Package index • E2E

Core Modeling Functions

Main functions for building and evaluating models.

models_dia(): Run Multiple Diagnostic Models

models_pro(): Run Multiple Prognostic Models

bagging_dia(): Train a Bagging Diagnostic Model

bagging_pro(): Train a Bagging Prognostic Model

voting_dia(): Train a Voting Ensemble Diagnostic Model

stacking_dia(): Train a Stacking Diagnostic Model

stacking_pro(): Train a Stacking Prognostic Model

imbalance_dia(): Train an EasyEnsemble Model for Imbalanced Classification

Helpers & Visualization

Functions for applying, evaluating, visualizing, and explaining models.

apply_dia(): Apply a Trained Diagnostic Model to New Data

apply_pro(): Apply a Trained Prognostic Model to New Data

evaluate_model_dia(): Evaluate Diagnostic Model Performance

evaluate_model_pro(): Evaluate Prognostic Model Performance

evaluate_predictions_pro(): Evaluate Prognostic Predictions

figure_dia(): Plot Diagnostic Model Evaluation Figures

figure_pro(): Plot Prognostic Model Evaluation Figures

figure_shap(): Generate and Plot SHAP Explanation Figures

print_model_summary_dia(): Print Diagnostic Model Summary

print_model_summary_pro(): Print Prognostic Model Summary

Setup & Customization

Functions for initialization and framework extension.

initialize_modeling_system_dia(): Initialize Diagnostic Modeling System

initialize_modeling_system_pro(): Initialize Prognostic Modeling System

register_model_dia(): Register a Diagnostic Model Function

register_model_pro(): Register a Prognostic Model Function

get_registered_models_dia(): Get Registered Diagnostic Models

get_registered_models_pro(): Get Registered Prognostic Models

Datasets

Example datasets included with the package.

train_dia: Training Data for Diagnostic Models

test_dia: Test Data for Diagnostic Models

train_pro: Training Data for Prognostic (Survival) Models

test_pro: Test Data for Prognostic (Survival) Models

Internal & Component Functions

These are lower-level functions, generally not called directly by the user.

apply_dia(): Apply a Trained Diagnostic Model to New Data

apply_pro(): Apply a Trained Prognostic Model to New Data

bagging_dia(): Train a Bagging Diagnostic Model

bagging_pro(): Train a Bagging Prognostic Model

calculate_metrics_at_threshold_dia(): Calculate Classification Metrics at a Specific Threshold

dt_dia(): Train a Decision Tree Model for Classification

en_dia(): Train an Elastic Net (L1 and L2 Regularized Logistic Regression) Model for Classification

en_pro(): Train an Elastic Net Cox Proportional Hazards Model

evaluate_model_dia(): Evaluate Diagnostic Model Performance

evaluate_model_pro(): Evaluate Prognostic Model Performance

evaluate_predictions_pro(): Evaluate Prognostic Predictions

figure_dia(): Plot Diagnostic Model Evaluation Figures

figure_pro(): Plot Prognostic Model Evaluation Figures

find_optimal_threshold_dia(): Find Optimal Probability Threshold

gbm_dia(): Train a Gradient Boosting Machine (GBM) Model for Classification

gbm_pro(): Train a Gradient Boosting Machine (GBM) for Survival Data

get_registered_models_dia(): Get Registered Diagnostic Models

get_registered_models_pro(): Get Registered Prognostic Models

imbalance_dia(): Train an EasyEnsemble Model for Imbalanced Classification

initialize_modeling_system_dia(): Initialize Diagnostic Modeling System

initialize_modeling_system_pro(): Initialize Prognostic Modeling System

lasso_dia(): Train a Lasso (L1 Regularized Logistic Regression) Model for Classification

lasso_pro(): Train a Lasso Cox Proportional Hazards Model

lda_dia(): Train a Linear Discriminant Analysis (LDA) Model for Classification

load_and_prepare_data_dia(): Load and Prepare Data for Diagnostic Models

load_and_prepare_data_pro(): Load and Prepare Data for Prognostic Models

mlp_dia(): Train a Multi-Layer Perceptron (Neural Network) Model for Classification

models_dia(): Run Multiple Diagnostic Models

models_pro(): Run Multiple Prognostic Models

nb_dia(): Train a Naive Bayes Model for Classification

print_model_summary_dia(): Print Diagnostic Model Summary

print_model_summary_pro(): Print Prognostic Model Summary

qda_dia(): Train a Quadratic Discriminant Analysis (QDA) Model for Classification

register_model_dia(): Register a Diagnostic Model Function

register_model_pro(): Register a Prognostic Model Function

rf_dia(): Train a Random Forest Model for Classification

ridge_dia(): Train a Ridge (L2 Regularized Logistic Regression) Model for Classification

ridge_pro(): Train a Ridge Cox Proportional Hazards Model

rsf_pro(): Train a Random Survival Forest Model

stacking_dia(): Train a Stacking Diagnostic Model

stacking_pro(): Train a Stacking Prognostic Model

stepcox_pro(): Train a Stepwise Cox Proportional Hazards Model

svm_dia(): Train a Support Vector Machine (Linear Kernel) Model for Classification

test_dia: Test Data for Diagnostic Models

test_pro: Test Data for Prognostic (Survival) Models

train_dia: Training Data for Diagnostic Models

train_pro: Training Data for Prognostic (Survival) Models

voting_dia(): Train a Voting Ensemble Diagnostic Model

xb_dia(): Train an XGBoost Tree Model for Classification

min_max_normalize(): Min-Max Normalization

Surv: re-export Surv from survival