Carries out mapping between assorted color spaces including RGB, HSV, HLS,
             CIEXYZ, CIELUV, HCL (polar CIELUV), CIELAB and polar CIELAB.
	     Qualitative, sequential, and diverging color palettes based on HCL colors
	     are provided along with corresponding ggplot2 color scales.
	     Color palette choice is aided by an interactive app (with either a Tcl/Tk
	     or a shiny GUI) and shiny apps with an HCL color picker and a
	     color vision deficiency emulator. Plotting functions for displaying
	     and assessing palettes include color swatches, visualizations of the
	     HCL space, and trajectories in HCL and/or RGB spectrum. Color manipulation
	     functions include: desaturation, lightening/darkening, mixing, and
	     simulation of color vision deficiencies (deutanomaly, protanomaly, tritanomaly).

datasets, utils, KernSmooth, MASS, kernlab, mvtnorm, vcd,
tcltk, shiny, shinyjs, ggplot2, dplyr, scales, grid, png, jpeg,
knitr, rmarkdown, RColorBrewer, rcartocolor, scico, viridis,
wesanderson

Converts R object into JSON objects and vice-versa.

The ellipsis is a powerful tool for extending functions. Unfortunately 
    this power comes at a cost: misspelled arguments will be silently ignored. 
    The ellipsis package provides a collection of functions to catch problems
    and alert the user.

c(
      person("Hadley", "Wickham", , "hadley@rstudio.com", role = c("aut", "cre")),
      person("RStudio", role = "cph")
    )

Hadley Wickham [aut, cre],
  RStudio [cph]

GlobalOptions (>= 0.1.2), shape, grDevices, utils, stats,
colorspace, methods, grid

knitr, dendextend (>= 1.0.1), ComplexHeatmap (>= 2.0.0),
gridBase, png

Circular layout is an efficient way for the visualization of huge 
    amounts of information. Here this package provides an implementation 
    of circular layout generation in R as well as an enhancement of available 
    software. The flexibility of the package is based on the usage of low-level 
    graphics functions such that self-defined high-level graphics can be easily 
    implemented by users for specific purposes. Together with the seamless 
    connection between the powerful computational and visual environment in R, 
    it gives users more convenience and freedom to design figures for 
    better understanding complex patterns behind multiple dimensional data. 
    The package is described in Gu et al. 2014 <doi:10.1093/bioinformatics/btu393>.