Title: A community modeling foundation for Eco-Hydrology.

Author: Fuka DR, Walter MT, Archibald JA, Steenhuis TS, and Easton ZM
Depends: R (>= 2.10), operators, topmodel, DEoptim, XML
Description: This package provides a flexible foundation for scientists,
engineers, and policy makers to base teaching exercises as well as for
more applied use to model complex eco-hydrological interactions.

Title: A Community Modeling Foundation for Eco-Hydrology
Description: Provides a flexible foundation for scientists,

Maintainer: C�stor Guisande Gonz�lez <castor@uvigo.es>
Description: Calculates several indices, such as of diversity, fluctuation, etc., and they are used to estimate ecological indicators.
Suggests: vegan, shape, Rarity, splancs, plotrix, FD, picante

Suggests: vegan, shape, Rarity, splancs, plotrix, FD, picante, sp,
geoR, mgcv, raster, alphahull, sf, methods

Title: Simulate and Sample from Ecological Interaction Networks
Description: Generate a wide range of random networks given expected degree, 'modularity',
and structure.  Simulations and sampling routines are implemented in 'FORTRAN',
providing efficient generation times even for large networks. Basic visualization
methods also included.  Algorithms implemented here are described in
person("Mathias", "Pires", role = "aut",
Author: Marcus de Aguiar [aut, cph] (<https://orcid.org/0000-0003-1379-7568>),
Carl Boettiger [aut, cre] (<https://orcid.org/0000-0003-4580-091X>)

Description: Randomly generate a wide range of interaction networks with
specified size, average degree, 'modularity', and topological
structure. Sample nodes and links from within simulated networks
randomly, by degree, by module, or by abundance. Simulations
and sampling routines are implemented in 'FORTRAN', providing
efficient generation times even for large networks. Basic
visualization methods also included. Algorithms implemented
here are described in de Aguiar et al. (2017) <arXiv:1708.01242>.

specified size, average degree, modularity, and topological
Suggests: spelling, testthat, covr, ggraph