* using log directory 'd:/Rcompile/CRANpkg/local/4.4/shapr.Rcheck' * using R version 4.4.3 (2025-02-28 ucrt) * using platform: x86_64-w64-mingw32 * R was compiled by gcc.exe (GCC) 13.3.0 GNU Fortran (GCC) 13.3.0 * 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 * used C++ compiler: 'g++.exe (GCC) 13.3.0' * checking installed package size ... NOTE installed size is 5.8Mb sub-directories of 1Mb or more: doc 3.3Mb libs 1.3Mb * 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 ... [1s] OK * checking whether the package can be unloaded cleanly ... [1s] OK * checking whether the namespace can be loaded with stated dependencies ... [1s] 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 ... [26s] OK * checking Rd files ... [4s] 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 ... [2s] OK * checking for unstated dependencies in 'tests' ... OK * checking tests ... [263s] ERROR Running 'testthat.R' [263s] 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: * 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 2025-09-05 08:36:46 -------------------------- 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_09_05_01_50_00_10480\RtmpSq0QWm\shapr_obj_17ccc3f495f74.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. -- 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: 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 ... [16s] OK * checking PDF version of manual ... [30s] OK * checking HTML version of manual ... [36s] OK * DONE Status: 1 ERROR, 1 NOTE