ctsem - Continuous Time Structural Equation Modelling
Hierarchical continuous (and discrete) 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) or difference equation, 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.
