Efficient variational inference methods for fully Bayesian univariate 
  and multivariate Gaussian and t-process regression models. Hierarchical shrinkage priors, 
  including the triple gamma prior, are used 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>.

Implements a two-stage Bayesian hierarchical modeling framework for
    applying shrinkage to subgroup-specific effects. The package separates
    model fitting (Stage 1) from hierarchical shrinkage (Stage 2), enabling
    modular sensitivity analyses without refitting expensive Markov chain
    Monte Carlo (MCMC) chains. Supports flexible prior specifications through
    the 'distributional' package, mixture approximations via 'mclust', and
    efficient 'Stan'-based inference.

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.

Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter 
  vector autoregressive models with stochastic volatility (TVP-VAR-SV) under shrinkage priors and dynamic shrinkage processes. 
  Details on the TVP-VAR-SV model and the shrinkage priors can be found in Cadonna et al. (2020) <doi:10.3390/econometrics8020020>, 
  details on the software can be found in Knaus et al. (2021) <doi:10.18637/jss.v100.i13>, while details on the dynamic shrinkage process
  can be found in Knaus and Frühwirth-Schnatter (2023) <doi:10.48550/arXiv.2312.10487>.