This illustrates a variety of example data structures that can be generated. These synthetic datasets represent common and challenging shapes found.
A simple spherical Gaussian cluster with very small variance in \(4\text{-}D\) space.
Uniform points in a hypercube with a hollow center, useful for detecting voids or gaps.
A curvilinear loop structure that forms a closed cycle in \(4\text{-}D\).
A tree-like curvilinear structure with six branches in \(6\text{-}D\), ideal for studying bifurcation patterns.
tree_data <- gen_orgcurvybranches(n = 600, p = 6, k = 6)
#> Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if
#> `.name_repair` is omitted as of tibble 2.0.0.
#> ℹ Using compatibility `.name_repair`.
#> ℹ The deprecated feature was likely used in the cardinalR package.
#> Please report the issue at
#> <https://github.com/JayaniLakshika/cardinalR/issues>.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
langevitour(tree_data, pointSize = 2)
A pointed cone shape in \(4\text{-}D\), controlled by height and radius ratio.
A spiral winding around a conical surface in \(5\text{-}D\) space.
A regularly spaced sphere with high point density, useful for manifold learning.
An S-shaped manifold in \(8\text{-}D\) with a missing section, to evaluate resilience to structural gaps.