What is rgrind? | Step 1: Load the package | Step 2: See what challenges are available | Step 3: Write your own solution | Step 4: Submit it with run_challenge() | Step 5: What happens when you're wrong? | Step 6: Try more challenges | What's next

Solving something to track | Understanding streaks | Checking your overall stats | Viewing your activity heatmap | Viewing your raw history | Starting fresh | What's next

Overview | 1. Prepare the data | 2. Fit a survival model | 3. Validate the model | 4. Automated pipeline review | 5. Generate a TRIPOD+AI-aligned report | Summary

Overview | 1. Load and prepare data | 2. Check and handle missing data | 3. Fit a model | 4. Validate the model | Comparing engines | 5. Explain the model | 6. Automated pipeline review | 7. Sensitivity analysis | 8. Generate a TRIPOD+AI-aligned report | Optional: AI-assisted method recommendation | Summary

Overview | 1. Load and prepare data | 2. Impute missing data | 3. Fit and compare three engines | 4. Explainability | 5. Automated pipeline review: interpreting the leakage flag | 6. Sensitivity analysis | Summary

Overview | Installation | Dataset | 1. Descriptive Statistics | 2. T-Tests | Independent Samples T-Test | One-Sample T-Test | Paired T-Test | 3. One-Way ANOVA | 4. Two-Way ANOVA | 5. MANOVA | 6. Chi-Square Test | 7. Correlation Analysis | Spearman Correlation | 8. Simple Linear Regression | 9. Multiple Linear Regression | 10. Logistic Regression | 11. Non-Parametric Tests | Mann-Whitney U Test | Wilcoxon Signed Rank Test | Kruskal-Wallis Test | 12. P-Value Interpretation | 13. The Master Function | 14. Fisher's Exact Test | 15. McNemar's Test | 16. Friedman Test | 17. Checking Assumptions | 18. Power Analysis | 19. The Shiny App | Summary