An easily accessible continuous (and discrete) time dynamic
modelling package for panel and time series data, reliant upon the OpenMx.
package (http://openmx.psyc.virginia.edu/) for computation. Most dynamic
modelling approaches to longitudinal data rely on the assumption that time
intervals between observations are consistent. When this assumption is
adhered to, the data gathering process is necessarily limited to a specific
schedule, and when broken, the resulting parameter estimates may be biased
and reduced in power. Continuous time models are conceptually similar to
vector autoregressive models (thus also the latent change models popularised
in a structural equation modelling context), however by explicitly including
the length of time between observations, continuous time models are freed
from the assumption that measurement intervals are consistent. This allows:
data to be gathered irregularly; the elimination of noise and bias due to
varying measurement intervals; parsimonious structures for complex dynamics.
The application of such a model in this SEM framework allows full-information
maximum-likelihood estimates for both N = 1 and N > 1 cases, multiple measured
indicators per latent process, and the flexibility to incorporate additional
elements, including individual heterogeneity in the latent process and
manifest intercepts, and time dependent and independent exogenous covariates.
Furthermore, due to the SEM implementation we are able to estimate a random
effects model where the impact of time dependent and time independent predictors
can be assessed simultaneously, but without the classic problems of random
effects models assuming no covariance between unit level effects and predictors.

c(person("Charles", "Driver", role =
c("aut","cre","cph"),email="driver@mpib-berlin.mpg.de"),
person("Manuel", "Voelkle", role = c("aut","cph")),
person("Han", "Oud", role = c("aut","cph") ))
A multivariate continuous (and discrete) time dynamic modelling
package for panel and time series data, using linear stochastic differential
equations. Contains a faster frequentist set of functions using OpenMx for
single subject and mixed-effects (random intercepts only) structural
equation models, or a hierarchical Bayesian implementation using Stan that
allows for random effects over all model parameters. Allows for modelling of
multiple noisy measurements of multiple stochastic processes, time varying
input / event covariates, and time invariant covariates used to predict the
parameters.
rstan, plyr, Matrix, stats, utils, graphics, methods,
grDevices, parallel
knitr, testthat, PSM, cts, yuima, shinystan
Charles Driver [aut, cre, cph],
Manuel Voelkle [aut, cph],
Han Oud [aut, cph]