Bayesian Variable Selection using Power-Expected-Posterior Prior
Selected models under different choices of prior on the model paramete...
Model averaged estimates
Heatmap for top models
Bayesian variable selection for Gaussian linear models using PEP throu...
Deprecated functions in package PEPBVS.
Bayesian variable selection using power--expected--posterior prior
Bayes factor for model comparison
Plots for object of class pep
Posterior predictive distribution under Bayesian model averaging
(Point) Prediction under PEP approach
Printing object of class pep
Performs Bayesian variable selection under normal linear models for the data with the model parameters following as prior distributions 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 over all models or the uniform on model dimension (a special case of the beta-binomial prior). The selection is performed by either implementing a full enumeration and evaluation 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 hypothesis testing, estimation and predictions under Bayesian model averaging, as well as, plotting and printing the results are also provided. The results can be compared to the ones obtained under other well-known priors on model parameters and model spaces.