Estimate penalized synthetic control models
    and perform hold-out validation to determine their 
    penalty parameter. This method is based on the work by
    Abadie & L'Hour (2021) <doi:10.1080/01621459.2021.1971535>.
    Penalized synthetic controls smoothly interpolate between 
    one-to-one matching and the synthetic control method.

Data sets associated with modeling examples in Craig Starbuck's book, "The Fundamentals of People Analytics: With Applications in R".

Data sets for statistical inference modeling related to People Analytics.  
  Contains various data sets from the book 'Handbook of Regression Modeling in People Analytics' 
  by Keith McNulty (2020).

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.