person("Daniel Laughlin", role = c("aut", "cre"),
email = "danielclaughlin@gmail.com")

c(
person("Daniel", "Laughlin", email = "danielclaughlin@gmail.com", role = "cre"),
person("Loic", "Chalmandrier", email = "lchalman@uwyo.edu", role = "aut"))
The objective of these functions is to derive a species assemblage
that satisfies a functional trait profile. Restoring resilient ecosystems
requires a flexible framework for selecting assemblages that are based on the
functional traits of species. However, current trait-based models have been
limited to algorithms that can only select species by optimising specific trait
values, and could not elegantly accommodate the common desire among restoration
ecologists to produce functionally diverse assemblages. We have solved this
problem by applying a non-linear optimisation algorithm that optimises Rao Q,
a closed-form functional trait diversity index that incorporates species
abundances, subject to other linear constraints. This framework generalises
previous models that only optimised the entropy of the community, and can
optimise both functional diversity and entropy simultaneously. This package
can also be used to generate experimental assemblages to test the effects
of community-level traits on community dynamics and ecosystem function. The
method is based on theory discussed in Laughlin (2014, Ecology Letters)
<doi.org/10.1111/ele.12288>.
Rsolnp, latticeExtra, grDevices, stats, lattice, graphics,
ade4, FD
Daniel Laughlin [cre],
Loic Chalmandrier [aut]

c(
person("Daniel", "Laughlin", email = "danielclaughlin@gmail.com", role = c("cre","aut")),
person("Loic", "Chalmandrier", email = "lchalman@uwyo.edu", role = "aut"))
Daniel Laughlin [cre, aut],
Loic Chalmandrier [aut]

A General Algorithm to Enhance the Performance of Variable
Selection Methods in Correlated Datasets
lars, glmnet, igraph, parallel, msgps, Rfast, methods,
Cascade, graphics, grDevices
c(
person(given = "Frederic", family= "Bertrand", role = c("cre", "aut"), email = "frederic.bertrand@math.unistra.fr", comment = c(ORCID = "0000-0002-0837-8281")),
person(given = "Myriam", family= "Maumy-Bertrand", role = c("aut"), email = "myriam.maumy-bertrand@math.unistra.fr", comment = c(ORCID = "0000-0002-4615-1512")),
person(given = "Ismail", family= "Aouadi", role = c("ctb"), email = "i.aouadi@unistra.fr"),
person(given = "Nicolas", family= "Jung", role = c("ctb"), email = "nicolas.jung@unistra.fr"))
Frederic Bertrand [cre, aut] (<https://orcid.org/0000-0002-0837-8281>),
Myriam Maumy-Bertrand [aut] (<https://orcid.org/0000-0002-4615-1512>),
Ismail Aouadi [ctb],
Nicolas Jung [ctb]
An implementation of the selectboost algorithm (Aouadi et al. 2018, <arXiv:1810.01670>), which is a general algorithm that improves the precision of any existing variable selection method. This algorithm is based on highly intensive simulations and takes into account the correlation structure of the data. It can either produce a confidence index for variable selection or it can be used in an experimental design planning perspective.