data.table, grDevices, Morpho, rgl, Rvcg

A Toolbox for Modelling Species Coexistence in R
c(person("David", "Garcia-Callejas", role = c("aut", "cre"),
email = "david.garcia.callejas@gmail.com",
comment = c(ORCID = "0000-0001-6982-476X")),
person("Ignasi", "Bartomeus", role = c("aut"),
email = "nacho.bartomeus@gmail.com",
comment = c(ORCID = "0000-0001-7893-4389")),
person("Oscar", "Godoy", role = c("aut"), email = "ogodoy.re@gmail.com",
comment = c(ORCID = "0000-0003-4988-6626")),
person("Maxime", "Lancelot", role = c("ctb")))
Recent developments in modern coexistence theory have advanced
our understanding on how species are able to persist and co-occur
with other species at varying abundances. However, applying this
mathematical framework to empirical data is still challenging,
precluding a larger adoption of the theoretical tools developed
by empiricists. This package provides a complete toolbox for modelling
interaction effects between species, and calculate fitness and
niche differences.
The functions are flexible, may accept covariates,
and different fitting algorithms can be used.
A full description of the underlying methods is available in
García-Callejas, D., Godoy, O., and Bartomeus, I. (2020) <doi:10.1111/2041-210X.13443>.
ggplot2, tidyr, dplyr, magrittr, knitr, stringr, rmarkdown,
testthat (>= 0.8.0), BB, ucminf, dfoptim, minqa, hydroPSO,
GenSA, DEoptimR, nloptr
David Garcia-Callejas [aut, cre]
(<https://orcid.org/0000-0001-6982-476X>),
Ignasi Bartomeus [aut] (<https://orcid.org/0000-0001-7893-4389>),
Oscar Godoy [aut] (<https://orcid.org/0000-0003-4988-6626>),
Maxime Lancelot [ctb]

R/C++ Tools for Literate Statistical Practice
Builds on the Rcpp package/library by providing an
application layer library on the C++ side that includes models
of fundamental R data structures like data frames and time
series. Includes an extensible collection of tools like special
functions and a financial date library. Extends Sweave by
permitting vignettes (or research compendium docs) to include
C++ code chunks. This package/library might be used to develop
R and C++ solutions in parallel, to speed the transition from
experimental R code to production C++ code, or to develop rich
research compendium documents following literate statistical
practice that have embedded R and C++ code chunks. There is a
package vignette that provides many examples.