Package: couplr 1.8.1

Installation | Overview | Who This Vignette Is For | Documentation Roadmap | Why Pixels? | Scientific Applications at a Glance | The General Matching Problem | Problem Formulation | Computational Challenge | Visual Illustration: Pixel Morphing | Exact Pixel Matching | Feature Quantization Morph (Strategy 1) | Hierarchical Morph (Strategy 2) | Three Approximation Strategies | Strategy 1: Feature Quantization | Mathematical Formulation | Complexity Reduction | Quality Trade-offs | Strategy 2: Hierarchical Decomposition | Complexity (Sketch) | High-Level Algorithm | Strategy 3: Resolution Reduction | Complexity | Strategy Comparison | Implementation Details of Exact Pixel Matching | Application to Scientific Domains | Ecology: Vegetation Plot Matching | Physics: Particle Tracking | Chemistry: Molecular Conformation Alignment | Implementation Notes | Customizing Morph Duration | Using the Example Code | Regenerating Examples | Mathematical Foundation: Optimal Transport | Monge Problem | Kantorovich Relaxation | Discrete Linear Assignment | Wasserstein Distance | Further Reading | Optimal Transport Theory | Scientific Applications | Assignment Algorithms | Connection to couplr Workflows | When Do These Strategies Apply? | Practical Recommendations | Limitations of Approximation Strategies | Summary | See Also

Installation | Overview | Evaluation Dataset | Comparison 1: MatchIt | MatchIt Approach | couplr Approach | Balance Comparison | Key Differences | When to Use Each | Comparison 2: optmatch | optmatch Approach | Performance Comparison | Comparison 3: designmatch | designmatch Approach | Comparison 4: Matching Package | Matching Package Approach | Runtime Scaling | Memory Usage | Feature Comparison Summary | Decision Guide | Choose couplr when: | Choose MatchIt when: | Choose optmatch when: | Choose designmatch when: | Workflow Integration | Sequential Pipeline | Complementary Use | Real-World Case Study: Job Training Evaluation | Background | Simulating Lalonde-Style Data | Challenge: Large Control Pool | Balance Assessment | Interpretation | Key Takeaways | References | See Also

Installation | Overview | Who This Vignette Is For | Documentation Roadmap | The Running Example: Job Training Evaluation | Key Features | Problem Definition | The Matching Problem | Why Matching Matters | Distance Metrics | Basic Usage | Creating a Matched Sample | Understanding the Output | Automatic Preprocessing | Why Preprocessing Matters | Smart Scaling with auto_scale = TRUE | Scaling Methods | Health Checks and Warnings | Optimal vs Greedy Matching | When to Use Each Approach | Greedy Strategies | Caliper Constraints | Why Use Calipers | Choosing Caliper Width | Blocking and Stratification | Why Use Blocking | Exact Blocking with matchmaker() | Cluster-Based Blocking | Balance Diagnostics | Key Balance Metrics | How to Interpret Balance Results | Visualizing Balance | Real-World Example: Treatment Effect Estimation | Scenario | Step 1: Data Preparation | Step 2: Perform Matching | Step 3: Assess Balance | Step 4: Estimate Treatment Effect | Step 5: Publication-Ready Output | Performance Considerations | Scalability | Memory Usage | Optimization Tips | What Can Go Wrong | Problem: Poor Balance Despite Matching | Problem: Very Few Matches | Problem: Matching Takes Too Long | Problem: Memory Error | Summary | Advanced Matching Techniques | Full Matching (Variable-Ratio Groups) | Ratio Matching (k:1) | With-Replacement Matching | Propensity Score Matching | Cardinality Matching | Coarsened Exact Matching (CEM) | Subclassification | Ecosystem Integration | Sensitivity Analysis | Visualizing Results with autoplot | See Also

Installation | What couplr Does | Documentation Roadmap | Your First Match | Understanding the Output | Why Scaling Matters | Checking Match Quality | Visualizing Match Quality | Large Datasets: Use Greedy Matching | Setting a Maximum Distance (Caliper) | Matching Within Groups (Blocking) | Full Matching: Keep Every Unit | Other Matching Methods | Complete Example | Next Steps | Additional: Direct Assignment Problem Solving | lap_solve(): Matrix-Based Assignment | Forbidden Assignments | Maximization | Grouped Data | K-Best Solutions | See Also

Installation | Overview | Who This Vignette Is For | The Race | The Problem | The LP Formulation | Duality: The Key to Efficiency | The Classics | Hungarian Algorithm (1955) | Jonker-Volgenant Algorithm (1987) | The Scaling Revolution | Auction Algorithm (1988) | Cost-Scaling Algorithm / CSA (1995) | Gabow-Tarjan Algorithm (1989) | The Network View | Network Simplex | Push-Relabel Algorithm | The Specialists | HK01: Binary Costs | SAP and LAPMOD: Sparse Problems | Ramshaw-Tarjan: Rectangular Problems (2012) | Beyond Standard Assignment | K-Best Solutions (Murty's Algorithm) | Bottleneck Assignment | Sinkhorn: Soft Assignment | Dual Variables | The Benchmark | Quick Reference | References

Installation | Overview | Infeasible Problems | Symptom | Cause | Diagnosis | Solutions | Poor Balance Despite Matching | Causes | Performance Issues | Memory Errors | Different Results with Different Methods | Missing Values in Data | Installation Issues | C++ Compilation Errors | Common Error Messages | "method_used attribute is NULL" | "Cost matrix contains non-finite values" | "All rows assigned to Inf columns" | "subscript out of bounds" | Getting Help | See Also