A collection of tools for downstream analysis of
    'VirusHunterGatherer' output. Processing of hittables 
    and plotting of results, enabling better interpretation,
    is made easier with the provided functions.

Provides a comprehensive R interface to the VirusTotal API (v2 and v3), 
    a Google service that analyzes files and URLs for viruses, worms, trojans and 
    other malware. Features include file/URL scanning, domain categorization, 
    passive DNS information, IP reputation analysis, and comment/voting systems.
    Implements rate limiting, error handling, and response validation for robust 
    security analysis workflows.

Provides easy-to-use tools for data analysis and visualization for hyperspectral remote sensing (also known as imaging spectroscopy), with 
    a particular focus on vegetation hyperspectral data analysis. It consists of a set of functions, ranging from the organization of hyperspectral data 
    in the proper data structure for spectral feature selection, calculation of vegetation index, multivariate analysis, as well as to the visualization 
    of spectra and results of analysis in the 'ggplot2' style.

Provides a set of wrapper functions for 'Visa Chart Components'.
  'Visa Chart Components' <https://github.com/visa/visa-chart-components> is an accessibility focused,
  framework agnostic set of data experience design systems components for the web.

Implementation of 'shiny' app to visualize adverse events based on the Common Terminology Criteria for Adverse Events (CTCAE) using stacked correspondence analysis as described in Diniz et. al (2021)<doi:10.1186/s12874-021-01368-w>.

Provides methods to calculate diagnostics for multicollinearity among predictors in a linear or
  generalized linear model. It also provides methods to visualize those diagnostics following Friendly & Kwan (2009),
  "Where’s Waldo: Visualizing Collinearity Diagnostics", <doi:10.1198/tast.2009.0012>.
  These include better tabular presentation of collinearity diagnostics that highlight the important numbers,
  a semi-graphic tableplot of the diagnostics to make warning and danger levels more salient,
  and a "collinearity biplot" of the smallest dimensions of predictor space, where collinearity is most apparent.