Packaged: Mon Nov 17 11:52:16 2008; paul

Depends: R (>= 2.12.0), methods, grid, XML

Suggests: lattice, cluster, colorspace, survival

Depends: R (>= 3.0.0), methods, grid, XML
Authors@R: c(person("Paul", "Murrell", role = c("aut", "cre"),
person("Richard", "Walton", role = "aut"),
person("Simon", "Potter", role = "ctb"))
Description: Functions for converting, importing, and drawing PostScript

Depends: R (>= 3.0.0), methods, grDevices, graphics, grid, XML

Imports: methods, grDevices, grid, XML, png, jpeg, base64enc
Authors@R: c(person("Simon", "Potter", role = c("aut"),
person("Paul", "Murrell", role = c("ctb", "cre"),
Description: Functions for importing external vector images and
drawing them as part of 'R' plots.  This package is different from the
'grImport' package because, where that package imports 'PostScript'
format images, this package imports 'SVG' format images.  Furthermore,
this package imports a specific subset of 'SVG', so external images
must be preprocessed using a package like 'rsvg' to produce 'SVG'
that this package can import.  'SVG' features that are not supported
by 'R' graphics, e.g., gradient fills, can be imported and then

person("Paul", "Murrell", role = c("aut", "cre"),

Title: Graphviz Pipeline Plot Based on Grids (grPipe: Graphviz
Authors@R: person("Daniel", "Gaspar Gonçalves", email = "daniel.gaspar.goncalves@gmail.com", role = c("aut", "cre"))
Description: Create a grid-based graphviz using the following functions:
1 - Creating the data.frame where the nodes are;
Imports: DiagrammeR, DiagrammeRsvg, dplyr, magrittr, rsvg, png, grid
Author: Daniel Gaspar Gonçalves [aut, cre]
Maintainer: Daniel Gaspar Gonçalves <daniel.gaspar.goncalves@gmail.com>

Title: Stochastic Quasi-Gradient Differential Evolution Optimization
Description: An optim-style implementation of the Stochastic Quasi-Gradient Differential Evolution (SQG-DE) optimization algorithm first published by Sala, Baldanzini, and Pierini (2018; <doi:10.1007/978-3-319-72926-8_27>). This optimization algorithm fuses the robustness of the population-based global optimization algorithm "Differential Evolution" with the efficiency of gradient-based optimization. The derivative-free algorithm uses population members to build stochastic gradient estimates, without any additional objective function evaluations. Sala, Baldanzini, and Pierini argue this algorithm is useful for 'difficult optimization problems under a tight function evaluation budget.' This package can run SQG-DE in parallel and sequentially.