# `lacunr` Quick-start guide

The standard workflow for `lacunr` is fairly simple:

1. Convert point cloud data to voxels using `voxelize()`
2. Arrange the voxels into a 3-dimensional array using `bounding_box()`
3. Calculate a lacunarity curve using `lacunarity()`

    library(lacunr)
    # create a data.frame of simulated point cloud data
    set.seed(5678)
    pc <- data.frame(X = rnorm(1000, 10), Y = rnorm(1000, 50), Z = rnorm(1000, 25))
    # convert to voxels of size 0.5
    vox <- voxelize(pc, edge_length = c(0.5, 0.5, 0.5))
    # generate 3D array
    box <- bounding_box(vox)
    # calculate lacunarity curve
    lac_curve <- lacunarity(box)

Lacunarity and H(r) curves can be plotted using `lac_plot()`, `lacnorm_plot()`, or `hr_plot()`:

    # plot lacunarity curve
    plot <- lac_plot(lac_curve)
    print(plot)

3D arrays generated by `bounding_box()` can have their dimensions selectively increased using `pad_array()`:

    # add two layers of empty space to the Z axis of the array
    box_pad1 <- pad_array(box, z = 2)
    # add two layers of occupied space to the Y axis of the array
    box_pad2 <- pad_array(box, y = 2, fill = 1)

For more extensive explanation on these functions and their use, please see the package documentation (available by typing `?lacunr` into your console), or the other vignettes via `browseVignettes("lacunr")`.