c(
person("Pedro", "Cardoso", email = "pedro.cardoso@helsinki.fi", role = c("aut", "cre")),
person("Stefano", "Mammola", role = c("aut")),
person("Francois", "Rigal", role = c("aut")),
person("Jose", "Carvalho", role = c("aut")))
Includes algorithms to assess alpha and beta diversity
in all their dimensions (taxonomic, phylogenetic and functional).
It allows performing a number of analyses based on species
identities/abundances, phylogenetic/functional distances, trees,
convex-hulls or kernel density n-dimensional hypervolumes
depicting species relationships.
Cardoso et al. (2015) <doi:10.1111/2041-210X.12310>.
geometry, graphics, hypervolume, nls2, raster, stats, utils,
vegan
Pedro Cardoso [aut, cre],
Stefano Mammola [aut],
Francois Rigal [aut],
Jose Carvalho [aut]

ape, geometry, graphics, hypervolume, MASS, nls2, raster,
stats, utils, vegan

ape, geometry, graphics, hypervolume, MASS, nls2, parallel,
raster, stats, utils, vegan

ape, geometry, graphics, hypervolume, MASS, methods, nls2,
parallel, raster, stats, utils, vegan

Victor Manuel Aguirre-Torres, Nery Sofia Huerta-Pacheco,  Edgar A. Lopez
Definitive Screening Designs are a class of experimental designs that under factor sparsity have the potential to estimate linear, quadratic and interaction effects with little experimental effort. BAYESDEF is a package that performs a five step strategy to analyze this kind of experiments that makes use of tools coming from the Bayesian approach. It also includes the least absolute shrinkage and selection operator (lasso) as a check (Aguirre VM. (2016) <DOI:10.1002/asmb.2160>).

On Bayesian analysis of Threshold autoregressive model (BAYSTAR)
Cathy W. S. Chen, Edward M.H. Lin, F.C. Liu, and Richard Gerlach
The manuscript introduces the BAYSTAR package, which provides the functionality for Bayesian estimation in autoregressive threshold models.

On Bayesian Analysis of Threshold Autoregressive Models
Cathy W. S. Chen, Edward M.H. Lin, F.C. Liu, and Richard
Gerlach
Fit two-regime threshold autoregressive (TAR) models by Markov chain Monte Carlo methods.