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:10.48550/arXiv.2312.06289>.
  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.

Bindings to the 'libgraphqlparser' C++ library. Parses GraphQL
    <https://graphql.org> syntax and exports the AST in JSON format.

Implements the Rank In Similarity Graph Edge-count two-sample test 
    (RISE) for high-dimensional and non-Euclidean data. 
    The method constructs similarity-based graphs, such as k-nearest neighbor graph (k-NNG),
    k-minimum spanning tree (k-MST), and k-minimum distance non-bipartite pairing (k-MDP),
    and evaluates rank-based within-sample edge counts 
    with asymptotic and permutation p-values.
    For methodological details, see Zhou and Chen (2023)
    <https://proceedings.mlr.press/v195/zhou23a.html>.

Functions to develop simulated continuous data (e.g., gene expression) from a sigma covariance matrix derived from a graph structure in 'igraph' objects. Intended to extend 'mvtnorm' to take 'igraph'  structures rather than sigma matrices as input. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. This allows the use of simulated data that correctly accounts for pathway relationships and correlations. Here we present a versatile statistical framework to simulate  correlated gene expression data from biological pathways, by sampling from a multivariate normal distribution derived from a graph structure. This package allows the simulation of biological pathways from a graph structure based on a statistical model of gene expression. For example methods to infer biological pathways and gene regulatory networks from gene expression data can be tested on simulated datasets using this framework. This also allows for pathway structures to be considered as a confounding variable when simulating gene expression data to test the performance of genomic analyses.