Running 'testthat.R' [210s]
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 2025-10-26 14:41:03 --------------------------
  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_26_01_50_00_14777\RtmpMjlaMQ\shapr_obj_b8a079154a9b.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. 
  
  -- 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. 
  
  -- 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()` -------------------------------------------------
  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: 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()` -------------------------------------------------
  
  -- Explanation overview --
  
  * Model class: <lm>
  * 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
          <int>    <char>       <char>        <char>       <char>       <char>
  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
           <char>
  1: -2.20 (2.47)
  2: -2.74 (0.96)
  3:  2.20 (0.96)
  
  -- Starting `shapr::explain()` -------------------------------------------------
  
  -- Explanation overview --
  
  * Model class: <lm>
  * 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
          <int>    <char>       <char>        <char>       <char>       <char>
  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
           <char>
  1: -3.06 (0.29)
  2: -2.67 (0.16)
  3:  2.08 (0.06)
  [ FAIL 2 | WARN 1 | SKIP 56 | PASS 48 ]
  
  ══ Skipped tests (56) ══════════════════════════════════════════════════════════
  • On CRAN (56): 'test-asymmetric-causal-output.R:14:1',
    'test-asymmetric-causal-setup.R:4:3', 'test-asymmetric-causal-setup.R:232:3',
    'test-asymmetric-causal-setup.R:256:3',
    'test-asymmetric-causal-setup.R:321:3', 'test-forecast-output.R:2:1',
    'test-forecast-setup.R:7:3', 'test-forecast-setup.R:36:3',
    'test-forecast-setup.R:114:3', 'test-forecast-setup.R:139:3',
    'test-forecast-setup.R:166:3', 'test-forecast-setup.R:228:3',
    'test-forecast-setup.R:302:3', 'test-forecast-setup.R:352:3',
    'test-forecast-setup.R:448:3', 'test-forecast-setup.R:521:3',
    'test-iterative-output.R:1:1', 'test-iterative-setup.R:79:3',
    'test-iterative-setup.R:313:3', 'test-iterative-setup.R:398:3',
    'test-plot.R:1:1', 'test-regression-output.R:1:1',
    'test-regression-setup.R:11:3', 'test-regression-setup.R:49:3',
    'test-regression-setup.R:177:3', 'test-regression-setup.R:235:3',
    'test-regression-setup.R:297:3', 'test-regression-setup.R:338:3',
    'test-regular-output.R:1:1', 'test-regular-setup.R:5:3',
    'test-regular-setup.R:38:3', 'test-regular-setup.R:121:3',
    'test-regular-setup.R:243:3', 'test-regular-setup.R:262:3',
    'test-regular-setup.R:320:3', 'test-regular-setup.R:397:3',
    'test-regular-setup.R:558:3', 'test-regular-setup.R:681:3',
    'test-regular-setup.R:797:3', 'test-regular-setup.R:818:3',
    'test-regular-setup.R:876:3', 'test-regular-setup.R:934:3',
    'test-regular-setup.R:1040:3', 'test-regular-setup.R:1152:3',
    'test-regular-setup.R:1225:3', 'test-regular-setup.R:1269:3',
    'test-regular-setup.R:1794:3', 'test-regular-setup.R:1829:3',
    'test-regular-setup.R:1852:3', 'test-semi-deterministic-output.R:1:1',
    'test-semi-deterministic-setup.R:2:3',
    'test-semi-deterministic-setup.R:23:3',
    'test-semi-deterministic-setup.R:48:3',
    'test-semi-deterministic-setup.R:97:3',
    'test-semi-deterministic-setup.R:126:3', 'test-summary.R:1:1'
  
  ══ Failed tests ════════════════════════════════════════════════════════════════
  ── Error ('test-regular-setup.R:1632:3'): vaeac_set_seed_works ─────────────────
  <std::runtime_error/C++Error/error/condition>
  Error in `cpp_torch_manual_seed(as.character(seed))`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.
  Backtrace:
      ▆
   1. └─shapr::explain(...) at test-regular-setup.R:1632:3
   2.   ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
   3.   └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
   4.     ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
   5.     └─shapr (local) `<fn>`(...)
   6.       └─torch::torch_manual_seed(seed)
   7.         └─torch:::cpp_torch_manual_seed(as.character(seed))
  ── Error ('test-regular-setup.R:1678:3'): vaeac_pretreained_vaeac_model ────────
  <std::runtime_error/C++Error/error/condition>
  Error in `cpp_torch_manual_seed(as.character(seed))`: Lantern is not loaded. Please use `install_torch()` to install additional dependencies.
  Backtrace:
      ▆
   1. └─shapr::explain(...) at test-regular-setup.R:1678:3
   2.   ├─shapr::setup_approach(internal, model = model, predict_model = predict_model)
   3.   └─shapr:::setup_approach.vaeac(internal, model = model, predict_model = predict_model)
   4.     ├─base::do.call(vaeac_train_model, c(vaeac_all_parameters, list(x_train = internal$data$x_train)))
   5.     └─shapr (local) `<fn>`(...)
   6.       └─torch::torch_manual_seed(seed)
   7.         └─torch:::cpp_torch_manual_seed(as.character(seed))
  
  [ FAIL 2 | WARN 1 | SKIP 56 | PASS 48 ]
  Error: Test failures
  Execution halted