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>.

Provides utility functions for standardizing economic entity 
  (economy, aggregate, institution, etc.) name and id in economic datasets such 
  as those published by the International Monetary Fund and World Bank. Aims to 
  facilitate consistent data analysis, reporting, and joining across datasets. 
  Used as a foundational building block in the 'EconDataverse' family of 
  packages (<https://www.econdataverse.org>).

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.

Provides a consistent set of functions for enriching and analyzing 
    sovereign-level economic data. Economists, data scientists, and financial 
    professionals can use the package to add standardized identifiers, 
    demographic and macroeconomic indicators, and derived metrics such as 
    gross domestic product per capita or government expenditure shares.

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>.