Changes in Version 0.2:
    o Added 'tsData' function to prepare data
    o graphicalVAR now supports multiple subjects (fixed effects only) and day effects
    o 'beepvar' can now be used to handle missing beeps
    o Added 'mimic' argument to 'graphicalVAR' to mimic 0.1.2 and 0.1.4 behavior.
    o Lambda sequence for kappa now uses the maximum absolute correlation as maximal value rather than 1.
    o Added 'mlGraphicalVAR' for pooled and individual estimation of N > 1 datasets
    o Added 'simMLgvar' to simulate N > 1 data
    o Fixed a bug in 'graphicalVARsim' where mean structure was not used in data generation

Changes in Version 0.1.4:
    o 'graphicalVAR' again standardizes variables before running by default. Can be controlled using the 'scale' argument
    o New arguments to 'graphicalVAR':
        o deleteMissings
        o penalize.diagonal
        o lambda_min_kappa
        o lambda_min_beta
        o scale
    o Added 'randomGVARmodel' function to simulate graphicalVAR model matrices
    o Added unregularized estimation when both lambda_kappa = 0 and lambda_beta = 0
    o Greatly updated the tuning parameter sequence generating algorithm. The sequence should now be better chosen. However, note that this change leads to different estimated networks as with previous graphicalVAR versions (as different LASSO tuning parameters are used)
    o Added 'lbound' and 'ubound' arguments to graphicalVARsim

Changes in Version 0.1.3:
    o graphicalVAR now only centers and does not standardize