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

## 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