Provides a general framework to perform statistical inference of each gene pair 
        and global inference of whole-scale gene pairs in gene networks using the well known 
        Gaussian graphical model (GGM) in a time-efficient manner. We focus on the high-dimensional 
        settings where p (the number of genes) is allowed to be far larger than n (the number of subjects). 
        Four main approaches are supported in this package: (1) the bivariate nodewise scaled Lasso 
        (Ren et al (2015) <doi:10.1214/14-AOS1286>) (2) the de-sparsified nodewise scaled Lasso 
        (Jankova and van de Geer (2017) <doi:10.1007/s11749-016-0503-5>) (3) the de-sparsified 
        graphical Lasso (Jankova and van de Geer (2015) <doi:10.1214/15-EJS1031>) (4) the GGM 
        estimation with false discovery rate control (FDR) using scaled Lasso or Lasso 
        (Liu (2013) <doi:10.1214/13-AOS1169>). Windows users should install 'Rtools' before the 
        installation of this package.

Generate common data forms for complex data suitable for conversions and
 transmission by decomposition as paths or primitives. Paths are sequentially-linked records, 
 primitives are basic atomic elements and both can model many forms and be grouped into hierarchical 
 structures.  The universal models 'SC0' (structural) and 'SC' (labelled, relational) are composed of 
 edges and can represent any hierarchical form. Specialist models 'PATH', 'ARC' and 'TRI' provide the 
 most common intermediate forms used for converting from one form to another. The methods are
 inspired by the simplicial complex <https://en.wikipedia.org/wiki/Simplicial_complex> and 
 provide intermediate forms that relate spatial data structures to this mathematical construct.

Implements a simple, novel clustering algorithm based on
  optimizing the silhouette width. See <doi:10.1101/2023.11.07.566055>
  for details.

Simultaneous inference procedures for high-dimensional linear models as described by Zhang, X., and Cheng, G. (2017) <doi:10.1080/01621459.2016.1166114>.