Provides methods for estimating species niche position and niche
    breadth under continuous environmental gradients. The package implements
    canonical correspondence analysis (CCA), partial CCA (pCCA), generalized
    additive models (GAM), and Levins' niche breadth metrics for species-level
    and community-level analyses. Methods are based on ter Braak (1986)
    <doi:10.2307/1938672>, Okie et al. (2015) <doi:10.1098/rspb.2014.2630>,
    Feng et al. (2020) <doi:10.1111/mec.15441>, Wood (2017)
    <doi:10.1201/9781315370279>, and Levins (1968, ISBN:978-0691080628).

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>.