Running 'testthat.R' [56s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
  > library(testthat)
  > library(cvms)
  > 
  > test_check("cvms")
  -- 1. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#1
  gaussian_baseline_summ$RMSE not equal to c(...).
  7/9 mismatches (average diff: 0.0239)
  [1] 0.5398 - 0.5382 ==  0.00159
  [2] 0.5200 - 0.5328 == -0.01275
  [3] 0.0554 - 0.0311 ==  0.02422
  [4] 0.0610 - 0.0219 ==  0.03912
  [5] 0.6455 - 0.6009 ==  0.04457
  [6] 0.4932 - 0.5051 == -0.01193
  [9] 0.4938 - 0.5270 == -0.03326
  
  -- 2. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#1
  gaussian_baseline_summ$MAE not equal to c(0.5, 0.5, 0, 0, 0.5, 0.5, 0, 0, 0.5).
  7/9 mismatches (average diff: 0.041)
  [1] 0.4705 - 0.5 == -0.0295
  [2] 0.4661 - 0.5 == -0.0339
  [3] 0.0361 - 0.0 ==  0.0361
  [4] 0.0360 - 0.0 ==  0.0360
  [5] 0.5500 - 0.5 ==  0.0500
  [6] 0.4167 - 0.5 == -0.0833
  [9] 0.4815 - 0.5 == -0.0185
  
  -- 3. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#1
  gaussian_baseline_summ$AIC not equal to c(...).
  7/9 mismatches (average diff: 72.5)
  [1]  -21.81 - 16.48 ==  -38.294
  [2]   12.40 - 16.77 ==   -4.374
  [3]  113.08 -  5.09 ==  107.983
  [4]    7.16 -  6.34 ==    0.822
  [5]   21.48 - 24.03 ==   -2.553
  [6] -343.36 -  9.03 == -352.389
  [9]   29.22 - 28.01 ==    1.209
  
  -- 4. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#1
  gaussian_baseline_summ$`Training Rows` not equal to c(9.6, 10, 3.062316, 4.5, 14, 5, 0, 0, 18).
  4/9 mismatches (average diff: 1.37)
  [1] 7.90 -  9.60 == -1.700
  [2] 7.00 - 10.00 == -3.000
  [3] 3.35 -  3.06 ==  0.286
  [4] 4.00 -  4.50 == -0.500
  
  -- 5. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#1
  gaussian_baseline_summ$AICc not equal to c(...).
  7/9 mismatches (average diff: 71.8)
  [1]  -18.18 - 18.93 ==  -37.113
  [2]   16.38 - 18.49 ==   -2.107
  [3]  112.19 -  3.68 ==  108.512
  [4]    3.45 -  4.34 ==   -0.885
  [5]   22.57 - 25.12 ==   -2.553
  [6] -337.36 - 13.18 == -350.546
  [9]   30.02 - 28.81 ==    1.209
  
  -- 6. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#1
  gaussian_baseline_summ$BIC not equal to c(...).
  7/9 mismatches (average diff: 72.6)
  [1]  -21.8 - 16.90 ==  -38.727
  [2]   12.1 - 17.38 ==   -5.287
  [3]  113.4 -  5.79 ==  107.589
  [4]    8.1 -  7.38 ==    0.721
  [5]   22.8 - 25.31 ==   -2.553
  [6] -344.1 -  8.25 == -352.389
  [9]   31.0 - 29.79 ==    1.209
  
  -- 7. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#1
  gaussian_baseline_reval$RMSE not equal to c(...).
  9/10 mismatches (average diff: 0.0425)
  [1]  0.493 - 0.601 == -0.10764
  [2]  0.510 - 0.527 == -0.01694
  [3]  0.564 - 0.539 ==  0.02542
  [4]  0.645 - 0.513 ==  0.13236
  [5]  0.493 - 0.505 == -0.01193
  [6]  0.505 - 0.527 == -0.02214
  [7]  0.530 - 0.539 == -0.00855
  [8]  0.624 - 0.583 ==  0.04140
  [10] 0.494 - 0.510 == -0.01611
  
  -- 8. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#1
  gaussian_baseline_reval$MAE not equal to rep(0.5, 10).
  10/10 mismatches (average diff: 0.0395)
  [1] 0.483 - 0.5 == -0.0167
  [2] 0.464 - 0.5 == -0.0357
  [3] 0.440 - 0.5 == -0.0595
  [4] 0.417 - 0.5 == -0.0833
  [5] 0.488 - 0.5 == -0.0119
  [6] 0.468 - 0.5 == -0.0321
  [7] 0.454 - 0.5 == -0.0463
  [8] 0.550 - 0.5 ==  0.0500
  [9] 0.450 - 0.5 == -0.0500
  ...
  
  -- 9. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#1
  gaussian_baseline_reval$AIC not equal to c(...).
  9/10 mismatches (average diff: 45.2)
  [1]    11.05 -  9.18 ==    1.871
  [2]    21.48 - 12.00 ==    9.478
  [3]     9.16 - 16.77 ==   -7.608
  [4]  -343.36 - 22.16 == -365.522
  [5]    14.02 - 24.03 ==  -10.017
  [6]    20.79 - 20.01 ==    0.784
  [7]    13.74 - 16.77 ==   -3.030
  [9]     9.03 - 16.77 ==   -7.746
  [10]   16.95 - 18.11 ==   -1.155
  
  -- 10. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#
  gaussian_baseline_reval$`Training Rows` not equal to c(6, 6, 10, 13, 14, 12, 10, 5, 10, 10).
  9/10 mismatches (average diff: 3.89)
  [1]   5 -  6 == -1
  [2]  14 -  6 ==  8
  [3]   7 - 10 == -3
  [4]   5 - 13 == -8
  [5]   7 - 14 == -7
  [6]  13 - 12 ==  1
  [7]   9 - 10 == -1
  [9]   5 - 10 == -5
  [10]  9 - 10 == -1
  
  -- 11. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#
  sum(all_predictions$Target) not equal to 60.
  1/1 mismatches
  [1] 70 - 60 == 10
  
  -- 12. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#
  sum(all_predictions$Prediction) not equal to 82.24176.
  1/1 mismatches
  [1] 81.2 - 82.2 == -1.02
  
  -- 13. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#
  all_coeffs$estimate not equal to c(...).
  9/10 mismatches (average diff: 0.186)
  [1]  0.600 - 0.833 == -0.2333
  [2]  0.714 - 0.667 ==  0.0476
  [3]  0.857 - 0.700 ==  0.1571
  [4]  1.000 - 0.615 ==  0.3846
  [6]  0.692 - 0.667 ==  0.0256
  [7]  0.778 - 0.700 ==  0.0778
  [8]  0.200 - 0.800 == -0.6000
  [9]  0.800 - 0.700 ==  0.1000
  [10] 0.556 - 0.600 == -0.0444
  
  -- 14. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#
  all_coeffs$std.error not equal to c(...).
  9/10 mismatches (average diff: 0.0504)
  [1]  2.45e-01 - 0.167 ==  0.07828
  [2]  1.25e-01 - 0.211 == -0.08552
  [3]  1.43e-01 - 0.153 == -0.00990
  [4]  9.93e-17 - 0.140 == -0.14044
  [5]  2.02e-01 - 0.137 ==  0.06478
  [6]  1.33e-01 - 0.142 == -0.00890
  [7]  1.47e-01 - 0.153 == -0.00577
  [9]  2.00e-01 - 0.153 ==  0.04725
  [10] 1.76e-01 - 0.163 ==  0.01238
  
  -- 15. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#
  all_coeffs$p.value not equal to c(...).
  6/10 mismatches (average diff: 0.0835)
  [1]  7.05e-02 - 0.00410 ==  0.06638
  [2]  7.28e-05 - 0.02503 == -0.02496
  [5]  3.00e-02 - 0.00111 ==  0.02891
  [8]  3.74e-01 - 0.01613 ==  0.35777
  [9]  1.61e-02 - 0.00132 ==  0.01481
  [10] 1.33e-02 - 0.00512 ==  0.00823
  
  -- 16. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlist$AUC not equal to c(0.76388889, 0.02083333).
  2/2 mismatches (average diff: 0.0741)
  [1] 0.762 - 0.7639 == -0.00231
  [2] 0.167 - 0.0208 ==  0.14583
  
  -- 17. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlist$`Lower CI` not equal to c(0.5855803, 0).
  1/2 mismatches
  [2] 0.0175 - 0 == 0.0175
  
  -- 18. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlist$`Upper CI` not equal to c(0.9421975, 0.05157582).
  2/2 mismatches (average diff: 0.134)
  [1] 0.938 - 0.9422 == -0.00416
  [2] 0.316 - 0.0516 ==  0.26427
  
  -- 19. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlist$Kappa not equal to c(0.4927536, -0.5217391).
  1/2 mismatches
  [2] -0.364 - -0.522 == 0.158
  
  -- 20. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlist$Specificity not equal to c(0.8888889, 0.5).
  1/2 mismatches
  [2] 0.667 - 0.5 == 0.167
  
  -- 21. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlist$`Neg Pred Value` not equal to c(0.7619048, 0.4285714).
  1/2 mismatches
  [2] 0.5 - 0.429 == 0.0714
  
  -- 22. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlist$`Detection Prevalence` not equal to c(0.3, 0.3).
  1/2 mismatches
  [2] 0.2 - 0.3 == -0.1
  
  -- 23. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlist$`Balanced Accuracy` not equal to c(0.7361111, 0.25).
  1/2 mismatches
  [2] 0.333 - 0.25 == 0.0833
  
  -- 24. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlist$MCC not equal to c(0.5048268, -0.5345225).
  1/2 mismatches
  [2] -0.408 - -0.535 == 0.126
  
  -- 25. Failure: binomial models work with cross_validate() (@test_cross_validate
  nrow(CVbinomlist$ROC[[1]]) not equal to 31.
  1/1 mismatches
  [1] 29 - 31 == -2
  
  -- 26. Failure: binomial models checks that dependent variable is numeric with c
  CVbinomlist$AUC not equal to c(0.76388889, 0.02083333).
  2/2 mismatches (average diff: 0.0741)
  [1] 0.762 - 0.7639 == -0.00231
  [2] 0.167 - 0.0208 ==  0.14583
  
  -- 27. Failure: binomial models checks that dependent variable is numeric with c
  CVbinomlist$`Lower CI` not equal to c(0.5855803, 0).
  1/2 mismatches
  [2] 0.0175 - 0 == 0.0175
  
  -- 28. Failure: binomial models checks that dependent variable is numeric with c
  CVbinomlist$`Upper CI` not equal to c(0.9421975, 0.05157582).
  2/2 mismatches (average diff: 0.134)
  [1] 0.938 - 0.9422 == -0.00416
  [2] 0.316 - 0.0516 ==  0.26427
  
  -- 29. Failure: binomial models checks that dependent variable is numeric with c
  CVbinomlist$Kappa not equal to c(0.4927536, -0.5217391).
  1/2 mismatches
  [2] -0.364 - -0.522 == 0.158
  
  -- 30. Failure: binomial models checks that dependent variable is numeric with c
  CVbinomlist$Specificity not equal to c(0.8888889, 0.5).
  1/2 mismatches
  [2] 0.667 - 0.5 == 0.167
  
  -- 31. Failure: binomial models checks that dependent variable is numeric with c
  CVbinomlist$`Neg Pred Value` not equal to c(0.7619048, 0.4285714).
  1/2 mismatches
  [2] 0.5 - 0.429 == 0.0714
  
  -- 32. Failure: binomial models checks that dependent variable is numeric with c
  CVbinomlist$`Detection Prevalence` not equal to c(0.3, 0.3).
  1/2 mismatches
  [2] 0.2 - 0.3 == -0.1
  
  -- 33. Failure: binomial models checks that dependent variable is numeric with c
  CVbinomlist$`Balanced Accuracy` not equal to c(0.7361111, 0.25).
  1/2 mismatches
  [2] 0.333 - 0.25 == 0.0833
  
  -- 34. Failure: binomial models checks that dependent variable is numeric with c
  CVbinomlist$MCC not equal to c(0.5048268, -0.5345225).
  1/2 mismatches
  [2] -0.408 - -0.535 == 0.126
  
  -- 35. Failure: binomial models checks that dependent variable is numeric with c
  nrow(CVbinomlist$ROC[[1]]) not equal to 31.
  1/1 mismatches
  [1] 29 - 31 == -2
  
  -- 36. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlistrand$AUC not equal to c(0.8425926, 0.02083333).
  2/2 mismatches (average diff: 0.0822)
  [1] 0.861 - 0.8426 == 0.0185
  [2] 0.167 - 0.0208 == 0.1458
  
  -- 37. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlistrand$`Lower CI` not equal to c(0.6892322, 0).
  2/2 mismatches (average diff: 0.0193)
  [1] 0.7103 - 0.689 == 0.0211
  [2] 0.0175 - 0.000 == 0.0175
  
  -- 38. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlistrand$`Upper CI` not equal to c(0.995953, 0.05157582).
  2/2 mismatches (average diff: 0.134)
  [1] 1.000 - 0.9960 == 0.00405
  [2] 0.316 - 0.0516 == 0.26427
  
  -- 39. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlistrand$Kappa not equal to c(0.6575342, -0.5217391).
  1/2 mismatches
  [2] -0.364 - -0.522 == 0.158
  
  -- 40. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlistrand$Specificity not equal to c(0.8333333, 0.5).
  1/2 mismatches
  [2] 0.667 - 0.5 == 0.167
  
  -- 41. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlistrand$`Neg Pred Value` not equal to c(0.8823529, 0.4285714).
  1/2 mismatches
  [2] 0.5 - 0.429 == 0.0714
  
  -- 42. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlistrand$`Detection Prevalence` not equal to c(0.4333333, 0.3).
  1/2 mismatches
  [2] 0.2 - 0.3 == -0.1
  
  -- 43. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlistrand$`Balanced Accuracy` not equal to c(0.8333333, 0.25).
  1/2 mismatches
  [2] 0.333 - 0.25 == 0.0833
  
  -- 44. Failure: binomial models work with cross_validate() (@test_cross_validate
  CVbinomlistrand$MCC not equal to c(0.659082, -0.5345225).
  1/2 mismatches
  [2] -0.408 - -0.535 == 0.126
  
  -- 45. Failure: binomial models work with cross_validate() (@test_cross_validate
  nrow(CVbinomlistrand$ROC[[1]]) not equal to 25.
  1/1 mismatches
  [1] 27 - 25 == 2
  
  -- 46. Failure: binomial models work with cross_validate() (@test_cross_validate
  nrow(CVbinomlistrand$ROC[[2]]) not equal to 10.
  1/1 mismatches
  [1] 11 - 10 == 1
  
  -- 47. Failure: gaussian model with cross_validate() (@test_cross_validate.R#178
  CVed$RMSE not equal to 16.5418.
  1/1 mismatches
  [1] 17.2 - 16.5 == 0.626
  
  -- 48. Failure: gaussian model with cross_validate() (@test_cross_validate.R#179
  CVed$MAE not equal to 13.82438.
  1/1 mismatches
  [1] 14.3 - 13.8 == 0.445
  
  -- 49. Failure: gaussian model with cross_validate() (@test_cross_validate.R#180
  CVed$r2m not equal to 0.2723672.
  1/1 mismatches
  [1] 0.264 - 0.272 == -0.00829
  
  -- 50. Failure: gaussian model with cross_validate() (@test_cross_validate.R#181
  CVed$r2c not equal to 0.2723672.
  1/1 mismatches
  [1] 0.264 - 0.272 == -0.00829
  
  -- 51. Failure: gaussian mixed models with cross_validate() (@test_cross_validat
  CVed$RMSE not equal to c(8.764387, 17.231632).
  2/2 mismatches (average diff: 1.46)
  [1]  9.66 -  8.76 ==  0.895
  [2] 15.20 - 17.23 == -2.029
  
  -- 52. Failure: gaussian mixed models with cross_validate() (@test_cross_validat
  CVed$MAE not equal to c(6.809691, 15.614625).
  2/2 mismatches (average diff: 1.19)
  [1]  7.15 -  6.81 ==  0.336
  [2] 13.58 - 15.61 == -2.038
  
  -- 53. Failure: gaussian mixed models with cross_validate() (@test_cross_validat
  CVed$r2m not equal to c(0.29097372, 0.03803014).
  2/2 mismatches (average diff: 0.0168)
  [1] 0.2822 - 0.291 == -0.00878
  [2] 0.0132 - 0.038 == -0.02483
  
  -- 54. Failure: gaussian mixed models with cross_validate() (@test_cross_validat
  CVed$r2c not equal to c(0.8080003, 0.5228147).
  2/2 mismatches (average diff: 0.0124)
  [1] 0.804 - 0.808 == -0.00369
  [2] 0.502 - 0.523 == -0.02121
  
  -- 55. Failure: gaussian mixed models with cross_validate() (@test_cross_validat
  CVed$AIC not equal to c(175.6952, 193.8477).
  2/2 mismatches (average diff: 0.521)
  [1] 176 - 176 == 0.254
  [2] 195 - 194 == 0.788
  
  -- 56. Failure: gaussian mixed models with cross_validate() (@test_cross_validat
  CVed$AICc not equal to c(177.9978, 196.1504).
  2/2 mismatches (average diff: 0.521)
  [1] 178 - 178 == 0.255
  [2] 197 - 196 == 0.788
  
  -- 57. Failure: gaussian mixed models with cross_validate() (@test_cross_validat
  CVed$BIC not equal to c(180.1403, 198.2929).
  2/2 mismatches (average diff: 0.521)
  [1] 180 - 180 == 0.255
  [2] 199 - 198 == 0.788
  
  -- 58. Failure: binomial models work with control specified in cross_validate() 
  CVbinomlistrand$AUC not equal to c(0.8842593).
  1/1 mismatches
  [1] 0.785 - 0.884 == -0.0995
  
  -- 59. Failure: gaussian models work with control specified in cross_validate() 
  CVgausslistrand$RMSE not equal to c(9.604585).
  1/1 mismatches
  [1] 10.4 - 9.6 == 0.838
  
  -- 60. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$AUC not equal to c(0.7708333, 0.07291667).
  2/2 mismatches (average diff: 0.136)
  [1] 0.749 - 0.7708 == -0.022
  [2] 0.323 - 0.0729 ==  0.250
  
  -- 61. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$`Lower CI` not equal to c(0.5962955, 0).
  2/2 mismatches (average diff: 0.0809)
  [1] 0.567 - 0.596 == -0.0296
  [2] 0.132 - 0.000 ==  0.1322
  
  -- 62. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$`Upper CI` not equal to c(0.9453712, 0.1588227).
  2/2 mismatches (average diff: 0.185)
  [1] 0.931 - 0.945 == -0.0144
  [2] 0.514 - 0.159 ==  0.3548
  
  -- 63. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$Kappa not equal to c(0.4927536, -0.3858696).
  2/2 mismatches (average diff: 0.141)
  [1]  0.461 -  0.493 == -0.0321
  [2] -0.136 - -0.386 ==  0.2495
  
  -- 64. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$Specificity not equal to c(0.8888889, 0.5).
  2/2 mismatches (average diff: 0.139)
  [1] 0.861 - 0.889 == -0.0278
  [2] 0.750 - 0.500 ==  0.2500
  
  -- 65. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$`Pos Pred Value` not equal to c(0.7777778, 0.125).
  2/2 mismatches (average diff: 0.0819)
  [1] 0.739 - 0.778 == -0.0389
  [2] 0.250 - 0.125 ==  0.1250
  
  -- 66. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$`Neg Pred Value` not equal to c(0.7619048, 0.4642857).
  2/2 mismatches (average diff: 0.0521)
  [1] 0.756 - 0.762 == -0.00595
  [2] 0.562 - 0.464 ==  0.09821
  
  -- 67. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$F1 not equal to c(0.6666667, NA).
  1/2 mismatches
  [1] 0.652 - 0.667 == -0.0152
  
  -- 68. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$`Detection Prevalence` not equal to c(0.3, 0.35).
  2/2 mismatches (average diff: 0.0833)
  [1] 0.317 - 0.30 ==  0.0167
  [2] 0.200 - 0.35 == -0.1500
  
  -- 69. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$`Balanced Accuracy` not equal to c(0.7361111, 0.3125).
  2/2 mismatches (average diff: 0.0694)
  [1] 0.722 - 0.736 == -0.0139
  [2] 0.438 - 0.312 ==  0.1250
  
  -- 70. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$MCC not equal to c(0.5048268, -0.3922612).
  2/2 mismatches (average diff: 0.138)
  [1]  0.469 -  0.505 == -0.0359
  [2] -0.153 - -0.392 ==  0.2392
  
  -- 71. Failure: binomial models work with repeated cross_validate() (@test_cross
  nrow(CVbinomlist$ROC[[1]]) not equal to 60.
  1/1 mismatches
  [1] 58 - 60 == -2
  
  -- 72. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$`Confusion Matrix`[[1]]$N not equal to c(7, 5, 2, 16, 7, 5, 2, 16).
  2/8 mismatches (average diff: 1)
  [7]  3 -  2 ==  1
  [8] 15 - 16 == -1
  
  -- 73. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$Results[[1]]$`Balanced Accuracy` not equal to c(0.7361111, 0.7361111).
  1/2 mismatches
  [2] 0.708 - 0.736 == -0.0278
  
  -- 74. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$Results[[1]]$F1 not equal to c(0.6666667, 0.6666667).
  1/2 mismatches
  [2] 0.636 - 0.667 == -0.0303
  
  -- 75. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$Results[[1]]$Specificity not equal to c(0.8888889, 0.8888889).
  1/2 mismatches
  [2] 0.833 - 0.889 == -0.0556
  
  -- 76. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$Results[[1]]$`Pos Pred Value` not equal to c(0.7777778, 0.7777778).
  1/2 mismatches
  [2] 0.7 - 0.778 == -0.0778
  
  -- 77. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$Results[[1]]$`Neg Pred Value` not equal to c(0.7619048, 0.7619048).
  1/2 mismatches
  [2] 0.75 - 0.762 == -0.0119
  
  -- 78. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$Results[[1]]$AUC not equal to c(0.7638889, 0.7777778).
  2/2 mismatches (average diff: 0.022)
  [1] 0.762 - 0.764 == -0.00231
  [2] 0.736 - 0.778 == -0.04167
  
  -- 79. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$Results[[1]]$`Lower CI` not equal to c(0.5855803, 0.6070107).
  1/2 mismatches
  [2] 0.548 - 0.607 == -0.0588
  
  -- 80. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$Results[[1]]$`Upper CI` not equal to c(0.9421975, 0.9485449).
  2/2 mismatches (average diff: 0.0144)
  [1] 0.938 - 0.942 == -0.00416
  [2] 0.924 - 0.949 == -0.02455
  
  -- 81. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$Results[[1]]$Kappa not equal to c(0.4927536, 0.4927536).
  1/2 mismatches
  [2] 0.429 - 0.493 == -0.0642
  
  -- 82. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$Results[[1]]$`Detection Prevalence` not equal to c(0.3, 0.3).
  1/2 mismatches
  [2] 0.333 - 0.3 == 0.0333
  
  -- 83. Failure: binomial models work with repeated cross_validate() (@test_cross
  CVbinomlist$Results[[1]]$MCC not equal to c(0.5048268, 0.5048268).
  1/2 mismatches
  [2] 0.433 - 0.505 == -0.0718
  
  -- 84. Failure: binomial models work with positive as.character in cross_validat
  CVbinomlist$F1 not equal to c(0.8049933, 0.5641026).
  1/2 mismatches
  [2] 0.538 - 0.564 == -0.0256
  
  -- 85. Failure: binomial models work with positive as.character in cross_validat
  CVbinomlist$F1 not equal to c(0.8049933, 0.5641026).
  1/2 mismatches
  [2] 0.538 - 0.564 == -0.0256
  
  -- 86. Failure: binomial models work with positive as.character in cross_validat
  CVbinomlist$F1 not equal to c(0.8049933, 0.5641026).
  1/2 mismatches
  [2] 0.538 - 0.564 == -0.0256
  
  -- 87. Failure: binomial models work with positive as.character in cross_validat
  CVbinomlist$F1 not equal to c(0.8049933, 0.5641026).
  1/2 mismatches
  [2] 0.538 - 0.564 == -0.0256
  
  -- 88. Failure: binomial models work with positive as.character in cross_validat
  CVbinomlist$F1 not equal to c(0.8049933, 0.5641026).
  1/2 mismatches
  [2] 0.538 - 0.564 == -0.0256
  
  -- 89. Failure: gaussian models work with repeated cross_validate() (@test_cross
  CVgausslist$RMSE not equal to c(16.6695, 22.6363).
  2/2 mismatches (average diff: 1.29)
  [1] 16.7 - 16.7 ==  0.0258
  [2] 20.1 - 22.6 == -2.5635
  
  -- 90. Failure: gaussian models work with repeated cross_validate() (@test_cross
  CVgausslist$MAE not equal to c(13.8321, 18.96102).
  2/2 mismatches (average diff: 1.22)
  [1] 13.9 - 13.8 ==  0.0889
  [2] 16.6 - 19.0 == -2.3543
  
  -- 91. Failure: gaussian models work with repeated cross_validate() (@test_cross
  CVgausslist$r2m not equal to c(0.2704824, 0.03761387).
  2/2 mismatches (average diff: 0.0153)
  [1] 0.26870 - 0.2705 == -0.00178
  [2] 0.00872 - 0.0376 == -0.02890
  
  -- 92. Failure: gaussian models work with repeated cross_validate() (@test_cross
  CVgausslist$r2c not equal to c(0.2704824, 0.03761387).
  2/2 mismatches (average diff: 0.0153)
  [1] 0.26870 - 0.2705 == -0.00178
  [2] 0.00872 - 0.0376 == -0.02890
  
  -- 93. Failure: gaussian models work with repeated cross_validate() (@test_cross
  CVgausslist$AIC not equal to c(194.7412, 201.2037).
  2/2 mismatches (average diff: 0.389)
  [1] 195 - 195 == -0.0619
  [2] 202 - 201 ==  0.7152
  
  -- 94. Failure: gaussian models work with repeated cross_validate() (@test_cross
  CVgausslist$AICc not equal to c(196.0471, 202.5096).
  2/2 mismatches (average diff: 0.389)
  [1] 196 - 196 == -0.0619
  [2] 203 - 203 ==  0.7152
  
  -- 95. Failure: gaussian models work with repeated cross_validate() (@test_cross
  CVgausslist$BIC not equal to c(198.0751, 204.5376).
  2/2 mismatches (average diff: 0.389)
  [1] 198 - 198 == -0.0619
  [2] 205 - 205 ==  0.7151
  
  -- 96. Failure: gaussian models with cross_validate_single_fn() (@test_cross_val
  cv_result$RMSE not equal to 8.76.
  1/1 mismatches
  [1] 9.66 - 8.76 == 0.899
  
  -- 97. Failure: gaussian models with cross_validate_single_fn() (@test_cross_val
  cv_result$r2m not equal to 0.291.
  1/1 mismatches
  [1] 0.282 - 0.291 == -0.00881
  
  -- 98. Failure: gaussian models with cross_validate_single_fn() (@test_cross_val
  cv_result$r2c not equal to 0.808.
  1/1 mismatches
  [1] 0.804 - 0.808 == -0.00369
  
  -- 99. Failure: gaussian models with cross_validate_single_fn() (@test_cross_val
  cv_result$AIC not equal to 175.6952.
  1/1 mismatches
  [1] 176 - 176 == 0.254
  
  -- 100. Failure: gaussian models with cross_validate_single_fn() (@test_cross_va
  cv_result$AICc not equal to 177.9978.
  1/1 mismatches
  [1] 178 - 178 == 0.255
  
  -- 101. Failure: gaussian models with cross_validate_single_fn() (@test_cross_va
  cv_result$BIC not equal to 180.1403.
  1/1 mismatches
  [1] 180 - 180 == 0.255
  
  -- 102. Failure: gaussian models with cross_validate_single_fn() (@test_cross_va
  cv_result$RMSE not equal to 16.5418.
  1/1 mismatches
  [1] 17.2 - 16.5 == 0.626
  
  -- 103. Failure: gaussian models with cross_validate_single_fn() (@test_cross_va
  cv_result$r2m not equal to 0.2723672.
  1/1 mismatches
  [1] 0.264 - 0.272 == -0.00829
  
  -- 104. Failure: gaussian models with cross_validate_single_fn() (@test_cross_va
  cv_result$r2c not equal to 0.2723672.
  1/1 mismatches
  [1] 0.264 - 0.272 == -0.00829
  
  -- 105. Failure: binomial models with cross_validate_single_fn() (@test_cross_va
  cv_result$AUC not equal to 0.843.
  1/1 mismatches
  [1] 0.861 - 0.843 == 0.0181
  
  -- 106. Failure: binomial models with cross_validate_single_fn() (@test_cross_va
  cv_result$`Lower CI` not equal to 0.6892.
  1/1 mismatches
  [1] 0.71 - 0.689 == 0.0211
  
  -- 107. Failure: binomial models with cross_validate_single_fn() (@test_cross_va
  cv_result$`Upper CI` not equal to 0.995953.
  1/1 mismatches
  [1] 1 - 0.996 == 0.00405
  
  -- 108. Failure: binomial models with cross_validate_single_fn() (@test_cross_va
  cv_result$AUC not equal to 0.7638889.
  1/1 mismatches
  [1] 0.762 - 0.764 == -0.00231
  
  -- 109. Failure: binomial models with cross_validate_single_fn() (@test_cross_va
  cv_result$`Upper CI` not equal to 0.942.
  1/1 mismatches
  [1] 0.938 - 0.942 == -0.00397
  
  -- 110. Failure: gaussian models with link functions with cross_validate() (@tes
  CV_gauss$RMSE not equal to 39.56006.
  1/1 mismatches
  [1] 40.6 - 39.6 == 1.01
  
  -- 111. Failure: gaussian models with link functions with cross_validate() (@tes
  CV_gauss$RMSE not equal to 39.48817.
  1/1 mismatches
  [1] 40.5 - 39.5 == 1.01
  
  -- 112. Failure: gaussian models with link functions with cross_validate() (@tes
  CV_gauss$AIC not equal to 187.5134.
  1/1 mismatches
  [1] 187 - 188 == -0.864
  
  -- 113. Failure: gaussian models with link functions with cross_validate() (@tes
  CV_gauss$RMSE not equal to 42.832.
  1/1 mismatches
  [1] 43.9 - 42.8 == 1.04
  
  -- 114. Failure: gaussian models with link functions with cross_validate() (@tes
  CV_gauss$RMSE not equal to 16.5418.
  1/1 mismatches
  [1] 17.2 - 16.5 == 0.626
  
  -- 115. Failure: binomial models with link functions with cross_validate() (@tes
  CV_binom$AUC not equal to 0.7638889.
  1/1 mismatches
  [1] 0.762 - 0.764 == -0.00231
  
  -- 116. Failure: binomial models with link functions with cross_validate() (@tes
  CV_binom$AUC not equal to 0.8425926.
  1/1 mismatches
  [1] 0.861 - 0.843 == 0.0185
  
  -- 117. Failure: binomial models with link functions with cross_validate() (@tes
  CV_binom$AUC not equal to 0.7638889.
  1/1 mismatches
  [1] 0.771 - 0.764 == 0.00694
  
  -- 118. Failure: binomial models with link functions with cross_validate() (@tes
  `cross_validate(...)` did not throw an error.
  
  -- 119. Failure: binomial models with link functions with cross_validate() (@tes
  CV_binom$AUC not equal to 0.75.
  1/1 mismatches
  [1] 0.752 - 0.75 == 0.00231
  
  -- 120. Failure: binomial models with link functions with cross_validate() (@tes
  CV_binom$Sensitivity not equal to 0.5.
  1/1 mismatches
  [1] 0.583 - 0.5 == 0.0833
  
  -- 121. Failure: binomial model work with validate() (@test_validate.R#34)  ----
  Vbinom_results$AUC not equal to 0.8888889.
  1/1 mismatches
  [1] 0.944 - 0.889 == 0.0556
  
  -- 122. Failure: binomial model work with validate() (@test_validate.R#35)  ----
  Vbinom_results$`Lower CI` not equal to 0.6312155.
  1/1 mismatches
  [1] 0.79 - 0.631 == 0.159
  
  -- 123. Failure: binomial model work with validate() (@test_validate.R#65)  ----
  nrow(Vbinom_results$ROC[[1]]) not equal to 10.
  1/1 mismatches
  [1] 9 - 10 == -1
  
  -- 124. Failure: binomial mixed model work with validate() (@test_validate.R#81)
  dat$.partitions not equal to factor(...).
  18 string mismatches
  
  -- 125. Failure: binomial mixed model work with validate() (@test_validate.R#97)
  Vbinom_results$AUC not equal to 0.8333333.
  1/1 mismatches
  [1] 0.944 - 0.833 == 0.111
  
  -- 126. Failure: binomial mixed model work with validate() (@test_validate.R#98)
  Vbinom_results$`Lower CI` not equal to 0.626735.
  1/1 mismatches
  [1] 0.816 - 0.627 == 0.189
  
  -- 127. Failure: binomial mixed model work with validate() (@test_validate.R#100
  Vbinom_results$Kappa not equal to 0.5.
  1/1 mismatches
  [1] 0.333 - 0.5 == -0.167
  
  -- 128. Failure: binomial mixed model work with validate() (@test_validate.R#101
  Vbinom_results$Sensitivity not equal to 1.
  1/1 mismatches
  [1] 0.333 - 1 == -0.667
  
  -- 129. Failure: binomial mixed model work with validate() (@test_validate.R#102
  Vbinom_results$Specificity not equal to 0.5.
  1/1 mismatches
  [1] 1 - 0.5 == 0.5
  
  -- 130. Failure: binomial mixed model work with validate() (@test_validate.R#103
  Vbinom_results$`Pos Pred Value` not equal to 0.6666667.
  1/1 mismatches
  [1] 1 - 0.667 == 0.333
  
  -- 131. Failure: binomial mixed model work with validate() (@test_validate.R#104
  Vbinom_results$`Neg Pred Value` not equal to 1.
  1/1 mismatches
  [1] 0.6 - 1 == -0.4
  
  -- 132. Failure: binomial mixed model work with validate() (@test_validate.R#105
  Vbinom_results$F1 not equal to 0.8.
  1/1 mismatches
  [1] 0.5 - 0.8 == -0.3
  
  -- 133. Failure: binomial mixed model work with validate() (@test_validate.R#107
  Vbinom_results$`Detection Rate` not equal to 0.5.
  1/1 mismatches
  [1] 0.167 - 0.5 == -0.333
  
  -- 134. Failure: binomial mixed model work with validate() (@test_validate.R#108
  Vbinom_results$`Detection Prevalence` not equal to 0.75.
  1/1 mismatches
  [1] 0.167 - 0.75 == -0.583
  
  -- 135. Failure: binomial mixed model work with validate() (@test_validate.R#111
  Vbinom_results$`Balanced Accuracy` not equal to 0.75.
  1/1 mismatches
  [1] 0.667 - 0.75 == -0.0833
  
  -- 136. Failure: binomial mixed model work with validate() (@test_validate.R#114
  Vbinom_results$`Singular Fit Messages` not equal to 0.
  1/1 mismatches
  [1] 1 - 0 == 1
  
  -- 137. Failure: binomial mixed model work with validate() (@test_validate.R#130
  nrow(Vbinom_results$ROC[[1]]) not equal to 6.
  1/1 mismatches
  [1] 12 - 6 == 6
  
  -- 138. Failure: binomial model work with test_data in validate() (@test_validat
  Vbinom_results$AUC not equal to 0.8888889.
  1/1 mismatches
  [1] 0.944 - 0.889 == 0.0556
  
  -- 139. Failure: binomial model work with test_data in validate() (@test_validat
  Vbinom_results$`Lower CI` not equal to 0.6312155.
  1/1 mismatches
  [1] 0.79 - 0.631 == 0.159
  
  -- 140. Failure: binomial model work with test_data in validate() (@test_validat
  nrow(Vbinom_results$ROC[[1]]) not equal to 10.
  1/1 mismatches
  [1] 9 - 10 == -1
  
  -- 141. Failure: gaussian model with validate() (@test_validate.R#222)  --------
  Vgauss_results$RMSE not equal to 5.41618.
  1/1 mismatches
  [1] 8.09 - 5.42 == 2.67
  
  -- 142. Failure: gaussian model with validate() (@test_validate.R#223)  --------
  Vgauss_results$r2m not equal to 0.2011114.
  1/1 mismatches
  [1] 0.166 - 0.201 == -0.0353
  
  -- 143. Failure: gaussian model with validate() (@test_validate.R#224)  --------
  Vgauss_results$r2c not equal to 0.688687.
  1/1 mismatches
  [1] 0.767 - 0.689 == 0.0787
  
  -- 144. Failure: gaussian model with validate() (@test_validate.R#225)  --------
  Vgauss_results$AIC not equal to 151.137.
  1/1 mismatches
  [1] 148 - 151 == -3.41
  
  -- 145. Failure: gaussian model with validate() (@test_validate.R#226)  --------
  Vgauss_results$AICc not equal to 154.2139.
  1/1 mismatches
  [1] 151 - 154 == -3.41
  
  -- 146. Failure: gaussian model with validate() (@test_validate.R#227)  --------
  Vgauss_results$BIC not equal to 154.6985.
  1/1 mismatches
  [1] 151 - 155 == -3.41
  
  == testthat results  ===========================================================
  OK: 374 SKIPPED: 6 WARNINGS: 2 FAILED: 146
  1. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#101) 
  2. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#105) 
  3. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#108) 
  4. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#112) 
  5. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#120) 
  6. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#124) 
  7. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#129) 
  8. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#133) 
  9. Failure: gaussian evaluation are correct in baseline() (@test_baseline.R#136) 
  1. ...
  
  Error: testthat unit tests failed
  Execution halted