A set of functions providing several visualization tools for exploring the behavior of the components in a network meta-analysis of multi-component (complex) interventions: 
   - components descriptive analysis
   - heat plot of the two-by-two component combinations 
   - leaving one component combination out scatter plot 
   - violin plot for specific component combinations' effects
   - density plot for components' effects  
   - waterfall plot for the interventions' effects that differ by a certain component combination 
   - network graph of components
   - rank heat plot of components for multiple outcomes.
   The implemented tools are described by Seitidis et al. (2023) <doi:10.1002/jrsm.1617>.

Functionality for creating phase portraits of functions in the
    complex number plane. Works with R base graphics, whose full 
    functionality is available. Parallel processing is used for optimum 
    performance.

Visualizing of distributions of covariance matrices.
    The package implements the methodology described in Tokuda, T., Goodrich, B., Van Mechelen, I., Gelman, A., & Tuerlinckx, F. (2012) <https://sites.stat.columbia.edu/gelman/research/unpublished/Visualization.pdf>.

Create preliminary exploratory data visualisations of an entire 
    dataset to identify problems or unexpected features using 'ggplot2'.

Designed to help the user to determine the sensitivity of an proposed causal effect to unconsidered common causes. Users can create visualizations of sensitivity, effect sizes, and determine which pattern of effects would support a causal claim for between group differences. Number needed to treat formula from Kraemer H.C. & Kupfer D.J. (2006) <doi:10.1016/j.biopsych.2005.09.014>.

A graphical R package designed to visualize behavioral observations over time. Based on raw time data extracted from video recorded sessions of experimental observations, ViSiElse grants a global overview of a process by combining the visualization of multiple actions timestamps for all participants in a single graph. Individuals and/or group behavior can easily be assessed. Supplementary features allow users to further inspect their data by adding summary statistics (mean, standard deviation, quantile or statistical test) and/or time constraints to assess the accuracy of the realized actions.