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>.

Comprehensive set of tools for analyzing and manipulating functional
 data with non-uniform lengths. This package addresses two common scenarios in
 functional data analysis: Variable Domain Data, where the observation domain
 differs across samples, and Partially Observed Data, where observations are
 incomplete over the domain of interest. 'VDPO' enhances the flexibility and
 applicability of functional data analysis in 'R'.
 See Amaro et al. (2024) <doi:10.48550/arXiv.2401.05839>,
 Hernandez-Amaro et al. (2025) <doi:10.48550/arXiv.2510.26917>, and
 Hernandez-Amaro et al. (2026) <doi:10.48550/arXiv.2605.03633>.

Although model selection is ubiquitous in scientific discovery, the stability and uncertainty of the selected model is often hard to evaluate. How to characterize the random behavior of the model selection procedure is the key to understand and quantify the model selection uncertainty. This R package offers several graphical tools to visualize the distribution of the selected model. For example, Gplot(), Hplot(), VDSM_scatterplot() and VDSM_heatmap(). To the best of our knowledge, this is the first attempt to visualize such a distribution. About what distribution of selected model is and how it work please see Qin,Y.and Wang,L. (2021) "Visualization of Model Selection Uncertainty" <https://homepages.uc.edu/~qinyn/VDSM/VDSM.html>.