Theory | Bayesian update rule | Players nested within team | Glicko rating system | Glicko2 rating system | Bayesian Bradley Terry | Dynamic Bayesian Logit | Additional controls | lambda | kappa | weight

Basic marimekko plot | Pearson residuals | Three-variable nested mosaic | Y-axis labels | Data extraction with fortify | Extending with custom ggplot2 layers | Extending with StatMarimekkoTiles | How it works | Example: bubble overlay | Example: residual markers | Example: rectangle outlines | StatMarimekkoTiles vs fortify_marimekko() | Combining layers | Independent x/y gaps | Colour palette | Plotly interactivity | In-aesthetic expressions | Namespace-qualified usage | Summary of parameters

What is a marimekko plot? | Installation | Your first marimekko plot | Aesthetics | Gap control | Marginal percentages | Adding text labels | Theming | Faceting | Next steps

Why migrate? | Function mapping | Side-by-side examples | One dimensional — spine plot and bar chart | Two dimensional — mosaic plot and stacked bar chart | Three dimensional | Three variables — nested mosaic | Conditioning with conds | Faceting | Divider types and standardize | Offset / spacing | Text labels | Axis labels and percentages | HairEyeColor — a larger example | Key differences to be aware of | 1. No product() wrapper | 2. Automatic axis labels | 3. No divider argument | 4. inherit.aes defaults to TRUE | 5. Text labels need explicit label aesthetic | plotly support | Search-and-replace checklist | References

Basic usage | Index-based windows | Evaluation at specific points | Varying window size and lag | NA padding | Data frames and matrices | dplyr integration | Parallel execution | Built-in functions

Running aggregations | min_run, max_run, sum_run, mean_run | Using idx for time-based windows | streak_run | Utility functions | lag_run | fill_run | which_run

Overview | Window types | Sliding window (k) | Lag (lag) | Index-based windows (idx) | Evaluation at specific points (at)

Unique elements in a 7-day window | Weekly trimmed mean | Prediction from rolling 2-week regression | Grouped rolling sums with uneven time gaps | Rolling distinct count by group | Grouped rolling regression with run_by | Cross-referencing another data.frame in grouped context

About | Package Usage | Available Data | Estimate dynamic ratings | Output | Advanced sport | Formula | Controlling update size by weight | Avoiding excessive RD shrinkage with kappa | Control output uncertainty by lambda | Players nested within teams