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.

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.

Phase I clinical trials are the first step in drug development to test
    a new drug or drug combination on humans. Typical designs of Phase I 
    trials use toxicity as the primary endpoint and aim to find the maximum 
    tolerable dosage. However, these designs are poorly applicable for the 
    development of cancer therapeutic vaccines because the expected safety 
    concerns for these vaccines are not as much as cytotoxic agents. The 
    primary objectives of a cancer therapeutic vaccine phase I trial thus 
    often include determining whether the vaccine shows biologic activity 
    and the minimum dose necessary to achieve a full immune or even clinical 
    response. This package implements a Bayesian Phase I cancer vaccine
    trial design that allows simultaneous evaluation of safety and
    immunogenicity outcomes. See Wang et al. (2019) <DOI:10.1002/sim.8021>
    for further details.