Local explanations | Raw Shapley values | Global explanations | Parallel processing

Introduction | Constructing VIPs in R | Model-specific VI | Decision trees and tree ensembles | Linear models | Neural networks | Model-agnostic VI | Variance-based methods | Permutation method | A classification example | Benchmarks | Shapley method | Drawbacks of existing methods | Summary | Acknowledgments | References

Introduction | The mat function | Character method | Better printing for "large" matrices | Data frames and block matrices | Making data frames with dmat | Construct block matrices with bmat | Convenience functions | Summary

Introduction | Prerequisites | How statlingua Works: The explain() Function and ellmer | Understanding explain()'s Arguments | The Power of context: Why It Matters | Some Examples in Action! | Example 1: Linear Regression (lm) - Sales of Child Car Seats | Interpretation of Linear Regression Model Output | Call | Residuals Summary | Coefficients Table | Signif. codes | Residual Standard Error | R-squared | F-statistic | Suggestions for Checking Assumptions | Follow-up Question: Interpreting R-squared | Example 2: Logistic GLM (glm) - Pima Indians Diabetes | Explanation of the Binomial GLM with Logit Link Output | Example 3: Cox Proportional Hazards Model (coxph) - Lung Cancer Survival | Explanation of Cox Proportional Hazards Model Output | Example 4: Linear Mixed-Effects Model (lmer from lme4) - Sleep Study | Requesting Plain Text Output (style = "text") | Requesting JSON Output (style = "json") | Inspecting LLM Interaction | Conclusion

Getting started

investr: An R Package for Inverse Estimation

What is basic pattern analysis? | What is the bpa package? | Basic usage