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>Maps the stan function so the same code works in R.
</p>

<table summary="R argblock"><tr valign="top">
<td><code>x</code></td>
<td>
<p>value to calculate the inverse logit for.</p>
</td>
</tr></table>

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

inv_logit

Description

Usage

Arguments

Examples

ctsem

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