Package index • E2E

Integrated Pipelines

One-click functions for comprehensive model comparison across multiple algorithms and ensemble methods.

int_dia(): Comprehensive Diagnostic Modeling Pipeline
int_imbalance(): Imbalanced Data Diagnostic Modeling Pipeline
int_pro(): Comprehensive Prognostic Modeling Pipeline
plot_integrated_results(): Visualize Integrated Modeling Results

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 Bagging Ensemble for Prognosis
voting_dia(): Train a Voting Ensemble Diagnostic Model
stacking_dia(): Train a Stacking Diagnostic Model
stacking_pro(): Train Stacking Ensemble for Prognosis
imbalance_dia(): Train an EasyEnsemble Model for Imbalanced Classification

Helpers & Visualization

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

apply_dia(): Apply a Trained Model to New Data
apply_pro(): Apply Prognostic Model to New Data
evaluate_model_dia(): Evaluate Diagnostic Model Performance
evaluate_model_pro(): Evaluate Prognostic Model Performance
evaluate_predictions_dia(): Evaluate Predictions from a Data Frame
evaluate_predictions_pro(): Evaluate External 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 Prognosis Modeling System
register_model_dia(): Register a Diagnostic Model Function
register_model_pro(): Register a Prognostic Model
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 Model to New Data
apply_pro(): Apply Prognostic Model to New Data
bagging_dia(): Train a Bagging Diagnostic Model
bagging_pro(): Train Bagging Ensemble for Prognosis
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 Elastic Net Cox Model
evaluate_model_dia(): Evaluate Diagnostic Model Performance
evaluate_model_pro(): Evaluate Prognostic Model Performance
evaluate_predictions_dia(): Evaluate Predictions from a Data Frame
evaluate_predictions_pro(): Evaluate External 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 Gradient Boosting Machine (GBM) for Survival
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 Prognosis Modeling System
int_dia(): Comprehensive Diagnostic Modeling Pipeline
int_pro(): Comprehensive Prognostic Modeling Pipeline
lasso_dia(): Train a Lasso (L1 Regularized Logistic Regression) Model for Classification
lasso_pro(): Train 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
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
pls_pro(): Train Partial Least Squares Cox (PLS-Cox)
predict_pro(): Generic Prediction Interface for Prognostic Models
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
rf_dia(): Train a Random Forest Model for Classification
ridge_dia(): Train a Ridge (L2 Regularized Logistic Regression) Model for Classification
ridge_pro(): Train Ridge Cox Model
rsf_pro(): Train Random Survival Forest (RSF)
stacking_dia(): Train a Stacking Diagnostic Model
stacking_pro(): Train Stacking Ensemble for Prognosis
stepcox_pro(): Train Stepwise Cox Model (AIC-based)
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
xgb_pro(): Train XGBoost Cox Model
min_max_normalize(): Min-Max Normalization
Surv: re-export Surv from survival