DESCRIPTION

Type: Package
Package: innsight
Title: Get the Insights of Your Neural Network
Version: 0.3.1
Authors@R: c(
    person("Niklas", "Koenen", , "niklas.koenen@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0002-4623-8271")),
    person("Raphael", "Baudeu", , "raphael.baudeu@gmail.com", role = "ctb")
  )
Description: 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>).
License: MIT + file LICENSE
URL: https://bips-hb.github.io/innsight/,
    https://github.com/bips-hb/innsight/
BugReports: https://github.com/bips-hb/innsight/issues/
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)
VignetteBuilder: 
    knitr
Config/testthat/edition: 3
Encoding: UTF-8
Language: en-US
Roxygen: list(markdown = TRUE, r6 = TRUE)
RoxygenNote: 7.2.3
Collate: 
    'AgnosticMethods.R'
    'AgnosticWrapper.R'
    'ConnectionWeights.R'
    'Convert_keras.R'
    'Convert_neuralnet.R'
    'Convert_torch.R'
    'ConvertedModel.R'
    'Converter.R'
    'DeepLift.R'
    'GradienBased.R'
    'InterpretingLayer.R'
    'InterpretingMethod.R'
    'LRP.R'
    'Layer_conv1d.R'
    'Layer_conv2d.R'
    'Layer_dense.R'
    'Layer_normalization.R'
    'Layer_other.R'
    'Layer_pooling.R'
    'innsight.R'
    'utils.R'
    'utils_ggplot.R'
    'utils_plotly.R'
    'innsight_sugar.R'
    'innsight_ggplot2.R'
    'innsight_plotly.R'