Computes a series of indices commonly used in the fields of economic geography, economic complexity, and evolutionary economics to describe the location, distribution, spatial organization, structure, and complexity of economic activities. Functions include basic spatial indicators such as the location quotient, the Krugman specialization index, the Herfindahl or the Shannon entropy indices but also more advanced functions to compute different forms of normalized relatedness between economic activities or network-based measures of economic complexity. Most of the functions use matrix calculus and are based on bipartite (incidence) matrices consisting of region - industry pairs. These are described in Balland (2017) <http://econ.geo.uu.nl/peeg/peeg1709.pdf>.

A wrapper of different methods from Linear Algebra for the equations
  introduced in The Atlas of Economic Complexity and related literature. This
  package provides standard matrix and graph output that can be used seamlessly
  with other packages. See <doi:10.21105/joss.01866> for a summary
  of these methods and its evolution in literature.

Tools for using null models to analyse ecological
    networks (e.g. food webs, flower-visitation networks, seed-dispersal
    networks) and detect resource preferences or non-random interactions among
    network nodes. Tools are provided to run null models, test for and plot
    preferences, plot and analyse bipartite networks, and export null model
    results in a form compatible with other network analysis packages. The 
    underlying null model was developed by Agusti et al. (2003) Molecular 
    Ecology <doi:10.1046/j.1365-294X.2003.02014.x> and the full application to 
    ecological networks by Vaughan et al. (2018) econullnetr: an R package 
    using null models to analyse the structure of ecological networks and 
    identify resource selection. Methods in Ecology & Evolution, 
    <doi:10.1111/2041-210X.12907>.

Estimates power by simulation for multivariate
    abundance data to be used for sample size estimates. Multivariate
    equivalence testing by simulation from a Gaussian copula model.
    The package also provides functions for parameterising multivariate effect
    sizes and simulating multivariate abundance data jointly. The discrete
    Gaussian copula approach is described in
    Popovic et al. (2018) <doi:10.1016/j.jmva.2017.12.002>.