fairml (0.4)

  * preliminary implementation of the fair ridge regression model.
  * fairness.profile.plots() no longer plots the intercept of the model.
  * the "epsilon" argument has been renamed to "unfairness" thorough the
     package.
  * loss() has been renamed to cv.loss().
  * added cv.unfairness() to match cv.loss().

fairml (0.3)

  * support custom covariance matrix estimators in nclm(); Komiyama et al.
     (2018) plugged various kernel estimators in the model estimation.
  * added an optional argument to regularize nclm() with a ridge penalty.
  * implemented cross-validation in fairml.cv() and an associated loss() 
     function.

fairml (0.2)

  * improved argument sanitization.
  * improved nclm() numeric stability by standardizing variables.
  * added data sets used in Komiyama et al. (2018).

fairml (0.1)

  * initial release.
  * preliminary implementation of the regression model with fairness
     constraints from Komiyama et al. (2018), without kernel regularization.
  * implemented print(), summary(), coef(), fitted(), residuals(), sigma(),
     nobs(), sigma(), predict() and all.equal() methods.
  * added some profile plots in fairness.profile.plots().
