Description
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 power-expected-posterior (PEP) or the intrinsic (a special case of the former) (Fouskakis and Ntzoufras (2022) <doi: 10.1214/21-BA1288>, Fouskakis and Ntzoufras (2020) <doi: 10.3390/econometrics8020017>). 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). The selection can be done either with full enumeration of all possible models or using the Markov Chain Monte Carlo Model Composition (MC3) algorithm (Madigan and York (1995) <doi: 10.2307/1403615>). Complementary functions for making predictions, as well as plotting and printing the results are also provided.