ctsem

Continuous Time Structural Equation Modelling

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://www.researchgate.net/publication/310747987_Introduction_to_Hierarchical_Continuous_Time_Dynamic_Modelling_With_ctsem> , 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.

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Description file content

Package
ctsem
Type
Package
Title
Continuous Time Structural Equation Modelling
Version
2.9.6
Date
2019-5-28
Description
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 , linear mixed effects SDE's estimated via maximum likelihood and optimization are possible. Using the Stan based formulation, described in , 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 . Exogenous inputs may also be included, for an overview of such possibilities see . Stan based functions are not available on 32 bit Windows systems at present.
License
GPL-3
Depends
R (>= 3.5.0), Rcpp (>= 0.12.16), OpenMx (>= 2.9.0)
URL
Imports
rstan (>= 2.17.1), rstantools (>= 1.5.0), plyr, tools, data.table, Matrix, datasets, stats, graphics, grDevices, parallel, shiny, MASS, methods, utils, ggplot2, mvtnorm, KernSmooth, ucminf, GGally, pkgbuild
Encoding
UTF-8
LazyData
true
ByteCompile
true
LinkingTo
StanHeaders (>= 2.17.0), rstan (>= 2.17.1), BH (>= 1.66.0-1), Rcpp (>= 0.12.16), RcppEigen (>= 0.3.3.4.0)
SystemRequirements
GNU make
NeedsCompilation
yes
Suggests
knitr, testthat, devtools, DEoptim
VignetteBuilder
knitr
RoxygenNote
6.1.1
Packaged
2019-05-29 09:16:59 UTC; Driver
Author
Charles Driver [aut, cre, cph], Manuel Voelkle [aut, cph], Han Oud [aut, cph], Trustees of Columbia University [cph]
Maintainer
Charles Driver
Repository
CRAN
Date/Publication
2019-05-29 13:40:09 UTC

install.packages('ctsem')

2.9.6

a month ago

https://github.com/cdriveraus/ctsem

Charles Driver

GPL-3

Depends on

R (>= 3.5.0), Rcpp (>= 0.12.16), OpenMx (>= 2.9.0)

Imports

rstan (>= 2.17.1), rstantools (>= 1.5.0), plyr, tools, data.table, Matrix, datasets, stats, graphics, grDevices, parallel, shiny, MASS, methods, utils, ggplot2, mvtnorm, KernSmooth, ucminf, GGally, pkgbuild

Suggests

knitr, testthat, devtools, DEoptim

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