R Under development (unstable) (2025-11-16 r89026 ucrt) -- "Unsuffered Consequences" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-w64-mingw32/x64 R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > # 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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: * 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 2025-11-17 15:50:09 -------------------------- 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: * 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_11_17_15_45_16_7643\RtmpqUcJ9i\shapr_obj_113b41f9f683f.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: * 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: * 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: * 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: * 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: * 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: * 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: * 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. Torch libraries are installed but loading them was unsuccessful. Torch libraries are installed but loading them was unsuccessful. -- 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: * 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()` ------------------------------------------------- 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: * v(S) estimation class: Monte Carlo integration * Approach: gaussian * Procedure: Non-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 -- 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: * v(S) estimation class: Monte Carlo integration * Approach: independence * 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: * v(S) estimation class: Monte Carlo integration * Approach: ctree * 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 18 of 32 coalitions, 6 new. -- Iteration 5 ----------------------------------------------------------------- i Using 24 of 32 coalitions, 6 new. -- Iteration 6 ----------------------------------------------------------------- i Using 28 of 32 coalitions, 4 new. -- Iteration 7 ----------------------------------------------------------------- i Using 30 of 32 coalitions, 2 new. -- Iteration 8 ----------------------------------------------------------------- i Using 32 of 32 coalitions, 2 new. -- Starting `shapr::explain()` ------------------------------------------------- -- Explanation overview -- * Model class: * v(S) estimation class: Monte Carlo integration * Approach: independence * Procedure: Non-iterative * Number of Monte Carlo integration samples: 50 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 18 of 32 coalitions. -- Convergence info v Iterative Shapley value estimation stopped at 18 coalitions after 1 iterations, due to: Maximum number of iterations (1) reached! Maximum number of coalitions (18) reached! Final estimated Shapley values (sd) explain_id none Solar.R Wind Temp Month 1: 1 42.44 (0) -3.39 (0.80) 7.95 (0.62) 14.86 (3.27) -4.63 (2.39) 2: 2 42.44 (0) 3.08 (0.62) -3.56 (0.36) -4.64 (0.97) -6.03 (1.03) 3: 3 42.44 (0) 3.73 (0.60) -18.90 (0.68) -1.04 (1.40) -3.56 (1.36) Day 1: -2.20 (2.47) 2: -2.74 (0.96) 3: 2.20 (0.96) -- Starting `shapr::explain()` ------------------------------------------------- -- Explanation overview -- * Model class: * v(S) estimation class: Monte Carlo integration * Approach: independence * Procedure: Non-iterative * Number of Monte Carlo integration samples: 50 * Number of feature-wise Shapley values: 5 * Number of observations to explain: 3 -- Main computation started -- i Using 20 of 32 coalitions. -- Convergence info v Iterative Shapley value estimation stopped at 20 coalitions after 1 iterations, due to: Maximum number of iterations (1) reached! Maximum number of coalitions (20) reached! Final estimated Shapley values (sd) explain_id none Solar.R Wind Temp Month 1: 1 42.44 (0) -4.33 (0.59) 7.52 (0.79) 17.47 (0.29) -5.01 (0.72) 2: 2 42.44 (0) 2.87 (0.55) -4.41 (0.35) -4.71 (0.16) -4.97 (0.50) 3: 3 42.44 (0) 3.35 (0.18) -18.35 (0.16) -1.83 (0.06) -2.82 (0.21) Day 1: -3.06 (0.29) 2: -2.67 (0.16) 3: 2.08 (0.06) [ FAIL 0 | WARN 0 | SKIP 58 | PASS 60 ] ══ Skipped tests (58) ══════════════════════════════════════════════════════════ • On CRAN (56): 'test-asymmetric-causal-output.R:14:1', 'test-asymmetric-causal-setup.R:1:1', 'test-asymmetric-causal-setup.R:229:1', 'test-asymmetric-causal-setup.R:253:1', 'test-asymmetric-causal-setup.R:318:1', 'test-forecast-output.R:2:1', 'test-forecast-setup.R:3:1', 'test-forecast-setup.R:33:1', 'test-forecast-setup.R:107:1', 'test-forecast-setup.R:136:1', 'test-forecast-setup.R:163:1', 'test-forecast-setup.R:225:1', 'test-forecast-setup.R:299:1', 'test-forecast-setup.R:349:1', 'test-forecast-setup.R:445:1', 'test-forecast-setup.R:518:1', 'test-iterative-output.R:1:1', 'test-iterative-setup.R:75:1', 'test-iterative-setup.R:263:1', 'test-iterative-setup.R:397:1', 'test-plot.R:1:1', 'test-regression-output.R:1:1', 'test-regression-setup.R:8:1', 'test-regression-setup.R:46:1', 'test-regression-setup.R:174:1', 'test-regression-setup.R:232:1', 'test-regression-setup.R:294:1', 'test-regression-setup.R:335:1', 'test-regular-output.R:1:1', 'test-regular-setup.R:1:1', 'test-regular-setup.R:26:1', 'test-regular-setup.R:118:1', 'test-regular-setup.R:236:1', 'test-regular-setup.R:259:1', 'test-regular-setup.R:317:1', 'test-regular-setup.R:394:1', 'test-regular-setup.R:555:1', 'test-regular-setup.R:678:1', 'test-regular-setup.R:793:1', 'test-regular-setup.R:814:1', 'test-regular-setup.R:872:1', 'test-regular-setup.R:930:1', 'test-regular-setup.R:1037:1', 'test-regular-setup.R:1149:1', 'test-regular-setup.R:1222:1', 'test-regular-setup.R:1262:1', 'test-regular-setup.R:1782:1', 'test-regular-setup.R:1828:1', 'test-regular-setup.R:1849:1', 'test-semi-deterministic-output.R:1:1', 'test-semi-deterministic-setup.R:1:1', 'test-semi-deterministic-setup.R:22:1', 'test-semi-deterministic-setup.R:47:1', 'test-semi-deterministic-setup.R:71:1', 'test-semi-deterministic-setup.R:100:1', 'test-summary.R:1:1' • torch::torch_is_installed() is not TRUE (2): 'test-regular-setup.R:1630:3', 'test-regular-setup.R:1673:3' [ FAIL 0 | WARN 0 | SKIP 58 | PASS 60 ] > > proc.time() user system elapsed 175.01 4.60 184.53