CRAN Package Check Results for Package shapr

Last updated on 2025-10-13 20:49:55 CEST.

Flavor	Version	Tinstall	Tcheck	Ttotal	Status	Flags
r-devel-linux-x86_64-debian-clang	1.0.5	81.03	439.24	520.27	OK
r-devel-linux-x86_64-debian-gcc	1.0.5	52.16	308.70	360.86	OK
r-devel-linux-x86_64-fedora-clang	1.0.5			848.26	OK
r-devel-linux-x86_64-fedora-gcc	1.0.5			812.07	OK
r-devel-windows-x86_64	1.0.5	97.00	347.00	444.00	ERROR
r-patched-linux-x86_64	1.0.5	84.27	416.77	501.04	OK
r-release-linux-x86_64	1.0.5	83.24	412.48	495.72	OK
r-release-macos-arm64	1.0.5			206.00	OK
r-release-macos-x86_64	1.0.5			408.00	WARN
r-release-windows-x86_64	1.0.5	96.00	343.00	439.00	ERROR
r-oldrel-macos-arm64	1.0.5			196.00	NOTE
r-oldrel-macos-x86_64	1.0.5			359.00	NOTE
r-oldrel-windows-x86_64	1.0.5	116.00	461.00	577.00	ERROR

Check Details

Version: 1.0.5
Check: tests
Result: ERROR Running 'testthat.R' [192s] Running the tests in 'tests/testthat.R' failed. Complete output: > # CRAN OMP THREAD LIMIT > Sys.setenv("OMP_THREAD_LIMIT" = 1) > > library(testthat) > library(shapr) Attaching package: 'shapr' The following object is masked from 'package:testthat': setup > > test_check("shapr") -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Non-iterative * Number of Monte Carlo integration samples: 5 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Non-iterative * Number of Monte Carlo integration samples: 5 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_features = 128`, and is therefore set to `2^n_features = 128`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 7 * Number of observations to explain: 2 -- Main computation started -- i Using 128 of 128 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_features = 64`, and is therefore set to `2^n_features = 64`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 6 * Number of observations to explain: 2 -- Main computation started -- i Using 64 of 64 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 2 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of group-wise Shapley values: 2 * Number of observations to explain: 2 -- Main computation started -- i Using 4 of 4 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of group-wise Shapley values: 2 * Number of observations to explain: 2 -- Main computation started -- i Using 4 of 4 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of group-wise Shapley values: 2 * Number of observations to explain: 2 -- Main computation started -- i Using 4 of 4 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence * Procedure: Iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 6 of 32 coalitions, 6 new. -- Iteration 2 ----------------------------------------------------------------- i Using 8 of 32 coalitions, 2 new. -- Iteration 3 ----------------------------------------------------------------- i Using 10 of 32 coalitions, 2 new. -- Iteration 4 ----------------------------------------------------------------- i Using 12 of 32 coalitions, 2 new. -- Iteration 5 ----------------------------------------------------------------- i Using 14 of 32 coalitions, 2 new. -- Iteration 6 ----------------------------------------------------------------- i Using 16 of 32 coalitions, 2 new. -- Iteration 7 ----------------------------------------------------------------- i Using 18 of 32 coalitions, 2 new. -- Iteration 8 ----------------------------------------------------------------- i Using 20 of 32 coalitions, 2 new. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 6 of 32 coalitions, 6 new. -- Iteration 2 ----------------------------------------------------------------- i Using 8 of 32 coalitions, 2 new. -- Iteration 3 ----------------------------------------------------------------- i Using 12 of 32 coalitions, 4 new. -- Iteration 4 ----------------------------------------------------------------- i Using 16 of 32 coalitions, 4 new. -- Iteration 5 ----------------------------------------------------------------- i Using 18 of 32 coalitions, 2 new. -- Iteration 6 ----------------------------------------------------------------- i Using 22 of 32 coalitions, 4 new. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 32`, and is therefore set to `2^n_groups = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Iterative * Number of Monte Carlo integration samples: 1000 * Number of group-wise Shapley values: 5 * Feature groups: Solar.R: {"Solar.R"}; Wind: {"Wind"}; Temp: {"Temp"}; Month: {"Month"}; Day: {"Day"} * Number of observations to explain: 3 -- Iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 6 of 32 coalitions, 6 new. -- Iteration 2 ----------------------------------------------------------------- i Using 8 of 32 coalitions, 2 new. -- Iteration 3 ----------------------------------------------------------------- i Using 12 of 32 coalitions, 4 new. -- Iteration 4 ----------------------------------------------------------------- i Using 16 of 32 coalitions, 4 new. -- Iteration 5 ----------------------------------------------------------------- i Using 18 of 32 coalitions, 2 new. -- Iteration 6 ----------------------------------------------------------------- i Using 22 of 32 coalitions, 4 new. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 10 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of group-wise Shapley values: 3 * Feature groups: A: {"Solar.R", "Wind"}; B: {"Temp", "Month_factor"}; C: {"Day"} * Number of observations to explain: 3 -- Main computation started -- i Using 6 of 8 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` at 2025-10-09 07:30:04 -------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Computations (temporary) saved at: 'D:\temp\2025_10_09_01_50_00_2422\RtmpSqacxL\shapr_obj_ba4c5c136ffd.rds' -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence, empirical, gaussian, and copula * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence, empirical, gaussian, and copula * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence, empirical, gaussian, and copula * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian, gaussian, gaussian, and gaussian * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence, empirical, independence, and empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence, empirical, independence, and empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: vaeac * Procedure: Non-iterative * Number of Monte Carlo integration samples: 10 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. Flavor: r-devel-windows-x86_64

Version: 1.0.5
Check: Rd files
Result: WARN additional_regression_setup.Rd: Sections \title, and \name must exist and be unique in Rd files aicc_full_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files aicc_full_single_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files append_vS_list.Rd: Sections \title, and \name must exist and be unique in Rd files categorical_to_one_hot_layer.Rd: Sections \title, and \name must exist and be unique in Rd files check_categorical_valid_MCsamp.Rd: Sections \title, and \name must exist and be unique in Rd files check_convergence.Rd: Sections \title, and \name must exist and be unique in Rd files check_groups.Rd: Sections \title, and \name must exist and be unique in Rd files check_verbose.Rd: Sections \title, and \name must exist and be unique in Rd files cli_compute_vS.Rd: Sections \title, and \name must exist and be unique in Rd files cli_iter.Rd: Sections \title, and \name must exist and be unique in Rd files cli_startup.Rd: Sections \title, and \name must exist and be unique in Rd files cli_topline.Rd: Sections \title, and \name must exist and be unique in Rd files coalition_matrix_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files Error writing to connection: No space left on device compute_estimates.Rd: Sections \title, and \name must exist and be unique in Rd files compute_shapley.Rd: Sections \title, and \name must exist and be unique in Rd files compute_time.Rd: Sections \title, and \name must exist and be unique in Rd files compute_vS.Rd: Sections \title, and \name must exist and be unique in Rd files convert_feature_name_to_idx.Rd: Sections \title, and \name must exist and be unique in Rd files correction_matrix_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files Error writing to connection: No space left on device create_ctree.Rd: Sections \title, and \name must exist and be unique in Rd files create_marginal_data_cat.Rd: Sections \title, and \name must exist and be unique in Rd files create_marginal_data_gaussian.Rd: Sections \title, and \name must exist and be unique in Rd files create_marginal_data_training.Rd: Sections \title, and \name must exist and be unique in Rd files default_doc_export.Rd: Sections \title, and \name must exist and be unique in Rd files default_doc_internal.Rd: Sections \title, and \name must exist and be unique in Rd files exact_coalition_table.Rd: Sections \title, and \name must exist and be unique in Rd files Error writing to connection: No space left on device Error writing to connection: No space left on device finalize_explanation.Rd: Sections \title, and \name must exist and be unique in Rd files format_convergence_info.Rd: Sections \title, and \name must exist and be unique in Rd files format_info_basic.Rd: Sections \title, and \name must exist and be unique in Rd files Warning in for (i in seq_along(specs)) { : closing unused connection 6 () Warning in for (i in seq_along(specs)) { : closing unused connection 5 () Warning in for (i in seq_along(specs)) { : closing unused connection 4 () Warning in for (i in seq_along(specs)) { : closing unused connection 3 () format_info_extra.Rd: Sections \title, and \name must exist and be unique in Rd files format_round.Rd: Sections \title, and \name must exist and be unique in Rd files format_shapley_info.Rd: Sections \title, and \name must exist and be unique in Rd files gauss_cat_loss.Rd: Sections \title, and \name must exist and be unique in Rd files gauss_cat_parameters.Rd: Sections \title, and \name must exist and be unique in Rd files gauss_cat_sampler_most_likely.Rd: Sections \title, and \name must exist and be unique in Rd files gauss_cat_sampler_random.Rd: Sections \title, and \name must exist and be unique in Rd files gaussian_transform.Rd: Sections \title, and \name must exist and be unique in Rd files gaussian_transform_separate.Rd: Sections \title, and \name must exist and be unique in Rd files get_S_causal_steps.Rd: Sections \title, and \name must exist and be unique in Rd files get_cov_mat.Rd: Sections \title, and \name must exist and be unique in Rd files get_data_forecast.Rd: Sections \title, and \name must exist and be unique in Rd files get_data_specs.Rd: Sections \title, and \name must exist and be unique in Rd files Error writing to connection: No space left on device get_extra_parameters.Rd: Sections \title, and \name must exist and be unique in Rd files get_feature_specs.Rd: Sections \title, and \name must exist and be unique in Rd files get_iterative_args_default.Rd: Sections \title, and \name must exist and be unique in Rd files get_max_n_coalitions_causal.Rd: Sections \title, and \name must exist and be unique in Rd files get_model_specs.Rd: Sections \title, and \name must exist and be unique in Rd files get_mu_vec.Rd: Sections \title, and \name must exist and be unique in Rd files get_nice_time.Rd: Sections \title, and \name must exist and be unique in Rd files get_output_args_default.Rd: Sections \title, and \name must exist and be unique in Rd files get_predict_model.Rd: Sections \title, and \name must exist and be unique in Rd files Error writing to connection: No space left on device get_supported_approaches.Rd: Sections \title, and \name must exist and be unique in Rd files get_supported_models.Rd: Sections \title, and \name must exist and be unique in Rd files get_valid_causal_coalitions.Rd: Sections \title, and \name must exist and be unique in Rd files group_forecast_setup.Rd: Sections \title, and \name must exist and be unique in Rd files hat_matrix_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files inv_gaussian_transform_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files lag_data.Rd: Sections \title, and \name must exist and be unique in Rd files mahalanobis_distance_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files mcar_mask_generator.Rd: Sections \title, and \name must exist and be unique in Rd files memory_layer.Rd: Sections \title, and \name must exist and be unique in Rd files model_checker.Rd: Sections \title, and \name must exist and be unique in Rd files num_str.Rd: Sections \title, and \name must exist and be unique in Rd files observation_impute.Rd: Sections \title, and \name must exist and be unique in Rd files observation_impute_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files paired_sampler.Rd: Sections \title, and \name must exist and be unique in Rd files Error writing to connection: No space left on device Error writing to connection: No space left on device Error writing to connection: No space left on device Error writing to connection: No space left on device Error writing to connection: No space left on device Error writing to connection: No space left on device prepare_data.Rd: Sections \title, and \name must exist and be unique in Rd files prepare_data_causal.Rd: Sections \title, and \name must exist and be unique in Rd files prepare_data_copula_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files prepare_data_copula_cpp_caus.Rd: Sections \title, and \name must exist and be unique in Rd files prepare_data_gaussian_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files prepare_data_gaussian_cpp_caus.Rd: Sections \title, and \name must exist and be unique in Rd files prepare_data_single_coalition.Rd: Sections \title, and \name must exist and be unique in Rd files prepare_next_iteration.Rd: Sections \title, and \name must exist and be unique in Rd files print.shapr.Rd: Sections \title, and \name must exist and be unique in Rd files print_iter.Rd: Sections \title, and \name must exist and be unique in Rd files process_factor_data.Rd: Sections \title, and \name must exist and be unique in Rd files quantile_type7_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files reg_forecast_setup.Rd: Sections \title, and \name must exist and be unique in Rd files regression.check_namespaces.Rd: Sections \title, and \name must exist and be unique in Rd files regression.check_parameters.Rd: Sections \title, and \name must exist and be unique in Rd files regression.check_recipe_func.Rd: Sections \title, and \name must exist and be unique in Rd files regression.check_sur_n_comb.Rd: Sections \title, and \name must exist and be unique in Rd files regression.check_vfold_cv_para.Rd: Sections \title, and \name must exist and be unique in Rd files regression.cv_message.Rd: Sections \title, and \name must exist and be unique in Rd files regression.get_string_to_R.Rd: Sections \title, and \name must exist and be unique in Rd files Warning in for (block in blocks) { : closing unused connection 10 () Warning in for (block in blocks) { : closing unused connection 9 () Warning in for (block in blocks) { : closing unused connection 8 () Warning in for (block in blocks) { : closing unused connection 7 () Warning in for (block in blocks) { : closing unused connection 6 () Warning in for (block in blocks) { : closing unused connection 5 () Warning in for (block in blocks) { : closing unused connection 4 () Warning in for (block in blocks) { : closing unused connection 3 () round_manual.Rd: Sections \title, and \name must exist and be unique in Rd files rss_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files Error writing to connection: No space left on device sample_coalitions_cpp_str_paired.Rd: Sections \title, and \name must exist and be unique in Rd files sample_combinations.Rd: Sections \title, and \name must exist and be unique in Rd files sample_ctree.Rd: Sections \title, and \name must exist and be unique in Rd files save_results.Rd: Sections \title, and \name must exist and be unique in Rd files Error writing to connection: No space left on device Error writing to connection: No space left on device shapley_setup.Rd: Sections \title, and \name must exist and be unique in Rd files shapley_weights.Rd: Sections \title, and \name must exist and be unique in Rd files shapr-package.Rd: Sections \title, and \name must exist and be unique in Rd files skip_connection.Rd: Sections \title, and \name must exist and be unique in Rd files specified_masks_mask_generator.Rd: Sections \title, and \name must exist and be unique in Rd files specified_prob_mask_generator.Rd: Sections \title, and \name must exist and be unique in Rd files summary.shapr.Rd: Sections \title, and \name must exist and be unique in Rd files test_predict_model.Rd: Sections \title, and \name must exist and be unique in Rd files testing_cleanup.Rd: Sections \title, and \name must exist and be unique in Rd files Error writing to connection: No space left on device vaeac_categorical_parse_params.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_check_activation_func.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_check_cuda.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_check_epoch_values.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_check_extra_named_list.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_check_logicals.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_check_mask_gen.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_check_masking_ratio.Rd: Sections \title, and \name must exist and be unique in Rd files Error writing to connection: No space left on device vaeac_check_positive_integers.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_check_positive_numerics.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_check_probabilities.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_check_save_names.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_check_save_parameters.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_check_which_vaeac_model.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_check_x_colnames.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_compute_normalization.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_dataset.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_extend_batch.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_get_current_save_state.Rd: Sections \title, and \name must exist and be unique in Rd files Error writing to connection: No space left on device vaeac_get_evaluation_criteria.Rd: Sections \title, and \name must exist and be unique in Rd files Error writing to connection: No space left on device vaeac_get_full_state_list.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_get_mask_generator_name.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_get_model_from_checkp.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_get_n_decimals.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_get_optimizer.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_get_save_file_names.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_get_val_iwae.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_get_x_explain_extended.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_impute_missing_entries.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_kl_normal_normal.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_normal_parse_params.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_normalize_data.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_postprocess_data.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_preprocess_data.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_print_train_summary.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_save_state.Rd: Sections \title, and \name must exist and be unique in Rd files Error writing to connection: No space left on device Error writing to connection: No space left on device vaeac_train_model_continue.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_update_para_locations.Rd: Sections \title, and \name must exist and be unique in Rd files vaeac_update_pretrained_model.Rd: Sections \title, and \name must exist and be unique in Rd files weight_matrix.Rd: Sections \title, and \name must exist and be unique in Rd files weight_matrix_cpp.Rd: Sections \title, and \name must exist and be unique in Rd files problems found in ‘additional_regression_setup.Rd’, ‘aicc_full_cpp.Rd’, ‘aicc_full_single_cpp.Rd’, ‘append_vS_list.Rd’, ‘categorical_to_one_hot_layer.Rd’, ‘check_categorical_valid_MCsamp.Rd’, ‘check_convergence.Rd’, ‘check_groups.Rd’, ‘check_verbose.Rd’, ‘cli_compute_vS.Rd’, ‘cli_iter.Rd’, ‘cli_startup.Rd’, ‘cli_topline.Rd’, ‘coalition_matrix_cpp.Rd’, ‘compute_MSEv_eval_crit.Rd’, ‘compute_estimates.Rd’, ‘compute_shapley.Rd’, ‘compute_time.Rd’, ‘compute_vS.Rd’, ‘convert_feature_name_to_idx.Rd’, ‘correction_matrix_cpp.Rd’, ‘create_coalition_table.Rd’, ‘create_ctree.Rd’, ‘create_marginal_data_cat.Rd’, ‘create_marginal_data_gaussian.Rd’, ‘create_marginal_data_training.Rd’, ‘default_doc_export.Rd’, ‘default_doc_internal.Rd’, ‘exact_coalition_table.Rd’, ‘explain.Rd’, ‘explain_forecast.Rd’, ‘finalize_explanation.Rd’, ‘format_convergence_info.Rd’, ‘format_info_basic.Rd’, ‘format_info_extra.Rd’, ‘format_round.Rd’, ‘format_shapley_info.Rd’, ‘gauss_cat_loss.Rd’, ‘gauss_cat_parameters.Rd’, ‘gauss_cat_sampler_most_likely.Rd’, ‘gauss_cat_sampler_random.Rd’, ‘gaussian_transform.Rd’, ‘gaussian_transform_separate.Rd’, ‘get_S_causal_steps.Rd’, ‘get_cov_mat.Rd’, ‘get_data_forecast.Rd’, ‘get_data_specs.Rd’, ‘get_extra_comp_args_default.Rd’, ‘get_extra_parameters.Rd’, ‘get_feature_specs.Rd’, ‘get_iterative_args_default.Rd’, ‘get_max_n_coalitions_causal.Rd’, ‘get_model_specs.Rd’, ‘get_mu_vec.Rd’, ‘get_nice_time.Rd’, ‘get_output_args_default.Rd’, ‘get_predict_model.Rd’, ‘get_results.Rd’, ‘get_supported_approaches.Rd’, ‘get_supported_models.Rd’, ‘get_valid_causal_coalitions.Rd’, ‘group_forecast_setup.Rd’, ‘hat_matrix_cpp.Rd’, ‘inv_gaussian_transform_cpp.Rd’, ‘lag_data.Rd’, ‘mahalanobis_distance_cpp.Rd’, ‘mcar_mask_generator.Rd’, ‘memory_layer.Rd’, ‘model_checker.Rd’, ‘num_str.Rd’, ‘observation_impute.Rd’, ‘observation_impute_cpp.Rd’, ‘paired_sampler.Rd’, ‘plot.shapr.Rd’, ‘plot_MSEv_eval_crit.Rd’, ‘plot_SV_several_approaches.Rd’, ‘plot_vaeac_eval_crit.Rd’, ‘plot_vaeac_imputed_ggpairs.Rd’, ‘predict_model.Rd’, ‘prepare_data.Rd’, ‘prepare_data_causal.Rd’, ‘prepare_data_copula_cpp.Rd’, ‘prepare_data_copula_cpp_caus.Rd’, ‘prepare_data_gaussian_cpp.Rd’, ‘prepare_data_gaussian_cpp_caus.Rd’, ‘prepare_data_single_coalition.Rd’, ‘prepare_next_iteration.Rd’, ‘print.shapr.Rd’, ‘print_iter.Rd’, ‘process_factor_data.Rd’, ‘quantile_type7_cpp.Rd’, ‘reg_forecast_setup.Rd’, ‘regression.check_namespaces.Rd’, ‘regression.check_parameters.Rd’, ‘regression.check_recipe_func.Rd’, ‘regression.check_sur_n_comb.Rd’, ‘regression.check_vfold_cv_para.Rd’, ‘regression.cv_message.Rd’, ‘regression.get_string_to_R.Rd’, ‘round_manual.Rd’, ‘rss_cpp.Rd’, ‘sample_coalition_table.Rd’, ‘sample_coalitions_cpp_str_paired.Rd’, ‘sample_combinations.Rd’, ‘sample_ctree.Rd’, ‘save_results.Rd’, ‘setup.Rd’, ‘setup_approach.Rd’, ‘shapley_setup.Rd’, ‘shapley_weights.Rd’, ‘shapr-package.Rd’, ‘skip_connection.Rd’, ‘specified_masks_mask_generator.Rd’, ‘specified_prob_mask_generator.Rd’, ‘summary.shapr.Rd’, ‘test_predict_model.Rd’, ‘testing_cleanup.Rd’, ‘vaeac.Rd’, ‘vaeac_categorical_parse_params.Rd’, ‘vaeac_check_activation_func.Rd’, ‘vaeac_check_cuda.Rd’, ‘vaeac_check_epoch_values.Rd’, ‘vaeac_check_extra_named_list.Rd’, ‘vaeac_check_logicals.Rd’, ‘vaeac_check_mask_gen.Rd’, ‘vaeac_check_masking_ratio.Rd’, ‘vaeac_check_parameters.Rd’, ‘vaeac_check_positive_integers.Rd’, ‘vaeac_check_positive_numerics.Rd’, ‘vaeac_check_probabilities.Rd’, ‘vaeac_check_save_names.Rd’, ‘vaeac_check_save_parameters.Rd’, ‘vaeac_check_which_vaeac_model.Rd’, ‘vaeac_check_x_colnames.Rd’, ‘vaeac_compute_normalization.Rd’, ‘vaeac_dataset.Rd’, ‘vaeac_extend_batch.Rd’, ‘vaeac_get_current_save_state.Rd’, ‘vaeac_get_data_objects.Rd’, ‘vaeac_get_evaluation_criteria.Rd’, ‘vaeac_get_extra_para_default.Rd’, ‘vaeac_get_full_state_list.Rd’, ‘vaeac_get_mask_generator_name.Rd’, ‘vaeac_get_model_from_checkp.Rd’, ‘vaeac_get_n_decimals.Rd’, ‘vaeac_get_optimizer.Rd’, ‘vaeac_get_save_file_names.Rd’, ‘vaeac_get_val_iwae.Rd’, ‘vaeac_get_x_explain_extended.Rd’, ‘vaeac_impute_missing_entries.Rd’, ‘vaeac_kl_normal_normal.Rd’, ‘vaeac_normal_parse_params.Rd’, ‘vaeac_normalize_data.Rd’, ‘vaeac_postprocess_data.Rd’, ‘vaeac_preprocess_data.Rd’, ‘vaeac_print_train_summary.Rd’, ‘vaeac_save_state.Rd’, ‘vaeac_train_model.Rd’, ‘vaeac_train_model_auxiliary.Rd’, ‘vaeac_train_model_continue.Rd’, ‘vaeac_update_para_locations.Rd’, ‘vaeac_update_pretrained_model.Rd’, ‘weight_matrix.Rd’, ‘weight_matrix_cpp.Rd’ Flavor: r-release-macos-x86_64

Version: 1.0.5
Check: tests
Result: ERROR Running 'testthat.R' [189s] Running the tests in 'tests/testthat.R' failed. Complete output: > # CRAN OMP THREAD LIMIT > Sys.setenv("OMP_THREAD_LIMIT" = 1) > > library(testthat) > library(shapr) Attaching package: 'shapr' The following object is masked from 'package:testthat': setup > > test_check("shapr") -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Non-iterative * Number of Monte Carlo integration samples: 5 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Non-iterative * Number of Monte Carlo integration samples: 5 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_features = 128`, and is therefore set to `2^n_features = 128`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 7 * Number of observations to explain: 2 -- Main computation started -- i Using 128 of 128 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_features = 64`, and is therefore set to `2^n_features = 64`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 6 * Number of observations to explain: 2 -- Main computation started -- i Using 64 of 64 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 2 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of group-wise Shapley values: 2 * Number of observations to explain: 2 -- Main computation started -- i Using 4 of 4 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of group-wise Shapley values: 2 * Number of observations to explain: 2 -- Main computation started -- i Using 4 of 4 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of group-wise Shapley values: 2 * Number of observations to explain: 2 -- Main computation started -- i Using 4 of 4 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence * Procedure: Iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 6 of 32 coalitions, 6 new. -- Iteration 2 ----------------------------------------------------------------- i Using 8 of 32 coalitions, 2 new. -- Iteration 3 ----------------------------------------------------------------- i Using 10 of 32 coalitions, 2 new. -- Iteration 4 ----------------------------------------------------------------- i Using 12 of 32 coalitions, 2 new. -- Iteration 5 ----------------------------------------------------------------- i Using 14 of 32 coalitions, 2 new. -- Iteration 6 ----------------------------------------------------------------- i Using 16 of 32 coalitions, 2 new. -- Iteration 7 ----------------------------------------------------------------- i Using 18 of 32 coalitions, 2 new. -- Iteration 8 ----------------------------------------------------------------- i Using 20 of 32 coalitions, 2 new. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 6 of 32 coalitions, 6 new. -- Iteration 2 ----------------------------------------------------------------- i Using 8 of 32 coalitions, 2 new. -- Iteration 3 ----------------------------------------------------------------- i Using 12 of 32 coalitions, 4 new. -- Iteration 4 ----------------------------------------------------------------- i Using 16 of 32 coalitions, 4 new. -- Iteration 5 ----------------------------------------------------------------- i Using 18 of 32 coalitions, 2 new. -- Iteration 6 ----------------------------------------------------------------- i Using 22 of 32 coalitions, 4 new. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 32`, and is therefore set to `2^n_groups = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Iterative * Number of Monte Carlo integration samples: 1000 * Number of group-wise Shapley values: 5 * Feature groups: Solar.R: {"Solar.R"}; Wind: {"Wind"}; Temp: {"Temp"}; Month: {"Month"}; Day: {"Day"} * Number of observations to explain: 3 -- Iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 6 of 32 coalitions, 6 new. -- Iteration 2 ----------------------------------------------------------------- i Using 8 of 32 coalitions, 2 new. -- Iteration 3 ----------------------------------------------------------------- i Using 12 of 32 coalitions, 4 new. -- Iteration 4 ----------------------------------------------------------------- i Using 16 of 32 coalitions, 4 new. -- Iteration 5 ----------------------------------------------------------------- i Using 18 of 32 coalitions, 2 new. -- Iteration 6 ----------------------------------------------------------------- i Using 22 of 32 coalitions, 4 new. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 10 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of group-wise Shapley values: 3 * Feature groups: A: {"Solar.R", "Wind"}; B: {"Temp", "Month_factor"}; C: {"Day"} * Number of observations to explain: 3 -- Main computation started -- i Using 6 of 8 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` at 2025-10-06 12:29:26 -------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Computations (temporary) saved at: 'D:\temp\2025_10_06_01_50_00_7952\RtmpuwDw34\shapr_obj_2b89c2b78ce1.rds' -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence, empirical, gaussian, and copula * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence, empirical, gaussian, and copula * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence, empirical, gaussian, and copula * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian, gaussian, gaussian, and gaussian * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence, empirical, independence, and empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence, empirical, independence, and empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: vaeac * Procedure: Non-iterative * Number of Monte Carlo integration samples: 10 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. Flavor: r-release-windows-x86_64

Version: 1.0.5
Check: installed package size
Result: NOTE installed size is 8.6Mb sub-directories of 1Mb or more: doc 3.3Mb libs 4.1Mb Flavors: r-oldrel-macos-arm64, r-oldrel-macos-x86_64, r-oldrel-windows-x86_64

Version: 1.0.5
Check: tests
Result: ERROR Running 'testthat.R' [269s] Running the tests in 'tests/testthat.R' failed. Complete output: > # CRAN OMP THREAD LIMIT > Sys.setenv("OMP_THREAD_LIMIT" = 1) > > library(testthat) > library(shapr) Attaching package: 'shapr' The following object is masked from 'package:testthat': setup > > test_check("shapr") -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Non-iterative * Number of Monte Carlo integration samples: 5 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Non-iterative * Number of Monte Carlo integration samples: 5 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_features = 128`, and is therefore set to `2^n_features = 128`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 7 * Number of observations to explain: 2 -- Main computation started -- i Using 128 of 128 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_features = 64`, and is therefore set to `2^n_features = 64`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 6 * Number of observations to explain: 2 -- Main computation started -- i Using 64 of 64 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 2 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of group-wise Shapley values: 2 * Number of observations to explain: 2 -- Main computation started -- i Using 4 of 4 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of group-wise Shapley values: 2 * Number of observations to explain: 2 -- Main computation started -- i Using 4 of 4 coalitions. -- Starting `shapr::explain_forecast()` ---------------------------------------- i Feature names extracted from the model contain `NA`. Consistency checks between model and data are therefore disabled. i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 4`, and is therefore set to `2^n_groups = 4`. -- Explanation overview -- * Model class: <Arima> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of group-wise Shapley values: 2 * Number of observations to explain: 2 -- Main computation started -- i Using 4 of 4 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence * Procedure: Iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 6 of 32 coalitions, 6 new. -- Iteration 2 ----------------------------------------------------------------- i Using 8 of 32 coalitions, 2 new. -- Iteration 3 ----------------------------------------------------------------- i Using 10 of 32 coalitions, 2 new. -- Iteration 4 ----------------------------------------------------------------- i Using 12 of 32 coalitions, 2 new. -- Iteration 5 ----------------------------------------------------------------- i Using 14 of 32 coalitions, 2 new. -- Iteration 6 ----------------------------------------------------------------- i Using 16 of 32 coalitions, 2 new. -- Iteration 7 ----------------------------------------------------------------- i Using 18 of 32 coalitions, 2 new. -- Iteration 8 ----------------------------------------------------------------- i Using 20 of 32 coalitions, 2 new. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 6 of 32 coalitions, 6 new. -- Iteration 2 ----------------------------------------------------------------- i Using 8 of 32 coalitions, 2 new. -- Iteration 3 ----------------------------------------------------------------- i Using 12 of 32 coalitions, 4 new. -- Iteration 4 ----------------------------------------------------------------- i Using 16 of 32 coalitions, 4 new. -- Iteration 5 ----------------------------------------------------------------- i Using 18 of 32 coalitions, 2 new. -- Iteration 6 ----------------------------------------------------------------- i Using 22 of 32 coalitions, 4 new. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_groups = 32`, and is therefore set to `2^n_groups = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Iterative * Number of Monte Carlo integration samples: 1000 * Number of group-wise Shapley values: 5 * Feature groups: Solar.R: {"Solar.R"}; Wind: {"Wind"}; Temp: {"Temp"}; Month: {"Month"}; Day: {"Day"} * Number of observations to explain: 3 -- Iterative computation started -- -- Iteration 1 ----------------------------------------------------------------- i Using 6 of 32 coalitions, 6 new. -- Iteration 2 ----------------------------------------------------------------- i Using 8 of 32 coalitions, 2 new. -- Iteration 3 ----------------------------------------------------------------- i Using 12 of 32 coalitions, 4 new. -- Iteration 4 ----------------------------------------------------------------- i Using 16 of 32 coalitions, 4 new. -- Iteration 5 ----------------------------------------------------------------- i Using 18 of 32 coalitions, 2 new. -- Iteration 6 ----------------------------------------------------------------- i Using 22 of 32 coalitions, 4 new. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 10 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of group-wise Shapley values: 3 * Feature groups: A: {"Solar.R", "Wind"}; B: {"Temp", "Month_factor"}; C: {"Day"} * Number of observations to explain: 3 -- Main computation started -- i Using 6 of 8 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: ctree * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` at 2025-10-08 10:54:01 -------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 * Computations (temporary) saved at: 'D:\temp\2025_10_08_01_50_00_15188\RtmpmaYAvM\shapr_obj_6ed442e12d91.rds' -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence, empirical, gaussian, and copula * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence, empirical, gaussian, and copula * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence, empirical, gaussian, and copula * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: gaussian, gaussian, gaussian, and gaussian * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence, empirical, independence, and empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: independence, empirical, independence, and empirical * Procedure: Non-iterative * Number of Monte Carlo integration samples: 1000 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. -- Starting `shapr::explain()` ------------------------------------------------- i `max_n_coalitions` is `NULL` or larger than `2^n_features = 32`, and is therefore set to `2^n_features = 32`. -- Explanation overview -- * Model class: <lm> * v(S) estimation class: Monte Carlo integration * Approach: vaeac * Procedure: Non-iterative * Number of Monte Carlo integration samples: 10 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 32 of 32 coalitions. Flavor: r-oldrel-windows-x86_64