Tools to create and query database from the U.S. National Library
  of Medicine's Clinical Trials database <https://clinicaltrials.gov/>. Functions
  provide access a variety of techniques for searching the data using range
  queries, categorical filtering, and by searching for full-text keywords.

Computerized tomography (CT) can be used to assess certain wood properties when wood disks or logs are scanned. Wood density profiles (i.e. variations of wood density from pith to bark) can yield important information used for studies in forest resource assessment, wood quality and dendrochronology studies. The first step consists in transforming grey values from the scan images to density values. The packages then proposes a unique method to automatically locate the pith by combining an adapted Hough Transform method and a one-dimensional edge detector. Tree ring profiles (average ring density, earlywood and latewood density, ring width and percent latewood for each ring) are then obtained.

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. 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.