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:Marcus W. Beck [aut, cre]

NeuralNetTools_1.5.3.tar.gz
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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

Datasets:

neuraldat - Simulated dataset for function examples

On CRAN:

9.79 score 72 stars 6 packages 524 scripts 4.6k downloads 4 mentions 8 exports 40 dependencies

Last updated 3 years agofrom:4fd777d025. Checks:OK: 7. Indexed: yes.

Target	Result	Date
Doc / Vignettes	OK	Dec 25 2024
R-4.5-win	OK	Dec 25 2024
R-4.5-linux	OK	Dec 25 2024
R-4.4-win	OK	Dec 25 2024
R-4.4-mac	OK	Dec 25 2024
R-4.3-win	OK	Dec 25 2024
R-4.3-mac	OK	Dec 25 2024

Exports:garson lekgrps lekprofile neuralskips neuralweights olden plotnet pred_sens

Dependencies:cli colorspace cpp11 dplyr fansi farver generics ggplot2 glue gtable isoband labeling lattice lifecycle magrittr MASS Matrix mgcv munsell nlme nnet pillar pkgconfig plyr purrr R6 RColorBrewer Rcpp reshape2 rlang scales stringi stringr tibble tidyr tidyselect utf8 vctrs viridisLite withr

Overview

Rendered fromOverview.Rmdusingknitr::rmarkdownon Dec 25 2024.

Last update: 2022-01-06
Started: 2022-01-06

Citation

Development and contributors

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