{ "_id": "6a1168c4acfb0bcc41cf2b76", "Type": "Package", "Package": "innsight", "Title": "Get the Insights of Your Neural Network", "Version": "0.3.2.9000", "Authors@R": "c(\nperson(\"Niklas\", \"Koenen\", , \"niklas.koenen@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0002-4623-8271\")),\nperson(\"Raphael\", \"Baudeu\", , \"raphael.baudeu@gmail.com\", role = \"ctb\")\n)", "Description": "Interpretation methods for analyzing the behavior and\nindividual predictions of modern neural networks in a\nthree-step procedure: Converting the model, running the\ninterpretation method, and visualizing the results. Implemented\nmethods are, e.g., 'Connection Weights' described by Olden et\nal. (2004) , layer-wise\nrelevance propagation ('LRP') described by Bach et al. (2015)\n, deep learning important\nfeatures ('DeepLIFT') described by Shrikumar et al. (2017)\n and gradient-based methods like\n'SmoothGrad' described by Smilkov et al. (2017)\n, 'Gradient x Input' or 'Vanilla\nGradient'. Details can be found in the accompanying scientific\npaper: Koenen & Wright (2024, Journal of Statistical Software,\n).", "License": "MIT + file LICENSE", "URL": "https://bips-hb.github.io/innsight/,\nhttps://github.com/bips-hb/innsight/", "BugReports": "https://github.com/bips-hb/innsight/issues/", "VignetteBuilder": "knitr", "Config/testthat/edition": "3", "Encoding": "UTF-8", "Language": "en-US", "Roxygen": "list(markdown = TRUE, r6 = TRUE)", "RoxygenNote": "7.3.3", "Collate": "'AgnosticMethods.R' 'AgnosticWrapper.R' 'ConnectionWeights.R'\n'Convert_keras.R' 'Convert_neuralnet.R' 'Convert_torch.R'\n'ConvertedModel.R' 'Converter.R' 'DeepLift.R' 'GradienBased.R'\n'torch_gradients.R' 'InterpretingLayer.R'\n'InterpretingMethod.R' 'LRP.R' 'Layer_conv1d.R'\n'Layer_conv2d.R' 'Layer_dense.R' 'Layer_normalization.R'\n'Layer_other.R' 'Layer_pooling.R' 'innsight.R' 'utils.R'\n'utils_ggplot.R' 'utils_plotly.R' 'innsight_sugar.R'\n'innsight_ggplot2.R' 'innsight_plotly.R'", "Repository": 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