Bayesian regression tree models with shrinkage priors on 
  step heights. Supports continuous, binary, and right-censored (survival) 
  outcomes. Used for high-dimensional prediction and causal inference.

Provides nonparametric Steinian shrinkage estimators of the covariance matrix that are suitable in high dimensional settings, that is when the number of variables is larger than the sample size.

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully 
    Bayesian estimation of dynamic survival models with shrinkage priors. 
    Details on the algorithms used are provided in Wagner (2011) <doi:10.1007/s11222-009-9164-5>, 
    Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and
    Cadonna et al. (2020) <doi:10.3390/econometrics8020020>.

Approximate Bayesian regularization using Gaussian approximations. The input is a vector of estimates
             and a Gaussian error covariance matrix of the key parameters. Bayesian shrinkage is then applied
             to obtain parsimonious solutions. The method is described on 
             Karimova, van Erp, Leenders, and Mulder (2024) <DOI:10.31234/osf.io/2g8qm>. Gibbs samplers are used
             for model fitting. The shrinkage priors that are supported are Gaussian (ridge) priors, Laplace
             (lasso) priors (Park and Casella, 2008 <DOI:10.1198/016214508000000337>), and horseshoe priors
             (Carvalho, et al., 2010; <DOI:10.1093/biomet/asq017>). These priors include an option
             for grouped regularization of different subsets of parameters (Meier et al., 2008; 
             <DOI:10.1111/j.1467-9868.2007.00627.x>). F priors are used for the penalty
             parameters lambda^2 (Mulder and Pericchi, 2018 <DOI:10.1214/17-BA1092>). This correspond to
             half-Cauchy priors on lambda (Carvalho, Polson, Scott, 2010 <DOI:10.1093/biomet/asq017>).

Efficient variational inference methods for fully Bayesian Gaussian 
  Process Regression (GPR) models with hierarchical shrinkage priors, 
  including the triple gamma prior for effective variable selection and 
  covariance shrinkage in high-dimensional settings. The package leverages normalizing 
  flows to approximate complex posterior distributions. For details on implementation, 
  see Knaus (2025) <doi:10.48550/arXiv.2501.13173>.