Allows to perform the tests of equal predictive accuracy for panels of forecasts. Main references: Qu et al. (2024) <doi:10.1016/j.ijforecast.2023.08.001> and Akgun et al. (2024) <doi:10.1016/j.ijforecast.2023.02.001>.

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.

Is designed to make easier printing summary statistics (for continues and factor level) tables in Latex, and plotting by factor.

Designed for prediction error estimation
        through resampling techniques, possibly accelerated by parallel
        execution on a compute cluster. Newly developed model fitting
        routines can be easily incorporated. Methods used in the package are detailed in
        Porzelius Ch., Binder H. and Schumacher M. (2009) <doi:10.1093/bioinformatics/btp062>
        and were used, for instance, in
        Porzelius Ch., Schumacher M. and Binder H. (2011) <doi:10.1007/s00180-011-0236-6>.