• using R Under development (unstable) (2026-08-13 r90397)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc-16 (Debian 16.1.0-3) 16.1.0     GNU Fortran (Debian 16.1.0-3) 16.1.0
• running under: Debian GNU/Linux forky/sid
• using session charset: UTF-8 * current time: 2026-08-14 13:07:18 UTC
• checking for file ‘SuperLearner/DESCRIPTION’ ... OK
• checking extension type ... Package
• this is package ‘SuperLearner’ version ‘2.0-40’
• checking CRAN incoming feasibility ... [1s/1s] OK
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• checking package dependencies ... OK
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• checking for portable file names ... OK
• checking for sufficient/correct file permissions ... OK
• checking serialization versions ... OK
• checking whether package ‘SuperLearner’ can be installed ... OK See the install log for details.
• checking package directory ... OK
• checking for future file timestamps ... 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 ... [0s/0s] OK
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• 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 ... [9s/11s] OK
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• checking for code/documentation mismatches ... INFO Exported functions without usage information:   All SL.bayesglm SL.caret SL.caret.rpart SL.earth SL.gam SL.gbm   SL.glm.interaction SL.ipredbagg SL.knn SL.leekasso SL.loess SL.logreg   SL.mean SL.nnet SL.nnls SL.polymars SL.randomForest SL.ridge SL.rpart   SL.rpartPrune SL.step SL.step.forward SL.step.interaction SL.stepAIC   SL.svm SL.template coef.CV.SuperLearner coef.SuperLearner   mcSuperLearner method.template predict.SL.bayesglm predict.SL.caret   predict.SL.cforest predict.SL.earth predict.SL.gam predict.SL.gbm   predict.SL.ipredbagg predict.SL.knn predict.SL.leekasso   predict.SL.loess predict.SL.logreg predict.SL.mean predict.SL.nnet   predict.SL.nnls predict.SL.polymars predict.SL.randomForest   predict.SL.ridge predict.SL.rpart predict.SL.step predict.SL.stepAIC   predict.SL.svm predict.SL.template print.CV.SuperLearner   print.SuperLearner screen.SIS screen.corP screen.corRank   screen.glmnet screen.randomForest screen.template screen.ttest   snowSuperLearner
• checking Rd \usage sections ... OK
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• checking for unstated dependencies in examples ... OK
• checking installed files from ‘inst/doc’ ... OK
• checking files in ‘vignettes’ ... OK
• checking examples ... [6s/7s] ERROR Running examples in ‘SuperLearner-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: SL.glmnet > ### Title: Elastic net regression, including lasso and ridge > ### Aliases: SL.glmnet > > ### ** Examples > > > # Load a test dataset. > data(PimaIndiansDiabetes2, package = "mlbench") Warning in data(PimaIndiansDiabetes2, package = "mlbench") :   data set ‘PimaIndiansDiabetes2’ not found > data = PimaIndiansDiabetes2 Error: object 'PimaIndiansDiabetes2' not found Execution halted Examples with CPU (user + system) or elapsed time > 5s              user system elapsed SL.biglasso 5.663 0.105 6.223
• checking for unstated dependencies in ‘tests’ ... OK
• checking tests ... [54s/60s] ERROR   Running ‘testthat.R’ [53s/60s] Running the tests in ‘tests/testthat.R’ failed. Complete output:   > library(testthat)   > library(SuperLearner)   Loading required package: nnls   Loading required package: gam   Loading required package: splines   Loading required package: foreach   Loaded gam 1.22-7      Super Learner   Version: 2.0-40   Package created on 2025-12-14      >   > test_check("SuperLearner")      Call:   SuperLearner(Y = Y_reg, X = X, family = gaussian(), SL.library = c(SL.library,       xgb_grid$names), cvControl = list(V = 2))                            Risk Coef   SL.mean_All 1.060266 0.00000000   SL.xgboost_All 1.248342 0.17377091   SL.xgb.1_All 1.046647 0.00000000   SL.xgb.2_All 1.039310 0.48619144   SL.xgb.3_All 1.033972 0.27648732   SL.xgb.4_All 1.046058 0.00000000   SL.xgb.5_All 1.057237 0.00000000   SL.xgb.6_All 1.053070 0.00000000   SL.xgb.7_All 1.054268 0.00000000   SL.xgb.8_All 1.049384 0.06355032   SL.xgb.9_All 1.059925 0.00000000   SL.xgb.10_All 1.059592 0.00000000   SL.xgb.11_All 1.059665 0.00000000   SL.xgb.12_All 1.058975 0.00000000   Error in .jnew("bartMachine.bartMachineRegressionMultThread") :     java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector   Error in .jnew("bartMachine.bartMachineRegressionMultThread") :     java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector   Error in .jnew("bartMachine.bartMachineRegressionMultThread") :     java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector   Error in .jnew("bartMachine.bartMachineRegressionMultThread") :     java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector   Error in .jnew("bartMachine.bartMachineRegressionMultThread") :     java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector   Error in .jnew("bartMachine.bartMachineRegressionMultThread") :     java.lang.NoClassDefFoundError: jdk/incubator/vector/Vector   lasso-penalized linear regression with n=506, p=13   At minimum cross-validation error (lambda=0.0222):   -------------------------------------------------     Nonzero coefficients: 11     Cross-validation error (deviance): 23.29     R-squared: 0.72     Signal-to-noise ratio: 2.63     Scale estimate (sigma): 4.826   lasso-penalized logistic regression with n=506, p=13   At minimum cross-validation error (lambda=0.0026):   -------------------------------------------------     Nonzero coefficients: 12     Cross-validation error (deviance): 0.66     R-squared: 0.48     Signal-to-noise ratio: 0.94     Prediction error: 0.123   lasso-penalized linear regression with n=506, p=13   At minimum cross-validation error (lambda=0.0362):   -------------------------------------------------     Nonzero coefficients: 11     Cross-validation error (deviance): 23.30     R-squared: 0.72     Signal-to-noise ratio: 2.62     Scale estimate (sigma): 4.827   lasso-penalized logistic regression with n=506, p=13   At minimum cross-validation error (lambda=0.0016):   -------------------------------------------------     Nonzero coefficients: 13     Cross-validation error (deviance): 0.63     R-squared: 0.50     Signal-to-noise ratio: 0.99     Prediction error: 0.132      Call:   SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",       "SL.biglasso"), cvControl = list(V = 2))                             Risk Coef   SL.mean_All 84.62063 0.02136708   SL.biglasso_All 26.01864 0.97863292      Call:   SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",       "SL.biglasso"), cvControl = list(V = 2))                              Risk Coef   SL.mean_All 0.2346857 0   SL.biglasso_All 0.1039122 1      Call: glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,       model = model)      Coefficients:   (Intercept) crim zn indus chas nox     3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01            rm age dis rad tax ptratio     3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01         black lstat     9.312e-03 -5.248e-01      Degrees of Freedom: 505 Total (i.e. Null); 492 Residual   Null Deviance: 42720   Residual Deviance: 11080 AIC: 3028      Call:   glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,       model = model)      Coefficients:                 Estimate Std. Error t value Pr(>|t|)   (Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***   crim -1.080e-01 3.286e-02 -3.287 0.001087 **   zn 4.642e-02 1.373e-02 3.382 0.000778 ***   indus 2.056e-02 6.150e-02 0.334 0.738288   chas 2.687e+00 8.616e-01 3.118 0.001925 **   nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***   rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***   age 6.922e-04 1.321e-02 0.052 0.958229   dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***   rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***   tax -1.233e-02 3.760e-03 -3.280 0.001112 **   ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***   black 9.312e-03 2.686e-03 3.467 0.000573 ***   lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***   ---   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      (Dispersion parameter for gaussian family taken to be 22.51785)          Null deviance: 42716 on 505 degrees of freedom   Residual deviance: 11079 on 492 degrees of freedom   AIC: 3027.6      Number of Fisher Scoring iterations: 2         Call:   glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,       model = model)      Coefficients:                Estimate Std. Error z value Pr(>|z|)   (Intercept) 10.682635 3.921395 2.724 0.006446 **   crim -0.040649 0.049796 -0.816 0.414321   zn 0.012134 0.010678 1.136 0.255786   indus -0.040715 0.045615 -0.893 0.372078   chas 0.248209 0.653283 0.380 0.703989   nox -3.601085 2.924365 -1.231 0.218170   rm 1.155157 0.374843 3.082 0.002058 **   age -0.018660 0.009319 -2.002 0.045252 *   dis -0.518934 0.146286 -3.547 0.000389 ***   rad 0.255522 0.061391 4.162 3.15e-05 ***   tax -0.009500 0.003107 -3.057 0.002233 **   ptratio -0.409317 0.103191 -3.967 7.29e-05 ***   black -0.001451 0.002558 -0.567 0.570418   lstat -0.318436 0.054735 -5.818 5.96e-09 ***   ---   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      (Dispersion parameter for binomial family taken to be 1)          Null deviance: 669.76 on 505 degrees of freedom   Residual deviance: 296.39 on 492 degrees of freedom   AIC: 324.39      Number of Fisher Scoring iterations: 7       [1] "coefficients" "residuals" "fitted.values"    [4] "effects" "R" "rank"    [7] "qr" "family" "linear.predictors"   [10] "deviance" "aic" "null.deviance"   [13] "iter" "weights" "prior.weights"   [16] "df.residual" "df.null" "y"   [19] "converged" "boundary" "call"   [22] "formula" "terms" "data"   [25] "offset" "control" "method"   [28] "contrasts" "xlevels"      Call:   glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,       model = model)      Coefficients:                 Estimate Std. Error t value Pr(>|t|)   (Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***   crim -1.080e-01 3.286e-02 -3.287 0.001087 **   zn 4.642e-02 1.373e-02 3.382 0.000778 ***   indus 2.056e-02 6.150e-02 0.334 0.738288   chas 2.687e+00 8.616e-01 3.118 0.001925 **   nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***   rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***   age 6.922e-04 1.321e-02 0.052 0.958229   dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***   rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***   tax -1.233e-02 3.760e-03 -3.280 0.001112 **   ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***   black 9.312e-03 2.686e-03 3.467 0.000573 ***   lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***   ---   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      (Dispersion parameter for gaussian family taken to be 22.51785)          Null deviance: 42716 on 505 degrees of freedom   Residual deviance: 11079 on 492 degrees of freedom   AIC: 3027.6      Number of Fisher Scoring iterations: 2         Call:   glm(formula = Y ~ ., family = family, data = X, weights = obsWeights,       model = model)      Coefficients:                Estimate Std. Error z value Pr(>|z|)   (Intercept) 10.682635 3.921395 2.724 0.006446 **   crim -0.040649 0.049796 -0.816 0.414321   zn 0.012134 0.010678 1.136 0.255786   indus -0.040715 0.045615 -0.893 0.372078   chas 0.248209 0.653283 0.380 0.703989   nox -3.601085 2.924365 -1.231 0.218170   rm 1.155157 0.374843 3.082 0.002058 **   age -0.018660 0.009319 -2.002 0.045252 *   dis -0.518934 0.146286 -3.547 0.000389 ***   rad 0.255522 0.061391 4.162 3.15e-05 ***   tax -0.009500 0.003107 -3.057 0.002233 **   ptratio -0.409317 0.103191 -3.967 7.29e-05 ***   black -0.001451 0.002558 -0.567 0.570418   lstat -0.318436 0.054735 -5.818 5.96e-09 ***   ---   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      (Dispersion parameter for binomial family taken to be 1)          Null deviance: 669.76 on 505 degrees of freedom   Residual deviance: 296.39 on 492 degrees of freedom   AIC: 324.39      Number of Fisher Scoring iterations: 7         Call:   SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",       "SL.glm"))                         Risk Coef   SL.mean_All 84.74142 0.0134192   SL.glm_All 23.62549 0.9865808          V1    Min. :-3.921    1st Qu.:17.514    Median :22.124    Mean :22.533    3rd Qu.:27.345    Max. :44.376      Call:   SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",       "SL.glm"))                           Risk Coef   SL.mean_All 0.23580362 0.01315872   SL.glm_All 0.09519266 0.98684128          V1    Min. :0.004942    1st Qu.:0.035424    Median :0.196222    Mean :0.375494    3rd Qu.:0.781687    Max. :0.991313   Saving _problems/test-glmnet-11.R   Saving _problems/test-kernelKnn-10.R   Saving _problems/test-knn-10.R   Call:   lda(X, grouping = Y, prior = prior, method = method, tol = tol,       CV = CV, nu = nu)      Prior probabilities of groups:           0 1   0.6245059 0.3754941      Group means:          crim zn indus chas nox rm age dis   0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307   1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371           rad tax ptratio black lstat   0 11.588608 459.9209 19.19968 340.6392 16.042468   1 6.157895 322.2789 17.21789 383.3425 7.015947      Coefficients of linear discriminants:                     LD1   crim 0.0012515925   zn 0.0095179029   indus -0.0166376334   chas 0.1399207112   nox -2.9934367740   rm 0.5612713068   age -0.0128420045   dis -0.3095403096   rad 0.0695027989   tax -0.0027771271   ptratio -0.2059853828   black 0.0006058031   lstat -0.0816668897   Call:   lda(X, grouping = Y, prior = prior, method = method, tol = tol,       CV = CV, nu = nu)      Prior probabilities of groups:           0 1   0.6245059 0.3754941      Group means:          crim zn indus chas nox rm age dis   0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307   1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371           rad tax ptratio black lstat   0 11.588608 459.9209 19.19968 340.6392 16.042468   1 6.157895 322.2789 17.21789 383.3425 7.015947      Coefficients of linear discriminants:                     LD1   crim 0.0012515925   zn 0.0095179029   indus -0.0166376334   chas 0.1399207112   nox -2.9934367740   rm 0.5612713068   age -0.0128420045   dis -0.3095403096   rad 0.0695027989   tax -0.0027771271   ptratio -0.2059853828   black 0.0006058031   lstat -0.0816668897      Call:   stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)      Coefficients:   (Intercept) crim zn indus chas nox     3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01            rm age dis rad tax ptratio     3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01         black lstat     9.312e-03 -5.248e-01         Call:   stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)      Residuals:       Min 1Q Median 3Q Max   -15.595 -2.730 -0.518 1.777 26.199      Coefficients:                 Estimate Std. Error t value Pr(>|t|)   (Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***   crim -1.080e-01 3.286e-02 -3.287 0.001087 **   zn 4.642e-02 1.373e-02 3.382 0.000778 ***   indus 2.056e-02 6.150e-02 0.334 0.738288   chas 2.687e+00 8.616e-01 3.118 0.001925 **   nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***   rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***   age 6.922e-04 1.321e-02 0.052 0.958229   dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***   rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***   tax -1.233e-02 3.760e-03 -3.280 0.001112 **   ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***   black 9.312e-03 2.686e-03 3.467 0.000573 ***   lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***   ---   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      Residual standard error: 4.745 on 492 degrees of freedom   Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338   F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16         Call:   stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)      Residuals:        Min 1Q Median 3Q Max   -0.80469 -0.23612 -0.03105 0.23080 1.05224      Coefficients:                 Estimate Std. Error t value Pr(>|t|)   (Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***   crim 0.0003028 0.0023585 0.128 0.897888   zn 0.0023028 0.0009851 2.338 0.019808 *   indus -0.0040254 0.0044131 -0.912 0.362135   chas 0.0338534 0.0618295 0.548 0.584264   nox -0.7242540 0.2741160 -2.642 0.008501 **   rm 0.1357981 0.0299915 4.528 7.48e-06 ***   age -0.0031071 0.0009480 -3.278 0.001121 **   dis -0.0748924 0.0143135 -5.232 2.48e-07 ***   rad 0.0168160 0.0047612 3.532 0.000451 ***   tax -0.0006719 0.0002699 -2.490 0.013110 *   ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***   black 0.0001466 0.0001928 0.760 0.447370   lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***   ---   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      Residual standard error: 0.3405 on 492 degrees of freedom   Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065   F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16       [1] "coefficients" "residuals" "fitted.values" "effects"    [5] "weights" "rank" "assign" "qr"    [9] "df.residual" "xlevels" "call" "terms"      Call:   stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)      Residuals:       Min 1Q Median 3Q Max   -15.595 -2.730 -0.518 1.777 26.199      Coefficients:                 Estimate Std. Error t value Pr(>|t|)   (Intercept) 3.646e+01 5.103e+00 7.144 3.28e-12 ***   crim -1.080e-01 3.286e-02 -3.287 0.001087 **   zn 4.642e-02 1.373e-02 3.382 0.000778 ***   indus 2.056e-02 6.150e-02 0.334 0.738288   chas 2.687e+00 8.616e-01 3.118 0.001925 **   nox -1.777e+01 3.820e+00 -4.651 4.25e-06 ***   rm 3.810e+00 4.179e-01 9.116 < 2e-16 ***   age 6.922e-04 1.321e-02 0.052 0.958229   dis -1.476e+00 1.995e-01 -7.398 6.01e-13 ***   rad 3.060e-01 6.635e-02 4.613 5.07e-06 ***   tax -1.233e-02 3.760e-03 -3.280 0.001112 **   ptratio -9.527e-01 1.308e-01 -7.283 1.31e-12 ***   black 9.312e-03 2.686e-03 3.467 0.000573 ***   lstat -5.248e-01 5.072e-02 -10.347 < 2e-16 ***   ---   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      Residual standard error: 4.745 on 492 degrees of freedom   Multiple R-squared: 0.7406, Adjusted R-squared: 0.7338   F-statistic: 108.1 on 13 and 492 DF, p-value: < 2.2e-16         Call:   stats::lm(formula = Y ~ ., data = X, weights = obsWeights, model = model)      Residuals:        Min 1Q Median 3Q Max   -0.80469 -0.23612 -0.03105 0.23080 1.05224      Coefficients:                 Estimate Std. Error t value Pr(>|t|)   (Intercept) 1.6675402 0.3662392 4.553 6.67e-06 ***   crim 0.0003028 0.0023585 0.128 0.897888   zn 0.0023028 0.0009851 2.338 0.019808 *   indus -0.0040254 0.0044131 -0.912 0.362135   chas 0.0338534 0.0618295 0.548 0.584264   nox -0.7242540 0.2741160 -2.642 0.008501 **   rm 0.1357981 0.0299915 4.528 7.48e-06 ***   age -0.0031071 0.0009480 -3.278 0.001121 **   dis -0.0748924 0.0143135 -5.232 2.48e-07 ***   rad 0.0168160 0.0047612 3.532 0.000451 ***   tax -0.0006719 0.0002699 -2.490 0.013110 *   ptratio -0.0498376 0.0093885 -5.308 1.68e-07 ***   black 0.0001466 0.0001928 0.760 0.447370   lstat -0.0197591 0.0036395 -5.429 8.91e-08 ***   ---   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      Residual standard error: 0.3405 on 492 degrees of freedom   Multiple R-squared: 0.5192, Adjusted R-squared: 0.5065   F-statistic: 40.86 on 13 and 492 DF, p-value: < 2.2e-16         Call:   SuperLearner(Y = Y_gaus, X = X, family = gaussian(), SL.library = c("SL.mean",       "SL.lm"))                         Risk Coef   SL.mean_All 84.74142 0.0134192   SL.lm_All 23.62549 0.9865808          V1    Min. :-3.921    1st Qu.:17.514    Median :22.124    Mean :22.533    3rd Qu.:27.345    Max. :44.376      Call:   SuperLearner(Y = Y_bin, X = X, family = binomial(), SL.library = c("SL.mean",       "SL.lm"))                          Risk Coef   SL.mean_All 0.2358036 0   SL.lm_All 0.1115499 1          V1    Min. :0.0000    1st Qu.:0.1281    Median :0.3530    Mean :0.3899    3rd Qu.:0.6091    Max. :1.0000      Call:   SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,       method = "method.NNLS", verbose = F, cvControl = list(V = 2))                            Risk Coef   SL.rpart_All 0.2219962 0   SL.glmnet_All 0.1675829 1   SL.mean_All 0.2491000 0   Error in (function (Y, X, newX, ...) : bad algorithm   Error in (function (Y, X, newX, ...) : bad algorithm   Error in (function (Y, X, newX, ...) : bad algorithm      Call:   SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,       "SL.bad_algorithm"), method = "method.NNLS", verbose = T, cvControl = list(V = 2))                                      Risk Coef   SL.rpart_All 0.2062976 0.00000000   SL.glmnet_All 0.1656934 0.97040848   SL.mean_All 0.2464000 0.02959152   SL.bad_algorithm_All NA 0.00000000      Call:   SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,       method = "method.NNLS2", verbose = F, cvControl = list(V = 2))                            Risk Coef   SL.rpart_All 0.2071145 0.000000000   SL.glmnet_All 0.1644480 0.996278512   SL.mean_All 0.2491000 0.003721488      Call:   SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,       method = "method.NNloglik", verbose = F, cvControl = list(V = 2))                            Risk Coef   SL.rpart_All 0.5646032 0.1435759   SL.glmnet_All 0.4860594 0.8564241   SL.mean_All 0.6956962 0.0000000   Error in (function (Y, X, newX, ...) : bad algorithm   Error in (function (Y, X, newX, ...) : bad algorithm   Error in (function (Y, X, newX, ...) : bad algorithm      Call:   SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,       "SL.bad_algorithm"), method = "method.NNloglik", verbose = T, cvControl = list(V = 2))                                      Risk Coef   SL.rpart_All Inf 0   SL.glmnet_All 0.4866903 1   SL.mean_All 0.7160070 0   SL.bad_algorithm_All NA 0      Call:   SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,       method = "method.CC_LS", verbose = F, cvControl = list(V = 2))                            Risk Coef   SL.rpart_All 0.2058305 0   SL.glmnet_All 0.1542369 1   SL.mean_All 0.2464000 0      Call:   SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,       method = "method.CC_nloglik", verbose = F, cvControl = list(V = 2))                           Risk Coef   SL.rpart_All 230.4838 0.1247876   SL.glmnet_All 192.7564 0.8752124   SL.mean_All 274.6155 0.0000000   Error in (function (Y, X, newX, ...) : bad algorithm   Error in (function (Y, X, newX, ...) : bad algorithm   Error in (function (Y, X, newX, ...) : bad algorithm      Call:   SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,       "SL.bad_algorithm"), method = "method.CC_nloglik", verbose = T, cvControl = list(V = 2))                                     Risk Coef   SL.rpart_All 226.0518 0.173761529   SL.glmnet_All 188.4293 0.818098488   SL.mean_All 274.6155 0.000000000   SL.bad_algorithm_All NA 0.008139983   Saving _problems/test-methods-218.R   Saving _problems/test-methods-220.R   Saving _problems/test-methods-227.R   Saving _problems/test-methods-229.R      Call:   SuperLearner(Y = Y, X = X, family = binomial(), SL.library = SL.library,       method = "method.AUC", verbose = FALSE, cvControl = list(V = 2))                            Risk Coef   SL.rpart_All 0.2287439 0.5103394   SL.glmnet_All 0.1693385 0.4896606   SL.mean_All 0.6014610 0.0000000   Error in (function (Y, X, newX, ...) : bad algorithm   Error in (function (Y, X, newX, ...) : bad algorithm   Removing failed learners: SL.bad_algorithm_All   Error in (function (Y, X, newX, ...) : bad algorithm      Call:   SuperLearner(Y = Y, X = X, family = binomial(), SL.library = c(SL.library,       "SL.bad_algorithm"), method = "method.AUC", verbose = TRUE, cvControl = list(V = 2))                                      Risk Coef   SL.rpart_All 0.2298600 0.3333333   SL.glmnet_All 0.1532062 0.3333333   SL.mean_All 0.5000000 0.3333333   SL.bad_algorithm_All NA 0.0000000   Call:   qda(X, grouping = Y, prior = prior, method = method, tol = tol,       CV = CV, nu = nu)      Prior probabilities of groups:           0 1   0.6245059 0.3754941      Group means:          crim zn indus chas nox rm age dis   0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307   1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371           rad tax ptratio black lstat   0 11.588608 459.9209 19.19968 340.6392 16.042468   1 6.157895 322.2789 17.21789 383.3425 7.015947   Call:   qda(X, grouping = Y, prior = prior, method = method, tol = tol,       CV = CV, nu = nu)      Prior probabilities of groups:           0 1   0.6245059 0.3754941      Group means:          crim zn indus chas nox rm age dis   0 5.2936824 4.708861 13.622089 0.05379747 0.5912399 5.985693 77.93228 3.349307   1 0.8191541 22.431579 7.003316 0.09473684 0.4939153 6.781821 53.01211 4.536371           rad tax ptratio black lstat   0 11.588608 459.9209 19.19968 340.6392 16.042468   1 6.157895 322.2789 17.21789 383.3425 7.015947   Ranger result      Call:    ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)      Type: Regression   Number of trees: 500   Sample size: 506   Number of independent variables: 13   Mtry: 3   Target node size: 5   Variable importance mode: none   Splitrule: variance   OOB prediction error (MSE): 10.60169   R squared (OOB): 0.8746648   Ranger result      Call:    ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)      Type: Probability estimation   Number of trees: 500   Sample size: 506   Number of independent variables: 13   Mtry: 3   Target node size: 1   Variable importance mode: none   Splitrule: gini   OOB prediction error (Brier s.): 0.08149979   Ranger result      Call:    ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)      Type: Regression   Number of trees: 500   Sample size: 506   Number of independent variables: 13   Mtry: 3   Target node size: 5   Variable importance mode: none   Splitrule: variance   OOB prediction error (MSE): 10.39997   R squared (OOB): 0.8770496   Ranger result      Call:    ranger::ranger(`_Y` ~ ., data = cbind(`_Y` = Y, X), num.trees = num.trees, mtry = mtry, min.node.size = min.node.size, replace = replace, sample.fraction = sample.fraction, case.weights = obsWeights, write.forest = write.forest, probability = probability, num.threads = num.threads, verbose = verbose)      Type: Probability estimation   Number of trees: 500   Sample size: 506   Number of independent variables: 13   Mtry: 3   Target node size: 1   Variable importance mode: none   Splitrule: gini   OOB prediction error (Brier s.): 0.08457002   Generalized Linear Model of class 'speedglm':      Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)      Coefficients:   (Intercept) crim zn indus chas nox     3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01            rm age dis rad tax ptratio     3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01         black lstat     9.312e-03 -5.248e-01      Generalized Linear Model of class 'speedglm':      Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)      Coefficients:    ------------------------------------------------------------------                 Estimate Std. Error t value Pr(>|t|)   (Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***   crim -1.080e-01 0.032865 -3.2865 1.087e-03 **   zn 4.642e-02 0.013727 3.3816 7.781e-04 ***   indus 2.056e-02 0.061496 0.3343 7.383e-01   chas 2.687e+00 0.861580 3.1184 1.925e-03 **   nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***   rm 3.810e+00 0.417925 9.1161 1.979e-18 ***   age 6.922e-04 0.013210 0.0524 9.582e-01   dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***   rad 3.060e-01 0.066346 4.6129 5.071e-06 ***   tax -1.233e-02 0.003761 -3.2800 1.112e-03 **   ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***   black 9.312e-03 0.002686 3.4668 5.729e-04 ***   lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***      -------------------------------------------------------------------   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      ---   null df: 505; null deviance: 42716.3;   residuals df: 492; residuals deviance: 11078.78;   # obs.: 506; # non-zero weighted obs.: 506;   AIC: 3027.609; log Likelihood: -1498.804;   RSS: 11078.8; dispersion: 22.51785; iterations: 1;   rank: 14; max tolerance: 1e+00; convergence: FALSE.   Generalized Linear Model of class 'speedglm':      Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)      Coefficients:    ------------------------------------------------------------------                Estimate Std. Error z value Pr(>|z|)   (Intercept) 10.682635 3.921395 2.7242 6.446e-03 **   crim -0.040649 0.049796 -0.8163 4.143e-01   zn 0.012134 0.010678 1.1364 2.558e-01   indus -0.040715 0.045615 -0.8926 3.721e-01   chas 0.248209 0.653283 0.3799 7.040e-01   nox -3.601085 2.924365 -1.2314 2.182e-01   rm 1.155157 0.374843 3.0817 2.058e-03 **   age -0.018660 0.009319 -2.0023 4.525e-02 *   dis -0.518934 0.146286 -3.5474 3.891e-04 ***   rad 0.255522 0.061391 4.1622 3.152e-05 ***   tax -0.009500 0.003107 -3.0574 2.233e-03 **   ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***   black -0.001451 0.002558 -0.5674 5.704e-01   lstat -0.318436 0.054735 -5.8178 5.964e-09 ***      -------------------------------------------------------------------   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      ---   null df: 505; null deviance: 669.76;   residuals df: 492; residuals deviance: 296.39;   # obs.: 506; # non-zero weighted obs.: 506;   AIC: 324.3944; log Likelihood: -148.1972;   RSS: 1107.5; dispersion: 1; iterations: 7;   rank: 14; max tolerance: 7.55e-12; convergence: TRUE.   Generalized Linear Model of class 'speedglm':      Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)      Coefficients:    ------------------------------------------------------------------                 Estimate Std. Error t value Pr(>|t|)   (Intercept) 3.646e+01 5.103459 7.1441 3.283e-12 ***   crim -1.080e-01 0.032865 -3.2865 1.087e-03 **   zn 4.642e-02 0.013727 3.3816 7.781e-04 ***   indus 2.056e-02 0.061496 0.3343 7.383e-01   chas 2.687e+00 0.861580 3.1184 1.925e-03 **   nox -1.777e+01 3.819744 -4.6513 4.246e-06 ***   rm 3.810e+00 0.417925 9.1161 1.979e-18 ***   age 6.922e-04 0.013210 0.0524 9.582e-01   dis -1.476e+00 0.199455 -7.3980 6.013e-13 ***   rad 3.060e-01 0.066346 4.6129 5.071e-06 ***   tax -1.233e-02 0.003761 -3.2800 1.112e-03 **   ptratio -9.527e-01 0.130827 -7.2825 1.309e-12 ***   black 9.312e-03 0.002686 3.4668 5.729e-04 ***   lstat -5.248e-01 0.050715 -10.3471 7.777e-23 ***      -------------------------------------------------------------------   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      ---   null df: 505; null deviance: 42716.3;   residuals df: 492; residuals deviance: 11078.78;   # obs.: 506; # non-zero weighted obs.: 506;   AIC: 3027.609; log Likelihood: -1498.804;   RSS: 11078.8; dispersion: 22.51785; iterations: 1;   rank: 14; max tolerance: 1e+00; convergence: FALSE.   Generalized Linear Model of class 'speedglm':      Call: speedglm::speedglm(formula = Y ~ ., data = X, family = family, weights = obsWeights, maxit = maxit, k = k)      Coefficients:    ------------------------------------------------------------------                Estimate Std. Error z value Pr(>|z|)   (Intercept) 10.682635 3.921395 2.7242 6.446e-03 **   crim -0.040649 0.049796 -0.8163 4.143e-01   zn 0.012134 0.010678 1.1364 2.558e-01   indus -0.040715 0.045615 -0.8926 3.721e-01   chas 0.248209 0.653283 0.3799 7.040e-01   nox -3.601085 2.924365 -1.2314 2.182e-01   rm 1.155157 0.374843 3.0817 2.058e-03 **   age -0.018660 0.009319 -2.0023 4.525e-02 *   dis -0.518934 0.146286 -3.5474 3.891e-04 ***   rad 0.255522 0.061391 4.1622 3.152e-05 ***   tax -0.009500 0.003107 -3.0574 2.233e-03 **   ptratio -0.409317 0.103191 -3.9666 7.291e-05 ***   black -0.001451 0.002558 -0.5674 5.704e-01   lstat -0.318436 0.054735 -5.8178 5.964e-09 ***      -------------------------------------------------------------------   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1      ---   null df: 505; null deviance: 669.76;   residuals df: 492; residuals deviance: 296.39;   # obs.: 506; # non-zero weighted obs.: 506;   AIC: 324.3944; log Likelihood: -148.1972;   RSS: 1107.5; dispersion: 1; iterations: 7;   rank: 14; max tolerance: 7.55e-12; convergence: TRUE.   Linear Regression Model of class 'speedlm':      Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)      Coefficients:   (Intercept) crim zn indus chas nox     3.646e+01 -1.080e-01 4.642e-02 2.056e-02 2.687e+00 -1.777e+01            rm age dis rad tax ptratio     3.810e+00 6.922e-04 -1.476e+00 3.060e-01 -1.233e-02 -9.527e-01         black lstat     9.312e-03 -5.248e-01      Linear Regression Model of class 'speedlm':      Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)      Coefficients:    ------------------------------------------------------------------                     coef se t p.value   (Intercept) 36.459488 5.103459 7.144 3.283e-12 ***   crim -0.108011 0.032865 -3.287 1.087e-03 **   zn 0.046420 0.013727 3.382 7.781e-04 ***   indus 0.020559 0.061496 0.334 7.383e-01   chas 2.686734 0.861580 3.118 1.925e-03 **   nox -17.766611 3.819744 -4.651 4.246e-06 ***   rm 3.809865 0.417925 9.116 1.979e-18 ***   age 0.000692 0.013210 0.052 9.582e-01   dis -1.475567 0.199455 -7.398 6.013e-13 ***   rad 0.306049 0.066346 4.613 5.071e-06 ***   tax -0.012335 0.003761 -3.280 1.112e-03 **   ptratio -0.952747 0.130827 -7.283 1.309e-12 ***   black 0.009312 0.002686 3.467 5.729e-04 ***   lstat -0.524758 0.050715 -10.347 7.777e-23 ***      -------------------------------------------------------------------   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1   ---   Residual standard error: 4.745298 on 492 degrees of freedom;   observations: 506; R^2: 0.741; adjusted R^2: 0.734;   F-statistic: 108.1 on 13 and 492 df; p-value: 0.   Linear Regression Model of class 'speedlm':      Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)      Coefficients:    ------------------------------------------------------------------                    coef se t p.value   (Intercept) 1.667540 0.366239 4.553 6.670e-06 ***   crim 0.000303 0.002358 0.128 8.979e-01   zn 0.002303 0.000985 2.338 1.981e-02 *   indus -0.004025 0.004413 -0.912 3.621e-01   chas 0.033853 0.061829 0.548 5.843e-01   nox -0.724254 0.274116 -2.642 8.501e-03 **   rm 0.135798 0.029992 4.528 7.483e-06 ***   age -0.003107 0.000948 -3.278 1.121e-03 **   dis -0.074892 0.014313 -5.232 2.482e-07 ***   rad 0.016816 0.004761 3.532 4.515e-04 ***   tax -0.000672 0.000270 -2.490 1.311e-02 *   ptratio -0.049838 0.009389 -5.308 1.677e-07 ***   black 0.000147 0.000193 0.760 4.474e-01   lstat -0.019759 0.003639 -5.429 8.912e-08 ***      -------------------------------------------------------------------   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1   ---   Residual standard error: 0.340537 on 492 degrees of freedom;   observations: 506; R^2: 0.519; adjusted R^2: 0.506;   F-statistic: 40.86 on 13 and 492 df; p-value: 0.   Linear Regression Model of class 'speedlm':      Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)      Coefficients:    ------------------------------------------------------------------                     coef se t p.value   (Intercept) 36.459488 5.103459 7.144 3.283e-12 ***   crim -0.108011 0.032865 -3.287 1.087e-03 **   zn 0.046420 0.013727 3.382 7.781e-04 ***   indus 0.020559 0.061496 0.334 7.383e-01   chas 2.686734 0.861580 3.118 1.925e-03 **   nox -17.766611 3.819744 -4.651 4.246e-06 ***   rm 3.809865 0.417925 9.116 1.979e-18 ***   age 0.000692 0.013210 0.052 9.582e-01   dis -1.475567 0.199455 -7.398 6.013e-13 ***   rad 0.306049 0.066346 4.613 5.071e-06 ***   tax -0.012335 0.003761 -3.280 1.112e-03 **   ptratio -0.952747 0.130827 -7.283 1.309e-12 ***   black 0.009312 0.002686 3.467 5.729e-04 ***   lstat -0.524758 0.050715 -10.347 7.777e-23 ***      -------------------------------------------------------------------   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1   ---   Residual standard error: 4.745298 on 492 degrees of freedom;   observations: 506; R^2: 0.741; adjusted R^2: 0.734;   F-statistic: 108.1 on 13 and 492 df; p-value: 0.   Linear Regression Model of class 'speedlm':      Call: speedglm::speedlm(formula = Y ~ ., data = X, weights = obsWeights)      Coefficients:    ------------------------------------------------------------------                    coef se t p.value   (Intercept) 1.667540 0.366239 4.553 6.670e-06 ***   crim 0.000303 0.002358 0.128 8.979e-01   zn 0.002303 0.000985 2.338 1.981e-02 *   indus -0.004025 0.004413 -0.912 3.621e-01   chas 0.033853 0.061829 0.548 5.843e-01   nox -0.724254 0.274116 -2.642 8.501e-03 **   rm 0.135798 0.029992 4.528 7.483e-06 ***   age -0.003107 0.000948 -3.278 1.121e-03 **   dis -0.074892 0.014313 -5.232 2.482e-07 ***   rad 0.016816 0.004761 3.532 4.515e-04 ***   tax -0.000672 0.000270 -2.490 1.311e-02 *   ptratio -0.049838 0.009389 -5.308 1.677e-07 ***   black 0.000147 0.000193 0.760 4.474e-01   lstat -0.019759 0.003639 -5.429 8.912e-08 ***      -------------------------------------------------------------------   Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1   ---   Residual standard error: 0.340537 on 492 degrees of freedom;   observations: 506; R^2: 0.519; adjusted R^2: 0.506;   F-statistic: 40.86 on 13 and 492 df; p-value: 0.   [ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]      ══ Skipped tests (4) ═══════════════════════════════════════════════════════════   • empty test (4): , , ,      ══ Failed tests ════════════════════════════════════════════════════════════════   ── Error ('test-glmnet.R:11:1'): (code run outside of `test_that()`) ───────────   <objectNotFoundError/error/condition>   Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found   ── Error ('test-kernelKnn.R:10:1'): (code run outside of `test_that()`) ────────   <objectNotFoundError/error/condition>   Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found   ── Error ('test-knn.R:10:1'): (code run outside of `test_that()`) ──────────────   <objectNotFoundError/error/condition>   Error in `eval(code, test_env)`: object 'PimaIndiansDiabetes2' not found   ── Failure ('test-methods.R:218:1'): (code run outside of `test_that()`) ───────   Expected `min(sl_bad$SL.predict)` >= 0.   Actual comparison: NA < 0.0   ── Failure ('test-methods.R:220:1'): (code run outside of `test_that()`) ───────   Expected `max(sl_bad$SL.predict)` <= 1.   Actual comparison: NA > 1.0   ── Failure ('test-methods.R:227:1'): (code run outside of `test_that()`) ───────   Expected `min(pred$pred)` >= 0.   Actual comparison: NA < 0.0   ── Failure ('test-methods.R:229:1'): (code run outside of `test_that()`) ───────   Expected `max(pred$pred)` <= 1.   Actual comparison: NA > 1.0      [ FAIL 7 | WARN 27 | SKIP 4 | PASS 55 ]   Error:   ! Test failures.   Execution halted
• checking for unstated dependencies in vignettes ... OK
• checking package vignettes ... OK
• checking re-building of vignette outputs ... [135s/139s] OK
• checking PDF version of manual ... [5s/7s] OK
• checking HTML version of manual ... [2s/2s] OK
• checking for non-standard things in the check directory ... OK
• DONE

Status: 2 ERRORs