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
{"title":"What's for dynr: A Package for Linear and Nonlinear Dynamic Modeling in R.","authors":"Lu Ou, Michael D Hunter, Sy-Miin Chow","doi":"10.32614/rj-2019-012","DOIUrl":null,"url":null,"abstract":"<p><p>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 <b>dynr</b> 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 <b>dynr</b> R package, and present two illustrative examples to demonstrate the unique features of <b>dynr</b>.</p>","PeriodicalId":51285,"journal":{"name":"R Journal","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2019-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8297742/pdf/nihms-1719194.pdf","citationCount":"38","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"R Journal","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.32614/rj-2019-012","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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.

Abstract Image

Abstract Image

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
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。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
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.
期刊最新文献
binGroup2: Statistical Tools for Infection Identification via Group Testing. glmmPen: High Dimensional Penalized Generalized Linear Mixed Models. Three-Way Correspondence Analysis in R nlstac: Non-Gradient Separable Nonlinear Least Squares Fitting A Workflow for Estimating and Visualising Excess Mortality During the COVID-19 Pandemic
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1