Create interactive analytic networks. It joins the data analysis power of R to obtain coincidences, co-occurrences and correlations, and the visualization libraries of 'JavaScript' in one package.

Infer system functioning with empirical NETwork COMparisons. These methods are part of a growing paradigm in network science that uses relative comparisons of networks to infer mechanistic classifications and predict systemic interventions. They have been developed and applied in Langendorf and Burgess (2021) <doi:10.1038/s41598-021-99251-7>, Langendorf (2020) <doi:10.1201/9781351190831-6>, and Langendorf and Goldberg (2019) <doi:10.48550/arXiv.1912.12551>.

Implementations of various control theory methods for use 
            in brain and psychological networks. Contains controllability
            statistics from Pasqualetti, Zampieri & Bullo (2014) <doi:10.1109/TCNS.2014.2310254>,
            optimal control algorithms from Lewis, Vrabie & Syrmos (2012, ISBN:978-0-470-63349-6), and various utilities.

The 'NetCoupler' algorithm identifies potential direct effects of 
    correlated, high-dimensional variables formed as a network with an external 
    variable. The external variable may act as the dependent/response variable 
    or as an independent/predictor variable to the network.

Efficient procedures for fitting and cross-validating the overlapping group Lasso (implemented in C++) for Cox models with time-dependent covariates. The penalty term is a weighted sum of infinity norms of (overlapping) groups of coefficients, which can select variables structurally with a specific grouping structure.

Implementation of discriminant analysis with network structures in predictors accommodated to do classification and prediction.

Empirical statistical analysis, visualization and simulation of
    diffusion and contagion processes on networks. The package implements algorithms
    for calculating network diffusion statistics such as transmission rate, hazard
    rates, exposure models, network threshold levels, infectiousness (contagion),
    and susceptibility. The package is inspired by work published in Valente,
    et al., (2015) <DOI:10.1016/j.socscimed.2015.10.001>; Valente (1995) <ISBN:
    9781881303213>, Myers (2000) <DOI:10.1086/303110>, Iyengar and others (2011)
    <DOI:10.1287/mksc.1100.0566>, Burt (1987) <DOI:10.1086/228667>; among others.