Data objects in R can be rendered as HTML tables using the
    JavaScript library 'DataTables' (typically via R Markdown or Shiny).
    The 'DataTables' library has been included in this R package. The
    package name 'DT' is an abbreviation of 'DataTables'.

A modern R binding for 'DataTables' V2 with modular
    extension loading, 'Bootstrap 5' styling, 'Shiny' integration (proxy, events,
    inline inputs), server-side processing helpers, and standalone (non-Shiny)
    support. Configure 'DataTables' options directly via R lists, a 1:1 mapping
    to the 'JavaScript' API.

Offers statistical methods to compare diagnostic performance between two binary diagnostic tests on the same subject in clinical studies. Includes functions for generating formatted tables to display diagnostic outcomes, facilitating a clear and comprehensive comparison directly through the R console. Inspired by and extending the functionalities of the 'DTComPair', 'tableone', and 'gtsummary' packages.

Fits the hierarchical summary receiver operating characteristic (HSROC) model of
    Rutter and Gatsonis (2001) <doi:10.1002/sim.942>, the bivariate binomial-normal model of
    Reitsma et al. (2005) <doi:10.1016/j.jclinepi.2005.02.022>, the threshold-based bivariate
    time-to-event model of Hoyer et al. (2018) <doi:10.1002/jrsm.1273>, and the latent class
    extensions of Liu et al. (2015) <doi:10.1111/biom.12264> for diagnostic studies without
    a perfect reference standard. Provides subgroup analyses, HSROC meta-regression,
    likelihood-ratio tests, summary ROC plots, and coupled forest plots.

Deconvolving cell types from high-throughput gene profiling data. For more information on dtangle see Hunt et al. (2019) <doi:10.1093/bioinformatics/bty926>.

Function to create forest plots. Functions to use posterior samples from Bayesian bivariate meta-analysis model, Bayesian hierarchical summary receiver operating characteristic (HSROC) meta-analysis model or Bayesian latent class (LC) meta-analysis model to create Summary Receiver Operating Characteristic (SROC) plots using methods described by Harbord et al (2007)<doi:10.1093/biostatistics/kxl004>.