Implements network analysis and graph theory measures used in neuroscience, cognitive science, and psychology. Methods include various filtering methods and approaches such as threshold, dependency (Kenett, Tumminello, Madi, Gur-Gershgoren, Mantegna, & Ben-Jacob, 2010 <doi:10.1371/journal.pone.0015032>), Information Filtering Networks (Barfuss, Massara, Di Matteo, & Aste, 2016 <doi:10.1103/PhysRevE.94.062306>), and Efficiency-Cost Optimization (Fallani, Latora, & Chavez, 2017 <doi:10.1371/journal.pcbi.1005305>). Brain methods include the recently developed Connectome Predictive Modeling (see references in package). Also implements several network measures including local network characteristics (e.g., centrality), community-level network characteristics (e.g., community centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.
Matrix, psych, corrplot, fdrtool, R.matlab, igraph, qgraph,
ppcor, parallel, foreach, doParallel, MASS, pwr, IsingFit,
pbapply

psych, corrplot, fdrtool, R.matlab, igraph, qgraph, ppcor,
parallel, foreach, doParallel, MASS, pwr, IsingFit, pbapply

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.
ggplot2, neuralnet, nnet, reshape2, RSNNS, scales
person(given = "Marcus W.", family = "Beck",
role = c("aut","cre"),
email = "mbafs2012@gmail.com")

ggplot2 (>= 2.0.0), nnet, reshape2, scales, tidyr

person(given = "Marcus W.", family = "Beck",
role = c("aut","cre"),
email = "marcusb@sccwrp.org")

caret, neuralnet, RSNNS, knitr, rmarkdown

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.
José Portela González [aut],
Jaime Pizarroso Gonzalo [ctb, cre]
ggplot2, gridExtra, NeuralNetTools, reshape2, caret,
fastDummies, stringr
c(
person(given = "José", family = "Portela González",
email = "Jose.Portela@iit.comillas.edu", role = "aut"),
person(given = "Jaime", family = "Pizarroso Gonzalo",
email = "jpizarroso@alu.comillas.edu", role = c("ctb", "cre"))
)