Estimates micro effects on macro structures (MEMS) and average micro mediated effects (AMME).
    URL: <https://github.com/sduxbury/netmediate>.
    BugReports: <https://github.com/sduxbury/netmediate/issues>.
    Robins, Garry, Phillipa Pattison, and Jodie Woolcock (2005) <doi:10.1086/427322>.
    Snijders, Tom A. B., and Christian E. G. Steglich (2015) <doi:10.1177/0049124113494573>.
    Imai, Kosuke, Luke Keele, and Dustin Tingley (2010) <doi:10.1037/a0020761>.
    Duxbury, Scott (2023) <doi:10.1177/00811750231209040>.
    Duxbury, Scott (2024) <doi:10.1177/00811750231220950>.

Provides measures to describe and manipulate one-mode, two-mode,
    multiplex, and multilevel networks using matrix algebra. Implements
    functions for network centrality, cohesive subgroups, structural holes,
    similarity measures, path distances, signed networks, and random network
    generation. Supports ego-centric and whole-network analyses, including
    dyadic and triadic census, structural balance, and bipartite projections.
    Key references: Bonacich (1972) <doi:10.1080/0022250X.1972.9989806>,
    Breiger (1974) <doi:10.2307/2576011>,
    Kivelä et al. (2014) <doi:10.1093/comnet/cnu016>,
    Espinosa-Rada et al. (2024) <doi:10.1016/j.socnet.2023.11.008>.

The field of immunology benefits from software that can
    predict which peptide sequences trigger an immune response.
    'NetMHCIIpan' is a such a tool: it predicts the
    binding strength of a short peptide to a Major Histocompatibility
    Complex class II (MHC-II) molecule.
    'NetMHCIIpan' can be used from a web server at 
    <https://services.healthtech.dtu.dk/services/NetMHCIIpan-3.2/>
    or from the command-line, using a local installation. This package
    allows to call 'NetMHCIIpan' from R.

Stochastic collapsed variational inference on mixed-membership stochastic blockmodel for networks,
             incorporating node-level predictors of mixed-membership vectors, as well as 
             dyad-level predictors. For networks observed over time, the model defines a hidden
             Markov process that allows the effects of node-level predictors to evolve in discrete,
             historical periods. In addition, the package offers a variety of utilities for 
             exploring results of estimation, including tools for conducting posterior 
             predictive checks of goodness-of-fit and several plotting functions. The package 
             implements methods described in Olivella, Pratt and Imai (2019) 'Dynamic Stochastic
             Blockmodel Regression for Social Networks: Application to International Conflicts',
             available at <https://www.santiagoolivella.info/pdfs/socnet.pdf>.