Optimal Designs for Nonlinear Models

Finds optimal designs for nonlinear models using a metaheuristic algorithm called imperialist competitive algorithm ICA. See, for details, Masoudi et al. (2017) .


Imperialist Competitive Algorithm (ICA) to find optimal designs for nonlinear models.

Example: locally D-optimal design for the exponential model mica(fimfunc = "FIM_exp_2par", lx = 0, ux = 1, lp = c(2, 3), up = c(2, 3), iter = 40, k = 2, type = "locally", control = list(seed = 215))

How to isntall:

install.packages("ICAOD") require(ICAOD)

The most important function is mica that finds locally, minimax and standardized maximin D-optimal design for nonlinear models. on_average_ica also finds optim on the average optimal designs.

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Reference manual

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install.packages("ICAOD")

0.9.8 by Ehsan Masoudi, 2 months ago


https://github.com/ehsan66/ICAOD


Report a bug at https://github.com/ehsan66/ICAOD/issues


Browse source code at https://github.com/cran/ICAOD


Authors: Ehsan Masoudi [aut, cre] , Heinz Holling [aut] , Weng Kee Wong [aut]


Documentation:   PDF Manual  


Task views: Design of Experiments (DoE) & Analysis of Experimental Data


GPL (>= 2) license


Imports Rcpp, nloptr, stats, graphics, grDevices, cubature, sn, mnormt, methods, mvQuad

Suggests rgl, lattice, knitr, rmarkdown

Linking to Rcpp, RcppEigen


See at CRAN