shrinkGPR - Scalable Gaussian Process Regression with Hierarchical Shrinkage Priors
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>.
Last updated 1 months ago
3.48 score 1 stars 186 downloadsshrinkTVP - Efficient Bayesian Inference for Time-Varying Parameter Models with Shrinkage
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>.
Last updated 1 years ago
openblasgslcpp
3.12 score 2 stars 2 dependents 11 scripts 486 downloadsshrinkTVPVAR - Efficient Bayesian Inference for TVP-VAR-SV Models with Shrinkage
Efficient Markov chain Monte Carlo (MCMC) algorithms for fully Bayesian estimation of time-varying parameter vector autoregressive models with shrinkage priors. Details on the algorithms used are provided in Cadonna et al. (2020) <doi:10.3390/econometrics8020020> and Knaus et al. (2021) <doi:10.18637/jss.v100.i13>.
Last updated 6 months ago
openblascpp
1.00 score 1 stars 2 scripts 215 downloads