End-to-End Sparse Gaussian Graphical Model Simulation, Estimation, Visualization, Evaluation and Application

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Documentation for package ‘JointNets’ version 2.0.1

• DESCRIPTION file.
• Code demos. Use demo() to run them.

Help Pages

aal116coordinates	AAL116 brain atlas coordinates in MNI space
ABIDE_aal116_timeseries	ABIDE I preprocessed time series grouped by control and autism and partitioned by AAL116 atlas
add_name_to_out	helper function to add row/col names to JointNets precision matrix output To help label igraph object in returngraph and plot
AUC	return AUC score for JointNets method
BIC	calculate BIC score for JointNets method
cancer	Microarray data set for breast cancer
compute_cov	helper function to add compute covariance matrix / kendall tau correlation matrix
diffee	Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model
dimension_reduce	reduce the dimensionality of the datalist if needed
exampleData	A simulated toy dataset that includes 2 data matrices (from 2 related tasks).
exampleDataGraph	A simulated toy dataset that includes 3 igraph objects
F1	Compute F1 score for JointNets result
F1.diffee	computes F1 score for jointnet result
F1.jeek	computes F1 score for jointnet result
F1.kdiffnet	computes F1 score for jointnet result
F1.simule	computes F1 score for jointnet result
F1.wsimule	computes F1 score for jointnet result
generateSampleList	function to generate a list of samples from simulatedGraph result
generateSamples	function to generate samples from a single precision matrix
jeek	A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models
jgl	wrapper for function JGL fromo package "JGL"
jointplot	core function to plot
kdiffnet	Fast and Scalable Estimator for Using Additional Knowledge in Learning Sparse Structure Change of High Dimensional of Sparse Changes in High-Dimensional Gaussian Graphical Models
nip_37_data	NIPS word count dataset
plot.diffee	plot diffee result specified by user input
plot.jeek	Plot jeek result specified by user input
plot.jgl	Plot jgl result specified by user input
plot.kdiffnet	plot kdiffnet result specified by user input
plot.simulation	Plot simulatedgraph result (generated from function simulation()) (class simulation)
plot.simule	Plot simule result specified by user input
plot.wsimule	Plot wsimule result specified by user input
plot_brain	plot 3d brain network from JointNets result
plot_brain.diffee	plot 3d brain network from diffee result
plot_brain.jeek	plot 3d brain network from jeek result
plot_brain.jgl	plot 3d brain network from jgl result
plot_brain.kdiffnet	plot 3d brain network from kdiffnet result
plot_brain.simule	plot 3d brain network from simule result
plot_brain.wsimule	plot 3d brain network from wsimule result
plot_brain_joint	plot 3d brain network
plot_gui	GUI of JointNets plot
QDA_eval	graphical model model evaluation using QDA as a classifier
returngraph	return igraph object from jointnet result specified by user input
returngraph.diffee	return igraph object from diffee result specified by user input
returngraph.jeek	return igraph object from jeek result specified by user input
returngraph.jgl	return igraph object from jgl result specified by user input
returngraph.kdiffnet	return igraph object from kdiffnet result specified by user input
returngraph.simulation	return igraph object from simulation result specified by user input
returngraph.simule	return igraph object from simule result specified by user input
returngraph.wsimule	return igraph object from wsimule result specified by user input
simulateGraph	function to simulate multiple sparse graphs
simulation	simulate multiple sparse graphs and generate samples
simule	A constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models Estimate multiple, related sparse Gaussian or Nonparanormal graphical
train_valid_test_split	split a datalist to train,validation and test
wsimule	A constrained and weighted l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models