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. These functions were split off from
    the main package of 'ctsem', as the main package uses the 'rstan' package as a backend now --
    offering estimation options from max likelihood to Bayesian.
    There are nevertheless use cases for the wide format SEM style approach as offered here, 
    particularly when there are no individual differences in observation timing and the
    number of individuals is large. For the main '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'.

An interactive document on  the topic of classification tree analysis using 'rmarkdown' and 'shiny' packages. Runtime examples are provided in the package function as well as at  <https://kartikeyab.shinyapps.io/CTShiny/>.

Perform state and parameter inference, and forecasting, in
    stochastic state-space systems using the 'ctsmTMB' class. This class,
    built with the 'R6' package, provides a user-friendly interface for
    defining and handling state-space models. Inference is based on
    maximum likelihood estimation, with derivatives efficiently computed
    through automatic differentiation enabled by the 'TMB'/'RTMB' packages
    (Kristensen et al., 2016) <doi:10.18637/jss.v070.i05>. The available
    inference methods include Kalman filters, in addition to a Laplace
    approximation-based smoothing method. For further details of these
    methods refer to the documentation of the 'CTSMR' package
    <https://ctsm.info/ctsmr-reference.pdf> and Thygesen (2025)
    <doi:10.48550/arXiv.2503.21358>. Forecasting capabilities include
    moment predictions and stochastic path simulations, both implemented
    in 'C++' using 'Rcpp' (Eddelbuettel et al., 2018)
    <doi:10.1080/00031305.2017.1375990> for computational efficiency.