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.

Original 'ctsem' (continuous time structural equation modelling)
    functionality, based on the 'OpenMx' software, as described in 
    Driver, Oud, Voelkle (2017) <doi:10.18637/jss.v077.i05>, with updated details in vignette. 
    Combines stochastic differential equations representing latent processes with 
    structural equation measurement models. This package is maintained for consistency with the 
    original 'ctsem' paper, but for the much newer and more capable 'ctsem' package, see 
    <https://cran.r-project.org/package=ctsem>.

An implementation of several functions for feature extraction in 
    categorical time series datasets. Specifically, some features related to 
    marginal distributions and serial dependence patterns can be computed. These 
    features can be used to feed clustering and classification algorithms for
    categorical time series, among others. The package also includes some
    interesting datasets containing biological sequences. Practitioners from a 
    broad variety of fields could benefit from the general framework provided 
    by 'ctsfeatures'.