Graphical model is an informative and powerful tool to explore the conditional dependence relationships among variables. The traditional Gaussian graphical model and its extensions either have a Gaussian assumption on the data distribution or assume the data are homogeneous. However, there are data with complex distributions violating these two assumptions. For example, the air pollutant concentration records are non-negative and, hence, non-Gaussian. Moreover, due to climate changes, distributions of these concentration records in different months of a year can be far different, which means it is uncertain whether datasets from different months are homogeneous. Methods with a Gaussian or homogeneous assumption may incorrectly model the conditional dependence relationships among variables. Therefore, we propose a heterogeneous graphical model for non-negative data (HGMND) to simultaneously cluster multiple datasets and estimate the conditional dependence matrix of variables from a non-Gaussian and non-negative exponential family in each cluster.

A set of routines to quickly download and import the
    HUGO Gene Nomenclature Committee (HGNC) data set on mapping of
    gene symbols to gene entries in other genomic databases or resources.

Contains functions for
 identifying and correcting HGNC human gene symbols and MGI mouse gene symbols 
 which have been converted to date format by Excel, withdrawn, or aliased.
 Also contains functions for reversibly converting between HGNC
 symbols and valid R names.

It is used to travel graphs, by using DFS and BFS to get the path from node to each leaf node.
  Depth first traversal(DFS) is a recursive algorithm for searching all the vertices of a graph or tree data structure.
  Traversal means visiting all the nodes of a graph.
  Breadth first traversal(BFS) algorithm is used to search a tree or graph data structure for a node that meets a set of criteria.
  It starts at the tree’s root or graph and searches/visits all nodes at the current depth level before moving on to the nodes at the next depth level.
  Also, it provides the matrix which is reachable between each node.
  Implement reference about Baruch Awerbuch (1985) <doi:10.1016/0020-0190(85)90083-3>.