Description: G2Sd package gives full descriptive statistics and a
physical description of sediment obtained with AFNOR or phi
sieves according to the grain size distribution.

Maintainer: Regis K. Gallon <reg.gallon@gmail.com>
physical description of sediment obtained with metric or phi

Description: gives full descriptive statistics and a

Title: Grain-Size Statistics and Description of Sediment
Imports: shiny, xlsx, rJava, xlsxjars, reshape2, ggplot2
Description: Full descriptive statistics, physical description of sediment,

Imports: shiny, xlsx, rJava, xlsxjars, reshape2, ggplot2, stats,

Description: An R package for optimization using genetic algorithms.
The package provides a flexible general-purpose set of tools
for implementing genetic algorithms search in both the
continuous and discrete case, whether constrained or not. Users
can easily define their own objective function depending on the
problem at hand. Several genetic operators are available and
can be combined to explore the best settings for the current
task. Furthermore, users can define new genetic operators and

Description: An R package for optimization using genetic algorithms. The package provides a flexible general-purpose set of tools for implementing genetic algorithms search in both the continuous and discrete case, whether constrained or not. Users can easily define their own objective function depending on the problem at hand. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. GAs can be run sequentially or in parallel.
Suggests: parallel, doParallel, foreach, iterators

Depends: R (>= 2.15), methods, foreach, iterators

Description: An R package for optimisation using genetic algorithms. The package provides a flexible general-purpose set of tools for implementing genetic algorithms search in both the continuous and discrete case, whether constrained or not. Users can easily define their own objective function depending on the problem at hand. Several genetic operators are available and can be combined to explore the best settings for the current task. Furthermore, users can define new genetic operators and easily evaluate their performances. Local search using general-purpose optimisation algorithms can be applied stochastically to exploit interesting regions. GAs can be run sequentially or in parallel, using an explicit master-slave parallelisation or a coarse-grain islands approach.
Authors@R: c(person("Luca", "Scrucca", role = c("aut", "cre"),
Depends: R (>= 3.0), methods, foreach, iterators
Imports: stats, graphics, grDevices, utils
Suggests: parallel, doParallel, doRNG (>= 1.6), knitr (>= 1.8)