ctsem

ctsem is an R package for continuous time structural equation modelling of panel (N &gt; 1) 
and time series (N = 1) data, using either a frequentist or Bayesian approach, or middle
ground forms like maximum a posteriori. 
The general workflow begins by specifying a model using the <code><a rd-options="" href="/link/ctModel?package=ctsem&version=3.3.11" data-mini-rdoc="ctsem::ctModel">ctModel</a></code> function, 
in which the <code>type</code> of model is also specified. Then the model is fit to data using 
<code><a rd-options="" href="/link/ctStanFit?package=ctsem&version=3.3.11" data-mini-rdoc="ctsem::ctStanFit">ctStanFit</a></code>. The ctFit function which allows for fitting using the OpenMx / SEM form,
as described in the original JSS ctsem paper, can now be found in the ctsemOMX package. 
The omx forms are no longer in 
development and for most purposes, the newer stan based forms are more robust and flexible.
For examples, see <code><a rd-options="" href="/link/ctStanFit?package=ctsem&version=3.3.11" data-mini-rdoc="ctsem::ctStanFit">ctStanFit</a></code>. 
For citation info, please run <code>citation('ctsem')</code> .

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.

Charles Driver

Continuous Time Structural Equation Modelling

Manuel Voelkle

Han Oud

Trustees of Columbia University 

ctsem function

ctsem is an R package for continuous time structural equation modelling of panel (N &gt; 1) 
and time series (N = 1) data, using either a frequentist or Bayesian approach, or middle
ground forms like maximum a posteriori. 
The general workflow begins by specifying a model using the <code><a rd-options='' href='ctModel'>ctModel</a></code> function, 
in which the <code>type</code> of model is also specified. Then the model is fit to data using 
<code><a rd-options='' href='ctStanFit'>ctStanFit</a></code>. The ctFit function which allows for fitting using the OpenMx / SEM form,
as described in the original JSS ctsem paper, can now be found in the ctsemOMX package. 
The omx forms are no longer in 
development and for most purposes, the newer stan based forms are more robust and flexible.
For examples, see <code><a rd-options='' href='ctStanFit'>ctStanFit</a></code>. 
For citation info, please run <code>citation('ctsem')</code> .

ctsem: ctsem

Description

Arguments

References