Example data | Static map | Interactive map | Trajectory geometries | Using the geometry layer directly

Shared data | 1. Static donuts without links | 2. Static map with curved coloured links | 3. Straight links | 4. Interactive map with filtered links | 5. Geometry-first workflow

Dates and missing measurements | Administrative records and formatted amounts | Exercise prompts and feedback | Static style feedback | Sources and snapshot limits

Dates and missing measurements | Administrative records and formatted amounts | Exercise prompts and feedback | Static style feedback | Sources and snapshot limits

Overview | Installation | Why circular data are different | Data included in the package | Directional versus axial data | Conventions for directions and bearings | First rose diagram | Counts, densities and proportions | Groups and facets | Circular density | Mean direction and resultant length | Uncertainty and circular tests | Compass display | Axial data | Movement data | Mixtures of von Mises distributions | Angular model diagnostics | Circular posterior draws | Experimental features | Statistical limitations | Next steps

Histogram-based density estimation | Estimators implemented in the package | Bandwidth and shift parameters | Scope of this package | References

Practical starting rules

Installation | Load the package | Estimate density at one point | Estimate density on a grid | Using the included data | Leave-one-out scores | Input expectations | Where to go next

Estimator families | Core workflow | Article guide | Input expectations

Simulated example | Pointwise estimation | Grid estimation | Visualization | Computational note

Run benchmark suite | MISE ranking by scenario | Sensitivity analysis (b, m) | Notes

Purpose | Boundary behavior | Axial behavior | Known mean direction | Von Mises mixture recovery | Optional comparison with circular | CRAN readiness checks | Interpretation

Definition | Linear versus circular histograms | Choosing the number of bins | Counts, densities and proportions | Area versus radius | Groups and facets | Axial data | Interpretation

Select a Reference Direction | Fit Homogeneous and Consensus Models | Predictions and Residuals | Special-Case Wrappers | Random-Intercept Fit

Prepare a reproducible example | Fit the main workflow | Predict on new angular covariates | Check residuals | Use the article in pkgdown

Fit the default two-step model | Predictions and residuals | Direct consensus fit

Simulated example | Model-specific functions | Practical notes

Learnr output | Quarto live output

Minimal reproducible workflow | Batch conversion | Source annotation tags | Explicit MCQ block schema

Source linting | Strict conversion | JSON report for CI

Load and validate | Reference bank questions in source files

Supported tags | Example