EBIR is an exact Bayesian algorithm applicable to both variable selection and model averaging problems. It employs a fully Bayesian approach that provides a complete characterization of the posterior ensemble of possible sub-models and consequently, the marginal probability of including each of the predictor variables when the number of variables is not too large. Thus, this fully Bayesian model can be used for variable selection, model averaging applications, and examination of the shape of the posterior space.