The R Journal: article published in 2019, volume 11:1

Whats for dynr: A Package for Linear and Nonlinear Dynamic Modeling in R PDF download
Lu Ou, Michael D. Hunter and Sy-Miin Chow , The R Journal (2019) 11:1, pages 91-111.

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 discreteand 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.

Received: 2018-02-02; online 2019-08-15, supplementary material, (4.2 KiB)
CRAN packages: dynr, dlm, KFAS, dse, OpenMx, ctsem, depmixS4, RHmm, MSwM, MSBVAR, MSGARCH, pomp, stats, Rcpp, RcppGSL, mice
CRAN Task Views cited directly: TimeSeries
CRAN Task Views implied by cited CRAN packages: TimeSeries, Finance, MissingData, Psychometrics, Bayesian, Cluster, DifferentialEquations, Environmetrics, HighPerformanceComputing, Multivariate, NumericalMathematics, OfficialStatistics, SocialSciences

CC BY 4.0
This article and supplementary materials are licensed under a Creative Commons Attribution 4.0 International license.

  author = {Lu Ou and Michael D. Hunter and Sy-Miin Chow},
  title = {{Whats for dynr: A Package for Linear and Nonlinear Dynamic Modeling in R}},
  year = {2019},
  journal = {{The R Journal}},
  doi = {10.32614/RJ-2019-012},
  url = {},
  pages = {91--111},
  volume = {11},
  number = {1}