fairml (0.6.3)

  * fixed predict() with just a single predictor variable (thanks Florian 
     Pfisterer).
  * better error message when the column names of predictors and sensitive
     attributes result in clashes in the respective design matrices (thanks
     Florian Pfisterer).

fairml (0.6.2)

  * changed the maintainer email.

fairml (0.6.1)

  * fixed conditionals involving is() vs inherit().

fairml (0.6)

  * preliminary implementation of a linear regression model with the fairness
     constraints from Zafar et al. (2019).
  * the equality-of-opportunity version of Komiyama's definition of fairness
     now works with fgrrm().
  * fairml.cv() now supports fgrrm().
  * added a logLik() method for all fair models.
  * added an RMSE profile plot for linear regressions.
  * Zafar's logistic regression is now faster and more robust.

fairml (0.5)

  * preliminary implementation of the logistic regression model with fairness
     constraints from Zafar et al. (2019).
  * frrm() can now enforce both statistical parity and equality of
     opportunity, as specified by the "definition" argument.
  * added an argument "cluster" to enable parallel computing in fairml.cv()
     and fairness.profile.plot().
  * added an optional argument to regularize the predictors in frrm() with a
     ridge penalty.
  * added an argument "save.auxiliary" (default: FALSE) to reduce the size of
     the model objects returned by nclm() and frrm() by not saving the fitted
     values and the residuals of the auxiliary models that computes the
     decorrelated predictors.
  * included the Adult and Bank data sets from UCI, used in Zafar et al.
     (2019).
  * added a precision-recall profile plot for classifiers, and more
     constraints profile plots.

fairml (0.4)

  * preliminary implementation of the fair ridge regression model.
  * fairness.profile.plot() 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 the 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().