Intensive longitudinal data have become increasingly prevalent in various scientific disciplines. Many such data sets are noisy, multivariate, and multisubject in nature. The change functions may also be continuous, or continuous but interspersed with periods of discontinuities (i.e., showing regime switches). The package 'dynr' (Dynamic Modeling in R) is an R package that implements a set of computationally efficient algorithms for handling a broad class of linear and nonlinear discrete and continuous time models with regimeswitching properties under the constraint of linear Gaussian measurement functions. The discretetime models can generally take on the form of a statespace or difference equation model. The continuoustime models are generally expressed as a set of ordinary or stochastic differential equations. All estimation and computations are performed in C, but users are provided with the option to specify the model of interest via a set of simple and easytolearn model specification functions in R. Model fitting can be performed using singlesubject time series data or multiplesubject longitudinal data. Ou, Hunter, & Chow (2019) <doi:10.32614%2FRJ2019012> provided a detailed introduction to the interface and more information on the algorithms.
Package details 


Author  Lu Ou [aut], Michael D. Hunter [aut, cre] (<https://orcid.org/0000000236516709>), SyMiin Chow [aut] (<https://orcid.org/000000031938027X>), Linying Ji [aut], Meng Chen [aut], HuiJu Hung [aut], Jungmin Lee [aut], Yanling Li [aut], Jonathan Park [aut], Massachusetts Institute of Technology [cph], S. G. Johnson [cph], Benoit Scherrer [cph], Dieter Kraft [cph] 
Maintainer  Michael D. Hunter <mike.dynr@gmail.com> 
License  GPL3 
Version  0.1.1691 
URL  https://dynrr.github.io/ https://github.com/mhunter1/dynr 
Package repository  View on CRAN 
Installation 
Install the latest version of this package by entering the following in R:

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