Using the Stan based formulation, described in <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> ,
Imports: rstan (>= 2.19.0), rstantools (>= 1.5.0), plyr, tools,
parallel, MASS, methods, utils, ggplot2, mvtnorm, mize,
Suggests: knitr, testthat, devtools, DEoptim, GGally, shiny

Suggests: knitr, testthat, devtools, DEoptim, GGally, shiny, gridExtra

Linear mixed effects SDE's estimated via maximum likelihood and optimization are the default.
Nonlinearities,  (state dependent parameters) and random effects on all parameters
are possible, using either max likelihood / max a posteriori optimization
Stan's Hamiltonian Monte Carlo sampling. See  <https://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf>
for details. Priors may be used. For the conceptual
<https://cdriver.netlify.app/> contains some tutorial blog posts.
Imports: cOde, expm, data.table (>= 1.12.8), datasets, Deriv, ggplot2,
graphics, grDevices, MASS, Matrix, methods, mize, mvtnorm,
parallel, pkgbuild, plyr, rstan (>= 2.19.0), stats, tools,
StanHeaders (>= 2.21.0), RcppParallel (>= 5.0.1)

StanHeaders (>= 2.19.0), RcppParallel (>= 5.0.1)

Suggests: knitr, testthat, devtools, DEoptim, tinytex, lme4, shiny,

Description: Hierarchical continuous (and discrete) time state space modelling, for linear
differential equation (SDE) or difference equation, measurement models are typically multivariate normal factor models.
(with optional importance sampling) or Stan's Hamiltonian Monte Carlo sampling.
for details. Priors may be used. For the conceptual overview of the hierarchical Bayesian

parallel, plyr, rstan (>= 2.19.0), stats, tools, utils,

Title: Continuous Time SEM - 'OpenMx' Based Functions
Description: Original 'ctsem' (continuous time structural equation modelling)
functionality, based on the 'OpenMx' software, as described in
Driver, Oud, Voelkle (2017) <doi:10.18637/jss.v077.i05>, with updated details in vignette.
Combines stochastic differential equations representing latent processes with
structural equation measurement models. These functions were split off from
the main package of 'ctsem', as the main package uses the 'rstan' package as a backend now --
offering estimation options from max likelihood to Bayesian.
There are nevertheless use cases for the wide format SEM style approach as offered here,
particularly when there are no individual differences in observation timing and the
number of individuals is large. For the main 'ctsem' package, see <https://cran.r-project.org/package=ctsem>.
Imports: graphics, grDevices, Matrix, methods, plyr, stats, utils