A system containing easy-to-use tools to compute the bioequivalence assessment in the univariate framework using the methods proposed in Boulaguiem et al. (2023) <doi:10.1101/2023.03.11.532179>.

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' (<https://clinicaltrials.gov/> and also 
        translating queries the retired classic interface), the 
        'ISRCTN' (<https://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'). 
        Protocols, statistical analysis plans, informed consent sheets and other
        documents in registers associated with trials can also be downloaded. 
        Other functions implement trial concepts canonically across registers,
        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 monitoring, meta- and trend-analysis of
        the design and conduct as well as of the results of clinical trials 
        across registers.
        See overview in Herold, R. (2025) <doi:10.1017/rsm.2025.10061>.

Implements the stacked-imputation workflow for conditional
    inference trees ('ctree') described in Sherlock et al. (2026)
    <doi:10.1080/00273171.2026.2661244>. When data contain missing values,
    multiply imputed datasets (e.g., from 'mice') are stacked vertically
    and a single 'ctree' is fit on the combined data. To correct for the
    artificially inflated sample size introduced by stacking, every
    node-level test statistic is divided by the number of imputations M,
    the node-level p-values are recomputed from the chi-squared reference
    distribution 'ctree' uses (including its multiplicity adjustment across
    candidate splitting variables), and the tree is compressed bottom-up
    (the Stack/M correction). Degrees of freedom are derived for each node
    and each candidate variable, so univariate, bivariate and
    higher-dimensional outcomes are all handled, as are unordered factor
    predictors, whose degrees of freedom depend on how many levels remain
    in a node. The result is a conservative but interpretable single tree
    that incorporates imputation uncertainty without requiring pooling of
    structurally different trees. Also exports stack_imputations(),
    rescale_statistic(), prune_stackM(), node_table() and
    report_ctreeMI() as standalone utilities. The underlying 'ctree'
    algorithm is provided by 'partykit' (Hothorn & Zeileis, 2015; Hothorn,
    Hornik & Zeileis, 2006 <doi:10.1198/106186006X133933>).