ctsemOMX: ctsemOMX
In ctsemOMX: Continuous Time SEM - 'OpenMx' Based Functions
ctsemOMX R Documentation
ctsemOMX
Description
ctsem is an R package for continuous time structural equation modelling of panel (N > 1)
and time series (N = 1) data, using either a frequentist or Bayesian approach, or middle
ground forms like maximum a posteriori. This ctsemOMX addition includes the original OpenMx based functions
which have been split off from the main package.
ctsem is an R package for continuous time structural equation modelling of panel (N > 1)
and time series (N = 1) data, using either a frequentist or Bayesian approach, or middle
ground forms like maximum a posteriori. This ctsemOMX addition includes the original OpenMx based functions
which have been split off from the main package.
Details
The general workflow begins by specifying a model using the ctModel function,
in which the type of model is also specified. Then the model is fit to data using
either ctFit if the original 'omx' (OpenMx, SEM, max likelihood) model is specified.
The omx forms are no longer in
development and for most purposes, the newer stan based forms (contained in the base ctsem package)
are more robust and flexible.
For citation info, please run citation('ctsem') .
The general workflow begins by specifying a model using the ctModel function,
in which the type of model is also specified. Then the model is fit to data using
either ctFit if the original 'omx' (OpenMx, SEM, max likelihood) model is specified.
The omx forms are no longer in
development and for most purposes, the newer stan based forms (contained in the base ctsem package)
are more robust and flexible.
For citation info, please run citation('ctsem') .
Author(s)
Maintainer: Charles Driver charles.driver@ibe.uzh.ch [copyright holder]
Authors:
Manuel Voelkle [copyright holder]
Han Oud [copyright holder]
References
https://www.jstatsoft.org/article/view/v077i05
Driver, C. C., & Voelkle, M. C. (2018). Hierarchical Bayesian continuous time dynamic modeling.
Psychological Methods. Advance online publication.http://dx.doi.org/10.1037/met0000168
https://www.jstatsoft.org/article/view/v077i05
Driver, C. C., & Voelkle, M. C. (2018). Hierarchical Bayesian continuous time dynamic modeling.
Psychological Methods. Advance online publication.http://dx.doi.org/10.1037/met0000168
See Also
Useful links:
ctsemOMX documentation built on Oct. 5, 2023, 5:06 p.m.
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| ctsemOMX | R Documentation |
ctsemOMX
Description
ctsem is an R package for continuous time structural equation modelling of panel (N > 1) and time series (N = 1) data, using either a frequentist or Bayesian approach, or middle ground forms like maximum a posteriori. This ctsemOMX addition includes the original OpenMx based functions which have been split off from the main package.
ctsem is an R package for continuous time structural equation modelling of panel (N > 1) and time series (N = 1) data, using either a frequentist or Bayesian approach, or middle ground forms like maximum a posteriori. This ctsemOMX addition includes the original OpenMx based functions which have been split off from the main package.
Details
The general workflow begins by specifying a model using the ctModel function,
in which the type of model is also specified. Then the model is fit to data using
either ctFit if the original 'omx' (OpenMx, SEM, max likelihood) model is specified.
The omx forms are no longer in
development and for most purposes, the newer stan based forms (contained in the base ctsem package)
are more robust and flexible.
For citation info, please run citation('ctsem') .
The general workflow begins by specifying a model using the ctModel function,
in which the type of model is also specified. Then the model is fit to data using
either ctFit if the original 'omx' (OpenMx, SEM, max likelihood) model is specified.
The omx forms are no longer in
development and for most purposes, the newer stan based forms (contained in the base ctsem package)
are more robust and flexible.
For citation info, please run citation('ctsem') .
Author(s)
Maintainer: Charles Driver charles.driver@ibe.uzh.ch [copyright holder]
Authors:
Manuel Voelkle [copyright holder]
Han Oud [copyright holder]
References
https://www.jstatsoft.org/article/view/v077i05
Driver, C. C., & Voelkle, M. C. (2018). Hierarchical Bayesian continuous time dynamic modeling. Psychological Methods. Advance online publication.http://dx.doi.org/10.1037/met0000168
https://www.jstatsoft.org/article/view/v077i05
Driver, C. C., & Voelkle, M. C. (2018). Hierarchical Bayesian continuous time dynamic modeling. Psychological Methods. Advance online publication.http://dx.doi.org/10.1037/met0000168
See Also
Useful links:
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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