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>.

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>.

Fit and sample from the ensemble model described in Spence et al (2018): "A general framework for combining ecosystem models"<doi:10.1111/faf.12310>.

Simulates the soil water balance (soil moisture, evapotranspiration, leakage and runoff), rainfall series by using the marked Poisson process and the vegetation growth through the normalized difference vegetation index (NDVI). Please see Souza et al. (2016) <doi:10.1002/hyp.10953>.

Calculates several indices, such as of diversity, fluctuation, etc., and they are used to estimate ecological indicators.

Provides measures to characterize the complexity of classification 
    and regression problems based on aspects that quantify the linearity of the 
    data, the presence of informative feature, the sparsity and dimensionality 
    of the datasets. This package provides bug fixes, generalizations and 
    implementations of many state of the art measures. The measures are 
    described in the papers: Lorena et al. (2019) <doi:10.1145/3347711> and 
    Lorena et al. (2018) <doi:10.1007/s10994-017-5681-1>.