| PEPBVS-package {PEPBVS} | R Documentation |
Bayesian variable selection using power-expected-posterior prior
Description
Performs Bayesian variable selection under normal linear models for the data with the model parameters following as prior either the PEP or the intrinsic (a special case of the former). The prior distribution on model space is the uniform on model space or the uniform on model dimension (a special case of the beta-binomial prior). Posterior model probabilities and marginal likelihoods can be derived in closed-form expressions under this setup. The selection can be done either with full enumeration of all possible models (for small–to–moderate model spaces) or using the MC3 algorithm (for large model spaces). Complementary functions for making predictions, as well as plotting and printing the results are also available.
References
Fouskakis, D. and Ntzoufras, I. (2022) Power-Expected-Posterior Priors as Mixtures of g-Priors in Normal Linear Models. Bayesian Analysis, 17(4): 1073-1099. doi:10.1214/21-BA1288
Fouskakis, D. and Ntzoufras, I. (2020) Bayesian Model Averaging Using Power-Expected-Posterior Priors. Econometrics, 8(2): 17. doi:10.3390/econometrics8020017