Changes in version 0.5.2                        

  - Fix newer xgboost R package compatibility:
      - Updated tree schema column name from Quality to Gain, matching
        xgboost commit 73713de ([R] rename Quality -> Gain (#9938), in
        upstream v2.1.0)
      - Implemented dynamic base_score extraction to replace
        hardcoded 0.5 intercept (modern xgboost auto-estimates
        base_score)
      - Fixed floating-point precision mismatch in C++ split comparators
        by casting to float, matching xgboost predictor behavior
      - Added node reindexing to ensure contiguous row ordering in tree
        matrices
      - For CRAN users, this schema change is observed in the later 3.x
        package line (for example 3.1.2.1+), which requires R >= 4.3.0
      - These changes ensure accurate model explanations for current
        CRAN xgboost releases

                        Changes in version 0.5.1                        

  - Fix path-dependent algorithm by computing the proper covers manually
  - Allow glex() to accept data frames as input

                        Changes in version 0.5.0                        

  - Optimize FastPD to be able to handle more features using bitmask
    represenation (#29)
  - Remove old probFuntion parameter to glex() in favor of
    weighting_method.
  - Add new progress bar when explaining many trees using glex()

                        Changes in version 0.4.2                        

  - Optimize FastPD by only computing components up to max_interaction
    (#24)

                        Changes in version 0.4.1                        

  - Added FastPD (arXiv) as default probFunction in glex.
  - Add rug plot to plot_*_effect[s] functions for continuous
    predictors, defaulting to showing a rug on the bottom side (rug_side
    = "b").

                        Changes in version 0.4.0                        

  - Add support for ranger objects to glex() (PR#17).
  - Add new optional parameter probFunction to glex() which specifies
    the probability function for weighting/marginalization of the leaves
    (PR#17).
    By default, glex() now uses the empirical marginal probabilities to
    perform the weighting. Previously, the weighting of the leaves was
    done based on a path-dependent method.
  - Add theme_glex() as a default theme to all plots.
    This is almost identical to [ggplot2::theme_minimal()] aside from
    increased base font size and convenience flags to toggle vertical
    and horizontal grid lines.
  - Add subset_components() and subset_component_names() to make it
    easier to extract only components belonging to a given main term.
  - Add pre-processed version of Bikeshare data from ISLR2 to
    streamlined examples.
  - Add plot_pdp(), a version of plot_main_effect() with the intercept
    added.
  - Limit max_interaction in glex.xgb.Booster to max_depth parameter of
    xgboost model. If max_depth is not set during model fit, the default
    value of 6 is assumed. This prevents glex from returning spurious
    higher-order interactions containing values numerically close to 0.
  - Extend plot functions to multiclass classification. In most cases
    that means facetting by the target class.
  - Overhaul glex_explain to a waterfall plot showing the SHAP
    decomposition for given predictors.
  - autoplot.glex_vi gains a max_interaction argument in line with
    glex_explain, and now similarly aggregates terms that either fall
    below threshold or exceed max_interaction.
  - Add glex.print for a more compact output in case of large numbers of
    terms.

                        Changes in version 0.3.0                        

  - Added plotting functions for main, 2- and 3-degree interaction terms
  - Added ggplot2::autoplot S3 method for glex objects.
  - Added pkgdown site
  - Added Bikesharing article
  - Added glex_vi() to compute variable importance scores including
    interaction terms, including a corresponding ggplot2::autoplot
    method.
  - Added glex_explain() to plot prediction components of a single
    observation.

                        Changes in version 0.2.0                        

  - Convert glex() to an S3 generic function with methods for xgboost
    and randomPlantedForest models.
  - Fix bug in xgboost method that could lead to wrongly computed shap
    values in certain cases.
  - Added a NEWS.md file to track changes to the package.