Title:	Methods and Measures for Brain, Cognitive, and Psychometric Network Analysis
Version:	1.4.4
Date:	2025-05-08
Maintainer:	Alexander Christensen <alexpaulchristensen@gmail.com>
Description:	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.
Depends:	R (≥ 3.6.0)
License:	GPL (≥ 3.0)
Encoding:	UTF-8
LazyData:	true
Imports:	corrplot, doParallel, fdrtool, foreach, igraph, IsingFit, MASS, methods, parallel, pbapply, ppcor, psych, pwr, R.matlab, qgraph
Suggests:	googledrive
RoxygenNote:	7.3.2
NeedsCompilation:	no
Packaged:	2025-05-08 15:54:17 UTC; alextops
Author:	Alexander Christensen [aut, cre], Guido Previde Massara [ctb]
Repository:	CRAN
Date/Publication:	2025-05-08 16:40:02 UTC

NetworkToolbox–package

Description

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, Information Filtering Networks, and Efficiency-Cost Optimization. Brain methods include the recently developed Connectome Predictive Modeling. Also implements several network measures including local network characteristics (e.g., centrality), global network characteristics (e.g., clustering coefficient), and various other measures associated with the reliability and reproducibility of network analysis.

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Christensen, A. P. (in press). NetworkToolbox: Methods and measures for brain, cognitive, and psychometric network analysis in R. The R Journal, 10, 422-439.

ECO Neural Network Filter

Description

Applies the ECO neural network filtering method

Usage

ECO(data, directed = FALSE)

Arguments

Value

A sparse association matrix

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Fallani, F. D. V., Latora, V., & Chavez, M. (2017). A topological criterion for filtering information in complex brain networks. PLoS Computational Biology, 13, e1005305.

Examples

eco.net <- ECO(neoOpen)

ECO+MaST Network Filter

Description

Applies the ECO neural network filtering method combined with the MaST filtering method

Usage

ECOplusMaST(data)

Arguments

Value

A sparse association matrix

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Fallani, F. D. V., Latora, V., & Chavez, M. (2017). A topological criterion for filtering information in complex brain networks. PLoS Computational Biology, 13, e1005305.

Examples

# half the variables for CRAN checks
ECOplusMaST.net <- ECOplusMaST(neoOpen[,c(1:24)])

Local/Global Inversion Method

Description

Applies the Local/Global method to estimate a Gaussian Graphical Model (GGM) using a TMFG-filtered network (see and cite Barfuss et al., 2016). Also used to convert clique and separator structure from MFCF into partial correlation and precision matrices

Usage

LoGo(
  data,
  cliques,
  separators,
  normal = TRUE,
  na.data = c("pairwise", "listwise", "fiml", "none"),
  partial = TRUE,
  ...
)

Arguments

Value

Returns the sparse LoGo-filtered inverse covariance matrix (partial = FALSE) or LoGo-filtered partial correlation matrix (partial = TRUE)

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Barfuss, W., Massara, G. P., Di Matteo, T., & Aste, T. (2016). Parsimonious modeling with information filtering networks. Physical Review E, 94, 062306.

Examples

# normal set to FALSE for CRAN tests
LoGonet <- LoGo(neoOpen, normal = FALSE, partial = TRUE)

Maximally Filtered Clique Forest

Description

Applies the Maximally Filtered Clique Forest (MFCF) filtering method (Please see and cite Massara & Aste).

Usage

MFCF(
  data,
  cases = NULL,
  na.data = c("pairwise", "listwise", "fiml", "none"),
  time.series = FALSE,
  gain.fxn = c("logLik", "logLik.val", "rSquared.val"),
  min_size = 0,
  max_size = 8,
  pval = 0.05,
  pen = 0,
  drop_sep = FALSE,
  use_returns = FALSE
)

Arguments

Value

Returns a list containing:

Author(s)

Guido Previde Massara <gprevide@gmail.com> and Alexander Christensen <alexpaulchristensen@gmail.com>

References

Massara, G. P. & Aste, T. (2019). Learning clique forests. ArXiv.

Examples

# Load data
data <- neoOpen

## Not run: 
# Use polychoric correlations and R-squared method
MFCF.net <- MFCF(qgraph::cor_auto(data), cases = nrow(neoOpen))$A


## End(Not run)

Maximum Spanning Tree

Description

Applies the Maximum Spanning Tree (MaST) filtering method

Usage

MaST(
  data,
  normal = TRUE,
  na.data = c("pairwise", "listwise", "fiml", "none"),
  depend = FALSE
)

Arguments

Value

A sparse association matrix

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Pearson's correlation only for CRAN checks
MaST.net <- MaST(neoOpen, normal = FALSE)

Triangulated Maximally Filtered Graph

Description

Applies the Triangulated Maximally Filtered Graph (TMFG) filtering method (Please see and cite Massara et al., 2016). The TMFG method uses a structural constraint that limits the number of zero-order correlations included in the network (3n - 6; where n is the number of variables). The TMFG algorithm begins by identifying four variables which have the largest sum of correlations to all other variables. Then, it iteratively adds each variable with the largest sum of three correlations to nodes already in the network until all variables have been added to the network. This structure can be associated with the inverse correlation matrix (i.e., precision matrix) to be turned into a GGM (i.e., partial correlation network) by using LoGo. See Details for more information on this network estimation method.

Usage

TMFG(
  data,
  normal = TRUE,
  na.data = c("pairwise", "listwise", "fiml", "none"),
  depend = FALSE
)

Arguments

Details

The TMFG method applies a structural constraint on the network, which restrains the network to retain a certain number of edges (3n-6, where n is the number of nodes; Massara et al., 2016). The network is also composed of 3- and 4-node cliques (i.e., sets of connected nodes; a triangle and tetrahedron, respectively). The TMFG method constructs a network using zero-order correlations and the resulting network can be associated with the inverse covariance matrix (yielding a GGM; Barfuss, Massara, Di Matteo, & Aste, 2016). Notably, the TMFG can use any association measure and thus does not assume the data is multivariate normal.

Construction begins by forming a tetrahedron of the four nodes that have the highest sum of correlations that are greater than the average correlation in the correlation matrix. Next, the algorithm iteratively identifies the node that maximizes its sum of correlations to a connected set of three nodes (triangles) already included in the network and then adds that node to the network. The process is completed once every node is connected in the network. In this process, the network automatically generates what’s called a planar network. A planar network is a network that could be drawn on a sphere with no edges crossing (often, however, the networks are depicted with edges crossing; Tumminello, Aste, Di Matteo, & Mantegna, 2005).

Value

Returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Christensen, A. P., Kenett, Y. N., Aste, T., Silvia, P. J., & Kwapil, T. R. (2018). Network structure of the Wisconsin Schizotypy Scales-Short Forms: Examining psychometric network filtering approaches. Behavior Research Methods, 50, 2531-2550.

Massara, G. P., Di Matteo, T., & Aste, T. (2016). Network filtering for big data: Triangulated maximally filtered graph. Journal of Complex Networks, 5, 161-178.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

Adaptive Alpha

Description

Compute an alpha value adjusted for sample size. The adjusted value is based on Perez and Pericchi's (2014) formula (equation 11, see below) using a reference sample, which can be defined a priori or estimated using the sample size calculation from power.

\frac{\alpha * \sqrt{n_0 \times (log(n_0) + \chi^{2}_{\alpha}(1))}}{\sqrt{n^* \times (log(n^*) + \chi^{2}_{\alpha}(1))}}

Usage

adapt.a(
  test = c("anova", "chisq", "cor", "one.sample", "two.sample", "paired"),
  ref.n = NULL,
  n = NULL,
  alpha = 0.05,
  power = 0.8,
  efxize = c("small", "medium", "large"),
  groups = NULL,
  df = NULL
)

Arguments

Value

A list containing the following objects:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Cohen, J. (1992). A power primer. Psychological Bulletin, 112, 155-159.

Perez, M. E., & Pericchi, L. R. (2014). Changing statistical significance with the amount of information: The adaptive a significance level. Statistics & Probability Letters, 85, 20-24.

Examples

#ANOVA
adapt.anova <- adapt.a(test = "anova", n = 200, alpha = .05, power = .80, groups = 3)

#Chi-square
adapt.chisq <- adapt.a(test = "chisq", n = 200, alpha = .05, power = .80, df = 3)

#Correlation
adapt.cor <- adapt.a(test = "cor", n = 200, alpha = .05, power = .80)

#One-sample t-test
adapt.one <- adapt.a(test = "one.sample", n = 200, alpha = .05, power = .80)

#Two-sample t-test
adapt.two <- adapt.a(test = "two.sample", n = 200, alpha = .05, power = .80)

#Paired sample t-test
adapt.paired <- adapt.a(test = "paired", n = 200, alpha = .05, power = .80, efxize = "medium")

NEO-PI-3 for Resting-state Data

Description

NEO-PI-3 Openness to Experience associated with resting-state data (n = 144).

Usage

data(behavOpen)

Format

behavOpen (vector, length = 144)

Details

Behavioral data of NEO-PI-3 associated with each connectivity matrix (open).

To access the resting-state brain data, please go to https://drive.google.com/file/d/1T7_mComB6HPxJxZZwwsLLSYHXsOuvOBt/view?usp=sharing

References

Beaty, R. E., Chen, Q., Christensen, A. P., Qiu, J., Silvia, P. J., & Schacter, D. L. (2018). Brain networks of the imaginative mind: Dynamic functional connectivity of default and cognitive control networks relates to Openness to Experience. Human Brain Mapping, 39, 811-821.

Beaty, R. E., Kenett, Y. N., Christensen, A. P., Rosenberg, M. D., Benedek, M., Chen, Q., ... & Silvia, P. J. (2018). Robust prediction of individual creative ability from brain functional connectivity. Proceedings of the National Academy of Sciences, 201713532.

Examples

data("behavOpen")

Betweenness Centrality

Description

Computes betweenness centrality of each node in a network

Usage

betweenness(A, weighted = TRUE)

Arguments

Value

A vector of betweenness centrality values for each node in the network

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

#Weighted BC
BCw <- betweenness(A)

#Unweighted BC
BC <- betweenness(A, weighted = FALSE)

Binarize Network

Description

Converts weighted adjacency matrix to a binarized adjacency matrix

Usage

binarize(A)

Arguments

Value

Returns an adjacency matrix of 1's and 0's

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

neoB <- binarize(A)

Closeness Centrality

Description

Computes closeness centrality of each node in a network

Usage

closeness(A, weighted = TRUE)

Arguments

Value

A vector of closeness centrality values for each node in the network

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

#Weighted LC
LC <- closeness(A)

#Unweighted LC
LC <- closeness(A, weighted = FALSE)

Clustering Coefficient

Description

Computes global clustering coefficient (CC) and local clustering coefficient (CCi)

Usage

clustcoeff(A, weighted = FALSE)

Arguments

Value

Returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

#Unweighted CC
CCu <- clustcoeff(A)

#Weighted CC
CCw <- clustcoeff(A, weighted=TRUE)

Communicating Nodes

Description

Computes the between-community strength for each node in the network

Usage

comcat(
  A,
  comm = c("walktrap", "louvain"),
  cent = c("strength", "degree"),
  absolute = TRUE,
  metric = c("across", "each"),
  diagonal = 0,
  ...
)

Arguments

Value

A vector containing the between-community strength value for each node

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Blanken, T. F., Deserno, M. K., Dalege, J., Borsboom, D., Blanken, P., Kerkhof, G. A., & Cramer, A. O. (2018). The role of stabilizing and communicating symptoms given overlapping communities in psychopathology networks. Scientific Reports, 8, 5854.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

communicating <- comcat(A, comm = "walktrap", cent = "strength", metric = "across")

Community Closeness Centrality

Description

Computes the community closeness centrality measure of each community in a network

Usage

comm.close(A, comm, weighted = FALSE)

Arguments

Value

A vector of community closeness centrality values for each specified community in the network (larger values suggest more central positioning)

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Christensen, A. P. (in press). NetworkToolbox: Methods and measures for brain, cognitive, and psychometric network analysis in R. The R Journal, 10, 422-439.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

comm <- igraph::walktrap.community(convert2igraph(abs(A)))$membership

#Weighted
result <- comm.close(A, comm)

#Unweighted
result <- comm.close(A, comm, weighted = FALSE)

Community Eigenvector Centrality

Description

Computes the flow.frac for each community in the network. The values are equivalent to the community's eigenvector centrality

Usage

comm.eigen(A, comm, weighted = TRUE)

Arguments

Value

A vector of community eigenvector centrality values for each specified community in the network (larger values suggest more central positioning)

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Giscard, P. L., & Wilson, R. C. (2018). A centrality measure for cycles and subgraphs II. Applied Network Science, 3, 9.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

comm <- igraph::walktrap.community(convert2igraph(abs(A)))$membership

result <- comm.eigen(A, comm)

Community Strength/Degree Centrality

Description

Computes the community strength/degree centrality measure of each community in a network or computes the strength/degree centrality measure of each community's connections to the other communities

Usage

comm.str(A, comm, weighted = TRUE, measure = c("within", "between"))

Arguments

Value

A vector of community strength/degree centrality values for each specified community in the network (larger values suggest more central positioning)

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

comm <- igraph::walktrap.community(convert2igraph(abs(A)))$membership

#Strength
within.ns <- comm.str(A, comm, measure = "within")
between.ns <- comm.str(A, comm, measure = "between")

#Degree
within.deg <- comm.str(A, comm, weighted = FALSE, measure = "within")
between.deg <- comm.str(A, comm, weighted = FALSE, measure = "between")

Network Connectivity

Description

Computes the average and standard deviation of the weights in the network

Usage

conn(A)

Arguments

Value

Returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

connectivity <- conn(A)

Convert Network(s) to igraph's Format

Description

Converts single or multiple networks into igraph's format for network analysis

Usage

convert2igraph(A, neural = FALSE)

Arguments

Value

Returns a network matrix in igraph's format or returns a list of brain connectivity matrices each of which have been convert to igraph's format

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

igraphNetwork <- convert2igraph(A)

## Not run: 
neuralarray <- convertConnBrainMat()

igraphNeuralList <- convert2igraph(neuralarray, neural = TRUE)

## End(Not run)

Import CONN Toolbox Brain Matrices to R format

Description

Converts a Matlab brain z-score connectivity array (n x n x m) where n is the n x n connectivity matrices and m is the participant. If you would like to simply import a connectivity array from Matlab, then see the examples

Usage

convertConnBrainMat(MatlabData, progBar = TRUE)

Arguments

Value

Returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

## Not run: 
neuralarray <- convertConnBrainMat()

#Import correlation connectivity array from Matlab
library(R.matlab)
neuralarray<-readMat(file.choose())

## End(Not run)

Convert Correlation Matrix to Covariance Matrix

Description

Converts a correlation matrix to a covariance matrix

Usage

cor2cov(cormat, data)

Arguments

Value

Returns a covariance matrix

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

cormat <- cor(neoOpen)

covmat <- cor2cov(cormat,neoOpen)

Core Items

Description

Automatically determines core, intermediary, and peripheral items in the network. The entire network or within-community gradations can be determined. Based on the hybrid centrality

Usage

core.items(A, comm, by = c("network", "communities"))

Arguments

Value

Returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

#network
# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

#core items by network
coreBYnetwork <- core.items(A, by = "network")

#theoretical factors
comm <- c(rep(1,8),rep(2,8),rep(3,8),rep(4,8),rep(5,8),rep(6,8))

#core items by communities
coreBYcomm <- core.items(A, comm, by = "communities")

Connectome-based Predictive Modeling

Description

Suite of functions for Connectome-based Predictive Modeling (CPM). See and cite Finn et al., 2015; Rosenberg et al., 2016; Shen et al., 2017

• cpmIV — Internal Validation method (Rosenberg et al., 2016; Shen et al., 2017). Using a leave-one-out approach, this method correlates a behavioral statistic bstat with each edge of a whole-brain network across participants. Using the significant edges in the network thresh, a connectome model is built (without the participant's network). A linear regression model is fit, with the behavioral statistic being regressed on the connectome model. The left out participants connectome model is then used with the linear regression weights to compute their predicted behavioral score. This is repeated for every participant. The predicted scores are correlated with their observed score. Significant values suggest that the connectome is related to the behavioral statistic

• cpmIVperm — Performs a permutation test of the results obtained by cpmIV. The permutation test quantifies whether the results obtained by the original cpmIV are significantly different than a random model (see Shen et al., 2017)

• cpmEV —

  UNDER DEVELOPMENT. External Validation method (Beaty et al., 2018). Performs similar function as cpmIV but uses data to train train_na the connectome model using a behavioral statistic train_b. This training connectome model is then used to predict another dataset valid_na, using the same behavioral statistic valid_b. The full training dataset FALSE or the leave-one-out overlap = TRUE approach can be used

• cpmFP — Fingerprinting method (Finn et al., 2015). Uses CPM approach to identify participants across two sessions

• cpmFPperm — Fingerprinting method (Finn et al., 2015). Uses permutation method to estimate the significance of the cpmFP results

• cpmPlot — Plots the CPM results

Usage

cpmIV(neuralarray, bstat, kfolds, covar, thresh = .01,
      connections = c("separate", "overall"), groups = NULL,
      method = c("mean", "sum"), model = c("linear","quadratic","cubic"),
      corr = c("pearson","spearman"), nEdges,
      standardize = FALSE, cores, progBar = TRUE, plots = TRUE)

cpmIVperm(iter = 1000, ...)

cpmEV(train_na, train_b, valid_na, valid_b, thresh = .01,
      overlap = FALSE, progBar = TRUE)

cpmFP(session1, session2, progBar = TRUE)

cpmFPperm(session1, session2, iter = 1000, progBar = TRUE)

cpmPlot(cpm.obj, visual.nets = FALSE)

Arguments

Value

cpmIV and cpmEV:

Returns a list containing:

cpmIVperm:

Returns a matrix containing p-values for positive and negative prediction models

cpmFP:

Returns a matrix containing the percentage and number of correctly identified subjects for sessions 1 and 2

cpmPlot:

Returns plot of connectivity differences between the positive and negative masks

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Beaty, R. E., Kenett, Y. N., Christensen, A. P., Rosenberg, M. D., Benedek, M., Chen, Q., Fink, A., Qiu, J., Kwapil, T. R., Kane, M. J., & Silvia, P. J. (2018). Robust prediction of individual creative ability from brain functional connectivity. Proceedings of the National Academy of Sciences, 115, 1087-1092.

Finn, E. S., Shen, X., Scheinost, D., Rosenberg, M. D., Huang, J., Chun, M. M., Papademetris, X., Constable, R. T. (2015). Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nature Neuroscience, 18, 1664-1671.

Rosenberg, M. D., Finn, E. S., Scheinost, D., Papademetris, X., Shen, X., Constable, R. T., Chun, M. M. (2016). A neuromarker of sustained attention from whole-brain functional connectivity. Nature Neuroscience, 19, 165-171.

Shen, X. Finn, E. S., Scheinost, D., Rosenberg, M. D., Chun, M. M., Papademetris, X., Constable, R. T. (2017). Using connectome-based predictive modeling to predict individual behavior from brain connectivity. Nature Protocols, 12, 506-518.

Wei, T. & Simko, V.(2017). R package "corrplot": Visualization of a correlation matrix (Version 0.84).

Examples

# Load data
behav <- behavOpen

## Not run: 

# Create path to temporary file
temp <- tempfile()

# Download to temporary file
googledrive::drive_download(
paste("https://drive.google.com/file/d/",
"1T7_mComB6HPxJxZZwwsLLSYHXsOuvOBt",
"/view?usp=sharing", sep = ""),
path = temp
)

# Load resting state brain data
load(temp)

# Run cpmIV
res <- cpmIV(neuralarray = restOpen, bstat = behav, cores = 4)

# Plot cpmIV results
cpmPlot(res)


## End(Not run)

Distance Correlation for ROI Time Series

Description

Computes the distance correlation (Yoo et al., 2019) for ROI time series data. This function is mainly a subroutine for the dCor.parallel function

Usage

dCor(neurallist, centering = c("U", "double"))

Arguments

Value

Returns a m x m matrix corresponding to distance correlations between ROIs

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Yoo, K., Rosenberg, M. D., Noble, S., Scheinost, D., Constable, R. T., & Chun, M. M. (2019). Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors. NeuroImage, 197, 212-223.

Examples

## Not run: 
# Import time series data
neurallist <- convertConnBrainMat()

# Run distance correlation
dCor(neurallist)


## End(Not run)

Parallelization of Distance Correlation for ROI Time Series

Description

Parallelizes the dCor function for faster computation times

Usage

dCor.parallel(neurallist, cores)

Arguments

Value

Returns a m x m x n array corresponding to distance correlations between ROIs (m x m matrix) for n participants

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Yoo, K., Rosenberg, M. D., Noble, S., Scheinost, D., Constable, R. T., & Chun, M. M. (2019). Multivariate approaches improve the reliability and validity of functional connectivity and prediction of individual behaviors. NeuroImage, 197, 212-223.

Examples

## Not run: 
# Import time series data 
for(i in 1:5)

# Run distance correlation
dCor.parallel(mat.list, cores = 2)


## End(Not run)

Degree

Description

Computes degree of each node in a network

Usage

degree(A)

Arguments

Value

A vector of degree values for each node in the network.

If directed network, returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

#Undirected network
## Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

deg <- degree(A)

#Directed network
## Not run: 
dep <- depend(neoOpen)

Adep <- TMFG(dep, depend = TRUE)$A

deg <- degree(Adep)

## End(Not run)

Dependency Network Approach

Description

Generates a dependency matrix of the data (index argument is still in testing phase)

Usage

depend(
  data,
  normal = FALSE,
  na.data = c("pairwise", "listwise", "fiml", "none"),
  index = FALSE,
  fisher = FALSE,
  progBar = TRUE
)

Arguments

Value

Returns an adjacency matrix of dependencies

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Kenett, D. Y., Tumminello, M., Madi, A., Gur-Gershgoren, G., Mantegna, R. N., & Ben-Jacob, E. (2010). Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market. PLoS one, 5, e15032.

Kenett, D. Y., Huang, X., Vodenska, I., Havlin, S., & Stanley, H. E. (2015). Partial correlation analysis: Applications for financial markets. Quantitative Finance, 15, 569-578.

Examples

## Not run: 
D <- depend(neoOpen)

Dindex <- depend(neoOpen, index = TRUE)

## End(Not run)

Dependency Neural Networks

Description

Applies the dependency network approach to neural network array

Usage

depna(neuralarray, cores, ...)

Arguments

Value

Returns an array of n x n x m dependency matrices

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Jacob, Y., Winetraub, Y., Raz, G., Ben-Simon, E., Okon-Singer, H., Rosenberg-Katz, K., ... & Ben-Jacob, E. (2016). Dependency Network Analysis (DEPNA) reveals context related influence of brain network nodes. Scientific Reports, 6, 27444.

Kenett, D. Y., Tumminello, M., Madi, A., Gur-Gershgoren, G., Mantegna, R. N., & Ben-Jacob, E. (2010). Dominating clasp of the financial sector revealed by partial correlation analysis of the stock market. PLoS one, 5, e15032.

Examples

## Not run: 
neuralarray <- convertConnBrainMat()

dependencyneuralarray <- depna(neuralarray)

## End(Not run)

Variable Descriptive Statistics

Description

Computes mean, standard deviation (sd), minimum value (min), maximum value (max), and univariate normal statistics (normal?) for a variable

Usage

desc(data, column, histplot = TRUE)

Arguments

Value

A data frame containing values for n (number of cases), missing (number of missing cases), mean, sd, min, and max. normal? will contain yes/no for whether the variable is normally distributed based on the shapiro.test for a variable

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

desc(neoOpen,1)

Dataset Descriptive Statistics

Description

Computes mean, standard deviation (sd), minimum value (min), maximum value (max), and univariate normal statistics (normal?) for the entire dataset

Usage

desc.all(data)

Arguments

Value

A data frame containing values for n (number of cases), missing (number of missing cases), mean, sd, min, and max. normal? will contain yes/no for whether the variable is normally distributed based on the shapiro.test for the entire dataset

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

desc.all(neoOpen)

Distance

Description

Computes distance matrix of the network

Usage

distance(A, weighted = FALSE)

Arguments

Value

A distance matrix of the network

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

#Unweighted
Du <- distance(A)

#Weighted
Dw <- distance(A, weighted = TRUE)

Diversity Coefficient

Description

Computes the diversity coefficient for each node. The diversity coefficient measures a node's connections to communitites outside of its own community. Nodes that have many connections to other communities will have higher diversity coefficient values. Positive and negative signed weights for diversity coefficients are computed separately.

Usage

diversity(A, comm = c("walktrap", "louvain"))

Arguments

Details

Values closer to 1 suggest greater between-community connectivity and values closer to 0 suggest greater within-community connectivity

Value

Returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

#theoretical communities
comm <- rep(1:8, each = 6)

gdiv <- diversity(A, comm = comm)

#walktrap communities
wdiv <- diversity(A, comm = "walktrap")

Edge Replication

Description

Computes the number of edges that replicate between two cross-sectional networks

Usage

edgerep(A, B, corr = c("pearson", "spearman", "kendall"))

Arguments

Value

Returns a list containing:

Lists for each network contain:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# normal set to FALSE for CRAN tests
tmfg <- TMFG(neoOpen, normal = FALSE)$A

# normal set to FALSE for CRAN tests
mast <- MaST(neoOpen, normal = FALSE)

edges <- edgerep(tmfg, mast)

Eigenvector Centrality

Description

Computes eigenvector centrality of each node in a network

Usage

eigenvector(A, weighted = TRUE)

Arguments

Value

A vector of eigenvector centrality values for each node in the network

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

#Weighted
EC <- eigenvector(A)

#Unweighted 
EC <- eigenvector(A, weighted = FALSE)

Flow Fraction

Description

Computes eigenvector centrality over nodes in a subset of nodes in the network. This measure generalizes across any subset of nodes and is not specific to communities

Usage

flow.frac(A, nodes)

Arguments

Value

Returns a flow fraction value

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Giscard, P. L., & Wilson, R. C. (2018). A centrality measure for cycles and subgraphs II. Applied Network Science, 3, 9.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

nodes <- seq(1,48,2)

result <- flow.frac(A, nodes)

MFCF Gain Functions

Description

These functions maximize a gain criterion for adding a node to a clique (and the larger network). The flexibility of MFCF allows for any multivariate function to be used as a scoring function.

• "logLik" — The log determinant of the matrix restricted to the separator minus the log determinant of the matrix restricted to the clique.

• "logLik.val" — "logLik" with a further validation based on the likelihood ratio. If the increase in gain is not significant the routine stops adding nodes to the separator.

• "rSquared.val" — The R squared from the regression of the node against the clique. Only the clique nodes with a regression coefficient significantly different from zero are added to the separator / new clique. The gain is different from zero only if the F-values is significant, It assumed that the data matrix is a dataset of realizations (i.e., p variables and n observations).

Usage

"logLik"
gfcnv_logdet(data, clique_id, cl, excl_nodes, ctreeControl)

"logLik.val"
gfcnv_logdet_val(data, clique_id, cl, excl_nodes, ctreeControl)

"rSquared.val"
gdcnv_lmfit(data, clique_id, cl, excl_nodes, ctreeControl)

Arguments

Value

Returns the value with the maximum gain

Author(s)

Guido Previde Massara <gprevide@gmail.com> and Alexander Christensen <alexpaulchristensen@gmail.com>

References

Massara, G. P. & Aste, T. (2019). Learning clique forests. ArXiv.

Gateway Coefficient

Description

Computes the gateway coefficient for each node. The gateway coefficient measures a node's connections between its community and other communities. Nodes that are solely responsible for inter-community connectivity will have higher gateway coefficient values. Positive and negative signed weights for gateway coefficients are computed separately.

Usage

gateway(
  A,
  comm = c("walktrap", "louvain"),
  cent = c("strength", "betweenness")
)

Arguments

Value

Returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Vargas, E. R., & Wahl, L. M. (2014). The gateway coefficient: A novel metric for identifying critical connections in modular networks. The European Physical Journal B, 87, 1-10.

Examples

#theoretical communities
comm <- rep(1:8, each = 6)

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

gw <- gateway(A, comm = comm)

#walktrap communities
wgw <- gateway(A, comm = "walktrap")

Hybrid Centrality

Description

Computes hybrid centrality of each node in a network

Usage

hybrid(A, BC = c("standard", "random"), beta)

Arguments

Value

A vector of hybrid centrality values for each node in the network (higher values are more central, lower values are more peripheral)

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Christensen, A. P., Kenett, Y. N., Aste, T., Silvia, P. J., & Kwapil, T. R. (2018). Network structure of the Wisconsin Schizotypy Scales-Short Forms: Examining psychometric network filtering approaches. Behavior Research Methods, 50, 2531-2550.

Pozzi, F., Di Matteo, T., & Aste, T. (2013). Spread of risk across financial markets: Better to invest in the peripheries. Scientific Reports, 3, 1655.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

HC <- hybrid(A)

Node Impact

Description

Computes impact measure or how much the average distance in the network changes with that node removed of each node in a network (Please see and cite Kenett et al., 2011)

Usage

impact(A)

Arguments

Value

A vector of node impact values for each node in the network (impact > 0, greater ASPL when node is removed; impact < 0, lower ASPL when node is removed)

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Cotter, K. N., Christensen, A. P., & Silvia, P. J. (in press). Understanding inner music: A dimensional approach to musical imagery. Psychology of Aesthetics, Creativity, and the Arts.

Kenett, Y. N., Kenett, D. Y., Ben-Jacob, E., & Faust, M. (2011). Global and local features of semantic networks: Evidence from the Hebrew mental lexicon. PLoS one, 6, e23912.

Examples

# normal set to FALSE for CRAN tests
A <- TMFG(neoOpen, normal = FALSE)$A

nodeimpact <- impact(A)

Determines if Network is Graphical

Description

Tests for whether the network is graphical. Input must be a partial correlation network. Function assumes that partial correlations were computed from a multivariate normal distribution

Usage

is.graphical(A)

Arguments

Value

Returns a TRUE/FALSE for whether network is graphical

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

## Not run: 
A <- LoGo(neoOpen, normal = TRUE, partial = TRUE)

is.graphical(A)

## End(Not run)

Kullback-Leibler Divergence

Description

Estimates the Kullback-Leibler Divergence which measures how one probability distribution diverges from the original distribution (equivalent means are assumed) Matrices must be positive definite inverse covariance matrix for accurate measurement. This is a relative metric

Usage

kld(base, test)

Arguments

Value

A value greater than 0. Smaller values suggest the probability distribution of the reduced model is near the full model

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Kullback, S., & Leibler, R. A. (1951). On information and sufficiency. The Annals of Mathematical Statistics, 22, 79-86.

Examples

A1 <- solve(cov(neoOpen))

## Not run: 
A2 <- LoGo(neoOpen)

kld_value <- kld(A1, A2)

## End(Not run)

Generates a Lattice Network

Description

Generates a lattice network

Usage

lattnet(nodes, edges)

Arguments

Value

Returns an adjacency matrix of a lattice network

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

latt <- lattnet(10, 27)

Leverage Centrality

Description

Computes leverage centrality of each node in a network (the degree of connected neighbors; Please see and cite Joyce et al., 2010)

Usage

leverage(A, weighted = TRUE)

Arguments

Value

A vector of leverage centrality values for each node in the network

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Joyce, K. E., Laurienti, P. J., Burdette, J. H., & Hayasaka, S. (2010). A new measure of centrality for brain networks. PLoS One, 5 e12200.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

#Weighted
levW <- leverage(A)

#Unweighted
levU <- leverage(A, weighted = FALSE)

Louvain Community Detection Algorithm

Description

Computes a vector of communities (community) and a global modularity measure (Q)

Usage

louvain(A, gamma, M0)

Arguments

Value

Returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008, P10008.

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

modularity <- louvain(A)

NEO-PI-3 Openness to Experience Data

Description

A response matrix (n = 802) of NEO-PI-3's Openness to Experience from Christensen, Cotter, & Silvia (2019).

Usage

data(neoOpen)

Format

A 802x48 response matrix

References

Christensen, A. P., Cotter, K. N., & Silvia, P. J. (2019). Reopening openness to experience: A network analysis of four openness to experience inventories. Journal of Personality Assessment, 101, 574-588.

Examples

data("neoOpen")

Network Coverage

Description

Computes the mean distance across a subset of nodes in a network. This measure can be used to identify the effectiveness of a subset of nodes' coverage of the network space

Usage

net.coverage(A, nodes, weighted = FALSE)

Arguments

Value

Returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com> and Mathias Benedek <mathias.benedek@uni-graz.at>

References

Christensen, A. P., Cotter, K. N., Silvia, P. J., & Benedek, M. (2018) Scale development via network analysis: A comprehensive and concise measure of Openness to Experience PsyArXiv, 1-40.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

nodes <- seq(1,48,2)

result <- net.coverage(A, nodes)

Network Coverage

Description

Computes the mean distance across a subset of nodes in a network. This measure can be used to identify the effectiveness of a subset of nodes' coverage of the network space

Usage

network.coverage(A, nodes, weighted = FALSE)

Arguments

Value

Returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com> and Mathias Benedek <mathias.benedek@uni-graz.at>

References

Christensen, A. P., Cotter, K. N., Silvia, P. J., & Benedek, M. (2018) Scale development via network analysis: A comprehensive and concise measure of Openness to Experience PsyArXiv, 1-40.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

nodes <- seq(1,48,2)

result <- network.coverage(A, nodes)

Permutation Test for Network Measures

Description

Computes a permutation test to determine whether there are difference in centrality and global network measures

Usage

network.permutation(
  sample1 = NULL,
  sample2 = NULL,
  iter,
  network = c("glasso", "ising", "TMFG", "LoGo"),
  measure = c("betweenness", "closeness", "strength", "eigenvector", "rspbc", "hybrid",
    "ASPL", "CC", "S", "Q"),
  alternative = c("less", "greater", "two.tailed"),
  ncores,
  prev.perm = NULL,
  ...
)

Arguments

Value

Returns a list containing two objects:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Split data (only for example)
split1 <- neoOpen[c(1:401),]
split2 <- neoOpen[c(402:802),]


# Perform permutation test
perm.str <- network.permutation(split1, split2, iter = 1000, network = "glasso",
measure = "strength", alternative = "two.tailed", ncores = 2)

# Check results
perm.str$result

# Permutation to check other measures (using networks from previous result)
perm.aspl <- network.permutation(prev.perm = perm.str, measure = "ASPL", ncores = 2)

# Check results
perm.aspl$result

Neural Network Filter

Description

Applies a network filtering methodology to neural network array. Removes edges from the neural network output from convertConnBrainMat using a network filtering approach

Usage

neuralnetfilter(
  neuralarray,
  method = c("TMFG", "MaST", "ECOplusMaST", "ECO", "threshold"),
  progBar = TRUE,
  ...
)

Arguments

Value

Returns an array of n x n x m filtered matrices

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

## Not run: neuralarray <- convertConnBrainMat()

filteredneuralarray <- neuralnetfilter(neuralarray, method = "threshold", thresh = .50)

dependencyarray <- depna(neuralarray)

filtereddependencyarray <- neuralnetfilter(dependencyarray, method = "TMFG", depend = TRUE)

## End(Not run)

Four Inventories of Openness to Experience

Description

A response matrix (n = 794) of all four Openness to Experience inventories from Christensen, Cotter, & Silvia (2019). The key provides inventory, facet, and item description information for the item labels. Note that because of NEO's copyrights the items have been shortened and paraphrased

Usage

data(openness)

data(openness.key)

Format

A 794 x 138 response matrix (openness) and 138 x 7 matrix (openness.key). Here are detailed descriptions of the key:

• Inventory — The personality inventory the item belongs to

• Facet — The personality inventory defined facet

• JPA.Domains — The broad domains identified by Christensen, Cotter, and Silvia (2019)

• JPA.Facets — The facets identified by Christensen, Cotter, and Silvia (2019)

• Item.Label — The labels used in Christensen, Cotter, and Silvia (2019)

• Item.Description — Descriptions of each item. Note that the NEO-PI-3 items are protected by copyright and therefore have been paraphrased. These item descriptions do not represent the item as given to the participant

• Reversed — Whether an item should be reversed or not (openness is already reversed)

References

Christensen, A. P., Cotter, K. N., & Silvia, P. J. (2019). Reopening openness to experience: A network analysis of four openness to experience inventories. Journal of Personality Assessment, 101, 574-588.

Examples

# Loading data
data("openness")
data("openness.key")

# Change item labels
colnames(openness) <- openness.key$Item.Description

Participation Coefficient

Description

Computes the participation coefficient for each node. The participation coefficient measures the strength of a node's connections within its community. Positive and negative signed weights for participation coefficients are computed separately.

Usage

participation(A, comm = c("walktrap", "louvain"))

Arguments

Details

Values closer to 0 suggest greater within-community connectivity and values closer to 1 suggest greater between-community connectivity

Value

Returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Guimera, R., & Amaral, L. A. N. (2005). Functional cartography of complex metabolic networks. Nature, 433, 895-900.

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

#theoretical factors
comm <- rep(1:8, each = 6)

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

pc <- participation(A, comm = comm)

# Walktrap factors
wpc <- participation(A, comm = "walktrap")

Characteristic Path Lengths

Description

Computes global average shortest path length, local average shortest path length, eccentricity, and diameter of a network

Usage

pathlengths(A, weighted = FALSE)

Arguments

Value

Returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

#Unweighted
PL <- pathlengths(A)

#Weighted
PL <- pathlengths(A, weighted = TRUE)

Plots CPM results

Description

Plots CPM results

Usage

## S3 method for class 'cpm'
plot(x, ...)

Arguments

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Generates a Random Network

Description

Generates a random binary network

Usage

randnet(nodes = NULL, edges = NULL, A = NULL)

Arguments

Value

Returns an adjacency matrix of a random network

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695.

Examples

rand <- randnet(10, 27)

Regression Matrix

Description

Computes regression such that one variable is regressed over all other variables

Usage

reg(
  data,
  family = c("binomial", "gaussian", "Gamma", "poisson"),
  symmetric = TRUE
)

Arguments

Value

A matrix of fully regressed coefficients where one variable is regressed over all others

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

#binarize responses
psyb <- ifelse(neoOpen>=4, 1, 0)

## Not run: 
#perform logistic regression
mat <- reg(psyb)
## End(Not run)

Repeated Responses Check

Description

Screens data to identify potential cases of repeated responding. The function is based on two criteria: no variance (i.e., a standard deviation of zero for given responses)and frequency proportion of the response values (which is set by freq.prop). Note that these criteria are highly related. Additional criteria will be added in the future.

Usage

resp.rep(data, scale.lens = NULL, max.val, reverse = NULL, freq.prop = 0.8)

Arguments

Details

If a case is returned, then it does not mean that it is a bad case. Researchers should thoroughly inspect each case that is returned. A general guideline is that if a participant responded with all middle values (e.g., all 3's on a 5-point Likert scale), then they should be dropped. Note that a participant who responds with all maximum or minimum values may be a real case or a bad case. It is up to the researcher to decide and justify why or why not a case is kept.

Value

Returns a matrix when scale.lens = NULL and a list with elements corresponding to the order of scales. In general, the output contains potential bad cases that should be further inspected by the researcher.

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

#Re-reverse responses
rev.vec <- c(TRUE,FALSE,TRUE,FALSE,TRUE,TRUE,TRUE,FALSE,TRUE,FALSE,
TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,FALSE,TRUE,
FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,FALSE,TRUE,
FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE)

#Maximum value (5-point Likert scale)
mv.vec <- 5

#Repeated responses check
resp.rep(neoOpen, reverse = rev.vec, max.val = mv.vec)

#Example with multiple scales

#Facet scale lengths of NEO-PI-3 Openness to Experience
s.len <- c(8, 8, 8, 8, 8, 8)

#Maximum values
mv.vec <- c(5, 5, 5, 5, 5, 5)

#Re-reverse responses
rev.vec <- c(TRUE,FALSE,TRUE,FALSE,TRUE,TRUE,TRUE,FALSE,TRUE,FALSE,
TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,FALSE,TRUE,
FALSE,FALSE,TRUE,FALSE,TRUE,FALSE,TRUE,TRUE,FALSE,TRUE,FALSE,TRUE,
FALSE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE,TRUE,FALSE,FALSE,TRUE,FALSE,TRUE)

#Repeated responses check
resp.rep(neoOpen, scale.lens = s.len, max.val = mv.vec, reverse = rev.vec)

Root Mean Square Error

Description

Computes the root mean square error (RMSE) of a sparse model to a full model

Usage

rmse(base, test)

Arguments

Value

RMSE value (lower values suggest more similarity between the full and sparse model)

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

A1 <- solve(cov(neoOpen))

## Not run: 
A2 <- LoGo(neoOpen)

root <- rmse(A1, A2)

## End(Not run)

Randomized Shortest Paths Betweenness Centrality

Description

Computes betweenness centrality based on randomized shortest paths of each node in a network (Please see and cite Kivimaki et al., 2016)

Usage

rspbc(A, beta = 0.01, comm = NULL)

Arguments

Value

A vector of randomized shortest paths betweenness centrality values for each node in the network

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Kivimaki, I., Lebichot, B., Saramaki, J., & Saerens, M. (2016). Two betweenness centrality measures based on Randomized Shortest Paths. Scientific Reports, 6, 19668.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

rspbc <- rspbc(A, beta=0.01)

Simulate Chordal Network

Description

Simulates a chordal network based on number of nodes. Data will also be simulated based on the true network structure

Usage

sim.chordal(
  nodes,
  inverse = c("cases", "matrix"),
  n = NULL,
  ordinal = FALSE,
  ordLevels = NULL,
  idio = NULL,
  eps = NULL
)

Arguments

Value

Returns a list containing:

Author(s)

Guido Previde Massara <gprevide@gmail.com>

References

Massara, G. P. & Aste, T. (2019). Learning clique forests. ArXiv.

Examples

#Continuous data
sim.Norm <- sim.chordal(nodes = 20, inverse = "cases", n = 1000)

#Ordinal data
sim.Likert <- sim.chordal(nodes = 20, inverse = "cases", n = 1000, ordinal = TRUE)

#Dichotomous data
sim.Binary <- sim.chordal(nodes = 20, inverse = "cases", n = 1000, ordinal = TRUE, ordLevels = 5)

Simulate Small-world Network

Description

Simulates a small-world network based on specified topological properties. Data will also be simulated based on the true network structure

Usage

sim.swn(
  nodes,
  n,
  pos = 0.8,
  ran = c(0.3, 0.7),
  nei = 1,
  p = 0.5,
  corr = FALSE,
  replace = NULL,
  ordinal = FALSE,
  ordLevels = NULL
)

Arguments

Value

Returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Csardi, G., & Nepusz, T. (2006). The igraph software package for complex network research. InterJournal, Complex Systems, 1695, 1-9.

Examples

#Continuous data
sim.Norm <- sim.swn(25, 500, nei = 3)

#Ordinal data
sim.Likert <- sim.swn(25, 500, nei = 3, replace = TRUE, ordinal = TRUE, ordLevels = 5)

#Dichotomous data
sim.Binary <- sim.swn(25, 500, nei = 3, replace = TRUE, ordinal = TRUE, ordLevels = 2)

Small-worldness Measure

Description

Computes the small-worldness measure of a network

Usage

smallworldness(
  A,
  iter = 100,
  progBar = FALSE,
  method = c("HG", "rand", "TJHBL")
)

Arguments

Details

For "rand", values > 1 indicate a small-world network. For "HG", values > 3 indicate a small-world network. For "TJHBL", values near 0 indicate a small-world network, while < 0 indicates a more regular network and > 0 indicates a more random network

Value

Returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Humphries, M. D., & Gurney, K. (2008). Network 'small-world-ness': A quantitative method for determining canonical network equivalence. PLoS one, 3, e0002051.

Telesford, Q. K., Joyce, K. E., Hayasaka, S., Burdette, J. H., & Laurienti, P. J. (2011). The ubiquity of small-world networks. Brain Connectivity, 1(5), 367-375.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A


swmHG <- smallworldness(A, method="HG")

swmRand <- smallworldness(A, method="rand")

swmTJHBL <- smallworldness(A, method="TJHBL")

Stabilizing Nodes

Description

Computes the within-community centrality for each node in the network

Usage

stable(
  A,
  comm = c("walktrap", "louvain"),
  cent = c("betweenness", "rspbc", "closeness", "strength", "degree", "hybrid"),
  absolute = TRUE,
  diagonal = 0,
  ...
)

Arguments

Value

A matrix containing the within-community centrality value for each node

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Blanken, T. F., Deserno, M. K., Dalege, J., Borsboom, D., Blanken, P., Kerkhof, G. A., & Cramer, A. O. (2018). The role of stabilizing and communicating symptoms given overlapping communities in psychopathology networks. Scientific Reports, 8, 5854.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

stabilizing <- stable(A, comm = "walktrap")

Node Strength

Description

Computes strength of each node in a network

Usage

strength(A, absolute = TRUE)

Arguments

Value

A vector of strength values for each node in the network.

If directed network, returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52 1059-1069.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

str <- strength(A)

#Directed network
## Not run: 
dep <- depend(neoOpen)

Adep <- TMFG(dep, depend = TRUE)$A

str <- strength(Adep)

## End(Not run)

Threshold Network Estimation Methods

Description

Filters the network based on an r-value, alpha, adaptive alpha, bonferroni, false-discovery rate (FDR), or proportional density (fixed number of edges) value

Usage

threshold(
  data,
  a,
  thresh = c("alpha", "adaptive", "bonferroni", "FDR", "proportional"),
  normal = FALSE,
  na.data = c("pairwise", "listwise", "fiml", "none"),
  ...
)

Arguments

Value

Returns a list containing:

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Strimmer, K. (2008). fdrtool: A versatile R package for estimating local and tail area-based false discovery rates. Bioinformatics, 24, 1461-1462.

Examples

threshnet<-threshold(neoOpen)

alphanet<-threshold(neoOpen, thresh = "alpha", a = .05)

bonnet<-threshold(neoOpen, thresh = "bonferroni", a = .05)

FDRnet<-threshold(neoOpen, thresh = "FDR", a = .10)

propnet<-threshold(neoOpen, thresh = "proportional", a = .15)

Transitivity

Description

Computes transitivity of a network

Usage

transitivity(A, weighted = FALSE)

Arguments

Value

Returns a value of transitivity

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

References

Rubinov, M., & Sporns, O. (2010). Complex network measures of brain connectivity: Uses and interpretations. NeuroImage, 52, 1059-1069.

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

trans <- transitivity(A, weighted=TRUE)

Convert Directed Network to Undirected Network

Description

Converts a directed network to an undirected network

Usage

un.direct(A, diagonal = 0)

Arguments

Value

Returns a symmetric adjacency matrix

Author(s)

Alexander Christensen <alexpaulchristensen@gmail.com>

Examples

# Pearson's correlation only for CRAN checks
A <- TMFG(neoOpen, normal = FALSE)$A

# create a directed network
dir <- A * sample(c(0,1), size = length(A), replace = TRUE)

# undirect the directed network
undir <- un.direct(dir)