Functions, data sets and examples for the calculation of various indices of biodiversity including species, functional and phylogenetic diversity. Part of the indices are expressed in terms of equivalent numbers of species. The package also provides ways to partition biodiversity across spatial or temporal scales (alpha, beta, gamma diversities). In addition to the quantification of biodiversity, ordination approaches are available which rely on diversity indices and allow the detailed identification of species, functional or phylogenetic differences between communities.

Provides an S3 class to represent graph adjacency lists using 'vctrs'.
    Allows for creation, subsetting, combining, and pretty printing of these lists.
    Adjacency lists can be easily converted to zero-indexed lists, which allows
    for easy passing of objects to low-level languages for processing.

Implements a constrained version of hierarchical agglomerative 
    clustering, in which each observation is associated to a position, and only 
    adjacent clusters can be merged. Typical application fields in 
    bioinformatics include Genome-Wide Association Studies or Hi-C data 
    analysis, where the similarity between items is a decreasing function of 
    their genomic distance. Taking advantage of this feature, the implemented 
    algorithm is time and memory efficient. This algorithm is described in 
    Ambroise et al (2019) <doi:10.1186/s13015-019-0157-4>.

Constructs sparse adjacency matrices from spatial coordinates,
    feature measurements, class labels, and temporal indices. Supports
    nearest-neighbor graphs, heat-kernel weights, graph Laplacians, diffusion
    operators, and bilateral smoothers for graph-based data analysis, following
    spectral graph methods in von Luxburg (2007)
    <doi:10.1007/s11222-007-9033-z>, diffusion maps in Coifman and Lafon (2006)
    <doi:10.1016/j.acha.2006.04.006>, and bilateral filtering in Tomasi and
    Manduchi (1998) <doi:10.1109/ICCV.1998.710815>.