Estimation of quantile regression models for survival data.

A system for querying, retrieving and analyzing
        protocol- and results-related information on clinical trials from
        four public registers, the 'European Union Clinical Trials Register'
        ('EUCTR', <https://www.clinicaltrialsregister.eu/>), 
        'ClinicalTrials.gov' ('CTGOV', using the classic interface at 
        <https://classic.clinicaltrials.gov/>, and 'CTGOV2', using the 
        current interface at <https://www.clinicaltrials.gov/>), the 
        'ISRCTN' (<http://www.isrctn.com/>) and the
        'European Union Clinical Trials Information System'
        ('CTIS', <https://euclinicaltrials.eu/>). 
        Trial information is downloaded, converted and stored in a database 
        ('PostgreSQL', 'SQLite', 'DuckDB' or 'MongoDB'; via package 'nodbi'). 
        Documents in registers associated with trials can also be downloaded. 
        Other functions identify deduplicated records, 
        easily find and extract variables (fields) of interest even 
        from complex nested data as used by the registers, 
        merge variables and update queries. 
        The package can be used for meta-analysis and trend-analysis of
        the design and conduct as well as of the results of clinical trials.

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. 
  Minimal graphical tools are also provided for interactively exploring the
  constructed data.

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.