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>.

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>.

Estimating individual-level covariate-outcome associations 
 using aggregate data ("ecological inference") or a combination of 
 aggregate and individual-level data ("hierarchical related regression").

A toolbox for implementing the Ecological Dynamic Regime framework 
    (Sánchez-Pinillos et al., 2023 <doi:10.1002/ecm.1589>) to characterize and 
    compare groups of ecological trajectories in multidimensional spaces defined 
    by state variables. The package includes the RETRA-EDR algorithm to identify 
    representative trajectories, functions to generate, summarize, and visualize 
    representative trajectories, and several metrics to quantify the distribution 
    and heterogeneity of trajectories in an ecological dynamic regime and quantify 
    the dissimilarity between two or more ecological dynamic regimes. The package 
    also includes a set of functions to assess ecological resilience based on 
    ecological dynamic regimes (Sánchez-Pinillos et al., 2024 <doi:10.1016/j.biocon.2023.110409>).