ctStanParnames

<p>vector of character strings, parameter names of the stan model
containing any of these strings will be returned. Useful strings may be 'pop_' for 
population means, 'popsd' for population standard deviations,
 or specific combinations such as 'pop_DRIFT' for the population
means of temporal dynamics parameters</p>

substrings

<p>Gets internal stan parameter names of a ctStanFit object based on specified substrings.</p>

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://github.com/cdriveraus/ctsem/raw/master/vignettes/hierarchicalmanual.pdf> ,
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.

ctStanParnames: ctStanParnames

Description

Usage

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Examples