NorskRegnesentral/shapr v1.0.4 on GitHub

Implement semi-deterministic sampling of coalitions similar to the default in the shap python library and described
and discussed as the PySHAP* strategy in Olsen & Jullum (2024). It is disabled by
default, but can be set via extra_computation_args = list(semi_deterministic_sampling = TRUE) in explain().
The functionality is available when paired coalition sampling (the default) is enabled. See
#449 for details.
Deletes the regression-surrogate parsnip object when testing to avoid future conflicts with model object changes.
(Second last commit in #447).
Improve and update the logic and print for setting the number of coalitions in the next iteration for iterative = TRUE
(#452)
Allow passing vS_batching_method to explain()/explain_forecast() to specify the batch computation method
(default is "future" for both, "forloop" available mainly for dev purposes)
(#452)
Transform to use the cli and rlang packages to provide all messages/warnings/stops with nicer formatting and layout.
The messages (via cli_inform()) now also obey the verbose argument and are displayed only if 'basic' %in% verbose is TRUE.
The header printout also differs between explain()/explain_forecast() and whether called from Python.
This also adds cli and rlang to imports. (#453)
Now using testthat::skip_if_not_installed for all tests requiring suggested packages to ensure they are skipped
gracefully when dependencies are unavailable (#451)

Other minor fixes

Two minor bugs related to KernelSHAP_reweighing() (#448)
Two minor bugs related to weighting for asymmetric Shapley values
(#449)
Check seed argument, and only pass to torch if not NULL
(#452)
Make explain_forecast() use future for batch computation as well (by default)
(#452)
Fix bug for the AICc + independence variants of approach = 'empirical' occurring when
n_features < n_explain (#453)