v0.7.3

30/11/2023

• Added new feature repeatcv() to apply repeated nested CV to the main
  nestedcv model functions for robust measurement of model performance.
• Added new feature via modifyX argument to all nestedcv models. This allows
  more powerful manipulation of the predictors such as scaling, imputing missing
  values, adding extra columns through variable manipulations. Importantly these
  are applied to train and test input data separately.
• Added predict() function for nestcv.SuperLearner()
• Added pred_SuperLearner wrapper for use with fastshap::explain
• Fixed parallelisation of nestcv.SuperLearner() on windows.
• Added support for multivariate Gaussian and Cox models in nestcv.glmnet()