About the multipathaic Package | Core Functions | Supported Models | Introduction | Load the Package and Data | Feature Engineering | Train/Test Split | Multi-Path AIC Procedure | Algorithm 1: Multi-Path Forward Selection | Algorithm 2: Stability Estimation | Algorithm 3: Plausible Model Selection | Model Evaluation on Test Set | Summary and Parameter Justification | Recommended Parameter Choices | Justification | References

Introduction | Problem Setup | Notation | Objective | Algorithm 1: Multi-Path Forward Selection | Motivation | The Multi-Path Approach | Algorithm Details | Input Parameters | Pseudocode | Key Design Choices | Example: Visualization | Algorithm 2: Stability Estimation | Bootstrap Stability | Design Considerations | Example Output | Algorithm 3: Plausible Model Selection | Filtering Criteria | Interpretation of Results | Plausible Models Table | Variable Summary Table | Example | Complete Pipeline: multipath_aic() | Output Structure | Parameter Tuning Guidelines | Quick Reference | Detailed Guidance | Exploration vs. Computation Tradeoff | Problem-Specific Adjustments | Statistical Justification | Why AIC? | Why Delta = 2? | Why Bootstrap Stability? | Why tau = 0.6? | Advantages Over Traditional Methods | vs. Single-Path Forward Selection | vs. All-Subsets Regression | vs. Lasso/Ridge Regression | Practical Recommendations | Workflow | Reporting Results | Limitations and Considerations | Computational Complexity | Model Family | Assumptions | References | Appendix: Mathematical Details | AIC Derivation | Stability Score Estimation | Average Stability for a Model

About the multipathaic Package | Core Functions | Supported Models | Introduction | Load the Package and Data | Feature Engineering | Train/Test Split | Multi-Path AIC Procedure | Algorithm 1: Multi-Path Forward Selection | Algorithm 2: Stability Estimation | Algorithm 3: Plausible Model Selection | Model Evaluation on Test Set | Summary and Parameter Justification | Recommended Parameter Choices | Justification | References

Introduction | Problem Setup | Notation | Objective | Algorithm 1: Multi-Path Forward Selection | Motivation | The Multi-Path Approach | Algorithm Details | Input Parameters | Pseudocode | Key Design Choices | Example: Visualization | Algorithm 2: Stability Estimation | Bootstrap Stability | Design Considerations | Example Output | Algorithm 3: Plausible Model Selection | Filtering Criteria | Interpretation of Results | Plausible Models Table | Variable Summary Table | Example | Complete Pipeline: multipath_aic() | Output Structure | Parameter Tuning Guidelines | Quick Reference | Detailed Guidance | Exploration vs. Computation Tradeoff | Problem-Specific Adjustments | Statistical Justification | Why AIC? | Why Delta = 2? | Why Bootstrap Stability? | Why tau = 0.6? | Advantages Over Traditional Methods | vs. Single-Path Forward Selection | vs. All-Subsets Regression | vs. Lasso/Ridge Regression | Practical Recommendations | Workflow | Reporting Results | Limitations and Considerations | Computational Complexity | Model Family | Assumptions | References | Appendix: Mathematical Details | AIC Derivation | Stability Score Estimation | Average Stability for a Model