The availability of smart devices has made it possible to collect intensive longitudinal data (ILD) from individuals, providing a unique opportunity to study the complex dynamics of psychological systems. Existing time-series methods often have limitations, such as assuming linear interactions or having restricted forms, leading to difficulties in capturing the complex nature of these systems. To address this issue, we introduce fitlandr, a method with implementation as an R package that integrates nonparametric estimation of the drift-diffusion function and stability landscape. The drift-diffusion function is estimated using the multivariate kernel estimator (MVKE; Bandi & Moloche, 2018), and the stability landscape is estimated through Monte-Carlo estimation of the steady-state distribution (Cui et al., 2021; Cui, Lichtwarck-Aschoff, et al., 2023). Using a simulated emotional system, we demonstrate that fitlandr can effectively recover bistable dynamics from data, even in the presence of moderate noise, and that it primarily relies on dynamic information from the system instead of distributional information. We then apply the method to two empirical single-participant experience sampling method data sets and compared the results with the simulation data sets. Whereas both data sets show a bimodal distribution, fitlandr only revealed bistability in one of them, indicating that bimodality in ILD does not necessarily imply the existence of bistability in the underlying system. These results demonstrate the potential of fitlandr as a tool for uncovering the rich, nonlinear dynamics of psychological systems from ILD. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

中文翻译：

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从密集的纵向数据中解锁非线性动力学和多稳定性：一种新颖的方法。


智能设备的出现使得收集个人的密集纵向数据（ILD）成为可能，为研究心理系统的复杂动态提供了独特的机会。现有的时间序列方法通常存在局限性，例如假设线性相互作用或具有受限的形式，导致难以捕获这些系统的复杂性质。为了解决这个问题，我们引入了 fitlandr，这是一种以 R 包的形式实现的方法，集成了漂移扩散函数和稳定性景观的非参数估计。使用多元核估计器来估计漂移扩散函数（MVKE；Bandi & Moloche，2018），并通过稳态分布的蒙特卡罗估计来估计稳定性景观（Cui et al.，2021；Cui，Lichtwarck） -Aschoff 等人，2023）。使用模拟情感系统，我们证明了 fitlandr 可以有效地从数据中恢复双稳态动态，即使存在中等噪声，并且它主要依赖于系统的动态信息而不是分布信息。然后，我们将该方法应用于两个经验单参与者经验抽样方法数据集，并将结果与​​模拟数据集进行比较。尽管两个数据集都显示出双峰分布，但 fitlandr 仅揭示了其中一个数据集的双稳态，表明 ILD 中的双峰分布并不一定意味着基础系统中存在双稳态。这些结果证明了 fitlandr 作为从 ILD 中揭示心理系统丰富的非线性动态的工具的潜力。 （PsycInfo 数据库记录 (c) 2023 APA，保留所有权利）。