dynr (0.1.10-10)

0 users

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.

Maintainer: Michael D. Hunter
Author(s): Lu Ou [aut], Michael D. Hunter [aut, cre], Sy-Miin Chow [aut]

License: Apache License (== 2.0)

Uses: ggplot2, latex2exp, magrittr, MASS, Matrix, mice, numDeriv, plyr, reshape2, xtable, testthat, roxygen2

Released 4 days ago.

3 previous versions



  (0 votes)


  (0 votes)

Log in to vote.


No one has written a review of dynr yet. Want to be the first? Write one now.

Related packages: ArDec, Ecdat, MSBVAR, TSdbi, boot, brainwaver, chron, cts, depmix, dlm, dse, dyn, dynlm, ensembleBMA, fArma, fGarch, fNonlinear, fame, fracdiff, fractal(20 best matches, based on common tags.)

Search for dynr on google, google scholar, r-help, r-devel.

Visit dynr on R Graphical Manual.