What is cram_bandit_sim()? | 📋 Inputs | Example: Cram Bandit Simulation | What Does It Return? | Example Output Preview | Notes | Recommended Use Cases | References

Introduction: What is the Cram Method? | The Cram Workflow | The cram_policy() Function | 🔑 Key Features of cram_policy() | Example: Running Cram Policy on simulated data | Interpreting Results | Visual Summary and Notes | 📐 Comparing Evaluation Strategies: Sample-Splitting, Cross-Validation, and Cram | 🔍 Sample-Splitting | 🔄 Cross-Validation | 📈 Cram | 🧠 Key Distinction Summary: What Is Being Evaluated? | References

🌟 What is this article about? | 🛠️Policy parameters explained | ✨ Epsilon-Greedy Policy | 🤖 Theoretical computation | 📊 Code helper | 🔢 LinUCB Disjoint Policy with ( \varepsilon )-Greedy | 🤓 Thompson Sampling (LinTS) | 👨‍💻 Practical Workflow | 🧪 Estimand Calculation in cram_bandit_sim() | 📂 Useful Links | 🌟 Acknowledgments

What is cram_bandit()? | Introduction: Bandits, Policies, and Cram | Understanding the inputs | Example: Use cram_bandit() on simulated data with batch size of 1 | References

Output of Cram ML | Built-in Model | loss_name argument | Regression Losses | Classification Losses | Custom Loss Functions | caret_params argument | Case of categorical target variable | Case 1: Predicting Class Labels | Case 2: Predicting Class Probabilities | Custom Model | 1. custom_fit(data, ...) | 2. custom_predict(model, data) | 3. custom_loss(predictions, data) | 4. Use cram_ml() with Custom Functions

Cram Policy | Built-in Model | How to set model_params? | Case of categorical target Y | Custom Model | 1. custom_fit(X, Y, D, ...) | 2. custom_predict(model, X_new, D_new) | 3. Use cram_policy() with custom_fit() and custom_predict() | References

🎯 What is cram_simulation()? | 📦 Inputs Overview | 📘 Example: Cram Policy Simulation | 📊 Output Summary

Introduction | Cram User file | 1. cram_policy() — Binary Policy Learning & Evaluation | Generate Simulated Data | Run cram_policy() with causal forest | Case of categorical target Y | Custom Models with cram_policy() | 2. cram_ml() — ML Learning & Evaluation | Regression with cram_ml() | Classification with cram_ml() | Case 1: Predicting Class labels | Case 2: Predicting Class Probabilities | 3. cram_bandit() — Contextual Bandits for On-policy Statistical Evaluation | Summary