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pdp is an R package forconstructingpartialdependenceplots (PDPs) andindividualconditionalexpectation (ICE) curves. PDPs andICE curves are part of a larger framework referred to asinterpretablemachine learning (IML), which also includes (but not limited to)variableimportanceplots (VIPs). While VIPs (available inthe R packagevip) helpvisualize feature impact (either locally or globally), PDPs and ICEcurves help visualize feature effects. An in-progress, butcomprehensive, overview of IML can be found at the following URL:https://github.com/christophM/interpretable-ml-book.

A detailed introduction topdphas been published in The R Journal: “pdp: An R Package for ConstructingPartial Dependence Plots”,https://journal.r-project.org/archive/2017/RJ-2017-016/index.html. Youcan track development athttps://github.com/bgreenwell/pdp. To reportbugs or issues, contact the main author directly or submit them tohttps://github.com/bgreenwell/pdp/issues. For additional documentationand examples, visit thepackagewebsite.

As of right now,pdp exports the following functions:

• partial() - compute partial dependence functions and individualconditional expectations (i.e., objects of class"partial" and"ice", respectively) from various fitted model objects;

• plotPartial()" - constructlattice-based PDPs and ICE curves;

• autoplot() - constructggplot2-based PDPs and ICE curves;

• topPredictors() extract most “important” predictors from varioustypes of fitted models. seevip instead for amore robust and flexible replacement;

• exemplar() - construct an exemplar record from a data frame(experimental feature that may be useful for constructing fast,approximate feature effect plots.)

# The easiest way to get pdp is to install it from CRAN:install.packages("pdp")# Alternatively, you can install the development version from GitHub:if (!("remotes"%in% installed.packages()[,"Package"])) {  install.packages("remotes")}remotes::install_github("bgreenwell/pdp")