Packaged: 2019-09-16 16:39:03 UTC; mleonawicz

This package has been peer-reviewed by rOpenSci (v. 0.3.4).
Suggests: testthat, knitr, rmarkdown, covr, tidyr, ggplot2

Authors@R: person("Matthew", "Leonawicz", email = "mfleonawicz@gmail.com", role = c("aut", "cre"), comment = c(ORCID = "0000-0001-9452-2771"))
Author: Matthew Leonawicz [aut, cre] (<https://orcid.org/0000-0001-9452-2771>)

Title: Explicit Interaction Community Models
email="mpbertolo@gmail.com", comment = c(ORCID="0000-0001-7640-0623"), role=c("aut", "cre")),
person(given="Pedro", family="Beja", comment = c(ORCID="0000-0001-8164-0760"), role="aut"))
Imports: methods, parallel, GA (>= 3.1.1), snow, doSNOW, iterators,
Description: Model fitting and species biotic interaction network topology selection for explicit
interaction community models. Explicit interaction community models are an extension of binomial
linear models for joint modelling of species communities, that incorporate both the effects of
species biotic interactions and the effects of missing covariates. Species interactions are modelled
as direct effects of each species on each of the others, and are estimated alongside the effects of
missing covariates, modelled as latent factors. The package includes a penalized maximum likelihood
fitting function, and a genetic algorithm for selecting the most parsimonious species interaction
Author: Miguel Porto [aut, cre] (<https://orcid.org/0000-0001-7640-0623>),

pso, ucminf, foreach, graphics, optimParallel

Author: The EigenDog Team <support@eigendog.com>
Maintainer: The EigenDog Team <support@eigendog.com>
Description: Evaluate binary classificaton models produced by the EigenDog web-service.

Description: Evaluate binary classification models produced by the

Title: Generates (dense) matrices that have a given set of eigenvalues
Description: Solves the ``inverse eigenvalue problem'' which is to
generate a real-valued matrix that has the specified real
eigenvalue spectrum.  It can generate infinitely many dense
matrices, symmetric or asymmetric, with the given set of
eigenvalues.  Algorithm can also generate stochastic and doubly