This release is intended to be the last before stable version 1.0.0.

Major change

Passing a background dataset bg_X is now optional.

If the explanation data X is sufficiently large (>= 50 rows), bg_X is derived as a random sample of bg_n = 200 rows from X. If X has less than bg_n rows, then simply
bg_X = X. If X has too few rows (< 50), you will have to pass an explicit bg_X.

Minor changes

• ranger() survival models now also work out-of-the-box without passing a tailored prediction function. Use the new argument survival = "chf" in kernelshap() and permshap() to distinguish cumulative hazards (default) and survival probabilities per time point.
• The resulting object of kernelshap() and permshap() now contain bg_X and bg_w used to calculate the SHAP values.