{
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  "Package": "seminr",
  "Type": "Package",
  "Title": "Building and Estimating Structural Equation Models",
  "Version": "2.5.0",
  "Date": "2026-05-21",
  "Authors@R": "c(person(\"Soumya\", \"Ray\", email = \"soumya.ray@gmail.com\", role = c(\"aut\", \"ths\")),\nperson(\"Nicholas Patrick\", \"Danks\", email = \"nicholasdanks@hotmail.com\", role = c(\"aut\",\"cre\")),\nperson(\"André\", \"Calero Valdez\", role = \"aut\", email = \"andrecalerovaldez@gmail.com\"),\nperson(\"Juan Manuel Velasquez\", \"Estrada\", role = \"ctb\"),\nperson(\"James\", \"Uanhoro\", role = \"ctb\", email = \"James.uanhoro@gmail.com\"),\nperson(\"Johannes\", \"Nakayama\", role = \"ctb\", email = \"johannes.nakayama@rwth-aachen.de\"),\nperson(\"Lilian\", \"Koyan\", role = \"ctb\", email = \"lilian.kojan@rwth-aachen.de\"),\nperson(\"Laura\", \"Burbach\", role = \"ctb\", email = \"laura.burbach@rwth-aachen.de\"),\nperson(\"Arturo Heynar\", \"Cano Bejar\", role = \"ctb\", email = \"arturocano997@gmail.com\"),\nperson(\"Susanne\", \"Adler\", role = \"ctb\", email = \"susanne.adler@ovgu.de\")\n)",
  "Description": "A powerful, easy to use syntax for specifying and\nestimating complex Structural Equation Models. Models can be\nestimated using Partial Least Squares Path Modeling or\nCovariance-Based Structural Equation Modeling or covariance\nbased Confirmatory Factor Analysis (Ray, Danks, and Valdez 2021\n<doi:10.2139/ssrn.3900621>).",
  "License": "GPL-3",
  "LazyData": "TRUE",
  "URL": "https://github.com/sem-in-r/seminr",
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  "Repository": "https://sem-in-r.r-universe.dev",
  "Date/Publication": "2026-05-25 09:14:30 UTC",
  "RemoteUrl": "https://github.com/sem-in-r/seminr",
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  "Author": "Soumya Ray [aut, ths],\nNicholas Patrick Danks [aut, cre],\nAndré Calero Valdez [aut],\nJuan Manuel Velasquez Estrada [ctb],\nJames Uanhoro [ctb],\nJohannes Nakayama [ctb],\nLilian Koyan [ctb],\nLaura Burbach [ctb],\nArturo Heynar Cano Bejar [ctb],\nSusanne Adler [ctb]",
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  "_created": "2026-05-25T11:59:03.000Z",
  "_published": "2026-05-25T12:04:47.598Z",
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  "_exports": [
    "all_composites",
    "all_factors",
    "all_non_interactions",
    "as.reflective",
    "associations",
    "boot_paths_df",
    "bootstrap_model",
    "browse_plot",
    "composite",
    "compute_itcriteria_weights",
    "construct_items",
    "construct_mode",
    "construct_name",
    "construct_names",
    "construct_type",
    "constructs",
    "correlation_weights",
    "csem2seminr",
    "dot_graph",
    "dot_graph_htmt",
    "edge_template_default",
    "edge_template_minimal",
    "estimate_cbsem",
    "estimate_cfa",
    "estimate_lavaan_ten_berge",
    "estimate_pls",
    "estimate_pls_mga",
    "fSquared",
    "get_theme_doc",
    "higher_composite",
    "higher_reflective",
    "interaction_term",
    "is_only_endogenous",
    "item_errors",
    "last_seminr_plot",
    "mean_replacement",
    "mode_A",
    "mode_B",
    "mode_plsc",
    "multi_items",
    "node_endo_template_default",
    "node_exo_template_default",
    "orthogonal",
    "path_factorial",
    "path_weighting",
    "paths",
    "plot_htmt",
    "plot_interaction",
    "plot_scores",
    "PLSc",
    "predict_DA",
    "predict_EA",
    "predict_pls",
    "product_indicator",
    "quadratic_term",
    "reflective",
    "regression_weights",
    "relationships",
    "report_missing",
    "report_paths",
    "rerun",
    "rho_A",
    "rhoC_AVE",
    "save_plot",
    "seminr_theme_academic",
    "seminr_theme_create",
    "seminr_theme_dark",
    "seminr_theme_default",
    "seminr_theme_get",
    "seminr_theme_set",
    "seminr_theme_smart",
    "set_last_seminr_plot",
    "simplePLS",
    "single_item",
    "slope_analysis",
    "specific_effect_significance",
    "specify_model",
    "total_indirect_ci",
    "two_stage",
    "unit_weights"
  ],
  "_datasets": [
    {
      "name": "corp_rep_data",
      "title": "Measurement Instrument for the Corporate Reputation Model",
      "object": "corp_rep_data",
      "class": [
        "data.frame"
      ],
      "fields": [
        "servicetype",
        "serviceprovider",
        "cusa",
        "cusl_1",
        "cusl_2",
        "cusl_3",
        "qual_1",
        "qual_2",
        "qual_3",
        "qual_4",
        "qual_5",
        "qual_6",
        "qual_7",
        "qual_8",
        "perf_1",
        "perf_2",
        "perf_3",
        "perf_4",
        "perf_5",
        "csor_1",
        "csor_2",
        "csor_3",
        "csor_4",
        "csor_5",
        "attr_1",
        "attr_2",
        "attr_3",
        "comp_1",
        "comp_2",
        "comp_3",
        "like_1",
        "like_2",
        "like_3",
        "qual_global",
        "perf_global",
        "csor_global",
        "attr_global",
        "switch_1",
        "switch_2",
        "switch_3",
        "switch_4"
      ],
      "rows": 344,
      "table": true,
      "tojson": true
    },
    {
      "name": "corp_rep_data2",
      "title": "A Second Measurement Instrument for the Corporate Reputation Model",
      "object": "corp_rep_data2",
      "class": [
        "data.frame"
      ],
      "fields": [
        "servicetype",
        "serviceprovider",
        "cusa",
        "cusl_1",
        "cusl_2",
        "cusl_3",
        "qual_1",
        "qual_2",
        "qual_3",
        "qual_4",
        "qual_5",
        "qual_6",
        "qual_7",
        "qual_8",
        "perf_1",
        "perf_2",
        "perf_3",
        "perf_4",
        "perf_5",
        "csor_1",
        "csor_2",
        "csor_3",
        "csor_4",
        "csor_5",
        "attr_1",
        "attr_2",
        "attr_3",
        "comp_1",
        "comp_2",
        "comp_3",
        "like_1",
        "like_2",
        "like_3",
        "qual_global",
        "perf_global",
        "csor_global",
        "attr_global",
        "switch_1",
        "switch_2",
        "switch_3",
        "switch_4"
      ],
      "rows": 347,
      "table": true,
      "tojson": true
    },
    {
      "name": "influencer_data",
      "title": "Measurement Instrument for the Influencer Model",
      "object": "influencer_data",
      "class": [
        "data.frame"
      ],
      "fields": [
        "pic_1",
        "pic_2",
        "pic_3",
        "pic_4",
        "pic_5",
        "sic_global",
        "sic_1",
        "sic_2",
        "sic_3",
        "sic_4",
        "sic_5",
        "sic_6",
        "sic_7",
        "pq_1",
        "pq_2",
        "pq_3",
        "pq_4",
        "pl_1",
        "pl_2",
        "pl_3",
        "pl_4",
        "pi_1",
        "pi_2",
        "pi_3",
        "pi_4",
        "pi_5",
        "wtp",
        "influencer_group"
      ],
      "rows": 222,
      "table": true,
      "tojson": true
    },
    {
      "name": "mobi",
      "title": "Measurement Instrument for the Mobile Phone Industry",
      "object": "mobi",
      "class": [
        "data.frame"
      ],
      "fields": [
        "CUEX1",
        "CUEX2",
        "CUEX3",
        "CUSA1",
        "CUSA2",
        "CUSA3",
        "CUSCO",
        "CUSL1",
        "CUSL2",
        "CUSL3",
        "IMAG1",
        "IMAG2",
        "IMAG3",
        "IMAG4",
        "IMAG5",
        "PERQ1",
        "PERQ2",
        "PERQ3",
        "PERQ4",
        "PERQ5",
        "PERQ6",
        "PERQ7",
        "PERV1",
        "PERV2"
      ],
      "rows": 250,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "all_composites",
      "title": "Get all composite constructs in a model",
      "topics": [
        "all_composites"
      ]
    },
    {
      "page": "all_factors",
      "title": "Get all common-factor (reflective) constructs in a model",
      "topics": [
        "all_factors"
      ]
    },
    {
      "page": "all_non_interactions",
      "title": "Select non-interaction constructs from a measurement model",
      "topics": [
        "all_non_interactions"
      ]
    },
    {
      "page": "as.reflective",
      "title": "Converts all contructs of a measurement model, or just a single construct into reflective factors.",
      "topics": [
        "as.reflective"
      ]
    },
    {
      "page": "as.reflective.construct",
      "title": "Converts a contruct of a measurement model into a reflective factor.",
      "topics": [
        "as.reflective.construct"
      ]
    },
    {
      "page": "as.reflective.interaction",
      "title": "Converts interaction of a measurement model into a reflective factors.",
      "topics": [
        "as.reflective.interaction"
      ]
    },
    {
      "page": "as.reflective.measurement_model",
      "title": "Converts all contructs of a measurement model, or just a single construct into reflective factors.",
      "topics": [
        "as.reflective.measurement_model"
      ]
    },
    {
      "page": "associations",
      "title": "Specifies inter-item covariances that should be supplied to CBSEM estimation ('estimate_cbsem') or CFA estimation ('estimate_cfa')",
      "topics": [
        "associations"
      ]
    },
    {
      "page": "boot_paths_df",
      "title": "Return all path bootstraps as a long dataframe. Columns of the dataframes are specified paths and rows are the estimated coefficients for the paths at each bootstrap iteration.",
      "topics": [
        "boot_paths_df"
      ]
    },
    {
      "page": "bootstrap_model",
      "title": "seminr bootstrap_model Function",
      "topics": [
        "bootstrap_model"
      ]
    },
    {
      "page": "browse_plot",
      "title": "Open Edotor graphViz Website with the preloaded in the Browser",
      "topics": [
        "browse_plot"
      ]
    },
    {
      "page": "composite",
      "title": "Composite construct measurement model specification",
      "topics": [
        "composite"
      ]
    },
    {
      "page": "compute_itcriteria_weights",
      "title": "Function to calculate Akaike weights for IT Criteria",
      "topics": [
        "compute_itcriteria_weights"
      ]
    },
    {
      "page": "construct_items",
      "title": "Get indicator item names for a construct",
      "topics": [
        "construct_items"
      ]
    },
    {
      "page": "construct_mode",
      "title": "Get the measurement mode of a construct",
      "topics": [
        "construct_mode"
      ]
    },
    {
      "page": "construct_name",
      "title": "Get the name of a single construct specification",
      "topics": [
        "construct_name"
      ]
    },
    {
      "page": "construct_names",
      "title": "Get construct names from a model or model component",
      "topics": [
        "construct_names"
      ]
    },
    {
      "page": "construct_type",
      "title": "Get the user-facing measurement type of a construct",
      "topics": [
        "construct_type"
      ]
    },
    {
      "page": "constructs",
      "title": "Measurement functions",
      "topics": [
        "constructs"
      ]
    },
    {
      "page": "cor_rsq",
      "title": "Returns R-sq of a dv given correlation matrix of ivs, dv cors <- cbsem_summary$descriptives$correlations$constructs cor_rsq(cors, dv_name = \"Value\", iv_names = c(\"Image\", \"Quality\"))",
      "topics": [
        "cor_rsq"
      ]
    },
    {
      "page": "corp_rep_data",
      "title": "Measurement Instrument for the Corporate Reputation Model",
      "topics": [
        "corp_rep_data"
      ]
    },
    {
      "page": "corp_rep_data2",
      "title": "A Second Measurement Instrument for the Corporate Reputation Model",
      "topics": [
        "corp_rep_data2"
      ]
    },
    {
      "page": "csem2seminr",
      "title": "seminr csem2seminr() function",
      "topics": [
        "csem2seminr"
      ]
    },
    {
      "page": "df_xtab_matrix",
      "title": "Cross-tabulates columns of a dataframe into a matrix with NAs for unspecified pairs",
      "topics": [
        "df_xtab_matrix"
      ]
    },
    {
      "page": "dot_component_mm",
      "title": "Generates the dot code for the measurement model",
      "topics": [
        "dot_component_mm"
      ]
    },
    {
      "page": "dot_graph",
      "title": "Generate a dot graph from various SEMinR models",
      "topics": [
        "dot_graph",
        "dot_graph.boot_seminr_model",
        "dot_graph.cbsem_model",
        "dot_graph.cfa_model",
        "dot_graph.measurement_model",
        "dot_graph.pls_model",
        "dot_graph.specified_model",
        "dot_graph.structural_model"
      ]
    },
    {
      "page": "dot_graph_htmt",
      "title": "Creates a dot string with a network graph of constructs based on HTMT measures",
      "topics": [
        "dot_graph_htmt"
      ]
    },
    {
      "page": "dot_subcomponent_mm",
      "title": "generates the dot code for a subgraph (per construct)",
      "topics": [
        "dot_subcomponent_mm"
      ]
    },
    {
      "page": "edge_template_default",
      "title": "The default template for labeling bootstrapped edges",
      "topics": [
        "edge_template_default"
      ]
    },
    {
      "page": "edge_template_minimal",
      "title": "A minimal template for labeling bootstrapped edges that only shows the bootstrapped mean value",
      "topics": [
        "edge_template_minimal"
      ]
    },
    {
      "page": "esc_node",
      "title": "Wrap a text in single quotes",
      "topics": [
        "esc_node"
      ]
    },
    {
      "page": "estimate_cbsem",
      "title": "seminr estimate_cbsem() function",
      "topics": [
        "estimate_cbsem"
      ]
    },
    {
      "page": "estimate_cfa",
      "title": "seminr estimate_cfa() function",
      "topics": [
        "estimate_cfa"
      ]
    },
    {
      "page": "estimate_lavaan_ten_berge",
      "title": "seminr estimate_lavaan_ten_berge() function",
      "topics": [
        "estimate_lavaan_ten_berge"
      ]
    },
    {
      "page": "estimate_pls",
      "title": "seminr estimate_pls() function",
      "topics": [
        "estimate_pls"
      ]
    },
    {
      "page": "estimate_pls_mga",
      "title": "Performs PLS-MGA to report significance of path differences between two subgroups of data",
      "topics": [
        "estimate_pls_mga"
      ]
    },
    {
      "page": "extract_bootstrapped_values",
      "title": "extract bootstrapped statistics from an edge using a row_index",
      "topics": [
        "extract_bootstrapped_values"
      ]
    },
    {
      "page": "extract_htmt_nodes",
      "title": "Helper function that applies formatting to each construct",
      "topics": [
        "extract_htmt_nodes"
      ]
    },
    {
      "page": "extract_mm_coding",
      "title": "extracts the constructs and their types from the model",
      "topics": [
        "extract_mm_coding"
      ]
    },
    {
      "page": "extract_mm_edge_value",
      "title": "gets the mm_edge value (loading, weight) for bootstrapped and regular models",
      "topics": [
        "extract_mm_edge_value"
      ]
    },
    {
      "page": "extract_mm_edges",
      "title": "extract mm edges from model for a given index of all constructs",
      "topics": [
        "extract_mm_edges"
      ]
    },
    {
      "page": "extract_mm_nodes",
      "title": "gets the individual nodes and applies formatting",
      "topics": [
        "extract_mm_nodes"
      ]
    },
    {
      "page": "extract_sm_nodes",
      "title": "Helper function that applies formatting to each construct",
      "topics": [
        "extract_sm_nodes"
      ]
    },
    {
      "page": "format_endo_node_label",
      "title": "Helps to render a node label for endogenous variables",
      "topics": [
        "format_endo_node_label"
      ]
    },
    {
      "page": "format_exo_node_label",
      "title": "Helps to render a node label for exogenous variables",
      "topics": [
        "format_exo_node_label"
      ]
    },
    {
      "page": "fSquared",
      "title": "seminr fSquared Function",
      "topics": [
        "fSquared"
      ]
    },
    {
      "page": "get_construct_element_size",
      "title": "Gets the optimal size for construct elements in the plot",
      "topics": [
        "get_construct_element_size"
      ]
    },
    {
      "page": "get_manifest_element_size",
      "title": "Gets the optimal size for manifest elements in the plot",
      "topics": [
        "get_manifest_element_size"
      ]
    },
    {
      "page": "get_mm_edge_style",
      "title": "individual styles for measurement model edges",
      "topics": [
        "get_mm_edge_style"
      ]
    },
    {
      "page": "get_mm_node_shape",
      "title": "Get a string to insert into a node specification using the themed shape",
      "topics": [
        "get_mm_node_shape"
      ]
    },
    {
      "page": "get_mm_node_style",
      "title": "get global measurement model node style",
      "topics": [
        "get_mm_node_style"
      ]
    },
    {
      "page": "get_sm_node_shape",
      "title": "Get a string to insert into a node specification using the themed shape",
      "topics": [
        "get_sm_node_shape"
      ]
    },
    {
      "page": "get_value_dependent_mm_edge_style",
      "title": "Formats the style of the structural model edges",
      "topics": [
        "get_value_dependent_mm_edge_style"
      ]
    },
    {
      "page": "get_value_dependent_sm_edge_style",
      "title": "Formats the style of the structural model edges",
      "topics": [
        "get_value_dependent_sm_edge_style"
      ]
    },
    {
      "page": "higher_composite",
      "title": "higher_composite",
      "topics": [
        "higher_composite"
      ]
    },
    {
      "page": "higher_reflective",
      "title": "higher_reflective",
      "topics": [
        "higher_reflective"
      ]
    },
    {
      "page": "influencer_data",
      "title": "Measurement Instrument for the Influencer Model",
      "topics": [
        "influencer_data"
      ]
    },
    {
      "page": "interaction_term",
      "title": "Interaction function",
      "topics": [
        "interaction_term"
      ]
    },
    {
      "page": "is_only_endogenous",
      "title": "Tests whether the i_th construct is endogenous or not",
      "topics": [
        "is_only_endogenous"
      ]
    },
    {
      "page": "item_errors",
      "title": "Specifies pair of items, or sets of items, that should covary. Used to specify error covariances for 'associations'.",
      "topics": [
        "item_errors"
      ]
    },
    {
      "page": "mean_replacement",
      "title": "Function to clean data of omitted values by mean replacement",
      "topics": [
        "mean_replacement"
      ]
    },
    {
      "page": "mobi",
      "title": "Measurement Instrument for the Mobile Phone Industry",
      "topics": [
        "mobi"
      ]
    },
    {
      "page": "mode_A",
      "title": "Outer weighting scheme functions to estimate construct weighting.",
      "topics": [
        "correlation_weights",
        "mode_A",
        "mode_A,"
      ]
    },
    {
      "page": "mode_B",
      "title": "Outer weighting scheme functions to estimate construct weighting.",
      "topics": [
        "mode_B",
        "mode_B,",
        "regression_weights"
      ]
    },
    {
      "page": "mode_plsc",
      "title": "Outer weighting scheme functions to estimate construct weighting.",
      "topics": [
        "mode_plsc"
      ]
    },
    {
      "page": "multi_items",
      "title": "Multi-items measurement model specification",
      "topics": [
        "multi_items"
      ]
    },
    {
      "page": "node_endo_template_default",
      "title": "The default template for labeling endogenous construct nodes",
      "topics": [
        "node_endo_template_default"
      ]
    },
    {
      "page": "node_exo_template_default",
      "title": "The default template for labeling exogenous construct nodes",
      "topics": [
        "node_exo_template_default"
      ]
    },
    {
      "page": "orthogonal",
      "title": "'orthogonal' creates interaction measurement items by using the orthogonalized approach wherein",
      "topics": [
        "orthogonal"
      ]
    },
    {
      "page": "path_factorial",
      "title": "Inner weighting scheme functions to estimate inner paths matrix",
      "topics": [
        "path_factorial"
      ]
    },
    {
      "page": "path_weighting",
      "title": "Inner weighting scheme functions to estimate inner paths matrix",
      "topics": [
        "path_weighting"
      ]
    },
    {
      "page": "plot_htmt",
      "title": "Plots a network graph of constructs based on HTMT measures",
      "topics": [
        "plot_htmt"
      ]
    },
    {
      "page": "plot_interaction",
      "title": "Function for plotting interaction plot for moderated PLS or CBSEM model",
      "topics": [
        "plot_interaction"
      ]
    },
    {
      "page": "plot.reliability_table",
      "title": "Function for plotting the measurement model reliability metrics of a PLS model",
      "topics": [
        "plot.reliability_table"
      ]
    },
    {
      "page": "plot.seminr_model",
      "title": "Plot various SEMinR models",
      "topics": [
        "plot.seminr_model"
      ]
    },
    {
      "page": "PLSc",
      "title": "seminr PLSc Function",
      "topics": [
        "PLSc"
      ]
    },
    {
      "page": "predict_DA",
      "title": "Predictive Scheme",
      "topics": [
        "predict_DA"
      ]
    },
    {
      "page": "predict_EA",
      "title": "Predictive Scheme",
      "topics": [
        "predict_EA"
      ]
    },
    {
      "page": "predict_pls",
      "title": "Predict_pls performs either k-fold or LOOCV on a SEMinR PLS model and generates predictions",
      "topics": [
        "predict_pls"
      ]
    },
    {
      "page": "predict.seminr_model",
      "title": "Predict method for SEMinR PLS models",
      "topics": [
        "predict.seminr_model"
      ]
    },
    {
      "page": "print.seminr_pls_mga",
      "title": "Summary function for PLS-MGA",
      "topics": [
        "print.seminr_pls_mga"
      ]
    },
    {
      "page": "product_indicator",
      "title": "'product_indicator' creates interaction measurement items by scaled product indicator approach.",
      "topics": [
        "product_indicator"
      ]
    },
    {
      "page": "quadratic_term",
      "title": "Quadratic term function",
      "topics": [
        "quadratic_term"
      ]
    },
    {
      "page": "reflective",
      "title": "Reflective construct measurement model specification",
      "topics": [
        "reflective"
      ]
    },
    {
      "page": "relationships",
      "title": "Structural specification functions for seminr package",
      "topics": [
        "paths",
        "relationships"
      ]
    },
    {
      "page": "report_missing",
      "title": "Function to report how missing data was handled and how much was missing.",
      "topics": [
        "report_missing"
      ]
    },
    {
      "page": "report_paths",
      "title": "Functions for reporting the Path Coefficients and R2 of endogenous constructs and for generating a scatterplot matrix of construct scores.",
      "topics": [
        "plot_scores",
        "report_paths"
      ]
    },
    {
      "page": "rerun",
      "title": "Reruns a previously specified seminr model/analysis",
      "topics": [
        "rerun"
      ]
    },
    {
      "page": "rerun.pls_model",
      "title": "Reruns a previously specified seminr PLS model",
      "topics": [
        "rerun.pls_model"
      ]
    },
    {
      "page": "rho_A",
      "title": "seminr rho_A Function",
      "topics": [
        "rho_A"
      ]
    },
    {
      "page": "rhoC_AVE",
      "title": "seminr rhoC_AVE() function",
      "topics": [
        "rhoC_AVE"
      ]
    },
    {
      "page": "save_plot",
      "title": "Saves a SEMinR model plot to file",
      "topics": [
        "save_plot"
      ]
    },
    {
      "page": "seminr_theme_academic",
      "title": "A theme function for a basic b/w theme",
      "topics": [
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      "page": "seminr_theme_create",
      "title": "Create a theme for a seminr graph visualization",
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    {
      "page": "seminr_theme_dark",
      "title": "The theme function for an inverted theme on black background.",
      "topics": [
        "seminr_theme_dark"
      ]
    },
    {
      "page": "seminr_theme_get",
      "title": "Get and set the active theme",
      "topics": [
        "seminr_theme_get",
        "seminr_theme_set"
      ]
    },
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      "page": "seminr_theme_default",
      "title": "A colored theme",
      "topics": [
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      "page": "simplePLS",
      "title": "seminr simplePLS Function",
      "topics": [
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      ]
    },
    {
      "page": "single_item",
      "title": "Single-item measurement model specification",
      "topics": [
        "single_item"
      ]
    },
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      "title": "Function for plotting a slope analysis for an interaction in a PLS model",
      "topics": [
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    },
    {
      "page": "specific_effect_significance",
      "title": "seminr specific effect significance function",
      "topics": [
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    {
      "page": "specify_model",
      "title": "seminr specify_model() function",
      "topics": [
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    {
      "page": "standardize_safely",
      "title": "Standardize (scale) a matrix/df and report interpretable errors",
      "topics": [
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    },
    {
      "page": "total_indirect_ci",
      "title": "seminr total indirect confidence intervals function",
      "topics": [
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      ]
    },
    {
      "page": "two_stage",
      "title": "Creates an interaction measurement item using a two-stage approach. The two-stage procedure for both PLS and CBSEM models estimates construct scores in the first stage, and uses them to produce a single-item product item for the interaction term in the second stage. For a PLS model, the first stage uses PLS to compute construct scores. For a CBSEM model, the first stage uses a CFA to produce ten Berge construct scores.",
      "topics": [
        "two_stage"
      ]
    },
    {
      "page": "unit_weights",
      "title": "Outer weighting scheme functions to estimate construct weighting.",
      "topics": [
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    {
      "source": "SEMinR.Rmd",
      "filename": "SEMinR.html",
      "title": "SEMinR",
      "author": "Soumya Ray & Nicholas Danks",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Introduction",
        "Setup",
        "Data",
        "Measurement model description",
        "Specifying measurement models with constructs",
        "Describe individual constructs as composite or reflective",
        "Converting composite models into reflective models",
        "Specifying construct measurement items",
        "Item associations (CBSEM only)",
        "Interaction terms",
        "Structural model description",
        "Specify structural model of relationships between constructs",
        "Specify structural paths with",
        "Model Estimation",
        "Consistent PLS (PLSc) estimation for common factors",
        "Bootstrapping PLS models for significance",
        "Reporting the model estimation results",
        "Reporting the estimated model",
        "Reporting results of a bootstrapped PLS",
        "Reporting confidence intervals for direct and mediated bootstrapped structural paths",
        "Reporting data descriptive statistics and construct descriptive statistics",
        "Prediction and cross-validation (PLSpredict)",
        "Single holdout prediction",
        "Cross-validated prediction with predict_pls()",
        "Parallelization",
        "Plotting models",
        "References"
      ],
      "created": "2016-10-11 04:33:11",
      "modified": "2026-05-25 09:14:30",
      "commits": 36
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}