Package: ggRandomForests 3.4.1

ggRandomForests: Visually Exploring Random Forests

Graphic elements for exploring Random Forests using the 'randomForest' or 'randomForestSRC' package for survival, regression and classification forests and 'ggplot2' package plotting. Implements visualisations of the methods described in Breiman (2001) <doi:10.1023/A:1010933404324> and Ishwaran, Kogalur, Blackstone, and Lauer (2008) <doi:10.1214/08-AOAS169>.

Authors:John Ehrlinger [aut, cre]

ggRandomForests_3.4.1.tar.gz
ggRandomForests_3.4.1.zip(r-4.7)ggRandomForests_3.4.1.zip(r-4.6)ggRandomForests_3.4.1.zip(r-4.5)
ggRandomForests_3.4.1.tgz(r-4.6-any)ggRandomForests_3.4.1.tgz(r-4.5-any)
ggRandomForests_3.4.1.tar.gz(r-4.7-any)ggRandomForests_3.4.1.tar.gz(r-4.6-any)
ggRandomForests_3.4.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
ggRandomForests/json (API)

# Install 'ggRandomForests' in R:
install.packages('ggRandomForests', repos = c('https://ehrlinger.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/ehrlinger/ggrandomforests/issues

Pkgdown/docs site:https://ehrlinger.github.io

On CRAN:

Conda:

quarto

10.56 score 153 stars 244 scripts 1.0k downloads 16 mentions 25 exports 83 dependencies

Last updated from:2daf8633fb. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK286
source / vignettesOK345
linux-release-x86_64OK284
macos-release-arm64OK299
macos-oldrel-arm64OK371
windows-develOK265
windows-releaseOK287
windows-oldrelOK244
wasm-releaseOK176

Exports:calc_auccalc_rocgg_beta_uvarprogg_beta_varprogg_briergg_errorgg_isoprogg_ivarprogg_partialgg_partial_rfsrcgg_partial_varprogg_partialprogg_rfsrcgg_rocgg_sdependentgg_survivalgg_udependentgg_variablegg_varprogg_vimpkaplannelsonquantile_ptssurv_partial.rfsrcvarpro_feature_names

Dependencies:BARTbase64encbitbit64bslibcachemclicliprcodetoolscpp11crayondata.treeDiagrammeRdigestdplyrevaluatefarverfastmapfontawesomeforeachfsgbmgenericsggplot2glmnetgluegtablehighrhmshtmltoolshtmlwidgetsigraphisobanditeratorsjquerylibjsonliteknitrlabelinglatticelifecyclemagrittrMatrixmemoisemimenlmepatchworkpillarpkgconfigprettyunitsprogresspurrrR6randomForestrandomForestSRCrappdirsRColorBrewerRcppRcppEigenreadrrlangrmarkdownrstudioapiS7sassscalesshapestringistringrsurvivaltibbletidyrtidyselecttinytextzdbutf8varProvctrsviridisLitevisNetworkvroomwithrxfunyaml

Exploring Random Forests with ggRandomForests
Error trajectories with gg_error() | Marginal dependence via gg_variable() | Variable importance with gg_vimp() | Balanced conditioning cuts with quantile_pts() | Next steps

Last update: 2026-07-01
Started: 2026-02-20

Exploring variable importance with varPro
What varPro is | Regression: Boston housing | Per-tree importance with gg_varpro() | Partial dependence with gg_partial_varpro() | Per-rule lasso refinement with gg_beta_varpro() | Unsupervised views: see the uvarpro vignette | Anomaly scoring with gg_isopro() | Local importance with gg_ivarpro() | Classification: iris | Class-conditional importance with gg_varpro(conditional = TRUE) | Partial dependence: gg_partial_varpro() on classification | Per-class lasso refinement with gg_beta_varpro() | Survival: PBC | Variable importance: gg_varpro() | Partial dependence: gg_partial_varpro() on survival | Anomaly scoring: gg_isopro() on the X-matrix | Not available for survival: gg_beta_varpro, gg_ivarpro | Cross-cutting reference | Family-support matrix | Factor-level ordering | Caching the expensive calls | Provenance shape | Further reading | References

Last update: 2026-07-01
Started: 2026-05-28

Variable selection without an outcome: unsupervised varPro
Importance without a response | One fit, three views | What depends on what: gg_udependent() | What ranks highest: gg_beta_uvarpro() | Where to draw the line: gg_sdependent() | Reading the three together | References

Last update: 2026-07-01
Started: 2026-07-01

Random Forest Regression with ggRandomForests
Work in progress | Introduction | Data: Boston Housing Values | Exploratory data analysis | Growing a Random Forest | OOB error convergence | OOB predictions | Variable Selection | Variable importance (VIMP) | Minimal depth | Variable Dependence | Variable dependence plots | Partial dependence | Variable Interactions and Conditioning Plots | Conditioning on a categorical variable | Conditioning on a continuous variable | Partial Dependence Surface | Conclusion | References

Last update: 2026-06-24
Started: 2026-03-28

Random Forest Survival Analysis with ggRandomForests
Work in progress | Introduction | Data: Primary Biliary Cirrhosis (PBC) | Data cleaning | Exploratory data analysis | Kaplan--Meier survival by treatment | Growing a Random Survival Forest | OOB error convergence | OOB predicted survival | Test set predictions | Variable Selection | Variable importance (VIMP) | Minimal depth | Variable Dependence | Variable dependence plots | Partial dependence | Conditional dependence | Partial Dependence Surfaces | Brier Score and CRPS | Conclusion | References

Last update: 2026-06-24
Started: 2026-03-28

Readme and manuals

Help Manual

Help pageTopics
ggRandomForests: Visually Exploring Random ForestsggRandomForests-package
'autoplot' methods for 'ggRandomForests' data objectsautoplot.gg autoplot.gg_brier autoplot.gg_error autoplot.gg_isopro autoplot.gg_partial autoplot.gg_partialpro autoplot.gg_partial_rfsrc autoplot.gg_partial_varpro autoplot.gg_rfsrc autoplot.gg_roc autoplot.gg_survival autoplot.gg_udependent autoplot.gg_variable autoplot.gg_varpro autoplot.gg_vimp
Area Under the ROC Curve calculatorcalc_auc calc_auc.gg_roc
Receiver Operator Characteristic calculatorcalc_roc calc_roc.randomForest calc_roc.rfsrc
Per-variable lasso-beta importance from an unsupervised varPro fitgg_beta_uvarpro
Per-variable lasso-beta importance from a varPro fitgg_beta_varpro
Brier score and CRPS for survival forestsgg_brier
Random forest error trajectory data objectgg_error gg_error.randomForest gg_error.randomForest.formula gg_error.rfsrc
Tidy data from a varPro isolation-forest fitgg_isopro
Individual (local) variable importance from a varPro fitgg_ivarpro
Split partial dependence data into continuous or categorical datasetsgg_partial
Partial dependence data from an rfsrc modelgg_partial_rfsrc
Partial dependence data from a varPro modelgg_partialpro gg_partial_varpro
Predicted response data objectgg_rfsrc gg_rfsrc.rfsrc
ROC (Receiver Operating Characteristic) curve data from a classification forest.gg_roc gg_roc.randomForest gg_roc.rfsrc
Signal-variable detection from an unsupervised varPro fitgg_sdependent
Nonparametric survival estimates.gg_survival gg_survival.default gg_survival.rfsrc
Variable dependency graph from a uvarpro modelgg_udependent
Marginal variable dependence data object.gg_variable gg_variable.random gg_variable.randomForest gg_variable.rfsrc
Variable importance data from a varPro modelgg_varpro
Variable Importance (VIMP) data objectgg_vimp gg_vimp.randomForest gg_vimp.randomForest.formula gg_vimp.rfsrc
nonparametric Kaplan-Meier estimateskaplan
nonparametric Nelson-Aalen estimatesnelson
Plot a 'gg_beta_uvarpro' objectplot.gg_beta_uvarpro
Plot a 'gg_beta_varpro' objectplot.gg_beta_varpro
Plot a 'gg_brier' objectplot.gg_brier
Plot a 'gg_error' objectplot.gg_error
Plot a varPro isolation-forest anomaly scoreplot.gg_isopro
Plot a 'gg_ivarpro' objectplot.gg_ivarpro
Plot a 'gg_partial' objectplot.gg_partial
Plot a 'gg_partial_rfsrc' objectplot.gg_partial_rfsrc
Plot a 'gg_partial_varpro' objectplot.gg_partialpro plot.gg_partial_varpro
Predicted response plot from a 'gg_rfsrc' object.plot.gg_rfsrc
ROC plot generic function for a 'gg_roc' object.plot.gg_roc
Plot a 'gg_sdependent' objectplot.gg_sdependent
Plot a 'gg_survival' object.plot.gg_survival
Plot a 'gg_udependent' variable dependency graphplot.gg_udependent
Plot a 'gg_variable' object,plot.gg_variable
Plot a 'gg_varpro' variable importance objectplot.gg_varpro
Plot a 'gg_vimp' object, extracted variable importance of a 'rfsrc' objectplot.gg_vimp
Print methods for gg_* data objectsprint.gg print.gg_beta_uvarpro print.gg_beta_varpro print.gg_brier print.gg_error print.gg_isopro print.gg_ivarpro print.gg_partial print.gg_partialpro print.gg_partial_rfsrc print.gg_partial_varpro print.gg_rfsrc print.gg_roc print.gg_sdependent print.gg_survival print.gg_udependent print.gg_variable print.gg_varpro print.gg_vimp print.summary.gg_udependent
Quantile-based cut points for coplotsquantile_pts
Summary methods for gg_* data objectsprint.summary.gg summary.gg summary.gg_beta_uvarpro summary.gg_beta_varpro summary.gg_brier summary.gg_error summary.gg_isopro summary.gg_ivarpro summary.gg_partial summary.gg_partialpro summary.gg_partial_rfsrc summary.gg_partial_varpro summary.gg_rfsrc summary.gg_roc summary.gg_sdependent summary.gg_survival summary.gg_udependent summary.gg_variable summary.gg_varpro summary.gg_vimp
Survival partial dependence data for one or more predictorssurv_partial.rfsrc
Recover original variable names from varpro one-hot encoded feature namesvarpro_feature_names