MOODE: An R Package for Multi-Objective Optimal Design of Experiments

TL;DR

The paper introduces MOODE, an R package that facilitates multi-objective optimal experimental design by integrating various criteria to efficiently address multiple research objectives.

Contribution

It presents a new R package that implements algorithms for multi-objective optimal design, allowing researchers to balance multiple experimental goals effectively.

Findings

Successfully applied to two case studies of different complexities.

Demonstrates the package's ability to find nearly optimal designs.

Integrates multiple criteria for comprehensive experimental optimization.

Abstract

We describe the R package MOODE and demonstrate its use to find multi-objective optimal experimental designs. Multi-Objective Optimal Design of Experiments (MOODE) targets the experimental objectives directly, ensuring that the full set of research questions is answered as economically as possible. In particular, individual criteria aimed at optimizing inference are combined with lack-of-fit and MSE-based components in compound optimality criteria to target multiple and competing objectives reflecting the priorities and aims of the experimentation. The package implements either a point exchange or coordinate exchange algorithm as appropriate to find nearly optimal designs. We demonstrate the functionality of MOODE through the application of the methodology to two case studies of varying complexity.