Optimal design is crucial for experimenters to maximize the information collected from experiments and estimate the model parameters most accurately. ForLion algorithms have been proposed to find D-optimal designs for experiments with mixed types of factors. In this paper, we introduce the ForLion package which implements the ForLion algorithm to construct locally D-optimal designs and the Expected Weighted (EW) ForLion algorithm to generate robust EW D-optimal designs, which maximize the determinant of the expected Fisher information matrix under parameter uncertainty. The package supports experiments under linear models (LM), generalized linear models (GLM), and multinomial logistic models (MLM) with continuous, discrete, or mixed-type factors. It provides both optimal approximate designs and an efficient function converting approximate designs into exact designs with integer-valued allocations of experimental units. Tutorials are included to show the package's usage across different scenarios.

翻译：最优设计对于实验者最大化从实验中收集的信息并最准确估计模型参数至关重要。ForLion算法已被提出用于寻找混合类型因子实验的D-最优设计。本文介绍了实现ForLion算法的ForLion软件包，该算法可构建局部D-最优设计，而期望加权（EW）ForLion算法则可生成稳健的EW D-最优设计，在参数不确定性下最大化期望Fisher信息矩阵的行列式。该软件包支持线性模型（LM）、广义线性模型（GLM）和多项逻辑模型（MLM）的实验，因子类型可为连续型、离散型或混合型。它同时提供了最优近似设计，以及将近似设计转化为具有整数分配实验单元的精确设计的高效函数。文中附有教程，展示了该软件包在不同场景下的使用。