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.

Provides a not-so-comprehensive list of methods for estimating graphon,
    a symmetric measurable function, from a single or multiple of observed networks. 
    For a detailed introduction on graphon and popular estimation techniques, 
    see the paper by Orbanz, P. and Roy, D.M.(2014) <doi:10.1109/TPAMI.2014.2334607>.
    It also contains several auxiliary functions for generating sample networks
    using various network models and graphons.

Generates (U,W) mixture graphs where U is a line graph graphon and W is a dense graphon. Graphons are graph limits and graphon U can be written as sequence of positive numbers adding to 1. Graphs are sampled from U and W and joined randomly to obtain the mixture graph. Given a mixture graph, U can be inferred. Kandanaarachchi and Ong (2025) <doi:10.48550/arXiv.2505.13864>.

Implement some models for 
  correlation/covariance matrices including two approaches 
  to model correlation matrices from a graphical structure.
  One use latent parent variables as proposed in
  Sterrantino et. al. (2024) <doi:10.1007/s10260-025-00788-y>.
  The other uses a graph to specify conditional 
  relations between the variables.
  The graphical structure makes correlation matrices 
  interpretable and avoids the quadratic increase of 
  parameters as a function of the dimension. 
  In the first approach a natural sequence of simpler 
  models along with a complexity penalization is used.
  The second penalizes deviations from a base model.
  These can be used as prior for model parameters,
  considering C code through the 'cgeneric' interface 
  for the 'INLA' package (<https://www.r-inla.org>). 
  This allows one to use these models as building 
  blocks combined and to other latent Gaussian models 
  in order to build complex data models.