Suggests: knitr, testthat, cts, yuima, shinystan

Imports: rstan (>= 2.13), plyr, Matrix, stats, utils, graphics,

Description: A hierarchical, multivariate, continuous (and discrete) time dynamic modelling
package for panel and time series data, using stochastic differential
allows for random effects and non-linearity 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.
Imports: rstan (>= 2.17.3), rstantools (>= 1.5.0), plyr, Matrix,
datasets, stats, graphics, grDevices, parallel, shiny, MASS,

Imports: rstan (>= 2.17.1), rstantools (>= 1.5.0), plyr, tools,
data.table, Matrix, datasets, stats, graphics, grDevices,
parallel, shiny, MASS, methods, utils, corrplot, mvtnorm,
LinkingTo: StanHeaders (>= 2.17.0), rstan (>= 2.17.1), BH (>=

covariates used to predict the parameters. Bayesian formulation not available on

person("Han", "Oud", role = c("aut","cph") ),
Description: Hierarchical continuous time state space modelling, for linear
and nonlinear systems measured by  continuous variables, with limited support for
binary data. The subject specific dynamic system is modelled as a stochastic
differential equation (SDE), measurement models are typically multivariate normal factor models.
Using the original ctsem formulation based on OpenMx, described in the JSS paper
"Continuous Time Structural Equation Modeling with R Package ctsem", with updated version
as CRAN vignette <https://cran.r-project.org/web/packages/ctsem/vignettes/ctsem.pdf> ,
linear mixed effects SDE's estimated via maximum likelihood and optimization are possible.
Using the Stan based formulation, described in <https://www.researchgate.net/publication/310747987_Introduction_to_Hierarchical_Continuous_Time_Dynamic_Modelling_With_ctsem> ,
nonlinearity (state dependent parameters) and random effects on all parameters
are possible, using either optimization (with optional importance sampling) or
Stan's Hamiltonian Monte Carlo sampling. Priors may be used. For the conceptual
overview of the hierarchical Bayesian linear SDE approach, see <https://www.researchgate.net/publication/324093594_Hierarchical_Bayesian_Continuous_Time_Dynamic_Modeling>.
Exogenous inputs may also be included, for an overview of such possibilities see <https://www.researchgate.net/publication/328221807_Understanding_the_Time_Course_of_Interventions_with_Continuous_Time_Dynamic_Models> .
Stan based functions are not available on 32 bit Windows systems at present.
parallel, shiny, MASS, methods, utils, ggplot2, mvtnorm,
Suggests: knitr, testthat, devtools, DEoptim