A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models
A Fast and Scalable Joint Estimator for Learning Multiple Related Spar...
A Fast and Scalable Joint Estimator for Learning Multiple Related Spar...
List the degree of every node of each graph in the input list of multi...
List the edges of each graph in the input list of multiple graphs
Get degrees of the most connected nodes of each graph in the input lis...
Get neighbors of a node in each graph in the input list of multiple gr...
Plotting functions for displaying the list of multiple graphs generate...
This is an R implementation of "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models" (FASJEM). The FASJEM algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogonous data sets, this tool can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(fasjem) to learn the basic functions provided by this package. For more details, please see <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>.