Carry out network-based gene set analysis by incorporating external information about interactions among genes, as well as novel interactions learned from data. Implements methods described in Shojaie A, Michailidis G (2010) <doi:10.1093/biomet/asq038>, Shojaie A, Michailidis G (2009) <doi:10.1089/cmb.2008.0081>, and Ma J, Shojaie A, Michailidis G (2016) <doi:10.1093/bioinformatics/btw410>.

A multi-core R package that contains a set of tools based on copula graphical
             models for accomplishing the three interrelated goals in genetics and genomics in an
			 unified way: (1) linkage map construction, (2) constructing linkage disequilibrium
			 networks, and (3) exploring high-dimensional genotype-phenotype network and genotype-
			 phenotype-environment interactions networks. 
			 The 'netgwas' package can deal with biparental inbreeding and outbreeding species with
			 any ploidy level, namely diploid (2 sets of chromosomes), triploid (3 sets of chromosomes),
			 tetraploid (4 sets of chromosomes) and so on. We target on high-dimensional data where 
			 number of variables p is considerably larger than number of sample sizes (p >> n). 
			 The computations is memory-optimized using the sparse matrix output. The 'netgwas' 
			 implements the methodological developments in Behrouzi and Wit (2017)
             <doi:10.1111/rssc.12287> and Behrouzi and Wit (2017) <doi:10.1093/bioinformatics/bty777>.

The Bayesian hierarchical model named antigen-T cell interaction estimation is to estimate the history of the immune pressure on the evolution of the tumor clones.The model is based on the estimation result from Andrew Roth (2014) <doi:10.1038/nmeth.2883>.

Given a network (e.g. a food web), estimates several network indices. These include: Ascendency network indices, Direct and indirect dependencies, Effective measures, Environ network indices, General network indices, Pathway analysis, Network uncertainty indices and constraint efficiencies and the trophic level and omnivory indices of food webs.