BayesReg

R package bayesreg: Bayesian Regression Models with Global-Local Shrinkage Priors. Fits linear or logistic regression model using Bayesian global-local shrinkage prior hierarchies as described in Polson and Scott (2010) <doi:10.1093/acprof:oso/9780199694587.003.0017>. Handles ridge, lasso, horseshoe and horseshoe+ regression with logistic, Gaussian, Laplace or Student-t distributed targets using the algorithms summarized in Makalic and Schmidt (2016) <arXiv:1611.06649>.

Keywords for this software

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References in zbMATH (referenced in 5 articles , 1 standard article )

Showing results 1 to 5 of 5.
Sorted by year (citations)

  1. Giacomazzo, Mario; Kamarianakis, Yiannis: Bayesian estimation of subset threshold autoregressions: short-term forecasting of traffic occupancy (2020)
  2. Posch, Konstantin; Arbeiter, Maximilian; Pilz, Juergen: A novel Bayesian approach for variable selection in linear regression models (2020)
  3. Pour, Ali Foroughi; Dalton, Lori A.: Theory of optimal Bayesian feature filtering (2020)
  4. Bhadra, Anindya; Datta, Jyotishka; Polson, Nicholas G.; Willard, Brandon: Lasso meets horseshoe: a survey (2019)
  5. Daniel F. Schmidt, Enes Makalic: High-Dimensional Bayesian Regularised Regression with the BayesReg Package (2016) arXiv