Bayesian regression tree ensembles for survival analysis
    and causal inference. Implements BART, DART, Bayesian Causal
    Forests (BCF), and Horseshoe Forest models. Supports
    right-censored and interval-censored survival outcomes via
    accelerated failure time (AFT) formulations. Designed for
    high-dimensional prediction and heterogeneous treatment effect
    estimation.

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 (2025) <DOI:10.1016/j.jmp.2025.102925>. 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>).