Performs Bayesian t Regression where mean and scale parameters are modeling by lineal regression structures, and the degrees of freedom parameters are estimated.

Efficient Markov Chain Monte Carlo (MCMC) algorithms for the
    fully Bayesian estimation of vectorautoregressions (VARs) featuring
    stochastic volatility (SV). Implements state-of-the-art shrinkage
    priors following Gruber & Kastner (2025) <doi:10.1016/j.ijforecast.2025.02.001>.
    Efficient equation-per-equation estimation following Kastner & Huber
    (2020) <doi:10.1002/for.2680> and Carrerio et al. (2021)
    <doi:10.1016/j.jeconom.2021.11.010>.

Various algorithms for segmentation of 2D and 3D images, such
    as computed tomography and satellite remote sensing. This package implements
    Bayesian image analysis using the hidden Potts model with external field
    prior of Moores et al. (2015) <doi:10.1016/j.csda.2014.12.001>.
    Latent labels are sampled using chequerboard updating or Swendsen-Wang.
    Algorithms for the smoothing parameter include pseudolikelihood, path sampling,
    the exchange algorithm, approximate Bayesian computation (ABC-MCMC and ABC-SMC),
    and the parametric functional approximate Bayesian (PFAB) algorithm. Refer to
    Moores, Pettitt & Mengersen (2020) <doi:10.1007/978-3-030-42553-1_6> for an overview
    and also to <doi:10.1007/s11222-014-9525-6> and <doi:10.1214/18-BA1130> for
    further details of specific algorithms.

Bayesian Latent Class Analysis using several different
        methods.

Making probabilistic projections of life expectancy for all countries of the world, using a Bayesian hierarchical model <doi:10.1007/s13524-012-0193-x>. Subnational projections are also supported.

Estimates Bayesian models of list experiments with informative priors. It includes functionalities to estimate different types of list experiment models with varying prior information. See Lu and Traunmüller (2026) <doi:10.1017/psrm.2025.10084> for examples and details of estimation.