Package: innsight 0.3.2.9000

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:
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
Last updated from:bc63149b2b. Checks:9 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | OK | 197 | ||
| source / vignettes | OK | 254 | ||
| linux-release-x86_64 | OK | 195 | ||
| macos-release-arm64 | OK | 124 | ||
| macos-oldrel-arm64 | OK | 116 | ||
| windows-devel | OK | 227 | ||
| windows-release | OK | 181 | ||
| windows-oldrel | OK | 166 | ||
| wasm-release | OK | 129 |
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
