NEWS

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glex 0.6.0

• Extended compatibility with xgboost, now requiring xgboost (>= 3.0.0) in Suggests:

  • Updated tests and examples for the new API

• Plot colors are now configurable via options() and documented in ?glex_options: glex.palette (diverging palette for continuous interaction effects; NULL for the default shap-style gradient, or the name of a scico palette), glex.palette_discrete (palette for categorical predictors: a color vector, "okabe-ito", a scico palette name, or a brewer palette name), glex.colors_sign (negative/positive colors in glex_explain() and gradient endpoints), and glex.color_line (main effect line/column color).

• Default colors updated to follow the blue/red convention of the Python shap/shapiq packages: continuous interaction effects use a #008BFB → white → #FF0051 gradient (previously the cyclic scico palette "vikO"), and glex_explain() uses the same blue/red for negative/positive contributions.

glex 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

glex 0.5.1

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

glex 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()

glex 0.4.2

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

glex 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").

glex 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.

glex 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.

glex 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.