R Under development (unstable) (2024-01-20 r85814 ucrt) -- "Unsuffered Consequences"
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> library("testthat")
> test_check("gbm3", filter="^s")
Loading required package: gbm3
The best cross-validation iteration was 30.
The best test-set iteration was 30.
The best cross-validation iteration was 30.
A gradient boosted model with Bernoulli loss function.
 30 iterations were performed.

Cross-validation confusion matrix:
    0 1 Pred. Acc.
0 623 0        100
1 377 0          0

Cross-validation prediction Accuracy = 62.3%

Cross-validation confusion matrix:
    0 1 Pred. Acc.
0 623 0        100
1 377 0          0

Cross-validation prediction Accuracy = 62.3%

Summary of cross-validation residuals:
       0%       25%       50%       75%      100% 
0.3727498 0.4667348 0.5210784 1.4389107 1.5977016 

Cross-validation robust pseudo R-squared:  -Inf 

Summary of cross-validation residuals:
       0%       25%       50%       75%      100% 
0.3727498 0.4667348 0.5210784 1.4389107 1.5977016 

Cross-validation robust pseudo R-squared:  -Inf 

Summary of cross-validation residuals:
       0%       25%       50%       75%      100% 
0.3727498 0.4667348 0.5210784 1.4389107 1.5977016 

Cross-validation robust pseudo R-squared:  -Inf 

Summary of cross-validation residuals:
       0%       25%       50%       75%      100% 
0.3727498 0.4667348 0.5210784 1.4389107 1.5977016 

Cross-validation robust pseudo R-squared:  -Inf 

Summary of cross-validation residuals:
       0%       25%       50%       75%      100% 
0.3727498 0.4667348 0.5210784 1.4389107 1.5977016 

Cross-validation robust pseudo R-squared:  -Inf 

Summary of cross-validation residuals:
       0%       25%       50%       75%      100% 
0.3727498 0.4667348 0.5210784 1.4389107 1.5977016 

Cross-validation robust pseudo R-squared:  -Inf 

Summary of cross-validation residuals:
       0%       25%       50%       75%      100% 
0.3727498 0.4667348 0.5210784 1.4389107 1.5977016 

Cross-validation pseudo R-squared:  -3.1 

Summary of cross-validation residuals:
       0%       25%       50%       75%      100% 
0.3727498 0.4667348 0.5210784 1.4389107 1.5977016 

Cross-validation robust pseudo R-squared:  -Inf 

Summary of cross-validation residuals:
       0%       25%       50%       75%      100% 
0.3727498 0.4667348 0.5210784 1.4389107 1.5977016 

Cross-validation robust pseudo R-squared:  -Inf 

Summary of cross-validation residuals:
       0%       25%       50%       75%      100% 
0.3727498 0.4667348 0.5210784 1.4389107 1.5977016 

Cross-validation robust pseudo R-squared:  -Inf 

Summary of cross-validation residuals:
       0%       25%       50%       75%      100% 
0.3727498 0.4667348 0.5210784 1.4389107 1.5977016 

Cross-validation robust pseudo R-squared:  -Inf 

Summary of cross-validation residuals:
       0%       25%       50%       75%      100% 
0.3727498 0.4667348 0.5210784 1.4389107 1.5977016 

Cross-validation robust pseudo R-squared:  -Inf 

Summary of cross-validation residuals:
       0%       25%       50%       75%      100% 
0.3727498 0.4667348 0.5210784 1.4389107 1.5977016 

Cross-validation robust pseudo R-squared:  -Inf 

Summary of cross-validation residuals:
       0%       25%       50%       75%      100% 
0.3727498 0.4667348 0.5210784 1.4389107 1.5977016 

Cross-validation robust pseudo R-squared:  -Inf 
gbmt(formula = Y ~ X1 + X2 + X3, distribution = dist, data = data, 
    weights = w, offset = offset, train_params = params, var_monotone = c(0, 
        0, 0), cv_folds = 5, keep_gbm_data = TRUE, is_verbose = FALSE)
A gradient boosted model with Bernoulli loss function.
 30 iterations were performed.
The best cross-validation iteration was 30.
The best test-set iteration was 30.
There were 3 predictors of which 2 had non-zero influence.

Cross-validation confusion matrix:
    0 1 Pred. Acc.
0 623 0        100
1 377 0          0

Cross-validation prediction Accuracy = 62.3%
gbmt(formula = Y ~ X1 + X2 + X3, distribution = dist, data = data, 
    weights = w, offset = offset, train_params = params, var_monotone = c(0, 
        0, 0), cv_folds = 1, keep_gbm_data = TRUE, is_verbose = FALSE)
A gradient boosted model with Bernoulli loss function.
 30 iterations were performed.
The best test-set iteration was 30.
There were 3 predictors of which 3 had non-zero influence.
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 51 ]
> 
> proc.time()
   user  system elapsed 
   2.93    0.23    3.17