# ------------------------------------------------
# CITATION.cff file created with {cffr} R package
# See also: https://docs.ropensci.org/cffr/
# ------------------------------------------------
 
cff-version: 1.2.0
message: 'To cite package "ctsem" in publications use:'
type: software
license: GPL-3.0-only
title: 'ctsem: Continuous Time Structural Equation Modelling'
version: 3.11.0
doi: 10.1037/met0000168
identifiers:
- type: doi
  value: 10.32614/CRAN.package.ctsem
abstract: 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. See <https://osf.io/preprints/psyarxiv/4q9ex_v2> for a detailed tutorial.
  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>
  . <https://cdriver.netlify.app/> contains some tutorial blog posts.
authors:
- family-names: Driver
  given-names: Charles
  email: charles.driver2@uzh.ch
- family-names: Voelkle
  given-names: Manuel
- family-names: Oud
  given-names: Han
preferred-citation:
  type: article
  title: Hierarchical Bayesian continuous time dynamic modeling.
  authors:
  - family-names: Driver
    given-names: Charles C.
  - family-names: Voelkle
    given-names: Manuel C.
  journal: Psychological Methods
  year: '2018'
  volume: '23'
  issue: '4'
  doi: 10.1037/met0000168
  start: 774-799
repository: https://cran.r-universe.dev
repository-code: https://github.com/cdriveraus/ctsem
commit: 07845c0e0ec0f47c9d0c4fd9f366a8e8c4cf467f
url: https://github.com/cdriveraus/ctsem
date-released: '2026-06-30'
contact:
- family-names: Driver
  given-names: Charles
  email: charles.driver2@uzh.ch
references:
- type: article
  title: Continuous Time Structural Equation Modeling with R package ctsem
  authors:
  - family-names: Driver
    given-names: Charles C.
  - family-names: Oud
    given-names: Johan H. L.
  - family-names: Voelkle
    given-names: Manuel C.
  journal: Journal of Statistical Software
  year: '2017'
  volume: '77'
  issue: '5'
  doi: 10.18637/jss.v077.i05
  start: '1'
  end: '35'
- type: generic
  title: Hierarchical continuous time modeling
  authors:
  - family-names: Driver
    given-names: Charles C.
  - family-names: Voelkle
    given-names: Manuel C.
  editors:
  - family-names: Rauthmann
    given-names: John F.
  collection-title: The Handbook of Personality Dynamics and Processes
  collection-type: collection
  publisher:
    name: Academic Press
  year: '2021'
  isbn: 978-0-12-813995-0
  doi: 10.1016/B978-0-12-813995-0.00034-0
  start: 887-908