innsight: Get the Insights of your Neural Network
Interpretability methods to analyze the behavior and individual predictions of modern neural networks. Implemented methods are: '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) <arXiv:1704.02685> and gradient-based methods like 'SmoothGrad' described by Smilkov et al. (2017) <arXiv:1706.03825>, 'Gradient x Input' described by Baehrens et al. (2009) <arXiv:0912.1128> or 'Vanilla Gradient'.
| Version: | 0.1.1 |
| Depends: | R (≥ 3.5.0) |
| Imports: | checkmate, ggplot2, R6, torch |
| Suggests: | covr, keras, knitr, neuralnet, plotly, rmarkdown, tensorflow, testthat (≥ 3.0.0) |
| Published: | 2022-08-29 |
| Author: | Niklas Koenen 
[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 |
| Materials: | README NEWS |
| CRAN checks: | innsight results |
Documentation:
| Reference manual: | innsight.pdf |
| Vignettes: |
Custom Model Definition Introduction to innsight |
Downloads:
| Package source: | innsight_0.1.1.tar.gz |
| Windows binaries: | r-devel: innsight_0.1.1.zip, r-release: innsight_0.1.1.zip, r-oldrel: innsight_0.1.1.zip |
| macOS binaries: | r-release (arm64): innsight_0.1.1.tgz, r-oldrel (arm64): innsight_0.1.1.tgz, r-release (x86_64): innsight_0.1.1.tgz, r-oldrel (x86_64): innsight_0.1.1.tgz |
| Old sources: | innsight archive |
Linking:
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