A set of routines to quickly download and import the 'HGNC'
    data set on mapping of gene symbols to gene entries in other popular
    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 which have been converted to date format by Excel, withdrawn, or aliased.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>.

Estimation of high-dimensional multi-response regression with heterogeneous noises under Heterogeneous group square-root Lasso penalty. For details see: Ren, Z., Kang, Y., Fan, Y. and Lv, J. (2018)<arXiv:1606.03803>.

A handy collection of utility functions designed to aid in
    package development, plotting and scientific research.  Package
    development functionalities includes among others tools such as
    cross-referencing package imports with the description file, analysis
    of redundant package imports, editing of the description file and the
    creation of package badges for GitHub.  Some of the other
    functionalities include automatic package installation and loading,
    plotting points without overlap, creating nice breaks for plots,
    overview tables and many more handy utility functions.

This model divides coefficients into three types,
        i.e., local fixed effects, global fixed effects, and random effects (Hu et al., 2022)<doi:10.1177/23998083211063885>.
        If data have spatial hierarchical structures (especially are overlapping on some locations),
        it is worth trying this model to reach better fitness.