lime 0.5.4

• Make package work with all versions of xgboost. (#202)

lime 0.5.3

• Emil Hvitfelt is taking over maintenance
• General upkeep

lime 0.5.2

• Fixed use of order() on data.frame objects
• Moved htmlwidgets, shiny, and shinythemes to suggests

lime 0.5.1

• Fixed namespace import from glmnet following changes there

lime 0.5.0

• explain() will now pass ... on to the relevant predict() method (#150)
• explain.data.frame() gains a gower_pow argument to modify the calculated
  gower distance before use by raising it to the power of the given value (#158)
• Fixed a bug when calculating R^2 on single feature explanations (@pkopper, #157)
• Fixed formatting of text prediction html presentation (#145)
• Fixed a bug when setting feature select method to "none" (#141)
• Changes default colouring from green-red to blue-red (#137)
• lime() now warns when quantile binning is not feasible and uses standard
  binning instead (#154)
• Changed the lambda value in the local model fit to match the one used in the
  Python version according to the relationship given here:
  https://stats.stackexchange.com/a/270705
• Added pkgdown site at https://lime.data-imaginist.com
• Fixed a bug when using a proprocessor with data.frame explanations

lime v0.3.1

• Added a NEWS.md file to track changes to the package.
• Fixed bug when explaining regression models, due to drop=TRUE defaults (#33)
• Integer features are no longer converted to numeric during permutations (#32)
• Fix bug when working with xgboost and tabular predictions (@martinju #1)
• Training data can now contain NA values (#8)
• Keep ordering when plotting with plot_features() (#38)
• Fix support for mlr by extracting predictions correctly
• Added support for h2o (@mdancho84) (#40)
• Throws meaningful error when all permutations have 0 similarity to original
  observation (#47)
• Explaining data can now contain NA values (#45)
• Support for Date and POSIXt columns. They will be kept constant during
  permutations so that lime will explain the model behaviour at the given
  timepoint based on the remaining features (#39).
• Add plot_explanations() for an overview plot of a large explanation set