Package: innsight 0.3.2.9000

Niklas Koenen

innsight: Get the Insights of Your Neural Network

Interpretation methods for analyzing the behavior and individual predictions of modern neural networks in a three-step procedure: Converting the model, running the interpretation method, and visualizing the results. Implemented methods are, e.g., 'Connection Weights' described by Olden et al. (2004) <doi:10.1016/j.ecolmodel.2004.03.013>, layer-wise relevance propagation ('LRP') described by Bach et al. (2015) <doi:10.1371/journal.pone.0130140>, deep learning important features ('DeepLIFT') described by Shrikumar et al. (2017) <doi:10.48550/arXiv.1704.02685> and gradient-based methods like 'SmoothGrad' described by Smilkov et al. (2017) <doi:10.48550/arXiv.1706.03825>, 'Gradient x Input' or 'Vanilla Gradient'. Details can be found in the accompanying scientific paper: Koenen & Wright (2024, Journal of Statistical Software, <doi:10.18637/jss.v111.i08>).

Authors:Niklas Koenen [aut, cre], Raphael Baudeu [ctb]

innsight_0.3.2.9000.tar.gz
innsight_0.3.2.9000.zip(r-4.7)innsight_0.3.2.9000.zip(r-4.6)innsight_0.3.2.9000.zip(r-4.5)
innsight_0.3.2.9000.tgz(r-4.6-any)innsight_0.3.2.9000.tgz(r-4.5-any)
innsight_0.3.2.9000.tar.gz(r-4.7-any)innsight_0.3.2.9000.tar.gz(r-4.6-any)
innsight_0.3.2.9000.tgz(r-4.6-emscripten)
manual.pdf |manual.html
card.svg |card.png
innsight/json (API)
NEWS

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

Bug tracker:https://github.com/bips-hb/innsight/issues

Pkgdown/docs site:https://bips-hb.github.io

On CRAN:

Conda:

7.39 score 31 stars 1 packages 35 scripts 911 downloads 33 exports 31 dependencies

Last updated from:bc63149b2b. Checks:9 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-x86_64OK197
source / vignettesOK254
linux-release-x86_64OK195
macos-release-arm64OK124
macos-oldrel-arm64OK116
windows-develOK227
windows-releaseOK181
windows-oldrelOK166
wasm-releaseOK129

Exports:AgnosticWrapperas_innsight_resultConnectionWeightsconvertConverterDeepLiftDeepSHAPExpectedGradientget_resultGradientIntegratedGradientLIMELRPplotplot_globalprintrun_cwrun_deepliftrun_deepshaprun_expgradrun_gradrun_intgradrun_limerun_lrprun_shaprun_smoothgradSHAPshowSmoothGradtorch_expgradtorch_gradtorch_intgradtorch_smoothgrad

Dependencies:backportsbitbit64callrcheckmateclicorocpp11descfarverggplot2gluegtableisobandjsonlitelabelinglifecyclemagrittrprocessxpsR6RColorBrewerRcpprlangS7safetensorsscalestorchvctrsviridisLitewithr

Direct Torch Gradient Methods

Rendered fromtorch_gradients.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2026-02-22
Started: 2026-02-22

Example 1: Iris dataset with torch

Rendered fromExample_1_iris.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2023-12-21
Started: 2023-02-01

Example 2: Penguin dataset with torch and luz

Rendered fromExample_2_penguin.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2025-03-28
Started: 2023-02-01

In-depth explanation

Rendered fromdetailed_overview.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2025-03-28
Started: 2023-02-01

Introduction to innsight

Rendered frominnsight.Rmdusingknitr::rmarkdownon May 23 2026.

Last update: 2025-03-28
Started: 2021-11-22

Readme and manuals

Help Manual

Help pageTopics
Get the insight of your neural networkinnsight-package innsight
Indexing plots of 'innsight_ggplot2'[,innsight_ggplot2-method [.innsight_ggplot2 [<-,innsight_ggplot2-method [<-.innsight_ggplot2 [[,innsight_ggplot2-method [[.innsight_ggplot2 [[<-,innsight_ggplot2-method [[<-.innsight_ggplot2
Indexing plots of 'innsight_plotly'[,innsight_plotly-method [.innsight_plotly [[,innsight_plotly-method [[.innsight_plotly
Generic add function for 'innsight_ggplot2'+,innsight_ggplot2,ANY-method +.innsight_ggplot2
Super class for model-agnostic interpretability methodsAgnosticWrapper
Convert gradient results to an innsight result objectas_innsight_result
Connection weights methodConnectionWeights
Converted torch-based modelConvertedModel
Converter of an artificial neural networkConverter
Deep learning important features (DeepLift)DeepLift
Deep Shapley additive explanations (DeepSHAP)DeepSHAP
Expected GradientsExpectedGradient
Get the result of an interpretation methodget_result
Vanilla Gradient and Gradient\timesInputGradient
Super class for gradient-based interpretation methodsGradientBased
S4 class for ggplot2-based plotsinnsight_ggplot2
S4 class for plotly-based plotsinnsight_plotly
Syntactic sugar for object constructionconvert innsight_sugar run_cw run_deeplift run_deepshap run_expgrad run_grad run_intgrad run_lime run_lrp run_shap run_smoothgrad
Integrated GradientsIntegratedGradient
Super class for interpreting methodsInterpretingMethod
Local interpretable model-agnostic explanations (LIME)LIME
Layer-wise relevance propagation (LRP)LRP
Get the result of an interpretation methodplot_global
Generic print, plot and show for 'innsight_ggplot2'plot,innsight_ggplot2-method plot.innsight_ggplot2 print,innsight_ggplot2-method print.innsight_ggplot2 show,innsight_ggplot2-method show.innsight_ggplot2
Generic print, plot and show for 'innsight_plotly'plot,innsight_plotly-method plot.innsight_plotly print,innsight_plotly-method print.innsight_plotly show,innsight_plotly-method show.innsight_plotly
Shapley valuesSHAP
SmoothGrad and SmoothGrad\timesInputSmoothGrad
Direct Expected Gradients for torch modelstorch_expgrad
Direct gradient calculation for torch modelstorch_grad
Direct Integrated Gradients for torch modelstorch_intgrad
Direct SmoothGrad for torch modelstorch_smoothgrad