Implementation of a modular framework for ecosystem risk assessments, 
    combining existing risk assessment approaches tailored to semi-quantitative and
    quantitative analyses.

API wrapper to download statistical information from the Economic
             Statistics System (ECOS) of the Bank of Korea
             <https://ecos.bok.or.kr/api/#/>.

Classes and methods for implementing aquatic ecosystem models,
  for running these models, and for visualizing their results.

R interface to the Embedded COnic Solver (ECOS), an efficient
	     and robust C library for convex problems. Conic and equality
	     constraints can be specified in addition to integer and
	     boolean variable constraints for mixed-integer problems. This
	     R interface is inspired by the python interface and has
	     similar calling conventions.

Implements stochastic simulations of community assembly (ecological
    diversification) using customizable ecospace frameworks (functional trait
    spaces). Provides a wrapper to calculate common ecological disparity and
    functional ecology statistical dynamics as a function of species richness.
    Functions are written so they will work in a parallel-computing environment.

Collection of R functions and data sets for the support of spatial ecology analyses with a focus on pre, core and post modelling analyses of species distribution, niche quantification and community assembly. Written by current and former members and collaborators of the ecospat group of Antoine Guisan, Department of Ecology and Evolution (DEE) and Institute of Earth Surface Dynamics (IDYST), University of Lausanne, Switzerland. Read Di Cola et al. (2016) <doi:10.1111/ecog.02671> for details.

Fits a state-space mass-balance model for marine ecosystems,
	which implements dynamics derived from 
	'Ecopath with Ecosim' ('EwE') <https://ecopath.org/>
	while fitting to time-series of fishery catch, biomass indices,
	age-composition samples, and weight-at-age data.  
	'Ecostate' fits biological parameters (e.g., equilibrium mass)
	and measurement parameters (e.g., catchability coefficients)
	jointly with residual variation in process errors, and can include
	Bayesian priors for parameters.