Changes in version 0.5.4.9000                      

                 Changes in version 0.5.4 (2025-12-11)                  

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

                 Changes in version 0.5.3 (2022-08-19)                  

  - Emil Hvitfelt is taking over maintenance
  - General upkeep

                 Changes in version 0.5.2 (2021-02-24)                  

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

                 Changes in version 0.5.1 (2019-11-12)                  

  - Fixed namespace import from glmnet following changes there

                 Changes in version 0.5.0 (2019-06-24)                  

  - 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

                 Changes in version 0.4.1 (2018-11-21)                  

  - Add build-in support for parsnip and ranger
  - Add preprocess argument to lime.data.frame to keep it in line with
    the other types. Use it to transform your data.frame into a new
    input that your model expects after permutations
  - magick is now only in suggest to cut down on heavy hard dependencies
  - explain now returns a tbl_df so you get pretty printing if you have
    tibble loaded
  - When plotting regression explanations of non-binned features the
    feature weight is now multiplied by its value
  - More consistent support for keras
  - Fix bug when xgboost was used with with default objective
  - Better errors when handling bad models
  - plot_features now has a cases argument for subsetting the data
    before plotting

                         Changes in version 0.4                         

  - Add support for image explanation. The dispatch will be on paths
    pointing to valid image files. Image explanations can be visualised
    using plot_image_explanation (#35)
  - Add support for neural networks from the keras package
  - Add as_classifier() and as_regressor() for ad-hoc specification of
    the model type in case the heuristic implemented in lime doesn't
    hold. as_classifier() also lets you add/overwrite the class labels.
  - Use gower as the new default similarity measure for tabular data
  - If bin_continuous = FALSE the default behavior is now to sample from
    a kernel density estimation rather than assume a normal
    distribution.
  - Fix bug when numeric features in the training data were constant
    (#56)
  - Fix bug when plotting regression explanations with
    plot_explanations() (#60)
  - Logical columns in tabular data is now supported (#75)
  - Overhaul of plot_text_explanation() with better formatting and
    scrolling support for many explanations
  - All plots now show the fit of the explainer so the user can assess
    the quality of the explanation

                 Changes in version 0.3.1 (2017-11-24)                  

  - 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