Identifies code blocks that have a high level of similarity
  within a set of R files.

An easy-to-use yet powerful system for plotting grouped data effect sizes.
    Various types of effect size can be estimated, then plotted together with a
    representation of the original data. Select from many possible data representations
    (box plots, violin plots, raw data points etc.), and combine as desired.
    'Durga' plots are implemented in base R, so are compatible with base R methods
    for combining plots, such as 'layout()'. See Khan & McLean (2023) 
    <doi:10.1101/2023.02.06.526960>.

Functionality for analyzing dose-volume histograms (DVH)
        in radiation oncology: Read DVH text files, calculate DVH
        metrics as well as generalized equivalent uniform dose (gEUD),
        biologically effective dose (BED), equivalent dose in 2 Gy
        fractions (EQD2), normal tissue complication probability
        (NTCP), and tumor control probability (TCP). Show DVH
        diagrams, check and visualize quality assurance constraints
        for the DVH. Includes web-based graphical user interface.

Joint DNA-based disaster victim identification (DVI), as
    described in Vigeland and Egeland (2021)
    <doi:10.21203/rs.3.rs-296414/v1>. Identification is performed by
    optimising the joint likelihood of all victim samples and reference
    individuals. Individual identification probabilities, conditional on
    all available information, are derived from the joint solution in the
    form of posterior pairing probabilities. 'dvir' is part of the
    'pedsuite' collection of packages for pedigree analysis.

Contains functions that do something convenient (e.g. create BMI categories), functions for calculating moving-window statistics efficiently, and functions for generating various figures (e.g. histograms with fitted probability mass/density function).

A set of control charts for batch processes based on the VAR model. The package contains the implementation of T2.var and W.var control charts based on VAR model coefficients using the couple vectors theory. In each time-instant the VAR coefficients are estimated from a historical in-control dataset and a decision rule is made for online classifying of a new batch data. Those charts allow efficient online monitoring since the very first time-instant. The offline version is available too. In order to evaluate the chart's performance, this package contains functions to generate batch data for offline and online monitoring.See in Danilo Marcondes Filho and Marcio Valk (2020) <doi:10.1016/j.ejor.2019.12.038>.