Plot principal component histograms around a bivariate
        scatter plot.

Useful set of tools for plotting network diagrams in any kind of project.

Lots of plots, various labeling, axis and color scaling functions. The author/maintainer died in September 2023.

Most ROC curve plots obscure the cutoff values and inhibit
    interpretation and comparison of multiple curves. This attempts to address
    those shortcomings by providing plotting and interactive tools. Functions
    are provided to generate an interactive ROC curve plot for web use, and
    print versions. A Shiny application implementing the functions is also
    included.

Figures rendered on graphics devices are usually 
 rescaled to fit pre-determined device dimensions. 'plotscale' 
 implements the reverse: desired plot dimensions are specified 
 and device dimensions are calculated to accommodate marginal
 material, giving consistent proportions for plot elements.
 Default methods support grid graphics such as lattice and ggplot.
 See "example('devsize')" and "vignette('plotscale')".

Contains a graphical user interface to generate the diagnostic
    plots proposed by Bauer (2005; <doi:10.1207/s15328007sem1204_1>), 
    Pek & Chalmers (2015; <doi:10.1080/10705511.2014.937790>), and
    Pek, Chalmers, R. Kok, & Losardo (2015; <doi:10.3102/1076998615589129>) to investigate
    nonlinear bivariate relationships in latent regression models using structural
    equation mixture models (SEMMs).

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

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>.