{
  "_id": "6a1efd88b401979e73419bd6",
  "Package": "E2E",
  "Title": "Ensemble Learning Framework for Diagnostic and Prognostic\nModeling",
  "Version": "0.1.3",
  "Authors@R": "person(\"Shanjie\", \"Luan\", email = \"Luan20050519@163.com\", role = c(\"aut\", \"cre\"))",
  "Description": "Provides a framework to build and evaluate diagnosis or\nprognosis models using stacking, voting, and bagging ensemble\ntechniques with various base learners. The package also\nincludes tools for visualization and interpretation of models.\nThe development version of the package is available on 'GitHub'\nat <https://github.com/xiaojie0519/E2E>. The methods are based\non the foundational work of Breiman (1996)\n<doi:10.1007/BF00058655> on bagging and Wolpert (1992)\n<doi:10.1016/S0893-6080(05)80023-1> on stacking.",
  "License": "MIT + file LICENSE",
  "Encoding": "UTF-8",
  "URL": "https://xiaojie0519.github.io/E2E/",
  "BugReports": "https://github.com/xiaojie0519/E2E/issues",
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  "Repository": "https://xiaojie0519.r-universe.dev",
  "Date/Publication": "2026-03-19 15:25:30 UTC",
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    "apply_dia",
    "apply_pro",
    "bagging_dia",
    "bagging_pro",
    "calculate_metrics_at_threshold_dia",
    "dt_dia",
    "en_dia",
    "en_pro",
    "evaluate_model_dia",
    "evaluate_model_pro",
    "evaluate_predictions_dia",
    "evaluate_predictions_pro",
    "figure_dia",
    "figure_pro",
    "figure_shap",
    "find_optimal_threshold_dia",
    "gbm_dia",
    "gbm_pro",
    "get_registered_models_dia",
    "get_registered_models_pro",
    "imbalance_dia",
    "initialize_modeling_system_dia",
    "initialize_modeling_system_pro",
    "int_dia",
    "int_imbalance",
    "int_pro",
    "lasso_dia",
    "lasso_pro",
    "lda_dia",
    "load_and_prepare_data_dia",
    "min_max_normalize",
    "mlp_dia",
    "models_dia",
    "models_pro",
    "nb_dia",
    "plot_integrated_results",
    "pls_pro",
    "predict_pro",
    "print_model_summary_dia",
    "print_model_summary_pro",
    "qda_dia",
    "register_model_dia",
    "register_model_pro",
    "rf_dia",
    "ridge_dia",
    "ridge_pro",
    "rsf_pro",
    "stacking_dia",
    "stacking_pro",
    "stepcox_pro",
    "Surv",
    "svm_dia",
    "voting_dia",
    "xb_dia",
    "xgb_pro"
  ],
  "_datasets": [
    {
      "name": "test_dia",
      "title": "Test Data for Diagnostic Models",
      "object": "test_dia",
      "class": [
        "data.frame"
      ],
      "fields": [
        "sample",
        "outcome",
        "AC009242.1",
        "AC004637.1",
        "AC246817.1",
        "AL139241.1",
        "PRDM16.DT",
        "LINC01028",
        "LINC00639",
        "AL135841.1",
        "HYMAI",
        "KCNIP2.AS1",
        "LINC00922",
        "LINC01614",
        "LINC01644",
        "AC104237.3",
        "AC016735.1",
        "AC090125.1",
        "AC008459.1",
        "LINC00958",
        "AC112721.2",
        "LINC00924"
      ],
      "rows": 368,
      "table": true,
      "tojson": true
    },
    {
      "name": "test_pro",
      "title": "Test Data for Prognostic (Survival) Models",
      "object": "test_pro",
      "class": [
        "data.frame"
      ],
      "fields": [
        "sample",
        "outcome",
        "time",
        "LINC01497",
        "LINC01028",
        "AC084212.1",
        "AC104211.1",
        "AL603840.1",
        "AL590434.1",
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        "AC093515.1",
        "AC004990.1",
        "HOTAIR",
        "AC055854.1",
        "SIRLNT"
      ],
      "rows": 324,
      "table": true,
      "tojson": true
    },
    {
      "name": "train_dia",
      "title": "Training Data for Diagnostic Models",
      "object": "train_dia",
      "class": [
        "data.frame"
      ],
      "fields": [
        "sample",
        "outcome",
        "AC009242.1",
        "AC004637.1",
        "AC246817.1",
        "AL139241.1",
        "PRDM16.DT",
        "LINC01028",
        "LINC00639",
        "AL135841.1",
        "HYMAI",
        "KCNIP2.AS1",
        "LINC00922",
        "LINC01614",
        "LINC01644",
        "AC104237.3",
        "AC016735.1",
        "AC090125.1",
        "AC008459.1",
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        "AC112721.2",
        "LINC00924"
      ],
      "rows": 863,
      "table": true,
      "tojson": true
    },
    {
      "name": "train_pro",
      "title": "Training Data for Prognostic (Survival) Models",
      "object": "train_pro",
      "class": [
        "data.frame"
      ],
      "fields": [
        "sample",
        "outcome",
        "time",
        "LINC01497",
        "LINC01028",
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        "AC104211.1",
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        "AL590434.1",
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        "C9orf163",
        "AL391845.2",
        "HYMAI",
        "LINC01152",
        "AL133467.1",
        "LINC00165",
        "LINC02408",
        "AC092118.1",
        "AP000851.2",
        "AC105046.1",
        "LINC01929",
        "AP001434.1",
        "AC105219.1",
        "AC133644.1",
        "FAM153CP",
        "AC093515.1",
        "AC004990.1",
        "HOTAIR",
        "AC055854.1",
        "SIRLNT"
      ],
      "rows": 759,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "apply_dia",
      "title": "Apply a Trained Model to New Data",
      "topics": [
        "apply_dia"
      ]
    },
    {
      "page": "apply_pro",
      "title": "Apply Prognostic Model to New Data",
      "topics": [
        "apply_pro"
      ]
    },
    {
      "page": "bagging_dia",
      "title": "Train a Bagging Diagnostic Model",
      "topics": [
        "bagging_dia"
      ]
    },
    {
      "page": "bagging_pro",
      "title": "Train Bagging Ensemble for Prognosis",
      "topics": [
        "bagging_pro"
      ]
    },
    {
      "page": "calculate_metrics_at_threshold_dia",
      "title": "Calculate Classification Metrics at a Specific Threshold",
      "topics": [
        "calculate_metrics_at_threshold_dia"
      ]
    },
    {
      "page": "dt_dia",
      "title": "Train a Decision Tree Model for Classification",
      "topics": [
        "dt_dia"
      ]
    },
    {
      "page": "en_dia",
      "title": "Train an Elastic Net (L1 and L2 Regularized Logistic Regression) Model for Classification",
      "topics": [
        "en_dia"
      ]
    },
    {
      "page": "en_pro",
      "title": "Train Elastic Net Cox Model",
      "topics": [
        "en_pro"
      ]
    },
    {
      "page": "evaluate_model_dia",
      "title": "Evaluate Diagnostic Model Performance",
      "topics": [
        "evaluate_model_dia"
      ]
    },
    {
      "page": "evaluate_model_pro",
      "title": "Evaluate Prognostic Model Performance",
      "topics": [
        "evaluate_model_pro"
      ]
    },
    {
      "page": "evaluate_predictions_dia",
      "title": "Evaluate Predictions from a Data Frame",
      "topics": [
        "evaluate_predictions_dia"
      ]
    },
    {
      "page": "evaluate_predictions_pro",
      "title": "Evaluate External Predictions",
      "topics": [
        "evaluate_predictions_pro"
      ]
    },
    {
      "page": "figure_dia",
      "title": "Plot Diagnostic Model Evaluation Figures",
      "topics": [
        "figure_dia"
      ]
    },
    {
      "page": "figure_pro",
      "title": "Plot Prognostic Model Evaluation Figures",
      "topics": [
        "figure_pro"
      ]
    },
    {
      "page": "figure_shap",
      "title": "Generate and Plot SHAP Explanation Figures",
      "topics": [
        "figure_shap"
      ]
    },
    {
      "page": "find_optimal_threshold_dia",
      "title": "Find Optimal Probability Threshold",
      "topics": [
        "find_optimal_threshold_dia"
      ]
    },
    {
      "page": "gbm_dia",
      "title": "Train a Gradient Boosting Machine (GBM) Model for Classification",
      "topics": [
        "gbm_dia"
      ]
    },
    {
      "page": "gbm_pro",
      "title": "Train Gradient Boosting Machine (GBM) for Survival",
      "topics": [
        "gbm_pro"
      ]
    },
    {
      "page": "get_registered_models_dia",
      "title": "Get Registered Diagnostic Models",
      "topics": [
        "get_registered_models_dia"
      ]
    },
    {
      "page": "get_registered_models_pro",
      "title": "Get Registered Prognostic Models",
      "topics": [
        "get_registered_models_pro"
      ]
    },
    {
      "page": "imbalance_dia",
      "title": "Train an EasyEnsemble Model for Imbalanced Classification",
      "topics": [
        "imbalance_dia"
      ]
    },
    {
      "page": "initialize_modeling_system_dia",
      "title": "Initialize Diagnostic Modeling System",
      "topics": [
        "initialize_modeling_system_dia"
      ]
    },
    {
      "page": "initialize_modeling_system_pro",
      "title": "Initialize Prognosis Modeling System",
      "topics": [
        "initialize_modeling_system_pro"
      ]
    },
    {
      "page": "int_dia",
      "title": "Comprehensive Diagnostic Modeling Pipeline",
      "topics": [
        "int_dia"
      ]
    },
    {
      "page": "int_imbalance",
      "title": "Imbalanced Data Diagnostic Modeling Pipeline",
      "topics": [
        "int_imbalance"
      ]
    },
    {
      "page": "int_pro",
      "title": "Comprehensive Prognostic Modeling Pipeline",
      "topics": [
        "int_pro"
      ]
    },
    {
      "page": "lasso_dia",
      "title": "Train a Lasso (L1 Regularized Logistic Regression) Model for Classification",
      "topics": [
        "lasso_dia"
      ]
    },
    {
      "page": "lasso_pro",
      "title": "Train Lasso Cox Proportional Hazards Model",
      "topics": [
        "lasso_pro"
      ]
    },
    {
      "page": "lda_dia",
      "title": "Train a Linear Discriminant Analysis (LDA) Model for Classification",
      "topics": [
        "lda_dia"
      ]
    },
    {
      "page": "load_and_prepare_data_dia",
      "title": "Load and Prepare Data for Diagnostic Models",
      "topics": [
        "load_and_prepare_data_dia"
      ]
    },
    {
      "page": "min_max_normalize",
      "title": "Min-Max Normalization",
      "topics": [
        "min_max_normalize"
      ]
    },
    {
      "page": "mlp_dia",
      "title": "Train a Multi-Layer Perceptron (Neural Network) Model for Classification",
      "topics": [
        "mlp_dia"
      ]
    },
    {
      "page": "models_dia",
      "title": "Run Multiple Diagnostic Models",
      "topics": [
        "models_dia"
      ]
    },
    {
      "page": "models_pro",
      "title": "Run Multiple Prognostic Models",
      "topics": [
        "models_pro"
      ]
    },
    {
      "page": "nb_dia",
      "title": "Train a Naive Bayes Model for Classification",
      "topics": [
        "nb_dia"
      ]
    },
    {
      "page": "plot_integrated_results",
      "title": "Visualize Integrated Modeling Results",
      "topics": [
        "plot_integrated_results"
      ]
    },
    {
      "page": "pls_pro",
      "title": "Train Partial Least Squares Cox (PLS-Cox)",
      "topics": [
        "pls_pro"
      ]
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