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.

Graphical approach provides a useful framework for multiplicity adjustment in clinical trials with multiple endpoints. This package includes statistical methods to optimize sample size over initial weight and transition probability in a graphical approach under a common setting, which is to use marginal power for each endpoint in a trial design.
    See Zhang, F. and Gou, J. (2023). Sample size optimization for clinical trials using graphical approaches for multiplicity adjustment, Technical Report.