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.">

ctsem: Continuous Time Structural Equation Modelling (original) (raw)

Hierarchical continuous (and discrete) 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) or difference equation, 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.

Version: 3.10.1
Depends: R (≥ 4.2.0), Rcpp (≥ 0.12.16)
Imports: cOde, data.table (≥ 1.12.8), datasets, Deriv, expm, ggplot2, graphics, grDevices, MASS, Matrix, methods, mize, mvtnorm, parallel, plyr, RcppParallel (≥ 5.0.1), rstan (≥ 2.26.0), rstantools (≥ 2.3.0), stats, tibble, tools, utils, splines, statmod
LinkingTo: BH (≥ 1.66.0-1), Rcpp (≥ 0.12.16), RcppEigen (≥ 0.3.3.4.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.26), StanHeaders (≥ 2.26.0), RcppParallel (≥ 5.0.1)
Suggests: knitr, testthat, devtools, DEoptim, tinytex, lme4, shiny, gridExtra, arules, collapse, qgam, papaja
Published: 2024-08-19
DOI: 10.32614/CRAN.package.ctsem
Author: Charles Driver [aut, cre, cph], Manuel Voelkle [aut, cph], Han Oud [aut, cph], Trustees of Columbia University [cph]
Maintainer: Charles Driver <charles.driver2 at uzh.ch>
License: GPL-3
URL: https://github.com/cdriveraus/ctsem
NeedsCompilation: yes
SystemRequirements: GNU make
Citation: ctsem citation info
Materials: README NEWS
CRAN checks: ctsem results

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