{
  "_id": "6ad0b0e2f55ded6d77dfa4dd",
  "Package": "psychonetrics",
  "Type": "Package",
  "Title": "Structural Equation Modeling and Confirmatory Network Analysis",
  "Version": "0.17.8",
  "Authors@R": "person(given = \"Sacha\",family = \"Epskamp\", role = c(\"aut\", \"cre\"), email = \"mail@sachaepskamp.com\")",
  "Maintainer": "Sacha Epskamp <mail@sachaepskamp.com>",
  "Description": "Multi-group (dynamical) structural equation models in\ncombination with confirmatory network models from\ncross-sectional, time-series and panel data\n<doi:10.31234/osf.io/8ha93>. Allows for confirmatory testing\nand fit as well as exploratory model search.",
  "License": "GPL-2",
  "Copyright": "see file COPYRIGHTS. Bundles the LBFGS++ (LBFGSpp) headers\nby Yixuan Qiu and others (MIT license) in inst/include.",
  "ByteCompile": "true",
  "URL": "https://psychonetrics.org/",
  "BugReports": "https://github.com/SachaEpskamp/psychonetrics/issues",
  "StagedInstall": "true",
  "NeedsCompilation": "yes",
  "RoxygenNote": "7.3.3",
  "Packaged": {
    "Date": "2026-10-15 10:26:18 UTC",
    "User": "root"
  },
  "Author": "Sacha Epskamp [aut, cre]",
  "Repository": "https://cran.r-universe.dev",
  "Date/Publication": "2026-08-04 09:20:02 UTC",
  "RemoteUrl": "https://github.com/cran/psychonetrics",
  "RemoteRef": "HEAD",
  "RemoteSha": "1baf83d2287abac9e243260a29739330eddbef51",
  "_user": "cran",
  "_type": "src",
  "_file": "psychonetrics_0.17.8.tar.gz",
  "_fileid": "https://r2.ropensci.org/3de7fa721ddb614053ca5e9a5422bf6a088505d734358aa5425bbbb444eff252",
  "_filesize": 1558940,
  "_sha256": "3de7fa721ddb614053ca5e9a5422bf6a088505d734358aa5425bbbb444eff252",
  "_expires": "2027-01-23T10:54:17.000Z",
  "_created": "2026-10-15T10:26:18.000Z",
  "_published": "2026-10-15T10:54:25.936Z",
  "_buildurl": "https://github.com/r-universe/cran/actions/runs/38718868207",
  "_status": "success",
  "_host": "GitHub-Actions",
  "_jobs": [
    {
      "job": 116376061331,
      "time": 634,
      "config": "linux-devel-arm64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "11966721757"
    },
    {
      "job": 116376061568,
      "time": 729,
      "config": "linux-devel-x86_64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "11965678394"
    },
    {
      "job": 116376061392,
      "time": 649,
      "config": "linux-release-arm64",
      "r": "4.6.1",
      "check": "OK",
      "artifact": "11966013076"
    },
    {
      "job": 116376061668,
      "time": 719,
      "config": "linux-release-x86_64",
      "r": "4.6.1",
      "check": "OK",
      "artifact": "11965983176"
    },
    {
      "job": 116368408117,
      "time": 1159,
      "config": "source",
      "r": "4.6.1",
      "check": "OK",
      "artifact": "11964682783"
    },
    {
      "job": 116376061173,
      "time": 375,
      "config": "wasm-release",
      "r": "4.6.0",
      "check": "OK",
      "artifact": "11967030750"
    },
    {
      "job": 116376061537,
      "time": 699,
      "config": "windows-devel-arm64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "11967046794"
    },
    {
      "job": 116376061305,
      "time": 1160,
      "config": "windows-devel-x86_64",
      "r": "4.7.0",
      "check": "OK",
      "artifact": "11967197946"
    },
    {
      "job": 116376061407,
      "time": 897,
      "config": "windows-release-arm64",
      "r": "4.6.1",
      "check": "OK",
      "artifact": "11967087365"
    },
    {
      "job": 116376061490,
      "time": 812,
      "config": "windows-release-x86_64",
      "r": "4.6.1",
      "check": "OK",
      "artifact": "11966452499"
    }
  ],
  "_upstream": "https://github.com/cran/psychonetrics",
  "_commit": {
    "id": "1baf83d2287abac9e243260a29739330eddbef51",
    "author": "Sacha Epskamp <mail@sachaepskamp.com>",
    "committer": "cran-robot <csardi.gabor+cran@gmail.com>",
    "message": "version 0.17.8\n",
    "time": 1785835202
  },
  "_maintainer": {
    "name": "Sacha Epskamp",
    "email": "mail@sachaepskamp.com"
  },
  "_distro": "resolute",
  "_registered": true,
  "_dependencies": [
    {
      "package": "R",
      "version": ">= 4.3.0",
      "role": "Depends"
    },
    {
      "package": "Rcpp",
      "version": ">= 0.11.3",
      "role": "LinkingTo"
    },
    {
      "package": "RcppArmadillo",
      "role": "LinkingTo"
    },
    {
      "package": "RcppEigen",
      "role": "LinkingTo"
    },
    {
      "package": "pbv",
      "role": "LinkingTo"
    },
    {
      "package": "roptim",
      "role": "LinkingTo"
    },
    {
      "package": "methods",
      "role": "Imports"
    },
    {
      "package": "qgraph",
      "role": "Imports"
    },
    {
      "package": "numDeriv",
      "role": "Imports"
    },
    {
      "package": "dplyr",
      "role": "Imports"
    },
    {
      "package": "Matrix",
      "version": ">= 1.6-5",
      "role": "Imports"
    },
    {
      "package": "lavaan",
      "role": "Imports"
    },
    {
      "package": "corpcor",
      "role": "Imports"
    },
    {
      "package": "glasso",
      "role": "Imports"
    },
    {
      "package": "mgcv",
      "role": "Imports"
    },
    {
      "package": "optimx",
      "role": "Imports"
    },
    {
      "package": "nloptr",
      "role": "Imports"
    },
    {
      "package": "pbapply",
      "role": "Imports"
    },
    {
      "package": "parallel",
      "role": "Imports"
    },
    {
      "package": "magrittr",
      "role": "Imports"
    },
    {
      "package": "IsingSampler",
      "version": ">= 0.5",
      "role": "Imports"
    },
    {
      "package": "tidyr",
      "role": "Imports"
    },
    {
      "package": "psych",
      "role": "Imports"
    },
    {
      "package": "rlang",
      "role": "Imports"
    },
    {
      "package": "MASS",
      "role": "Imports"
    },
    {
      "package": "psychTools",
      "role": "Suggests"
    },
    {
      "package": "semPlot",
      "role": "Suggests"
    },
    {
      "package": "graphicalVAR",
      "role": "Suggests"
    },
    {
      "package": "metaSEM",
      "role": "Suggests"
    },
    {
      "package": "mvtnorm",
      "role": "Suggests"
    },
    {
      "package": "ggplot2",
      "role": "Suggests"
    },
    {
      "package": "tinytest",
      "role": "Suggests"
    }
  ],
  "_owner": "cran",
  "_selfowned": false,
  "_usedby": 3,
  "_updates": [
    {
      "week": "2025-43",
      "n": 1
    },
    {
      "week": "2026-09",
      "n": 1
    },
    {
      "week": "2026-29",
      "n": 1
    },
    {
      "week": "2026-32",
      "n": 1
    }
  ],
  "_tags": [
    {
      "name": "0.13.2",
      "date": "2025-10-22"
    },
    {
      "name": "0.15",
      "date": "2026-02-27"
    },
    {
      "name": "0.16.9",
      "date": "2026-07-16"
    },
    {
      "name": "0.17.8",
      "date": "2026-08-04"
    }
  ],
  "_stars": 0,
  "_userbio": {
    "uuid": 6899542,
    "type": "organization",
    "name": "cran",
    "followers": 630,
    "description": "Unofficial read-only mirror of all CRAN R packages"
  },
  "_downloads": {
    "count": 2559,
    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/psychonetrics"
  },
  "_mentions": 3,
  "_devurl": "https://github.com/sachaepskamp/psychonetrics",
  "_searchresults": 80,
  "_topics": [
    "openblas",
    "cpp",
    "openmp"
  ],
  "_rbuild": "4.6.1",
  "_assets": [
    "extra/citation.cff",
    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
    "extra/manual.pdf",
    "extra/NEWS.html",
    "extra/NEWS.txt",
    "extra/psychonetrics.html"
  ],
  "_homeurl": "https://github.com/sachaepskamp/psychonetrics",
  "_realowner": "sachaepskamp",
  "_cranurl": false,
  "_releases": [
    {
      "version": "0.3.2",
      "date": "2019-06-28"
    },
    {
      "version": "0.3.3",
      "date": "2019-07-06"
    },
    {
      "version": "0.4",
      "date": "2019-12-17"
    },
    {
      "version": "0.4.1",
      "date": "2019-12-18"
    },
    {
      "version": "0.5.0",
      "date": "2020-01-25"
    },
    {
      "version": "0.5.1",
      "date": "2020-01-27"
    },
    {
      "version": "0.6",
      "date": "2020-02-21"
    },
    {
      "version": "0.7",
      "date": "2020-04-01"
    },
    {
      "version": "0.7.1",
      "date": "2020-04-15"
    },
    {
      "version": "0.7.2",
      "date": "2020-06-24"
    },
    {
      "version": "0.8",
      "date": "2020-10-26"
    },
    {
      "version": "0.8.1",
      "date": "2021-01-28"
    },
    {
      "version": "0.9",
      "date": "2021-02-23"
    },
    {
      "version": "0.10",
      "date": "2021-10-25"
    },
    {
      "version": "0.11",
      "date": "2023-06-05"
    },
    {
      "version": "0.11.5",
      "date": "2023-10-03"
    },
    {
      "version": "0.11.6",
      "date": "2024-04-05"
    },
    {
      "version": "0.12",
      "date": "2024-05-21"
    },
    {
      "version": "0.13",
      "date": "2024-06-20"
    },
    {
      "version": "0.13.1",
      "date": "2025-06-02"
    },
    {
      "version": "0.13.2",
      "date": "2025-10-22"
    },
    {
      "version": "0.15",
      "date": "2026-02-27"
    },
    {
      "version": "0.16.9",
      "date": "2026-07-16"
    },
    {
      "version": "0.17.8",
      "date": "2026-08-04"
    }
  ],
  "_exports": [
    "addfit",
    "addMIs",
    "addSEs",
    "aggregate_bootstraps",
    "allequal",
    "bifactor",
    "BlumeCapel",
    "bootstrap",
    "changedata",
    "checkFisher",
    "checkJacobian",
    "cholesky",
    "CIplot",
    "compare",
    "corr",
    "covML",
    "covMLtoUB",
    "covUBtoML",
    "diagonalizationMatrix",
    "dlvm",
    "dlvm1",
    "duplicationMatrix",
    "eliminationMatrix",
    "emergencystart",
    "equalityScoreTest",
    "esa",
    "esa_manual",
    "factorscores",
    "find_penalized_lambda",
    "fit",
    "fixpar",
    "fixstart",
    "freepar",
    "fromlavaan",
    "generate",
    "getmatrix",
    "getVCOV",
    "ggm",
    "groupequal",
    "groupfree",
    "gvar",
    "identify",
    "intersectionmodel",
    "Ising",
    "latentgrowth",
    "lnm",
    "logbook",
    "loop_psychonetrics",
    "lrnm",
    "lvm",
    "meta_ggm",
    "meta_gvar",
    "meta_lvm",
    "meta_var",
    "meta_var1",
    "meta_varcov",
    "MIs",
    "ml_corr",
    "ml_ggm",
    "ml_gvar",
    "ml_gvar1",
    "ml_lnm",
    "ml_lrnm",
    "ml_lvm",
    "ml_rnm",
    "ml_ts_lvgvar",
    "ml_tsdlvm1",
    "ml_var",
    "ml_var1",
    "ml_varcov",
    "modelsearch",
    "panel_lvgvar",
    "panelgvar",
    "panellvgvar",
    "panelvar",
    "parameters",
    "parequal",
    "partialprune",
    "penalize",
    "penaltyVector",
    "prec",
    "precision",
    "prune",
    "refit",
    "resid",
    "residuals",
    "ri_clpm",
    "ri_clpm_search",
    "ri_clpm_stationary",
    "ri_clpn",
    "ri_clpn_search",
    "ri_clpn_stationary",
    "rnm",
    "runmodel",
    "setestimator",
    "setoptimizer",
    "setverbose",
    "show",
    "simplestructure",
    "stepup",
    "summary",
    "tolavaan",
    "transmod",
    "ts_lvgvar",
    "tsdlvm",
    "tsdlvm1",
    "unionmodel",
    "unpenalize",
    "update_baseline",
    "update_saturated",
    "usecpp",
    "var1",
    "varcov",
    "write_psychonetrics"
  ],
  "_datasets": [
    {
      "name": "Jonas",
      "title": "Jonas dataset",
      "object": "Jonas",
      "file": "Jonas.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "scientist",
        "jeans",
        "cares",
        "economics",
        "hardworking",
        "honest",
        "intouch",
        "knowledgeable",
        "makeupmind",
        "getsthingsdone",
        "familiar",
        "group"
      ],
      "rows": 215,
      "table": true,
      "tojson": true
    },
    {
      "name": "NA2020",
      "title": "Network Analysis 2020 Self-Report Data",
      "object": "NA2020",
      "file": "NA2020.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "regular_sleep",
        "worried_sleep",
        "sleep_interfere",
        "happy_health",
        "optimistic_future",
        "very_happy",
        "feel_alone",
        "happy_love_life"
      ],
      "rows": 501,
      "table": true,
      "tojson": true
    },
    {
      "name": "StarWars",
      "title": "Star Wars dataset",
      "object": "StarWars",
      "file": "StarWars.RData",
      "class": [
        "data.frame"
      ],
      "fields": [
        "Q1",
        "Q2",
        "Q3",
        "Q4",
        "Q5",
        "Q6",
        "Q7",
        "Q8",
        "Q9",
        "Q10",
        "Q11",
        "Q12",
        "Q13"
      ],
      "rows": 271,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "psychonetrics-package",
      "title": "Structural Equation Modeling and Confirmatory Network Analysis",
      "topics": [
        "psychonetrics-package",
        "psychonetrics"
      ]
    },
    {
      "page": "aggregate_bootstraps",
      "title": "Aggregate Bootstrapped Models",
      "topics": [
        "aggregate_bootstraps"
      ]
    },
    {
      "page": "allequal",
      "title": "Constrain all elements of a matrix equal within groups",
      "topics": [
        "allequal"
      ]
    },
    {
      "page": "bifactor",
      "title": "Bi-factor models",
      "topics": [
        "bifactor"
      ]
    },
    {
      "page": "BlumeCapel",
      "title": "Blume-Capel model",
      "topics": [
        "BlumeCapel"
      ]
    },
    {
      "page": "bootstrap",
      "title": "Bootstrap a psychonetrics model",
      "topics": [
        "bootstrap"
      ]
    },
    {
      "page": "changedata",
      "title": "Change the data of a psychonetrics object",
      "topics": [
        "changedata"
      ]
    },
    {
      "page": "CIplot",
      "title": "Plot Analytic Confidence Intervals",
      "topics": [
        "CIplot"
      ]
    },
    {
      "page": "compare",
      "title": "Model comparison",
      "topics": [
        "compare",
        "print.psychonetrics_compare"
      ]
    },
    {
      "page": "covML",
      "title": "Maximum likelihood covariance estimate",
      "topics": [
        "covML",
        "covMLtoUB",
        "covUBtoML"
      ]
    },
    {
      "page": "diagnostics",
      "title": "Diagnostic functions",
      "topics": [
        "checkFisher",
        "checkJacobian"
      ]
    },
    {
      "page": "dlvm1_family",
      "title": "Lag-1 dynamic latent variable model family of psychonetrics models for panel data",
      "topics": [
        "dlvm",
        "dlvm1",
        "panellvgvar",
        "panel_lvgvar"
      ]
    },
    {
      "page": "algebraMatrices",
      "title": "Model matrices used in derivatives",
      "topics": [
        "diagonalizationMatrix",
        "duplicationMatrix",
        "eliminationMatrix"
      ]
    },
    {
      "page": "emergencystart",
      "title": "Reset starting values to simple defaults",
      "topics": [
        "emergencystart"
      ]
    },
    {
      "page": "equalityScoreTest",
      "title": "Score (Lagrange Multiplier) test for cross-group equality constraints",
      "topics": [
        "equalityScoreTest"
      ]
    },
    {
      "page": "esa",
      "title": "Ergodic Subspace Analysis",
      "topics": [
        "esa",
        "esa_manual",
        "plot.esa",
        "plot.esa_manual",
        "print.esa",
        "print.esa_manual"
      ]
    },
    {
      "page": "factorscores",
      "title": "Compute factor scores",
      "topics": [
        "factorscores"
      ]
    },
    {
      "page": "find_penalized_lambda",
      "title": "Automated Lambda Selection for Penalized Estimation",
      "topics": [
        "find_penalized_lambda"
      ]
    },
    {
      "page": "fit",
      "title": "Print fit indices",
      "topics": [
        "fit"
      ]
    },
    {
      "page": "fixpar_freepar",
      "title": "Parameters modification",
      "topics": [
        "fixpar",
        "freepar"
      ]
    },
    {
      "page": "fixstart",
      "title": "Attempt to Fix Starting Values",
      "topics": [
        "fixstart"
      ]
    },
    {
      "page": "fromlavaan",
      "title": "Convert a lavaan model to a psychonetrics lvm",
      "topics": [
        "fromlavaan"
      ]
    },
    {
      "page": "generate",
      "title": "Generate data from a fitted psychonetrics object",
      "topics": [
        "generate"
      ]
    },
    {
      "page": "getmatrix",
      "title": "Extract an estimated matrix",
      "topics": [
        "getmatrix"
      ]
    },
    {
      "page": "getVCOV",
      "title": "Obtain the asymptotic covariance matrix",
      "topics": [
        "getVCOV"
      ]
    },
    {
      "page": "groupequal_groupfree",
      "title": "Group equality constraints",
      "topics": [
        "groupequal",
        "groupfree"
      ]
    },
    {
      "page": "Ising",
      "title": "Ising model",
      "topics": [
        "Ising"
      ]
    },
    {
      "page": "Jonas",
      "title": "Jonas dataset",
      "topics": [
        "Jonas"
      ]
    },
    {
      "page": "latentgrowth",
      "title": "Latent growth curve model",
      "topics": [
        "latentgrowth"
      ]
    },
    {
      "page": "logbook",
      "title": "Retrieve the psychonetrics logbook",
      "topics": [
        "logbook"
      ]
    },
    {
      "page": "loop_psychonetrics",
      "title": "Parallel loop for psychonetrics",
      "topics": [
        "loop_psychonetrics"
      ]
    },
    {
      "page": "lvm_family",
      "title": "Continuous latent variable family of psychonetrics models",
      "topics": [
        "lnm",
        "lrnm",
        "lvm",
        "rnm"
      ]
    },
    {
      "page": "meta_lvm",
      "title": "Meta-analytic latent variable model",
      "topics": [
        "meta_lvm"
      ]
    },
    {
      "page": "meta_var1",
      "title": "Meta-analytic VAR(1) model",
      "topics": [
        "meta_gvar",
        "meta_var",
        "meta_var1"
      ]
    },
    {
      "page": "meta_varcov",
      "title": "Variance-covariance and GGM meta analysis",
      "topics": [
        "meta_ggm",
        "meta_varcov"
      ]
    },
    {
      "page": "MIs",
      "title": "Print modification indices",
      "topics": [
        "MIs"
      ]
    },
    {
      "page": "ml_lvm",
      "title": "Multi-level latent variable model family",
      "topics": [
        "ml_lnm",
        "ml_lrnm",
        "ml_lvm",
        "ml_rnm"
      ]
    },
    {
      "page": "ml_tsdlvm1",
      "title": "Multi-level Lag-1 dynamic latent variable model family of psychonetrics models for time-series data",
      "topics": [
        "ml_tsdlvm1",
        "ml_ts_lvgvar"
      ]
    },
    {
      "page": "ml_var1",
      "title": "Multi-level lag-1 vector auto-regression family (random intercepts)",
      "topics": [
        "ml_gvar",
        "ml_gvar1",
        "ml_var",
        "ml_var1"
      ]
    },
    {
      "page": "ml_varcov",
      "title": "Multi-level variance-covariance family of psychonetrics models",
      "topics": [
        "ml_corr",
        "ml_ggm",
        "ml_varcov"
      ]
    },
    {
      "page": "modelsearch",
      "title": "Stepwise model search",
      "topics": [
        "modelsearch"
      ]
    },
    {
      "page": "NA2020",
      "title": "Network Analysis 2020 Self-Report Data",
      "topics": [
        "NA2020"
      ]
    },
    {
      "page": "panelvar_family",
      "title": "Lag-1 panel vector autoregression family of psychonetrics models",
      "topics": [
        "panelgvar",
        "panelvar"
      ]
    },
    {
      "page": "parameters",
      "title": "Print parameter estimates",
      "topics": [
        "parameters"
      ]
    },
    {
      "page": "parequal",
      "title": "Set equality constraints across parameters",
      "topics": [
        "parequal"
      ]
    },
    {
      "page": "partialprune",
      "title": "Partial pruning of multi-group models",
      "topics": [
        "partialprune"
      ]
    },
    {
      "page": "penalize",
      "title": "Penalized Maximum Likelihood Functions",
      "topics": [
        "penalize",
        "penaltyVector",
        "refit",
        "unpenalize"
      ]
    },
    {
      "page": "plot.psychonetrics",
      "title": "Plot the networks of a psychonetrics model",
      "topics": [
        "plot.psychonetrics"
      ]
    },
    {
      "page": "prune",
      "title": "Stepdown model search by pruning non-significant parameters",
      "topics": [
        "prune"
      ]
    },
    {
      "page": "psychonetrics_bootstrap-class",
      "title": "Class '\"psychonetrics_bootstrap\"'",
      "topics": [
        "psychonetrics_bootstrap-class",
        "show,psychonetrics_bootstrap-method"
      ]
    },
    {
      "page": "psychonetrics_log-class",
      "title": "Class '\"psychonetrics_log\"'",
      "topics": [
        "psychonetrics_log-class",
        "show,psychonetrics_log-method"
      ]
    },
    {
      "page": "modelupdate",
      "title": "Model updating functions",
      "topics": [
        "addfit",
        "addMIs",
        "addSEs",
        "identify"
      ]
    },
    {
      "page": "psychonetrics-class",
      "title": "Class '\"psychonetrics\"'",
      "topics": [
        "psychonetrics-class",
        "psychonetrics_samplestats-class",
        "resid,psychonetrics-method",
        "residuals,psychonetrics-method",
        "sessionInfo-class",
        "show,psychonetrics-method",
        "summary,psychonetrics-method"
      ]
    },
    {
      "page": "ri_clpm",
      "title": "Random intercept cross-lagged panel models and networks",
      "topics": [
        "print.ri_clpm_search",
        "ri_clpm",
        "ri_clpm_search",
        "ri_clpm_stationary",
        "ri_clpn",
        "ri_clpn_search",
        "ri_clpn_stationary"
      ]
    },
    {
      "page": "runmodel",
      "title": "Run a psychonetrics model",
      "topics": [
        "runmodel"
      ]
    },
    {
      "page": "convenience",
      "title": "Convenience functions",
      "topics": [
        "setestimator",
        "setoptimizer",
        "usecpp"
      ]
    },
    {
      "page": "setverbose",
      "title": "Should messages of computation progress be printed?",
      "topics": [
        "setverbose"
      ]
    },
    {
      "page": "simplestructure",
      "title": "Generate factor loadings matrix with simple structure",
      "topics": [
        "simplestructure"
      ]
    },
    {
      "page": "StarWars",
      "title": "Star Wars dataset",
      "topics": [
        "StarWars"
      ]
    },
    {
      "page": "stepup",
      "title": "Stepup model search along modification indices",
      "topics": [
        "stepup"
      ]
    },
    {
      "page": "tolavaan",
      "title": "Convert a psychonetrics lvm to a lavaan model",
      "topics": [
        "tolavaan"
      ]
    },
    {
      "page": "transmod",
      "title": "Transform between model types",
      "topics": [
        "transmod"
      ]
    },
    {
      "page": "tsdlvm1_family",
      "title": "Lag-1 dynamic latent variable model family of psychonetrics models for time-series data",
      "topics": [
        "tsdlvm",
        "tsdlvm1",
        "ts_lvgvar"
      ]
    },
    {
      "page": "unionmodel_intersectionmodel",
      "title": "Unify models across groups",
      "topics": [
        "intersectionmodel",
        "unionmodel"
      ]
    },
    {
      "page": "update_baseline_saturated",
      "title": "Set a custom baseline or saturated reference model",
      "topics": [
        "update_baseline",
        "update_saturated"
      ]
    },
    {
      "page": "var1_family",
      "title": "Lag-1 vector autoregression family of psychonetrics models",
      "topics": [
        "gvar",
        "var1"
      ]
    },
    {
      "page": "varcov_family",
      "title": "Variance-covariance family of psychonetrics models",
      "topics": [
        "cholesky",
        "corr",
        "ggm",
        "prec",
        "precision",
        "varcov"
      ]
    },
    {
      "page": "write_psychonetrics",
      "title": "Write comprehensive model output to a text file",
      "topics": [
        "write_psychonetrics"
      ]
    }
  ],
  "_rundeps": [
    "abind",
    "backports",
    "base64enc",
    "bslib",
    "cachem",
    "checkmate",
    "cli",
    "cluster",
    "colorspace",
    "corpcor",
    "cpp11",
    "data.table",
    "digest",
    "dplyr",
    "evaluate",
    "farver",
    "fastmap",
    "fdrtool",
    "fontawesome",
    "foreign",
    "Formula",
    "fs",
    "generics",
    "ggplot2",
    "glasso",
    "glue",
    "GPArotation",
    "gridExtra",
    "gtable",
    "gtools",
    "highr",
    "Hmisc",
    "htmlTable",
    "htmltools",
    "htmlwidgets",
    "igraph",
    "IsingSampler",
    "isoband",
    "jpeg",
    "jquerylib",
    "jsonlite",
    "knitr",
    "labeling",
    "lattice",
    "lavaan",
    "lifecycle",
    "magrittr",
    "MASS",
    "Matrix",
    "memoise",
    "mgcv",
    "mime",
    "mnormt",
    "nlme",
    "nloptr",
    "nnet",
    "numDeriv",
    "optimx",
    "pbapply",
    "pbivnorm",
    "pbv",
    "pillar",
    "pkgconfig",
    "plyr",
    "png",
    "pracma",
    "psych",
    "purrr",
    "qgraph",
    "quadprog",
    "R6",
    "rappdirs",
    "RColorBrewer",
    "Rcpp",
    "RcppArmadillo",
    "RcppEigen",
    "reshape2",
    "rlang",
    "rmarkdown",
    "roptim",
    "rpart",
    "rstudioapi",
    "S7",
    "sass",
    "scales",
    "stringi",
    "stringr",
    "tibble",
    "tidyr",
    "tidyselect",
    "tinytex",
    "utf8",
    "vctrs",
    "viridisLite",
    "withr",
    "xfun",
    "yaml"
  ],
  "_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"
    },
    {
      "shlib": "libgomp",
      "package": "libgomp1",
      "source": "gcc",
      "version": "16-20260322-1ubuntu1",
      "name": "openmp",
      "homepage": "http://gcc.gnu.org/",
      "description": "GCC OpenMP (GOMP) support library"
    }
  ],
  "_score": 4.344584028366079,
  "_indexed": false,
  "_nocasepkg": "psychonetrics",
  "_universes": [
    "cran"
  ],
  "_indexurl": "https://sachaepskamp.r-universe.dev/psychonetrics",
  "_binaries": [
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "0.17.8",
      "date": "2026-10-15T10:36:20.000Z",
      "distro": "resolute",
      "arch": "aarch64",
      "commit": "1baf83d2287abac9e243260a29739330eddbef51",
      "fileid": "https://r2.ropensci.org/6762e976be69cd649b4df0c222caeee19e985911833d0017733e34e65df0e8d9",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/38718868207"
    },
    {
      "r": "4.7.0",
      "os": "linux",
      "version": "0.17.8",
      "date": "2026-10-15T10:36:46.000Z",
      "distro": "resolute",
      "arch": "x86_64",
      "commit": "1baf83d2287abac9e243260a29739330eddbef51",
      "fileid": "https://r2.ropensci.org/c8690f6535de4b786b5e7ef8531596881b826563dd64c73b76c034d983781089",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/38718868207"
    },
    {
      "r": "4.6.1",
      "os": "linux",
      "version": "0.17.8",
      "date": "2026-10-15T10:36:23.000Z",
      "distro": "resolute",
      "arch": "aarch64",
      "commit": "1baf83d2287abac9e243260a29739330eddbef51",
      "fileid": "https://r2.ropensci.org/163943cbe8e2bed48d0fafdecfbf1f8b8dc3ff9a93d99e5bed62ae9fc2b727aa",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/38718868207"
    },
    {
      "r": "4.6.1",
      "os": "linux",
      "version": "0.17.8",
      "date": "2026-10-15T10:36:42.000Z",
      "distro": "resolute",
      "arch": "x86_64",
      "commit": "1baf83d2287abac9e243260a29739330eddbef51",
      "fileid": "https://r2.ropensci.org/d33154419ce67d8a1a8cd891da5dfc28c63a886a24d7b5f369389abe803095f6",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/38718868207"
    },
    {
      "r": "4.6.0",
      "os": "wasm",
      "version": "0.17.8",
      "date": "2026-10-15T10:36:47.000Z",
      "arch": "emscripten",
      "commit": "1baf83d2287abac9e243260a29739330eddbef51",
      "fileid": "https://r2.ropensci.org/b1c2a4b3b71e70eb11a4dfa11e20668dcb88c9e61beef5d2138b7c29d983ae33",
      "status": "success",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/38718868207"
    },
    {
      "r": "4.7.0",
      "os": "win",
      "version": "0.17.8",
      "date": "2026-10-15T10:39:14.000Z",
      "arch": "aarch64",
      "commit": "1baf83d2287abac9e243260a29739330eddbef51",
      "fileid": "https://r2.ropensci.org/d13ffb884322cb86bcdfd6ee735f4d8a612c394776a6b28b43edf93d8e87804c",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/38718868207"
    },
    {
      "r": "4.7.0",
      "os": "win",
      "version": "0.17.8",
      "date": "2026-10-15T10:36:55.000Z",
      "arch": "x86_64",
      "commit": "1baf83d2287abac9e243260a29739330eddbef51",
      "fileid": "https://r2.ropensci.org/924c2cad534a0976551c684ac7cc8c97a74abfcb48eee786203649bb84bc5533",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/38718868207"
    },
    {
      "r": "4.6.1",
      "os": "win",
      "version": "0.17.8",
      "date": "2026-10-15T10:37:42.000Z",
      "arch": "aarch64",
      "commit": "1baf83d2287abac9e243260a29739330eddbef51",
      "fileid": "https://r2.ropensci.org/0be83754b6a9be9f6dd3ff2a2832638fa3f1fad21c73616897dfd8c5f8ce007d",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/38718868207"
    },
    {
      "r": "4.6.1",
      "os": "win",
      "version": "0.17.8",
      "date": "2026-10-15T10:36:09.000Z",
      "arch": "x86_64",
      "commit": "1baf83d2287abac9e243260a29739330eddbef51",
      "fileid": "https://r2.ropensci.org/ec4073ace3caed0c5d776bd85554d68476f2d0df1baebca3c74d06bb53672ed0",
      "status": "success",
      "check": "OK",
      "buildurl": "https://github.com/r-universe/cran/actions/runs/38718868207"
    }
  ]
}