psuedo_density_mode_cluster_1d: Mode clustering for euclidean data (distance based)
In benjaminleroy/simulationBands: Conformal Prediction Bands with Simulations

Walks up a guassian kernel density estimate to find modes

psuedo_density_mode_cluster_1d(
  X_mat,
  G_mat = X_mat,
  sigma,
  maxT = 30,
  eps = 1e-05,
  verbose = TRUE,
  list_out = FALSE
)

`X_mat`	matrix of data points (in rows), that define the kernel density
`G_mat`	matrix of data points (in rows) that need to be "walked" to their modes
`sigma`	density scalar / sigma value
`maxT`	int, maximum number of iterations
`eps`	float, difference between 2 sequential points for which to stop walking toward the mode for a give point's walk
`verbose`	boolean, if this progression should be verbose
`list_out`	if we should return every step of the process (for fun visualization purposes and visual checks only)