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).

Provides functions for model fitting and selection of generalised hypergeometric ensembles of random graphs (gHypEG).
    To learn how to use it, check the vignettes for a quick tutorial.
    Please reference its use as Casiraghi, G., Nanumyan, V. (2019) <doi:10.5281/zenodo.2555300>
    together with those relevant references from the one listed below.
    The package is based on the research developed at the Chair of Systems Design, ETH Zurich.
    Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2016) <arXiv:1607.02441>.
    Casiraghi, G., Nanumyan, V., Scholtes, I., Schweitzer, F. (2017) <doi:10.1007/978-3-319-67256-4_11>.
    Casiraghi, G., (2017) <arXiv:1702.02048>
    Brandenberger, L., Casiraghi, G., Nanumyan, V., Schweitzer, F. (2019) <doi:10.1145/3341161.3342926>
    Casiraghi, G. (2019) <doi:10.1007/s41109-019-0241-1>.
    Casiraghi, G., Nanumyan, V. (2021) <doi:10.1038/s41598-021-92519-y>.
    Casiraghi, G. (2021) <doi:10.1088/2632-072X/ac0493>.