Variable importance, interaction measures and partial dependence plots are important summaries in the interpretation of statistical and machine learning models. In our R package vivid (variable importance and variable interaction displays) we create new visualisation techniques for exploring these model summaries. We construct heatmap and graph-based displays showing variable importance and interaction jointly, which are carefully designed to highlight important aspects of the fit. We also construct a new matrix-type layout showing all single and bivariate partial dependence plots, and an alternative layout based on graph Eulerians focusing on key subsets. Our new visualisations are model-agnostic and are applicable to regression and classification supervised learning settings. They enhance interpretation even in situations where the number of variables is large and the interaction structure complex. A practical example of the package in use can be found here:


The zenplots package (which is used within vivid) requires the graph package from BioConductor. To install the graph and zenplots packages use:

if (!requireNamespace("graph", quietly = TRUE)){

You can install the released version of vivid from CRAN with:


And the development version from GitHub with:

# install.packages("devtools")

You can then load the package with: