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 dense or sparse graphs using graphon mixtures and graphettes. Graphon mixtures uses two graphons U and W to generate graphs. Sparse graphs are generated in this case using the inverse line graph (root) operation. Graphettes have 3 components, the graphon W, a real-valued sequence and a graph edit function. Both techniques can generate dense or sparse graphs. Kandanaarachchi and Ong (2026) <doi:10.48550/arXiv.2505.13864>, Wijesinghe et al (2026) <doi:10.48550/arXiv.2602.23566>.

Estimation and display of various types of population attributable 
    fraction and impact fractions. As well as the usual calculations of 
    attributable fractions and impact fractions, functions are provided for 
    attributable fraction nomograms and fan plots, continuous exposures, for 
    pathway specific population attributable fractions, and for joint, average 
    and sequential population attributable fractions.

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.