A port of the Scarabee toolkit originally written as a 
  Matlab-based application. scaRabee provides a framework for simulation and optimization 
  of pharmacokinetic-pharmacodynamic models at the individual and population level.
  It is built on top of the neldermead package, which provides the direct search 
  algorithm proposed by Nelder and Mead for model optimization.

Allows you to make clean, good-looking scatter plots with the option to 
    easily add marginal density or box plots on the axes. It is also available as a module for 'jamovi'
    (see <https://www.jamovi.org> for more information). 'Scatr' is based on the 
    'cowplot' package by Claus O. Wilke and the 'ggplot2' package by Hadley Wickham.

Provides a powerful and flexible tool for visualizing proportional data across spatially resolved contexts. By combining the concepts of scatter plots and stacked bar charts, 'scatterbar' allows users to create scattered bar chart plots, which effectively display the proportions of different categories at each (x, y) location. This visualization is particularly useful for applications where understanding the distribution of categories across spatial coordinates is essential. This package features automatic determination of optimal scaling factors based on data, customizable scaling and padding options for both x and y axes, flexibility to specify custom colors for each category, options to customize the legend title, and integration with 'ggplot2' for robust and high-quality visualizations. For more details, see Velazquez et al. (2024) <doi:10.1101/2024.08.14.606810>.

Creates 'D3' 'JavaScript' scatterplots from 'R' with interactive
    features : panning, zooming, tooltips, etc.

The user has the option to utilize the two-dimensional density estimation techniques called smoothed density published by Eilers and Goeman (2004) <doi:10.1093/bioinformatics/btg454>, and pareto density which was evaluated for univariate data by Thrun, Gehlert and Ultsch, 2020 <doi:10.1371/journal.pone.0238835>. Moreover, it provides visualizations of the density estimation in the form of two-dimensional scatter plots in which the points are color-coded based on increasing density. Colors are defined by the one-dimensional clustering technique called 1D distribution cluster algorithm (DDCAL) published by Lux and Rinderle-Ma (2023) <doi:10.1007/s00357-022-09428-6>.