标题： 对于狮子：一个用于查找具有混合因子的最优实验设计的 R 包 显示英文标题

标题： ForLion: An R Package for Finding Optimal Experimental Designs with Mixed Factors

摘要： 最优设计对于实验者来说至关重要，以最大化从实验中收集的信息并最准确地估计模型参数。 ForLion算法已被提出用于寻找具有混合类型因子的实验的D最优设计。 在本文中，我们介绍了ForLion包，该包实现了ForLion算法来构建局部D最优设计，以及EW ForLion算法来生成稳健的EW D最优设计。 该包支持在线性模型（LM）、广义线性模型（GLM）和多项逻辑模型（MLM）下进行实验，这些模型包含连续型、离散型或混合类型因子。 它提供了最优近似设计，并提供了一个高效函数，可将近似设计转换为具有整数值分配的精确设计。 教程包括在不同场景中展示该包的使用方法。 显示英文摘要

摘要： 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 EW ForLion algorithm to generate robust EW D-optimal designs. 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.