{
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  "Title": "Complex Partial Least Squares Structural Equation Modeling",
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  "Authors@R": "person(given = \"Kjell\", family = \"Solem Slupphaug\",\nemail = \"slupphaugkjell@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0009-0005-8324-2834\"))",
  "Maintainer": "Kjell Solem Slupphaug <slupphaugkjell@gmail.com>",
  "Description": "Estimate complex Structural Equation Models (SEMs) by\nfitting Partial Least Squares Structural Equation Modeling\n(PLS-SEM) and Partial Least Squares consistent Structural\nEquation Modeling (PLSc-SEM) specifications that handle\ncategorical data, non-linear relations, and multilevel\nstructures. The implementation follows Lohmöller (1989) for the\nclassic PLS-SEM algorithm, Dijkstra and Henseler (2015) for\nconsistent PLSc-SEM, Dijkstra et al., (2014) for nonlinear\nPLSc-SEM, and Schuberth, Henseler, Dijkstra (2018) for ordinal\nPLS-SEM and PLSc-SEM. Additional extensions are under\ndevelopment. The MC-OrdPLSc algorithm, used to handle ordinal\ninteraction models is detailed in Slupphaug et al., (2026).\nReferences: Lohmöller, J.-B. (1989, ISBN:9783790803002).\n\"Latent Variable Path Modeling with Partial Least Squares.\"\nDijkstra, T. K., & Henseler, J. (2015).\n<doi:10.1016/j.jmva.2015.06.002>. \"Consistent partial least\nsquares path modeling.\" Dijkstra, T. K., & Schermelleh-Engel,\nK. (2014). <doi:10.1016/j.csda.2014.07.008>. \"Consistent\npartial least squares for nonlinear structural equation\nmodels.\" Schuberth, F., Henseler, J., & Dijkstra, T. K. (2018).\n<doi:10.1007/s11135-018-0767-9>. \"Partial least squares path\nmodeling using ordinal categorical indicators.\" Slupphaug, K.\nMehmetoglu, M. & Mittner, M. (2026).\n<doi:10.31234/osf.io/fwzj6_v1>. \"Consistent Estimates from\nBiased Estimators: Monte-Carlo Consistent Partial Least Squares\nfor Latent Interaction Models with Ordinal Indicators.\"",
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  "Repository": "https://kss2k.r-universe.dev",
  "Date/Publication": "2026-07-03 21:58:25 UTC",
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    "pls_implied_joint_corr",
    "pls_inspect",
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    "pls_srmr",
    "predict",
    "show",
    "summary",
    "unstandardized_estimates",
    "vcov"
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      "title": "Retrieve bootstrap coefficient matrix",
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        "boot,PlsModel-method",
        "pls_boot,PlsModel-method"
      ]
    },
    {
      "page": "coef-PlsModel-method",
      "title": "Extract coefficients from a 'PlsModel' model",
      "topics": [
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        "coefficients,PlsModel-method"
      ]
    },
    {
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      "topics": [
        "fit_measures",
        "fit_measures,PlsModel-method"
      ]
    },
    {
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      "title": "Check whether a fitted model has admissible parameter estimates",
      "topics": [
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        "is_admissible,PlsModel-method"
      ]
    },
    {
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      "title": "Check whether an object uses the MC-OrdPLSc estimator",
      "topics": [
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        "is_mcpls,PlsModel-method"
      ]
    },
    {
      "page": "mcpls_loglik",
      "title": "Loglikelihood of MC-PLS parameters",
      "topics": [
        "mcpls_loglik",
        "mcpls_loglik,PlsModel-method"
      ]
    },
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      "title": "oneIntOrdered",
      "topics": [
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      ]
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      "title": "Generic accessor for model parameter estimates",
      "topics": [
        "parameter_estimates"
      ]
    },
    {
      "page": "parameter_estimates-PlsModel-method",
      "title": "Parameter estimates for 'PlsModel' objects",
      "topics": [
        "parameter_estimates,PlsModel-method"
      ]
    },
    {
      "page": "pls",
      "title": "Fit Partial Least Squares Structural Equation Models",
      "topics": [
        "pls"
      ]
    },
    {
      "page": "pls_boot",
      "title": "Retrieve bootstrap coefficient matrix",
      "topics": [
        "pls_boot"
      ]
    },
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      "page": "pls_chisq",
      "title": "Chi-Square",
      "topics": [
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        "pls_chisq,PlsModel-method",
        "pls_chisq_df,PlsModel-method"
      ]
    },
    {
      "page": "pls_chisq_df",
      "title": "Chi-Square Degrees of Freedom",
      "topics": [
        "pls_chisq_df"
      ]
    },
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      "page": "pls_construct_scores",
      "title": "Construct latent variable scores",
      "topics": [
        "pls_construct_scores"
      ]
    },
    {
      "page": "pls_implied_construct_corr",
      "title": "Implied Construct Correlation Matrix",
      "topics": [
        "pls_implied_construct_corr",
        "pls_implied_construct_corr,PlsModel-method"
      ]
    },
    {
      "page": "pls_implied_indicator_corr",
      "title": "Implied Indicator Correlation Matrix",
      "topics": [
        "pls_implied_indicator_corr",
        "pls_implied_indicator_corr,PlsModel-method"
      ]
    },
    {
      "page": "pls_implied_joint_corr",
      "title": "Implied Joint Correlation Matrix",
      "topics": [
        "pls_implied_joint_corr",
        "pls_implied_joint_corr,PlsModel-method"
      ]
    },
    {
      "page": "pls_inspect",
      "title": "Inspect a fitted PLS-SEM model",
      "topics": [
        "pls_inspect",
        "pls_inspect,PlsModel-method"
      ]
    },
    {
      "page": "pls_predict",
      "title": "Predict from a fitted PLS-SEM model",
      "topics": [
        "pls_predict",
        "pls_predict,PlsModel-method"
      ]
    },
    {
      "page": "pls_rmsea",
      "title": "RMSEA",
      "topics": [
        "pls_rmsea",
        "pls_rmsea,PlsModel-method"
      ]
    },
    {
      "page": "pls_srmr",
      "title": "SRMR",
      "topics": [
        "pls_srmr",
        "pls_srmr,PlsModel-method"
      ]
    },
    {
      "page": "predict-PlsModel-method",
      "title": "Predict from a fitted 'PlsModel' (alias for 'pls_predict')",
      "topics": [
        "predict,PlsModel-method"
      ]
    },
    {
      "page": "print.PlsSemPredict",
      "title": "Print a 'PlsSemPredict' object",
      "topics": [
        "print.PlsSemPredict"
      ]
    },
    {
      "page": "print.SummaryPlsSem",
      "title": "Print a 'SummaryPlsSem' object",
      "topics": [
        "print.SummaryPlsSem"
      ]
    },
    {
      "page": "randomIntercepts",
      "title": "randomIntercepts",
      "topics": [
        "randomIntercepts"
      ]
    },
    {
      "page": "randomInterceptsOrdered",
      "title": "randomInterceptsOrdered",
      "topics": [
        "randomInterceptsOrdered"
      ]
    },
    {
      "page": "randomSlopes",
      "title": "randomSlopes",
      "topics": [
        "randomSlopes"
      ]
    },
    {
      "page": "randomSlopesOrdered",
      "title": "randomSlopesOrdered",
      "topics": [
        "randomSlopesOrdered"
      ]
    },
    {
      "page": "show-PlsModel-method",
      "title": "Show a 'PlsModel' object",
      "topics": [
        "show,PlsModel-method"
      ]
    },
    {
      "page": "summary-PlsModel-method",
      "title": "Summarize a fitted 'PlsModel' model",
      "topics": [
        "summary,PlsModel-method"
      ]
    },
    {
      "page": "titanic",
      "title": "Titanic Passenger Survival Data Set.",
      "topics": [
        "titanic"
      ]
    },
    {
      "page": "TPB_Ordered",
      "title": "TPB_Ordered",
      "topics": [
        "TPB_Ordered"
      ]
    },
    {
      "page": "unstandardized_estimates",
      "title": "Unstandardized Parameter Estimates",
      "topics": [
        "unstandardized_estimates",
        "unstandardized_estimates,PlsModel-method"
      ]
    },
    {
      "page": "vcov-PlsModel-method",
      "title": "Extract the variance-covariance matrix from a 'PlsModel' model",
      "topics": [
        "vcov,PlsModel-method"
      ]
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