• using R Under development (unstable) (2026-07-23 r90295)
• using platform: x86_64-pc-linux-gnu
• R was compiled by     gcc (GCC) 16.1.1 20260515 (Red Hat 16.1.1-2)     GNU Fortran (GCC) 16.1.1 20260515 (Red Hat 16.1.1-2)
• running under: Fedora Linux 44 (Server Edition)
• using session charset: UTF-8 * current time: 2026-07-24 07:20:44 UTC
• using option ‘--no-stop-on-test-error’
• checking for file ‘SuperLearner/DESCRIPTION’ ... OK
• checking extension type ... Package
• this is package ‘SuperLearner’ version ‘2.0-40’
• 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 executable files ... OK
• checking for hidden files and directories ... OK
• checking for portable file names ... OK
• checking for sufficient/correct file permissions ... OK
• checking whether package ‘SuperLearner’ can be installed ... OK See the install log for details.
• 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 ... OK
• checking whether the package can be loaded with stated dependencies ... OK
• checking whether the package can be unloaded cleanly ... OK
• checking whether the namespace can be loaded with stated dependencies ... OK
• checking whether the namespace can be unloaded cleanly ... OK
• checking loading without being on the library search path ... OK
• checking whether startup messages can be suppressed ... 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 ... OK
• checking Rd files ... OK
• checking Rd metadata ... OK
• checking Rd line widths ... 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 installed files from ‘inst/doc’ ... OK
• checking files in ‘vignettes’ ... OK
• checking examples ... ERROR Running examples in ‘SuperLearner-Ex.R’ failed The error most likely occurred in: > ### 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
• checking for unstated dependencies in ‘tests’ ... OK
• checking tests ... [47s/49s] ERROR   Running ‘testthat.R’ [47s/49s] 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 ... [104s/116s] OK
• checking PDF version of manual ... OK
• checking HTML version of manual ... OK
• checking for non-standard things in the check directory ... OK
• checking for detritus in the temp directory ... OK
• DONE

Status: 2 ERRORs