NeuralSens: Sensitivity Analysis of Neural Networks

Analysis functions to quantify inputs importance in neural network models. Functions are available for calculating and plotting the inputs importance and obtaining the activation function of each neuron layer and its derivatives. The importance of a given input is defined as the distribution of the derivatives of the output with respect to that input in each training data point.

Version: 0.0.5
Imports: ggplot2, gridExtra, NeuralNetTools, reshape2, caret, fastDummies, stringr
Suggests: h2o, neural, RSNNS, nnet, neuralnet
Published: 2019-07-11
Author: José Portela González [aut], Antonio Muñoz San Roque [aut], Jaime Pizarroso Gonzalo [ctb, cre]
Maintainer: Jaime Pizarroso Gonzalo <jpizarroso at alu.comillas.edu>
BugReports: https://github.com/JaiPizGon/NeuralSens/issues
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
URL: https://github.com/JaiPizGon/NeuralSens
NeedsCompilation: no
CRAN checks: NeuralSens results

Downloads:

Reference manual: NeuralSens.pdf
Package source: NeuralSens_0.0.5.tar.gz
Windows binaries: r-devel: NeuralSens_0.0.3.zip, r-release: NeuralSens_0.0.3.zip, r-oldrel: NeuralSens_0.0.5.zip
OS X binaries: r-release: NeuralSens_0.0.5.tgz, r-oldrel: NeuralSens_0.0.5.tgz
Old sources: NeuralSens archive

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