  Although there are established graphics that accompany the most common
functional data analyses, generating these graphics for each dataset and
analysis can be cumbersome and time consuming. Often, the barriers to
visualization inhibit useful exploratory data analyses and prevent the
development of intuition for a method and its application to a particular
dataset. The refund.shiny package was developed to address these issues for
several of the most common functional data analyses. After conducting an
analysis, the plot_shiny() function is used to generate an interactive
visualization environment that contains several distinct graphics, many of
which are updated in response to user input. These visualizations reduce the
burden of exploratory analyses and can serve as a useful tool for the
communication of results to non-statisticians.
