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.
Supplementary materials are available in addition to this article. It can be downloaded at RJ-2019-012.zip
dynr, dlm, KFAS, dse, OpenMx, ctsem, depmixS4, RHmm, MSwM, MSBVAR, MSGARCH, pomp, stats, Rcpp, RcppGSL, mice
TimeSeries, Finance, MissingData, Psychometrics, Bayesian, Cluster, DifferentialEquations, Environmetrics, HighPerformanceComputing, Multivariate, NumericalMathematics, OfficialStatistics, SocialSciences
Text and figures are licensed under Creative Commons Attribution CC BY 4.0. The figures that have been reused from other sources don't fall under this license and can be recognized by a note in their caption: "Figure from ...".
For attribution, please cite this work as
Ou, et al., "Whats for dynr: A Package for Linear and Nonlinear Dynamic Modeling in R", The R Journal, 2019
BibTeX citation
@article{RJ-2019-012,
author = {Ou, Lu and Hunter, Michael D. and Chow, Sy-Miin},
title = {Whats for dynr: A Package for Linear and Nonlinear Dynamic Modeling in R},
journal = {The R Journal},
year = {2019},
note = {https://doi.org/10.32614/RJ-2019-012},
doi = {10.32614/RJ-2019-012},
volume = {11},
issue = {1},
issn = {2073-4859},
pages = {91-111}
}