Optimal design ideas are increasingly used in different disciplines to rein in experimental costs. Given a nonlinear statistical model and a design criterion, optimal designs determine the number of experimental points to observe the responses, the design points and the number of replications at each design point. Currently, there are very few free and effective computing tools for finding different types of optimal designs for a general nonlinear model, especially when the criterion is not differentiable. We introduce an R package ICAOD to find various types of optimal designs and they include locally, minimax and Bayesian optimal designs for different nonlinear statistical models. Our main computational tool is a novel metaheuristic algorithm called imperialist competitive algorithm (ICA) and inspired by socio-political behavior of humans and colonialism. We demonstrate its capability and effectiveness using several applications. The package also includes several theory-based tools to assess optimality of a generated design when the criterion is a convex function of the design.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2022-043.zip
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For attribution, please cite this work as
Masoudi, et al., "ICAOD: An R Package for Finding Optimal designs for Nonlinear Statistical Models by Imperialist Competitive Algorithm", The R Journal, 2022
BibTeX citation
@article{RJ-2022-043,
author = {Masoudi, Ehsan and Holling, Heinz and Wong, Weng Kee and Kim, Seongho},
title = {ICAOD: An R Package for Finding Optimal designs for Nonlinear Statistical Models by Imperialist Competitive Algorithm},
journal = {The R Journal},
year = {2022},
note = {https://doi.org/10.32614/RJ-2022-043},
doi = {10.32614/RJ-2022-043},
volume = {14},
issue = {3},
issn = {2073-4859},
pages = {20-45}
}