Computes marginal likelihood in Gaussian graphical models through a
  novel telescoping block decomposition of the precision matrix which allows
  estimation of model evidence. The top level function used to estimate marginal
  likelihood is called evidence(), which expects the prior name, data, and
  relevant prior specific parameters. This package also provides an MCMC prior
  sampler using the same underlying approach, implemented in prior_sampling(),
  which expects a prior name and prior specific parameters. Both functions also
  expect the number of burn-in iterations and the number of sampling iterations
  for the underlying MCMC sampler.

Statistical methodology for sparse multivariate extreme value models. Methods are
             provided for exact simulation and statistical inference for multivariate Pareto distributions
             on graphical structures as described in the paper 'Graphical Models for Extremes' by
             Engelke and Hitz (2020) <doi:10.1111/rssb.12355>.

Multiple comparison procedures (MCPs) control the familywise error
    rate in clinical trials. Graphical MCPs include many commonly used
    procedures as special cases; see Bretz et al. (2011)
    <doi:10.1002/bimj.201000239>, Lu (2016) <doi:10.1002/sim.6985>, and Xi et
    al. (2017) <doi:10.1002/bimj.201600233>. This package is a low-dependency
    implementation of graphical MCPs which allow mixed types of tests. It also
    includes power simulations and visualization of graphical MCPs.

Estimates within and between time point interactions in experience sampling data, using the Graphical vector autoregression model in combination with regularization. See also Epskamp, Waldorp, Mottus & Borsboom (2018) <doi:10.1080/00273171.2018.1454823>.

Several new layout algorithms to visualize networks are provided which are not part of 'igraph'. 
    Most are based on the concept of stress majorization by Gansner et al. (2004) <doi:10.1007/978-3-540-31843-9_25>. 
    Some more specific algorithms allow the user to emphasize hidden group structures in networks or focus on specific nodes.