The qda() function from package 'MASS' is extended to calculate a weighted linear (LDA) and quadratic discriminant analysis (QDA) by changing the group variances and group means based on cell-wise uncertainties.
    The uncertainties can be derived e.g. through relative errors for each individual measurement (cell), not only row-wise or column-wise uncertainties.
    The method can be applied compositional data (e.g. portions of substances, concentrations) and non-compositional data.

Facilities for constructing variance dispersion graphs, fraction-
    of-design-space plots and similar graphics for exploring the properties of
    experimental designs. The design region is explored via random sampling, which
    allows for more flexibility than traditional variance dispersion graphs. A
    formula interface is leveraged to provide access to complex model formulae.
    Graphics can be constructed simultaneously for multiple experimental designs
    and/or multiple model formulae. Instead of using pointwise optimization to
    find the minimum and maximum scaled prediction variance curves, which can be
    inaccurate and time consuming, this package uses quantile regression as an
    alternative.

An extension to the 'testthat' package that makes it easy to
    add graphical unit tests. It provides a Shiny application to manage
    the test cases.

To ease the visualization of outputs from Diversity Motif Analyser ('DiMA';
    <https://github.com/BVU-BILSAB/DiMA>). 'vDiveR' allows visualization of the diversity 
    motifs (index and its variants – major, minor and unique) for elucidation of 
    the underlying inherent dynamics. Please refer <https://vdiver-manual.readthedocs.io/en/latest/>
    for more information.

Contains variable, diversity, and joining sequences and accompanying functions that enable both the extraction of and comparison between immune V-D-J genomic segments from a variety of species. Sources include IMGT from MP Lefranc (2009) <doi:10.1093/nar/gkn838> and Vgenerepertoire from publication DN Olivieri (2014) <doi:10.1007/s00251-014-0784-3>.