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  "Package": "risdr",
  "Title": "Regularised and Information-Theoretic Sufficient Dimension\nReduction",
  "Version": "0.4.0",
  "Authors@R": "person(\"Kabir\", \"Olorede\", email = \"kabirolorede@gmail.com\", role = c(\"aut\", \"cre\"))",
  "Description": "Implements covariance-stabilised sufficient dimension\nreduction for continuous responses, with an additional sparse\nengine for continuous, categorical, and right-censored survival\nresponses and information-theoretic structural dimension\nselection. Supported methods include sliced inverse regression,\nsliced average variance estimation, directional regression, and\nprincipal Hessian directions. Sample, ridge, Oracle\nApproximating Shrinkage, Ledoit-Wolf, and Maximum Entropy\nCovariance (MEC) estimators are provided alongside prediction,\nresampling, simulation, and diagnostic utilities. The\nsufficient dimension reduction methods build on Li (1991)\n<doi:10.1080/01621459.1991.10475035>, Li (1992)\n<doi:10.1080/01621459.1992.10476258>, and Li and Wang (2007)\n<doi:10.1198/016214507000000536>. Covariance shrinkage follows\nOlorede and Yahya (2019) <doi:10.48550/arXiv.1909.13017>,\nLedoit and Wolf (2004) <doi:10.1016/S0047-259X(03)00096-4> and\nChen et al. (2010) <doi:10.1109/TSP.2010.2053029>.",
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  "Author": "Kabir Olorede [aut, cre]",
  "Maintainer": "Kabir Olorede <kabirolorede@gmail.com>",
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  "_exports": [
    "adjusted_r_squared",
    "ar1_covariance",
    "C1F",
    "choose_dimension",
    "compute_dr",
    "compute_ic",
    "compute_ic_values",
    "compute_information_criteria",
    "compute_penalty_weights",
    "compute_phd",
    "compute_save",
    "compute_scores",
    "compute_sdr",
    "compute_sir",
    "cov_complexity_C1",
    "cov_complexity_C1F",
    "cov_condition_number",
    "cov_diagnostics",
    "cov_effective_rank",
    "cov_lw",
    "cov_mec",
    "cov_oas",
    "cov_ridge",
    "cov_sample",
    "criterion_weights",
    "dual_pca_scores",
    "estimate_cov",
    "evaluate_prediction",
    "evaluate_prediction_cv",
    "extract_loadings",
    "filter_low_variance",
    "fit_downstream_lm",
    "fit_risdr",
    "fit_risdr_dual",
    "fit_risdr_realdata",
    "fit_risdr_sparse",
    "fit_risdr_sparse_realdata",
    "fit_sparse_grid",
    "mae",
    "make_cv_folds",
    "make_slices",
    "mape",
    "plot_cv_dimension",
    "plot_dimension_selection",
    "plot_ladle",
    "plot_loadings",
    "plot_prediction",
    "plot_residuals",
    "plot_scree",
    "plot_sufficient",
    "plot_sufficient2d",
    "predict_downstream_lm",
    "prediction_correlation",
    "prepare_survival_response",
    "projection_matrix",
    "r_squared",
    "rmse",
    "run_one_simulation",
    "run_risdr_simulation",
    "select_dimension",
    "select_dimension_cv",
    "select_dimension_cv_icomp",
    "select_dimension_ladle",
    "simulate_risdr_data",
    "slice_covariances",
    "slice_means",
    "slice_proportions",
    "slice_response",
    "slice_summary",
    "stabilize_cov",
    "stabilize_eigenfloor",
    "stabilize_nearest_pd",
    "stabilize_ridge",
    "subspace_distance",
    "summarise_simulation",
    "summarise_sparsity",
    "weighted_soft_threshold",
    "weighted_sparsify_directions"
  ],
  "_help": [
    {
      "page": "risdr-package",
      "title": "risdr: Regularised and Information-Theoretic Sufficient Dimension Reduction",
      "topics": [
        "risdr-package",
        "risdr"
      ]
    },
    {
      "page": "adjusted_r_squared",
      "title": "Adjusted coefficient of determination",
      "topics": [
        "adjusted_r_squared"
      ]
    },
    {
      "page": "ar1_covariance",
      "title": "AR(1) covariance matrix",
      "topics": [
        "ar1_covariance"
      ]
    },
    {
      "page": "C1F",
      "title": "C1F complexity and induced adaptive weights",
      "topics": [
        "C1F"
      ]
    },
    {
      "page": "choose_dimension",
      "title": "Choose dimension from criteria table",
      "topics": [
        "choose_dimension"
      ]
    },
    {
      "page": "compute_dr",
      "title": "Compute Directional Regression",
      "topics": [
        "compute_dr"
      ]
    },
    {
      "page": "compute_ic",
      "title": "Compute one reduced-model information criterion",
      "topics": [
        "compute_ic"
      ]
    },
    {
      "page": "compute_ic_values",
      "title": "Compute all reduced-model information criteria",
      "topics": [
        "compute_ic_values"
      ]
    },
    {
      "page": "compute_information_criteria",
      "title": "Compute model selection criteria",
      "topics": [
        "compute_information_criteria"
      ]
    },
    {
      "page": "compute_penalty_weights",
      "title": "Compute adaptive penalty weights from C1F variable contributions",
      "topics": [
        "compute_penalty_weights"
      ]
    },
    {
      "page": "compute_phd",
      "title": "Compute Principal Hessian Directions",
      "topics": [
        "compute_phd"
      ]
    },
    {
      "page": "compute_save",
      "title": "Compute Sliced Average Variance Estimation",
      "topics": [
        "compute_save"
      ]
    },
    {
      "page": "compute_scores",
      "title": "Compute SDR scores for new data",
      "topics": [
        "compute_scores"
      ]
    },
    {
      "page": "compute_sdr",
      "title": "General SDR kernel dispatcher",
      "topics": [
        "compute_sdr"
      ]
    },
    {
      "page": "compute_sir",
      "title": "Compute Sliced Inverse Regression",
      "topics": [
        "compute_sir"
      ]
    },
    {
      "page": "cov_complexity_C1",
      "title": "Covariance complexity C1",
      "topics": [
        "cov_complexity_C1"
      ]
    },
    {
      "page": "cov_complexity_C1F",
      "title": "Scale-invariant covariance complexity C1F",
      "topics": [
        "cov_complexity_C1F"
      ]
    },
    {
      "page": "cov_condition_number",
      "title": "Covariance condition number",
      "topics": [
        "cov_condition_number"
      ]
    },
    {
      "page": "cov_diagnostics",
      "title": "Covariance eigenvalue summary",
      "topics": [
        "cov_diagnostics"
      ]
    },
    {
      "page": "cov_effective_rank",
      "title": "Effective covariance rank",
      "topics": [
        "cov_effective_rank"
      ]
    },
    {
      "page": "cov_lw",
      "title": "Ledoit-Wolf type covariance estimator",
      "topics": [
        "cov_lw"
      ]
    },
    {
      "page": "cov_mec",
      "title": "Maximum Entropy Covariance estimator",
      "topics": [
        "cov_mec"
      ]
    },
    {
      "page": "cov_oas",
      "title": "Oracle Approximating Shrinkage covariance estimator",
      "topics": [
        "cov_oas"
      ]
    },
    {
      "page": "cov_ridge",
      "title": "Ridge-type covariance estimator",
      "topics": [
        "cov_ridge"
      ]
    },
    {
      "page": "cov_sample",
      "title": "Sample covariance matrix",
      "topics": [
        "cov_sample"
      ]
    },
    {
      "page": "criterion_weights",
      "title": "Information criterion weights",
      "topics": [
        "criterion_weights"
      ]
    },
    {
      "page": "dual_pca_scores",
      "title": "Dual PCA score construction for p much larger than n",
      "topics": [
        "dual_pca_scores"
      ]
    },
    {
      "page": "estimate_cov",
      "title": "General covariance estimator dispatcher",
      "topics": [
        "estimate_cov"
      ]
    },
    {
      "page": "evaluate_prediction",
      "title": "Evaluate predictions",
      "topics": [
        "evaluate_prediction"
      ]
    },
    {
      "page": "evaluate_prediction_cv",
      "title": "Repeated cross-validation for predictive assessment",
      "topics": [
        "evaluate_prediction_cv"
      ]
    },
    {
      "page": "extract_loadings",
      "title": "Extract SDR loadings",
      "topics": [
        "extract_loadings"
      ]
    },
    {
      "page": "filter_low_variance",
      "title": "Filter low-variance predictors",
      "topics": [
        "filter_low_variance"
      ]
    },
    {
      "page": "fit_downstream_lm",
      "title": "Fit downstream regression model on SDR scores",
      "topics": [
        "fit_downstream_lm"
      ]
    },
    {
      "page": "fit_risdr",
      "title": "Fit regularised and information-theoretic SDR model",
      "topics": [
        "fit_risdr"
      ]
    },
    {
      "page": "fit_risdr_dual",
      "title": "Fit dual high-dimensional RISDR",
      "topics": [
        "fit_risdr_dual"
      ]
    },
    {
      "page": "fit_risdr_realdata",
      "title": "Fit RISDR to real high-dimensional data",
      "topics": [
        "fit_risdr_realdata"
      ]
    },
    {
      "page": "fit_risdr_sparse",
      "title": "Fit regularised and information-theoretic sufficient dimension reduction",
      "topics": [
        "fit_risdr_sparse"
      ]
    },
    {
      "page": "fit_risdr_sparse_realdata",
      "title": "Fit RISDR to real high-dimensional data",
      "topics": [
        "fit_risdr_sparse_realdata"
      ]
    },
    {
      "page": "fit_sparse_grid",
      "title": "Fit sparse SDR directions over a lambda grid",
      "topics": [
        "fit_sparse_grid"
      ]
    },
    {
      "page": "mae",
      "title": "Mean absolute error",
      "topics": [
        "mae"
      ]
    },
    {
      "page": "make_cv_folds",
      "title": "Create cross-validation folds",
      "topics": [
        "make_cv_folds"
      ]
    },
    {
      "page": "make_slices",
      "title": "Create response slices",
      "topics": [
        "make_slices"
      ]
    },
    {
      "page": "mape",
      "title": "Mean absolute percentage error",
      "topics": [
        "mape"
      ]
    },
    {
      "page": "plot_cv_dimension",
      "title": "Plot cross-validation dimension selection result",
      "topics": [
        "plot_cv_dimension"
      ]
    },
    {
      "page": "plot_dimension_selection",
      "title": "Plot dimension selection criteria",
      "topics": [
        "plot_dimension_selection"
      ]
    },
    {
      "page": "plot_ladle",
      "title": "Plot ladle dimension diagnostic",
      "topics": [
        "plot_ladle"
      ]
    },
    {
      "page": "plot_loadings",
      "title": "Plot SDR loadings",
      "topics": [
        "plot_loadings"
      ]
    },
    {
      "page": "plot_prediction",
      "title": "Prediction plot",
      "topics": [
        "plot_prediction"
      ]
    },
    {
      "page": "plot_residuals",
      "title": "Residual plot",
      "topics": [
        "plot_residuals"
      ]
    },
    {
      "page": "plot_scree",
      "title": "Scree plot of SDR eigenvalues",
      "topics": [
        "plot_scree"
      ]
    },
    {
      "page": "plot_sufficient",
      "title": "Sufficient summary plot",
      "topics": [
        "plot_sufficient"
      ]
    },
    {
      "page": "plot_sufficient2d",
      "title": "Two-direction sufficient summary plot",
      "topics": [
        "plot_sufficient2d"
      ]
    },
    {
      "page": "predict_downstream_lm",
      "title": "Predict from downstream SDR regression model",
      "topics": [
        "predict_downstream_lm"
      ]
    },
    {
      "page": "predict.risdr",
      "title": "Predict method for risdr objects",
      "topics": [
        "predict.risdr"
      ]
    },
    {
      "page": "predict.risdr_sparse",
      "title": "Predict from the sparse or dual SDR engine",
      "topics": [
        "predict.risdr_dual",
        "predict.risdr_sparse"
      ]
    },
    {
      "page": "prediction_correlation",
      "title": "Prediction correlation",
      "topics": [
        "prediction_correlation"
      ]
    },
    {
      "page": "prepare_survival_response",
      "title": "Prepare survival response",
      "topics": [
        "prepare_survival_response"
      ]
    },
    {
      "page": "print.risdr",
      "title": "Print risdr object",
      "topics": [
        "print.risdr"
      ]
    },
    {
      "page": "print.risdr_dual",
      "title": "Print method for dual RISDR objects",
      "topics": [
        "print.risdr_dual"
      ]
    },
    {
      "page": "print.risdr_realdata",
      "title": "Print real-data RISDR workflow",
      "topics": [
        "print.risdr_realdata"
      ]
    },
    {
      "page": "print.risdr_sparse",
      "title": "Print risdr object",
      "topics": [
        "print.risdr_sparse"
      ]
    },
    {
      "page": "print.risdr_sparse_realdata",
      "title": "Print real-data RISDR workflow",
      "topics": [
        "print.risdr_sparse_realdata"
      ]
    },
    {
      "page": "print.summary.risdr",
      "title": "Print summary of risdr object",
      "topics": [
        "print.summary.risdr"
      ]
    },
    {
      "page": "print.summary.risdr_sparse",
      "title": "Print summary of risdr object",
      "topics": [
        "print.summary.risdr_sparse"
      ]
    },
    {
      "page": "projection_matrix",
      "title": "Projection matrix",
      "topics": [
        "projection_matrix"
      ]
    },
    {
      "page": "r_squared",
      "title": "Coefficient of determination",
      "topics": [
        "r_squared"
      ]
    },
    {
      "page": "rmse",
      "title": "Root mean squared error",
      "topics": [
        "rmse"
      ]
    },
    {
      "page": "run_one_simulation",
      "title": "Run one SDR simulation replication",
      "topics": [
        "run_one_simulation"
      ]
    },
    {
      "page": "run_risdr_simulation",
      "title": "Run SDR simulation study",
      "topics": [
        "run_risdr_simulation"
      ]
    },
    {
      "page": "select_dimension",
      "title": "Select structural dimension",
      "topics": [
        "select_dimension"
      ]
    },
    {
      "page": "select_dimension_cv",
      "title": "Cross-validation dimension selection for SDR",
      "topics": [
        "select_dimension_cv"
      ]
    },
    {
      "page": "select_dimension_cv_icomp",
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