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: https://alaninglis.github.io/vivid/articles/vividVignette.html

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)){
install.packages("BiocManager")
::install("graph")
BiocManager
}install.packages("zenplots")
```

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

`install.packages("vivid")`

And the development version from GitHub with:

```
# install.packages("devtools")
::install_github("AlanInglis/vivid") devtools
```

You can then load the package with:

`library(vivid)`