Easily create overlapping grammar of graphics plots for scientific data visualization. 
  This style of plotting is particularly common in climatology and oceanography research communities.

A 'ggplot2' extension that provides flipped components:
    horizontal versions of 'Stats' and 'Geoms', and vertical versions
    of 'Positions'. This package is now superseded by 'ggplot2' itself
    which now has full native support for horizontal layouts. It
    remains available for backward compatibility.

To create the multiple polygonal point layer for easily discernible shapes, 
             we developed the package, it is like the 'geom_point' of 'ggplot2'.
             It can be used to draw the scatter plot.

Provides new statistics, new geometries and new positions for 
    'ggplot2' and a suite of functions to facilitate the creation of 
    statistical plots.

Extension of 'ggplot2', 'ggstatsplot' creates graphics with
    details from statistical tests included in the plots themselves. It
    provides an easier syntax to generate information-rich plots for
    statistical analysis of continuous (violin plots, scatterplots,
    histograms, dot plots, dot-and-whisker plots) or categorical (pie and
    bar charts) data. Currently, it supports the most common types of
    statistical approaches and tests: parametric, nonparametric, robust,
    and Bayesian versions of t-test/ANOVA, correlation analyses,
    contingency table analysis, meta-analysis, and regression analyses.
    References: Patil (2021) <doi:10.21105/joss.03236>.

Make smoothed stacked area charts in 'ggplot2'. Stream plots are useful to show magnitude trends over time.

Provides an extension to 'ggplot2'
  (Wickham, 2016, <doi:10.1007/978-3-319-24277-4>) for creating two types of
  continuous confidence interval plots (Violin CI and Gradient CI plots),
  typically for the sample mean. These plots contain multiple user-defined
  confidence areas with varying colours, defined by the underlying
  t-distribution used to compute standard confidence intervals for the mean of
  the normal distribution when the variance is unknown. Two types of plots are
  available, a gradient plot with rectangular areas, and a violin plot where
  the shape (horizontal width) is defined by the probability density function
  of the t-distribution. These visualizations are studied in
  (Helske, Helske, Cooper, Ynnerman, and Besancon, 2021) 
  <doi:10.1109/TVCG.2021.3073466>.