Rcpp (>= 0.11), Matrix, reshape2, ggplot2, magrittr, tidyr,
vegan, ape, phytools, mvtnorm, coda, graphics, grDevices,
methods, stats, depth, utils

Rcpp (>= 0.11), Matrix, reshape2, ggplot2, vegan, ape, expm,
matrixcalc, mvtnorm, coda, MCMCpack, graphics, grDevices,
methods, stats, utils
testthat, foreach, magrittr, grid, gridExtra, doParallel

Rcpp (>= 0.11), Matrix, reshape2, ggplot2, vegan, ape, expm,
matrixcalc, mvtnorm, coda, igraph, MCMCpack, graphics,
grDevices, methods, stats, utils

Ana Paula Assis, Diogo Melo, Edgar Zanella, Fabio Andrade Machado, Guilherme
Garcia
testthat, foreach, magrittr, grid, gridExtra, doParallel,
PHENIX, cowplot

Matrix, reshape2, ggplot2, vegan, ape, expm, matrixcalc,
mvtnorm, coda, igraph, MCMCpack, graphics, grDevices, methods,
stats, utils
dplyr, testthat, foreach, magrittr, grid, gridExtra,
doParallel, PHENIX, cowplot

Matrix, reshape2, ggplot2, vegan, ape, expm, mvtnorm, coda,
igraph, MCMCpack, graphics, grDevices, methods, stats, utils
dplyr, testthat, foreach, magrittr, grid, gridExtra,
doParallel, cowplot

person("Geir H.", "Bolstad", email = "geir.bolstad@nina.no",
role = c("aut", "cre"))
An implementation of the evolvability parameters defined in Hansen and Houle (2008).

Provides tools for calculating evolvability parameters from estimated G-matrices as defined
in Hansen and Houle (2008) <doi:10.1111/j.1420-9101.2008.01573.x> and fits phylogenetic
comparative models that link the rate of evolution of a trait to the state of another evolving
trait (see Hansen et al. 2021 Systematic Biology <doi:10.1093/sysbio/syab079>). The package was
released with Bolstad et al. (2014) <doi:10.1098/rstb.2013.0255>, which contains some examples of use.

Evolutionary Parameter Estimation for 'Repast Simphony' Models
c(
person("Antonio", "Prestes Garcia", email = "antonio.pgarcia@alumnos.upm.es", role = c("aut","cre")),
person("Alfonso", " Rodriguez-Paton", email = "arpaton@fi.upm.es", role = c("aut","ths")))
The EvoPER, Evolutionary Parameter Estimation for 'Repast Simphony'
Agent-Based framework, provides optimization driven parameter estimation methods
based on evolutionary computation techniques which could be more efficient and
require, in some cases, fewer model evaluations than other alternatives
relaying on experimental design.
Antonio Prestes Garcia [aut, cre],
Alfonso Rodriguez-Paton [aut, ths]