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  "Title": "Penalized Elastic Net S/MM-Estimator of Regression",
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  "Authors@R": "c(\nperson(\"David\", \"Kepplinger\", , \"david.kepplinger@gmail.com\",\nrole = c(\"aut\", \"cre\")),\nperson(\"Matías\", \"Salibián-Barrera\", role = c(\"aut\")),\nperson(\"Gabriela\", \"Cohen Freue\", role = \"aut\")\n)",
  "Copyright": "See the file COPYRIGHT for copyright details on some of the\nfunctions and algorithms used.",
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  "Description": "Robust penalized (adaptive) elastic net S and M estimators\nfor linear regression. The adaptive methods are proposed in\nKepplinger, D. (2023) <doi:10.1016/j.csda.2023.107730> and the\nnon-adaptive methods in Cohen Freue, G. V., Kepplinger, D.,\nSalibián-Barrera, M., and Smucler, E. (2019)\n<doi:10.1214/19-AOAS1269>. The package implements robust\nhyper-parameter selection with robust information sharing\ncross-validation according to Kepplinger & Wei (2025)\n<doi:10.1080/00401706.2025.2540970>.",
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    "enpy_initial_estimates",
    "enpy_options",
    "mloc",
    "mlocscale",
    "mm_algorithm_options",
    "mscale",
    "mscale_algorithm_options",
    "pense",
    "pense_cv",
    "prediction_performance",
    "prinsens",
    "regmest",
    "regmest_cv",
    "rho_function",
    "starting_point",
    "tau_size"
  ],
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      "title": "Coordinate Descent (CD) Algorithm to Compute Penalized Elastic Net S-estimates",
      "concept": [
        "Robust EN algorithms"
      ],
      "topics": [
        "cd_algorithm_options"
      ]
    },
    {
      "page": "change_cv_measure",
      "title": "Change the Cross-Validation Measure",
      "concept": [
        "functions to compute robust estimates with CV"
      ],
      "topics": [
        "change_cv_measure"
      ]
    },
    {
      "page": "coef.pense_cvfit",
      "title": "Extract Coefficient Estimates",
      "concept": [
        "functions for extracting components"
      ],
      "topics": [
        "coef.pense_cvfit"
      ]
    },
    {
      "page": "coef.pense_fit",
      "title": "Extract Coefficient Estimates",
      "concept": [
        "functions for extracting components"
      ],
      "topics": [
        "coef.pense_fit"
      ]
    },
    {
      "page": "rho-tuning-constants",
      "title": "Get the Constant for Consistency for the M-Scale and for Efficiency for the M-estimate of Location",
      "concept": [
        "Robustness control options"
      ],
      "topics": [
        "consistency_const",
        "efficiency_const"
      ]
    },
    {
      "page": "elnet",
      "title": "Compute the Least Squares (Adaptive) Elastic Net Regularization Path",
      "concept": [
        "functions for computing non-robust estimates"
      ],
      "topics": [
        "adaelnet",
        "adaen",
        "elnet"
      ]
    },
    {
      "page": "elnet_cv",
      "title": "Cross-validation for Least-Squares (Adaptive) Elastic Net Estimates",
      "concept": [
        "functions for computing non-robust estimates"
      ],
      "topics": [
        "elnet_cv"
      ]
    },
    {
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      "title": "Use the ADMM Elastic Net Algorithm",
      "concept": [
        "LS-EN algorithm options"
      ],
      "topics": [
        "en_admm_options"
      ]
    },
    {
      "page": "en_algorithm_options",
      "title": "Control the Algorithm to Compute (Weighted) Least-Squares Elastic Net Estimates",
      "concept": [
        "LS-EN algorithm options"
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        "en_algorithm_options"
      ]
    },
    {
      "page": "en_cd_options",
      "title": "Use Coordinate Descent to Solve Elastic Net Problems",
      "concept": [
        "LS-EN algorithm options"
      ],
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        "en_cd_options"
      ]
    },
    {
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      "title": "Use the DAL Elastic Net Algorithm",
      "concept": [
        "LS-EN algorithm options"
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        "en_dal_options"
      ]
    },
    {
      "page": "en_lars_options",
      "title": "Use the LARS Elastic Net Algorithm",
      "concept": [
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        "en_lars_options"
      ]
    },
    {
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      "concept": [
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      ]
    },
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      "concept": [
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      ]
    },
    {
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      "title": "Compute the M-estimate of Location",
      "concept": [
        "functions to compute robust estimates of location and scale"
      ],
      "topics": [
        "mloc"
      ]
    },
    {
      "page": "mlocscale",
      "title": "Compute the M-estimate of Location and Scale",
      "concept": [
        "functions to compute robust estimates of location and scale"
      ],
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        "mlocscale"
      ]
    },
    {
      "page": "mm_algorithm_options",
      "title": "MM-Algorithm to Compute Penalized Elastic Net S- and M-Estimates",
      "concept": [
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      ],
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        "mm_algorithm_options"
      ]
    },
    {
      "page": "mscale",
      "title": "Compute the M-Scale of Centered Values",
      "concept": [
        "functions to compute robust estimates of location and scale"
      ],
      "topics": [
        "mscale"
      ]
    },
    {
      "page": "mscale_algorithm_options",
      "title": "Options for the M-scale Estimation Algorithm",
      "concept": [
        "Robustness control options"
      ],
      "topics": [
        "mscale_algorithm_options"
      ]
    },
    {
      "page": "pense",
      "title": "Compute (Adaptive) Elastic Net S-Estimates of Regression",
      "concept": [
        "functions to compute robust estimates"
      ],
      "topics": [
        "adapense",
        "pense"
      ]
    },
    {
      "page": "pense_cv",
      "title": "Cross-validation for (Adaptive) PENSE Estimates",
      "concept": [
        "functions to compute robust estimates with CV"
      ],
      "topics": [
        "adapense_cv",
        "pense_cv"
      ]
    },
    {
      "page": "plot.pense_cvfit",
      "title": "Plot Method for Penalized Estimates With Cross-Validation",
      "concept": [
        "functions for plotting and printing"
      ],
      "topics": [
        "plot.pense_cvfit"
      ]
    },
    {
      "page": "plot.pense_fit",
      "title": "Plot Method for Penalized Estimates",
      "concept": [
        "functions for plotting and printing"
      ],
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        "plot.pense_fit"
      ]
    },
    {
      "page": "predict.pense_cvfit",
      "title": "Predict Method for PENSE Fits",
      "concept": [
        "functions for extracting components"
      ],
      "topics": [
        "predict.pense_cvfit"
      ]
    },
    {
      "page": "predict.pense_fit",
      "title": "Predict Method for PENSE Fits",
      "concept": [
        "functions for extracting components"
      ],
      "topics": [
        "predict.pense_fit"
      ]
    },
    {
      "page": "prediction_performance",
      "title": "Prediction Performance of Adaptive PENSE Fits",
      "concept": [
        "functions for plotting and printing"
      ],
      "topics": [
        "prediction_performance",
        "print.pense_pred_perf"
      ]
    },
    {
      "page": "prinsens",
      "title": "Principal Sensitivity Components",
      "concept": [
        "functions for initial estimates"
      ],
      "topics": [
        "prinsens"
      ]
    },
    {
      "page": "regmest",
      "title": "Compute (Adaptive) Elastic Net M-Estimates of Regression",
      "concept": [
        "functions to compute robust estimates"
      ],
      "topics": [
        "regmest"
      ]
    },
    {
      "page": "regmest_cv",
      "title": "Cross-validation for (Adaptive) Elastic Net M-Estimates",
      "concept": [
        "functions to compute robust estimates with CV"
      ],
      "topics": [
        "adamest_cv",
        "regmest_cv"
      ]
    },
    {
      "page": "residuals.pense_cvfit",
      "title": "Extract Residuals",
      "concept": [
        "functions for extracting components"
      ],
      "topics": [
        "residuals.pense_cvfit"
      ]
    },
    {
      "page": "residuals.pense_fit",
      "title": "Extract Residuals",
      "concept": [
        "functions for extracting components"
      ],
      "topics": [
        "residuals.pense_fit"
      ]
    },
    {
      "page": "rho_function",
      "title": "List Available Rho Functions",
      "concept": [
        "Robustness control options"
      ],
      "topics": [
        "rho_function"
      ]
    },
    {
      "page": "starting_point",
      "title": "Create Starting Points for the PENSE Algorithm",
      "concept": [
        "functions for initial estimates"
      ],
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        "as_starting_point",
        "as_starting_point.enpy_starting_points",
        "as_starting_point.pense_cvfit",
        "as_starting_point.pense_fit",
        "starting_point"
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    },
    {
      "page": "summary.pense_cvfit",
      "title": "Summarize Cross-Validated PENSE Fit",
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      ],
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        "print.pense_cvfit",
        "summary.pense_cvfit"
      ]
    },
    {
      "page": "tau_size",
      "title": "Compute the Tau-Scale of Centered Values",
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      ]
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