José Portela González [aut],
Antonio Muñoz San Roque [aut],
Jaime Pizarroso Gonzalo [ctb, cre]
c(
person(given = "José", family = "Portela González",
email = "Jose.Portela@iit.comillas.edu", role = "aut"),
person(given = "Antonio", family = "Muñoz San Roque",
email = "antonio.munoz@iit.comillas.edu", role = "aut"),
person(given = "Jaime", family = "Pizarroso Gonzalo",
email = "jpizarroso@alu.comillas.edu", role = c("ctb", "cre"))
)

ggplot2, gridExtra, NeuralNetTools, reshape2, caret,
fastDummies, stringr, Hmisc

ggplot2, gridExtra, NeuralNetTools, reshape2, caret,
fastDummies, stringr, Hmisc, ggforce
c(
person(given = "José", family = "Portela González",
email = "Jose.Portela@iit.comillas.edu", role = "aut"),
person(given = "Antonio", family = "Muñoz San Roque",
email = "antonio.munoz@iit.comillas.edu", role = "aut"),
person(given = "Jaime", family = "Pizarroso Gonzalo",
email = "jpizarroso@comillas.edu", role = c("ctb", "cre"))
)

ggplot2, gridExtra, NeuralNetTools, reshape2, caret,
fastDummies, stringr, Hmisc, ggforce, scales

José Portela González [aut],
Antonio Muñoz San Roque [aut],
Jaime Pizarroso Gonzalo [aut, ctb, cre]
c(
person(given = "José", family = "Portela González",
email = "Jose.Portela@iit.comillas.edu", role = "aut"),
person(given = "Antonio", family = "Muñoz San Roque",
email = "antonio.munoz@iit.comillas.edu", role = "aut"),
person(given = "Jaime", family = "Pizarroso Gonzalo",
email = "jpizarroso@comillas.edu", role = c("aut","ctb", "cre"))
)

ggplot2, gridExtra, NeuralNetTools, reshape2, caret,
fastDummies, stringr, Hmisc, ggforce, scales, ggnewscale

h2o, neural, RSNNS, nnet, neuralnet, e1071

ggplot2, gridExtra, NeuralNetTools, reshape2, caret,
fastDummies, stringr, Hmisc, ggforce, scales, ggnewscale,
magrittr
h2o, neural, RSNNS, nnet, neuralnet, plotly, e1071

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 <doi:10.18637/jss.v102.i07>.