A 'GraphQL' client, with an R6 interface for initializing a
    connection to a 'GraphQL' instance, and methods for constructing
    queries, including fragments and parameterized queries. Queries are
    checked with the 'libgraphqlparser' C++ parser via the 'graphql'
    package.

A collection of commonly used visualizations of temporal and
    spatio-temporal health data including case counts, incidence rates, and
    covariates. The available plot types include time series, heatmaps,
    seasonality plots, maps and more. The package supports standard data
    transformations such as temporal and spatial aggregations, while
    offering extensive customization options for the resulting figures.

Supports modeling health outcomes using Bayesian hierarchical 
  spatio-temporal models with complex covariate effects (e.g., linear, 
  non-linear, interactions, distributed lag linear and non-linear 
  models) in the 'INLA' framework. It is designed to help users identify key 
  drivers and  predictors of disease risk by enabling streamlined model 
  exploration, comparison, and visualization of complex covariate effects. 
  See an application of the modelling framework in Lowe, Lee, O'Reilly et al. (2021)
  <doi:10.1016/S2542-5196(20)30292-8>.

Draw posterior samples to estimate the precision matrix for multivariate Gaussian data. Posterior means of the samples is the graphical horseshoe estimate by Li, Bhadra and Craig(2017) <arXiv:1707.06661>. The function uses matrix decomposition and variable change from the Bayesian graphical lasso by Wang(2012) <doi:10.1214/12-BA729>, and the variable augmentation for sampling under the horseshoe prior by Makalic and Schmidt(2016) <arXiv:1508.03884>. Structure of the graphical horseshoe function was inspired by the Bayesian graphical lasso function using blocked sampling, authored by Wang(2012) <doi:10.1214/12-BA729>.

Detailed functionality for working
        with the univariate and multivariate Generalized Hyperbolic
        distribution and its special cases (Hyperbolic (hyp), Normal
        Inverse Gaussian (NIG), Variance Gamma (VG), skewed Student-t
        and Gaussian distribution). Especially, it contains fitting
        procedures, an AIC-based model selection routine, and functions
        for the computation of density, quantile, probability, random
        variates, expected shortfall and some portfolio optimization
        and plotting routines as well as the likelihood ratio test. In
        addition, it contains the Generalized Inverse Gaussian
        distribution. See Chapter 3 of A. J. McNeil, R. Frey, and P. Embrechts. 
        Quantitative risk management: Concepts, techniques and tools. 
        Princeton University Press, Princeton (2005).