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>.

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter models with shrinkage priors, both dynamic and static. Details on the algorithms used are provided in Bitto and Frühwirth-Schnatter (2019) <doi:10.1016/j.jeconom.2018.11.006> and  
  Cadonna et al. (2020) <doi:10.3390/econometrics8020020> and Knaus and Frühwirth-Schnatter (2023)  <doi:10.48550/arXiv.2312.10487>. For details on the package, please see Knaus et al. (2021) <doi:10.18637/jss.v100.i13>. For the multivariate extension, see the 'shrinkTVPVAR' package.