Functions for estimating the gliding box lacunarity (GBL),
    covariance, and pair-correlation of a random closed set (RACS) in 2D
    from a binary coverage map (e.g. presence-absence land cover maps).
    Contains a number of newly-developed covariance-based estimators of
    GBL (Hingee et al., 2019) <doi:10.1007/s13253-019-00351-9> and
    balanced estimators, proposed by Picka (2000)
    <http://www.jstor.org/stable/1428408>, for covariance, centred
    covariance, and pair-correlation.  Also contains methods for
    estimating contagion-like properties of RACS and simulating 2D Boolean
    models.  Binary coverage maps are usually represented as raster images
    with pixel values of TRUE, FALSE or NA, with NA representing
    unobserved pixels.  A demo for extracting such a binary map from a
    geospatial data format is provided.  Binary maps may also be
    represented using polygonal sets as the foreground, however for most
    computations such maps are converted into raster images.  The package
    is based on research conducted during the author's PhD studies.

Calculates 3D lacunarity from voxel data. It is designed for use
    with point clouds generated from Light Detection And Ranging (LiDAR) scans
    in order to measure the spatial heterogeneity of 3-dimensional structures
    such as forest stands. It provides fast 'C++' functions to efficiently bin
    point cloud data into voxels and calculate lacunarity using different
    variants of the gliding-box algorithm originated by Allain & Cloitre (1991)
    <doi:10.1103/PhysRevA.44.3552>.

Calculate mean statistics and leaf angle distribution type from measured leaf inclination angles. LAD distribution is fitted using a two-parameters (mu, nu) Beta distribution and compared with six theoretical LAD distributions. Additional information is provided in Chianucci and Cesaretti (2022) <doi:10.1101/2022.10.28.513998>.

Set of tools for analyzing vertical fuel continuity at the tree level using Airborne Laser Scanning data. The workflow consisted of: 1) calculating the vertical height profiles of each segmented tree; 2) identifying gaps and fuel layers; 3) estimating the distance between fuel layers; and 4) retrieving the fuel layers base height and depth. Additionally, other functions recalculate previous metrics after considering distances greater than 1 m and calculate the canopy base height as the fuel base height located at the largest- and at the last-distance. Moreover, the package calculates: i) the percentage of Leaf Area Density comprised in each fuel layer, ii) remove fuel layers with Leaf Area Density percentage less than 25, iii) recalculate the distances among the reminder ones, and iv) identify the canopy base height as the fuel base height with the highest Leaf Area Density percentage. On the other hand, when there is only one fuel layer, it identifies the canopy base height performing a segmented linear regression (breaking points) on the cumulative sum of Leaf Area Density as a function of height. Finally, a collection of plotting functions is developed to represent: i) the initial gaps and fuel layers; ii) the fuels base height, depths and gaps with distances greater than 1 m and, iii) the fuels base height and depths after applying the breaking point method over trees with only one fuel layer. The methods implemented in this package are original and have not been published elsewhere.