Provides step-by-step automation for integrating biodiversity data from multiple online aggregators, merging and cleaning datasets while addressing challenges such as taxonomic inconsistencies, georeferencing issues, and spatial or environmental outliers. Includes functions to extract environmental data and to define the biogeographic ranges in which species are most likely to occur.

Work with the Ecological Community Data Design Pattern. 'ecocomDP' 
    is a flexible data model for harmonizing ecological community surveys, in a 
    research question agnostic format, from source data published across 
    repositories, and with methods that keep the derived data up-to-date as the 
    underlying sources change. Described in O'Brien et al. (2021), 
    <doi:10.1016/j.ecoinf.2021.101374>.

Creates 'graphs' of species associations (interactions) and ordination biplots from 
    co-occurrence data by fitting discrete gaussian copula graphical models. Methods described in
    Popovic, GC., Hui, FKC., Warton, DI., (2018) <doi:10.1016/j.jmva.2017.12.002>.

A framework to simulate ecosystem dynamics through ordinary differential equations (ODEs). 
    You create an ODE model, tells 'ecode' to explore its behaviour, and perform numerical 
    simulations on the model. 'ecode' also allows you to fit model parameters by machine learning 
    algorithms. Potential users include researchers who are interested in the dynamics of ecological 
    community and biogeochemical cycles.

Biotracers and stomach content analyses are combined in a Bayesian hierarchical model
    to estimate a probabilistic topology matrix (all trophic link probabilities) and a diet matrix 
    (all diet proportions).
    The package relies on the JAGS software and the 'jagsUI' package to run a Markov chain Monte Carlo 
    approximation of the different variables.

Dissimilarity-based analysis functions including ordination and Mantel test functions, intended for use with spatial and community ecological data. The original package description is in Goslee and Urban (2007) <doi:10.18637/jss.v022.i07>, with further statistical detail in Goslee (2010) <doi:10.1007/s11258-009-9641-0>.