Provides high-level API and a wide range of options to create stunning, publication-quality plots effortlessly.
    It is built upon 'ggplot2' and other plotting packages, and is designed to be easy to use and to work seamlessly with 'ggplot2' objects.
    It is particularly useful for creating complex plots with multiple layers, facets, and annotations.
    It also provides a set of functions to create plots for specific types of data, such as Venn diagrams, alluvial diagrams, and phylogenetic trees.
    The package is designed to be flexible and customizable, and to work well with the 'ggplot2' ecosystem.
    The API can be found at <https://pwwang.github.io/plotthis/reference/index.html>.

Annotate plots with legends for continuous variables and colour
  spectra using the base graphics plotting tools; and manipulate irregular
  polygons. Includes palettes for colour-blind viewers.

Small self-contained plots for use in larger plots or to
    delegate plotting in other functions. Also contains a number of
    alternative color palettes and HSL color space based tools to modify
    colors or palettes.

Prediction limits for the Poisson distribution
  are produced from both frequentist and Bayesian viewpoints. Limiting results
  are provided in a Bayesian setting with uniform, Jeffreys and gamma as prior
  distributions. More details on the methodology are discussed in Bejleri and
  Nandram (2018) <doi:10.1080/03610926.2017.1373814> and Bejleri, Sartore and
  Nandram (2021) <doi:10.1007/s42952-021-00157-x>.

Power logit regression models for bounded
  continuous data, in which the density generator may be normal, Student-t, 
  power exponential, slash, hyperbolic, sinh-normal, or type II logistic. 
  Diagnostic tools associated with the fitted model, such as the residuals, 
  local influence measures, leverage measures, and goodness-of-fit statistics,
  are implemented. The estimation process follows the maximum likelihood approach
  and, currently, the package supports two types of estimators: the usual maximum 
  likelihood estimator and the penalized maximum likelihood estimator. More details
  about power logit regression models are described in 
  Queiroz and Ferrari (2022) <arXiv:2202.01697>.