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.

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>.

Fits the Piecewise Exponential distribution with random time grids using the clustering structure of the Product Partition Models. Details of the implemented model can be found in Demarqui et al. (2008) <doi:10.1007/s10985-008-9086-0>.

A PEP, or Portable Encapsulated Project, is a dataset that 
    subscribes to the PEP structure for organizing metadata. It is written using
    a simple YAML + CSV format, it is your one-stop solution to metadata 
    management across data analysis environments. This package reads this 
    standardized project configuration structure into R. 
    Described in Sheffield et al. (2021) <doi:10.1093/gigascience/giab077>.