Continuous (and discrete) time dynamic modeling in R, using both SEM and Bayesian approaches.
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.
R CRAN Nov 13, 2018
build cran 2.6.4 Jun 8, 2018
data cran Nov 8, 2018
inst cran Nov 7, 2018
man CRAN Nov 13, 2018
src cran Nov 14, 2018
tests/testthat cran Nov 14, 2018
tools ctStanFit: horseshoe prior and cleanup, CRAN Oct 30, 2018
vignettes cran Nov 7, 2018
.Rbuildignore cran Jun 1, 2018
.gitignore initial commit for ctsem 2.0 github Jun 24, 2016
.travis.yml . Nov 14, 2018
NEWS ... Nov 9, 2018 corrected vignette url in readme. Dec 22, 2016
README.rmd corrected vignette url in readme. Dec 22, 2016
ctsem.Rproj cran prep May 28, 2018
ctsemgit.Rproj initial commit for ctsem 2.0 github Jun 24, 2016

Build Status

See the NEWS file for recent updates!

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 Bayesian 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. ctsem version 1 is documented in a forthcoming JSS publication (Driver, Voelkle, Oud, in press), and in R vignette form at . The new Bayesian approach is outlined in the vignette, Introduction to Hierarchical Continuous Time Dynamic Modelling with ctsem, at . To cite ctsem please use the citation("ctsem") command in R.