Introduction | Data Structure | Example Data | Load the example MRT dataset | Estimate the Standardized Effect | Optional: Increase Bootstrap Replications | Plot the Estimated Effect

1. Introduction | Estimation framework | Three user-facing wrappers | 2. Installation | 3. Quick Start Example | 4. A More Complex Data Example | Columns (Variables) | Peek at the included dataset | 5. Detailed Walkthrough of mcee | 5.1 Basic usage (GLM; constant effects) | 5.2 Time-varying effects (linear in dp) | 5.3 Other learners for nuisance fitting | 5.4 Estimating effects at specific decision points | 6. Testing for Linear Combinations of Coefficients | 6.1 Estimates $mcee_fit | 6.2 Linear combinations | Example 1: Difference $\alpha - \beta$ for constant‑effect model (fit1) | Example 2: Effects at decision point 10 for linear‑trend model (fit2) | 6.3 Inspecting nuisance models | 7. Other Wrappers: mcee_general and mcee_userfit_nuisance | 7.1 mcee_general | Example: flexible nuisance configs | 7.2 mcee_userfit_nuisance | Example: manual nuisance fits with GLM | 7.3 Comparing results | 8. Best Practices | 9. Conclusion | Appendix: Estimating Equations and Asymptotic Variance | Reference

Introduction | Data Structure of MRT with Distal Outcomes | Distal Causal Excursion Effects | Example Dataset | Using dcee() | Fully Marginal Effect (no moderators) | Moderated Effect | GAM nuisance models | Random Forest / Ranger nuisance | Cross-Fitting | Inspecting Stage-1 Fits | References

Introduction | Data Structure of an MRT | Availability | Prepare Your Data Set for Analysis | Causal Excursion Effect Estimation for MRT with Continuous Outcomes | Fully Marginal Causal Excursion Effect | Moderated Causal Excursion Effect | Causal Excursion Effect Estimation for MRT with Binary Outcomes | Bibliography | Reference

Background | Quick Start | A numerical example | Example use of mrt_binary_ss() | Example use of mrt_binary_power() | Example use of power_vs_n_plot() | Example use of power_summary() | References