A simple way to assess the stability of candidate housekeeping genes is implemented in this package.

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.

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>.