NEWS

effectplots 0.2.1 (2025-01-11)

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.

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

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.

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.

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.

Initial release.