A set of functions providing the implementation of the network meta-analysis model with dose-response relationships, predicted values of the fitted model and dose-response plots in a frequentist way.

Social network analysis has become an essential tool in the study of complex systems. 'NetExplorer' allows to visualize and explore complex systems. It is based on 'd3js' library that brings 1) Graphical user interface;  2) Circular, linear, multilayer and force Layout; 3) Network live exploration and 4) SVG exportation.

A network-guided penalized regression framework that integrates network characteristics from Gaussian graphical models with partial penalization, accounting for both network structure (hubs and non-hubs) and clinical covariates in high-dimensional omics data, including transcriptomics and proteomics. The full methodological details can be found in our recent preprint by Ahn S and Oh EJ (2025) <doi:10.48550/arXiv.2505.22986>.

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>.