ynamic models are becoming increasingly popular to study the dynamic processes of dyadic interactions. In this article, we present a Dyadic Interaction Dynamics (DID) Shiny app which provides simulations and visualizations of data from several models that have been proposed for the analysis of dyadic data. We propose data generation as a tool to inspire and guide theory development and elaborate on how to connect substantive ideas to specific features of these models. We begin by discussing the basics of dynamic models with dyadic interactions. Then we present several models and illustrate model-implied behavior through generated data, accompanied by the DID Shiny app which allows researchers to generate and visualize their own data. Specifically, we consider: (a) the first-order vector autoregressive (VAR(1)) model; (b) the latent VAR(1) model; (c) the time-varying VAR(1) model; (d) the threshold VAR(1) model; (e) the hidden Markov model; and (f) the Markov-switching VAR(1) model. Finally, we demonstrate these models using empirical examples. We aim to give researchers more insight into what dynamic modeling approach fits their research question and data best. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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模拟和可视化二元交互动态的工具。

动态模型在研究二元相互作用的动态过程中变得越来越流行。在本文中，我们提出了一个二元交互动力学 (DID) Shiny 应用程序，它提供了来自多个模型的数据模拟和可视化，这些模型已被提议用于二元数据分析。我们建议将数据生成作为激发和指导理论发展的工具，并详细说明如何将实质性想法与这些模型的具体特征联系起来。我们首先讨论二元交互动态模型的基础知识。然后，我们提出几个模型，并通过生成的数据说明模型隐含的行为，并附带 DID Shiny 应用程序，该应用程序允许研究人员生成和可视化自己的数据。具体来说，我们考虑： (a) 一阶向量自回归 (VAR(1)) 模型；(b) 潜在 VAR(1) 模型；(c) 时变VAR(1)模型；(d) 阈值VAR(1)模型；(e) 隐马尔可夫模型；(f) 马尔可夫切换 VAR(1) 模型。最后，我们使用实证示例来演示这些模型。我们的目标是让研究人员更深入地了解哪种动态建模方法最适合他们的研究问题和数据。（PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。