README.Rmd

---
output:
  md_document:
    variant: gfm
---

```{r, echo = FALSE}
knitr::opts_chunk$set(
  cache = TRUE,
  cache.path = "tools/README-cache/",
  collapse = TRUE,
  comment = "#>",
  fig.align = "center",
  fig.path = "man/figures/README-"
)
```

# pdp <img src="man/figures/pdp-logo.png" align="right" width="130" height="150" />

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## Overview

[pdp](https://cran.r-project.org/package=pdp) is an R package for constructing _**p**artial **d**ependence **p**lots_ (PDPs) and _**i**ndividual **c**onditional **e**xpectation_ (ICE) curves. PDPs and ICE curves are part of a larger framework referred to as *interpretable machine learning* (IML), which also includes (but not limited to) _**v**ariable **i**mportance **p**lots_ (VIPs). While VIPs (available in the R package [vip](https://koalaverse.github.io/vip/index.html)) help visualize feature impact (either locally or globally), PDPs and ICE curves help visualize feature effects. An in-progress, but comprehensive, overview of IML can be found at the following URL: https://github.com/christophM/interpretable-ml-book.

A detailed introduction to [pdp](https://cran.r-project.org/package=pdp) has been published in The R Journal: "pdp: An R Package for Constructing Partial Dependence Plots", https://journal.r-project.org/archive/2017/RJ-2017-016/index.html. You can track development at https://github.com/bgreenwell/pdp. To report bugs or issues, contact the main author directly or submit them to https://github.com/bgreenwell/pdp/issues. For additional documentation and examples, visit the [package website](https://bgreenwell.github.io/pdp/index.html).

As of right now, `pdp` exports the following functions:

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

  * `plotPartial()"` - construct `lattice`-based PDPs and ICE curves;

  * `autoplot()` - construct `ggplot2`-based PDPs and ICE curves;

  * ~~`topPredictors()` extract most "important" predictors from various types of fitted models.~~ see [vip](https://koalaverse.github.io/vip/index.html) instead for a more 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.)


## Installation

```{r, eval=FALSE}
# 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")
```