Dose Titration Algorithm Tuning (DTAT) is a methodologic framework
             allowing dose individualization to be conceived as a continuous
             learning process that begins in early-phase clinical trials and
             continues throughout drug development, on into clinical practice.
             This package includes code that researchers may use to reproduce
             or extend key results of the DTAT research programme, plus tools
             for trialists to design and simulate a '3+3/PC' dose-finding study.
             Please see Norris (2017a) <doi:10.12688/f1000research.10624.3> and
             Norris (2017c) <doi:10.1101/240846>.

Diagnostic tools for auditing data analysis workflows built on
    'data.table'. Provides functions to validate join operations, compare
    data.tables, filter with diagnostic output, summarize data quality, check
    primary keys and variable relationships, and diagnose string columns.
    Designed to help analysts understand and document data transformations.

To calculate the sensitivity and specificity in the absence of gold standard using the Bayesian method.
    The Bayesian method can be referenced at Haiyan Gu and Qiguang Chen (1999) <doi:10.3969/j.issn.1002-3674.1999.04.004>.

Implement weighted higher-order initialization and angle-based iteration for multi-way spherical clustering under degree-corrected tensor block model. See reference Jiaxin Hu and Miaoyan Wang (2023) <doi:10.1109/TIT.2023.3239521>.

A system for combining two diagnostic tests using various approaches
              that include statistical and machine-learning-based methodologies. 
              These approaches are divided into four groups: linear combination 
              methods, non-linear combination methods, mathematical operators, 
              and machine learning algorithms. See 
              the <https://biotools.erciyes.edu.tr/dtComb/> website 
              for more information, documentation, and examples.

Comparison of the accuracy of two binary diagnostic tests in a "paired" study design, i.e. when each test is applied to each subject in the study.