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>.

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>.

Create epicurves, epigantt charts, and diverging bar charts
    using 'ggplot2'. Prepare data for visualisation or other reporting for
    infectious disease surveillance and outbreak investigation (time
    series data).  Includes tidy functions to solve date based
    transformations for common reporting tasks, like (A) seasonal date
    alignment for respiratory disease surveillance, (B) date-based case
    binning based on specified time intervals like isoweek, epiweek, month
    and more, (C) automated detection and marking of the new year based on
    the date/datetime axis of the 'ggplot2', (D) labelling of the last
    value of a time-series.  An introduction on how to use epicurves can
    be found on the US CDC website (2012,
    <https://www.cdc.gov/training/quicklearns/epimode/index.html>).