ESS++: Bayesian variable selection for linear regression using evolutionary Monte Carlo. ESS++ is a C++ implementation of a fully Bayesian variable selection approach for single and multiple response linear regression. ESS++ works well both when the number of observations is larger than the number of predictors and in the ’large p, small n’ case. In the current version (0.1), ESS++ can handle several hundred observations, thousands of predictors and a few responses simultaneously. The core engine of ESS++ for the selection of relevant predictors is based on evolutionary Monte Carlo. The C++ implementation of ESS++ is open source and distributed according to the GNU GPL licence.

References in zbMATH (referenced in 4 articles , 1 standard article )

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  1. Hubin, Aliaksandr; Storvik, Geir: Mode jumping MCMC for Bayesian variable selection in GLMM (2018)
  2. Benoît Liquet and Leonardo Bottolo and Gianluca Campanella and Sylvia Richardson and Marc Chadeau-Hyam: R2GUESS: A Graphics Processing Unit-Based R Package for Bayesian Variable Selection Regression of Multivariate Responses (2016) not zbMATH
  3. Castillo, Ismaël; Schmidt-Hieber, Johannes; van der Vaart, Aad: Bayesian linear regression with sparse priors (2015)
  4. Bottolo, Leonardo; Chadeau-Hyam, Marc; Hastie, David I.; Langley, Sarah R.; Petretto, Enrico; Tiret, Laurence; Tregouet, David; Richardson, Sylvia: \textitESS++: a C++ objected-oriented algorithm for Bayesian stochastic search model exploration (2011) ioport