First- and higher-order likelihood inference in
        meta-analysis and meta-regression models.

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/>.

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').

Identify time intervals of differentially abundant metagenomic features in longitudinal studies (Metwally AA, et al., Microbiome, 2018 <doi:10.1186/s40168-018-0402-y>).

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. When the primary studies are one-group 
    studies, the methods of McGrath et al. (2019) <doi:10.1002/sim.8013> and 
    Ozturk and Balakrishnan (2020) <doi:10.1002/sim.8738> can be applied to 
    estimate the pooled median. In the two-group context, the 
    methods of McGrath et al. (2020a) <doi:10.1002/bimj.201900036> can be 
    applied to estimate the pooled difference of medians across 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. See McGrath et al. (in press) 
    <doi:10.1002/jrsm.1686> for a detailed guide on using the package.