Package: NeuralNetTools 1.5.3
NeuralNetTools: Visualization and Analysis Tools for Neural Networks
Visualization and analysis tools to aid in the interpretation of neural network models. Functions are available for plotting, quantifying variable importance, conducting a sensitivity analysis, and obtaining a simple list of model weights.
Authors:
NeuralNetTools_1.5.3.tar.gz
NeuralNetTools_1.5.3.zip(r-4.5)NeuralNetTools_1.5.3.zip(r-4.4)NeuralNetTools_1.5.3.zip(r-4.3)
NeuralNetTools_1.5.3.tgz(r-4.4-any)NeuralNetTools_1.5.3.tgz(r-4.3-any)
NeuralNetTools_1.5.3.tar.gz(r-4.5-noble)NeuralNetTools_1.5.3.tar.gz(r-4.4-noble)
NeuralNetTools_1.5.3.tgz(r-4.4-emscripten)NeuralNetTools_1.5.3.tgz(r-4.3-emscripten)
NeuralNetTools.pdf |NeuralNetTools.html✨
NeuralNetTools/json (API)
# Install 'NeuralNetTools' in R: |
install.packages('NeuralNetTools', repos = c('https://fawda123.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/fawda123/neuralnettools/issues
- neuraldat - Simulated dataset for function examples
Last updated 3 years agofrom:4fd777d025. Checks:OK: 7. Indexed: yes.
Target | Result | Date |
---|---|---|
Doc / Vignettes | OK | Aug 27 2024 |
R-4.5-win | OK | Aug 27 2024 |
R-4.5-linux | OK | Aug 27 2024 |
R-4.4-win | OK | Aug 27 2024 |
R-4.4-mac | OK | Aug 27 2024 |
R-4.3-win | OK | Aug 27 2024 |
R-4.3-mac | OK | Aug 27 2024 |
Exports:garsonlekgrpslekprofileneuralskipsneuralweightsoldenplotnetpred_sens
Dependencies:clicolorspacecpp11dplyrfansifarvergenericsggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmennetpillarpkgconfigplyrpurrrR6RColorBrewerRcppreshape2rlangscalesstringistringrtibbletidyrtidyselectutf8vctrsviridisLitewithr
Readme and manuals
Help Manual
Help page | Topics |
---|---|
Plot connection weights for bias lines | bias_lines |
Plot bias points | bias_points |
Variable importance using Garson's algorithm | garson garson.default garson.mlp garson.nn garson.nnet garson.numeric garson.train |
Get y locations for layers in 'plotnet' | get_ys |
Plot connection weights | layer_lines |
Plot neural network nodes | layer_points |
Create optional barplot for 'lekprofile' groups | lekgrps |
Sensitivity analysis using Lek's profile method | lekprofile lekprofile.default lekprofile.mlp lekprofile.nn lekprofile.nnet lekprofile.train |
Simulated dataset for function examples | neuraldat |
Get weights for the skip layer in a neural network | neuralskips neuralskips.nnet |
Get weights for a neural network | neuralweights neuralweights.mlp neuralweights.nn neuralweights.nnet neuralweights.numeric |
Variable importance using connection weights | olden olden.default olden.mlp olden.nn olden.nnet olden.numeric olden.train |
Plot a neural network model | plotnet plotnet.default plotnet.mlp plotnet.nn plotnet.nnet plotnet.numeric plotnet.train |
Predicted values for Lek profile method | pred_sens |