Implements various simple function utilities and flexible pipelines to generate circular images for visualizing complex genomic and network data analysis features.

Tools to create and modify network objects.  The network class can represent a range of relational data types, and supports arbitrary vertex/edge/graph attributes.

We developed an inference tool based on approximate Bayesian computation to decipher network data and assess the strength of the inferred links between network's actors. It is a new multi-level approximate Bayesian computation (ABC) approach. At the first level, the method captures the global properties of the network, such as a scale-free structure and clustering coefficients, whereas the second level is targeted to capture local properties, including the probability of each couple of genes being linked. Up to now, Approximate Bayesian Computation (ABC) algorithms have been scarcely used in that setting and, due to the computational overhead, their application was limited to a small number of genes. On the contrary, our algorithm was made to cope with that issue and has low computational cost. It can be used, for instance, for elucidating gene regulatory network, which is an important step towards understanding the normal cell physiology and complex pathological phenotype. Reverse-engineering consists in using gene expressions over time or over different experimental conditions to discover the structure of the gene network in a targeted cellular process. The fact that gene expression data are usually noisy, highly correlated, and have high dimensionality explains the need for specific statistical methods to reverse engineer the underlying network.

Network changepoint analysis for undirected network data. The package implements a hidden Markov network change point model (Park and Sohn (2020)). Functions for break number detection using the approximate marginal likelihood and WAIC are also provided.

This permutation based hypothesis test, suited for several types of data 
    supported by the estimateNetwork function of the bootnet package (Epskamp & Fried, 2018), 
    assesses the difference between two networks based on several invariance measures (network 
    structure invariance, global strength invariance, edge invariance, several centrality 
    measures, etc.). Network structures are estimated with l1-regularization. The Network 
    Comparison Test is suited for comparison of independent (e.g., two different groups) and 
    dependent samples (e.g., one group that is measured twice). See van Borkulo et al. (2021),
    available from <doi:10.1037/met0000476>.