ICAOD: Imperialist Competitive Algorithm for Optimal Designs

Finding locally D-optimal, minimax D-optimal, standardized maximin D-optimal, optim-on-the-average and multiple objective optimal designs for nonlinear models. Different Fisher information matrices can also be set by user. There are also useful functions for verifying the optimality of the designs with respect to different criteria by equivalence theorem. ICA is a meta-heuristic evolutionary algorithm inspired from the socio-political process of humans. See Masoudi et al. (2016) <doi:10.1016/j.csda.2016.06.014>.

Version: 0.9.2
Depends: R (≥ 3.1.3)
Imports: Rcpp, Rsolnp, nloptr, stats, graphics, grDevices
LinkingTo: Rcpp, RcppEigen
Suggests: rgl, lattice
Published: 2017-01-09
Author: Ehsan Masoudi [aut, cre], Heinz Holling [aut], Weng Kee Wong [aut]
Maintainer: Ehsan Masoudi <ehsan.masoudi at wwu.de>
BugReports: https://github.com/ehsan66/ICAOD/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/ehsan66/ICAOD
NeedsCompilation: yes
Materials: README
In views: ExperimentalDesign
CRAN checks: ICAOD results

Downloads:

Reference manual: ICAOD.pdf
Package source: ICAOD_0.9.2.tar.gz
Windows binaries: r-devel: ICAOD_0.9.2.zip, r-release: ICAOD_0.9.2.zip, r-oldrel: ICAOD_0.9.2.zip
OS X El Capitan binaries: r-release: ICAOD_0.9.2.tgz
OS X Mavericks binaries: r-oldrel: ICAOD_0.9.2.tgz
Old sources: ICAOD archive

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