Uses 'ggplot2' to create normally distributed
    violin plots with specified means and standard deviations. This
    function can be useful in showing hypothetically normal distributions
    and confidence intervals.

Provides a theme, a discrete color palette, and continuous scales
    to make 'ggplot2' look like 'gnuplot'. This may be helpful if you use both
    'ggplot2' and 'gnuplot' in one project.

Allows plotting data on bathymetric maps using 'ggplot2'. Plotting
  oceanographic spatial data is made as simple as feasible, but also flexible
  for custom modifications. Data that contain geographic information from 
  anywhere around the globe can be plotted on maps generated by the basemap()
  or qmap() functions using 'ggplot2' layers separated by the '+' operator. The 
  package uses spatial shape- ('sf') and raster ('stars') files, geospatial 
  packages for R to manipulate, and the 'ggplot2' package to plot these 
  files. The package ships with low-resolution spatial data files and 
  higher resolution files for detailed maps are stored in the 
  'ggOceanMapsLargeData' repository on GitHub and downloaded automatically 
  when needed.

Discrete scales for the colorblind-friendly 'Okabe-Ito'
    palette, including 'color', 'fill', and 'edge_colour'. 'ggokabeito'
    provides 'ggplot2' and 'ggraph' scales to easily use the 'Okabe-Ito'
    palette in your data visualizations.

The 'vegan' package includes several functions for adding features
  to ordination plots: ordiarrows(),  ordiellipse(), ordihull(),
  ordispider() and ordisurf(). This package adds these same   features to 
  ordination plots made with 'ggplot2'. In addition, gg_ordibubble() sizes
  points relative to the value of an environmental variable.

Identify and visualize individuals with unusual association patterns of genetics and geography using the approach of Chang and Schmid (2023) <doi:10.1101/2023.04.06.535838>. It detects potential outliers that violate the isolation-by-distance assumption using the K-nearest neighbor approach. You can obtain a table of outliers with statistics and visualize unusual geo-genetic patterns on a geographical map. This is useful for landscape genomics studies to discover individuals with unusual geography and genetics associations from a large biological sample.