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' described by Baehrens et al. (2009) <doi:10.48550/arXiv.0912.1128> or 'Vanilla Gradient'.

Version: 0.3.0
Depends: R (≥ 3.5.0)
Imports: checkmate, cli, ggplot2, methods, R6, torch
Suggests: covr, fastshap, GGally, grid, gridExtra, gtable, keras, knitr, lime, luz, neuralnet, palmerpenguins, plotly, rmarkdown, ranger, spelling, tensorflow, testthat (≥ 3.0.0)
Published: 2023-12-21
Author: Niklas Koenen ORCID iD [aut, cre], Raphael Baudeu [ctb]
Maintainer: Niklas Koenen <niklas.koenen at gmail.com>
BugReports: https://github.com/bips-hb/innsight/issues/
License: MIT + file LICENSE
URL: https://bips-hb.github.io/innsight/, https://github.com/bips-hb/innsight/
NeedsCompilation: no
Language: en-US
Materials: README NEWS
CRAN checks: innsight results

Documentation:

Reference manual: innsight.pdf
Vignettes: Example 1: Iris dataset with torch
Example 2: Penguin dataset with torch and luz
In-depth explanation
Introduction to innsight

Downloads:

Package source: innsight_0.3.0.tar.gz
Windows binaries: r-devel: innsight_0.3.0.zip, r-release: innsight_0.3.0.zip, r-oldrel: innsight_0.3.0.zip
macOS binaries: r-release (arm64): innsight_0.3.0.tgz, r-oldrel (arm64): innsight_0.3.0.tgz, r-release (x86_64): innsight_0.3.0.tgz
Old sources: innsight archive

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