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CRAN: Package innsight

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>).

Version: 0.3.2 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: 2025-03-30 DOI: 10.32614/CRAN.package.innsight 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 Language: en-US Citation: innsight citation info Materials: README NEWS CRAN checks: innsight results Documentation: Downloads: Linking:

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