Calculate multiple or pairwise dissimilarity for orders q = 0-N 
    (CqN; Chao et al. 2008 <doi:10/fcvn63>) for a set of species assemblages or 
    interaction networks.

Spatial downscaling of coarse grid mapping to fine grid
    mapping using predictive covariates and a model fitted using the 'caret'
    package. The original dissever algorithm was published by Malone et al. 
    (2012) <doi:10.1016/j.cageo.2011.08.021>, and extended by Roudier et al.
    (2017) <doi:10.1016/j.compag.2017.08.021>.

Efficiently creates, manipulates, and subsets "dist" objects, commonly used in cluster analysis. Designed to minimise unnecessary conversions and computational overhead while enabling seamless interaction with distance matrices.

Tools to fit sample selection models in case of discrete response 
  variables, through a parametric formulation which represents a natural 
  extension of the well-known Heckman selection model are provided in the 
  package. The response variable can be of Bernoulli, Poisson or Negative 
  Binomial type. The sample selection mechanism allows to choose among a 
  Normal, Logistic or Gumbel distribution.

Extends the 'algebraic.dist' distribution algebra to
    random variables with internal structure: coherent reliability
    systems decomposed into components arranged by a structure function
    (series, parallel, k-out-of-n, bridge, and arbitrary topologies via
    minimal path sets). Every 'dist_structure' object is a 'dist', so
    the full distribution algebra (mean, vcov, sampler, surv, cdf) works
    automatically via default methods that compose component-level
    distributions through the topology. Adds structural queries:
    structure function evaluation, minimal path and cut sets, system
    signature, critical states, dual, Birnbaum structural importance,
    and system reliability. Topology shortcut constructors (series_dist,
    parallel_dist, kofn_dist, bridge_dist) produce ready-to-use dists
    from component dists and a chosen structure.