What's for dynr: A Package for Linear and Nonlinear Dynamic Modeling in R.

IF 2.3 4区 计算机科学 Q3 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS R Journal Pub Date : 2019-06-01 DOI:10.32614/rj-2019-012
Lu Ou, Michael D Hunter, Sy-Miin Chow
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引用次数: 38

Abstract

Intensive longitudinal data in the behavioral sciences are often noisy, multivariate in nature, and may involve multiple units undergoing regime switches by showing discontinuities interspersed with continuous dynamics. Despite increasing interest in using linear and nonlinear differential/difference equation models with regime switches, there has been a scarcity of software packages that are fast and freely accessible. We have created an R package called dynr that can handle a broad class of linear and nonlinear discrete- and continuous-time models, with regime-switching properties and linear Gaussian measurement functions, in C, while maintaining simple and easy-to-learn model specification functions in R. We present the mathematical and computational bases used by the dynr R package, and present two illustrative examples to demonstrate the unique features of dynr.

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什么是dynr:一个在R中的线性和非线性动态建模包。
行为科学中密集的纵向数据通常是嘈杂的、多变量的,并且可能涉及多个单元,通过显示穿插在连续动态中的不连续性来进行状态切换。尽管人们对使用具有状态切换的线性和非线性微分/差分方程模型越来越感兴趣,但缺乏快速且可自由访问的软件包。我们创建了一个名为dynr的R包,它可以用C处理一系列线性和非线性离散和连续时间模型,具有状态切换特性和线性高斯测量函数,同时在R中保持简单易学的模型规范函数。我们介绍了dynr R包使用的数学和计算基础,并给出了两个示例来说明dynr的独特特性。
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来源期刊
R Journal
R Journal COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-STATISTICS & PROBABILITY
CiteScore
2.70
自引率
0.00%
发文量
40
审稿时长
>12 weeks
期刊介绍: The R Journal is the open access, refereed journal of the R project for statistical computing. It features short to medium length articles covering topics that should be of interest to users or developers of R. The R Journal intends to reach a wide audience and have a thorough review process. Papers are expected to be reasonably short, clearly written, not too technical, and of course focused on R. Authors of refereed articles should take care to: - put their contribution in context, in particular discuss related R functions or packages; - explain the motivation for their contribution; - provide code examples that are reproducible.
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