• using R Under development (unstable) (2025-08-30 r88742 ucrt)
• using platform: x86_64-w64-mingw32
• R was compiled by     gcc.exe (GCC) 14.2.0     GNU Fortran (GCC) 14.2.0
• running under: Windows Server 2022 x64 (build 20348)
• using session charset: UTF-8
• checking for file 'shapr/DESCRIPTION' ... OK
• this is package 'shapr' version '1.0.5'
• package encoding: UTF-8
• checking package namespace information ... OK
• checking package dependencies ... OK
• checking if this is a source package ... OK
• checking if there is a namespace ... OK
• checking for hidden files and directories ... OK
• checking for portable file names ... OK
• checking whether package 'shapr' can be installed ... OK See the install log for details.
• used C++ compiler: 'g++.exe (GCC) 14.2.0'
• checking installed package size ... INFO   installed size is 5.9Mb   sub-directories of 1Mb or more:     doc 3.3Mb     libs 1.4Mb
• checking package directory ... OK
• checking 'build' directory ... OK
• checking DESCRIPTION meta-information ... OK
• checking top-level files ... OK
• checking for left-over files ... OK
• checking index information ... OK
• checking package subdirectories ... OK
• checking code files for non-ASCII characters ... OK
• checking R files for syntax errors ... OK
• checking whether the package can be loaded ... [1s] OK
• checking whether the package can be loaded with stated dependencies ... [0s] OK
• checking whether the package can be unloaded cleanly ... [1s] OK
• checking whether the namespace can be loaded with stated dependencies ... [0s] OK
• checking whether the namespace can be unloaded cleanly ... [1s] OK
• checking loading without being on the library search path ... [1s] OK
• checking whether startup messages can be suppressed ... [1s] OK
• checking use of S3 registration ... OK
• checking dependencies in R code ... OK
• checking S3 generic/method consistency ... OK
• checking replacement functions ... OK
• checking foreign function calls ... OK
• checking R code for possible problems ... [17s] OK
• checking Rd files ... [3s] OK
• checking Rd metadata ... OK
• checking Rd cross-references ... OK
• checking for missing documentation entries ... OK
• checking for code/documentation mismatches ... OK
• checking Rd \usage sections ... OK
• checking Rd contents ... OK
• checking for unstated dependencies in examples ... OK
• checking line endings in C/C++/Fortran sources/headers ... OK
• checking line endings in Makefiles ... OK
• checking compilation flags in Makevars ... OK
• checking for GNU extensions in Makefiles ... OK
• checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
• checking use of PKG_*FLAGS in Makefiles ... OK
• checking pragmas in C/C++ headers and code ... OK
• checking compiled code ... OK
• checking installed files from 'inst/doc' ... OK
• checking files in 'vignettes' ... OK
• checking examples ... [1s] OK
• checking for unstated dependencies in 'tests' ... OK
• checking tests ... [175s] ERROR   Running 'testthat.R' [175s] 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 2025-08-31 19:12:10 --------------------------   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_08_31_01_50_00_8774\Rtmp4iswgs\shapr_obj_8de046653a83.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.
• checking for unstated dependencies in vignettes ... OK
• checking package vignettes ... OK
• checking re-building of vignette outputs ... [14s] OK
• checking PDF version of manual ... [30s] OK
• checking HTML version of manual ... [32s] OK
• DONE

Status: 1 ERROR