An implementation of Bayesian survival models with graph-structured selection priors for sparse identification of omics features predictive of survival (Madjar et al., 2021 <doi:10.1186/s12859-021-04483-z>) and its extension to use a fixed graph via a Markov Random Field (MRF) prior for capturing known structure of omics features, e.g. disease-specific pathways from the Kyoto Encyclopedia of Genes and Genomes database (Hermansen et al., 2025 <doi:10.48550/arXiv.2503.13078>).

Provides an Markov-Chain-Monte-Carlo algorithm for Bayesian t-tests on the effect size. The underlying Gibbs sampler is based on a two-component Gaussian mixture and approximates the posterior distributions of the effect size, the difference of means and difference of standard deviations. A posterior analysis of the effect size via the region of practical equivalence is provided, too. For more details about the Gibbs sampler see Kelter (2019) <arXiv:1906.07524>.

Provides utilities to describe posterior
    distributions and Bayesian models. It includes point-estimates such as
    Maximum A Posteriori (MAP), measures of dispersion (Highest Density
    Interval - HDI; Kruschke, 2015 <doi:10.1016/C2012-0-00477-2>) and
    indices used for null-hypothesis testing (such as ROPE percentage, pd
    and Bayes factors). References: Makowski et al. (2021) <doi:10.21105/joss.01541>.

Making probabilistic projections of total fertility rate for all countries of the world, using a Bayesian hierarchical model <doi:10.1007/s13524-011-0040-5> <doi:10.18637/jss.v106.i08>. Subnational probabilistic projections are also supported <doi:10.4054/DemRes.2018.38.60>.

Provides tools for conducting Bayesian analyses and Bayesian model averaging 
    (Kass and Raftery, 1995, <doi:10.1080/01621459.1995.10476572>, 
    Hoeting et al., 1999, <doi:10.1214/ss/1009212519>). The package contains 
    functions for creating a wide range of prior distribution objects, mixing posterior 
    samples from 'JAGS' and 'Stan' models, plotting posterior distributions, and etc...
    The tools for working with prior distribution span from visualization, generating 'JAGS' 
    and 'bridgesampling' syntax to basic functions such as rng, quantile, and distribution functions.