Intensive longitudinal data have become increasingly prevalent in various scientific disciplines. Many such data sets are noisy, multivariate, and multi-subject 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 regime-switching properties under the constraint of linear Gaussian measurement functions. The discrete-time models can generally take on the form of a state- space or difference equation model. The continuous-time 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 easy-to-learn model specification functions in R. Model fitting can be performed using single- subject time series data or multiple-subject longitudinal data.
The DESCRIPTION file:
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Because the dynr package compiles C code in response to user input, more setup is required for the dynr package than for many others. We acknowledge that this additional setup can be bothersome, but we believe the ease of use for the rest of the package and the wide variety of models it is possible to fit with it will compensate for this initial burden. Hopefully you will agree!
See the installation vignette referenced in the Examples section below for installation instructions.
The naming convention for dynr exploits the pronunciation of the package name, dynr, pronounced the same as “dinner”. That is, the names of functions and methods are specifically designed to relate to things done surrounding dinner, such as gathering ingredients (e.g., the data), preparing recipes, cooking, and serving the finished product. The general procedure for using the dynr package can be summarized in five steps as below.
Data are prepared using with the
Recipes are prepared. To each part of a model there is a corresponding
prep.*() recipe function. Examples of such
prep.*() functions include:
dynr.model() mixes the data and recipes together into a model object of class
The model is cooked with
Results from model fitting and related estimation are served using functions such as
dynr.ggplot() (or its alias
State-space modeling, dynamic model, differential equation, regime switching, nonlinear
Lu Ou [aut], Michael D. Hunter [aut, cre], Sy-Miin Chow [aut]
Maintainer: Michael D. Hunter <[email protected]>
For other annotated tutorials using the dynr package see https://quantdev.ssri.psu.edu/resources/what%E2%80%99s-dynr-package-linear-and-nonlinear-dynamic-modeling-r
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# For installation instructions see the package vignette below vignette(package='dynr', 'InstallationForUsers') # This should open a pdf/html file to guide you through proper # installation and configuration. #For illustrations of the functions in dynr, check out some of the demo examples in: demo(package='dynr') #For example, to run the demo 'LinearSDE' type # the following without the comment character (#) in front of it. #demo('LinearSDE', package='dynr')
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