dynr: Dynamic Modeling in R

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 \pkg{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.

Version: 0.1.10-10
Depends: R (≥ 3.0.0), methods, ggplot2
Imports: MASS, Matrix, numDeriv, xtable, latex2exp, grid, reshape2, plyr, mice, magrittr
Suggests: testthat, roxygen2 (≥ 3.1)
Published: 2017-05-21
Author: Lu Ou [aut], Michael D. Hunter [aut, cre], Sy-Miin Chow [aut]
Maintainer: Michael D. Hunter <mhunter.ou at gmail.com>
License: Apache License (== 2.0)
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: NEWS
In views: TimeSeries
CRAN checks: dynr results


Reference manual: dynr.pdf
Vignettes: Installation for Users
Package source: dynr_0.1.10-10.tar.gz
Windows binaries: r-devel: dynr_0.1.10-10.zip, r-release: dynr_0.1.10-10.zip, r-oldrel: dynr_0.1.10-10.zip
OS X El Capitan binaries: r-release: dynr_0.1.10-10.tgz
OS X Mavericks binaries: r-oldrel: dynr_0.1.10-10.tgz
Old sources: dynr archive


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