Fit Bayesian Gaussian graphical models. The methods are separated into 
    two Bayesian approaches for inference: hypothesis testing and estimation. There are 
    extensions for confirmatory hypothesis testing, comparing Gaussian graphical models, 
    and node wise predictability. These methods were recently introduced in the Gaussian 
    graphical model literature, including 
    Williams (2019) <doi:10.31234/osf.io/x8dpr>, 
    Williams and Mulder (2019) <doi:10.31234/osf.io/ypxd8>,
    Williams, Rast, Pericchi, and Mulder (2019) <doi:10.31234/osf.io/yt386>.

Provides a Metropolis-coupled Markov chain Monte Carlo sampler,
    post-processing and parameter estimation functions, and plotting utilities
    for the generalized graded unfolding model of Roberts, Donoghue, and
    Laughlin (2000) <doi:10.1177/01466216000241001>.

A collection of box-geometry model (BGM) files for the Atlantis 
    ecosystem model. Atlantis is a deterministic, biogeochemical, 
    whole-of-ecosystem model (see <http://atlantis.cmar.csiro.au/> for more information).

The BGmisc R package offers a comprehensive suite of
    functions tailored for extended behavior genetics analysis, including
    model identification, calculating relatedness, pedigree conversion,
    pedigree simulation, and more.

Two partially supervised mixture modeling methods: 
        soft-label and belief-based modeling are implemented.
        For completeness, we equipped the package also with the
        functionality of unsupervised, semi- and fully supervised
        mixture modeling.  The package can be applied also to selection
        of the best-fitting from a set of models with different
        component numbers or constraints on their structures.
        For detailed introduction see:
        Przemyslaw Biecek, Ewa Szczurek, Martin Vingron, Jerzy
        Tiuryn (2012), The R Package bgmm: Mixture Modeling with
        Uncertain Knowledge, Journal of Statistical Software 
        <doi:10.18637/jss.v047.i03>.

Bayesian variable selection methods for analyzing the structure of a Markov Random Field model for a network of binary and/or ordinal variables. Details of the implemented methods can be found in: Marsman and Haslbeck (2023) <doi:10.31234/osf.io/ukwrf>.