A metadata structure for clinical data analysis
    and reporting based on Analysis Data Model (ADaM) datasets.
    The package simplifies clinical analysis and reporting tool development
    by defining standardized inputs, outputs, and workflow.
    The package can be used to create analysis and reporting planning grid,
    mock table, and validated analysis and reporting results based on
    consistent inputs.

Analyzes adverse events in clinical trials using the 'metalite'
    data structure. The package simplifies the workflow to create
    production-ready tables, listings, and figures discussed in the
    adverse events analysis chapters of
    "R for Clinical Study Reports and Submission"
    by Zhang et al. (2022) <https://r4csr.org/>.

Analyzes subject-level data in clinical trials using the 'metalite'
    data structure. The package simplifies the workflow to create
    production-ready tables, listings, and figures discussed in the
    subject-level analysis chapters of
    "R for Clinical Study Reports and Submission"
    by Zhang et al. (2022) <https://r4csr.org/>.

Create an interactive table of descriptive statistics in HTML. 
  This table is typically used for exploratory analysis in a clinical study (referred to as 'Table 1').

The 'MetAlyzer' S4 object provides methods to read and reformat metabolomics data for convenient data handling, statistics and downstream analysis. The resulting format corresponds to input data of the Shiny app 'MetaboExtract' (<https://www.metaboextract.shiny.dkfz.de/MetaboExtract/>).

Combination of either p-values or modified effect sizes from different
    studies to find differentially expressed genes.

Implements several methods to meta-analyze studies that report the 
    sample median of the outcome. The methods described by 
    McGrath et al. (2019) <doi:10.1002/sim.8013>, 
    Ozturk and Balakrishnan (2020) <doi:10.1002/sim.8738>, and 
    McGrath et al. (2020a) <doi:10.1002/bimj.201900036> can be 
    applied to directly meta-analyze the median or difference of medians between 
    groups. Additionally, a number of methods (e.g., McGrath et al. (2020b) 
    <doi:10.1177/0962280219889080>, Cai et al. (2021) 
    <doi:10.1177/09622802211047348>, and McGrath et al. (2023) 
    <doi:10.1177/09622802221139233>) are implemented to estimate 
    study-specific (difference of) means and their standard errors in order to 
    estimate the pooled (difference of) means. Methods for meta-analyzing median 
    survival times (McGrath et al. (2025) <doi:10.48550/arXiv.2503.03065>) are 
    also implemented. See McGrath et al. (2024) <doi:10.1002/jrsm.1686> for a 
    detailed guide on using the package.