Package: effectplots 0.2.3

effectplots: Effect Plots

High-performance implementation of various effect plots useful for regression and probabilistic classification tasks. The package includes partial dependence plots (Friedman, 2021, <doi:10.1214/aos/1013203451>), accumulated local effect plots and M-plots (both from Apley and Zhu, 2016, <doi:10.1111/rssb.12377>), as well as plots that describe the statistical associations between model response and features. It supports visualizations with either 'ggplot2' or 'plotly', and is compatible with most models, including 'Tidymodels', models wrapped in 'DALEX' explainers, or models with case weights.

Authors:Michael Mayer [aut, cre]

effectplots_0.2.3.tar.gz
effectplots_0.2.3.zip(r-4.7)effectplots_0.2.3.zip(r-4.6)effectplots_0.2.3.zip(r-4.5)
effectplots_0.2.3.tgz(r-4.6-x86_64)effectplots_0.2.3.tgz(r-4.6-arm64)effectplots_0.2.3.tgz(r-4.5-x86_64)effectplots_0.2.3.tgz(r-4.5-arm64)
effectplots_0.2.3.tar.gz(r-4.7-arm64)effectplots_0.2.3.tar.gz(r-4.7-x86_64)effectplots_0.2.3.tar.gz(r-4.6-arm64)effectplots_0.2.3.tar.gz(r-4.6-x86_64)
effectplots_0.2.3.tgz(r-4.6-emscripten)
manual.pdf |manual.html
DESCRIPTION |NEWS
card.svg |card.png
effectplots/json (API)

# Install 'effectplots' in R:
install.packages('effectplots', repos = c('https://mayer79.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/mayer79/effectplots/issues

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

machine-learningregressionxaicpp

4.29 score 23 stars 17 scripts 594 downloads 10 exports 66 dependencies

Last updated from:a1b1624226. Checks:13 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK204
linux-devel-x86_64OK189
source / vignettesOK182
linux-release-arm64OK196
linux-release-x86_64OK171
macos-release-arm64OK110
macos-release-x86_64OK309
macos-oldrel-arm64OK148
macos-oldrel-x86_64OK277
windows-develOK145
windows-releaseOK419
windows-oldrelOK138
wasm-releaseOK187

Exports:.ale.pdaleaverage_observedaverage_predictedbiaseffect_importancefcutfeature_effectspartial_dependence

Dependencies:askpassbase64encbslibcachemclicollapsecpp11crosstalkcurldata.tabledigestdplyrevaluatefarverfastmapfontawesomefsgenericsggplot2gluegtablehighrhtmltoolshtmlwidgetshttrisobandjquerylibjsonliteknitrlabelinglaterlazyevallifecyclemagrittrmemoisemimeopensslotelpatchworkpillarpkgconfigplotlypromisespurrrR6rappdirsRColorBrewerRcpprlangrmarkdownS7sassscalesstringistringrsystibbletidyrtidyselecttinytexutf8vctrsviridisLitewithrxfunyaml

Readme and manuals

Help Manual

Help pageTopics
Barebone Accumulated Local Effects (ALE).ale
Barebone Partial Dependence.pd
Accumulated Local Effects (ALE)ale ale.default ale.explainer ale.H2OModel ale.ranger
Average Observedaverage_observed
Average Predictionsaverage_predicted
Bias / Average Residualsbias
Variable Importanceeffect_importance
Fast cut()fcut
Feature Effectsfeature_effects feature_effects.default feature_effects.explainer feature_effects.H2OModel feature_effects.ranger
Partial Dependencepartial_dependence partial_dependence.default partial_dependence.explainer partial_dependence.H2OModel partial_dependence.ranger
Plots "EffectData" Objectplot.EffectData
Update "EffectData" Objectupdate.EffectData