{"id":13401407,"url":"https://github.com/bgreenwell/pdp","last_synced_at":"2025-04-09T15:07:28.138Z","repository":{"id":47772842,"uuid":"53458235","full_name":"bgreenwell/pdp","owner":"bgreenwell","description":"A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.","archived":false,"fork":false,"pushed_at":"2022-07-18T08:14:48.000Z","size":69260,"stargazers_count":94,"open_issues_count":18,"forks_count":13,"subscribers_count":5,"default_branch":"master","last_synced_at":"2025-04-09T15:07:20.416Z","etag":null,"topics":["black-box-model","machine-learning","partial-dependence-function","partial-dependence-plot","r","visualization"],"latest_commit_sha":null,"homepage":"http://bgreenwell.github.io/pdp","language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/bgreenwell.png","metadata":{"files":{"readme":"README.Rmd","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null}},"created_at":"2016-03-09T01:29:34.000Z","updated_at":"2025-03-25T15:37:50.000Z","dependencies_parsed_at":"2022-08-21T01:10:19.626Z","dependency_job_id":null,"html_url":"https://github.com/bgreenwell/pdp","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bgreenwell%2Fpdp","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bgreenwell%2Fpdp/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bgreenwell%2Fpdp/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/bgreenwell%2Fpdp/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/bgreenwell","download_url":"https://codeload.github.com/bgreenwell/pdp/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":248055284,"owners_count":21040157,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["black-box-model","machine-learning","partial-dependence-function","partial-dependence-plot","r","visualization"],"created_at":"2024-07-30T19:01:02.346Z","updated_at":"2025-04-09T15:07:28.115Z","avatar_url":"https://github.com/bgreenwell.png","language":"R","funding_links":[],"categories":["HTML","R"],"sub_categories":[],"readme":"---\noutput:\n  md_document:\n    variant: gfm\n---\n\n```{r, echo = FALSE}\nknitr::opts_chunk$set(\n  cache = TRUE,\n  cache.path = \"tools/README-cache/\",\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.align = \"center\",\n  fig.path = \"man/figures/README-\"\n)\n```\n\n# pdp \u003cimg src=\"man/figures/pdp-logo.png\" align=\"right\" width=\"130\" height=\"150\" /\u003e\n\n\u003c!-- badges: start --\u003e\n[![CRAN status](https://www.r-pkg.org/badges/version/investr)](https://CRAN.R-project.org/package=investr)\n[![R-CMD-check](https://github.com/bgreenwell/pdp/workflows/R-CMD-check/badge.svg)](https://github.com/bgreenwell/pdp/actions)\n[![Codecov test coverage](https://codecov.io/gh/bgreenwell/pdp/branch/master/graph/badge.svg)](https://app.codecov.io/gh/bgreenwell/pdp?branch=master)\n[![Total Downloads](https://cranlogs.r-pkg.org/badges/grand-total/pdp)](https://cranlogs.r-pkg.org/badges/grand-total/pdp)\n\u003c!-- badges: end --\u003e\n\n\n## Overview\n\n[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.\n\nA 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).\n\nAs of right now, `pdp` exports the following functions:\n\n  * `partial()` - compute partial dependence functions and individual conditional expectations (i.e., objects of class `\"partial\"` and `\"ice\"`, respectively) from various fitted model objects;\n\n  * `plotPartial()\"` - construct `lattice`-based PDPs and ICE curves;\n\n  * `autoplot()` - construct `ggplot2`-based PDPs and ICE curves;\n\n  * ~~`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;\n  \n  * `exemplar()` - construct an exemplar record from a data frame (**experimental** feature that may be useful for constructing fast, approximate feature effect plots.)\n\n\n## Installation\n\n```{r, eval=FALSE}\n# The easiest way to get pdp is to install it from CRAN:\ninstall.packages(\"pdp\")\n\n# Alternatively, you can install the development version from GitHub:\nif (!(\"remotes\" %in% installed.packages()[, \"Package\"])) {\n  install.packages(\"remotes\")\n}\nremotes::install_github(\"bgreenwell/pdp\")\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbgreenwell%2Fpdp","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fbgreenwell%2Fpdp","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fbgreenwell%2Fpdp/lists"}