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  "Package": "multivar",
  "Title": "Penalized Estimation of Multiple-Subject Vector Autoregressive\nModels",
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  "Description": "Simulate, estimate, and forecast vector autoregressive\n(VAR) models for multiple-subject data using structured\npenalization. Decomposes dynamics into shared (common) and\nsubject-specific (unique) components via adaptive LASSO with\nFISTA optimization. Supports cross-validation and extended BIC\nmodel selection and subgroup detection, and time-varying\nparameters.",
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    "canonical.multivar",
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    "constructModel",
    "cv_blocked",
    "cv_multivar",
    "cv.multivar",
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    "get_common_cols",
    "get_common_effects",
    "get_common_tvp_cols",
    "get_dynamics",
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    "get_n_periods",
    "get_period_rows",
    "get_subgrp_cols",
    "get_subgrp_cols_for_subject",
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    "get_total_effects",
    "get_unique_cols",
    "get_unique_effects",
    "get_unique_tvp_cols",
    "is_tvp",
    "lambda_grid",
    "multivar_sim",
    "multivar_sim_breaks",
    "multivar_sim_growth",
    "multivar_sim_subgroups",
    "multivar_sim_tvp",
    "plot_cv_lambda_grid",
    "plot_results",
    "plot_sim",
    "plot_transition_mat",
    "pretrained_var_stage1_glmnet",
    "print_dynamics",
    "print_dynamics_tvp",
    "print_edge_prevalence",
    "recover_intercepts",
    "select_by_ebic",
    "show",
    "summary_multivar",
    "validate_matrix_spec",
    "var_sim_tvp"
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      "object": "dat_multivar_sim",
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      ],
      "fields": [],
      "table": false,
      "tojson": true
    }
  ],
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      "page": "multivar-package",
      "title": "multivar: Penalized Estimation of Multiple-Subject Vector Autoregressive Models",
      "topics": [
        "multivar-package"
      ]
    },
    {
      "page": "build_matrix_spec",
      "title": "Build Design Matrix Specification",
      "topics": [
        "build_matrix_spec"
      ]
    },
    {
      "page": "build_scenario_table",
      "title": "Reconstruct scenario-level multipliers used to build W",
      "topics": [
        "build_scenario_table"
      ]
    },
    {
      "page": "canonical.multivar-methods",
      "title": "Canonical VAR Fitting Function for multivar",
      "topics": [
        "canonical.multivar",
        "canonical.multivar,multivar-method"
      ]
    },
    {
      "page": "compute_ebic",
      "title": "Compute EBIC for an array of fitted coefficient matrices",
      "topics": [
        "compute_ebic"
      ]
    },
    {
      "page": "constructModel",
      "title": "Construct an object of class multivar",
      "topics": [
        "constructModel"
      ]
    },
    {
      "page": "cv_blocked",
      "title": "Blocked Cross-Validation for multivar",
      "topics": [
        "cv_blocked"
      ]
    },
    {
      "page": "cv_multivar",
      "title": "Cross-Validation Dispatcher for multivar",
      "topics": [
        "cv_multivar"
      ]
    },
    {
      "page": "cv.multivar-methods",
      "title": "Cross Validation for multivar",
      "topics": [
        "cv.multivar",
        "cv.multivar,multivar-method"
      ]
    },
    {
      "page": "dat_multivar_sim",
      "title": "Simulated multi-VAR data.",
      "topics": [
        "dat_multivar_sim"
      ]
    },
    {
      "page": "estimate_initial_coefs",
      "title": "Estimate initial coefficients for adaptive weights",
      "topics": [
        "estimate_initial_coefs"
      ]
    },
    {
      "page": "eval_multivar_performance",
      "title": "Summarize multivar performance against simulation truth (robust to missing parts)",
      "topics": [
        "eval_multivar_performance"
      ]
    },
    {
      "page": "expand_range",
      "title": "Expand Column Range to Indices",
      "topics": [
        "expand_range"
      ]
    },
    {
      "page": "extract_multivar_hyperparams",
      "title": "Extract hyperparameter grid search results",
      "topics": [
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      ]
    },
    {
      "page": "get_common_cols",
      "title": "Get Common Effect Columns",
      "topics": [
        "get_common_cols"
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    },
    {
      "page": "get_common_tvp_cols",
      "title": "Get Common TVP Columns for a Period",
      "topics": [
        "get_common_tvp_cols"
      ]
    },
    {
      "page": "get_dynamics_helpers",
      "title": "Helper Functions for Accessing multivar Results",
      "topics": [
        "get_common_effects",
        "get_dynamics",
        "get_dynamics_all_subjects",
        "get_dynamics_helpers",
        "get_total_effects",
        "get_unique_effects"
      ]
    },
    {
      "page": "get_n_periods",
      "title": "Get Number of Periods in TVP Model",
      "topics": [
        "get_n_periods"
      ]
    },
    {
      "page": "get_period_rows",
      "title": "Get Row Range for a Subject's Period",
      "topics": [
        "get_period_rows"
      ]
    },
    {
      "page": "get_subgrp_cols",
      "title": "Get Subgroup Effect Columns",
      "topics": [
        "get_subgrp_cols"
      ]
    },
    {
      "page": "get_subgrp_cols_for_subject",
      "title": "Get Subgroup Columns for a Subject",
      "topics": [
        "get_subgrp_cols_for_subject"
      ]
    },
    {
      "page": "get_subject_rows",
      "title": "Get Row Range for a Subject",
      "topics": [
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      ]
    },
    {
      "page": "get_unique_cols",
      "title": "Get Unique Effect Columns for a Subject",
      "topics": [
        "get_unique_cols"
      ]
    },
    {
      "page": "get_unique_tvp_cols",
      "title": "Get Unique TVP Columns for a Subject and Period",
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        "get_unique_tvp_cols"
      ]
    },
    {
      "page": "is_tvp",
      "title": "Check if Model is Time-Varying",
      "topics": [
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      ]
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      "title": "Build the lambda1 path (one column per penalty scenario)",
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        "lambda_grid"
      ]
    },
    {
      "page": "maity_debiasing",
      "title": "Maity et al. (2022) MrLasso-style initial estimation",
      "topics": [
        "maity_debiasing"
      ]
    },
    {
      "page": "multivar_fit_methods",
      "title": "S3 Methods for multivar_fit Objects",
      "topics": [
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        "multivar_fit_methods",
        "plot.multivar_fit",
        "print.multivar_fit",
        "summary.multivar_fit"
      ]
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    {
      "page": "multivar_sim",
      "title": "Simulate multivar data.",
      "topics": [
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      ]
    },
    {
      "page": "multivar_sim_breaks",
      "title": "Simulate multivar data with time-varying dynamics according to a latent growth curve.",
      "topics": [
        "multivar_sim_breaks"
      ]
    },
    {
      "page": "multivar_sim_growth",
      "title": "Simulate multivar data with time-varying dynamics according to a latent growth curve.",
      "topics": [
        "multivar_sim_growth"
      ]
    },
    {
      "page": "multivar_sim_subgroups",
      "title": "Simulate multivar data with explicit subgroup structure",
      "topics": [
        "multivar_sim_subgroups"
      ]
    },
    {
      "page": "multivar_sim_tvp",
      "title": "Simulate multivar TVP data (piecewise or continuous)",
      "topics": [
        "multivar_sim_tvp"
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    },
    {
      "page": "multivar-class",
      "title": "multivar object class",
      "topics": [
        "multivar-class"
      ]
    },
    {
      "page": "plot_cv_lambda_grid",
      "title": "Plot CV error over (lambda1, lambda2)",
      "topics": [
        "plot_cv_lambda_grid"
      ]
    },
    {
      "page": "plot_results",
      "title": "Plot fitted multivar model results",
      "topics": [
        "plot_results"
      ]
    },
    {
      "page": "plot_sim",
      "title": "Plot simulated multivar ground truth",
      "topics": [
        "plot_sim"
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    },
    {
      "page": "plot_transition_mat",
      "title": "Plot arbitrary transition matrix",
      "topics": [
        "plot_transition_mat"
      ]
    },
    {
      "page": "pretrained_var_stage1_glmnet",
      "title": "Stage 1 (glmnet): common block only, with optional per-equation intercepts",
      "topics": [
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    {
      "page": "print_dynamics",
      "title": "Print dynamics matrix with clear formatting",
      "topics": [
        "print_dynamics"
      ]
    },
    {
      "page": "print_dynamics_tvp",
      "title": "Print time-varying dynamics (TVP models)",
      "topics": [
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    },
    {
      "page": "print_edge_prevalence",
      "title": "Print edge prevalence across subjects",
      "topics": [
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      "page": "print.matrix_spec",
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      "page": "select_by_ebic",
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