
        "biscwit"
      ]
    },
    {
      "page": "bfi",
      "title": "25 Personality items representing 5 factors",
      "topics": [
        "bfi",
        "bfi.dictionary",
        "bfi.keys"
      ]
    },
    {
      "page": "bi.bars",
      "title": "Draw pairs of bargraphs based on two groups",
      "topics": [
        "bi.bars"
      ]
    },
    {
      "page": "bigCor",
      "title": "Find large correlation matrices by stitching together smaller ones found more rapidly",
      "topics": [
        "bigCor"
      ]
    },
    {
      "page": "biplot.psych",
      "title": "Draw biplots of factor or component scores by factor or component loadings",
      "topics": [
        "biPlot",
        "biplot.psych"
      ]
    },
    {
      "page": "block.random",
      "title": "Create a block randomized structure for n independent variables",
      "topics": [
        "block.random"
      ]
    },
    {
      "page": "bock.table",
      "title": "Bock and Liberman (1970) data set of 1000 observations of the LSAT",
      "topics": [
        "bock",
        "bock.lsat",
        "bock.table",
        "lsat6",
        "lsat7"
      ]
    },
    {
      "page": "cattell",
      "title": "12 cognitive variables from Cattell (1963)",
      "topics": [
        "cattell"
      ]
    },
    {
      "page": "cfa",
      "title": "Confirmatory Factor Analysis Without Iteration",
      "topics": [
        "CFA",
        "CFA.bifactor",
        "irt.CFA"
      ]
    },
    {
      "page": "circ.tests",
      "title": "Apply four tests of circumplex versus simple structure",
      "topics": [
        "circ.tests"
      ]
    },
    {
      "page": "cluster.fit",
      "title": "cluster Fit: fit of the cluster model to a correlation matrix",
      "topics": [
        "cluster.fit"
      ]
    },
    {
      "page": "cluster.loadings",
      "title": "Find item by cluster correlations, corrected for overlap and reliability",
      "topics": [
        "cluster.loadings"
      ]
    },
    {
      "page": "cluster.plot",
      "title": "Plot factor/cluster loadings and assign items to clusters by their highest loading.",
      "topics": [
        "cluster.plot",
        "fa.plot",
        "factor.plot"
      ]
    },
    {
      "page": "cluster2keys",
      "title": "Convert a cluster vector (from e.g., kmeans) to a keys matrix suitable for scoring item clusters.",
      "topics": [
        "cluster2keys"
      ]
    },
    {
      "page": "cohen.d",
      "title": "Find Cohen d and confidence intervals",
      "topics": [
        "cd.validity",
        "cohen.d",
        "cohen.d.by",
        "cohen.d.ci",
        "cohenPooled",
        "d.ci",
        "d.robust",
        "d2CL",
        "d2OVL",
        "d2OVL2",
        "d2r",
        "d2t",
        "d2U3",
        "m2d",
        "m2t",
        "r2d",
        "t2d"
      ]
    },
    {
      "page": "kappa",
      "title": "Find Cohen's kappa and weighted kappa coefficients for correlation of two raters",
      "topics": [
        "cohen.kappa",
        "wkappa"
      ]
    },
    {
      "page": "comorbidity",
      "title": "Convert base rates of two diagnoses and their comorbidity into phi, Yule, and tetrachorics",
      "topics": [
        "comorbidity"
      ]
    },
    {
      "page": "congruence",
      "title": "Matrix and profile congruences and distances",
      "topics": [
        "cohen.profile",
        "congruence",
        "distance"
      ]
    },
    {
      "page": "cor.smooth",
      "title": "Smooth a non-positive definite correlation matrix to make it positive definite",
      "topics": [
        "cor.smooth",
        "cor.smoother"
      ]
    },
    {
      "page": "cor.wt",
      "title": "The sample size weighted correlation may be used in correlating aggregated data",
      "topics": [
        "cor.wt"
      ]
    },
    {
      "page": "cor2dist",
      "title": "Convert correlations to distances (necessary to do multidimensional scaling of correlation data)",
      "topics": [
        "cor2dist"
      ]
    },
    {
      "page": "cor.ci",
      "title": "Bootstrapped and normal confidence intervals for raw and composite correlations",
      "topics": [
        "cor.ci",
        "corCi"
      ]
    },
    {
      "page": "corFiml",
      "title": "Find a Full Information Maximum Likelihood (FIML) correlation or covariance matrix from a data matrix with missing data",
      "topics": [
        "corFiml"
      ]
    },
    {
      "page": "cor.plot",
      "title": "Create an image plot for a correlation or factor matrix",
      "topics": [
        "cor.plot",
        "cor.plot.upperLowerCi",
        "corPlot",
        "corPlotUpperLowerCi"
      ]
    },
    {
      "page": "correct.cor",
      "title": "Find dis-attenuated correlations given correlations and reliabilities",
      "topics": [
        "correct.cor"
      ]
    },
    {
      "page": "cortest.mat",
      "title": "Chi square tests of whether a single matrix is an identity matrix, or a pair of matrices are equal.",
      "topics": [
        "cortest",
        "cortest.jennrich",
        "cortest.mat",
        "cortest.normal"
      ]
    },
    {
      "page": "corr.test",
      "title": "Find the correlations, sample sizes, and probability values between elements of a matrix or data.frame.",
      "topics": [
        "corInfo",
        "corr.p",
        "corr.test",
        "corTest"
      ]
    },
    {
      "page": "cortest.bartlett",
      "title": "Bartlett's test that a correlation matrix is an identity matrix",
      "topics": [
        "cortest.bartlett"
      ]
    },
    {
      "page": "cosinor",
      "title": "Functions for analysis of circadian or diurnal data",
      "topics": [
        "circadian.cor",
        "circadian.F",
        "circadian.linear.cor",
        "circadian.mean",
        "circadian.phase",
        "circadian.reliability",
        "circadian.sd",
        "circadian.stats",
        "circular.cor",
        "circular.mean",
        "cosinor",
        "cosinor.period",
        "cosinor.plot"
      ]
    },
    {
      "page": "cta",
      "title": "Simulate the C(ues) T(endency) A(ction) model of motivation",
      "topics": [
        "cta",
        "cta.15"
      ]
    },
    {
      "page": "densityBy",
      "title": "Create a 'violin plot' or density plot of the distribution of a set of variables",
      "topics": [
        "densityBy",
        "violin",
        "violinBy"
      ]
    },
    {
      "page": "describe",
      "title": "Basic descriptive statistics useful for psychometrics",
      "topics": [
        "describe",
        "describeData",
        "describeFast"
      ]
    },
    {
      "page": "describe.by",
      "title": "Basic summary statistics by group",
      "topics": [
        "describe.by",
        "describeBy"
      ]
    },
    {
      "page": "diagram",
      "title": "Helper functions for drawing path model diagrams",
      "topics": [
        "dia.arrow",
        "dia.cone",
        "dia.curve",
        "dia.curved.arrow",
        "dia.ellipse",
        "dia.ellipse1",
        "dia.rect",
        "dia.self",
        "dia.shape",
        "dia.triangle",
        "diagram",
        "multi.arrow",
        "multi.curved.arrow",
        "multi.rect",
        "multi.self"
      ]
    },
    {
      "page": "draw.tetra",
      "title": "Draw a correlation ellipse and two normal curves to demonstrate tetrachoric correlation",
      "topics": [
        "draw.cor",
        "draw.tetra"
      ]
    },
    {
      "page": "dummy.code",
      "title": "Create dummy coded variables",
      "topics": [
        "dummy.code"
      ]
    },
    {
      "page": "dwyer",
      "title": "8 cognitive variables used by Dwyer for an example.",
      "topics": [
        "Dwyer"
      ]
    },
    {
      "page": "eigen.loadings",
      "title": "Convert eigen vectors and eigen values to the more normal (for psychologists) component loadings",
      "topics": [
        "eigen.loadings"
      ]
    },
    {
      "page": "ellipses",
      "title": "Plot data and 1 and 2 sigma correlation ellipses",
      "topics": [
        "ellipses",
        "minkowski"
      ]
    },
    {
      "page": "error.bars",
      "title": "Plot means and confidence intervals",
      "topics": [
        "error.bars",
        "error.bars.tab"
      ]
    },
    {
      "page": "error.bars.by",
      "title": "Plot means and confidence intervals for multiple groups",
      "topics": [
        "error.bars.by"
      ]
    },
    {
      "page": "error.crosses",
      "title": "Plot x and y error bars",
      "topics": [
        "error.crosses"
      ]
    },
    {
      "page": "error.dots",
      "title": "Show a dot.chart with error bars for different groups or variables",
      "topics": [
        "error.dots"
      ]
    },
    {
      "page": "error.circles",
      "title": "Two way plots of means, error bars, and sample sizes",
      "topics": [
        "errorCircles"
      ]
    },
    {
      "page": "esem",
      "title": "Perform and Exploratory Structural Equation Model (ESEM) by using factor extension techniques",
      "topics": [
        "cancorDiagram",
        "esem",
        "esem.diagram",
        "esemDiagram",
        "interbattery"
      ]
    },
    {
      "page": "fa",
      "title": "Exploratory Factor analysis using MinRes (minimum residual) as well as EFA by Principal Axis, Weighted Least Squares or Maximum Likelihood",
      "topics": [
        "fa",
        "fa.pooled",
        "fa.sapa",
        "fac"
      ]
    },
    {
      "page": "fa.diagram",
      "title": "Graph factor loading matrices",
      "topics": [
        "extension.diagram",
        "fa.diagram",
        "fa.graph",
        "fa.rgraph",
        "het.diagram"
      ]
    },
    {
      "page": "fa.extension",
      "title": "Apply Dwyer's factor extension to find factor loadings for extended variables",
      "topics": [
        "fa.extend",
        "fa.extension",
        "faReg",
        "faRegression"
      ]
    },
    {
      "page": "fa.lookup",
      "title": "A set of functions for factorial and empirical scale construction",
      "topics": [
        "fa.lookup",
        "item.lookup",
        "itemSort",
        "keys.lookup",
        "lmCorLookup",
        "lookup",
        "lookupFromKeys",
        "lookupItems"
      ]
    },
    {
      "page": "faMulti",
      "title": "Multi level (hierarchical) factor analysis",
      "topics": [
        "fa.multi",
        "fa.multi.diagram"
      ]
    },
    {
      "page": "fa.parallel",
      "title": "Scree plots of data or correlation matrix compared to random ``parallel\" matrices",
      "topics": [
        "fa.parallel",
        "fa.parallel.poly",
        "paSelect",
        "plot.poly.parallel"
      ]
    },
    {
      "page": "deprecated",
      "title": "Deprecated Exploratory Factor analysis functions.  Please use fa",
      "topics": [
        "fa.poly",
        "factor.minres",
        "factor.pa",
        "factor.wls"
      ]
    },
    {
      "page": "fa.random",
      "title": "A first approximation to Random Effects Exploratory Factor Analysis",
      "topics": [
        "fa.random"
      ]
    },
    {
      "page": "fa.sort",
      "title": "Sort factor analysis or principal components analysis loadings",
      "topics": [
        "fa.organize",
        "fa.sort"
      ]
    },
    {
      "page": "faCor",
      "title": "Correlations between two factor analysis solutions",
      "topics": [
        "faCor"
      ]
    },
    {
      "page": "factor.congruence",
      "title": "Coefficient of factor congruence",
      "topics": [
        "fa.congruence",
        "factor.congruence"
      ]
    },
    {
      "page": "factor.fit",
      "title": "How well does the factor model fit a correlation matrix. Part of the VSS package",
      "topics": [
        "factor.fit"
      ]
    },
    {
      "page": "factor.model",
      "title": "Find R = F F' + U2 is the basic factor model",
      "topics": [
        "factor.model"
      ]
    },
    {
      "page": "factor.residuals",
      "title": "R* = R- F F'",
      "topics": [
        "factor.residuals"
      ]
    },
    {
      "page": "factor.rotate",
      "title": "``Hand\" rotate a factor loading matrix",
      "topics": [
        "factor.rotate"
      ]
    },
    {
      "page": "factor.scores",
      "title": "Various ways to estimate factor scores for the factor analysis model",
      "topics": [
        "factor.scores"
      ]
    },
    {
      "page": "factor.stats",
      "title": "Find various goodness of fit statistics for factor analysis and principal components",
      "topics": [
        "fa.stats",
        "factor.stats"
      ]
    },
    {
      "page": "factor2cluster",
      "title": "Extract cluster definitions from factor loadings",
      "topics": [
        "factor2cluster"
      ]
    },
    {
      "page": "faRotate",
      "title": "Multiple rotations of factor loadings to find local minima",
      "topics": [
        "faRotations"
      ]
    },
    {
      "page": "fisherz",
      "title": "Transformations of r, d, and t including Fisher r to z and z to r and confidence intervals",
      "topics": [
        "chi2r",
        "cor2cov",
        "fisherz",
        "fisherz2r",
        "g2r",
        "r.con",
        "r2c",
        "r2chi",
        "r2p",
        "r2t",
        "t2r"
      ]
    },
    {
      "page": "fparse",
      "title": "Parse and extend formula input from a model and return the DV(s), IV(s), and associated terms.",
      "topics": [
        "fparse",
        "lavParse"
      ]
    },
    {
      "page": "fsi",
      "title": "Factor Score Indeterminacy estimates )",
      "topics": [
        "fsi"
      ]
    },
    {
      "page": "Garcia",
      "title": "Data from the sexism (protest) study of Garcia, Schmitt, Branscome, and Ellemers (2010)",
      "topics": [
        "Garcia",
        "GSBE",
        "protest"
      ]
    },
    {
      "page": "geometric.mean",
      "title": "Find the geometric mean of a vector or columns of a data.frame.",
      "topics": [
        "geometric.mean"
      ]
    },
    {
      "page": "glb.algebraic",
      "title": "Find the greatest lower bound to reliability.",
      "topics": [
        "glb.algebraic"
      ]
    },
    {
      "page": "Gleser",
      "title": "Example data from Gleser, Cronbach and Rajaratnam (1965) to show basic principles of generalizability theory.",
      "topics": [
        "Gleser"
      ]
    },
    {
      "page": "Gorsuch",
      "title": "Example data set from Gorsuch (1997) for an example factor extension.",
      "topics": [
        "Gorsuch"
      ]
    },
    {
      "page": "Harman",
      "title": "Six data sets from Harman (1967). 9 cognitive variables from Holzinger and 8 emotional variables from Burt",
      "topics": [
        "Harman",
        "Harman.5",
        "Harman.8",
        "Harman.Burt",
        "Harman.Holzinger",
        "Harman.political",
        "Harman_5"
      ]
    },
    {
      "page": "harmonic.mean",
      "title": "Find the harmonic mean of a vector, matrix, or columns of a data.frame",
      "topics": [
        "harmonic.mean"
      ]
    },
    {
      "page": "headtail",
      "title": "Combine calls to head and tail",
      "topics": [
        "headTail",
        "headtail",
        "quickView",
        "topBottom"
      ]
    },
    {
      "page": "ICC",
      "title": "Intraclass Correlations (ICC1, ICC2, ICC3 from Shrout and Fleiss)",
      "topics": [
        "ICC"
      ]
    },
    {
      "page": "ICLUST",
      "title": "iclust: Item Cluster Analysis - Hierarchical cluster analysis using psychometric principles",
      "topics": [
        "ICLUST",
        "iclust"
      ]
    },
    {
      "page": "ICLUST.cluster",
      "title": "Function to form hierarchical cluster analysis of items",
      "topics": [
        "ICLUST.cluster"
      ]
    },
    {
      "page": "iclust.diagram",
      "title": "Draw an ICLUST hierarchical cluster structure diagram",
      "topics": [
        "ICLUST.diagram",
        "iclust.diagram"
      ]
    },
    {
      "page": "ICLUST.graph",
      "title": "create control code for ICLUST graphical output",
      "topics": [
        "ICLUST.graph",
        "iclust.graph"
      ]
    },
    {
      "page": "ICLUST.rgraph",
      "title": "Draw an ICLUST graph using the Rgraphviz package",
      "topics": [
        "ICLUST.rgraph"
      ]
    },
    {
      "page": "ICLUST.sort",
      "title": "Sort items by absolute size of cluster loadings",
      "topics": [
        "ICLUST.sort",
        "iclust.sort"
      ]
    },
    {
      "page": "interp.median",
      "title": "Find the interpolated sample median, quartiles, or specific quantiles for a vector, matrix, or data frame",
      "topics": [
        "interp.boxplot",
        "interp.median",
        "interp.q",
        "interp.qplot.by",
        "interp.quantiles",
        "interp.quart",
        "interp.quartiles",
        "interp.values"
      ]
    },
    {
      "page": "irt.person.rasch",
      "title": "Item Response Theory estimate of theta (ability) using a Rasch (like) model",
      "topics": [
        "irt.0p",
        "irt.1p",
        "irt.2p",
        "irt.person.rasch"
      ]
    },
    {
      "page": "irt.fa",
      "title": "Item Response Analysis by Exploratory Factor Analysis of tetrachoric/polychoric correlations",
      "topics": [
        "fa2irt",
        "irt.fa",
        "irt.select"
      ]
    },
    {
      "page": "irt.item.diff.rasch",
      "title": "Simple function to estimate item difficulties using IRT concepts",
      "topics": [
        "irt.discrim",
        "irt.item.diff.rasch"
      ]
    },
    {
      "page": "irt.responses",
      "title": "Plot probability of multiple choice responses as a function of a latent trait",
      "topics": [
        "irt.responses"
      ]
    },
    {
      "page": "kaiser",
      "title": "Apply the Kaiser normalization when rotating factors",
      "topics": [
        "kaiser"
      ]
    },
    {
      "page": "KMO",
      "title": "Find the Kaiser, Meyer, Olkin Measure of Sampling Adequacy",
      "topics": [
        "KMO"
      ]
    },
    {
      "page": "lmCor",
      "title": "Multiple Regression, Canonical and Set Correlation from matrix or raw input",
      "topics": [
        "crossValidation",
        "crossValidationBoot",
        "lmCor",
        "lmCor.diagram",
        "lmDiagram",
        "mat.regress",
        "matPlot",
        "matReg",
        "set.cor",
        "setCor"
      ]
    },
    {
      "page": "logistic",
      "title": "Logistic transform from x to p and logit transform from p to x",
      "topics": [
        "logistic",
        "logistic.grm",
        "logit"
      ]
    },
    {
      "page": "lowerUpper",
      "title": "Combine two square matrices to have a lower off diagonal for one, upper off diagonal for the other",
      "topics": [
        "lowerUpper"
      ]
    },
    {
      "page": "make.keys",
      "title": "Create a keys matrix for use by score.items or cluster.cor",
      "topics": [
        "keys2list",
        "make.keys",
        "makePositiveKeys",
        "selectFromKeys"
      ]
    },
    {
      "page": "manhattan",
      "title": "\"Manhattan\" plots of correlations with a set of criteria.",
      "topics": [
        "manhattan"
      ]
    },
    {
      "page": "skew",
      "title": "Calculate univariate or multivariate (Mardia's test) skew and kurtosis for a vector, matrix, or data.frame",
      "topics": [
        "kurtosi",
        "mardia",
        "skew"
      ]
    },
    {
      "page": "mat.sort",
      "title": "Sort the elements of a correlation matrix to reflect factor loadings",
      "topics": [
        "mat.sort",
        "matSort"
      ]
    },
    {
      "page": "matrix.addition",
      "title": "A function to add two vectors or matrices",
      "topics": [
        "%+%",
        "matrix.addition"
      ]
    },
    {
      "page": "mediate",
      "title": "Estimate and display direct and indirect effects of mediators and moderator in path models",
      "topics": [
        "mediate",
        "mediate.diagram",
        "moderate.diagram"
      ]
    },
    {
      "page": "mixed.cor",
      "title": "Find correlations for mixtures of continuous, polytomous, and dichotomous variables",
      "topics": [
        "mixed.cor",
        "mixedCor"
      ]
    },
    {
      "page": "mssd",
      "title": "Find von Neuman's Mean Square of Successive Differences",
      "topics": [
        "autoR",
        "mssd",
        "rmssd"
      ]
    },
    {
      "page": "multi.hist",
      "title": "Multiple histograms with density and normal fits on one page",
      "topics": [
        "histBy",
        "histo.density",
        "multi.hist"
      ]
    },
    {
      "page": "multilevel.reliability",
      "title": "Find and plot various reliability/gneralizability coefficients for multilevel data",
      "topics": [
        "mlArrange",
        "mlPlot",
        "mlr",
        "multilevel.reliability"
      ]
    },
    {
      "page": "omega",
      "title": "Calculate McDonald's omega estimates of general and total factor saturation",
      "topics": [
        "directSl",
        "omega",
        "omegaDirect",
        "omegaFromSem",
        "omegah",
        "omegaSem"
      ]
    },
    {
      "page": "omega.graph",
      "title": "Graph hierarchical factor structures",
      "topics": [
        "omega.diagram",
        "omega.graph"
      ]
    },
    {
      "page": "omegaStats",
      "title": "Omega Statistics from a Factor Loading Matrix",
      "topics": [
        "omegaStats"
      ]
    },
    {
      "page": "outlier",
      "title": "Find and graph Mahalanobis squared distances to detect outliers",
      "topics": [
        "outlier"
      ]
    },
    {
      "page": "p.rep",
      "title": "Find the probability of replication for an F, t, or r and estimate effect size",
      "topics": [
        "p.rep",
        "p.rep.f",
        "p.rep.r",
        "p.rep.t"
      ]
    },
    {
      "page": "paired.r",
      "title": "Test the difference between (un)paired correlations",
      "topics": [
        "paired.r"
      ]
    },
    {
      "page": "pairs.panels",
      "title": "SPLOM, histograms and correlations for a data matrix",
      "topics": [
        "pairs.panels",
        "panel.cor",
        "panel.cor.scale",
        "panel.ellipse",
        "panel.hist",
        "panel.hist.density",
        "panel.lm",
        "panel.lm.ellipse",
        "panel.smoother"
      ]
    },
    {
      "page": "count.pairwise",
      "title": "Count number of pairwise cases for a data set with missing (NA) data and impute values.",
      "topics": [
        "count.pairwise",
        "pairwiseCount",
        "pairwiseCountBig",
        "pairwiseDescribe",
        "pairwiseImpute",
        "pairwisePlot",
        "pairwiseReport",
        "pairwiseSample",
        "pairwiseZero"
      ]
    },
    {
      "page": "parcels",
      "title": "Find miniscales (parcels) of size 2 or 3 from a set of items",
      "topics": [
        "keysort",
        "parcels"
      ]
    },
    {
      "page": "partial.r",
      "title": "Find the partial correlations for a set (x) of variables with set (y) removed.",
      "topics": [
        "partial.r"
      ]
    },
    {
      "page": "phi",
      "title": "Find the phi coefficient of correlation between two dichotomous variables",
      "topics": [
        "phi"
      ]
    },
    {
      "page": "phi.demo",
      "title": "A simple demonstration of the Pearson, phi, and polychoric corelation",
      "topics": [
        "phi.demo"
      ]
    },
    {
      "page": "phi2poly",
      "title": "Convert a phi coefficient to a tetrachoric correlation",
      "topics": [
        "phi2poly",
        "phi2tetra"
      ]
    },
    {
      "page": "Pinv",
      "title": "Compute the Moore-Penrose Pseudo Inverse of a matrix",
      "topics": [
        "Pinv"
      ]
    },
    {
      "page": "plot.psych",
      "title": "Plotting functions for the psych package of class ``psych\"",
      "topics": [
        "plot.irt",
        "plot.poly",
        "plot.psych",
        "plot.residuals"
      ]
    },
    {
      "page": "polar",
      "title": "Convert Cartesian factor loadings into polar coordinates",
      "topics": [
        "polar"
      ]
    },
    {
      "page": "polychor.matrix",
      "title": "Phi or Yule coefficient matrix to polychoric coefficient matrix",
      "topics": [
        "phi2poly.matrix",
        "polychor.matrix",
        "Yule2phi.matrix",
        "Yule2poly.matrix"
      ]
    },
    {
      "page": "predict.psych",
      "title": "Prediction function for factor analysis, principal components (pca), bestScales",
      "topics": [
        "predict.psych"
      ]
    },
    {
      "page": "predicted.validity",
      "title": "Find the predicted validities of a set of scales based on item statistics",
      "topics": [
        "item.validity",
        "predicted.validity",
        "validityItem"
      ]
    },
    {
      "page": "principal",
      "title": "Principal components analysis (PCA)",
      "topics": [
        "pca",
        "principal"
      ]
    },
    {
      "page": "print.psych",
      "title": "Print and summary functions for the psych class",
      "topics": [
        "print.psych",
        "summary.psych"
      ]
    },
    {
      "page": "Promax",
      "title": "Perform Procustes,bifactor, promax or targeted rotations and return the inter factor angles.",
      "topics": [
        "bifactor",
        "biquartimin",
        "equamax",
        "faRotate",
        "Procrustes",
        "Promax",
        "target.rot",
        "TargetQ",
        "TargetT",
        "varimin",
        "vgQ.bimin",
        "vgQ.targetQ",
        "vgQ.varimin"
      ]
    },
    {
      "page": "misc",
      "title": "Miscellaneous helper functions for the psych package",
      "topics": [
        "acs",
        "char2numeric",
        "cor2",
        "cs",
        "fromTo",
        "isCorrelation",
        "isCovariance",
        "levels2numeric",
        "lowerCor",
        "lowerMat",
        "matMult",
        "misc",
        "nchar2numeric",
        "progressBar",
        "psych.misc",
        "reflect",
        "SAPAfy",
        "shannon",
        "tableF",
        "test.all",
        "vech"
      ]
    },
    {
      "page": "r.test",
      "title": "Tests of significance for correlations",
      "topics": [
        "r.test"
      ]
    },
    {
      "page": "range.correction",
      "title": "Correct correlations for restriction of range. (Thorndike Case 2)",
      "topics": [
        "rangeCorrection"
      ]
    },
    {
      "page": "reliability",
      "title": "Reports 7 different estimates of scale reliabity including alpha, omega, split half",
      "topics": [
        "plot.reliability",
        "reliability"
      ]
    },
    {
      "page": "rescale",
      "title": "Function to convert scores to ``conventional \" metrics",
      "topics": [
        "rescale"
      ]
    },
    {
      "page": "residuals.psych",
      "title": "Extract residuals from various psych objects",
      "topics": [
        "resid.psych",
        "residuals.psych"
      ]
    },
    {
      "page": "reverse.code",
      "title": "Reverse the coding of selected items prior to scale analysis",
      "topics": [
        "reverse.code"
      ]
    },
    {
      "page": "RMSEA",
      "title": "Root Mean Squared Error of Approximation from chisq, df, and n",
      "topics": [
        "RMSEA"
      ]
    },
    {
      "page": "RV",
      "title": "Three measures of the correlations between sets of variables",
      "topics": [
        "RV"
      ]
    },
    {
      "page": "sat.act",
      "title": "3 Measures of ability: SATV, SATQ, ACT",
      "topics": [
        "sat.act"
      ]
    },
    {
      "page": "scaling.fits",
      "title": "Test the adequacy of simple choice, logistic, or Thurstonian scaling.",
      "topics": [
        "scaling.fits"
      ]
    },
    {
      "page": "scatter.hist",
      "title": "Draw a scatter plot with associated X and Y histograms, densities and correlation",
      "topics": [
        "scatter.hist",
        "scatterHist"
      ]
    },
    {
      "page": "schmid",
      "title": "Apply the Schmid Leiman transformation to a correlation matrix",
      "topics": [
        "schmid"
      ]
    },
    {
      "page": "Schmid.Leiman",
      "title": "12 variables created by Schmid and Leiman to show the Schmid-Leiman Transformation",
      "topics": [
        "Chen",
        "Schmid",
        "schmid.leiman",
        "West"
      ]
    },
    {
      "page": "score.alpha",
      "title": "Score scales and find Cronbach's alpha as well as associated statistics",
      "topics": [
        "score.alpha"
      ]
    },
    {
      "page": "score.multiple.choice",
      "title": "Score multiple choice items and provide basic test statistics",
      "topics": [
        "score.multiple.choice"
      ]
    },
    {
      "page": "score.irt",
      "title": "Find Item Response Theory (IRT) based scores for dichotomous or polytomous items",
      "topics": [
        "irt.se",
        "irt.stats.like",
        "irt.tau",
        "make.irt.stats",
        "score.irt",
        "score.irt.2",
        "score.irt.poly",
        "scoreIrt",
        "scoreIrt.1pl",
        "scoreIrt.2pl"
      ]
    },
    {
      "page": "score.items",
      "title": "Score item composite scales and find Cronbach's alpha, Guttman lambda 6 and item whole correlations",
      "topics": [
        "removeMissing",
        "response.frequencies",
        "responseFrequency",
        "score.items",
        "scoreFast",
        "scoreItems",
        "scoreVeryFast"
      ]
    },
    {
      "page": "cluster.cor",
      "title": "Find correlations of composite variables (corrected for overlap) from a larger matrix.",
      "topics": [
        "cluster.cor",
        "scoreBy",
        "scoreOverlap"
      ]
    },
    {
      "page": "scoreWtd",
      "title": "Score items using regression or correlation based weights",
      "topics": [
        "scoreWtd"
      ]
    },
    {
      "page": "scrub",
      "title": "A utility for basic data cleaning and recoding.  Changes values outside of minimum and maximum limits to NA.",
      "topics": [
        "scrub"
      ]
    },
    {
      "page": "SD",
      "title": "Find the Standard deviation for a vector, matrix, or data.frame - do not return error if there are no cases",
      "topics": [
        "SD"
      ]
    },
    {
      "page": "sim",
      "title": "Functions to simulate psychological/psychometric data.",
      "topics": [
        "sim",
        "sim.minor",
        "sim.simplex"
      ]
    },
    {
      "page": "sim.anova",
      "title": "Simulate a 3 way balanced ANOVA or linear model, with or without repeated measures.",
      "topics": [
        "sim.anova"
      ]
    },
    {
      "page": "sim.congeneric",
      "title": "Simulate a congeneric data set with or without minor factors",
      "topics": [
        "congeneric.sim",
        "make.congeneric",
        "sim.congeneric"
      ]
    },
    {
      "page": "sim.hierarchical",
      "title": "Create a population or sample correlation matrix, perhaps with hierarchical structure.",
      "topics": [
        "make.hierarchical",
        "sim.bonds",
        "sim.hierarchical"
      ]
    },
    {
      "page": "sim.irt",
      "title": "Functions to simulate psychological/psychometric data.",
      "topics": [
        "sim.irt",
        "sim.npl",
        "sim.npn",
        "sim.poly",
        "sim.poly.ideal",
        "sim.poly.ideal.npl",
        "sim.poly.ideal.npn",
        "sim.poly.mat",
        "sim.poly.npl",
        "sim.poly.npn",
        "sim.rasch"
      ]
    },
    {
      "page": "sim.item",
      "title": "Generate simulated data structures for circumplex, spherical, or simple structure",
      "topics": [
        "circ.sim",
        "con2cat",
        "item.dichot",
        "item.sim",
        "sim.circ",
        "sim.dichot",
        "sim.item",
        "sim.spherical"
      ]
    },
    {
      "page": "sim.multilevel",
      "title": "Simulate multilevel data with specified within group and between group correlations",
      "topics": [
        "sim.multi",
        "sim.multilevel"
      ]
    },
    {
      "page": "sim.omega",
      "title": "Further functions to simulate psychological/psychometric data.",
      "topics": [
        "sim.general",
        "sim.omega",
        "sim.parallel"
      ]
    },
    {
      "page": "sim.structural",
      "title": "Create correlation matrices or data matrices with a particular measurement and structural model",
      "topics": [
        "sim.correlation",
        "sim.structural",
        "sim.structure",
        "simCor"
      ]
    },
    {
      "page": "sim.VSS",
      "title": "create VSS like data",
      "topics": [
        "sim.VSS",
        "VSS.sim",
        "VSS.simulate"
      ]
    },
    {
      "page": "simulation.circ",
      "title": "Simulations of circumplex and simple structure",
      "topics": [
        "circ.sim.plot",
        "circ.simulation",
        "simulation.circ"
      ]
    },
    {
      "page": "small.msq",
      "title": "A small example data set taken from a larger data set",
      "topics": [
        "small.msq"
      ]
    },
    {
      "page": "smc",
      "title": "Find the Squared Multiple Correlation (SMC) of each variable with the remaining variables in a matrix",
      "topics": [
        "smc"
      ]
    },
    {
      "page": "spider",
      "title": "Make \"radar\" or \"spider\" plots.",
      "topics": [
        "radar",
        "spider"
      ]
    },
    {
      "page": "splitHalf",
      "title": "Alternative estimates of test reliabiity",
      "topics": [
        "glb",
        "glb.fa",
        "guttman",
        "splitHalf",
        "tenberge"
      ]
    },
    {
      "page": "statsBy",
      "title": "Find statistics (including correlations) within and between groups for basic multilevel analyses",
      "topics": [
        "faBy",
        "statsBy",
        "statsBy.boot",
        "statsBy.boot.summary"
      ]
    },
    {
      "page": "structure.diagram",
      "title": "Draw a structural equation model specified by two measurement models and a structural model",
      "topics": [
        "lavaan.diagram",
        "sem.diagram",
        "sem.graph",
        "structure.diagram",
        "structure.graph",
        "structure.sem"
      ]
    },
    {
      "page": "structure.list",
      "title": "Create factor model matrices from an input list",
      "topics": [
        "phi.list",
        "structure.list"
      ]
    },
    {
      "page": "super.matrix",
      "title": "Form a super matrix from two sub matrices.",
      "topics": [
        "super.matrix",
        "superCor",
        "superMatrix"
      ]
    },
    {
      "page": "table2matrix",
      "title": "Convert a table with counts to a matrix or data.frame representing those counts.",
      "topics": [
        "table2df",
        "table2matrix"
      ]
    },
    {
      "page": "tal_or",
      "title": "Data set testing causal direction in presumed media influence",
      "topics": [
        "pmi",
        "Tal.Or",
        "Tal_Or",
        "tctg"
      ]
    },
    {
      "page": "test.irt",
      "title": "A simple demonstration (and test) of various IRT scoring algorthims.",
      "topics": [
        "test.irt"
      ]
    },
    {
      "page": "test.psych",
      "title": "Testing of functions in the psych package",
      "topics": [
        "test.psych"
      ]
    },
    {
      "page": "testRetest",
      "title": "Find various test-retest statistics, including test, person and item reliability",
      "topics": [
        "testReliability",
        "testRetest"
      ]
    },
    {
      "page": "tetrachor",
      "title": "Tetrachoric, polychoric, biserial and polyserial correlations from various types of input",
      "topics": [
        "biserial",
        "poly.mat",
        "polychoric",
        "polydi",
        "polyserial",
        "tetrachor",
        "tetrachoric"
      ]
    },
    {
      "page": "thurstone",
      "title": "Thurstone Case V scaling",
      "topics": [
        "thurstone"
      ]
    },
    {
      "page": "tr",
      "title": "Find the trace of a square matrix",
      "topics": [
        "tr"
      ]
    },
    {
      "page": "Tucker",
      "title": "9 Cognitive variables discussed by Tucker and Lewis (1973)",
      "topics": [
        "Tucker"
      ]
    },
    {
      "page": "unidim",
      "title": "Several indices of the unidimensionality of a set of variables.",
      "topics": [
        "unidim"
      ]
    },
    {
      "page": "VSS",
      "title": "Apply the Very Simple Structure, MAP, and other criteria to determine the appropriate number of factors.",
      "topics": [
        "eigenCi",
        "MAP",
        "nfactors",
        "VSS",
        "vss",
        "vssSelect"
      ]
    },
    {
      "page": "VSS.parallel",
      "title": "Compare real and random VSS solutions",
      "topics": [
        "VSS.parallel"
      ]
    },
    {
      "page": "VSS.plot",
      "title": "Plot VSS fits",
      "topics": [
        "VSS.plot"
      ]
    },
    {
      "page": "VSS.scree",
      "title": "Plot the successive eigen values for a scree test",
      "topics": [
        "scree",
        "VSS.scree"
      ]
    },
    {
      "page": "winsor",
      "title": "Find the Winsorized scores, means, sds or variances for a vector, matrix, or data.frame",
      "topics": [
        "winsor",
        "winsor.mean",
        "winsor.means",
        "winsor.sd",
        "winsor.var"
      ]
    },
    {
      "page": "withinBetween",
      "title": "An example of the distinction between within group and between group correlations",
      "topics": [
        "withinBetween"
      ]
    },
    {
      "page": "Yule",
      "title": "From a two by two table, find the Yule coefficients of association, convert to phi, or tetrachoric, recreate table the table to create the Yule coefficient.",
      "topics": [
        "Yule",
        "Yule.inv",
        "Yule2phi",
        "Yule2poly",
        "Yule2tetra",
        "YuleBonett",
        "YuleCor"
      ]
    }
  ],
  "_rundeps": [
    "GPArotation",
    "lattice",
    "mnormt",
    "nlme"
  ],
  "_vignettes": [
    {
      "source": "scoring.Rnw",
      "filename": "scoring.pdf",
      "title": "Scoring scales with psych",
      "engine": "knitr::knitr",
      "headings": [],
      "created": "2020-10-05 13:30:02",
      "modified": "2026-05-16 04:06:58",
      "commits": 12
    }
  ],
  "_score": 14.41458479594304,
  "_indexed": true,
  "_nocasepkg": "psych",
  "_universes": [
    "revelle"
  ],
  "_binaries": [
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "2.6.5",
      "date": "2026-05-16T06:07:04.000Z",
      "distro": "noble",
      "commit": "2b17d1e78b536c0e4cc807d8a83f546e0a0da85d",
      "fileid": "663ebee486a5d3d2441bc2813d3f2c7cf3944fc4fc02445d48875e52b618bea7",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/revelle/actions/runs/25954446202"
    },
    {
      "r": "4.6.0",
      "os": "linux",
      "version": "2.6.5",
      "date": "2026-05-16T06:07:18.000Z",
      "distro": "noble",
      "commit": "2b17d1e78b536c0e4cc807d8a83f546e0a0da85d",
      "fileid": "efc7a3567a9c4edf1ad63211f3ddccbc932336eed32aff2f999d4f8a6444ed55",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/revelle/actions/runs/25954446202"
    },
    {
      "r": "4.5.3",
      "os": "mac",
      "version": "2.6.5",
      "date": "2026-05-16T06:16:37.000Z",
      "commit": "2b17d1e78b536c0e4cc807d8a83f546e0a0da85d",
      "fileid": "52e6db49b34317a64a548cdf67859e1fea48aeb886b460cd682163645bdd253a",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/revelle/actions/runs/25954446202"
    },
    {
      "r": "4.6.0",
      "os": "mac",
      "version": "2.6.5",
      "date": "2026-05-16T06:16:20.000Z",
      "commit": "2b17d1e78b536c0e4cc807d8a83f546e0a0da85d",
      "fileid": "479092131d2be1f2950f21ca9b407dc46ac220384273f2c6e78f5752f0fb88e6",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/revelle/actions/runs/25954446202"
    },
    {
      "r": "4.7.0",
      "os": "win",
      "version": "2.6.5",
      "date": "2026-05-16T06:06:21.000Z",
      "commit": "2b17d1e78b536c0e4cc807d8a83f546e0a0da85d",
      "fileid": "6d839a066b3b8ccd75e71af0652af729e83b28a1269db0a7c30817aeb6637761",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/revelle/actions/runs/25954446202"
    },
    {
      "r": "4.5.3",
      "os": "win",
      "version": "2.6.5",
      "date": "2026-05-16T06:06:19.000Z",
      "commit": "2b17d1e78b536c0e4cc807d8a83f546e0a0da85d",
      "fileid": "680b62e5769d83c11a233f990e6f9cffef3dce66ccbe7649c645ada029bb1bb9",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/revelle/actions/runs/25954446202"
    },
    {
      "r": "4.6.0",
      "os": "win",
      "version": "2.6.5",
      "date": "2026-05-16T06:06:08.000Z",
      "commit": "2b17d1e78b536c0e4cc807d8a83f546e0a0da85d",
      "fileid": "69f0b05f9b5cbe9c71dce097dd03f2560d05553ca72d7a6f1bde7f76e966921b",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/revelle/actions/runs/25954446202"
    },
    {
      "r": "4.6.0",
      "os": "wasm",
      "version": "2.6.5",
      "date": "2026-06-02T18:48:48.000Z",
      "commit": "2b17d1e78b536c0e4cc807d8a83f546e0a0da85d",
      "fileid": "20e96d2157364500b297c6c4cfaa7aea19033324261a5431e93e6ed81d60beea",
      "status": "success",
      "buildurl": "https://github.com/r-universe/revelle/actions/runs/25954446202"
    }
  ]
}