ICAOD: ICAOD: Finding Optimal Designs for Nonlinear Models Using the...

In ICAOD: Optimal Designs for Nonlinear Models via ICA

ICAOD: Finding Optimal Designs for Nonlinear Models Using the Imperialist Competitive Algorithm

Description

Different functions are available to find optimal designs for linear and nonlinear models using the imperialist competitive algorithm (ICA). Because optimality criteria depend on unknown model parameters, users may choose among the following methods:

locally

Finds locally optimal designs.

bayes

Finds Bayesian optimal designs with a continuous prior.

robust

Finds robust average-optimal designs using a discrete prior.

minimax

Finds minimax and standardized maximin optimal designs.

multiple

Locally multiple-objective optimal designs for Hill models.

bayescomp

Compound DP‑optimal Bayesian designs for binary models.

Additional sensitivity-verification functions:

senslocally

Sensitivity for locally optimal designs.

sensrobust

Sensitivity for robust optimal designs.

sensbayes

Sensitivity for Bayesian designs.

sensminimax

Sensitivity for minimax designs.

sensmultiple

Sensitivity for multiple objective designs.

sensbayescomp

Sensitivity for compound DP Bayesian designs.

Details

Further methodological background can be found in Masoudi et al. (2017, 2019).

Author(s)

Maintainer: Ehsan Masoudi [e-mail]

Authors:

• Heinz Holling

• Weng Kee Wong

Other contributors:

• Seongho Kim [contributor]

References

Masoudi E, Holling H, Wong WK (2017). *Application of Imperialist Competitive Algorithm to Find Minimax and Standardized Maximin Optimal Designs*. Computational Statistics & Data Analysis, 113, 330–345.

Masoudi E, Holling H, Duarte BP, Wong WK (2019). *Metaheuristic Adaptive Cubature‑Based Algorithm to Find Bayesian Optimal Designs for Nonlinear Models*. Journal of Computational and Graphical Statistics.

ICAOD documentation built on Feb. 3, 2026, 5:07 p.m.