Changes in version 0.2.3                        

  - More unit tests.
  - Better code explanations for .ale().

                 Changes in version 0.2.2 (2025-03-09)                  

Minor improvement

  - update(, collapse_m = m) will now produce a level "other p" for the
    p least frequent categories #52.
  - update() has received new default: collapse_m = 15 (was 30) #54.
  - Missing values in y are now checked after removing observations
    without positive weights #56.
  - Plotted lines now skip missing values on the y-axis, also in the
    ribbons #57.
  - Factors with explicit NA level are respected, #59.
  - Empty factor levels are being dropped, #60.

Maintenance

  - Improved logic to find discrete grids for PDPs, #61.
  - Better test coverage for fcut(), #63.

Minor changes

                 Changes in version 0.2.1 (2025-01-11)                  

Major improvement

  - NA values on x axis are always plotted, even for numeric features
    #49.

Minor changes

  - ggplot plots with no y variation would not show the exposure bars.
    This has been fixed in #49.
  - Added {labeling} and {scales} explicitly to list of dependencies.
    Both are required by {ggplot2} anyway #49.

                 Changes in version 0.2.0 (2024-12-11)                  

Major bug fixes

  - The outlier clipping algorithm has unintentionally modified the
    values in place, i.e., also in the original dataframe. This is fixed
    by #24.

Efficiency improvements

  - Significant speed-up and memory reduction for numeric features #16,
    #24, #25.
  - The barebone ALE function .ale() has become faster thanks to issue
    #11 by @SebKrantz.
  - Subsampling indices for outlier capping is now done only once,
    instead of once per feature #15.

Minor bug fixes

  - NA values in feature columns have not been counted in the counts
    "N".
  - Ordered factors are now working properly.
  - ALE are correct also with empty bins at the border (could happen
    with user-defined breaks).
  - update(collapse_m = ...) has collapsed wrong categories #31, #34,
    and #35.

Documentation

  - README has received examples for Tidymodels and probabilistic
    classification.
  - Updated function documentation #41.

Other changes

  - Plots with more than one line now use "Effect" als default y label.
  - Automatic break count selection via "FD", "Scott" and via function
    is not possible anymore #24.
  - Export of fcut(), a fast variant of cut() #25.
  - x axes are not collected anymore by {patchwork} #27.
  - The default of discrete_m = 5 has been increased to 13 #29.
  - Slightly different check/preparation of predictions (and the
    argument pred). Helps to simplify the use of {h2o} #32.
  - Updated Plotly subplots layout #33, #43, #44, #45.
  - Better test coverage, e.g., #34.
  - (Slowish) support for h2o models #36.
  - Row names of statistics of numeric features are now removed #37.
  - ALE values are now plotted at the right bin break (instead of bin
    mean) #38.
  - Empty factor levels in features are not anymore dropped. However,
    you can use update(..., drop_empty = TRUE) to drop them after
    calculations #40.
  - Better input checks for average_observed(), average_predicted(), and
    bias() #41.
  - plot(): Renamed argument num_points to continuous_points and
    cat_lines to discrete_lines #42.
  - update(): New argument to_factor to turn discrete non-factors to
    factors #42.
  - EffectData class: Discrete feature values in the output class are
    represented by their original data types instead of converting them
    to factors #42.
  - EffectData class: The data.frames in the output now contain an
    attributes discrete to distinguish continuous from discrete features
    #42.
  - effect_importance() will produce an error when sorting on
    non-existent statistic #45.

                 Changes in version 0.1.0 (2024-11-18)                  

Initial release.