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ctsem allows for easy specification and fitting of a range of continuous and discrete time dynamic models, including multiple indicators (dynamic factor analysis), multiple, potentially higher order processes, and time dependent (varying within subject) and time independent (not varying within subject) covariates. Classic longitudinal models like latent growth curves and latent change score models are also possible. Version 1 of ctsem provided SEM based functionality by linking to the OpenMx software, allowing mixed effects models (random means but fixed regression and variance parameters) for multiple subjects. For version 2 of the R package ctsem, we include a hierarchical specification and fitting routine that uses the Stan probabilistic programming language, via the rstan package in R. This allows for all parameters of the dynamic model to individually vary, using an estimated population mean and variance, and any time independent covariate effects, as a prior. Version 3 allows for state dependencies in the parameter specification (i.e. time varying parameters).

The curent manual is at https://cran.r-project.org/package=ctsem/vignettes/hierarchicalmanual.pdf. The original ctsem is documented in a JSS publication (Driver, Voelkle, Oud, 2017), and in R vignette form at https://cran.r-project.org/package=ctsemOMX/vignettes/ctsem.pdf, however these OpenMx based functions have been split off into a sub package, ctsemOMX.

To cite ctsem please use the citation(“ctsem”) command in R.

To install the github version and (if needed) configure your system, from a fresh R session run:

source(file = 'https://github.com/cdriveraus/ctsem/raw/master/installctsem.R')

If there are problems with the above script, you can try:

Manually install rstan, Rtools

remotes::install_github('cdriveraus/ctsem', INSTALL_opts = "--no-multiarch", dependencies = c("Depends", "Imports"))

Or just use the CRAN version, but rstan compiler setup is needed separately for some models:

install.packages('ctsem')

Troubleshooting Rstan / Rtools install for Windows:

Ensure recent version of R and Rtools is installed. If the installctsem.R code has never been run before, be sure to run that (see above).

Place this line in ~/.R/makevars.win , and if there are other lines, delete them:

CXX14FLAGS += -mtune=native -march=native -Wno-ignored-attributes -Wno-deprecated-declarations

see for details

If makevars does not exist, re-run the install code above.

In case of compile errors like g++ not found, ensure the devtools package is installed:

install.packages('devtools')

and include the following in your .Rprofile, replacing c:/Rtools with the appropriate path – sometimes Rbuildtools/4.0/ .

library(devtools)
Sys.setenv(PATH = paste("C:/Rtools/bin", Sys.getenv("PATH"), sep=";"))
Sys.setenv(PATH = paste("C:/Rtools/mingw_64/bin", Sys.getenv("PATH"), sep=";"))
Sys.setenv(BINPREF = "C:/Rtools/mingw_$(WIN)/bin/")

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Hierarchical continuous time state space modelling

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