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.
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
(with optional importance sampling) or
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
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.
<https://cdriver.netlify.app/> contains some tutorial blog posts.

Continuous Time Structural Equation Modelling

package-ubuntu-20.04-r-4.0.5#cran#ctsem-3.4.3

<p>Simulated example dataset for the ctsem package
</p>

<p>100 by 17 matrix containing containing ctsem wide format data. 
6 measurement occasions of leisure time and happiness and 5 measurement intervals for each of 100 individuals.
</p>

package-ubuntu-20.04-r-4.0.5#cran#ctsem-3.4.3#docs#ctExample1

ctExample1

Description

Format

ctsem

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